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

2012 ◽  
Author(s):  
M.F. Othman ◽  
M. Mahfouf ◽  
D.A. Linkens
Keyword(s):  
Power System ◽  
Fuzzy Inference ◽  
Design Procedure ◽  
Test System ◽  
Power System Stabilizer ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Three Phase ◽  
Input Signals ◽  
System Stabilizer

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

2012 ◽  
Author(s):  
Hua Nong Ting ◽  
Jasmy Yunus ◽  
Sheikh Hussain Shaikh Salleh
Keyword(s):  
Power System ◽  
Fuzzy Inference ◽  
Design Procedure ◽  
Test System ◽  
Power System Stabilizer ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Three Phase ◽  
Input Signals ◽  
System Stabilizer

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.

scholarly journals Design of Neuro-Fuzzy Controller for Stabilization of Single Phase Stand-Alone PV Power System

2018 ◽  
Vol 7 (3) ◽  
pp. 176
Author(s):  
Hitendra Singh Thakur ◽  
Ram Narayan Patel
Keyword(s):  
Power System ◽  
Single Phase ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Phase System ◽  
Inference System ◽  
Current Harmonics ◽  
Synchronous Reference Frame ◽  
Neuro Fuzzy ◽  
Three Phase

For the three phase power electronic and drive applications, vector control or the synchronous reference frame (SRF) based control concept is well accepted and settled amongst the research communities. Although the SRF concept has gained popularity and appreciation in developing the three phase controllers, still the concept has not reached the same level in case of a single phase system. The work presented in this paper is mainly concerned to the design of a hybrid Artificial Neural Network and Fuzzy Logic based controller for a single phase stand-alone photo-voltaic (PV) power system. The adaptive neuro fuzzy inference system (ANFIS) controller proposed in this paper is chiefly meant for improving the transient and steady state responses; for minimizing the distorting effect of the low order load current harmonics encountered particularly in case of switching the drive based inductive loads and to help maintain the inverter output voltage constant under different loading circumstances. The result obtained through simulation work, shows the effectiveness of the proposed controller as compared with the previously established research works.

A Simple Design Approach for Excitation Controller and Power System Stabilizer

2010 ◽  
Author(s):  
G. Fusco ◽  
M. Russo
Keyword(s):  
Power System ◽  
Transient Stability ◽  
Short Circuit ◽  
Design Procedure ◽  
Voltage Regulation ◽  
Operating Conditions ◽  
Power System Stabilizer ◽  
Simple Design ◽  
Wide Range ◽  
System Stabilizer

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.

Recurrent ANFIS-Coordinated Controller Design for Multimachine Power System with FACTS Devices

2016 ◽  
Vol 26 (02) ◽  
pp. 1750034 ◽  
Author(s):  
J. Sangeetha ◽  
P. Renuga
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Controller Design ◽  
Test System ◽  
Operating Conditions ◽  
Superior Performance ◽  
Control Technique ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Oscillation Damping

This paper proposes the design of auxiliary-coordinated controller for static VAR compensator (SVC) and thyristor-controlled series capacitor (TCSC) devices by adaptive fuzzy optimized technique for oscillation damping in multimachine power systems. The performance of the coordinated control of SVC and TCSC devices based on feedforward adaptive neuro fuzzy inference system (F-ANFIS) is compared with that of the adaptive neuro fuzzy inference system (ANFIS) structure based on recurrent adaptive neuro fuzzy inference system (R-ANFIS) network architecture. The objective of the coordinated controller design is to tune the parameters of SVC and TCSC fuzzy lead lag compensator simultaneously to minimize the deviation of rotor angle and rotor speed of the generators. The performance of the system is enhanced by optimally tuning the membership functions of fuzzy lead lag controller parameter of the flexible AC transmission system (FACTS) by R-ANFIS controller. The training data for F-ANFIS and R-ANFIS are generated by conventional linear control technique under various operating conditions. The offline trained controller tunes the parameter of lead lag controller in online. The oscillation damping ability of the system is analyzed for three-machine test system by calculating the standard deviation and cost function. The superior performance of R-ANFIS controller is compared with various particle swarm optimization-based feedforward ANFIS controllers available in literature.

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