The efficiency of PV systems can be improved by accurate estimation of PV parameters. Parameter estimation of PV cells and modules is a challenging task as it requires accurate operation of PV cells and modules followed by an optimization tool that estimates their associated parameters. Mostly, population-based optimization tools are utilized for PV parameter estimation problems due to their computational intelligent behavior. However, most of them suffer from premature convergence problems, high computational burden, and often fall into local optimum solution. To mitigate these limitations, this paper presents an improved variant of particle swarm optimization (PSO) aiming to reduce shortcomings offered by conventional PSO for estimation of PV parameters. PSO is improved by introducing two strategies to control inertia weight and acceleration coefficients. At first, a sine chaotic inertia weight strategy is employed to attain an appropriate balance between local and global search. Afterward, a tangent chaotic strategy is utilized to guide acceleration coefficients in search of an optimal solution. The proposed algorithm is utilized to estimate the parameters of the PWP201 PV module, RTC France solar cell, and a JKM330P-72 PV module-based practical system. The obtained results indicate that the proposed technique avoids premature convergence and local optima stagnation of conventional PSO. Moreover, a comparison of obtained results with techniques available in the literature proves that the proposed methodology is an efficient, effective, and optimal tool to estimate PV modules and cells’ parameters.

Doubly-fed induction generators (DFIGs), commonly used in a wind power plant (WPP), effectively convert wind energy into electrical power. However, a DFIG-based WPP (DWPP) has limited flexibility in generating reactive power (Q) due to interface converters that cause delays in fault recovery voltage. Insufficient Q support leads to dynamic voltage stability (DVS) and reliability problems. To address this, FACTS devices such as SVC and STATCOM with proportional–integral (PI) controllers are widely employed to regulate reactive power and enhance voltage stability. Conventional PI-based control often exhibits limited performance under severe grid disturbances. To this end, a deep reinforcement learning (DRL) based control is proposed to enhance the Q management capability of an SVC and a STATCOM, both in steady-state and transient conditions. The performance analysis of the suggested controller is conducted in the MATLAB/Simulink environment, considering different types of disturbances using a modified IEEE nine-bus network. The simulation results show that the suggested controller improves the Q management capability and DVS of the test network. Moreover, a comparative analysis of the DRL controller with PI and fuzzy logic-based controllers shows the superior performance of the proposed controller when used with either an SVC or a STATCOM.