Journal articles: 'Predictive-Reactive strategy'

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Tighazoui, Ayoub, Christophe Sauvey, and Nathalie Sauer. "Predictive-reactive strategy for identical parallel machine rescheduling." Computers & Operations Research 134 (October 2021): 105372. http://dx.doi.org/10.1016/j.cor.2021.105372.

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Kalinowski, Krzysztof, Damian Krenczyk, and Cezary Grabowik. "Predictive - Reactive Strategy for Real Time Scheduling of Manufacturing Systems." Applied Mechanics and Materials 307 (February 2013): 470–73. http://dx.doi.org/10.4028/www.scientific.net/amm.307.470.

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In this paper a solution of soft real time scheduling in manufacturing systems is presented. The basic requirements of scheduling as a real time system are discussed. The proposed rescheduling method uses predictive-reactive strategy and multi thread searching approach with rule-based heuristics, meta-heuristics and random modules.

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Di, Zhengfei, Demin Xu, and Kehan Zhang. "Continuous Control Set Model Predictive Control for an Indirect Matrix Converter." Energies 14, no. 14 (July 8, 2021): 4114. http://dx.doi.org/10.3390/en14144114.

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A continuous control set model predictive power control strategy for an indirect matrix converter is proposed in this paper. The load reactive power, the load active power, and the input reactive power are controlled simultaneously. This control strategy can obtain output waveforms with fixed switching frequency. Additionally, an optimal switching sequence is proposed to simplify the commutations of the indirect matrix converter. To suppress the input filter resonance, an active damping method is proposed. Experimental results prove that the proposed method features controllable input reactive power, controllable load active and reactive power, fixed switching frequency output waveforms, zero-current switching operations, and effectively suppresses input filter resonance.

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Zhang, Ming Guang, and Xiao Jing Chen. "Control Strategy of Low Voltage Ride-Through for Grid-Connected Photovoltaic Power System Based on Predictive Current." Applied Mechanics and Materials 556-562 (May 2014): 1753–56. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.1753.

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The control strategy based on predictive current is proposed to solve problems that destruct stable operation of grid-connected photovoltaic system during asymmetrical fall. A mathematical model of PV inverter is established to calculate current instruction; a method of tracking based on predictive current is proposed to reduce the fluctuations of 2 times frequency. In the meantime, PV inverter provides reactive power to support voltage recovery according to the depth of grid voltage sags and realize LVRT. The result also shows that the proposed control strategy can reduce wave of DC voltage and provide reactive power to support voltage recovery.

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Nawaz, Muhammad, Muhammad Asghar Saqib, Syed Abdul Rahman Kashif, and Mehr Gul. "Constrained model predictive control for an induction heating load." Transactions of the Institute of Measurement and Control 41, no. 1 (March 28, 2018): 210–18. http://dx.doi.org/10.1177/0142331218758887.

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This paper explores a model predictive control (MPC) strategy with constraints satisfaction for a high power induction heating load. The MPC predicts the state variables and future control sequence of the system in advance and achieves on-line-optimization with a reduced error. The state-space model of the system with a parallel resonant load is developed and then MPC is applied. The proposed approach controls the DC link current at the rectifier output and reactive component of the supply current. The DC current is used to regulate the power of the heating load and the reactive component of input current is kept at zero to attain the unity power factor. The results show that the proposed strategy regulates the power of the heating load, achieves unity power factor at input of the system and handles the variables within the defined constraints effectively.

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MELLAH, Hacene, Amar MAAFA, Hamza SAHRAOUI, Abdelghani YAHIOU, and Houria SMAIL. "Generalized Predictive Control of the Active and Reactive Stator Powers of the DFIG for Wind Energy Generation." Eurasia Proceedings of Science Technology Engineering and Mathematics 26 (December 30, 2023): 295–305. http://dx.doi.org/10.55549/epstem.1409597.

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This paper proposes a Generalized Predictive Control (GPC) strategy for Wind Power Generation (WPG) based on a doubly fed induction generator (DFIG). The objective is to study and apply a robust active and reactive power control strategy based on GPC of the DFIG, this is likely to optimize the energy production and improve the quality of the energy produced. In order to maximize the amount of WPG taken even when the turbine is uncertain or the wind speed varies abruptly, the design is built utilizing the Maximum Power Point Tracking (MPPT) theory. Through numerical modeling with the aid of the Matlab/Simulink software, the predictive control (GPC) of the active and reactive stator powers of the DFIG was validated. A comparison between the GPC of the GADA and the indirect method based on a typical PI controller is developed in order to illustrate the viability of the suggested method. According to the simulation results of this comparison, the suggested method is viable and has promising results.

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Zhu, Jian-hong, Pengkun Zhang, Xinsong Zhang, Lin Qin, Chengxiang Sun, and Han Li. "Model Predictive Control on Transient Flux Linkage and Reactive Power Compensation of Doubly Fed Induction Wind Generator." International Journal of Energy Research 2024 (March 5, 2024): 1–15. http://dx.doi.org/10.1155/2024/6648691.

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For weak grid scenario with high new energy proportion, large fluctuations of load are prone to cause low-voltage ride through. Moreover, stator transient magnetic flux will cause overvoltage and overcurrent problems in the rotor of doubly fed induction generator. Based on model predictive control, a control strategy for transient flux linkage and reactive power compensation is proposed. Firstly, regarding the issue of reactive power allocation of grid side converters (GSC) and rotor side converters (RSC), an allocation strategy is derived under minimizing winding energy loss on case of low-voltage ride through, enabling the wind energy conversion system to provide reactive power support during grid voltage recovery process. Meanwhile, an improved mixed second- and third-order generalized integrator (MSTOGI) phase-locked loop (PLL) is used to extract the positive and negative sequence components of the power grid voltage, further for RSC control. Secondly, in response to power grid faults, considering the influence of stator DC transient flux and negative sequence flux components on rotor current, by injecting rotor transient compensation current and stator flux feedforward compensation into RSC, the rotor impulse voltage and loop current are reduced. Moreover, combined with model predictive control algorithm, a control strategy of rotor current is designed. Finally, a simulation platform is built to validate the effectiveness of the proposed method based on comparing with several traditional vector control low-voltage ride through methods.

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Tighazoui, Ayoub, Christophe Sauvey, and Nathalie Sauer. "Predictive-reactive Strategy for Flowshop Rescheduling Problem: Minimizing the Total Weighted Waiting Times and Instability." Journal of Systems Science and Systems Engineering 30, no. 3 (April 19, 2021): 253–75. http://dx.doi.org/10.1007/s11518-021-5490-8.

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Rahima, Bachar, Golea Amar, Benchouia Mohamed Toufik, and Chebaani Mohamed. "High-performance active power filter implementation based on predictive current control." International Journal of Power Electronics and Drive Systems (IJPEDS) 10, no. 1 (March 1, 2019): 277. http://dx.doi.org/10.11591/ijpeds.v10.i1.pp277-287.

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This paper presents the use of the predictive strategy concept to improve the Active Power Filter (APF) performance, by compensation of the reactive power and elimination of the harmonic currents generated by non-linear loads. Predictive control is generating considerable interest when it comes to implementing current control strategies in active power filter. The proposed strategy provides a simple controller incorporating Phase Locked Loop (PLL) independency. The prediction is evaluated using a cost function that quantiﬁes the desired system behavior. The cost function used in this work evaluates the ﬁltered error of the currents. This strategy minimized the number of sensors, ease of practical implementation and reduced system size and cost. The effectiveness of the proposed controller is confirmed through simulation and experimental validation using a hardware prototype based on dSPACE-1104

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He, Tingting, Dylan Dah-Chuan Lu, Mingli Wu, Qinyao Yang, Teng Li, and Qiujiang Liu. "Four-Quadrant Operations of Bidirectional Chargers for Electric Vehicles in Smart Car Parks: G2V, V2G, and V4G." Energies 14, no. 1 (December 31, 2020): 181. http://dx.doi.org/10.3390/en14010181.

