This paper presents a review on the state-of-the-art maximum power point tracking (MPPT) techniques for PV power system applications. The main techniques that will be deliberated are the Perturb and Observe, Incremental Conductance and Hill Climbing. The coverage will also encompass their variations and adaptive forms. In addition, the more recent MPPT approaches using soft computing methods such as Fuzzy Logic Control, Artificial Neural Network and Evolutionary Algorithms are included. Whilst the paper provides as thorough treatment of MPPT at normal (uniform) insolation, its focus will be on the applications of the abovementioned techniques during partial shading conditions. It is envisaged that this review work will be a source of valuable information for PV professionals to keep abreast with the latest progress in this area, as well as for new researchers to get started on MPPT.

A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition

The application of soft computing methods for MPPT of PV system: A technological and status review

An artificial bee colony based MPPT under partially shaded conditions is proposed.Photovoltaic systems are considered.A co-simulation methodology combining Simulink and Pspice has been adopted.Excellent efficiency and tracking performance compared to the PSO-based MPPT.The effectiveness of the proposed method has been confirmed experimentally. Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink? and Cadence/Pspice?, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.

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Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

The energy problem is one of the most important and serious problems that humanity is faced with it and this is while the fossil fuels are running out, so finding new sources of energy is one of the challenges of modern man. Solar energy is an available, newable and almost eternal energy which can be converted directly to electrical energy by photovoltaic (PV) cells. Although the use of sunlight costs nothing but PV cells are relatively expensive so it's necessary to extract maximum power from these cells because of economic reasons. To achieve the maximum power point, there are many techniques and also many review papers but just few papers have compared these techniques from economical and technical point of view. This paper presents a review of MPPT techniques using of comprehensive and relatively new classification with emphasizing on comparison of methods.

Comparative and comprehensive review of maximum power point tracking methods for PV cells

Solar energy is one of the important renewable energy resources as it is pollution free, clean, never-ending and abundant. Solar photovoltaic technologies are mounting vigor attention in the modern electrical power applications due to fast growth in the relative sectors of semiconductor and power electronics. It is significant to operate solar photovoltaic energy conversion systems to its maximum power output to raise the efficiency. Maximum power point tracking plays a very important role for extracting maximum power from the solar photovoltaic module and transferring that power to the load. The present work highlights a survey on perturb and observe method maximum power point tracking technique for solar photovoltaic system undertaken by considering the various works already listed relative study has been carried out, which includes different perturb and observe method on maximum power point tracking techniques and draw their advantages and drawbacks. These techniques vary in many aspects, which can be broadly categorized on the basis of simplicity, way of implementing, type of sensors, total cost of the system, range of effectiveness, hardware requirement and speed of convergence. It is expected that this review work will provide precious information for solar photovoltaic professionals to keep alongside with the latest progress in this area, as well as for new researchers to get started on maximum power point tracking.

Maximum power point tracking of solar photovoltaic system using modified perturbation and observation method

In order to deal with problems of premature convergence and slow convergence rate, this paper proposes a Complex-method-based Particle Swarm Optimization algorithm, namely CPSO. At the beginning of the evolution, the PSO is applied to implement global search, and when the group of particles has entered into the local optimum region, the Complex method is used to quickly get the local optimal point, which can effectively improve the ability of local search. The mutation inertia weight is also used in the CPSO to jump out of the local optimum, which can increase the diversity of the population and solve the premature convergence problem. This new algorithm is applied for the maximum power point tracking (MPPT) in the photovoltaic system, and the effectiveness of this algorithm is demonstrated in the experimental findings.

Qiang Fu

Papers

A Complex-Method-Based PSO Algorithm for the Maximum Power Point Tracking in Photovoltaic System