Speaker:

Mr. Muhammad Inshal Shahzad

Graduate. Student

Control and Instrumentation Engineering Department

King Fahd University of Petroleum and Minerals

Dhahran, Saudi Arabia

Abstract:

In recent times, renewable energy has gained significant global interest due to its cost-effectiveness and sustainability. Therefore, the existence of it in power systems is an unavoidable reality. Therefore, a proposal is made for load frequency control (LFC) in multi-area power systems that include photovoltaic (PV), Electric Vehicles, Wind Turbines and Thermal plants as sources. The study examines the competing cascaded controllers proportional integral and derivative with filter-PI (PIDn-PI). The results of these controllers are then compared with the classic PI and PIDn controllers. The suggested study introduces an advanced coyote optimization algorithm (ECOA) to efficiently determine the best parameters for the proposed controllers. Moreover, the uncertainty is taken into account by varying the system parameters within a range of ± 40%. The effectiveness of the suggested alternative controllers is evaluated by subjecting them to a dynamic load change, which is applied separately in each location. These controllers are used to control two different test cases with different sets of disturbances. The acquired data are being compared with a range of previously documented approaches. The simulated comparisons demonstrate the exceptional effectiveness and strong resilience of the proposed cascaded PIDn-PI control system based on ECOA for managing the Load Frequency Control (LFC) in multi-area power systems.

Speaker Bio :

Muhammad Inshal Shahzad received his B.Sc. degree in Mechatronics Engineering in 2021 from the Air University Islamabad (AU), Pakistan. Mr. Muhammad Inshal Shahzad is currently pursuing his master’s degree in System and Control Engineering (SCE) at King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. His research interests include control optimization techniques in hybrid multi-area power systems using renewable energy sources.