Modelling and Tracking of the Global Maximum Power Point in Shaded Solar PV Systems Using Computational Intelligence

Solar Photovoltaic (PV) systems are renewable energy sources that are environmentally friendly and are now widely used as a source of power generation. The power produced by solar PV varies with temperature, solar irradiance and load. This variation is nonlinear and it is difficult to predict how much power will be produced by the solar PV system. When the solar panel is directly coupled to the load, the power delivered is not optimal unless the load is properly matched to the PV system. In the case of a matched load the variation of irradiance and temperature will change this matching so a maximum peak power point tracking is therefore necessary for maximum efficiency. The complete PV system with a maximum power point tracking (MPPT) includes the solar panel array, MPPT algorithm and a DC-DC converter topology. Each subsystem is modelled and simulated in MATLAB/Simulink environment. The components are then combined with a DC resistive load to assess the overall performance when the PV panels are subjected to different weather conditions. The PV panel is modelled based on the Shockley diode equation and is used to predict the electrical characteristic curves under different irradiances and temperatures. In this dissertation, five MPPT algorithms were investigated. These algorithms include the standard Perturb and Observe (PnO), Incremental conductance (IC), Fuzzy Logic (FL), Particle Swarm Optimisation (PSO) and the Firefly Optimisation (FA). The algorithms are tested under different weather conditions including partial shading. The Particle Swarm and Firefly algorithm performed relatively the same and were chosen to be the best under all test conditions as they were the most efficient and were able to track the global maximum power point under partial shading. The PnO and IC performed well under static and varying irradiance, the PnO was seen to lose track of the MPP under rapid increasing irradiance. The PnO was tested under partial shaded conditions and it was seen that it is not reliable under these conditions. The Fuzzy logic performed better than the PnO and IC but was not as good as the PSO and FA. Since the fuzzy logic requires extensive tuning to converge it was not tested under partial shaded conditions. A DC-DC boost converter interface study between a DC source and the DC load are performed. This includes the steady state and dynamic analysis of the Boost converter. The converter is linearised about its steady state operating point and the transfer function is obtained using the state space averaged model. The simulation results of the complete PV system show that PSO and Firefly algorithm provided the best results under all weather conditions compared to other algorithms. They provided less oscillations at steady state, high efficiency in tracking (99%), quick convergence time at maximum power point and where able to track global power under partial shaded weather conditions for all partial shaded patterns. The Fuzzy logic performed well for what it was tested for which are static irradiance and rapid varying irradiance. The PnO and IC also performed relatively well but showed a lot of ringing at steady state. The PnO failed to track the MPP at certain instances under rapid increasing irradiance and the IC was shown to be unstable at low irradiance. The PnO was not reliable in tracking the global maximum power point under partial shaded conditions as it converged at local maximum power points for some partial shaded patterns

A Comprehensive Analysis on Solar PV Maximum Power Point Tracking Techniques

In this paper, electrical power is generated from solar energy using Solar PV cell. The system is designed with 1- KW PV module and connected with DC/DC converters with DC load.There are two main drawbacks with PV plants, the high cost of PV cells and their conversion efficiency. In the I-V characteristics of PV module which is non-linear but has a unique maximum power point. To increase or to maximize the output power of photo-voltaic system Maximum power point tracking (MPPT) techniques are used. These techniques give maximum output power, irrespective of the irradiation condition, temperature and load electrical characteristics. For the purpose of tracking the maximum power the MPPT techniques use some electronic converters. This paper presents comparative analysis of two well-known maximum power point tracking algorithms- perturb-and- observe (P&O), and Fuzzy Logic Control Technique (FLC) are utilized with DC-DC Buck-Boost converters to observe various output response like power and voltage and comparisons between the controlling techniques using FLC and P&O for Solar PV system which provides a convenient choice of right technique for DC micro grid system and find out the efficiency of the converter

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

On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions

This article analyzes and compares the integration of two different maximum power point tracking (MPPT) control methods, which are tested under partial shading and fast ramp conditions. These MPPT methods are designed by Improved Particle Swarm Optimization (IPSO) and a combination technique between a Neural Network and the Perturb and Observe method (NN-P&amp;O). These two methods are implemented and simulated for photovoltaic systems (PV), where various system responses, such as voltage and power, are obtained. The MPPT techniques were simulated using the MATLAB/Simulink environment. A comparison of the performance of the IPSO and NN-P&amp;O algorithms is carried out to confirm the best accomplishment of the two methods in terms of speed, accuracy, and simplicity.</p

Maximum Power Point Tracking Algorithm for Advanced Photovoltaic Systems

Photovoltaic (PV) systems are the major nonconventional sources for power generation for present power strategy. The power of PV system has rapid increase because of its unpolluted, less noise and limited maintenance. But whole PV system has two main disadvantages drawbacks, that is, the power generation of it is quite low and the output power is nonlinear, which is influenced by climatic conditions, namely environmental temperature and the solar irradiation. The natural limiting factor is that PV potential in respect of temperature and irradiation has nonlinear output behavior. An automated power tracking method, for example, maximum power point tracking (MPPT), is necessarily applied to improve the power generation of PV systems. The MPPT methods undergo serious challenges when the PV system is under partial shade condition because PV shows several peaks in power. Hence, the exploration method might easily be misguided and might trapped to the local maxima. Therefore, a reasonable exploratory method must be constructed, which has to determine the global maxima for PV of shaded partially. The traditional approaches namely constant voltage tracking (CVT), perturb and observe (P&amp;O), hill climbing (HC), Incremental Conductance (INC), and fractional open circuit voltage (FOCV) methods, indeed some of their improved types, are quite incompetent in tracking the global MPP (GMPP). Traditional techniques and soft computing-based bio-inspired and nature-inspired algorithms applied to MPPT were reviewed to explore the possibility for research while optimizing the PV system with global maximum output power under partially shading conditions. This paper is aimed to review, compare, and analyze almost all the techniques that implemented so far. Further this paper provides adequate details about algorithms that focuses to derive improved MPPT under non-uniform irradiation. Each algorithm got merits and demerits of its own with respect to the converging speed, computing time, complexity of coding, hardware suitability, stability and so on

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)

Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions

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