Automatic Load Frequency Control of Six Areas’ Hybrid Multi-Generation Power Systems Using Neuro-Fuzzy Intelligent Controller

Abstract

This manuscript deals with the implementation of adaptive neuro-fuzzy inference system (ANFIS) approach to automatic load frequency control (ALFC) of six unequal areas’ multi-generation hybrid interconnected power system. ANFIS controller has the advantages of fuzzy as well as neural network and stands for adaptive neuro-fuzzy inference system. Areas 1 and 2 comprise thermal and reheat thermal plants, area 3 consists of hydro plant with hydraulic governor system whereas areas 4, 5 and 6 comprise nuclear, diesel and gas turbine power plants, respectively. The hybrid model of interconnected power plant is developed with intelligent ANN (artificial neural network) and hybrid neuro-fuzzy controllers, i.e. proposed ANFIS and existing proportional and integral (PI) controller separately. The simulation work was carried out and the performance evaluation is checked by the proposed control approaches with similar step load change in each areas. Sliding surface is introduced with each controller to enhance the performance of controllers. Evaluation of ANN, ANFIS and PI-based approaches exhibits the advantage of the proposed ANFIS controller over conventional PI controller and ANN controller for tie-line power and frequency deviation in all six areas of interconnected hybrid power system.

KEYWORDS:

• ANFIS
• ANN control technique
• Area control error (ACE)
• Automatic load frequency control (ALFC)
• MATLAB
• PI
• tie-line power

Additional information

Funding

The present work is supported by SEED money grant of Electrical and Instrumentation Engineering Department, Thapar University, Patiala, Punjab.

Notes on contributors

Surya Prakash

Surya Prakash received his Bachelor of Engineering degree from the Institution of Engineers (India) in 2003. He obtained his MTech. in Electrical Engg. (Power System) from KNIT, Sultanpur, India, in 2009 and PhD in Electrical Engg. (Power System) from SHIATS-DU (formerly AAI-DU, Allahabad, India) in 2014. Presently, he is working as Assistant Professor in the Department of Electrical & Instrumentation Engineering, Thapar University, Patiala. His field of interest includes power system operation & control, artificial intelligent control, and distributed generation.

Sunil K. Sinha

Sunil K. Sinha received his BSc Engg degree in Electrical from R.I.T. Jamshedpur, Jharkhand, India, in 1984, the M.Tech. degree in Electrical Engineering from the Institute of Technology, B.H.U, Varanasi, India, in 1987, and the PhD degree from IIT, Roorkee, India, in 1997. Currently, he is working as Professor in the Department of Electrical Engineering, Kamla Nehru Institute of Technology, Sultanpur, India. His field of interest includes state estimation, fuzzy control, robotics and AI applications.

E-mail: [email protected]