Fuzzy Logic-based Power System Stabilizer Research Articles

A Novel Fuzzy Rule Matrix Design for Fuzzy Logic-based Power System Stabilizer

In this paper, fuzzy logic-based power system stabilizer (FPSS) is designed to have effective damping. As to achieve impressive performance of a controlled plant, the rule base/fuzzy rule matrix (FRM) of a fuzzy logic controller (FLC) is designed by trial–error method, which is a time-consuming process. In this paper, a systematic procedure is given to design FRM for effective damping of single-machine infinite-bus (SMIB) and two-area four-machine ten-bus power system. To design the novel FRM, the existing rule tables, proposed by different researchers, are tested with SMIB system, and empirical relationships have been drawn. The response validity and rule justification is carried out using linguistic phase-plane trajectory method. Finally, the contribution to design FRM is made to get effective damping for SMIB as well as a multi-machine system. The effectiveness of the FPSS based on the proposed FRM over a wide range of operating conditions on SMIB system and the comparison of system response without PSS, with Conventional PSS, and with FPSS is carried out. Similarly, the multimachine power system is considered without PSS and with FPSS, and performance comparison is carried out. The response of FPSS with proposed FRM enables to show the effective damping of system oscillations.

Design of optimal input–output scaling factors based fuzzy PSS using bat algorithm

In this article, a fuzzy logic based power system stabilizer (FPSS) is designed by tuning its input–output scaling factors. Two input signals to FPSS are considered as change of speed and change in power, and the output signal is considered as a correcting voltage signal. The normalizing factors of these signals are considered as the optimization problem with minimization of integral of square error in single-machine and multi-machine power systems. These factors are optimally determined with bat algorithm (BA) and considered as scaling factors of FPSS. The performance of power system with such a designed BA based FPSS (BA-FPSS) is compared to that of response with FPSS, Harmony Search Algorithm based FPSS (HSA-FPSS) and Particle Swarm Optimization based FPSS (PSO-FPSS). The systems considered are single-machine connected to infinite-bus, two-area 4-machine 10-bus and IEEE New England 10-machine 39-bus power systems for evaluating the performance of BA-FPSS. The comparison is carried out in terms of the integral of time-weighted absolute error (ITAE), integral of absolute error (IAE) and integral of square error (ISE) of speed response for systems with FPSS, HSA-FPSS and BA-FPSS. The superior performance of systems with BA-FPSS is established considering eight plant conditions of each system, which represents the wide range of operating conditions.

Application of Bat Algorithm to Optimize Scaling Factors of Fuzzy Logic-Based Power System Stabilizer for Multimachine Power System

Abstract This article presents the design of optimized fuzzy logic-based power system stabilizer (FPSS) to enhance small signal stability using bat algorithm (BA). The proposed optimization of scaling factors of FPSS is considered with an objective function based on square error minimization to guarantee the stability of nonlinear models of test system using BA. The BA-optimized FPSS (BAFPSS) controller is applied to the standard IEEE ten-machine thirty-nine-bus test power system model in the decentralized manner, and the performance is compared with the robust fuzzy controller. The robustness is tested by considering four different models of the test power system with different fault locations to establish the superiority of the proposed BAFPSS over the FPSS.

Dynamic Stability Enhancement of Power System Using Fuzzy Power System Stabilizer Under Different Loading Conditions

The power system is dynamic in nature and is constantly being subjected to disturbances. It enters into dynamic instability when there is an imbalance between generation and varying load demand which calls for use of Power System Stabilizer (PSS). PSS is a device which provides additional signal to the voltage regulator derived from speed deviation, excitation deviation and accelerating power for damping critical oscillations. The PSS used should be capable to produce appropriate stabilizing signals against a wide range of operating conditions and disturbances. For this purpose, a PSS based on fuzzy logic control under multi-operating conditions is proposed in this paper. The performance of Fuzzy Logic based PSS(FLPSS) applied to Single Machine Infinite Bus (SMIB) system is studied for three different operating conditions; nominal load, heavy load and fault condition in transmission line. For Fuzzy PSS, speed deviation and acceleration are taken as input. The system is simulated in SIMULINK platform and its dynamic response is analyzed for system without PSS and with PSS. The results are compared for system with Conventional PSS and Fuzzy Logic PSS.

