Relevant bibliographies by topics / IEEE bus 14-network

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Academic literature on the topic 'IEEE bus 14-network'

Author: Grafiati
Published: 3 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "IEEE bus 14-network"

1

Araga, Idris A., and A. E. Airoboman. "Enhancement of voltage stability in an interconnected network using unified power flow controller." Journal of Advances in Science and Engineering 4, no. 1 (2021): 65–74. http://dx.doi.org/10.37121/jase.v4i1.141.

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In this paper, the optimal placement of Unified Power Flow Controllers (UPFC) in a large-scale transmission network in order to improve the loadability margin was considered. In other to achieve this aim, the Line Stability Factor (LQP) as a technique for the optimal location of UPFC in the IEEE 14-bus network and 56-bus Nigerian national grid was adopted. The power injection model for the UPFC was employed to secure improvements in the loading margin of the IEEE 14-bus network and 56-bus Nigerian national grid system. Continuation power flow was used to assess the effect of UPFC on the loadability margin. Steady-state simulations using Power System Analysis Toolbox (PSAT) on MATLAB was applied to determine the effectiveness of placing UPFC between bus 13 and bus 14 in the IEEE 14-bus network and between bus 44 (Ikot-Ekpene) and bus 56 (Odukpani) in the 56-bus Nigerian national grid system. The results showed that the loadability margin increased by 8.52 % after UPFC was optimally placed in the IEEE 14-bus network and increased by 195.5 % after UPFC was optimally placed in the 56-bus Nigerian national grid system. Thus, these enhance the voltage stability of both network and utilizing the network efficiently.
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Babu, Rohit, and Biplab Bhattacharyya. "A-star algorithm based on admissible searching for strategically placing PMU considering redundancy and cost/benefit analysis." International Journal of Applied Power Engineering (IJAPE) 11, no. 2 (2022): 165. http://dx.doi.org/10.11591/ijape.v11.i2.pp165-178.

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This research examines an admissible search algorithm-dependent A-star strategy and takes into account redundancy and cost/benefit analysis under normal operating conditions. The goal is to allocate a phasor measurement unit (PMU) for maximal observability of the interconnected power network. To determine the fewest number of PMU required to make the connected power network totally observable using redundancy analysis, the A-star approach is utilized. The redundancy analysis of the power network is carried out in order to determine the appropriate PMU placement, which results in the acquisition of total power network observability and reliability. To put the suggested technique through its paces, it has been tested on IEEE-standard test systems such as IEEE-14 bus, IEEE-30 bus, New England-39 bus, IEEE-57 bus, and IEEE-118 bus. The results obtained using the suggested methodology are compared to those obtained through standard literature research. The experimental findings of the suggested method revealed the resilience and accuracy of the A-star algorithm as well as its effectiveness in achieving maximum observability of the connected power network.
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Ahmed, Muhammad Musadiq, Muhammad Amjad, Muhammad Ali Qureshi, Muhammad Omer Khan, and Zunaib Maqsood Haider. "Optimal PMU Placement to Enhance Observability in Transmission Networks Using ILP and Degree of Centrality." Energies 17, no. 9 (2024): 2140. http://dx.doi.org/10.3390/en17092140.

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The optimal PMU placement problem is placing the minimum number of PMUs in the network to ensure complete network observability. It is an NP-complete optimization problem. PMU placement based on cost and critical nodes is solved separately in the literature. This paper proposes a novel approach, a degree of centrality in the objective function, to combine the effect of both strategies to place PMUs in the power network optimally. The contingency analysis and the effect of zero-injection buses are solved to ensure the reliability of network monitoring and attain a minimum number of PMUs. Integer linear programming is used on the IEEE 7-bus, IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus, and IEEE 118-bus systems to solve this problem. The results are evaluated based on two performance measures: the bus observability index (BOI) and the sum of redundancy index (SORI). On comparison, it is found that the proposed methodology has significantly improved results, i.e., a reduced number of PMUs and increased network overall observability (SORI). This methodology is more practical for implementation as it focuses on critical nodes. Along with improvement in the results, the limitations of existing indices are also discussed for future work.
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Rohit, Babu, and Bhattacharyya Biplab. "A-star algorithm based on admissible searching for strategically placing PMU considering redundancy and cost/benefit analysis." International Journal of Applied Power Engineering 11, no. 2 (2022): 165~178. https://doi.org/10.11591/ijape.v11.i2.pp165-178.

