Intelligent Control Schemes for Maximum Power Extraction from Photovoltaic Arrays under Faults

Investigation of power output from PV arrays under different fault conditions is an essential task to enhance performance of a photovoltaic system under all operating conditions. Significant reduction in power output can occur during various PV faults such as module disconnection, bypass diode failure, bridge fault, and short circuit fault under non-uniform shading conditions. These PV faults may cause several peaks in the characteristics curve of PV arrays, which can lead to failure of the MPPT control strategy. In fact, impact of a fault can differ depending on the type of PV array, and it can make the control of the system more complex. Therefore, consideration of suitable PV arrays with an effective control design is necessary for maximum power output from a PV system. For this purpose, the proposed study presents a comparative study of two intelligent control schemes, i.e., fuzzy logic (FL) and particle swarm optimization (PSO), with a conventional control scheme known as perturb and observe (P&O) for power extraction from a PV system. The comparative analysis is based on the performance of the control strategies under several faults and the types of PV modules, i.e., monocrystalline and thin-film PV arrays. In this study, numerical analysis for complex fault scenarios like multiple faults under partial shading have also been performed. Different from the previous literature, this study will reveal the performance of FL-, PSO-, and P&O-based MPPT strategies to track maximum peak power during multiple severe fault conditions while considering the accuracy and fast-tracking efficiencies of the control techniques. A thorough analysis along with in-depth quantitative data are presented, confirming the superiority of intelligent control techniques under multiple faults and different PV types

Maximum power point tracking implementation by Dspace controller integrated through Z-Source inverter using particle swarm optimization technique for photovoltaic applications

Maximum Power Point Tracking (MPPT) technique is used to extract maximum power from the photovoltaic system. This paper involves working on an enhanced Particle Swarm Optimization (PSO) based MPPT method for the photovoltaic (PV) system integrated through Z-Source inverter. The main benefit of the proposed method is the diminishing of the steady-state oscillation when the maximum power point (MPP) is located. Additionally, during an extreme environmental condition, such as partial shading and large fluctuations of irradiance and temperature, the proposed method has the capability to track the MPP. This algorithm is implemented in dspace 1104 controller. MATLAB simulations are carried out under varying irradiance and temperature conditions to evaluate its effectiveness. Its performance is compared with a conventional method like Perturb and observe (P&O) method

A Review on Photo Voltaic MPPT Algorithms

A photovoltaic generator exhibits nonlinear voltage-current characteristics and its maximum power point varies with solar radiation and cell temperature. A Dc/Dc power converter is used to match the photovoltaic system to the load and to operate the PV (photo voltaic) cell array at maximum power point. Maximum Power Point Tracking (MPPT) is a process which tracks one maximum power point from PV array input, varying the ratio between the voltage and current delivered to get the most power it can. There are different techniques proposed with lot of algorithms are being used in the MPPT controller to extract the maximum power. It is very difficult for the photo voltaic designers, researchers and academic experts to select a particular MPPT technique for a particular application which requires the background knowledge and comparative features of various MPPT algorithms. This paper will be avaluable source for those who work in the photo voltaic generation, so its objective is to review the main MPPT algorithms in practice and analyzes the merits and demerits with various factors