<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper proposes a newly adaptive single-neuron proportional integral derivative (SNPID) controller that uses fuzzy logic as an adaptive system. The main problem of the classical controller is lacking the required robustness against disturbers, measurement noise in industrial applications. The new formula of the proposed controller helps in fixing this problem based on the fuzzy logic technique. In addition, the genetic algorithm (GA) is used to optimize parameters of the SNPID controller. Because of the high demands on the availability and efficiency of electrical power production, the design of robust load-frequency controller is becoming increasingly important due to its potential in increasing the reliability, maintainability and safety of power systems. So, the proposed controller has been applied for load-frequency control (LFC) of a single-area power system. The effectiveness of the proposed SNPID controller has been compared with the conventional controllers. The simulation results show that the proposed controller approach provides better damping of oscillations with a smaller settling time. This confirms its superiority against its counterparts. In addition, the results show the robustness of the proposed controller against the parametric variation of the system.</p></abstract>

This paper proposes a newly adaptive single-neuron proportional integral derivative (SNPID) controller that uses fuzzy logic as an adaptive system. The main problem of the classical controller is lacking the required robustness against disturbers, measurement noise in industrial applications. The new formula of the proposed controller helps in fixing this problem based on the fuzzy logic technique. In addition, the genetic algorithm (GA) is used to optimize parameters of the SNPID controller. Because of the high demands on the availability and efficiency of electrical power production, the design of robust load-frequency controller is becoming increasingly important due to its potential in increasing the reliability, maintainability and safety of power systems. So, the proposed controller has been applied for load-frequency control (LFC) of a single-area power system. The effectiveness of the proposed SNPID controller has been compared with the conventional controllers. The simulation results show that the proposed controller approach provides better damping of oscillations with a smaller settling time. This confirms its superiority against its counterparts. In addition, the results show the robustness of the proposed controller against the parametric variation of the system.

Novel Fuzzy-Based Self-Adaptive Single Neuron PID Load Frequency Controller for Power System

Mechanical Engineering Department, Mechatronics Division, Faculty of Engineering

Power Electronics and Drives

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper proposes a newly adaptive single-neuron proportional integral derivative (SNPID) controller that uses fuzzy logic as an adaptive system. The main...

Novel Fuzzy-Based Self-Adaptive Single Neuron PID Load Frequency Controller for Power System

Online veröffentlicht: 26. Nov. 2019

Seitenbereich: 141 - 150

Eingereicht: 05. Dez. 2018

Akzeptiert: 27. Dez. 2018

DOI: https://doi.org/10.2478/pead-2019-0002

Schlüsselwörter
Power system, Load frequency, Intelligent Control, PID, Single Neuron

© 2019 M. Abdullah Eissa, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Novel Fuzzy-Based Self-Adaptive Single Neuron PID Load Frequency Controller for Power System

Online veröffentlicht: 26. Nov. 2019

Seitenbereich: 141 - 150

Eingereicht: 05. Dez. 2018

Akzeptiert: 27. Dez. 2018

DOI: https://doi.org/10.2478/pead-2019-0002

SchlüsselwörterPower system, Load frequency, Intelligent Control, PID, Single Neuron

© 2019 M. Abdullah Eissa, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Power system, Load frequency, Intelligent Control, PID, Single Neuron