Mayfly Optimization Algorithm Applied to the Design of PSS and SSSC-POD Controllers for Damping Low-Frequency Oscillations in Power Systems

Abstract

In this paper, it is proposed to apply the mayfly optimization algorithm (MOA) to perform the coordinated and simultaneous tuning of the parameters of supplementary damping controllers, i.e., power system stabilizer (PSS) and power oscillation damping (POD), that actuate together with the automatic voltage regulators of the synchronous generators and the static synchronous series compensator (SSSC), respectively, for damping low-frequency oscillations in power systems. The performance of the MOA is compared with the performances of the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm for solving this problem. The dynamics of the power system is represented using the current sensitivity model, and, because of that, a current injections model is proposed for the SSSC, which uses proportional-integral (PI) controllers and the residues of the current injections at the buses, obtained from the Newton–Raphson method. Tests were carried out using the New England system and the two-area symmetrical system. Both static and dynamic analyses of the operation of the SSSC were performed. To validate the proposed optimization techniques, two sets of tests were conducted: first, with the purpose of verifying the performance of the most effective algorithm for tuning the parameters of PSSs, PI, and POD controllers, and second, with the purpose of performing studies focused on small-signal stability. The results have validated the current injections model for the SSSC, as well as have indicated the superior performance of the MOA for solving the problem, accrediting it as a powerful tool for small-signal stability studies in power systems.

A New Scheme of Harris Hawk Optimizer With Memory Saving Strategy (HHO-MSS) for Controlling Parameters of Power System Stabilizer and Virtual Inertia in Renewable Microgrid Power System

“…Nowadays, metaheuristic algorithms have rapidly developed with countless inspirations in the world and it has been implemented in modern power systems, including for controlling parameters in PSS or VI cases. For example, in [15], [27], the Mayfly Optimizer Algorithm (MOA) has been applied for PSS-SVC and PSS-SSSC. Quantum Artificial Gorilla Troop Optimizer (QGTO), Harris Hawk Optimizer (HHO), Equilibrium Optimizer (EO), and Arithmetic Optimizer (AO) have been implemented in parameter tuning of PSS in Single Machine Infinite Bus (SMIB) and Multi Machine systems [8], [28]- [30].…”

“…Moreover, it can be found the combination of the objective function increases the accuracy. In [27], a combination of the damping ratio and frequency component is presented. The damping factor and damping ratio are also combined as the objective function [31].…”

“…The damping factor and damping ratio are also combined as the objective function [31]. Both [27] and [31] used the weighting sum scalarization technique. In this technique, determining these parameters is difficult and confusing due to each user having their preferences.…”

Optimal Coordination of TCSC and PSS2B Controllers in Electric Power Systems Using MOPSO Multiobjective Algorithm

“…Te Hefron and Phillips model is one of the most mainly used dynamic models of the power system. Te Park's equations for the 4 th order model of a power system can be formulated as following equations [32]:…”

Stability Enhancement of Sulselrabar Electricity System Using Mayfly Algorithm Based on Static Var Compensator and Multi-Band Power System Stabilizer PSS2B

“…In addition, the application of the MOA as an optimization method for power systems has been explored and optimal results have been demonstrated. In [44], MOA was applied to facilitate the coordinated and simultaneous tuning of the parameters of the PSS auxiliary damping controller. In [45], the application of the MOA method was proposed to configure the gain of a PID controller.…”