Relevant bibliographies by topics / Power system models

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Power system models'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 June 2025

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Journal articles on the topic "Power system models"

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Zorzano Santamaría, P. J., A. Falces de Andrés, L. A. Fernández Jiménez, et al. "Hybrid Power Systems Planning with Geographical Information System Models." Renewable Energy and Power Quality Journal 1, no. 08 (2010): 1180–85. http://dx.doi.org/10.24084/repqj08.618.

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Sahadet Hossain, Mohammad, and M. Monir Uddin. "Reduce Order Modelling of Power System Models Using Interpolatory Projections Technique." International Journal of Modeling and Optimization 5, no. 3 (2015): 228–33. http://dx.doi.org/10.7763/ijmo.2015.v5.467.

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Kumar, A. Kranthi. "Study of Reduced System Models for Power System Monitoring." HELIX 8, no. 2 (2018): 3087–92. http://dx.doi.org/10.29042/2018-3087-3092.

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Luiz Carlos P. da Silva, Vivaldo F. "Power System Voltage Stability Assessment Using Enhanced Power Flow Models." Electric Power Components and Systems 29, no. 4 (2001): 349–60. http://dx.doi.org/10.1080/15325000151125658.

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Henville, C. F. "Digital relay reports verify power system models." IEEE Computer Applications in Power 13, no. 1 (2000): 26–32. http://dx.doi.org/10.1109/67.814663.

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Rosehart, William D., and Claudio A. Cañizares. "Bifurcation analysis of various power system models." International Journal of Electrical Power & Energy Systems 21, no. 3 (1999): 171–82. http://dx.doi.org/10.1016/s0142-0615(98)00037-4.

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Bogdan Proca, Amuliu, and Ali Keyhani. "Identification of power steering system dynamic models." Mechatronics 8, no. 3 (1998): 255–70. http://dx.doi.org/10.1016/s0957-4158(98)00003-8.

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Henville, C. F. "Digital relay reports verify power system models." IEEE Transactions on Power Delivery 13, no. 2 (1998): 386–93. http://dx.doi.org/10.1109/61.660905.

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Granda, Nelson, and Karen Paguanquiza. "System Frequency Response Models for the Ecuadorian Interconnected Power System." Revista Técnica "energía" 21, no. 1 (2024): 22–33. http://dx.doi.org/10.37116/revistaenergia.v21.n1.2024.637.

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En el presente trabajo se presenta una metodología, basada en modelos de repuesta de la frecuencia, para estimar el valor mínimo o máximo -nadir- que alcanza la frecuencia cuando en el sistema eléctrico de potencia se presenta un desbalance generación – carga. Con este fin, se determina el modelo reducido de 1er orden del sistema de control potencia – frecuencia del generador usando el “Estimador de Parámetros” de Matlab/Simulink. Luego, se establecen ecuaciones que provienen del modelo equivalente reducido para estimar el valor de la máxima desviación transitoria de la frecuencia y su tiempo de ocurrencia. Para ilustrar la aplicación de la metodología presentada se ha elegido el sistema de prueba IEEE New England de 39 barras y 10 generadores. Hecho esto, la metodología propuesta se aplica al Sistema Nacional Interconectado ecuatoriano. Se presentan resultados de simulaciones en el dominio del tiempo usando el programa PowerFactory, mismos que son comparados con los resultados calculados mediante el modelo equivalente reducido y el modelo analítico. Se concluye que la metodología propuesta estima con alta precisión la máxima desviación transitoria de la frecuencia y su tiempo de ocurrencia.
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Mashalov, Eugene, Vladimir Neuymin, Denis Snegirev, Andrej Mihajlenko, Daniil Teplukhin, and Viktor Klassen. "Prospective power systems models development algorithms." E3S Web of Conferences 584 (2024): 01034. http://dx.doi.org/10.1051/e3sconf/202458401034.

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This research aims to automate the creation of prospective models essential for long-term power system planning. Several algorithms and accompanying tools have been developed to transform an existing power system simulation model (SM) into a series of prospective simulation models (PSMs). This article outlines a general approach to SM conversion using a command system, enabling the binding of SM variants to specific planning periods and operating conditions (OC). Several tools to ensure PSM quality and adequacy, including forecasted power consumption balancing and security-constrained optimal power flow (SCOPF), are presented. Advanced techniques for assessing transient stability model quality and implementing hierarchical power system modeling are also integral parts of this development process.
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Dissertations / Theses on the topic "Power system models"

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Holm, Gustav. "Automated Model Transformation for Cyber-Physical Power System Models." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214750.