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This paper presents the four-quadrant operation modes of bidirectional chargers for electric vehicles (EVs) framed in smart car parks. A cascaded model predictive control (MPC) scheme for the bidirectional two-stage off-board chargers is proposed. The controller is constructed in two stages. The model predictive direct power control for the grid side is applied to track the active/reactive power references. The model predictive direct current control is proposed to achieve constant current charging/discharging for the EV load side. With this MPC strategy, EV chargers are able to transmit the active and reactive powers between the EV batteries and the power grid. Apart from exchanging the active power, the vehicle-for-grid (V4G) mode is proposed, where the chargers are used to deliver the reactive power to support the grid, simultaneously combined with grid-to-vehicle or vehicle-to-grid operation modes. In the V4G mode, the EV battery functions as the static var compensator. According to the simulation results, the system can operate effectively in the full control regions of the active and reactive power (PQ) plane under the aforementioned operation modes. Fast dynamic response and great steady-state system performances can be verified through various simulation and experimental results.

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Yang, Aimin, Jianfeng Yang, and Ting Li. "Research on the overvoltage suppression strategy of single-phase photovoltaic grid-connected systems based on FCS-MPDPC." Journal of Physics: Conference Series 2591, no. 1 (September 1, 2023): 012051. http://dx.doi.org/10.1088/1742-6596/2591/1/012051.

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Abstract For the overvoltage problem brought by the access of high-penetration photovoltaic power plants to the low-voltage distribution network, this paper chooses to deal with it by using the reactive power regulation capability of photovoltaic power plant inverters, which reduces the equipment investment in distribution line voltage regulation. Based on the reactive power regulation theory of the inverter, a finite set model predictive direct power control method is proposed by constructing a virtual voltage vector, taking a single-phase cascaded 2H bridge grid-connected inverter as an example, to realize independent control of active and reactive power. By setting different power reference values, the inverter can be made to have two functions: grid-connected per-unit power factor and reactive power control during overvoltage. Finally, the two modes of inverter operation are verified by Simulink simulation, and the verification by example shows that the method can limit the grid-connected voltage deviation to within 7% and meet the requirements of grid-connected regulations.

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Di, Zhengfei, Demin Xu, and Kehan Zhang. "Vector Modulation-Based Model Predictive Current Control with Filter Resonance Suppression and Zero-Current Switching Sequence for Two-Stage Matrix Converter." Energies 14, no. 12 (June 21, 2021): 3685. http://dx.doi.org/10.3390/en14123685.

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This paper proposes a novel model predictive current control scheme for two-stage matrix converter. The switching frequency is kept constant by fixing the switching instant. The control strategy achieves to control source reactive power in the input side and output currents in the output side. In addition, the advantage of the proposed strategy compared with conventional model predictive control is firstly proved using the principle of vector synthesis and the law of sines in the vector distribution area. Moreover, a zero-current switching sequence is proposed and implemented to insure zero-current switching operations and reduce the switching losses. Furthermore, in order to suppress the input filter resonance, which is easier to be inspired by the model predictive control, compared with traditional control strategies, an innovative active damping technique is proposed and implemented. Finally, both simulation and experiment are implemented to verify the performance of the proposed strategy. The results demonstrate that the control system features both good steady and transient performance.

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Zeng, Lin Suo, and Zhong Lei Chen. "Study on Application of Improved Predictive Current Method in STATCOM." Advanced Materials Research 433-440 (January 2012): 6089–95. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6089.

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Predictive current method as a control strategy of the STATCOM is more and more applied to practical production. The traditional predictive current method can only predict the reactive current in the broad sense, and not detailed to predict for any harmonic; and the delay caused by sampling, analysis and calculation, prediction do not led to a good predictor for control requirements during the traditional prediction. In this paper, the above issues are appropriately improved. And the simulation results verify its operability.

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Letchumanan, Iswary, M. K. Md Arshad, S. R. Balakrishnan, and Subash C. B. Gopinath. "Gold-nanorod enhances dielectric voltammetry detection of c-reactive protein: A predictive strategy for cardiac failure." Biosensors and Bioelectronics 130 (April 2019): 40–47. http://dx.doi.org/10.1016/j.bios.2019.01.042.

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Uribe-Sánchez, Andrés, and Alex Savachkin. "Predictive and Reactive Distribution of Vaccines and Antivirals during Cross-Regional Pandemic Outbreaks." Influenza Research and Treatment 2011 (June 5, 2011): 1–14. http://dx.doi.org/10.1155/2011/579597.

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As recently pointed out by the Institute of Medicine, the existing pandemic mitigation models lack the dynamic decision support capability. We develop a large-scale simulation-driven optimization model for generating dynamic predictive distribution of vaccines and antivirals over a network of regional pandemic outbreaks. The model incorporates measures of morbidity, mortality, and social distancing, translated into the cost of lost productivity and medical expenses. The performance of the strategy is compared to that of the reactive myopic policy, using a sample outbreak in Fla, USA, with an affected population of over four millions. The comparison is implemented at different levels of vaccine and antiviral availability and administration capacity. Sensitivity analysis is performed to assess the impact of variability of some critical factors on policy performance. The model is intended to support public health policy making for effective distribution of limited mitigation resources.

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Di, Zhengfei, Demin Xu, Luca Tarisciotti, and Pat Wheeler. "A Novel Predictive Control Method with Optimal Switching Sequence and Filter Resonance Suppression for Two-Stage Matrix Converter." Energies 14, no. 12 (June 19, 2021): 3652. http://dx.doi.org/10.3390/en14123652.

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This paper proposes a vector modulation-based model predictive current control strategy for a two-stage matrix converter. The switching frequency is kept constant by fixing the switching instantly. The control scheme controls the source reactive power on the input side and output currents on the output side. Besides, the advantage of the proposed strategy compared with conventional model predictive control is firstly proved using the principle of vector synthesis and the law of sines in the vector distribution area. Moreover, to ensure zero-current switching operations and reduce the switching losses, an optimal switching sequence is proposed and implemented. Furthermore, considering that the input filter resonance is easier to be inspired by the model predictive control, compared with conventional linear control strategies, an innovative active damping technique is proposed to suppress the input filter resonance. To assess the performance of the proposed method, simulation and experimental results are demonstrated, showing that the control system features both good steady-state and transient performance.

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Cui, Hesong, Xueping Li, Gongping Wu, Yawei Song, Xiao Liu, and Derong Luo. "MPC Based Coordinated Active and Reactive Power Control Strategy of DFIG Wind Farm with Distributed ESSs." Energies 14, no. 13 (June 29, 2021): 3906. http://dx.doi.org/10.3390/en14133906.

