A State of Art of Intelligent h-HE-PO-GA Controller for the Load Frequency Control of Multi-Area Power System Network

The article presents the load frequency control (LFC) of a multi-area power system (MAPS) network using a hybrid Hesso-Poisson-genetic (h-HE-PO-GA) intelligent controller. In a multi-area or two-area power system network, continuous frequency deviations occur due to load disturbances. Such fluctuations in frequency also affect other parameters, which is one of the greatest concerns for researchers. Conventionally, the LFC of a multi-area system was assessed using state space analysis, which is complex in nature and more variable. Such critical issues will be resolved by developing multi-objective functions and considering the effects of the minimum and maximum limits of disturbance which is the novelty. Further, the multi-objective function will be trained by a hybrid intelligent controller in order to neutralize the effect of disturbances. The hybrid intelligent controller has been referred to as an h-HE-PO-GA controller, which is developed by combining the three topologies, namely the hessian matrix controller, the Poisson distribution, and the genetic algorithm. The hybrid intelligent controller is simple, with fewer computation variables, which is the novelty and achievement of the article. The overall analysis begins with mathematical modeling of various generating units in each area, such as solar photovoltaic (SPV), wind turbines (WT), battery energy storage systems (BESS), and conventional sources (CS), which are integrated with the common grid. Two area networks are simulated to analyze the various parameters using the h-HE-PO-GA controller. The various performance parameters, like frequency deviations, mechanical power deviations, area control error, settling time, and total harmonic distortion (THD) of grid voltage, will be measured. All such assessments will be analyzed in three different cases: variable load disturbance, variable solar irradiation, and variable wind operation. The performance parameters have also been assessed with sliding mode control (SMC) and particle swarm optimization (PSO). At the end, it is observed that the least value of change in frequency, mechanical power deviation, settling time, and THD (%) are attained with the h-HE-PO-GA controller in comparison to SMC and PSO which is one of greatest achievement of proposed controller. Further, the stability of the intelligent hybrid controller has also been validated with Routh Hurwitz (R-H) criteria.