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This research examines an admissible search algorithm-dependent A-star strategy and takes into account redundancy and cost/benefit analysis under normal operating conditions. The goal is to allocate a phasor measurement unit (PMU) for maximal observability of the interconnected power network. To determine the fewest number of PMU required to make the connected power network totally observable using redundancy analysis, the A-star approach is utilized. The redundancy analysis of the power network is carried out in order to determine the appropriate PMU placement, which results in the acquisition of total power network observability and reliability. To put the suggested technique through its paces, it has been tested on IEEEstandard test systems such as IEEE-14 bus, IEEE-30 bus, New England-39 bus, IEEE-57 bus, and IEEE-118 bus. The results obtained using the suggested methodology are compared to those obtained through standard literature research. The experimental findings of the suggested method revealed the resilience and accuracy of the A-star algorithm as well as its effectiveness in achieving maximum observability of the connected power network.
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Kumar, K. Sowjan, B. Nagaraju, K. Satvika, et al. "Optimal Placement and Size Selection 0f Photovoltaic System in IEEE 14 Bus Power Transmission Network." International Journal of Innovative Research in Engineering and Management 9, no. 4 (2022): 161–68. http://dx.doi.org/10.55524/ijirem.2022.9.4.31.

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The analysis and design of energy systems is usually carried out using power flow analysis. This is used for determining the magnitude and phase angle of the load buses and the real and reactive power flows on the transmission lines, as well as the real and reactive powers injected into the buses. In this paper single photo voltaic system is connected to IEEE 14 bus system and run the power flow system and find thecomparison between single and double voltaic systems. In the paper of larger systems computation time increases in this condition, Newton Raphson method.This paper aims to develop a MATLAB program to calculate voltages and active and reactive power at each bus for IEEE 14 bus systems.
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Gupta, Santosh Kumar, and Sanjeev Kumar Mallik. "Fast assessment of the voltage stability using reconfigured power system network and artificial neural network approaches." Engineering Research Express 5, no. 3 (2023): 035047. http://dx.doi.org/10.1088/2631-8695/acf189.

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Abstract Planning and running power systems must take voltage stability into account. Instability is mostly caused by the power system’s failure to supply the demand for reactive power. The voltage stability margin must be understood by utilities if they are to operate the power system with the highest level of security and dependability. This paper uses reconfigured 12 bus, 10 bus, and 8-bus reconfigured networks of the interconnected IEEE 14 bus system to demonstrate the proposed quick method for assessing the voltage stability. The original (IEEE 14 bus) and the reconfigured (12 bus, 10 bus, and 8-bus) systems’ voltage stability has been evaluated using the line stability index indicators: fast voltage stability index (FVSI), line voltage stability index (LVSI), and line stability index (Lmn). Based on the maximum loadability factor, the contingencies for the original and reconfigured systems are ranked. The system loadability factor is used as the input parameter, and the LVSI, Lmn, and FVSI indices for the critical line under critical contingency are used as an output to train the ANN network. It has been found that there is no discernible difference between the actual (NR method) and predicted (ANN approach) output. For accessing the voltage stability of the IEEE 14 bus system by its reconfigured networks using the proposed approach, the computational time and error are very low, showing the effectiveness, rapidity, and accuracy of the suggested approach.
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Dhana Sai Sri, M., and P. Srinivasa Varma. "Evaluation and Analysis of Available Transfer Capability in Deregulated Power System Environment." International Journal of Engineering & Technology 7, no. 1.8 (2018): 188. http://dx.doi.org/10.14419/ijet.v7i1.8.16399.

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Reliability of network is need of the hour in the present power system market and is constrained by capability of the network. The network calculations are performed using accurate and high efficient strategies. In order to perform power transactions in the system, the computation of available transfer capability is essential which a metric of capability of the system. Generally, effect wattless power is not taken into account in the methodologies for computation of linear available transfer capability. In this paper, a methodology which considers the reactive power flows for enhancement of linear ATC is presented. In order to perform analysis theoretically, a standard IEEE 3 bus system is considered. Another case study i.e., 14 bus system available in IEEE test systems is used for simulation analysis. FACTS technology is incorporated in the existing system in order to enhance capability of the network. To facilitate transfer maximum power in the system, an optimal power-flow-based ATC enhancement model is formulated and presented along with simulation results. Studies based on the IEEE 3-bus system and 14-bus systems with TCSC demonstrate the effectiveness of FACTS control on ATC enhancement.
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Gongada, Sandhya Rani, Muktevi Chakravarthy, and Bhukya Mangu. "Power system contingency classification using machine learning technique." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3091–98. http://dx.doi.org/10.11591/eei.v11i6.4031.

