Power system stabilizer design using hybrid multi-objective particle swarm optimization with chaos

Abstract

A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed, by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC). Firstly, a modification in the particle swarm optimization (PSO) was made by introducing passive congregation (PC). It helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search. Secondly, the MPSO and chaos were hybridized (MPSOC) to improve the global searching capability and prevent the premature convergence due to local minima. The robustness of the proposed PSS tuning technique was verified on a multi-machine power system under different operating conditions. The performance of the proposed MPSOC was compared to the MPSO, PSO and GA through eigenvalue analysis, nonlinear time-domain simulation and statistical tests. Eigenvalue analysis shows acceptable damping of the low-frequency modes and time domain simulations also show that the oscillations of synchronous machines can be rapidly damped for power systems with the proposed PSSs. The results show that the presented algorithm has a faster convergence rate with higher degree of accuracy than the GA, PSO and MPSO.

Decentralized multi-machine power system excitation control using continuous higher-order sliding mode technique

“…Considering the multi-machine power system (8), if the excitation control law is designed as (11), (14), and (18), and the time-varying gain is conceived as:…”

“…Meanwhile, since the power system is subject to variations like the changes in system configuration and the loading, it is a highly nonlinear dynamic system [7]. Therefore, many advanced control approaches, such as feedback linearization control [1,8], nonlinear predictive control [9], fuzzy control [10,11], neural networks [12], synergetic control [13] and chaotic optimization algorithm [14] have been designed to achieve high dynamic performance under large and unexpected contingencies. These advanced controls enable operating region to be extended and transient stability to be partly improved, yet the control systems are still sensitive to the plant parameter variations and external disturbances.…”

Modelling and Simulation of Quality Risk Forecasting in a Supply Chain

“…So, we put forward the combining method of chaos particle swarm optimization and support vector machine. Because the contribution of chaos to the diversity of particle and the iteration of particles [15], to some extent, the efficiency of algorithm and accuracy of support vector machines classifier can be improved.…”

Fuzzy Energy Management Optimization for a Parallel Hybrid Electric Vehicle using Chaotic Non-dominated sorting Genetic Algorithm

“…For instance, Guo et al [26] applied chaos optimization in the initial population and final optima solution local search. Mahdiyeh [27] introduced chaotic sequence into particle swarm optimization to improve global searching capability and escape premature convergence to local minima for a power system stabilizer design. Although researchers have studied approaches that combined chaos with evolutionary algorithms in various fields, the research on combing chaotic operators with NSGA-II has not been well addressed [22,24,26] and will be explored in detail in this study.…”