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

A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability

Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm

The contribution of solar photovoltaics (PV׳s) in generation of electric power is continually increasing. PV cells are commonly modelled as circuits. Finding appropriate circuit model parameters of PV cells is crucial for performance evaluation, control, efficiency computations and maximum power point tracking of solar PV systems. The problem of finding circuit model parameters of solar PV cells is referred to as “PV cell model parameter estimation problem,” and is highly attracted by researchers. In this paper, the existing research works on PV cell model parameter estimation problem are classified into three categories and the research works of those categories are reviewed. Based on the conducted review, some recommendations for future research are provided.

Parameter estimation of solar photovoltaic (PV) cells: A review

In recent years solar energy has received worldwide attention in the field of renewable energy systems Among the various research thrusts in solar PV, the most proverbial area is extracting maximum power from solar PV system Application dof Maximum Power Point Tracking (MPPT) for extracting maximum power is very much appreciated and holds the key in developing efficient solar PV system In this paper, a state of the art review on various maximum power point techniques for solar PV systems covering timeworn conventional methods and latest soft computing algorithms is presented To date critical analysis on each of the method in terms of (1) tracking speed, (2) algorithm complexity, (3) Dynamic tracking under partial shading and (4) hardware implementation is not been carried out In this regard the authors have attempted to compile a comprehensive review on various solar PV MPPT techniques based on the above criteria Further, it is envisaged that the information presented in this review paper will be a valuable gathering of information for practicing engineers as well as for new researchers

A comprehensive review on solar PV maximum power point tracking techniques

A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions

Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm

A hybrid maximum power point tracking for partially shaded photovoltaic systems in the tropics

Maximum power point (MPP) tracking (MPPT) is an important technique for maximizing the power extraction from photovoltaic (PV) systems under varying climatic conditions. In an array of PV modules it is possible to observe multiple peaks in the power versus voltage (P-V) curve due to the current versus voltage (I–V) PV cell mismatch caused by differences in the received irradiance, such as occurs during partial shading. In these circumstances, the ability of the MPPT devices to track the global MPP of the PV array directly influences the system efficiency. In the literature, various MPPT techniques have been proposed. Among them, computational intelligence (CI) algorithm based MPPT methods have demonstrated the ability to find the global MPP. This paper presents a detailed and specific review of CI- based MPPT techniques. Each method type is classified into one of several subcategories according to its application strategy. The various ways of applying CI into MPPTs are analyzed in detail. The advantages and disadvantages of each method are discussed and compared. The purpose of this study is to provide a compendium on CI-based MPPT techniques for users to understand and select an appropriate method based on application requirements and system constraints.

Computational intelligence techniques for maximum power point tracking in PV systems: A review

Long term exposure of photovoltaic (PV) systems under relatively harsh and changing environmental conditions can result in fault conditions developing during the operational lifetime. The present solution is for system operators to manually perform condition monitoring of the PV system. However, it is time-consuming, inaccurate and dangerous. Thus, automatic fault detection and diagnosis is a critical task to ensure the reliability and safety in PV systems. The current state-of-the-art techniques either cannot provide enough detailed fault information with high accuracy or have too much complexity. This work presents an automatic fault detection and diagnosis method for string based PV systems. It combines an artificial neural network (ANN) with the conventional analytical method to conduct the fault detection and diagnosis tasks. A two-layered ANN is applied to predict the expected power which is then compared with the measured power from the real PV system. Based on the difference between the ANN estimated power and the measured power, the open circuit voltage and short circuit current of the PV string determined using analytical equations are used to identify any of the six defined fault types. The proposed method has a fast detection, compact structure and good accuracy. Simulation results show the effective fault detection and diagnosis capability of the proposed method.

Lian Lian Jiang

Papers

A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions

Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm

A hybrid maximum power point tracking for partially shaded photovoltaic systems in the tropics

Computational intelligence techniques for maximum power point tracking in PV systems: A review

Automatic fault detection and diagnosis for photovoltaic systems using combined artificial neural network and analytical based methods