Designing Power System Stabilizer for Multimachine Power System Using Neuro-Fuzzy Algorithm

This paper describes a design procedure for a fuzzy logic based power system stabilizer (FLPSS) and adaptive neuro–fuzzy inference system (ANFIS) and investigates their robustness for a multi–machine power system. Speed deviation of a machine and its derivative are chosen as the input signals to the FLPSS. A four–machine and a two–area power system is used as the case study. Computer simulations for the test system subjected to transient disturbances i.e. a three phase fault, were carried out and the results showed that the proposed controller is able to prove its effectiveness and improve the system damping when compared to a conventional lead–lag based power system stabilizer controller.

Speaker-Independent Malay Syllable Recognition Using Singular and Modular Neural Networks

This paper describes a design procedure for a fuzzy logic based power system stabilizer (FLPSS) and adaptive neuro–fuzzy inference system (ANFIS) and investigates their robustness for a multi–machine power system. Speed deviation of a machine and its derivative are chosen as the input signals to the FLPSS. A four–machine and a two–area power system is used as the case study. Computer simulations for the test system subjected to transient disturbances i.e. a three phase fault, were carried out and the results showed that the proposed controller is able to prove its effectiveness and improve the system damping when compared to a conventional lead–lag based power system stabilizer controller.

Neurofuzzy Power System Stabilizer

An adaptive fuzzy logic power system stabilizer (AFPSS) consisting of a generalized neuron (GN)-based predictor and a fuzzy logic controller (FLC) is described. The inference mechanism of the FLC is represented by a rule-base and a database. Two parameters, decided on the basis of the GN-predictor output and the current system conditions, are used to tune the AFPSS. This mechanism of tuning makes the fuzzy logic-based power system stabilizer adaptive to changes in the operating conditions. Therefore, variation in the system response, under a wide range of operating conditions, is less compared to the system response with a fixed-parameter conventional PSS. The performance of the AFPSS has been tested by simulation and experimental studies.

Design of a robust power system stabilizer using fuzzy logic for a multi-machine power system

This paper presents a design procedure for a fuzzy logic based power system stabilizer (FPSS) and investigates the robustness of the FPSS for a multi-machine power system. Speed deviation of synchronous generator and its derivative are chosen as the input signals to the FPSS. A two area, five generator power system is used to illustrate the robustness of the FPSS. A normalized sum-squared deviation (NSSD) index is used for designing the FPSS and demonstrating its robustness. Nonlinear simulation tests for different disturbances and their results are discussed.

A fuzzy logic based power system stabilizer with learning ability

A fuzzy logic-based power system stabilizer (PSS) with learning ability is proposed in this paper. The proposed PSS employs a multilayer adaptive network. The network is trained directly from the input and the output of the generating unit. The algorithm combines the advantages of artificial neural networks (ANNs) and fuzzy logic control (FLC) schemes. Studies show that the proposed adaptive network-based fuzzy logic PSS (ANF PSS) can provide good damping of power systems over a wide range of operating conditions and improve the dynamic performance of the power system.

Optimization of fuzzy controllers as real-time power system stabilizers

The optimization and implementation of fuzzy controllers to improve the stability of power systems is presented in the paper. Elements from fuzzy set theory were used in the development of two different types of fuzzy controllers. The first one uses a traditional fuzzy logic controller (FLC) as a power system stabilizer. The input and output membership functions were adjusted according to an evaluation index in order to achieve the optimal performance. The initial FLC design with a complete filled rule table (49 rules) can be reduced using neural-network techniques to improve the dynamic performance. The second type of fuzzy controller is a fuzzy-logic-based power system stabilizer (FLBPSS) which has been simulated to determine its optimum settings using new nonlinear fuzzy membership functions. The performance of the optimized FLBPSS has been compared to the FLC and found to be better. The design was implemented on 5 kVA generators in the Power System Laboratory using an IBM-PC with A/D and D/A converters acting as the real-time controller. Both the simulation and implementation results on one-machine and two-machine infinite bus power systems show that the proposed FLBPSS is quite robust for both small and large perturbations and need not be changed for changes in the systems operating conditions and parameters.