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The ESS is considered as an effective tool for enhancing the flexibility and controllability of a wind farm, and the optimal control scheme of a wind farm with distributed ESSs is vital to the stable operation of wind power generation. In this paper, a coordinated active and reactive power control strategy based on model predictive control (MPC) is proposed for doubly fed induction generator (DFIG)-based wind farm (WF) with distributed energy storage systems (ESSs). The proposed control scheme coordinates the active and reactive power output among DFIG wind turbines (WTs), grid-side converters (GSCs), and distributed ESSs inside the WF, and the aim is to decrease fatigue loads of WTs, make the WT terminal voltage inside the extent practicable, and take the WF economic operation into consideration. Moreover, the best reactive power references of DFIG stator and GSC are produced independently based on their dynamics. At last, the control scheme generates optimal power references for all ESS to make the SOC of each ESS converge to their average state. With the distributed ESSs, the WF controller regulates the WTs inside WF more flexibly. A WF composed of 10 DFIG WTs was utilized to verify the control performance of the proposed coordinated active and reactive power control strategy.

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Lupangu, Cedrick, Jackson J. Justo, and Ramesh C. Bansal. "Model Predictive for Reactive Power Scheduling Control Strategy for PV–Battery Hybrid System in Competitive Energy Market." IEEE Systems Journal 14, no. 3 (September 2020): 4071–78. http://dx.doi.org/10.1109/jsyst.2020.2968926.

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Ma, Chenghao, Jiahang Sun, Jingguang Huang, and Kaijie Wang. "Transient Stability Enhancement Strategy for Islanded Microgrids Based on Energy Storage–Virtual Synchronous Machine Control." Energies 16, no. 17 (September 3, 2023): 6390. http://dx.doi.org/10.3390/en16176390.

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In a high percentage of new energy-islanded microgrids, the overall inertia of the system gradually decreases, and the transient stability requirements of the microgrid frequency and voltage become more and more demanding under low-inertia conditions. To improve the transient stability of low-inertia islanded microgrid frequencies and voltages, this paper proposes a transient stability enhancement strategy for islanded microgrids based on energy storage system (ESS)–virtual synchronous generator (VSG) control. Model predictive control (MPC) is added within the active control loop of the VSG to achieve dynamic correction of the active power reference value of the VSG; PI control link is added within the reactive control loop to achieve a fast dynamic response of the reactive power command value. The ESS achieves fast and accurate regulation of frequency and voltage according to the power reference value of the VSG active control loop and the power command value of the reactive control loop simultaneously. Considering the need to ensure the ability of VSG to operate stably during transients, a comprehensive current-limiting technique combining virtual impedance and phase limiting is used to limit the fault current of VSG and maintain its synchronization and stability. Finally, the simulation results verify the strategy’s effectiveness and the superiority of the transient stability enhancement effect.

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Zocca, Elena, Silva Seraceni, Teresa Cafaro, Tamara Emanuela Cervone, Laura Cardarelli, Massimiliano Valisi, Isabella Polidori, Massimo Pieri, Flaminia Tomassetti, and Francesco Broccolo. "Evaluation of Two-Assay Serological Testing Strategies for Anti-HCV Screening in Italian Populations: A Dual Screening Approach." Diagnostics 14, no. 6 (March 7, 2024): 570. http://dx.doi.org/10.3390/diagnostics14060570.

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(1) Background: Hepatitis C virus (HCV) screening mostly uses a one-assay anti-HCV testing approach, which has a higher probability of false-positive results in populations with low HCV prevalence. (2) Methods: In this investigation, 17,926 participants were screened for HCV, and the reactives were tested using a two-assay anti-HCV approach: Elecsys ElectroChemiLuminescence (ECL) and a ChemiLuminescence ImmunoAssay (CLIA), respectively. A recombinant immunoblot assay (RIBA) was performed to confirm anti-HCV positivity. Statistical analysis was performed. (3) Results: A total of 350 specimens were reactive in the ECL screening, of which CLIA retesting showed that 292 (83.4%) were anti-HCV reactive (283 positives, 9 indeterminate, none negative by RIBA), but 58 (16.6%) were not anti-HCV reactive (15 positive, 12 indeterminate, 31 negatives by RIBA). The two-assay strategy significantly improved the positive predictive value (PPV: 95.00%) with χ2: 7.59 (p < 0.01) compared to the PPV assessed by one assay (PPV: 90.6%) with χ2: 34.51 (p < 0.001). The ROC curve defined a sensibility and specificity for the dual approach of 99.66% and 100.00%. (4) Conclusions: Compared with a one-assay testing strategy, the two-assay testing strategy may significantly reduce false positives in anti-HCV testing and identify inactive HCV infection in low seroprevalence populations.

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Chen, Yandong, An Luo, Zhikang Shuai, and Sanjun Xie. "Robust predictive dual‐loop control strategy with reactive power compensation for single‐phase grid‐connected distributed generation system." IET Power Electronics 6, no. 7 (August 2013): 1320–28. http://dx.doi.org/10.1049/iet-pel.2013.0011.

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Xiaojuan, Lu, Wang Xinju, and Dong Haiying. "Model Predictive Direct Power Control of DC Micro Grid Rectifier Based on Neutral Point Potential Balance." Mathematical Problems in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6571659.

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Aimed at the problem of DC micro grid rectifier control delay and the DC bus voltage stability, the method of two-step model predictive direct power control (TMPDPC) combined with neutral point potential control is proposed. Model predictive direct power control (MPDPC) is designed for rectifier. A cost function is then used to evaluate the active and reactive power ripples, from which the vector that generates the lowest power ripple will be applied during the next sampling interval. Two-step predictive control is designed to compensate for the delay of one-step predictive control. On the basis of this, the two-capacitor voltage unbalance problem in DC side is considered, and the neutral point potential control is added. In the Matlab/Simulink simulation, compared with control effect of direct power control (DPC) and one-step model predictive direct power control (OMPDPC), the control strategy of neutral point potential added to TMPDPC can make the system stability and control accuracy better. The validity of this scheme was validated by physical simulation at last.

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Vasista, T. G. K., and A. M. AlAbdullatif. "Role of Electronic Customer Relationship Management in Demand Chain Management." International Journal of Information Systems and Supply Chain Management 10, no. 1 (January 2017): 53–67. http://dx.doi.org/10.4018/ijisscm.2017010104.

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In 21st century, collaborative business supply chain environments are required to be proactive rather than reactive so that they can better deal with the uncertainty, growing competition, shorter cycle times, more demanding customers and pressure to cut costs. Demand chain management as a new business model requires investing in consumer insights and closer relationships in the supply chain to conduct predictive analysis of retail intelligent solutions. In this regard new kinds of methodologies are required to be discussed. However, at the execution level the limitations in terms of scalability, data integration and knowledge based decision support to providers or suppliers in terms of strategy building and in providing deductive inference capabilities are to be addressed. Therefore, it is required to describe how predictive analytics helps in constructing the knowledge base to conduct verification and validation in terms of semantic predictive analytic for the domain of demand chain management.

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Marciel, Esteban I., Carlos R. Baier, Roberto O. Ramírez, Carlos A. Muñoz, Marcelo A. Pérez, and Mauricio Arevalo. "Operation Assessment of a Hybrid Distribution Transformer Compensating for Voltage and Power Factor Using Predictive Control." Mathematics 12, no. 5 (March 5, 2024): 774. http://dx.doi.org/10.3390/math12050774.

