الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Recently, the demand for electricity has risen and the demand for electricity is
projected to increase dramatically in the immediate future. There is a movement
toward renewable energy sources to meet this expected demand because they are
environmentally sound and economically best considered. This has contributed to the
growth of small-scale power generation systems called microgrids. Local loads and
distributed generation sources such as wind turbines, photovoltaic systems, fuel cells
and energy storage systems make up a microgrid. Microgrid can operate in the gridconnected mode as well as island mode contingency. The power generated from most
renewable resources is direct current (DC), but the utility grid is alternating current
(AC). The inverter must be used inside the microgrid to convert the DC to AC. This
inverter is considered the most significant element in the microgrid. There are many
inverters in the microgrid system operating in parallel. The parallel operation of the
inverters ensures the stability of the microgrid. The control of inverters is expected to
provide active and reactive powers while maintaining the frequency and voltage
variability within the permissible limits. The droop control method is used to control
the inverters for this purpose. The droop control technique produces power sharing in
accordance with constrained voltage and frequency limits. Such droop controllers are
tuned with identical parameters by trial and error method. Nevertheless, this approach
has a significant limitation in achieving optimum parameters or even the correct
performance.In order to address many engineering problems and obstacles, researchers are
likely to use optimization algorithms, but for researchers, there is a question: ” Is there any optimization technique that can solve all problems? ”. To answer this question,
many researchers have conducted new researches for new types of optimization
techniques or improved existing algorithms. There is a theory supporting this trend
called the No Free Lunch Law (NFL). In this thesis, two types of improved
optimization techniques that are enhanced and inspired by salp swarm inspired
algorithm and Henry Gas Solubility Optimization are developed and presented.
The hybridization between salp swarm inspired algorithm (SSIA) and particle
swarm optimization (PSO) is the first type of hybrid optimization technology called
hybrid SSIA-PSO. To illustrate the improved SSIA capability, a robust comparative
study will be conducted between it and the other seven types. The hybrid SSIA-PSO was proposed to solve one of the most common technical microgrid problems. To
ensure equal power sharing between various sources, the optimal design for the
proportional-integral (PI) controller parameters and droop control parameters is
performed. Texas Instruments Launchpad TMS320F28379D is used to develop a realtime test bench to check the theoretical results of the suggested optimization
technique. In addition, to demonstrate the effectiveness of the proposed SSIA-PSO, a
detailed comparative review is carried out between the simulation and experimental
results.
To address the optimal parameters of a decentralized control system focused on
two controller types, PI droop control (PIDC) and fuzzy droop control (FDC), a new
hybrid Henry Gas Solubility Optimization (HGSO) with Sine Cosine Algorithm
(SCA) is used (FDC). Using the Texas Instruments Launchpad TMS320F28379D, the
implemented FDC control strategies, based on HGSO-SCA, are tested experimentally
in a real-time environment.