Gaussian quantum particle swarm optimization-based wide-area power system stabilizer for damping inter-area oscillations

Abstract

Purpose The extensive increase in power demand has challenged the ability of power systems to deal with small-signal oscillations such as inter-area oscillations, which occur under unseen operating conditions. A wide-area measurement system with a phasor measurement unit (PMU) in the power network enhances the observability of the power grid under a wide range of operating conditions. This paper aims to propose a wide-area power system stabilizer (WAPSS) based on Gaussian quantum particle swarm optimization (GQPSO) using the wide-area signals from a PMU to handle the inter-area oscillations in the system with a higher degree of controllability. Design/methodology/approach In the design of the wide-area stabilizer, a dead band is introduced to mitigate the influence of ambient signal frequency fluctuations. The location and the input signal of the wide-area stabilizer are selected using the participation factor and controllability index calculations. An improved particle swarm optimization (PSO) technique, namely, GQPSO, is used to optimize the variables of the WAPSS to move the unstable inter-area modes to a stable region in the s-plane, thereby improving the overall system stability. Findings The proposed GQPSO-based WAPSS is compared with the PSO-based WAPSS, genetic algorithm-based WAPSS and power system stabilizer. Eigenvalue analysis, time-domain simulation responses and performance index analysis are used to assess performance. The various evaluation techniques show that GQPSO WAPSS has a consistently good performance, with a higher damping ratio, faster convergence with fewer oscillations and a minimum error in the performance index analysis, indicating a more stable system with effective oscillation damping. Originality/value This paper proposes an optimally tuned design for the WAPSS with a wide-area input along with a dead-band structure for damping the inter-area oscillations. Tie line power is used as the input to the WAPSS and optimal tuning of the WAPSS is performed using an improved PSO algorithm, known as Gaussian quantum PSO.

A Novel Improved Sea-Horse Optimizer for Tuning Parameter Power System Stabilizer

“…The application of metaheuristic methods in adjusting the power system stabilizer has provided promising performance. Several metaheuristic algorithms have been applied to power system stabilizers in recent years, such as Atomic Search Optimization [53], Ant Colony Optimization Algorithm [54], [55], Crow Search Algorithm [56], Tunicate Swarm Algorithm [57], Harris Hawk Optimizer [58], [59], Moth Search Algorithm [60], [61], Mayfly Optimization Algorithm [62], Sine-Cosine Algorithm [63], Rat Swarm Optimization [64], Whale Optimization Algorithm [65], [66] and Particle Swarm Optimization [67]- [70] Several researchers have also presented a combination of metaheuristic algorithms for tuning PSS parameters, such as Gude et al demonstrated a combination of butterfly optimization algorithm and particle swarm optimization [71]. Kalegowda Algorithm and the Tabu Search Algorithm [74].…”