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Hybrid Distribution Transformers (HDTs) offer a compelling alternative to traditional low-frequency transformers (LFTs), providing auxiliary services in addition to standard functionalities. By integrating LFTs with power converters, HDTs enhance the operational capabilities of the system. The specific configuration in which converters are connected to the transformer allows for the provision of multiple services. This can not only prevent network failures but also extend the lifespan of its components, an outcome that is highly desirable in a distribution grid. This article discusses an HDT developed to mitigate voltage fluctuations in the grid and to decrease the reactive power drawn from the secondary side of traditional LFTs. A finite-control-set model predictive control (FCS-MPC), in conjunction with linear controllers, is utilized for the effective management of the HDT converters. Two separate control loops are established to regulate voltage and reactive power on the secondary side of the transformer. Results from Hardware-in-the-Loop (HIL) testing affirm the proficiency of HDT in reducing grid voltage variations by 15% and in cutting reactive power consumption by up to 94%. The adopted control strategy and topology are demonstrated to be effective in stabilizing voltage and reactive power fluctuations while concurrently facilitating the charging of the converters’ DC link directly from the grid.

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Zwolińska, Bożena, and Jakub Wiercioch. "Selection of Maintenance Strategies for Machines in a Series-Parallel System." Sustainability 14, no. 19 (September 22, 2022): 11953. http://dx.doi.org/10.3390/su141911953.

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In this paper, an assessment of the failure frequency of machines in a series-parallel structure was conducted. The analyses contain the decomposition of the system according to the general theory of complex systems. Based on the results obtained, a model for an optimal determination of the mean time to failure (MTTF) according to the expected value of the gamma distribution was proposed. For this purpose, the method of moments was used to determine the optimal values of the parameters of the estimated gamma distribution. The article is designed to be analytical. The object of consideration in this analysis is the real production system working in accordance with make-to-order, with a high degree of product customisation. Moreover, in the considered system occurs a dichotomy of mutually exclusive flows: push and pull. In the article, the main emphasis was placed on the applicability of the proposed MTTF value-shaping algorithm. Then, the maintenance strategy for each machine (reactive, preventive or predictive) was proposed. Maintenance strategy selection considered sustainable development principles in the criterion of minimizing maintenance actions, fulfilling the assumption of not interrupting the flow of the processed material. Based on inductive analyses, the concepts of improvement actions individually for each machine in the analysed subsystem were deductively defined. As a result, it was proved that a reactive maintenance strategy is appropriate for machines that have manufacturing reserves and are low priority. The equipment possessing manufacturing reserves but also having an impact on the risk of interrupting the flow of the processed material should be operated in accordance with a preventive maintenance strategy. A predictive maintenance strategy was proposed for the machines with the highest priority, which simultaneously do not have manufacturing reserves and the risk of manufacturing line operation interruption is high. The considerations were conducted with a holistic approach, taking into account the main functional areas of the enterprise.

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Akcal, M. Ugur, and N. Kemal Ure. "Predictive Missile Guidance with Online Trajectory Learning." Defence Science Journal 67, no. 3 (April 25, 2017): 332. http://dx.doi.org/10.14429/dsj.67.10657.

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<p>This study presents a predictive guidance scheme for tactical missiles. The modern day targets, with improved manoeuverability, have revealed insufficient performance of the conventional guidance laws. The underlying cause of this poor performance is the reactive nature of the conventional guidance laws such as proportional navigation (PN) and pure pursuit (PP). Predictive guidance offers an alternative approach to the classical methods by taking proactive actions by estimating target’s future trajectory. However, most of the existing predictive guidance approaches assume that the interceptor have a model of the target dynamics. A guidance strategy is developed in this study, that can learn the target dynamics iteratively and adapt the interceptor actions accordingly. A recursive least squares (RLS) estimation algorithm is employed for learning and estimating the possible future target positions, and a fixed horizon nonlinear program is employed for selecting the optimal interception action. Monte-Carlo simulations show that the guidance algorithm introduced in this work demonstrates a significantly improved performance compared to the alternatives in terms of interception time and miss distance.</p>

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Ferreira, Silvia Costa, João Gabriel Luppi Foster, Robson Bauwelz Gonzatti, Rondineli Rodrigues Pereira, Guilherme Gonçalves Pinheiro, and Bruno P. Braga Guimarães. "Online Adaptive Parameter Estimation of a Finite Control Set Model Predictive Controlled Hybrid Active Power Filter." Energies 16, no. 9 (April 29, 2023): 3830. http://dx.doi.org/10.3390/en16093830.

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This paper presents a novel strategy for online parameter estimation in a hybrid active power filter (HAPF). This HAPF makes use of existing capacitor banks which it combines with an active power filter (APF) in order to dynamically compensate reactive power. The equipment is controlled with finite control set model predictive control (FCS-MPC) due to its already well-known fast dynamic response. The HAPF model is similar to a grid-connected LCL-filtered converter, so the direct control of the HAPF current can cause resonances and instabilities. To solve this, indirect control, using the capacitor voltage and the inverter-side current, is applied in the cost function, which creates high dependency between the system parameters and the equipment capability to compensate the load reactive power. This dependency is evaluated by simulations, in which the capacitor bank reactance is shown to be the most sensitive parameter, and, thus, responsible for inaccuracies in the FCS-MPC references. In order to minimize this problem without increasing the complexity of the FCS-MPC algorithm, an estimation technique, based on adaptive notch filters, is proposed. The proposed algorithm is tested in a laboratory prototype to demonstrate its ability to follow variations in the HAPF capacitor reactance, effectively correcting the reactive power reference and providing dynamic reactive power compensation. During the tests, the proposed algorithm was capable of keeping the supplied reactive power within a 1% error, even in a situation with 33% variation in the HAPF capacitor reactance.

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Hassan, Raaed Faleh, and Suha Sabah Shyaa. "Design and Analysis of the STATCOM Based on Diode Clamped Multilevel Converter Using Model Predictive Current Control Strategy." European Journal of Electrical Engineering 23, no. 3 (June 21, 2021): 221–28. http://dx.doi.org/10.18280/ejee.230306.

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In recent decades, multi-level converters have become popular and used in many power systems applications. Compared with conventional converters, multi-level converters contribute to reducing the voltage stress on the switching devices and enhancing the power quality delivered to the load. In this paper, the study of the five-level diode clamped multilevel converter based static synchronous compensator has been accomplished. Model Predictive current control strategy which a type of modern control algorithms was employed for driving the proposed compensator. The suggested five level converter controlled by model predictive current control is firstly examined to verify that this control algorithm is appropriate for achieving the desired performance. Then the proposed converter and control combination is employed and simulated as a static synchronous compensator in distributed power system. Moreover, in order to examine the robustness of this compensator, the load status is suggested to be heavy inductive. Simulation process has been performed using MATLAB – SIMULINK software package. The results show that the implemented configuration (converter and control algorithm) provides high power quality improvement with adequate reactive power compensation.

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Ferro, Giulio, Michela Robba, and Roberto Sacile. "A Model Predictive Control Strategy for Distribution Grids: Voltage and Frequency Regulation for Islanded Mode Operation." Energies 13, no. 10 (May 21, 2020): 2637. http://dx.doi.org/10.3390/en13102637.

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In the last few years, one of the most important challenges of power technologies has been the integration of traditional energy production systems and distributed energy resources. Large-scale photovoltaic systems and wind farms may decrease the quality of the electrical grid service, mainly due to voltage and frequency peaks and fluctuations. Besides, new functionalities, such as the operation in islanded mode of some portions of the medium-voltage grid, are more and more required. In this respect, a model predictive control for voltage and frequency regulation in interconnected local distribution systems is presented. In the proposed model, each local system represents a collection of intelligent buildings and microgrids with a large capacity in active and reactive power regulation. The related model formalization includes a linear approximation of the power flow equations, based on stochastic variables related to the electrical load and to the production from renewable sources. A model predictive control problem is formalized, and a closed-loop linear control law has been obtained. In the results section, the proposed approach has been tested on the Institute of Electrical and Electronics Engineers(IEEE) 5 bus system, considering multiple loads and renewable sources variations on each local system.