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Standardized information and mathematicalmodels, which model the characteristics of the power generationand power transmission systems, are requirements for futuredevelopment and maintenance of different applications tooperate the electrical grid. Available databases such as Nordpoolprovides large amounts of data for power supply and demand [1].The typical misconception with open availability of data is thatexisting power system software tools can interact and process thisdata. Difficulties occur mainly because of two reasons. The firston is the amount of data produced. When the topology of theelectrical grid changes e.g. when a switch opens or closes, the flowof electrical power changes. This event produce changes ingeneration, transmission and distribution of the energy anddifferent data sets are produced. The second problem is therepresentation of information [2]. There are a limited number ofsoftware tools that can analyze this data, but each software toolrequires a specific data format structure to run. Dealing withthese difficulties requires an effective way to transform theprovided data representation into new data structures that canbe used in different execution platforms. This project aims tocreate a generic Model-to-Text (M2T) transformation capable oftransforming standardized power system information modelsinto input files executable by the Power System Analysis Tool(PSAT). During this project, a working M2T transformation wasnever achieved. However, missing functionality in someprograms connected to sub processes resulted in unexpectedproblems. This led to a new task of updating the informationmodel interpreter PyCIM. This task is partially completed andcan load basic power system information models.
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Anderson, Sharon Lee. "Reduced order power system models for transient stability studies." Thesis, This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-09052009-040743/.

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Hammer, Anders. "Analysis of IEEE Power System Stabilizer Models." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elkraftteknikk, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-14035.

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Student:Anders HammerSupervisor:Kjetil UhlenContact:Daniel MotaCollaboration with:Voith HydroProblem descriptionIEEE (Institute of Electrical and Electronics Engineers) presented in 2005 a new PSS structure named IEEE PSS4B (Figure 0 1). Voith Hydro wants to analyse the pros and cons of using this new type compared to older structures. The PSS4B is a multi-band stabilizer that has three separate bands and is specially designed to handle different oscillation frequencies in a wide range. Until now, Voith Hydro has used the common PSS2B in their installations, but in the future they will probably start to implement the new PSS4B. This master thesis will seek to find an answer on following questions:•How should the PSS4B be tuned to give the best damping of the local and inter-area oscillation mode?•Will an implementation of PSS4B give a better result compared to PSS2B?•What are the pros and cons of PSS2B and PSS4B? Figure 0 1: The multi-band stabilizer, IEEE PSS4B [1].MethodIn order to test and compare different PSS models, a simple two-area network model is created in a computer simulation programme (SIMPOW). One of the generating units is a hydro generator, which has a model of a static excitation system made by Voith Hydro. This network is characterised by a poorly damped inter-area oscillation mode, and in addition some local oscillation modes related to each machine. Different PSS structures (PSS2B and PSS4B) are then tuned and installed in the excitation system of the hydro generator, in order to improve the stability of the network. Different tuning methods of the PSS4B are designed, tested and later compared with the more common stabilizer the PSS2B. Simplifications are made where parts of the stabilizer is disconnected in order to adapt the control structure to the applied network and its oscillations. Totally 5 different tuning methods are presented, and all these methods are based on a pole placement approach and tuning of lead/lag-filters. ResultsInitial eigenvalues of the different setups are analysed and several disturbances are studied in time domain analysis, in order to describe the robustness of the system. Figure 2 illustrates the rotor speed of the generator, where the different PSS’s are implemented. PSS4B is clearly resulting in increased damping of all speed oscillations in this network. The same results can also be seen in an eigenvalue analysis.Conclusion The best overall damping obtained in this master thesis occurs when the high frequency band of the PSS4B is tuned first, and in order to maximize the damping of the local oscillation mode in the network. The intermediate frequency band is then tuned as a second step, according to the inter-area oscillation mode. Results of this tuning technique show a better performance of the overall damping in the network, compared to PSS2B. The improvement of the damping of the inter-area oscillation mode is not outstanding, and the reason is that the applied machine is relative small compared to the other generating units in the network. The oscillation modes in the network (local and inter-area) have a relative small frequency deviation. A network containing a wider range of oscillation frequencies will probably obtain a greater advantage of implementing a multi-band stabilizer.
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Scruggs, James N. "Power system availability determination through Petri net simulation." Ohio : Ohio University, 1995. http://www.ohiolink.edu/etd/view.cgi?ohiou1178910568.

