Multi Area Load Frequency Control of a Hybrid Power System with Advanced Machine Learning Controller: Case Study of Andhra Pradesh

Mahaboob Shareef Syed *, Ch.V.Suresh**, S. Siva Nagaraju ***
* Research Scholar, Department of Electrical and Electronics Engineering, Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India.
** Associate Professor, Department of Electrical and Electronics Engineering, Vasireddy Venkatadri Institute of Technology, Nambur, Guntur Andhra Pradesh, India.
*** Professor, Department of Electrical and Electronics Engineering, Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India.
Periodicity:February - April'2018
DOI : https://doi.org/10.26634/jps.6.1.14043

Abstract

In the present work, the Load Frequency Control (LFC) problem of a real time bus test system of Andhra Pradesh state 124 (AP-124), India, has been studied. The system comprises of hydro, thermal, gas, and nuclear plants. The entire system is segregated into four control areas. Area 2 and Area 4 form a hybrid power system with wind and solar plants integration, as the availability of wind and solar resources are abundant in the respective areas. To overcome the chaotic behaviour of load and renewable energy generation, it is indeed necessary to develop a sophisticated controller. A hybrid Persistent – Extreme Learning Machine (P-ELM) controller was used to encounter the frequency and tie-line power deviations. The LFC has been studied by the Conventional Integral Control, Neural Network (NN) Control and P-ELM Control, with variations in load, wind, and solar generations as well.

Keywords

Load Frequency Control, Persistent – Extreme Learning Machine Controller, Hybrid Power System, Machine Learning, Neural Network

How to Cite this Article?

Syed, M. S., Suresh, V., and Sivanagaraju, S. (2018). Multi Area Load Frequency Control of a Hybrid Power System with Advanced Machine Learning Controller: Case Study of Andhra Pradesh. i-manager’s Journal on Power Systems Engineering, 6(1), 1-13. https://doi.org/10.26634/jps.6.1.14043

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