• Energy Research

Abstract An accurate model of photovoltaic (PV) panel is indispensable for simulations studies. In general, the PV circuit parameters for simulation studies are extracted from the manufacturer’s data sheet under different environmental conditions. The PV characterizing equations are nonlinear and requires a more involved computation. This paper presents a fast convergent third order Newton-type method to solve such nonlinear equations and thereby, to accurately parameterize any of the possible PV circuit models. The applicability and suitability of the proposed method are demonstrated through modeling of multi and mono-crystalline PV cells. Further an algorithm to evaluate the efficacy of the available methods and the proposed method is presented. PV characteristics of the suitable circuit model at various levels of temperature and irradiation are also examined. Finally, the effectiveness of the developed method is comprehensively assessed through comparison with the most recent and available effective techniques by considering various performance indices based on current-voltage, power-voltage curves and experimental data is carried out.

Use of a robust controller for handling the operational uncertainties has become imperative in real time. This paper presents a modified fitness function based automated robust controller with the aid of quantitative feedback theory (QFT) using Genetic algorithm (GA). A controller exhibiting the desired decreasing modular plot and descending phase response is devised. The addition of arctangent function as one of the fitness function term is the proposed modification that facilitates in capturing the ideal controller characteristics. The proposed controller is applied to extract maximum power from a permanent magnet synchronous generator based autonomous wind power system. The step by step design guidelines for the automated QFT robust controller is deliberated in detail. The performance evaluation is carried out for step change and stochastically varying wind speed. Finally, benchmarking of the proposed controller against those available in the literature is accomplished through extensive simulations and it will be shown that the maximum power extraction along with least electromagnetic torque oscillations are achieved with the proposed fitness function based automated QFT controller.