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Persson, Jonas. "Linear models of non-linear power system components." Licentiate thesis, KTH, Electrical Systems, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-1415.

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Cresswell, Charles. "Steady state load models for power system analysis." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3846.

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The last full review of load models used for power system studies occurred in the 1980s. Since then, new types of loads have been introduced and system load mix has changed considerably. The examples of newly introduced loads include drive-controlled motors, low energy consumption light sources and other modern power electronic loads. Their numbers have been steadily increasing in recent years, a trend which is expected to escalate. Accordingly, the majority of load models used in traditional power system studies are becoming outdated, as they are unable to accurately represent power demand characteristics of existing and future loads. Therefore, in order to accurately predict both active and non-active power demand characteristics of aggregated modern power system loads in different load sectors (e.g. residential, commercial or industrial), existing load models should be updated and new models developed. This thesis aims to fill this gap by developing individual, generic and aggregated steady state models of the most common loads in use today, as well as of those expected to show significant growth in the future. The component-based approach is adopted for load modelling, where individual load models are obtained in detailed simulations of physical devices. Whenever possible, the developed individual load models are validated by measurements. These detailed individual load models are then simplified and expressed as equivalent circuit and analytical models, which allowed the establishment of generic load models that can be easily aggregated. It should be noted that since all non-active power characteristics are correctly represented, the developed aggregated load models allow for a full harmonic analysis, which is not the case with the standard steady state load models. Therefore, the proposed load models form an extensive library of comprehensive load models that are suitable for use in multiple areas of power system research. Based on the results of research related to typical domestic/residential sector load mix, the newly developed load models are aggregated and then applied to a typical UK/Scotland distribution network. Considerable differences are seen between network characteristics of newly proposed and previously developed models. The voltage distortion of a typical distribution system bus is investigated, and it is shown that distortion of the system voltage is likely to increase significantly in the future. The results of the presented research also suggest that neglecting the harmonic characteristics from the set of general load attributes may introduce errors in standard load flow studies.
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Nteka, Makhetsi Flora. "Development and assessment of reduced order power system models." Thesis, Cape Peninsula University of Technology, 2013. http://hdl.handle.net/20.500.11838/1088.

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Thesis submitted in fulfilment of the requirements for the degree Master of Technology: Electrical Engineering in the Faculty of Engineering at the Cape Peninsula University of Technology 2013<br>The demand for electrical energy has kept on increasing, thus causing power systems to be more complex and bringing the challenging problems of electrical energy generation, transmission, stability, as well as storage to be examined more thoroughly. With the advent of high-speed computation and the desire to analyze increasingly complex behaviour in power systems, simulation techniques are gaining importance and prevalence. Nevertheless, while simulations of large, interconnected complex power systems are feasible, they remain time-consuming. Moreover, the models and parameters used in simulations are uncertain, due to measurement uncertainty, the need to represent a complex behaviour with low-order models, and the inherent changing nature of the power system. This research explores the use of a model reduction technique and the applications of a Real-Time Digital Simulator (RTDS) to reduce the uncertainty in large-scale complex power system models. The main goal of the research is to develop a reduced order model and to investigate the applications of the RTDS simulator in reduction of large, interconnected power systems models. The first stage of the study is to build and simulate the full model of the power system using the DigSILENT and RTDS simulators. The second phase is to apply model reduction technique to the full model and to determine the parameters in the reduced-order model as well as how the process of reduction increases this model uncertainty. In the third phase the results of the model reduction technique are compared based on the results of the original model - IEEE standard benchmark models has been used. The RTDS was used for comparative purposes. The thesis investigations use a particular model reduction technique as Coherency based Method. Though the method ideas are applicable more generally, a concrete demonstration of its principles is instructive and necessary. Further, while this particular technique is not relevant to every system, it does apply to a broad class of systems and illustrates the salient features of the proposed methodology. The results of the thesis can be used in the development of reduced models of complex power systems, simulation in real-time during power system operation, education at universities, and research. Keywords: IEEE benchmark models, reduced models, Coherency based Method, DigSILENT, RTDS, model uncertainty, power system stability
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Raoofsheibani, Davood [Verfasser]. "Online Power System Security Analysis and State Prediction : Enhanced Power System Models and Tools / Davood Raoofsheibani." Düren : Shaker, 2021. http://d-nb.info/1240853947/34.

