A hybrid MPPT algorithm based on DE-IC for photovoltaic systems under partial shading conditions

This paper presents a hybrid maximum power point tracking (MPPT), which combines a metaheuristic algorithm and a traditional MPPT method applied in a photovoltaic system operating under partial shading conditions. The MPPTs based on traditional methods are not able to track the global maxi-mum power point (GMPP) when partial shadings occur. Thus, MPPT algorithms based on metaheuristic algorithms, which are used for global optimization, have presented efficiency to extract the maximum power from photovoltaic arrays. However, these methods are random, resulting in large power oscillations in transients of small variations in solar irradiance. Therefore, this paper proposes the metaheuristic algorithm called Differential Evolution (DE) to seek and track the GMPP. After the DE convergence, the MPPT algorithm is switched to Incremental Conductance (IC) in order to refine the tracking. The effectiveness of the algorithm is proved through simulation results. Furthermore, comparative analyses are provided for each algorithm (DE and IC) to evaluate their performances in the PV system

A Novel MPPT Technique based on Hybrid Radial Movement Optimization with Teaching Learning Based Optimization for PV system

Because of its pure and plentiful accessibility, solar power is a remarkable resource of energy for the generation of electrical power. The solar photovoltaic mechanism transforms sunlight striking the photovoltaic solar panel or array of photovoltaic panels directly into non-linear DC power. Due to the nonlinear characteristics of solar photovoltaic panels, power must be tracked for their effective usage. When the photovoltaic arrays are shaded, the problem of nonlinearity becomes more pronounced, resulting in large power loss and intensive heating in a few areas of the photovoltaic arrangement. The tracking challenge is made more difficult by the fact that bypass diodes, which are used to completely eradicate the shading effect, generate numerous power peak levels on the power vs. voltage (P-V) curve. Traditional methods for tracing the global peak point are unable to examine the entire P-V curve as they frequently get stuck at the local peak point. Recently, machine learning or optimization algorithms have been used to determine the global peak point. Because these algorithms are random, they search the entire search area, reducing the possibility of being caught in the local maximum value. This article proposes a hybrid of two optimization approaches: radial movement optimization and teaching-learning optimization (HRMOTLBO). The proposed MPPT method was thoroughly investigated and tested in a wide range of photovoltaic partial shading combinations. The recommended HRMOTLBO MPPT approach outperforms and is more reliable than a recent Jaya-based MPPT approach in terms of tracing time and power variation under dynamic and static partial shading conditions. Experimental as well as simulation outcomes demonstrate that the proposed MPPT successfully traces the global peak point in less time and with fewer fluctuations during various partial shading conditions

Analysis of spatial fixed PV arrays configurations to maximize energy harvesting in BIPV applications

This paper presents a new approach for efficient utilization of building integrated photovoltaic (BIPV) systems under partial shading conditions in urban areas. The aim of this study is to find out the best electrical configuration by analyzing annual energy generation of the same BIPV system, in terms of nominal power, without changing physical locations of the PV modules in the PV arrays. For this purpose, the spatial structure of the PV system including the PV modules and the surrounding obstacles is taken into account on the basis of virtual reality environment. In this study, chimneys which are located on the residential roof-top area are considered to create the effect of shading over the PV array. The locations of PV modules are kept stationary, which is the main point of this paper, while comparing the performances of the configurations with the same surrounding obstacles that causes partial shading conditions. The same spatial structure with twelve distinct PV array configurations is considered. The same settling conditions on the roof-top area allow fair comparisons between PV array configurations. The payback time analysis is also performed with considering local and global maximum power points (MPPs) of PV arrays by comparing the annual energy yield of the different configurationsPeer ReviewedPostprint (author’s final draft

A hierarchical architecture for increasing efficiency of large photovoltaic plants under non-homogeneous solar irradiation

Under non-homogeneous solar irradiation, photovoltaic (PV) panels receive different solar irradiance, resulting in a decrease in efficiency of the PV generation system. There are a few technical options to fix this issue that goes under the name of mismatch. One of these is the reconfiguration of the PV generation system, namely changing the connections of the PV panels from the initial configuration to the optimal one. Such technique has been widely considered for small systems, due to the excessive number of required switches. In this paper, the authors propose a new method for increasing the efficiency of large PV systems under non-homogeneous solar irradiation using Series-Parallel (SP) topology. In the first part of the paper, the authors propose a method containing two key points: a switching matrix to change the connection of PV panels based on SP topology and the proof that the SP-based reconfiguration method can increase the efficiency of the photovoltaic system up to 50%. In the second part, the authors propose the extension of the method proposed in the first part to improve the efficiency of large solar generation systems by means of a two-levels architecture to minimize the cost of fabrication of the switching matrix

A modified particle swarm optimization based maximum power point tracking for photovoltaic converter system

