This paper presents the automatic load frequency control (ALFC) of two-area multisource hybrid power system (HPS). The interconnected HPS model consists of conventional and renewable energy sources operating in disparate combinations to balance the generation and load demand of the system. In the proffered work, the stability analysis of nonlinear dynamic HPS model was analyzed using the Hankel method of model order reduction. Also, an attempt was made to apply cascade proportional integralproportional derivative (PI-PD) control for HPS. The gains of the controller were optimized by minimizing the integral absolute error (IAE) of area control error using particle swarm optimization-gravitational search algorithm (PSO-GSA) optimization technique. The performance of cascade control was compared with other classical controllers and the efficiency of this approach was studied for various cases of HPS model. The result shows that the cascade control produced better transient and steady state performances than those of the other classical controllers. The robustness analysis also reveals that the system overshoots/undershoots in frequency response pertaining to random change in wind power generation and load perturbations were significantly reduced by the proposed cascade control. In addition, the sensitivity analysis of the system was performed, with the variation in step load perturbation (SLP) of 1% to 5%, system loading and inertia of the system by ±25% of nominal values to prove the efficiency of the controller. Furthermore, to prove the efficiency of PSO-GSA tuned cascade control, the results were compared with other artificial intelligence (AI) methods presented in the literature. Further, the stability of the system was analyzed in frequency domain for different operating cases.INDEX TERMS Automatic load frequency control (ALFC), hybrid power system (HPS), cascade control scheme (CCs), proportional integral -proportional derivative (PI-PD) control, Hankel method, particle swarm optimization -gravitational search algorithm (PSO-GSA).

The large-scale penetration of intermittent renewable energy (RE) sources such as wind and solar power generation may cause a problem of frequency aberration, power quality and instability to the system. This occurs when the load frequency control of interconnected system is unable to compensate the power balance between generation and load demand. To overcome these issues, this paper proposes a method to analyze the frequency stability of Hybrid Power System (HPS) through adaptive intelligent control techniques for delivering reliable power supply under the stochastic nature of RE and load demand. A recently developed physics-inspired Atom Search Optimization algorithm was applied for tuning the parameters of Fractional Order Proportional-Integral-Derivative controller for Automatic Load Frequency control of HPS. In addition, an effort was made to analyze the frequency stability of HPS using Matignon's theorem. The interconnected HPS consists of reheat thermal power system, RE sources such as wind and solar thermal power generation associated with energy storage devices namely aqua electrolyzer, fuel cell and electric vehicle. The gain and fractional terms of the controller were obtained by minimizing the Integral Time Absolute Error of interconnected system. The robustness of ASO-tuned FOPID controller is tested on two-area HPS that was modelled using MATLAB/Simulink. The results obtained were then compared with other fractional order and classical integer order controllers. From the simulation results, it is inferred that the proposed ASO-tuned FOPID controller gives superior transient and steady-state response compared with other controllers. Moreover, the self-adaptiveness and robustness of the controller was validated to account for the change in RE power generations and system parameters. Furthermore, the effectiveness of the method is proved by comparing its performance with the recent literature works. The real-time applicability of proffered controller is validated in hardware-in-the-loop simulation using Real Time Digital Simulator.

Natural ester oils are the current target of many industries and electrical utilities as electrically insulating fluids to replace the conventional mineral oils. However, previously investigated most natural ester oils are based on edible products, causing a negative impact on the food crisis. Accordingly, nonedible green nanofluids based on cottonseed oil have been targeted in the present study. Additive graphene nanoparticles (0.0015 wt%, 0.003 wt%, 0.006 wt%, and 0.01 wt%) along with surfactant sodium dodecylbenzene sulfonate (SDBS) were used (1:1) due to their promising impact on dielectric and thermal properties. Experimental methods introduced were including characterization of graphene and preparation of dielectric nanofluids (DNFs). The main concern for any nanofluid to be usable in transformer applications is its long-term stability. The effect of various ultrasonication period (10, 20, 30 and 60minute) on short-term stability of nanofluids was preliminary investigated by visual inspection, highest short-term stability was obtained at 30-min and 60-min. Considering short-term stability results, the two most stable samples were investigated and compared for long-term stability through Ultraviolet visible (UV-Vis) spectroscopy to find the suitable ultrasonication time. In addition, dielectric and thermal properties of these samples were investigated and compared. Physical mechanisms were discussed for the obtained enhancements considering the effect of ultrasonication period on the number of dispersed nanoparticle sheets per unit volume and the corresponding effect on dielectric and thermal properties.

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