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Rony, MA, MH Rana, and MN Uddin. "Analysis Using Simulations of Predictive Control Algorithms Applied to the Matrix Converter." Saudi Journal of Civil Engineering 7, no. 10 (November 2, 2023): 241–51. http://dx.doi.org/10.36348/sjce.2023.v07i10.001.

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In this final work, a model-based predictive control strategy is proposed for three-phase matrix converter (MC) in order to control the power delivered to the balanced/linear loads for distributed generation applications. The discrete-time model of the MC, filter and load are used to predict the future behavior of the load currents for each of the possible switching states. The proposed predictive current method chooses, on one hand, a switching state that minimizes the error between the output currents and their references, and on the other hand, the reactive power output and his reference. The feasibility of the proposed method is verified through simulation results, evaluating these results using as a performance parameter the total harmonic distortion. These results validate the dynamic performance and efficiency of the proposed method.

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Wolf, S., and J. Miethlinger. "Control Strategies for Reactive Extrusion of Polypropylene by Peroxide Degradation A Brief Review and an Experimental Study." International Polymer Processing 36, no. 5 (November 1, 2021): 472–98. http://dx.doi.org/10.1515/ipp-2020-4088.

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Abstract Industry 4.0 and digitalization are widely argued for the future success of numerous industrial solutions. Big data management might lead to the assumption that every issue can be solved numerically without any physical background. To some extent, this strategy will help within the plastics industry in general and in the extrusion technology in particular. However, a deep process knowledge together with process-relevant sensors, as well as the right process arrangements within the processing chain combined with smart data mining methods will be still the key success of industry 4.0. This presentation illustrates, based on a brief review on existing control strategies (Part 1), including sensory and predictive control models for reactive extrusion applied at a real-life on-site best practice project (Part 2), possibilities in combination of process tasks with digitalization approaches for PP-Polymer production. Specifically, rheological research conducted with a novel, patented multi-point rheometer (part 3), will provide a deeper insight into dynamic processes such as reactive extrusion. With those results and derivations thereof, improvements in predictive process control in addition to artificial control systems are made and might even lead to further interesting opportunities.

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Shehadeh, Hazem Khaled. "Evaluating the Impact of Transitioning Maintenance Strategy from Reactive to Proactive in Power Generation Companies: An Empirical Analysis." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 21 (March 22, 2024): 820–38. http://dx.doi.org/10.37394/23207.2024.21.69.

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This empirical study rigorously investigates the impact of transitioning from a reactive maintenance strategy to a proactive approach within the context of power generation companies. The central aim is to quantify and provide a comparative analysis of the efficiency, cost implications, and overall operational impact of adopting proactive versus reactive maintenance strategies in a power plant setting. Drawing on meticulously collected data, the research considers an array of key performance indicators, including maintenance costs, equipment breakdowns, downtime duration, total power output, equipment lifespan, safety incidents, regulatory compliance violations, and investment in staff training and predictive maintenance tools. The findings of this study are both revealing and quantitatively substantial. A transition to a proactive maintenance strategy has demonstrated a reduction in maintenance costs by approximately 20%, coupled with a 35% decrease in the number of equipment breakdowns. Downtime duration was significantly reduced by 40%, enhancing operational efficiency and power output. Notably, the total power output increased by 15%, and the equipment lifespan was extended by an average of 25%. Furthermore, a marked decrease of 50% in safety incidents was observed, reflecting the profound impact of proactive strategies on enhancing safety protocols. However, these improvements are juxtaposed with an initial investment surge, where staff training costs increased by 30%, and expenditure on predictive maintenance tools rose by 25%. This research underscores the critical importance of a comprehensive and quantified understanding of maintenance strategies and their broader impacts on power plant performance. The study illustrates that while proactive maintenance demands initial investments, the long-term benefits significantly outweigh these costs, leading to enhanced operational efficiency, safety, and cost-effectiveness. The insights gleaned from this study provide invaluable guidance for power plant operators, stakeholders, and policymakers in their pursuit to optimize operations, improve safety standards, and achieve economic efficiencies, thereby advocating for a strategic shift towards more proactive maintenance approaches in power plant operations.

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Saeed, Amer Tayes, Mohammed Qasim Taha, and Abdullah Khalid Ahmed. "Tracking technique for the sudden change of PV inverter load." International Journal of Power Electronics and Drive Systems (IJPEDS) 10, no. 4 (December 1, 2019): 2076. http://dx.doi.org/10.11591/ijpeds.v10.i4.pp2076-2083.

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Many power electronics applications require a power calculation in the control system. To get a suitable output, engineers need to control the process and regulate the power exchange with the grid. Since real and reactive power calculations are so crucial a topic, a novel control strategy for a single-phase photovoltaic (PV) inverter has been developed. Therefore, Direct power control (DPC) and a single-phase three-level space vector pulse width modulation (SVPWM) combine as a control and modulation system. In this paper, predictive real and reactive power control and SVPWM method are conferred in the inner loop. A voltage controller based on a proportionalintegral (PI) scheme is used in the outer loop to acquire constant output voltage and provide power refers to the DPC. The performance of the proposed method is verified by using MATLAB/SIMULINK.

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Tanta, Mohamed, J. G. Pinto, Vitor Monteiro, Antonio P. Martins, Adriano S. Carvalho, and Joao L. Afonso. "Deadbeat Predictive Current Control for Circulating Currents Reduction in a Modular Multilevel Converter Based Rail Power Conditioner." Applied Sciences 10, no. 5 (March 8, 2020): 1849. http://dx.doi.org/10.3390/app10051849.

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This paper presents a deadbeat predictive current control methodology to reduce the circulating currents in a modular multilevel converter (MMC) when it operates as a rail power conditioner (RPC) in a conventional railway system-based V/V connection. For this purpose, a half-bridge MMC based on half-bridge submodules, operating as an RPC is explained, and the total system is denominated as a simplified rail power conditioner (SRPC). The SRPC in this study is used to compensate harmonics, reactive power, and the negative sequence component of currents. This paper explains the SRPC system architecture, the key control algorithms, and the deadbeat predictive current control methodology. Mathematical analysis, based on the MMC equivalent circuit, is described and the reference equations are presented. Moreover, simulation results of the deadbeat predictive current control methodology are compared with the results of the conventional proportional-integral (PI) controller. This comparison is to verify the effectiveness of the proposed control strategy. Simulation results of the SRPC show reduced circulating currents in the MMC phases when using the predictive control approach, besides accomplishing power quality improvement at the three-phase power grid side.

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Torana Kamble, Et al. "Predictive Resource Allocation Strategies for Cloud Computing Environments Using Machine Learning." Journal of Electrical Systems 19, no. 2 (January 25, 2024): 68–77. http://dx.doi.org/10.52783/jes.692.

