Shipboard Power System Stabilizer Optimization Using GA and QPSO Algorithm

Author(s):  
Wei Zhang ◽  
Weifeng Shi ◽  
Jinbao Zhuo

In order to improve shipboard power system dynamic stability, two bio-inspired algorithms, the genetic algorithm (GA) and quantum-behaved particle swarm optimization (QPSO), method are proposed for the shipboard power system stabilizer (PSS) optimization. The proposed PSS optimization method is inspired by a hybrid-coordinated stabilizer for diesel engine generator and the bio-inspired algorithm. The simulations are conducted under load change disturbance and short-circuit fault case for the marine generator with/without the diesel engine speed governor. Simulation results show that the quantum particle swarm optimize strategy could improve the dynamic performance of the marine generator better than the GA method. The dynamic performance for shipboard power system always indicates the effectiveness, feasibility and robustness of the proposed approach.

Author(s):  
G. Fusco ◽  
M. Russo

This paper proposes a simple design procedure to solve the problem of controlling generator transient stability following large disturbances in power systems. A state-feedback excitation controller and power system stabilizer are designed to guarantee robustness against uncertainty in the system parameters. These controllers ensure satisfactory swing damping and quick decay of the voltage regulation error over a wide range of operating conditions. The controller performance is evaluated in a case study in which a three-phase short-circuit fault near the generator terminals in a four-bus power system is simulated.


2018 ◽  
Vol 7 (4) ◽  
pp. 17-55 ◽  
Author(s):  
Dasu Butti ◽  
Siva Kumar Mangipudi ◽  
Srinivasarao Rayapudi

In this article, a multi objective and a novel objective based Power System Stabilizer (PSS) design is proposed for a modified Heffron - Philiphs model (MHP) using bio inspired algorithms. A conventional Heffron – Philphs (CHP) model is developed by taking infinite bus voltage as reference, whereas MHP model is developed by taking transformer high voltage bus voltage as reference, which makes independent of external system data for the PSS design. PSS parameters are optimized using differential evolution (DE) algorithm and Firefly (FF) algorithm to obtain better dynamic response. The proposed method is tested on various operating conditions under different typical disturbances to test efficacy and robustness. Simulation results prove that better dynamic performance is obtained with the proposed stabilizers over the fixed gain stabilizers. This method of tuning would become a better alternative to conventional stabilizers as conventional stabilizers require retuning of parameters mostly when operating condition changes, which is a time-consuming process and laborious. Eigen value analysis is also done to prove the efficacy of the proposed method over the conventional methods.


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