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

Open access


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.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Bensenouci A. and Ghany A.A. (2010). Performance analysis and comparative study of LMI-based iterative PID load-frequency controllers of a singlearea power system. Power 4 p.7.

  • Blevins T. and Nixon M. (2011). Control Loop Foundation Batch and Continuous Processes. North Carolina: International Society of Automation.

  • Duman S. Yorukeren N. and Altas I. H. (2012). Load frequency control of a single area power system using Gravitational Search Algorithm. International Symposium on Innovations in Intelligent Systems and Applications pp. 1–5.

  • Guha D. Roy P.K. and Banerjee S. (2016). Load frequency control of interconnected power system using grey wolf optimization. Swarm and Evolutionary Computation 27 pp. 97–115.

  • Gupta M. Walia A. Gupta S. and Sikander A. (2017). Modelling and identification of single area power system for load frequency control. In: 4th IEEE International Conference on Signal Processing Computing and Control (ISPCC) (pp. 436–439) September 2017.

  • Hu T.T. Zhuang Y.F. and Yu J. (2012). An improved single neuron adaptive PID controller based on levenberg-marquardt algorithm. In: International Conference on Brain Inspired Cognitive Systems (pp. 288–295). Berlin Heidelberg: Springer.

  • Isermann R. (2006). Fault-Diagnosis Systems. An Introduction from Fault Detection to Fault Tolerance. Berlin-Heidelberg: Springer.

  • Lüy M. Kocaarslan I. Çam E. & Taplamacioǧlu M. C. (2008). Load frequency control in a single area power system by artificial neural network (ANN). In 4 International Conference on TPE (pp. 26-29).

  • Martin P.G. and Hale G. (2010). Automation Made Easy Everything you Wanted to Know About Automation and Need to Ask. North Carolina: International Society of Automation.

  • Ponce-Cruz P. and Ramírez-Figueroa F.D. (2009). Intelligent Control Systems with LabVIEWTM. Berlin: Springer Science & Business Media.

  • Sakhavati A. Gharehpetian G. and Hosseini S.H. (2011). Decentralized robust load-frequency control of power system based on quantitative feedback theory. Turkish Journal of Electrical Engineering & Computer Sciences 19(4) pp. 513–530.

  • Shahrokhi M. and Zomorrodi A. 2013. Comparison of PID controller tuning methods. Department of Chemical & Petroleum Engineering Sharif University of Technology pp.1–2.

  • Wang Q. Shuang Y. (2015). A single neuron PID control algorithm of memristor-based. Computer Information System 14 no. 20143104 pp. 5023– 5030.

  • Xiao-dan et al. (2017). Application of single neuron adaptive PID approach in rolling tension control. In: 2nd International Conference on Materials Science Machinery and Energy Engineering (MSMEE) 2017.

Journal information
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 386 386 11
PDF Downloads 264 264 23