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Cloud computing revolutionizes fast-changing technology. Companies' computational resource use is changing. Businesses can quickly adapt to changing market conditions and operational needs with cloud-based solutions' adaptability, scalability, and cost-efficiency. IT operations and service delivery have changed due to widespread computational resource access. Cloud computing efficiently allocates resources in cloud environments, making it crucial to this transformation. Resource allocation impacts efficiency, cost, performance, and SLAs. Users and providers can allocate cloud resources based on workloads using elasticity, scalability, and on-demand provisioning. IT economics and operational effectiveness have changed due to rapid and flexible resource allocation. Proactive versus reactive resource allocation is key to understanding cloud resource management challenges and opportunities. Reactive strategies allocate resources only when shortages or surpluses occur at demand. This responsive strategy often leads to inefficiencies like over- or under-allocation, which raises costs and lowers performance. Predictive analysis and workload forecasting predict resource needs in proactive resource allocation. Optimize resource use to avoid shortages and over-provisioning. Attention has been drawn to proactive predictive resource allocation. These methods predict resource needs using historical data, machine learning, and predictive analytics. Predictive strategies optimize resource allocation by considering future decisions. Reduced bottlenecks boost user satisfaction and lower operational costs. Matching resource distribution to workloads optimizes cloud resource management. Resource allocation prediction improves with deep learning. CNN, LSTM, and Transformer cloud resource forecasting algorithms are promising. New tools for accurate and flexible workload predictions have come from their ability to spot intricate patterns in historical data. This paper compares CNN, LSTM, and Transformer deep learning algorithms for cloud computing resource allocation forecasting. This study determines the best predictive accuracy and workload ada[1]ptability algorithm using Google Cluster Data (GCD). The study evaluates upgrading cloud computing resource allocation with the Transformer model. This study advances predictive resource allocation strategies, which can help cloud service providers and organizations improve resource utilization, cost-effectiveness, and performance in the face of rapid technological change.

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Zhang, Xiao Ying, and Xue Jie Ma. "Predictive Direct Capacitor Power Control Based on Fractional-Order PID Controller of Three-Phase Voltage Source PWM Rectifiers." Advanced Materials Research 1070-1072 (December 2014): 1115–18. http://dx.doi.org/10.4028/www.scientific.net/amr.1070-1072.1115.

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In order to improve the dynamic performance of three-phase voltage type PWM rectifiers predictive direct capacitor power control, in view of the deficiencies of slow response and poor dynamic performance of present PI control of load disturbance, the fractional order PID controller of the DC side voltage control section is introduced to the capacitive power control. The new predictive direct power capacitor control strategy has been proposed. It consists of two parts: the outer loop of voltage square model of fractional order PID and the inner loop of capacitor power and the reactive power in network side. Through decoupling control with the prediction control of the inner loop and the voltage outer loop of fractional PID, the dynamic performance of a direct power control has been improved. The Matlab/Simulink simulation results prove that the system has better dynamic performance, robustness and response speed of direct power control.

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Kasim, Mostafa Q., and Raaed Faleh Hassan. "Model Predictive Current Control with Asymmetric Stacked Multilevel Inverter and LCL-Filter Based STATCOM." Journal of Techniques 3, no. 2 (June 30, 2021): 19–30. http://dx.doi.org/10.51173/jt.v3i2.305.

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The work presented in this paper deals with a proposal of a new topology of a multilevel inverter to act as a Static synchronous Compensator (STATCOM). The proposed inverter is the five-level Asymmetric Stacked Multi-Level Inverter (ASMLI). One of the essential features of this inverter that distinguishes it from the conventional types is that it achieves the required voltage levels with fewer switching devices, leading to simplifying the control process. Moreover, the work includes using a Finite Control Set Model Predictive Current Control (FCS-MPCC) to control the proposed structure. The FCS-MPCC control strategy performs the finite optimization process at the current sampling instant to provide the optimum switching states to the inverter at the next sampling instant. Therefore, this control strategy allows injecting harmonic current and reactive power compensation to reduce source current distortion and improve the voltage profile and power factor. The optimization mechanism reduces the cost function, which is a function of measuring the network current's deviation from the reference value and how the capacitor voltage deviates from the required values. LCL-filter was used to connect this setup to the grid, and its resonance was actively damped using the multivariable capabilities of the FCS-MPCC. The proposed control framework was simulated using MATLAB/Simulink 9.1 environment and tested in a distorted and healthy network compared to a conventional two-level converter with RL-filter. The STATCOM was used to inject reactive power to raise the source power factor to unity and reduce source current harmonics by injecting harmonic current. The proposed prototype could absorb 70% of source current harmonics, which is nearly 25% better than a conventional inverter, inject an appropriate amount of reactive power, and raise the source power factor to unity in two case scenarios. The performance achieved was promising at steady-state operation and speedy response during transients with balanced capacitors voltages.

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Dazok Donald Jambol, Oludayo Olatoye Sofoluwe, Ayemere Ukato, and Obinna Joshua Ochulor. "Transforming equipment management in oil and gas with AI-Driven predictive maintenance." Computer Science & IT Research Journal 5, no. 5 (May 5, 2024): 1090–112. http://dx.doi.org/10.51594/csitrj.v5i5.1117.

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The oil and gas industry faces significant challenges in managing equipment maintenance due to the complexity and criticality of its assets. Traditional maintenance approaches are often reactive and inefficient, leading to costly downtime and safety risks. However, the emergence of artificial intelligence (AI) and predictive maintenance technologies offers a transformative solution to these challenges. This paper explores the role of AI-driven predictive maintenance in revolutionizing equipment management in the oil and gas sector. AI-driven predictive maintenance leverages machine learning algorithms to analyze equipment data and predict when maintenance is required before a breakdown occurs. By monitoring equipment performance in real-time, AI can identify potential issues early, allowing operators to take proactive maintenance actions. This approach helps minimize downtime, reduce maintenance costs, and improve overall equipment reliability and safety. The implementation of AI-driven predictive maintenance requires a comprehensive strategy that includes data collection, analysis, and integration with existing maintenance practices. Successful adoption of AI-driven predictive maintenance can lead to significant benefits for oil and gas companies, including increased equipment uptime, extended asset lifespan, and enhanced operational efficiency. This paper reviews the current landscape of equipment management in the oil and gas industry, highlighting the limitations of traditional maintenance practices and the need for a more proactive approach. It then examines the principles and benefits of AI-driven predictive maintenance, showcasing real-world examples of its successful implementation. Finally, the paper discusses the challenges and considerations for implementing AI-driven predictive maintenance and provides recommendations for oil and gas companies looking to transform their equipment management practices. Keywords: Transforming Equipment; Management; Oil and Gas; AI-Driven; Predictive Maintenance.

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Chen, Xiaotao, Weimin Wu, Ning Gao, Jiahao Liu, Henry Shu-Hung Chung, and Frede Blaabjerg. "Finite Control Set Model Predictive Control for an LCL-Filtered Grid-Tied Inverter with Full Status Estimations under Unbalanced Grid Voltage." Energies 12, no. 14 (July 13, 2019): 2691. http://dx.doi.org/10.3390/en12142691.

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This paper proposes a novel finite control set model predictive control (FCS-MPC) strategy with merely grid-injected current sensors for an inductance-capacitance-inductance (LCL)-filtered grid-tied inverter, which can obtain a sinusoidal grid-injected current whether three-phase grid voltages are balanced or not. Compared with the conventional FCS-MPC method, four compositions are added in the proposed FCS-MPC algorithm, where the grid voltage observer (GVO) and Luenberger observer are combined together to achieve full status estimations (including grid voltage, capacitor voltage, inverter-side current, and grid-injected current), while the sequence extractor and the reference generator are applied to eliminate the double frequency ripples of the active or reactive power, or the negative sequence component (NSC) of the grid-injected current caused by the unbalanced grid voltage. Simulation model and experimental platform are established to verify the effectiveness of the proposed FCS-MPC strategy, with full status estimations under both balanced and unbalanced grid voltage conditions.