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Demiray, Turhan Hilmi. "Simulation of power system dynamics using dynamic phasor models /." Zürich : ETH, 2008. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17607.

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Persson, Jonas. "Bandwidth-reduced Linear Models of Non-continuous Power System Components." Doctoral thesis, Stockholm : Electric Power Systems, School of Electrical Engineering, Royal Institute of Technology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3984.

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Books on the topic "Power system models"

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Das, J. C. Power System Analysis. Marcel Dekker, Inc., 2003.

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Al-Khusaibi, T. M. S. Gas turbine models for power system analysis. UMIST, 1993.

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Blair, Nate. Comparison of photovoltaic models in the System Advisor Model: Preprint. National Renewable Energy Laboratory, 2013.

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Schuermans, Stefan, and Rainer Leupers. Power Estimation on Electronic System Level using Linear Power Models. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-01875-7.

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J, Arrillaga, ed. Power system harmonic analysis. Wiley, 1997.

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Pardalos, P. M. Handbook of Power Systems I. Springer, 2010.

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Zhu, Jizhong. Optimization of power system operation. Wiley-IEEE, 2009.

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Savage, John E. Models of computation: Exploring the power of computing. Addison Wesley, 1998.

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IEEE Power Engineering Society. Energy Development and Power Generation Committee. and IEEE Standards Board, eds. IEEE recommended practice for excitation system models for power system stability studies. Institute of Electrical and Electronics Engineers, 1992.

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Pai, M. A. Energy function analysis for power system stability. Kluwer Academic Publishers, 1989.

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Book chapters on the topic "Power system models"

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Conejo, Antonio J., and Luis Baringo. "Power System Components: Models." In Power Electronics and Power Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69407-8_3.

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Zhu, Yue. "Power System Load Models and Load Modelling." In Power System Loads and Power System Stability. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37786-1_2.

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Anari, Ali, and James W. Kolari. "Profit System Models for Industries." In The Power of Profit. Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0649-6_5.

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Chattopadhyay, Surajit, Madhuchhanda Mitra, and Samarjit Sengupta. "Passivity and Activity Based Models of Polyphase System." In Power Systems. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0635-4_18.

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Hossain, Jahangir, and Hemanshu Roy Pota. "Power System Voltage Stability and Models of Devices." In Power Systems. Springer Singapore, 2014. http://dx.doi.org/10.1007/978-981-287-116-9_2.

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Morison, Kip, and Lei Wang. "Reduction of Large Power System Models: A Case Study." In Power Electronics and Power Systems. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-1803-0_7.

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Lubosny, Zbigniew. "Models of a WTGS Operating in a Power System." In Power Systems. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-10944-1_6.

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Mewada, Arvind, Maninder Singh, Harsh Pratap Singh, and Nagendra Singh. "Voting ensemble-based machine learning models for early detection of power system faults." In Power System Management. CRC Press, 2025. https://doi.org/10.1201/9781003516156-14.

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Anari, Ali, and James W. Kolari. "Profit System Models of the Corporate Sector." In The Power of Profit. Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0649-6_4.

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Yousuf, Hana, Asma Y. Zainal, Muhammad Alshurideh, and Said A. Salloum. "Artificial Intelligence Models in Power System Analysis." In Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51920-9_12.

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Conference papers on the topic "Power system models"

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Rai, Keerti, Surjeet Yadav, and Shalini R. "Nonlinear Regression Models in Power System Optimization." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725129.

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Wei, Aoqiu, Shengli Wang, Li Xie, et al. "A Review of Models and Methods for Precise Load Control Strategies." In 2024 Boao New Power System International Forum - Power System and New Energy Technology Innovation Forum (NPSIF). IEEE, 2024. https://doi.org/10.1109/npsif64134.2024.10883482.

