A Hybrid Method for Optimal Allocation of Shunt Compensators to Mitigate Fault Induced Delayed Voltage Recovery (FIDVR)

Subject Areas : Generation, transmission and distribution

Maryam Bahramgiri ¹ , Mehdi Ehsan ² , Seyed Babak Mozafari ³

1 - Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
3 - Faculty of Mechanics, Electrical Power and Computer- Science and Research Branch, Islamic Azad University, Tehran, Iran

Received: 2024-02-03 Accepted : 2024-05-28 Published : 2025-08-06

Keywords: Composite load model, fault-induced delayed voltage recovery, multi-objective particle swarm optimization, residential air conditioners, volt/ampere reactive.,

Abstract :

The widespread use of residential air conditioning (RAC) systems in modern power systems has resulted in an increase in the phenomenon of fault-induced delayed voltage recovery (FIDVR). This phenomenon leads to short-term voltage instability and sometimes even voltage collapse. To address this issue, parallel FACTS devices such as SVC and STATCOM can be used. In this paper, a data-driven hybrid approach based on volt-ampere reactive (VAR) placement is proposed to reduce FIDVR events. This approach uses a new and efficient index for voltage evaluation after faults and determines the optimal location and size of VAR resources considering economic and technical constraints. A multi-layer perceptron (MLP) neural network is used to solve the multi-dimensional mapping problem considering reactive power injections into buses. Then, a multi-objective optimization is proposed to identify the optimal size of VAR resources to address short-term voltage instability and prevent FIDVR events using intelligent optimization methods. First, optimization is performed for the single-objective function with predefined weights by the PSO algorithm, and then the results are compared with the artificial bee colony (ABC) algorithm, ant colony optimization for continuous domains (ACO_R), and differential evolution (DE) algorithms. Additionally, this paper focuses on identifying a Pareto front of non-dominated solutions using multi-objective particle swarm optimization (MOPSO). The proposed approach is tested on the 39-bus IEEE system considering a time-varying dynamic model for residential air conditioning loads. The results show that this approach is highly effective in solving reactive power optimization problems and reducing FIDVR effects.

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