فیزیکی هوشمند مبتنی بر فاز واکنش گرما قدرت یک سیستم قدرت هیبرید منحصر به فرد

In this paper, an isolated wind-diesel hybrid power system model is considered for its on-line reactive power compensation. In the studied power system model, a diesel engine based synchronous generator (SG) and a wind turbine based induction generator (IG) are used for power generation. IG offers many advantages over the SG but it requires reactive power support for its operation. So, there is a gap between the reactive power demand and its supply. To minimize this gap between reactive power generation and its demand, variable source of reactive power such as static VAR compensator (SVC) is used. The different tunable parameters of the studied hybrid power system model are optimized by a novel opposition-based gravitational search algorithm (OGSA). Gravitational search algorithm (GSA) is based on the law of gravity and the interaction between the masses. In GSA, the searcher agents are a collection of masses and their interactions are based on the Newtonian laws of gravity and motion. To further improve the optimization performance of the GSA, opposition-based learning is employed for population initialization and also for generation jumping. The performance analysis of a Sugeno fuzzy logic (SFL) based controller for the studied isolated hybrid power system model is also carried out which tracks the degree of reactive power compensation for any sort of input perturbation in real-time. Time-domain simulation of the investigated power system model reveals that the proposed OGSA-SFL yields on-line, off-nominal optimal SVC parameters resulting in on-line optimal terminal voltage response.