Intelligent Design of Multi-Machine Power System Stabilizers (PSSs) Using Improved Particle Swarm Optimization

Abstract

In this paper, an improved version of the particle swarm optimization algorithm is proposed for the online tuning of power system stabilizers in a standard four-machine two-area power system to mitigate local and inter-area mode oscillations. Moreover, an innovative objective function is proposed for performing the optimization, which is a weight function of two functions. The first part of fitness is the function of the angular velocity deviation of the generators, and the other part is a function based on the percentage of undershoot and maximum overshoot, and also the damping time of the power system oscillations. The performance of the proposed stabilization method is compared with the genetic algorithm and bacteria foraging algorithm results. Simulations are made in three different power system operation conditions by changing the system load. The simulation results indicate the superiority of the proposed method over the genetic algorithm and bacteria foraging algorithm. In all the scenarios, power system oscillations are damped faster and with lower amplitude when the power system stabilizers coordinate with the proposed optimization method.

Optimal Tuning of Power System Stabilizers for a Multi-Machine Power Systems Using Hybrid Gorilla Troops and Gradient-Based Optimizers

“…There are several unique intelligent control design approaches, such as artificial neural networks [3] and fuzzy logic [4], [5], have been researched. As per literature there are so many used metaheuristic optimization algorithms for the optimum tuning of PSS like Particle Swarm Optimization [6], [7], JAYA Algorithm [8], Improved Moth Flame [9], whale optimization algorithm [10], sine cosine algorithm [11], A hybrid modified grey wolf optimization-sine cosine algorithm [12], modified Sperm Swarm Optimization [13], Improved Salp Swarm Optimization Algorithm [14], Adaptive Rat Swarm Optimization [15], improved particle swarm optimization [16], Bat Algorithm [17], Runge Kutta optimizer [18], Quantum Artificial Gorilla Troops Optimizer [19], Cuckoo Search Optimization Algorithm [20], slime mould algorithm [21], honey bee mating optimization [22], Backtracking Search Algorithm [23], kidney-inspired algorithm [24], henry gas solubility optimization algorithm [25], modified arithmetic optimization algorithm [26],Water Cycle-Moth Flame Optimization [27], modified sine cosine algorithm [28], nondominated sorting genetic algorithm [29], Improved Harris Hawk Optimizer [30], genetic algorithm-neural network techniques [31].…”

Solar, Wind and Their Hybridization Integration for Multi-Machine Power System Oscillation Controllers Optimization: A Review

“…Nevertheless, one of the major challenges with particle swarm optimization (PSO) is its stagnation in the local minima [142]. An improved PSO is presented in [142][143][144] for tackling the local minima trials.…”

Suppression to Angular Oscillation among Synchronous Generators by Optimizing Parameters and Set-Points of Synchronous Condenser and High-Voltage DC

“…Considering that Q SC is not included in (6), the derivative method for the compound function is used to derive the sensitivities of V Gx , V Gy , I Gx , and I Gy to Q SC . V Gx and V Gy are decided by V G and θ G , so the sensitivities of the former to Q SC may be derived from the sensitivities of the latter to Q SC in (7).…”

“…In [5], the parameters of the PSS and the static var compensator are optimized in coordination with the particle swarm optimization (PSO) to suppress the oscillations. In [6], the tuning scheme of the PSS is proposed to suppress local and interarea mode oscillations with the weight function of the angular velocity deviation and the damping time. With the high-voltage DC (HVDC) widely applied [7], the oscillation in the AC/HVDC power system is more complex and needs to be suppressed [8,9].…”