In this work, a photovoltaic (PV) system integrated with a non-inverting DC-DC buck-boost converter to extract maximum power under varying environmental conditions such as irradiance and temperature is considered. In order to extract maximum power (via maximum power transfer theorem), a robust nonlinear arbitrary order sliding mode-based control is designed for tracking the desired reference, which is generated via feed forward neural networks (FFNN). The proposed control law utilizes some states of the system, which are estimated via the use of a high gain differentiator and a famous flatness property of nonlinear systems. This synthetic control strategy is named neuro-adaptive arbitrary order sliding mode control (NAAOSMC). The overall closed-loop stability is discussed in detail and simulations are carried out in Simulink environment of MATLAB to endorse effectiveness of the developed synthetic control strategy. Finally, comparison of the developed controller with the backstepping controller is done, which ensures the performance in terms of maximum power extraction, steady-state error and more robustness against sudden variations in atmospheric conditions.

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Maximum Power Extraction from a Standalone Photo Voltaic System via Neuro-Adaptive Arbitrary Order Sliding Mode Control Strategy with High Gain Differentiation

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Modelling and Control of a Grid-connected PV System for Smart Grid Integration

This Paper presents a MPPT (maximum power point tracking) design for a Grid connected PV (Photo-voltaic) system using FB-PSO (Forward Backward particle swarm optimization) Technique. The FB-PSO is a new improved method which results in maximum efficiency, fast tracking of maximum power point, less steady state oscillations compare to PO (Perturb and Observe) and CPSO (Conventional PSO) methods. The proposed scheme is examine under PSC (partial shading conditions) and its results are compare with other two methods. The FB-PSO algorithm is implemented in MATLAB/SIMULINK and it is observed that proposed method is best compare to PO and CPSO MPPTs.

Grid connected PV system using FB-PSO

The Photovoltaic (PV) system is an eco-friendly renewable energy system that is integrated with a DC-DC buck-boost converter to generate electrical energy as per the variations in solar irradiance and outdoor temperature. This article proposes a novel Adaptive Fractional Order PID (A-FOPID) compensator with self-adjusting fractional orders to extract maximum power from a stand-alone PV system as ambient conditions change. The reference voltage is generated using a feed-forward neural network. The conventional FOPID compensator, which operates on the output voltage error of the interleaved buck-boost converter, is employed as the baseline maximum-power-point-tracking (MPPT) controller. The baseline controller is retrofitted with an online state-error-driven adaptation law that dynamically modifies the fractional orders of the controller’s integral and differential operators. The adaptation law is formulated as a nonlinear hyperbolic scaling function of the system’s state error and error-derivative variables. This augmentation supplements the controller’s adaptability, enabling it to manipulate flexibly the tightness of the applied control effort as the operating conditions change. The efficacy of the proposed control law is analyzed by carrying out customized simulations in the MATLAB Simulink environment. The simulation results show that the proposed MPPT control scheme yields a mean improvement of 25.4% in tracking accuracy and 11.3% in transient response speed under varying environmental conditions.

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Robust MPPT Control of Stand-Alone Photovoltaic Systems via Adaptive Self-Adjusting Fractional Order PID Controller

As the use of energy is increasing, the requirements for the quality of the supplied electrical energy are more tighten. Energy is the most basic and essential of all resources. As conventional sources of energy are rapidly depleting and the cost of energy is rising, photovoltaic energy becomes a promising alternative source. Photovoltaic (PV) generation is becoming increasingly important as a renewable source since it exhibits a great many merit such as cleanness, little maintenance and no noise. The output power of PV arrays is always changing with weather conditions, i.e., solar irradiation and atmospheric temperature. Therefore, a Maximum Power Point Tracking (MPPT) control to extract maximum power from the PV arrays at real time becomes indispensable in PV generation system. In recent years, a large number of techniques have been proposed for tracking the maximum power point (MPP). MPPT is used in photovoltaic (PV) systems to maximize the photovoltaic array output power, irrespective of the temperature and radiation conditions and of the load electrical characteristics the PV array output power is used to directly control the dc/dc converter, thus reducing the complexity of the system. The resulting system has high-efficiency. This paper presents in details of most popular MPPT algorithms techniques which are Perturb & Observe algorithm (P&O).Perturb & Observe algorithm (IPO). Modeling and designing a PV system with Perturb & Observe algorithm (PO) is remarkably more complex than implementing a standard MPPT technique.

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Reeya Pandya

Papers

Grid-connected pv system with mppt control and p&o technique