This thesis presents a modified Particle Swarm Optimization based Maximum Power Point Tracking for Photovoltaic Converter system. All over the world, many governments are striving to exploit the vast potential of renewable energy to meet the growing energy requirements mainly when the price of oil is high. Maximum Power Point Tracking (MPPT) is a method that ensures power generated in Photovoltaic (PV) systems is optimized under various conditions. Due to partial shading or change in irradiance and temperature conditions in PV, the power-voltage characteristics exhibit multiple local peaks; one such phenomenon is the global peak. These conditions make it very challenging for MPPT to locate the global maximum power point. Many MPPT algorithms have been proposed for this purpose. In this thesis, a modified Particle Swarm Optimisation (PSO)-based MPPT method for PV systems is proposed. Unlike the conventional PSO-based MPPT methods, the proposed method accelerates convergence of the PSO algorithm by consistently decreasing weighting factor, cognitive and social parameters thus reducing the steps of iterations and improved the tracking response time. The advantage of the proposed method is that it requires fewer search steps (converges to the desired solution in a reasonable time) compared to other MPPT methods. It requires only the idea of series cells; thus, it is system independent. The control scheme was first created in MATLAB/Simulink and compared with other MPPT methods and then validated using hardware implementation. The TMS320F28335 eZDSP board was used for implementing the developed control algorithm. The results show good performance in terms of speed of convergence and also guaranteed convergence to global MPP with faster time response compared to the other MPPT methods under typical conditions (partial shading, change in irradiance and temperature, load profile). This demonstrates the effectiveness of the proposed method

Development of Maximum Power Extraction Algorithms for PV system With Non-Uniform Solar Irradiances

This thesis addresses the problem of extraction of maximum power from PV arrays subjected to non-uniform solar irradiances e.g partial shading. In the past, a number of maximum power point tracking algorithms (MPPTs) such as Perturb & Observe, Hill climbing, Incremental Conductance, etc. have been proposed. These are extensively used for obtaining maximum power from a PV module to maximize power yield from PV systems under uniform solar irradiance. However, these techniques have not considered partial shading conditions and the stochastic nature of solar insolation. In the event of non-uniform solar insolation, a number multiple maximum power points (MPPs) appear in the power-voltage characteristic of the PV module. In the present thesis, the stochastic nature of the solar insolation is considered to obtain the global MPP of a PV module with a focus on developing global optimization techniques for MPPT that would handle the multiple MPPs. Thus, the thesis will address the above problem by developing a number of global MPPT algorithms. In this thesis, an extensive review on MPPT algorithms for both uniform and non-uniform insolation levels is presented. Subsequently, an analysis with respect to their merits, demerits and applications have been provided in order to design new MPPTs to achieve higher MPPT efficiency under non-uniform solar irradiances. Firstly, PV modules are modelled with and without bypass diodes for handling Partial shading conditions (PSCs). Then, a new Ring pattern (RP) configuration has been proposed which is compared with different existing configurations such as Series parallel (SP), Total cross tied(TCT) and Bridge linked(BL) configurations on the basis of maximum power and fill factor. As described earlier, under non-uniform irradiances the MPPT problem boil down to determining the global MPP. Thus, the MPPT problem can be cast as a global optimization problem. It may be noted that evolutionary computing approaches are extensively used for obtaining global optimum solutions. One of the most recent evolutionary optimization techniques called grey wolf optimization technique has gained enormous popularity as an efficient global optimization approach. In view of this, Grey wolf optimization is employed to design a global MPPT such that maximum power from PV modules can be extracted which will work under partial shading conditions. Its performance has been compared with two existing MPPTs namely P&O and IPSO based MPPT methods. From the obtained simulation and experimental results, it was found that the GWO based MPPT exhibits superior MPPT performance as compared to both P&O and IPSO MPPTs on the basis of dynamic response, faster convergence to GP and higher tracking efficiency. Further, in order to scale down the search space of GWO which helps to speed up for achieving convergence towards the GP, a fusion of GWO-MPPT with P&O MPPT for obtaining maximum power from a PV system with different possible patterns is developed. An experimental setup of 600W solar simulator is used in the laboratory having characteristics of generating partial shading situation. Firstly, the developed algorithms were implemented for a PV system using MATLAB/SIMULINK. Subsequently, the aforesaid experimental setup is used to implement the proposed global MPPT algorithms. From the obtained simulation and experimental results it is observed that the Hybrid-MPPT converges to the GP with least time enabling highest possible maximum power from the solar PV system. In this thesis, analytical modeling of PV modules for handling non-uniform irradiances is pursued as well as a new RP configuration of PV modules is developed to achieve maximum power and fill factor. In order to extract maximum power from PV panels subjected to non-uniform solar irradiances, two new MPPT algorithms are developed namely Grey wolf optimization based MPPT (GWO-MPPT) and GWO assisted PO (GWO-PO)