taheri, B., Ghasemzade, F., Farhadi, P. (2018). Optimal Placement of Phasor Measurement Units to Maintain CompleteObservability Considering Maximum Reliability by Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Journal of Artificial Intelligence in Electrical Engineering, 7(26), 11-22.
bahman taheri; Farzad Ghasemzade; Payam Farhadi. "Optimal Placement of Phasor Measurement Units to Maintain CompleteObservability Considering Maximum Reliability by Non-dominated Sorting Genetic Algorithm-II (NSGA-II)". Journal of Artificial Intelligence in Electrical Engineering, 7, 26, 2018, 11-22.
taheri, B., Ghasemzade, F., Farhadi, P. (2018). 'Optimal Placement of Phasor Measurement Units to Maintain CompleteObservability Considering Maximum Reliability by Non-dominated Sorting Genetic Algorithm-II (NSGA-II)', Journal of Artificial Intelligence in Electrical Engineering, 7(26), pp. 11-22.
taheri, B., Ghasemzade, F., Farhadi, P. Optimal Placement of Phasor Measurement Units to Maintain CompleteObservability Considering Maximum Reliability by Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Journal of Artificial Intelligence in Electrical Engineering, 2018; 7(26): 11-22.
Optimal Placement of Phasor Measurement Units to Maintain CompleteObservability Considering Maximum Reliability by Non-dominated Sorting Genetic Algorithm-II (NSGA-II)
1Department of Electrical Engineering, Bilesavar Branch, Islamic Azad University, Bilesavar, Iran
2Department of Electrical Engineering, Parsabad Moghan Branch, Islamic Azad University, Parsabad , Iran
Abstract
Ever-increasing energy demand has led to geographic expansion of transmission lines and their complexity. In addition, higher reliability is expected in the transmission systemsdue to their vital role in power systems. It is very difficult to realize this goal by conventional monitoring and control methods. Thus, phasor measurement units (PMUs) are used to measure system parameters. Although installation of PMUsincreases the observability and system reliability, high installation costs of these devices requireplacing them appropriately in proper positions. In this research, multi-objective placement of PMUs with the aims of improving investment and risk costs in power systems is performed along with observability constraint. Then, PMU placement problem is solved as an optimization problem using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Finally, the performance of the proposed method is tested on standard IEEE 24-bus test system and Roy Billiton IEEE 31-bus test system.
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