Comparative study of ANFIS fuzzy logic and neural network scheduling based load frequency control for two-area hydro thermal system

• Main Authors: Yadav, Piyush Kumar, Bhasker, Rajnish, Upadhyay, Satyam Kumar
• Format: Conference Proceeding
• Language: English
• Subjects: Adaptive Neuro-Fuzzy Inference System (ANFIS); Area Control Error (ACE); Artificial Neural Network (ANN); Automatic Load Frequency Controller (AFLC); Fuzzy Logic Controller; MATLAB/Simulink 2020 Software
• ISSN: 2214-7853; 2214-7853
• DOI: 10.1016/j.matpr.2021.12.041

In this study effort, power systems are studied using two hydro-thermal system regions linked by tie lines. The primary objective of the system generation control is to balance the system generation with load and losses in order to maintain the appropriate frequency and power exchange with adjacent systems. This paper focuses primarily on technical problems in structured power systems related to load frequency control (LFC). The PI & PID controllers are extremely easy to build and provide greater dynamical response, but their performance deteriorates as the system complexity grows as a result of disturbances such as dynamic load fluctuation boiler. A controller is thus needed to solve this issue. In this regard, artificial intelligent controllers like Fuzzy and Neural methods are more appropriate. Fuzzy system has been used with promising results to load frequency control issues. These have advanced setup of adaptive control. The NNs may monitor the frequency of the system when the controller exits its control command. The neural control method has many benefits over the basic fixed parameter schemes and the newly created more sophisticated adaptive control technique. The suggested ANFIS controller combines the benefits of the ANN's fluid controller as well as its rapid reaction and flexibility. The smart controls such as Fuzzy logic, the ANN and Hybrid Fuzzy Neural Network methods are utilized for the two sectors of linked power systems for automatic generation control. Area-1 consists of a thermal power plant whereas area-2 consists of an electric governor hydropower project. Performance assessment is done out utilizing intelligent control methods (ANFIS, ANN and Fuzzy). The controls are provided to improve the performance of the sliding controller surface i.e. changeable structure control.