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One of the most effective ways for estimating the impact and severity of line failures on the static security of the power system is contingency analysis. The contingency categorization approach uses the overall performance index to measure the system's severity (OPI). The newton raphson (NR) load flow technique is used to extract network variables in a contingency situation for each transmission line failure. Static security is categorised into five categories in this paper: secure (S), critically secure (CS), insecure (IS), highly insecure (HIS), and most insecure (MIS). The K closest neighbor machine learning strategy is presented to categorize these patterns. The proposed machine learning classifiers are trained on the IEEE 30 bus system before being evaluated on the IEEE 14, IEEE 57, and IEEE 118 bus systems. The suggested k-nearest neighbor (KNN) classifier increases the accuracy of power system security assessments categorization. A fuzzy logic approach was also investigated and implemented for the IEEE 14 bus test system to forecast the aforementioned five classifications.
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Sandhya, Rani Gongada, Chakravarthy Muktevi, and Mangu Bhukya. "Power system contingency classification using machine learning technique." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3091~3098. https://doi.org/10.11591/eei.v11i6.4031.

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One of the most effective ways for estimating the impact and severity of line failures on the static security of the power system is contingency analysis. The contingency categorization approach uses the overall performance index to measure the system's severity (OPI). The newton raphson (NR) load flow technique is used to extract network variables in a contingency situation for each transmission line failure. Static security is categorised into five categories in this paper: secure (S), critically secure (CS), insecure (IS), highly insecure (HIS), and most insecure (MIS). The K closest neighbor machine learning strategy is presented to categorize these patterns. The proposed machine learning classifiers are trained on the IEEE 30 bus system before being evaluated on the IEEE 14, IEEE 57, and IEEE 118 bus systems. The suggested k-nearest neighbor (KNN) classifier increases the accuracy of power system security assessments categorization. A fuzzy logic approach was also investigated and implemented for the IEEE 14 bus test system to forecast the aforementioned five classifications.
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Rohit, Babu, and Bhattacharyya Biplab. "Optimal placement of PMU for complete observability of the interconnected power network considering zero-injection bus: A numerical approach." International Journal of Applied Power Engineering 9, no. 2 (2020): 135~146. https://doi.org/10.5281/zenodo.7349686.

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This paper presents an approach to place the phasor measurement unit (PMU) optimally, which minimizes the setup cost of PMU. This methodology attains complete state estimation of the interconnected power networks. An integer linear programming (ILP) method is explored for the optimal PMU placement problem. It is used to determine the optimal location and minimum number of PMUs necessary to make the interconnected power network completely observable. ILP may provide many solutions if acquainting buses to zero injection buses are unhandled. In the case of more than one solution, a bus observability redundancy index and total system observability redundancy index is proposed to find the most promising solutions set for redundancy measurement. The proposed algorithm is applied to benchmark the optimal PMU placement solutions for the IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 118-bus, and NRPG 246-bus test systems. The obtained results of the proposed approach are compared with the existing standard algorithm, and it is observed that the proposed approach achieves complete observability of the interconnected power network under base-load conditions.
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Book chapters on the topic "IEEE bus 14-network"

1

Bhadani, Rakesh, and K. C. Roy. "Different Loading of Distributed Generation on IEEE 14-Bus Test System to Find Out the Optimum Size of DG to Allocation in Transmission Network." In Proceedings of International Conference on Data Science and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7561-7_6.

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Sarkar, Dipu, and Joyanta Kumar Roy. "Artificial Neural Network (ANN) in Network Reconfiguration for Improvement of Voltage Stability." In Advances in Computer and Electrical Engineering. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9911-3.ch010.

