In power systems, Low Frequency Oscillations (LFO) in the range of 0.1 - 2.5 Hz have been solved through Power System Stabilizer (PSS). Proportional Integral Derivative (PID) controller is the simplest and effective solution to the most of control engineering applications today. Based on advantage, the PID controller combined with PSS to enhance the stability in power system. In practice most of the PID controller and parameters of PSS are tuned manually and fixed for certain operating conditions. In general power systems are non linear, conventional methods had lack of robustness. Therefore it is necessary to take advantage in simplifying the problem and implementation by utilizing most efficient optimization methods. From this view, many optimization methods and algorithms have been employed to tune the PID gains and PSS parameters. This paper broadly reviews the optimization methods and algorithms such as Conventional methods, Soft Computing, Genetic Algorithm (GA), Evolutionary Programming (EP), Differential Evolution (DE) and Swarm Intelligence methods were available for tuning the PID gains and PSS parameters successfully. Research showed the design of controllers based on conventional methods; soft computing and population based algorithms suffer from limitations. However, swarm intelligence techniques proved to be able to overcome these limitations. Swarm intelligence based coordinated controller (PID+PSS), will effectively enhance the small signal stability and transient stability in power system. An effort is made in this paper to present a broad analysis of tuning the PID gains and PSS parameters by various researchers.

Coordination of PSS and PID Controller for Power System Stability Enhancement – Overview

/pdf/2009-international-conference-on-electric-power-and-energy-58ngd7eb49.pdf

2009 International Conference on Electric Power and Energy Conversion Systems (EPECS 2009)

This paper compares PSS based various controller structures on a single machine connected to infinite bus system. The controllers hold the types of PID, PD, PI, and lead-lag controllers. These controllers were tuned by a technique called Particle Swarm Optimization (PSO). The PSO was undertaken for multiple operating points related to the system in order to find a global solution. The simulation results had shown that the PSO optimized the PSS based PID controller structure has achieved the best performance in stabilizing the system when a disturbance occurred.

Comparative study between various controllers for power system stabilizer using Particle Swarm Optimization

In this paper, a new evolutionary algorithm, namely the biogeography-based optimization (BBO) algorithm, is proposed to tune the Proportional Integral Derivative (PID) controller and power system stabilizer (PSS) parameters. The efficiency of BBO algorithm is verified on the single-machine infinite-bus (SMIB) system for various operating conditions. The proposed method suppresses the (0.1–2.5 Hz) low-frequency electromechanical oscillations and increases the power system stability via minimizing the objective function such as integral square error (ISE). Simulations of BBO-based PID, BBO-based PSS, BBO-based PID-PSS and conventional PSS are performed by using MATLAB/Simulink. The results show that BBO-based PID-PSS method improves the system performance and provides better dynamic performance.

Implementation and analysis of BBO algorithm for better damping of rotor oscillations of a synchronous machine

A real coded genetic algorithm based PID controller (RCGAPID) is presented in this paper to improve power system dynamic stability. The proposed RCGAPID parameters are optimized using real coded genetic algorithm (RCGA). The design problem of the proposed controller is formulated as an optimization problem and RCGA is employed to search for optimal PID controller parameters. Integral absolute error (IAE) and integral squared error (ISE) performance indexes have been used for the determination of the PID controller parameters in power systems stabilization. The performance of the proposed RCGAPID under various loading and small disturbance conditions are analyzed and compared in single machine infinite bus (SMIB) power system. Simulation results show the effectiveness and robustness of proposed controllers to improve the stability performance of power system. Copyright © 2010 Praise Worthy Prize S.r.l. - All rights reserved.

Robust Design of PID Controller for Power System Stabilization by Using Real Coded Genetic Algorithm

This paper considers the stabilization of a synchronous machine connected to an infinite bus via a PID. The PID parameters are tuned using particle swarm optimization (PSO). The optimum controller's parameters where found by minimizing an objective function that moved the unstable eigenvalues to the left-hand side of the complex s-plane. The PSO based PID controller was initially designed for a single operating point of the power system and later the design was extended to stabilize multiple operating points to ensure robustness of the controller. The results proved the effectiveness of the PSO tuned PID controller in stabilizing the power system for a wide range of operating points.

Design of PID controller for power system stabilization using particle swarm optimization

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Design of PID controller for power system stabilization using particle swarm optimization