Gain Schedule PI Fuzzy Load Frequency Control for Two-Area Electric Power System

Abstract

The use of proportional integral (PI) load frequency control (LFC) to ensure the stable and reliable operation of electric power system is practical important. Any imbalance between synchronous generators and consumption loads will cause frequency unstable within the complete power system. The purpose of the load frequency control (LFC) is to keep the power system frequency and the inter-area tie power as near equilibrium point. This article introduces a gain schedule PI fuzzy load frequency control (GLFC) applying to two area electric power system. The GLFC consists of two level control systems, where the PI controller in the conventional form and its parameters are tuned in real time by fuzzy system. A fuzzy rule base is constructed in the form set of IF-THEN that describe how to choose the PI parameters under different operating conditions. The simulation has been conducted in MATLAB Simulink package. The effectiveness of the GLFC is measured by comparison with conventional PI load frequency controller.

Recent Strategies for Automatic Generation Control of Power Systems With Diverse Energy Sources

“…Pham et al (2017) developed a time-delay system to generate double-scroll chaotic attractors irrespective of model and presented model suitability. Hirpara et al (2020) worked for three first-order differential equations coupled with two algebraic equations for stochastic for wind speed, the wind turbine-generator system.A gain schedule PI fuzzy load frequency control (GLFC) with two area system (Elmenfy, 2020) tuned to worked in real time.…”

Power system load frequency management using fuzzy logic

“…The most commonly utilized controllers in multi area systems [4], particularly in two area systems, are "fuzzy logic with PI controllers" [20], "fuzzy logic based PID controllers" [17], and "neuro-fuzzy controllers" [19]. Additionally, fuzzy controllers help the system regain its stability and robustness [7,8,12]. In addition to traditional controllers, newer controllers that use evolutionary algorithms [6,15,21] are important in AGC.…”