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Issues related to power system voltage levels have become increasingly important issue during last two and half decades. In power networks, low voltage situations may result in the loss of stability, voltage collapse and eventually to cascading power outages. Large number of incidents of voltage collapse has been reported in different countries across the globe. A simple indicator that has the potential in real time, i.e. L indicator has been used to find voltage profile at different switching condition and simulated using ANN in network reconfiguration for the improvement of voltage stability. A method for improving voltage stability in a power network comprising of multiple lines and switches has been suggested in this chapter based on system reconfiguration approach. ANN based fast and efficient methodology has been developed to obtain the optimum switching combination to achieve best voltage stability. The proposed scheme has been tested on an IEEE 14-bus system.
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Sarkar, Dipu, and Joyanta Kumar Roy. "Artificial Neural Network (ANN) in Network Reconfiguration for Improvement of Voltage Stability." In Deep Learning and Neural Networks. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch012.

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Issues related to power system voltage levels have become increasingly important issue during last two and half decades. In power networks, low voltage situations may result in the loss of stability, voltage collapse and eventually to cascading power outages. Large number of incidents of voltage collapse has been reported in different countries across the globe. A simple indicator that has the potential in real time, i.e. L indicator has been used to find voltage profile at different switching condition and simulated using ANN in network reconfiguration for the improvement of voltage stability. A method for improving voltage stability in a power network comprising of multiple lines and switches has been suggested in this chapter based on system reconfiguration approach. ANN based fast and efficient methodology has been developed to obtain the optimum switching combination to achieve best voltage stability. The proposed scheme has been tested on an IEEE 14-bus system.
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Bentarzi, Hamid. "PMU Placement Optimization for Fault Observation Using Different Techniques." In Advances in Computer and Electrical Engineering. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4027-5.ch009.

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This chapter presents different techniques for obtaining the optimal number of the phasor measurement units (PMUs) that may be installed in a smart power grid to achieve full network observability under fault conditions. These optimization techniques such as binary teaching learning based optimization (BTLBO) technique, particle swarm optimization, the grey wolf optimizer (GWO), the moth-flame optimization (MFO), the cuckoo search (CS), and the wind-driven optimization (WDO) have been developed for the objective function and constraints alike. The IEEE 14-bus benchmark power system has been used for testing these optimization techniques by simulation. A comparative study of the obtained results of previous works in the literature has been conducted taking into count the simplicity of the model and the accuracy of characteristics.
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Irshaad Oozeer, Mohammad, and Simon Haykin. "Cognitive Dynamic System for AC State Estimation and Cyber-Attack Detection in Smart Grid." In Smart Cities [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94093.

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The work presented in this chapter is an extension of our previous research of bringing together the Cognitive Dynamic System (CDS) and the Smart Grid (SG) by focusing on AC state estimation and Cyber-Attack detection. Under the AC power flow model, state estimation is complex and computationally expensive as it relies on iterative procedures. On the other hand, the False Data Injection (FDI) attacks are a new category of cyber-attacks targeting the SG that can bypass the current bad data detection techniques in the SG. Due to the complexity of the nonlinear system involved, the amount of published works on AC based FDI attacks have been fewer compared to their DC counterpart. Here, we will demonstrate how the entropic state, which is the objective function of the CDS, can be used as a metric to monitor the grid’s health and detect FDI attacks. The CDS, acting as the supervisor of the system, improves the entropic state on a cycle to cycle basis by dynamically optimizing the state estimation process through the reconfiguration of the weights of the sensors in the network. In order to showcase performance of this new structure, computer simulations are carried out on the IEEE 14-bus system for optimal state estimation and FDI attack detection.
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Conference papers on the topic "IEEE bus 14-network"

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Pattanaik, Piyush Prakash, and Chinmoy Kumar Panigrahi. "Stability and fault analysis in a power network considering IEEE 14 bus system." In 2018 2nd International Conference on Inventive Systems and Control (ICISC). IEEE, 2018. http://dx.doi.org/10.1109/icisc.2018.8398981.

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Le Roux, P. F., M. K. Ngwenyama, and T. C. Aphane. "14-Bus IEEE Electrical Network Compensated for Optimum Voltage Enhancement using FACTS Technologies." In 2022 3rd International Conference for Emerging Technology (INCET). IEEE, 2022. http://dx.doi.org/10.1109/incet54531.2022.9824172.

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Maruch Barreto, Nathan Elias, Ciro Monteiro Baer, Mateus Jaensen Daros, et al. "Artificial Neural Network Solution for Anomalous Operation Detection in Power Systems." In Simpósio Brasileiro de Sistemas Elétricos - SBSE2020. sbabra, 2020. http://dx.doi.org/10.48011/sbse.v1i1.2158.