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Dubey, Shatakshi, Abhishek Kumar, and R. Subash. "Enhancing AI Proctoring System Using Various ML Models." In 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT). IEEE, 2024. http://dx.doi.org/10.1109/iccpct61902.2024.10672673.

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Lu, Liangyuchen, Yanzhen Zhou, Zhengcheng Wang, Jinbo Liu, Hongyang Jin, and Qinglai Guo. "Power System Simulation Intelligent Assistant: A Chatbot Integrating Large Language Models and Domain Models." In 2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2024. https://doi.org/10.1109/ei264398.2024.10991688.

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Allen, Eric, Dmitry Kosterev, and Pouyan Pourbeik. "Validation of power system models." In Energy Society General Meeting. IEEE, 2010. http://dx.doi.org/10.1109/pes.2010.5589874.

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Kuch, Markus, and Christian Rehtanz. "Transformation of electric power system models into information and communication system models." In 2017 52nd International Universities Power Engineering Conference (UPEC). IEEE, 2017. http://dx.doi.org/10.1109/upec.2017.8231953.

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Hiskens, Ian A. "Trajectory deadlock in power system models." In 2011 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2011. http://dx.doi.org/10.1109/iscas.2011.5938167.

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Bogatenkov, S. A., S. N. Malovechko, and V. V. Kosterin. "Secure state power system models building." In 2016 2nd International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). IEEE, 2016. http://dx.doi.org/10.1109/icieam.2016.7911642.

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Rosenberg, R. C., and T. Zhou. "Power-Based Simplification of Dynamic System Models." In ASME 1988 Design Technology Conferences. American Society of Mechanical Engineers, 1988. http://dx.doi.org/10.1115/detc1988-0062.

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Abstract Simplification of system models is done in a variety of ways. A new approach based on measures of power interaction within a system graph model is presented in this paper. The method can detect the existence and location of weak coupling in both linear and nonlinear systems. Weak coupling may be exploited by partitioning the system into subsystems or by eliminating unimportant effects. Both types of simplification typically lead to reduced computational loads and greater model insight.
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Simmons, Jeremy W., and James D. Van de Ven. "Pipeline Model Fidelity for Wave Energy System Models." In ASME/BATH 2021 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/fpmc2021-68484.

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Abstract Ocean wave energy conversion plants that use hydraulic power take-offs (PTOs) have been configured so that the working fluid must travel a significant distance (of several hundred to a few thousand meters) from the wave energy converter (WEC) located offshore to equipment onshore. With the pulsatile flow generated by the WEC having a peak period in the range of 3 to 12 seconds, the wavelengths of the excited pressure waves approach the length of the pipelines themselves. By the standards for modeling pipelines presented in popular fluid power and related textbooks, the system models for these plants should include distributed parameter models of the pipeline dynamics that capture the pressure wave delay effects. This work tests the importance of pipeline model fidelity for wave energy conversion plants. Simulations have been conducted of a simple but representative hydraulic PTO for wave energy conversion and incorporate several common lumped and distributed parameter pipeline models for comparison. These results are used to show the degree to which model fidelity effects several design metrics that are especially useful in the preliminary design phase of system development. The pipeline models used include: 1) a short line model that includes lumped resistive effects only, 2) a medium line model that also includes lumped inertial and capacitive effects for a single pipeline segment, 3) a long line model that uses repeated, lumped parameter line segments to approximate the distributed parameters of a real pipeline, 4) a simple method of characteristics solution to the one-dimensional momentum and continuity equations assuming a fixed wave speed, and 5) a discrete free-gas cavity model augmenting the simple method of characteristic pipeline model. The results suggest a relaxed standard for modeling pipelines in the case of this type of system, in which case, the recommended model is easily implemented in variable time step solvers and CAD software such as Simscape Fluids and can be used within the WEC-Sim modeling framework developed by the National Renewable Energy Lab.
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Reports on the topic "Power system models"

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Rice, Mark, Stephen Elbert, Olga Kuchar, David Pinney, and Laurentiu Marinovici. Data Repository for Power system Open models With Evolving Resources (DR POWER) Final Scientific/Technical Report. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1761209.

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Diakov, Victor, Wesley Cole, Patrick Sullivan, Gregory Brinkman, and Robert Margolis. Improving Power System Modeling. A Tool to Link Capacity Expansion and Production Cost Models. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1233204.

