Performance Comparison between LQR, GPC and ANFIS controllers for Load Frequency Control in a Small Hydro Power Plant

Campos-Pena P.A.; Aliaga-Yauri J.O.; Barrera-Esparta D.L.; Gomez-Casasola M.R. · 2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control: For the Development of Sustainable Agricultural Systems, ICA-ACCA 2022 (2022) · DOI: 10.1109/ica-acca56767.2022.10006007

Resumen

For hydroelectric power plants, it is essential to generate power at constant frequency. Frequency variations in generator sets reduce their efficiency and reliability. To ensure a high quality of power supply, conventional controllers, such as Proportional Integral Derivative (PID), are used. However, these types of controllers applied to speed governors do not have good enough performance to keep a constant frequency when there are load variations. This paper presents the design of a Linear Quadratic Regulator (LQR) controller and a Generalized Predictive Control (GPC) controller that includes load variation as a measurable disturbance, for power system frequency stabilization in a small hydro power plant unit. Furthermore, these controllers were used to train Sugeno fuzzy architecture-based adaptive neuro-fuzzy inference system (ANFIS) models to create fuzzy controller models of each one. To simulate the proposed controllers, Matlab and Simulink software were used, considering a 20 MW Francis Turbine. Finally, a performance comparison analysis using performance indexes like integral of error (IE), integral of squared error (ISE), integral of absolute error (IAE), integral of time absolute error (ITAE) and integral of time squared error (ITSE) was performed. The results show that all the designed controllers stabilize the frequency of the system against external disturbances without steady-state error and perform better than a tuned PID controller. Besides, compared with the other controllers designed, the GPC-based ANFIS controller achieved the best performance.

Instituciones: Universidad Nacional de Ingeniería