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Liu, Yuxin, Yi Mei, Mengjie Zhang, and Zili Zhang. "A Predictive-Reactive Approach with Genetic Programming and Cooperative Coevolution for the Uncertain Capacitated Arc Routing Problem." Evolutionary Computation 28, no. 2 (June 2020): 289–316. http://dx.doi.org/10.1162/evco_a_00256.

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The uncertain capacitated arc routing problem is of great significance for its wide applications in the real world. In the uncertain capacitated arc routing problem, variables such as task demands and travel costs are realised in real time. This may cause the predefined solution to become ineffective and/or infeasible. There are two main challenges in solving this problem. One is to obtain a high-quality and robust baseline task sequence, and the other is to design an effective recourse policy to adjust the baseline task sequence when it becomes infeasible and/or ineffective during the execution. Existing studies typically only tackle one challenge (the other being addressed using a naive strategy). No existing work optimises the baseline task sequence and recourse policy simultaneously. To fill this gap, we propose a novel proactive-reactive approach, which represents a solution as a baseline task sequence and a recourse policy. The two components are optimised under a cooperative coevolution framework, in which the baseline task sequence is evolved by an estimation of distribution algorithm, and the recourse policy is evolved by genetic programming. The experimental results show that the proposed algorithm, called Solution-Policy Coevolver, significantly outperforms the state-of-the-art algorithms to the uncertain capacitated arc routing problem for the ugdb and uval benchmark instances. Through further analysis, we discovered that route failure is not always detrimental. Instead, in certain cases (e.g., when the vehicle is on the way back to the depot) allowing route failure can lead to better solutions.

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Nakagawa, N., F. Inanc, A. Frishman, R. Bruce Thompson, W. R. Junker, F. H. Ruddy, A. R. Dulloo, J. M. Beatty, and N. G. Arlia. "On-Line NDE and Structural Health Monitoring for Advanced Reactors." Key Engineering Materials 321-323 (October 2006): 234–39. http://dx.doi.org/10.4028/www.scientific.net/kem.321-323.234.

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This paper considers the use of on-line structural health monitoring in advanced nuclear power systems such as IRIS. The motivation for the on-line health monitoring is to prevent routine maintenance from interrupting long-term continuous reactor operation. However, the outcome of the on-line monitoring implementation has a broader impact, and amounts to a paradigm shift in maintenance strategy from outage-based maintenance to continuous real-time monitoring of operational and structural integrity. Indeed, on-line health monitoring data will provide a foundation for diagnostics and prognostics (i.e., predictive) capabilities that will detect component degradation prior to failure, thus allowing for proactive rather than reactive maintenance strategies. Specifically, this paper briefly reports on our studies on (1) on-line monitoring strategy and its benefits, (2) candidate reactor components where on-line monitoring provides maximum benefits, (3) applicable on-line NDE sensor methodologies and conceptual sensor designs, and (4) model-based sensor performance estimations.

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Vilian, A. T. Ezhil, Wonyoung Kim, Bumjun Park, Seo Yeong Oh, TaeYoung Kim, Yun Suk Huh, Chang Kwon Hwangbo, and Young-Kyu Han. "Efficient electron-mediated electrochemical biosensor of gold wire for the rapid detection of C-reactive protein: A predictive strategy for heart failure." Biosensors and Bioelectronics 142 (October 2019): 111549. http://dx.doi.org/10.1016/j.bios.2019.111549.

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Alanio, Cécile, Fabrice Lemaitre, Helen K. W. Law, Milena Hasan, and Matthew L. Albert. "Enumeration of human antigen–specific naive CD8+ T cells reveals conserved precursor frequencies." Blood 115, no. 18 (May 6, 2010): 3718–25. http://dx.doi.org/10.1182/blood-2009-10-251124.

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Abstract The number of antigen-specific naive CD8+ T cells is believed to be important in the shaping of adaptive immune responses, and is predictive for the magnitude of priming responses in mouse models. Because of extremely low precursor frequencies, knowledge about these cells comes from indirect techniques and estimations. Here, we present a strategy based on the combination of tetramer staining, magnetic-bead enrichment, and multiparametric cytometry, which permitted direct detection and analysis of CD8+ T cells reactive for 6 different naive epitopes (MART-126-35, HIV-1 Gag p1777-85, hepatitis C virus [HCV] NS31406-1415, HCV Core132-140, NY-ESO-1157-165, and cytomegalovirus [CMV] pp65495-503). Interestingly, we detected higher than 100-fold differences in precursor frequency across these epitopes (from 0.6 × 10−6 to 1.3 × 10−4), but conserved frequencies among humans. Development of a procedure for direct assessment of T-cell precursor frequency in humans has important implications, with particular relevance to vaccine development and monitoring of tumor and self-reactive T cells.

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Kim, Jong-Lim, Jin Yong Shin, Si-Gyun Roh, Suk Choo Chang, and Nae-Ho Lee. "Predictive Laboratory Findings of Lower Extremity Amputation in Diabetic Patients: Meta-analysis." International Journal of Lower Extremity Wounds 16, no. 4 (November 16, 2017): 260–68. http://dx.doi.org/10.1177/1534734617737660.

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Lower extremity amputation is a source of morbidity and mortality among diabetic patients. This meta-analysis aimed to identify significant laboratory data in patients with diabetic foot ulcer with high rates of lower extremity amputation. We performed a systematic literature review and meta-analysis using MEDLINE, EMBASE, and Cochrane databases. We extracted and evaluated 11 variables from the included studies based on amputation rates. This study used the Newcastle-Ottawa Scale to assess the quality of the studies. The search strategy identified 101 publications from which we selected 16 articles for review. We identified HbA1c, fasting blood glucose, white blood cells, C-reactive protein, and erythrocyte sedimentation rate as predictive variables of higher major amputation rate. Although further investigation of long-term and prospective studies is needed, we identified 5 variables as predisposing factors for higher major amputation in diabetic patients through meta-analysis.

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Li, Hong, Yang Liu, Rende Qi, and Yu Ding. "A Novel Multi-vector Model Predictive Current Control of Three-Phase Active Power Filter." European Journal of Electrical Engineering 23, no. 1 (February 28, 2021): 71–78. http://dx.doi.org/10.18280/ejee.230109.

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This paper proposes the application of a novel finite control set model predictive control (FCS-MPC) strategy in active power filter (APF). In the process of APF compensating harmonic and reactive power, the traditional single vector model predictive current control (MPCC) has low tracking accuracy to harmonic current, while the multi-vector MPCC has the problems of complex calculation and long calculation time, a new multi-vector MPCC control method has proposed in this paper. Firstly, the harmonic reference value is transformed into d-q coordinate system, according to the sector, the slope is calculated and the action time is obtained. Six new expected vectors are synthesized from six effective vectors and zero vectors. The value function is established to loop and calculate the optimal virtual vector, which is applied to APF. Compared with single vector control and traditional multi-vector control, it has a wider vector action area and faster calculation speed. The compensation results and dynamic performance are improved. The simulation results show that the total harmonic distortion (THD) is low.

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Martiny, Patrícia Borba, Fernanda de-Paris, Alice Beatriz Mombach Pinheiro Machado, Ricardo Obalski de Mello, Martha Bergman Senger, Maria Clara Medina Corrêa, Luiz Carlos Werres Junior, and Carolina Fischinger Moura de Souza. "Comparison of the performance of polymerase chain reaction and pp65 antigenemia for the detection of human cytomegalovirus in immunosuppressed patients." Revista da Sociedade Brasileira de Medicina Tropical 44, no. 3 (May 27, 2011): 286–89. http://dx.doi.org/10.1590/s0037-86822011005000029.

