Coordinated control and parameters optimization for PSS, POD and SVC to enhance the transient stability with the integration of DFIG based wind power systems

Abstract

This paper presents stability enhancement of a test system that is connected with a Wind Farm (WF) by using Power System Stabilizer (PSS) for Synchronous Generator (SG) and Power Oscillation Damper (POD) for Static Var Compensator (SVC). This paper also proposes a coordination mechanism for the controller to effectively damp out the oscillations and make the power system more stable by considering the uncertainties. The uncertainty is considered as wind speed variation and wind power penetration and different locations. The Particle Swarm Optimization (PSO) is used to overcome the controller parameter tuning drawbacks and controller coordination. The SG rotor speed deviation is selected as an objective function with various constraints for PSO. The transient stability analysis is carried out by considering large disturbance that is a three-phase fault. The nonlinear dynamic simulation results are obtained by integrating WF and SG replacement with the same rating WF. Evaluation and analysis are performed for various cases and different combination of without and with controllers. From the simulation results, it is noticed that oscillations in the system are minimized, and stability is enhanced at the maximum level. It also observed that the capability of SG and DFIG under three-phase fault is intensified by using PSO for optimized coordinated controller parameters. The robustness and effectiveness of the proposed approaches are evaluated on the IEEE-11 bus test system.

Transient stability analysis of DFIG-based wind farm-integrated power system considering gearbox ratio and reactive power control

“…Furthermore, from the existing wind power generation technologies, DFIG can be mentioned as a very attractive technology that has the most application in the industrial field. Accordingly, in the last decade, the transient stability analysis in power systems with the high penetration of DFIG-based WFs has become an interesting field of research [24][25][26][27][28][29][30][31][32][33][34][35]. As the first performed research in this field, in [24], the simultaneous implicit (SI) based on time-domain (T-D) simulation has been used and a suitable model has been provided for transient stability assessment (TSA).…”

“…Semi-aggregation method through a nonlinear integral backstepping control [31] and active diode bridge fault current limiter topology by limiting the torque transients, currents peak, drop, active and reactive powers, and terminal voltage [32], also can be utilized. Further, a coordination approach to damp out the oscillations in a DFIG-integrated power system aiming at transient stability improvement using a power oscillation damper for static var compensator and power system stabilizer is developed in [33]. Reference [34] employs DFIG by TEF technique and super-twisting differentiator in the multi-machine power system to improve transient stability.…”

A Novel Hybrid Sine Cosine Algorithm and Pattern Search for Optimal Coordination of Power System Damping Controllers

“…Most of this research has been focused on the coordinated design of SVC and PSS controllers. For the coordinated design of power system controllers, a large number of such algorithms have recently been offered, including: Teaching-Learning Algorithm (TLA) [15], Bacterial Foraging Optimization (BFO) [16], Brainstorm optimization algorithm (BOA) [17], Coyote Optimization Algorithm (COA) [18], ant colony optimization (ACO) [19], bat algorithm (BAT) [20], bee colony algorithm (BCA) [7], Genetic Algorithm (GA) [21], particle swarm optimization (PSO) [22], flower pollination algorithm (FPA) [23], gravitational search algorithm (GSA) [24,25], sine-cosine algorithm (SCA) [26], grey wolf optimizer (GWO) [27], firefly algorithm (FA) [28], Differential Evolution (DE) [29], Biogeography-Based Optimization (BBO) [30], Cuckoo Search (CS) algorithm [31], Harmony Search (HS) [32], Seeker Optimization Algorithm (SOA) [33], Imperialist Competitive Algorithm (ICA) [34], Harris Hawk Optimization (HHO) [35], Sperm Swarm Optimization (SSO) [36], Tabu Search (TS) [37], Simulated Annealing [38], Multi-Verse Optimizer (MVO) [39], Moth-flame Optimization (MFO) [40], Tunicate Swarm Algorithm (TSA) [41] and collective decision optimization (CDO) [42]. Although metaheuristics algorithms could provide relatively satisfactory results, no algorithm could provide superior performance than others in solving all optimizing problems.…”

Improved Salp Swarm Optimization Algorithm for Damping Controller Design for Multimachine Power System

“…1. As a result, substantial research has been done to apply various optimization strategies for the answer, such as the genetic algorithm (GA) [6,7], particle swarm optimization (PSO) [5,[8][9][10][11], differential evolution (DE) [12], and gravitational search algorithm (GSA) [13] , an important variety of similar algorithms have lately been proposed for coordinated design of power system controllers, including: ant colony optimization (ACO) [14], bee colony algorithm (BCA) [15], Genetic Algorithm (GA) [16], sine-cosine algorithm (SCA) [17,18], grey wolf optimizer (GWO) [19], Cuckoo Search (CS) algorithm [20], Sperm Swarm Optimization (SSO) [21], Tabu Search (TS) [22], Simulated Annealing [23], Firefly Algorithm (FA) [24,25], Multi-Verse Optimizer (MVO) [26] and Tunicate Swarm Algorithm (TSA) [27] have been successfully implemented to efficiently and effectively address basic and complicated problems. Natural evolution is the inspiration for the majority of populationbased search strategies.…”