الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
The need for power has gone up significantly. It is expected that it will rise further in the near future. Therefore, there is a drive toward distributed energy resources (DERs) to meet the anticipated demand. DERS are both ecologically responsible and the most cost-effective option. Due to this, the number of microgrids, small-scale power generation systems, has expanded widely. A microgrid is made up of local loads and distributed generation (DG) resources. A microgrid may operate in either an islanded mode or in a grid-connected mode.
The major goal of this study is to keep a balance between the load demands and the generating power sources. It proposes different optimized controllers for the islanded microgrid. These controllers can maintain the desired voltage level for the different points of common coupling. It can also enhance the system’s dynamic response. Additionally, each proposed controller can handle fault conditions and disturbance issues on the system. Also, several optimization algorithms are used to solve a variety of technical issues and challenges.
This dissertation presents an application of gorilla troops optimization algorithm (GTO) for managing and controlling the microgrid voltages. The proposed proportional-integral (PI) controller parameters are properly designed by the GTO algorithm. It is based on the integral squared error (ISE) to minimize the system’s objective function (ISE). The novel optimal controller is applied to regulate the system voltage at the different points of common coupling despite the load variation. The performance of the PI controller based on the GTO algorithm is compared with the optimal PI controller that was designed by a particle swarm optimization (PSO) algorithm. It is observed that the microgrid functioning operation is improved even more by applying a GTO-based than the PSO. While the system transient response (regarding the maximum overshoots /undershoots percentages, settling time, and steady-state error) is required for more adjustments. So, Fuzzy logic controls (FLCs) are suggested to improve the control management performance.
This dissertation also presents a comparative analysis of an autonomous microgrid system based on an optimized FLC and the optimized PI controller. The fuzzy logic control parameters are also designed by using both the African vulture optimization algorithm (AVOA) and the PSO algorithm. At the same time, they are also compared with the best results obtained from the PI controller based on the GTO algorithm. The attained results prove the superiority of the FLC based on the AVOA over the other optimal control techniques. The AVOA is also applied to give an ideal design for the FLC parameters to preserve adequate power to the different loads. The optimal FLC is also able to bring a robust and effective control with a higher accuracy level than optimal PI control techniques under different studied scenarios.
Finally, this dissertation presents a novel application of an artificial hummingbird algorithm (AHA) for achieving optimal control for a super-twisting sliding mode control (ST-SMC) of an autonomous microgrid. It also presents another comparative analysis for the optimal ST-SMC with the best results obtained from both optimal FLC and optimal PI controllers. The proposed ST-SMC parameters are adequately designed by using the AHA optimization technique. A MATLAB/Simulink environment is used to verify the effectiveness of all the suggested optimal control methods. Also, the overall system modelling, designing, and controlling are tested. The investigation of the ST-SMC based on AHA, FLC based on AVOA, and PI based on GTO approaches are also implemented in real-time. OPAL-RT 4510 hardware in the loop and rapid control prototyping and OPAL-RT 8660 data acquisition platforms are used to implement the best results for all the suggested controllers. The verified theoretical and experimental results prove the superiority of the ST-SMC based on the AHA optimization technique over all the other optimal control techniques.