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Herrera, Gerardo. Analysis of Lobe Power Calculator and Indication System with Physics and Cycle Based Models. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/2440143.

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Obringer, Renee, Rohini Kumar, and Kaveh Madani. Harnessing the Power of AI for Climate Change Impact Assessment. United Nations University Institute for Water, Environment and Health (UNU INWEH), 2024. http://dx.doi.org/10.53328/inr24ror012.

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Climate change impact assessment is critical for creating adequate climate change mitigation and adaptation policies and plans. Critical infrastructure systems (e.g., water and energy systems) are particularly at risk for climate change impacts. In fact, a better understanding of climate change impacts on the water and energy systems would facilitate the fulfillment of SDG2 (end hunger), SDG 6 (clean water and sanitation), SDG 7 (affordable and clean energy), SDG 11 (sustainable cities and communities), and SDG 13 (climate action), with many indirect benefits across many other areas. Nonetheless, conducting climate change impact assessment, particularly at the community-level, is not an easy task. Often, the impact assessment models require access to substantial computational resources to run the complex models, as well as the expertise to work with those models and interpret their results, which may not be possible for all communities. As such, there is a need to expand climate change impact assessment to include more accessible models that can handle high-resolution, local data that is of interest to communities. Here, we highlight how climate impact assessment studies can benefit from the power of artificial intelligence (AI). The report details the use of AI model to conduct a computationally efficient climate change impact assessment. This model is applied to a case study across the United States of America (U.S.) as an example to showcase the insights it generates in real-world applications. To demonstrate this process, the study will focus on the impacts on coupled water and electricity demand (e.g., the water-electricity demand nexus). To conduct the impact assessment, the report demonstrates two different means of collecting future climate data—Coupled Model Intercomparison Project 5 (CMIP5) Earth System Models (ESMs) and contemporary climate analogs. Our results show significant increases across the Midwestern U.S. when using ESM-derived data. Similar results were found through the climate analog-derived data, suggesting that the analogs can be used successfully as proxies for traditional ESM data in communities that might not have access to the larger CMIP suite of models. Understanding the impacts of climate change on critical infrastructure is important for building sustainable and equitable policies for climate change mitigation and adaptation. These infrastructure systems are often interconnected (e.g., the water-energy nexus) and managed by local entities. Thus, while climate change is a global problem requiring cooperation across countries and sectors, many solutions require local action. In this sense, the results presented here can be used to deepen our scientific understanding of climate change impacts on the water-energy nexus, as well as develop novel methodologies that integrate AI with traditional climate change impact assessment to better prepare local communities for the future.
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Jose Reyes. Natural Circulation in Water Cooled Nuclear Power Plants Phenomena, models, and methodology for system reliability assessments. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/836896.

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Yang, Yu, Hen-Geul Yeh, and Bryan Aguirre. Fuel Cell System Development for Heavy Duty Vehicles. Mineta Transportation Institute, 2025. https://doi.org/10.31979/mti.2025.2441.

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As California advances its ambitious goals for transportation electrification to combat climate change, hydrogen-powered fuel cells are emerging as a viable solution for overcoming the challenges of heavy-duty vehicles, offering an efficient alternative to lithium-ion batteries because they produce minimal chemical, thermal, and carbon emissions. One type of hydrogen fuel cell technology called proton exchange membrane fuel cells (PEMFCs) has garnered the most attention due to its distinct advantages, including relatively low operating temperatures (60–80 °C) and reliable performance at high current densities. However, despite their promise, PEMFCs face challenges, including in optimizing stack power output and safety concerns. To tackle these issues, accurate modeling and control strategies are essential. This study focuses on using data-driven modeling (specifically using a process known as “closed-loop system identification” under proportional controller and pseudo-random binary sequence excitation methods) to better understand and manage PEMFC systems. Various transfer functions models were analyzed, including first-order, first-order plus time delay, second-order, and second-order plus time delay models. The resulting closed-loop identification approach was applied on the humidifier, cooling, and oxygen supplier subsystems of simulated PEMFC to build their models under controlled operations. The results of this study highlight the potential of closed-loop system identification techniques to improve fuel cell vehicle performance in power supply, water, and heat management, without interrupting PEMFC operations. These findings demonstrate the significance of precise modeling as a cornerstone for advancing PEMFC control strategies and optimizing their application in renewable transportation and a more sustainable future.
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Hale, Elaine, Matt Leach, Brady Cowiestoll, Yashen Lin, and Daniel Levie. Methods for Computing Physically Realistic Estimates of Electric Water Heater Demand Response Resource Suitable for Bulk Power System Planning Models. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1899987.