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This paper presents an anomalous operation detection system for power systems using the artificial neural network approach while discussing its advantages and disadvantages. The initial data for the proposed technique is a set of simulated post-fault bus voltages and currents obtained in a sampling rate so as to emulate a phasor measurement unit network. Several types of faults are dealt with, such as three-phase to ground, two-phase, two-phase to ground and single-phase to the ground as well as line and load contingencies. All fault and steady-state simulations were performed on MATLAB using Graham Rogers’ Power System Toolbox. The artificial neural network was designed on MATLAB, using an architecture proper for pattern recognition with supervised learning and obtaining high accuracy predictions within a short amount of time. The test system used in all simulations is the IEEE 39-Bus New England Power System, which presents 10 generation units, 21 loads and three distinct areas alongside transient and sub transient models, with phasor measurement units in 14 buses. Future works are discussed, showing the possibilities for feature engineering in this type of problem, fault type detection and fault location in operation using analogous dataset and neural network structures.
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Nazifa, Saraban, Roubaiath Islam, Md Ahsan Kabir, and Md Mahadi Hasan. "Analysis of the Impact of PV Penetration in Distribution Network: A Case Study in IEEE 14 Bus System." In 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI). IEEE, 2021. http://dx.doi.org/10.1109/acmi53878.2021.9528090.

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Jalalat, Hamed, Sahand Ghaseminejad Liasi, and Mohammad Tavakoli Bina. "Estimation of Number of Voltage Sags in the IEEE 14-Bus Network Using Bayesian and Artificial Neural Network: A Case Study." In 2020 28th Iranian Conference on Electrical Engineering (ICEE). IEEE, 2020. http://dx.doi.org/10.1109/icee50131.2020.9260574.

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Mojumder, Md Rayid Hasan, and Naruttam Kumar Roy. "Effect of Dataset Size and Hidden Layers on the Stability Classification of IEEE-14 Bus System Using Deep Neural Network." In 2022 International Conference on Energy and Power Engineering (ICEPE). IEEE, 2022. http://dx.doi.org/10.1109/icepe56629.2022.10044902.

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Ghosal, Malini, and Vittal Rao. "Mitigation of Adverse Effect of False Data Injection in Optimally Controlled Smart Grid." In ASME 2015 Power Conference collocated with the ASME 2015 9th International Conference on Energy Sustainability, the ASME 2015 13th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2015 Nuclear Forum. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/power2015-49689.

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The ‘smart grid’ is one of the largest critical infrastructure systems of any nation. Preventing the grid from data integrity attacks is vital for reliable operation of the grid. Various Phasor Measurement Units (PMUs) and other intelligent electronic devices play crucial role in real-time operations of the grid. Control actions are taken based on information received from such devices. However, all these modern measurement systems communicate with the control center via wireless network making them vulnerable to several security threats, data integrity attack being one of them. This paper addresses the issues associated with the injection of malicious data into the measurements. A model based ‘trustiness’ technique has been proposed to mitigate the adverse effect of such attacks in the performance of the electrical power grid. The proposed method has been demonstrated using the IEEE 14-bus test system.
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Piacenza, Joseph R., Scott Proper, Mir Abbas Bozorgirad, Irem Y. Tumer, and Christopher Hoyle. "Robust Topology Design of Complex Infrastructure Systems." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46560.

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Optimizing the topology of complex infrastructure systems can minimize the impact of cascading failures due to an initiating failure event. This paper presents a novel approach for the concept-stage design of complex infrastructure systems by integrating model-based design with network analysis to increase system robustness. This approach focuses on system performance after cascading has occurred, and examines design trade-offs of the resultant (or degraded) system state. In this research, robustness is defined as the invariability of system performance due to uncertain failure events. Where a robust network has the ability to meet minimum performance requirements despite the impact of cascading failures. This research is motivated by catastrophic complex infrastructure system failures such as the August 13th Blackout of 2003, highlighting the vulnerability of systems such as the North American Power Grid (NAPG). A mathematical model was developed using an adjacency matrix, where removing a network connection simulates uncertain failure events. Performance degradation is iteratively calculated as failures cascade throughout the system, and robustness is measured by the lack of performance variability over multiple cascading failure scenarios. Two case studies are provided: an extrapolated IEEE 14 test bus, and the Oregon State University campus power network. The overarching goal of this research is to understand key system design trade-offs between robustness, performance objectives, and cost. In addition, optimizing network topologies to mitigate performance loss during concept-stage design will enable system robustness.
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