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&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

Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions

This paper presents an accelerated particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithm to track global maximum power point (MPP) of photovoltaic (PV) generation under partial shading conditions. Conventional PSO-based MPPT algorithms have common weaknesses of a long convergence time to reach the global MPP and oscillations during the searching. The proposed algorithm includes a standard PSO and a perturb-and-observe algorithm as the accelerator. It has been experimentally tested and compared with conventional MPPT algorithms. Experimental results show that the proposed MPPT method is effective in terms of high reliability, fast dynamic response, and high accuracy in tracking the global MPP

Hybrid Improved Differential Evolution and Splinebased Jaya for Photovoltaic MPPT Technique

Some Soft Computing algorithms to solve themaximum power point tracking (MPPT) method problem ofthe photovoltaic system under partially shaded conditions willstop tracking Global Maxima and produce reference voltage orthe best duty-cycle if the difference between the worst and thebest candidate solution is smaller than the specified threshold.A large threshold value will produce fast converging, but theaccuracy value will be low, and vice versa, then thedetermination of the threshold value will be very dilemma.Therefore, this study proposed a combination of ImprovedDifferential Evolution (IDE) and Jaya optimization based onpredictive curves using cubic spline interpolation to determinethe best particles after the IDE reaches convergent criteria, sothat with a large threshold value it will still get high accuracyand high convergent speed. Furthermore, the algorithmproposed in this study is known as Improved DifferentialEvolution and Jaya Based Spline (IDESJaya). The proposedalgorithm is compared with conventional P&O, Jaya based onSpline, and IDE. Simulation results show that the IDESJayatechnique is faster converging, provides a better overalltracking efficiency and higher accuracy

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)

Modelling and Analysis of Photovoltaic System under Partially Shaded Conditions using Improved Harmony Search Algorithm

With the increasing penetration of solar electricity in residential, institutional and commercial centres around the globe, the phenomenon of partial shading (PS) in Photovoltaic (PV) power generation is gaining attention. Under Partial shading condition (PSC), cells that are shaded tends to have an equivalent current with cells that are unshaded in series-connection, due to this, the shaded cells operates in reverse bias and consequently becomes load and consumes the generated power. This causes a serious problem known as hotspot. This is characterized by the presence of excessive heat which consequently reduces the total generated power. Recently, researchers use the technique of bypass diodes across the PV cells so that the problem of partial shading can be reduced, but this solution taken alone, has made the nonlinearity and complexity of the system to increase. The shaded cells generate multiple peaks with only one global peak. Conventional Maximum Power Point Tracking (MPPT) algorithms do not differentiates the global peak from local peaks which may end up tracking local peak as global peak, this results in serious power loss. This paper seeks to solve this problem by modelling a PV system under PSC and through the application of Improved Harmony Search algorithm (IHSA) and variable step Perturb & Observe (P&O) to track the global peak instead of local peaks. Simulation was done in MATLAB/Simulink 2018a environment, and the results under standard test condition (STC) and PSC showed that the proposed IHSA had an improvement of 25%, 3.17% and 2.27%, 3.07% and 2.21%, 3.26% and 2.26% when compared with the improved particle swarm optimization (IPSO) under STC and PSC conditions respectively, which had a better advantage of minimizing power oscillation and improving the efficiency of the system, improved MPPT tracking, reduced error and a better tracking efficiency in both conditions. Keywords: MPPT, photovoltaic system, partial shading, tracking efficiency, Harmony search algorith

Design of Improved Soft Computing based Maximum Power Point Tracking System for Operational Efficiency Enhancement of Solar Photovoltaic Energy System

The most promising renewable energy source is solar energy, which has enormous potential but has not yet been fully investigated or converted into useful power. The process of converting solar radiation into electrical power is marked by fluctuations in output and waste. These losses are associated with the processes of conversion, transformation, and usage. Temperature, irradiance, and shade are the main operating conditions that affect how well a solar photovoltaic system performs. The efficiency with which power is converted from the panel to the load determines the operational efficiency. Charge controllers are made to convert solar photovoltaic power into electricity for an external circuit. The goal of the research is to look into efficient algorithms that can improve the solar photovoltaic energy system's operating efficiency. In order to increase the operational efficiency of solar photovoltaic systems, research is concentrated on developing maximum power point tracking systems (MPPT) under a variety of operating scenarios, including partial shade and fluctuating irradiance. The performance of the system under various operating conditions has been investigated through the simulation of an equivalent mathematical model. A unique method for charge controller duty cycle control and solar system cooling has been developed. It is based on hybridization of PV-T System and cuckoo search optimization. Simulations of the suggested system have been run under standard, complex, and changing shading pattern and operating conditions. Simulations have shown that the devised method performs better in terms of tracked power, tracking time, and tracking stability. Comparing the suggested research to heuristic approaches based on conventional and soft computing, the solar photovoltaic system's operational performance is much improved

MPPT Schemes for PV System Under Normal and Partial Shading Condition: a Review

The photovoltaic system is one of the renewable energy device, which directly converts solar radiation into electricity. The I-V characteristics of PV system are nonlinear in nature and under variable Irradiance and temperature, PV system has a single operating point where the power output is maximum, known as Maximum Power Point (MPP) and the point varies on changes in atmospheric conditions and electrical load. Maximum Power Point Tracker (MPPT) is used to track MPP of solar PV system for maximum efficiency operation. The various MPPT techniques together with implementation are reported in literature. In order to choose the best technique based upon the requirements, comprehensive and comparative study should be available. The aim of this paper is to present a comprehensive review of various MPPT techniques for uniform insolation and partial shading conditions. Furthermore, the comparison of practically accepted and widely used techniques has been made based on features, such as control strategy, type of circuitry, number of control variables and cost. This review work provides a quick analysis and design help for PV systems. Article History: Received March 14, 2016; Received in revised form June 26th 2016; Accepted July 1st 2016; Available online How to Cite This Article: Sameeullah, M. and Swarup, A. (2016). MPPT Schemes for PV System under Normal and Partial Shading Condition: A Review. Int. Journal of Renewable Energy Development, 5(2), 79-94. http://dx.doi.org/10.14710/ijred.5.2.79-9

Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition

Partial shading condition is one of the adverse phenomena which effects the power output of photovoltaic (PV) systems due to inaccurate tracking of global maximum power point. Conventional Maximum Power Point Tracking (MPPT) techniques like Perturb and Observe, Incremental Conductance and Hill Climbing can track the maximum power point effectively under uniform shaded condition, but fails under partial shaded condition. An attractive solution under partial shaded condition is application of meta-heuristic algorithms to operate at global maximum power point. Hence in this paper, an Enhanced Grey Wolf Optimizer (EGWO) based maximum power point tracking algorithm is proposed to track the global maximum power point of PV system under partial shading condition. A Mathematical model of PV system is developed under partial shaded condition using single diode model and EGWO is applied to track global maximum power point. The proposed method is programmed in MATLAB environment and simulations are carried out on 4S and 2S2P PV configurations for dynamically changing shading patterns. The results of the proposed method are analyzed and compared with GWO and PSO algorithms. It is observed that proposed method is effective in tracking global maximum power point with more accuracy in less computation time compared to other methods.Article History: Received June 12nd 2017; Received in revised form August 13rd 2017; Accepted August 15th 2017; Available onlineHow to Cite This Article: Kumar, C.H.S and Rao, R.S. (2017 Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition. Int. Journal of Renewable Energy Development, 6(3), 203-212.https://doi.org/10.14710/ijred.6.3.203-21