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Nasr, Elhami, Tariq Shehab, Nigel Blampied, and Vinit Kanani. Estimating Models for Engineering Costs on the State Highway Operation and Protection Program (SHOPP) Portfolio of Projects. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2365.

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The State Highway Operation and Protection Program (SHOPP) is crucial for maintaining California’s 15,000-mile state highway system, which includes projects like pavement rehabilitation, bridge repair, safety enhancements, and traffic management systems. Administered by Caltrans, SHOPP aims to preserve highway efficiency and safety, supporting economic growth and public safety. This research aimed to develop robust cost-estimating models to improve budgeting and financial planning, aiding Caltrans, the California Transportation Commission (CTC), and the Legislature. The research team collected and refined comprehensive data from Caltrans project expenditures from 1983 to 2021, ensuring a high-quality dataset. Subject matter experts validated the data, enhancing its reliability. Two models were developed: a statistical model using exponential regression to account for non-linear cost growth, and an AI model employing neural networks to handle complex relationships in the data. Model performance was evaluated based on accuracy and reliability through repeated testing and validation. Key findings indicated that the new models significantly improved the precision of cost forecasts, reducing the variance between predicted and actual project costs. This advancement minimizes budget overruns and enhances resource allocation efficiency. Additionally, leveraging historical data with current market trends refined the models’ predictive power, boosting stakeholder confidence in project budgeting and financial planning. The study’s innovative approach, integrating machine learning and big data analytics, transforms traditional estimation practices and serves as a reference for other state highway programs. Continuous improvement and broader application of these models are recommended to further enhance cost estimation accuracy and support informed decision-making in transportation infrastructure management.
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Voisin, Nathalie, Dominique Bain, Jordan Macknick, and Rebecca O'Neil. Improving Hydropower Representation in Power System Models - Report Summary of PNNL-NREL Technical Workshop Held March 6-7, 2019 in Salt Lake City, UT. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1726280.

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Olsen and Willson. L51916 Pressure Based Parametric Emission Monitoring Systems (PEMS). Pipeline Research Council International, Inc. (PRCI), 2002. http://dx.doi.org/10.55274/r0010181.

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The natural gas industry operates over 8000 stationary large bore (bore greater than 14 in) natural gas engines for natural gas compression on pipelines and power generation. As emissions regulations become increasingly more stringent, the need for low cost methods for compliance demonstration arises. A PEMS model is one such approach. Research in this area has increased significantly during the last decade. PEMS models for this application utilize parameters commonly measured on industrial engines in the field to predict engine-out emissions. Monitoring emissions in this manner represents a significant cost savings over the periodic use of chemiluminescence NOX analyzers, which are not standard equipment in natural gas compressor stations. PEMS model accuracy is dependent on the quality of the input data, both the training NOX measurements and the selection of input parameters. Hence, it is important to have both reliable data measurement methods and an understanding of engine operating parameters relation to NOX. This work is part of the body of work referred to as the Integrated Test Plan (ITP), performed at the Engines and Energy Conversion Laboratory (EECL). This report details an investigation into Parametric Emissions Monitoring System (PEMS) models. It is the final document to be delivered under the ITP program. Much of the work performed under the ITP program focused on Hazardous Air Pollutants (HAPs) research. However, the emphasis of the PEMS work is on the prediction of oxides of nitrogen (NOX) emissions from large bore natural gas engines. In this work two different PEMS models are developed, a semi-empirical model and a neural network model. The semi-empirical model is based on general relationships between NOX emissions and engine parameters, but contains empirical constants that are determined based on the best fit to engine experimental data. The neural network model utilizes a similar set of input parameters, but relies on the neural network code to determine the relationships between input parameters and measured NOX emissions. The neural network model also contains empirical constants. The mathematics involved in both models is described. A single term semi-empirical model, which has been utilized in the literature as a PEMS model, is applied for comparative purposes.
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