A hybrid approach of artificial neural network-particle swarm optimization algorithm for optimal load shedding strategy

Abstract

This paper proposes an under-frequency load shedding (UFLS) method by using the optimization technique of artificial neural network (ANN) combined with particle swarm optimization (PSO) algorithm to determine the minimum load shedding capacity. The suggested technique using a hybrid algorithm ANN-PSO focuses on 2 main goals: determine whether process shedding plan or not and the distribution of the minimum of shedding power on each demand load bus in order to restore system’s frequency back to acceptable values. In the hybrid algorithm ANN-PSO, the PSO algorithm takes responsible for searching the optimal weights in the neural network structure, which can help to optimize the network training in terms of training speed and accuracy. The distribution of shedding power at each node considering the primary control and secondary control of the generators’ unit and the phase electrical distance between the outage generators and load nodes. The effectiveness of the proposed method is experimented with multiple generators outage cases at various load levels in the IEEE-37 Bus scheme where load shedding cases are considered compared with other traditional technique.

UFLS and Smart Load for Frequency Regulation in Electrical Power System: A Review

“…The application of computational techniques in UFLS schemes is part of the adaptive UFLS techniques and aims to achieve optimal load shedding by minimizing the load or finding the optimal shedding time, among other objectives. The most common computational techniques used in UFLS include genetic algorithms [55]- [57], fuzzy logic [58]- [60], neural networks [61], [62], particle swarm optimization [63], [64], adaptive neuro-fuzzy inference systems [65], [66], mixed-integer linear programming [25], [67]- [69], grasshopper optimization algorithms [70], and stochastic optimization [71], among others. UFLS schemes based on computational techniques are considered superior because they focus on optimizing their performance [72]- [76].…”