Indirect adaptive fuzzy finite time synergetic control for power systems

Authors

DOI:

https://doi.org/10.20998/2074-272X.2023.1.08

Keywords:

adaptive fuzzy systems, synergetic control theory, finite time convergence, power system stabilizer, multi-machine power system

Abstract

Introduction. Budget constraints in a world ravenous for electrical power have led utility companies to operate generating stations with full power and sometimes at the limit of stability. In such drastic conditions the occurrence of any contingency or disturbance may lead to a critical situation starting with poorly damped oscillations followed by loss of synchronism and power system instability. In the past decades, the utilization of supplementary excitation control signals for improving power system stability has received much attention. Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp low-frequency oscillations caused by load disturbances or short-circuit faults. Problem. Adaptive power system stabilizers have been proposed to adequately deal with a wide range of operating conditions, but they suffer from the major drawback of requiring parameter model identification, state observation and on-line feedback gain computation. Power systems are nonlinear systems, with configurations and parameters that fluctuate with time that which require a fully nonlinear model and an adaptive control scheme for a practical operating environment. A new nonlinear adaptive fuzzy approach based on synergetic control theory which has been developed for nonlinear power system stabilizers to overcome above mentioned problems. Aim. Synergetic control theory has been successfully applied in the design of power system stabilizers is a most promising robust control technique relying on the same principle of invariance found in sliding mode control, but without its chattering drawback. In most of its applications, synergetic control law was designed based on an asymptotic stability analysis and the system trajectories evolve to a specified attractor reaching the equilibrium in an infinite time. In this paper an indirect finite time adaptive fuzzy synergetic power system stabilizer for damping local and inter-area modes of oscillations for power systems is presented. Methodology. The proposed controller design is based on an adaptive fuzzy control combining a synergetic control theory with a finite-time attractor and Lyapunov synthesis. Enhancing existing adaptive fuzzy synergetic power system stabilizer, where fuzzy systems are used to approximate unknown system dynamics and robust synergetic control for only providing asymptotic stability of the closed-loop system, the proposed technique procures finite time convergence property in the derivation of the continuous synergetic control law. Analytical proofs for finite time convergence are presented confirming that the proposed adaptive scheme can guarantee that system signals are bounded and finite time stability obtained. Results. The performance of the proposed stabilizer is evaluated for a single machine infinite bus system and for a multi machine power system under different type of disturbances. Simulation results are compared to those obtained with a conventional adaptive fuzzy synergetic controller.

Author Biographies

A. Khatir, Mostefa Ben Boulaïd University of Batna 2

PhD Student, LSTEB Laboratory, Department of Electrical Engineering

Z. Bouchama, Ferhat Abbas University of Setif 1

PhD, Associate Professor, QUERE Laboratory, Department of Electrical Engineering

S. Benaggoune, Mostefa Ben Boulaïd University of Batna 2

Professor, LSTEB Laboratory, Department of Electrical Engineering

N. Zerroug, Ferhat Abbas University of Setif 1

PhD, Associate Professor, QUERE Laboratory, Department of Electrical Engineering

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Published

2023-01-04

How to Cite

Khatir, A., Bouchama, Z., Benaggoune, S., & Zerroug, N. (2023). Indirect adaptive fuzzy finite time synergetic control for power systems. Electrical Engineering & Electromechanics, (1), 57–62. https://doi.org/10.20998/2074-272X.2023.1.08

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Section

Power Stations, Grids and Systems