NDLI: Optimizing PSS parameters for a multi-machine power system using genetic algorithm and neural network techniques

Content Provider	Springer Nature Link
Author	Jebali, Mariam Kahouli, Omar Hadj Abdallah, Hsan
Copyright Year	2016
Abstract	Power system stabilizers (PSSs) are generally used to solve the low-frequency oscillation problem. To overcome this type of oscillation, this paper presented a new method by using the genetic algorithm (GA) to identify PSS settings. The tuning of PSS parameters was formulated relying on an eigenvalue-based objective function aiming at maximizing the stability margin. This was achieved by increasing the sum of the squares of negative real parts of the system eigenvalues. The small disruptions in the form of load change take place routinely; the controller parameters should be adjusted to the changing conditions. To remedy this defect, an adjustment of parameters as a function of the load was required. Thus, a neuronal model by using a historical database determined by the GA solutions for various load levels can approximate the simulation studies, since it could estimate the stabilizer parameters in real time after the learning phase. The validity of the proposed technique was checked through the evolution simulation of the regulator parameters for a load forecast curve. These concepts were discussed in details on a multi-machine network Western System Coordinating Council (WSCC) comprising three generators and nine nodes. The eigenvalue analysis and time domain simulation results were presented at different operating conditions and under various disturbances in order to show the effectiveness of this study.
Starting Page	2669
Ending Page	2688
Page Count	20
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	90
Issue Number	9-12
e-ISSN	14333015
Language	English
Publisher	Springer London
Publisher Date	2016-10-21
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Low-frequency oscillations Power system stabilizer (PSS) Genetic algorithm (GA) Eigenvalue analysis Neural network (NN) Industrial and Production Engineering Media Management Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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