Tuning of power system stabilizer for small signal stability improvement of interconnected power system

Abstract

This paper reports a new technique for achieving optimized design for power system stabilizers. In any large scale interconnected systems, disturbances of small magnitudes are very common and low frequency oscillations pose a major problem. Hence small signal stability analysis is very important for analyzing system stability and performance. Power System Stabilizers (PSS) are used in these large interconnected systems for damping out low-frequency oscillations by providing auxiliary control signals to the generator excitation input. In this paper, collective decision optimization (CDO) algorithm, a meta-heuristic approach based on the decision making approach of human beings, has been applied for the optimal design of PSS. PSS parameters are tuned for the objective function, involving eigenvalues and damping ratios of the lightly damped electromechanical modes over a wide range of operating conditions. Also, optimal locations for PSS placement have been derived. Comparative study of the results obtained using CDO with those of grey wolf optimizer (GWO), differential Evolution (DE), Whale Optimization Algorithm (WOA) and crow search algorithm (CSA) methods, established the robustness of the algorithm in designing PSS under different operating conditions.

Optimal integration of wind energy with a shunt-FACTS controller for reductions in electrical power loss

“…One of the stability indices that is applied to improve system performance in this study is the small-signal stability. This power system stability index reflects the system's ability to return to regular or stable operation after several minor disturbances, as indicated by the eigenvalues of the system matrix, which characterize the stability of the system [19]. The increase in system loading (MLB) system and a large injection of WT into the grid can affect the stability of the distribution and transmission system [4], mainly due to the nonlinear dynamic behavior of WTG [5].…”

Power System Stabilizer Tuning Algorithm in a Multimachine System Based on S-Domain and Time Domain System Performance Measures

“…Gain KS defines a circuit gain and damping torque. Gain increase leads to damping increase until it reaches a maximum point after which a further gain increase results in less damping torque [40]. Gain should be tuned following the maximum damping but The main structure of the PSS presented in Figure 2 (also known in [39] as PSS1A), consists of the gain, wash-out filter, and phase compensation blocks (lead-lag filters).…”

Implementation of Particle Swarm Optimization (PSO) Algorithm for Tuning of Power System Stabilizers in Multimachine Electric Power Systems

“…Certainly, genetic algorithms have been one the most used heuristics to solve the tuning of the PSS. However, there are diverse optimization techniques such as the Tabu search [11], the Firefly algorithm [12,13], the Seeker Optimization algorithm (SOA) [14], the Simulated Annealing algorithm (SA) [15], the Artificial Bee Colony (ABC) [16], the Genetic Programming algorithm [17], the Collective Decision Optimization algorithm (CDO) [18], the Cuckoo search algorithm [19], or the Gradual Hybrid Differential algorithm (GHDE) [20].…”