Title: A FIS-GA combined approach for transmission congestion management in restructured power system
Authors: Saswati Kumari Behera; Nalin Kant Mohanty
Addresses: EEE Department, Sri Sai Ram Engineering College, Chennai, India ' EEE Department, Sri Venkateswara College of Engineering, Sriperumbudur, Tamil Nadu, India
Abstract: A power system under goes frequent changes due to disturbances. If the power system survives after the disturbance it will be operating in a new steady state, in which one or more transmission lines may be over loaded. The over loading of the transmission line can be eliminated by generator rescheduling and/or load shedding. This paper proposes fuzzy inference system (FIS)-based algorithm for overload indication in transmission line. The system parameters such as line overload factor (OF) and transmission congestion distribution factors (TCDFs) are given as the FIS input. The output from the FIS indicates whether the line is overloaded or not. Then generator rescheduling/load shedding is done to relief the congested line. The effectiveness of the congestion clusters method for CM is discussed by formulating two objective functions. Rescheduling of generation is formulated as an optimisation problem with the objective of obtaining minimum cost and proposed objective of achieving minimum real power loss. Attractive features of genetic algorithm (GA) are used for solving the formulated problem with the objectives considered separately. The effectiveness of the proposed approach has been tested for 5 bus system, 30 bus systems in MATLAB environment.
Keywords: fuzzy inference system; FIS; congestion management; transmission line overload; transmission congestion distribution; overload indication; load shedding; genetic algorithms; GAs; economic load dispatch; ELD; fuel costs; transmission congestion management; restructured power systems; fuzzy logic; generator rescheduling; simulation.
International Journal of Mathematical Modelling and Numerical Optimisation, 2016 Vol.7 No.3/4, pp.307 - 322
Received: 01 Feb 2016
Accepted: 28 Aug 2016
Published online: 27 Jan 2017 *