Coordinated Complex-Valued Encoding Dragonfly Algorithm and Artificial Emotional Reinforcement Learning for Coordinated Secondary Voltage Control and Automatic Voltage Regulation in Multi-Generator Power Systems

Abstract

This paper proposes a coordinated optimization and control algorithm for coordinated secondary voltage control (CSVC) in multi-generator power systems. Firstly, to obtain a smaller voltage deviation and avoid the curse of dimensionality simultaneously, an artificial emotional reinforcement learning (AERL) is applied to automatic voltage regulation (AVR). Secondly, to obtain a smaller fitness value with lesser random for the decentralized independent variables optimization problem of the CSVC, a complex-valued encoding dragonfly algorithm (CDA) is proposed. Thirdly, the CDA and the AERL are coordinated for the CSVC and the AVR in multi-generator power systems. To verify the control performance of the AERL and the convergence of the proposed CDA, three simulation cases, i.e., IEEE 57-bus, 118-bus and 300-bus systems, are considered. The simulation results show that the CDA-AERL effectively obtains the smallest control objectives and the convergence for the CSVC in multi-generator power systems.INDEX TERMS Coordinated secondary voltage control; artificial emotional reinforcement learning; complex-valued encoding dragonfly algorithm; automatic voltage regulation; multi-generator power systems.

A Complex-Valued Encoding Multichain Seeker Optimization Algorithm for Engineering Problems

“…In this section, the parameter set of the PSO [47], SA_GA [48], GSA [6], SCA [8], MVO [9], SOA [29], and CMSOA is presented. According to references [6,8,23,29,47,48], we did a lot of practice tests and comparative studies for the parameter set.…”

“…ese complex-valued encoding intelligent optimization algorithms have proven to be feasible optimization algorithms and have been used in practical engineering. For instance, the complex-valued encoding dragonfly algorithm optimized the power systems [23]. A gray wolf optimization based on plural encoding optimized the filter model [24].…”

Clustering methods for the efficient voltage regulation in smart grids

“…However, it simplified the testing process and was compatible with tuning methods. Recently, learning‐based approaches are being implemented, including spectral clustering [8], reinforcement learning [9] and deep learning [10–12].…”

Optimal tuning of the novel voltage regulation controller considering the real model of the automatic voltage regulation system

“…Generalized disturbance, which includes both system parameter uncertainties and external disturbance, was estimated using a fifth-order extended state observer. Furthermore, it is very important to mention the advanced artificial intelligence (AI) techniques for application in AVR systems [ [36] , [37] , [38] , [39] , [40] , [41] ]. In Refs.…”

“…In Refs. [ [36] , [37] , [38] ] the authors deal with the application of reinforcement learning for voltage control. Precisely, reinforcement learning is applied in Ref.…”