INTELLIGENT LOAD-FREQUENCY CONTROL IN A DEREGULATED ENVIRONMENT: This study presents an intelligent solution for load-frequency control in a restructured power system using a modified traditional frequency response model, suitable for a bilateral-based deregulation policy. The new approach is based on an extended classifier system with continuous-valued inputs (XCSR) which is the most successful learning classifier systems.< Final Year Project > The proposed intelligent solution does not require an accurate model of the system and is more flexible in specifying the control objectives. Also it is an automated learning-based approach. It means there is not any need to training data and expert knowledge of the system to determine the states and actions, which is a very time-consuming and difficult stage of designing reinforcement learning-based solutions. To demonstrate the effectiveness of the proposed method, its performance on a three-area restructured power system with possible contract scenarios, large load demands and area disturbances has been compared with multi-agent reinforcement learning-based controller. The results show that the proposed intelligent solution achieves good robust performance for a wide range of load changes in the presence of system nonlinearities and has good ability to track the contracted and non-contracted demands.
Abstract—INTELLIGENT LOAD-FREQUENCY CONTROL IN A DEREGULATED ENVIRONMENT: CONTINUOUS-VALUED INPUT, EXTENDED CLASSIFIER SYSTEM APPROACH
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