Abstract
Microcontroller based maximum power point tracking (MPPT) has been the most popular MPPT approach in photovoltaic systems due to its high flexibility and efficiency in different photovoltaic systems. It is well known that PV systems typically operate under a range of uncertain environmental parameters and disturbances, which implies that MPPT controllers generally suffer from some unknown stochastic perturbations. To address this issue, a novel Newton-based stochastic extremum seeking MPPT method is proposed. Treating stochastic perturbations as excitation signals, the proposed MPPT controller has a good tolerance of stochastic perturbations in nature. Different from conventional gradient-based extremum seeking MPPT algorithm, the convergence rate of the proposed controller can be totally user-assignable rather than determined by unknown power map. The stability and convergence of the proposed controller are rigorously proved. We further discuss the effects of partial shading and PV module ageing on the proposed controller. Numerical simulations and experiments are conducted to show the effectiveness of the proposed MPPT algorithm.
Highlights
Recent years have seen a growing interest in the research of solar energy, which is mainly due to the advantages solar energy has over traditional fossil energies, including safety, sustainable source of energy, less environment pollution, and numerous market potentials
The theoretical part of this paper is based on the findings of [14, 15] and we further propose a Newton-based stochastic extremum seeking maximum power point tracking (MPPT) controller considering physical features of photovoltaic systems
In order to illustrate the effectiveness of the proposed MPPT method more intuitively, we provide the numerical comparison between the proposed algorithm and classical extremum seeking MPPT algorithm under different irradiance
Summary
Recent years have seen a growing interest in the research of solar energy, which is mainly due to the advantages solar energy has over traditional fossil energies, including safety, sustainable source of energy, less environment pollution, and numerous market potentials. Due to the inherent nature of photovoltaic cells, the powervoltage curves of PV cells depend nonlinearly on temperature and irradiation intensity; see [3,4,5] This fact, means that the operating current or voltage that maximizes the output power will change with environmental conditions. The IncCond algorithm tracks the maximum power point by comparing the instantaneous and incremental conductances of the PV array. It can track the rapidly changing irradiance. (ii) In existing extremum seeking MPPT controllers, the convergence speed is typically defined by the gradient of power map of PV systems, which implies that control systems will be highly influenced by unknown and changing environmental conditions.
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