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INTRODUCTION: Human cytomegalovirus (HCMV) is often reactive in latently infected immunosuppressed patients. Accordingly, HCMV remains one of the most common infections following solid organ and hemopoietic stem cell transplantations, resulting in significant morbidity, graft loss and occasional mortality. The early diagnosis of HCMV disease is important in immunosuppressed patients, since in these individuals, preemptive treatment is useful. The objective of this study was to compare the performance of the in-house qualitative polymerase chain reaction (PCR) and pp65 antigenemia to HCMV infection in immunosuppressed patients in the Hospital de Clínicas of Porto Alegre (HCPA). METHODS: A total of 216 blood samples collected between August 2006 and January 2007 were investigated. RESULTS: Among the samples analyzed, 81 (37.5%) were HCMV-positive by PCR, while 48 (22.2%) were positive for antigenemia. Considering antigenemia as the gold standard, sensitivity, specificity, positive predictive values and negative predictive values for PCR were 87.5%, 76.8%, 51.8% and 95.5% respectively. CONCLUSIONS: These results demonstrated that qualitative PCR has high sensitivity and negative predictive value (NPV). Consequently PCR is especially indicated for the initial diagnosis of HCMV infection. In the case of preemptive treatment strategy, identification of patients at high-risk for HCMV disease is fundamental and PCR can be useful tool.

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Elgammal, Adel, and Curtis Boodoo. "Optimal Energy Management System Control of Permanent Magnet Direct Drive Linear Generator for Grid-Connected FC-Battery-Wave Energy Conversion." European Journal of Engineering and Technology Research 8, no. 5 (September 29, 2023): 25–30. http://dx.doi.org/10.24018/ejeng.2023.8.5.3083.

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The Wave Energy Conversion System (WECS) control strategy is presented in this study to make sure the system operates at its best under fluctuating wave resource situations. The suggested system consists of a MOPSO based MPC approach, a point absorber WEC oscillating in heave, back-to-back power converter for grid connections, and a linear permanent magnet generator. Despite the benefits of model predictive control, problems including switching frequency variations, steady-state errors, high processing costs, and constrained prediction horizons continue to exist. The article presents a method that incorporates the switching control action into the cost function while maintaining the finite nature of a model predictive control to handle the switching frequency issue. In order to minimise switching frequency variations while also addressing other control goals, such as regulating the direct current linked voltage and controlling the flow of active and reactive power, the switching control weight factors are optimised. In order to increase power quality, a fuel cell-based short-term energy storage system is also included to direct current link between the back-to-back converters.

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Ma, Xiaoxin, Yongli Wang, Hongyu Wu, Fei Li, Xiping Feng, Yingxin Xie, Danshu Xie, Wenji Wang, Edward Chin Man Lo, and Haixia Lu. "Periodontal health related–inflammatory and metabolic profiles of patients with end-stage renal disease: potential strategy for predictive, preventive, and personalized medicine." EPMA Journal 12, no. 2 (April 22, 2021): 117–28. http://dx.doi.org/10.1007/s13167-021-00239-0.

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Abstract Objectives To compare the periodontal health related–inflammatory and metabolic differences between patients with end-stage renal disease (ESRD) and healthy controls (HC), and to identify potential biomarkers in gingival crevicular fluid (GCF) and serum of ESRD patients for predictive, preventive, and personalized medicine (PPPM). Methods Patients with ESRD (ESRD group; n = 52) and healthy controls (HC group; n = 44) were recruited. Clinical periodontal parameters were recorded. The differential metabolites in the GCF and serum were identified by liquid chromatography/mass spectrometry (LC/MS). Inflammatory markers including interleukin-1β (IL-1β), interleukin-6 (IL-6), interleukin-8 (IL-8), and C-reactive protein (CRP) were also assessed. Results In the ESRD group, IL-8 and CRP were significantly higher in GCF, whereas IL-6 and CRP were significantly higher in serum, compared with HC group (all P < 0.05). In the case of GCF, taurine levels were positively correlated with IL-8 levels in both groups (all P < 0.05). In the case of serum, l-phenylalanine and p-hydroxyphenylacetic acid levels were positively correlated with CRP levels in both groups (all P < 0.05). Significant positive correlations were observed between metabolites (including pseudouridine, l-phenylalanine, and p-hydroxyphenylacetic acid) and IL-6 levels only in ESRD group. Conclusions IL-8 and CRP are potential inflammatory makers that reflect the periodontal health of ESRD individual, which may be considered the valuable predictive diagnostics in the agreement with PPPM philosophy. Besides, metabolites of taurine in GCF as well as l-phenylalanine and p-hydroxyphenylacetic acid in serum are possible biomarkers correlated with inflammatory markers. All these biomarkers may also be highly recommended as a novel predictive/diagnostic tool for the assessment of inflammatory status from the perspectives of PPPM in view of susceptible population and individual screening.

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Jimenez, Jose-Fernando, Abdelghani Bekrar, Damien Trentesaux, and Paulo Leitão. "A switching mechanism framework for optimal coupling of predictive scheduling and reactive control in manufacturing hybrid control architectures." International Journal of Production Research 54, no. 23 (May 2, 2016): 7027–42. http://dx.doi.org/10.1080/00207543.2016.1177237.

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ECKI, M., H. POILVACHE, M. RANDY BUZISA, M. VAN CAUTER, H. RODRIGUEZ-VILLALOBOS, J. C. YOMBI, and O. CORNU. "Are C-reactive protein (CRP) and neutrophil to lymphocyte ratio (NLR) predictive markers of successful two-stage prosthetic joint infection management?" Acta Orthopaedica Belgica 89, no. 4 (December 2023): 659–64. http://dx.doi.org/10.52628/89.4.11123.

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The treatment of prosthetic joint infections (PJI) regularly involves a two-stage surgical strategy. The second stage is generally performed when the surgeon estimates that the infection is under control based on either the treatment length or clinical and biological data. We have raised the question whether standardization of C-reactive protein (CRP) and neutrophil to lymphocyte ratio (NLR) values could be either indicative of infection control or predictive of infection recurrence. This was an analytical retrospective and monocentric cross-sectional observational study. The CRP and NLR values were recorded prior to the second-stage surgery in a sample of 100 patients who underwent a two-stage revision arthroplasty following a PJI, and these values were correlated with the absence of infectious recurrence within a 2-year follow-up. The statistical analysis consisted of evaluating the diagnostic validity of each marker, first individually, and then in combination. CRP was shown associated with a 68% sensitivity, 40% specificity, 27% positive predictive value (PPV), and 79% negative predictive value (NPV). The ROC curve was 51.1%. The NLR displayed a 12% sensitivity, 89% specificity, 27% PPV, and 75% NPV. The ROC curve was 47.9%. The combination of the two markers did not fundamentally improve the statistical results, with only a 43% concordance of the two markers, 27% sensitivity, 84% specificity, 37% PPV, and 77% NPV. Measuring CRP and NLR values, either individually or in combination, before the second-stage revision arthroplasty did not turn out to be predictive of either infection recurrence or cure within two years of follow-up. Therefore, an absolute test is still lacking, which would enable us to predict without failure the absence of control or the control of a PJI before or after second-stage revision. Level of evidence: Level III, retrospective cross-sectional study.

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Journal articles on the topic 'Predictive-Reactive strategy'

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