Faults in any components (modules, connection lines, converters, inverters, etc.) of photovoltaic (PV) systems (stand-alone, grid-connected or hybrid PV systems) can seriously affect the efficiency, energy yield as well as the security and reliability of the entire PV plant, if not detected and corrected quickly. In addition, if some faults persist (e.g. arc fault, ground fault and line-to-line fault) they can lead to risk of fire. Fault detection and diagnosis (FDD) methods are indispensable for the system reliability, operation at high efficiency, and safety of the PV plant. In this paper, the types and causes of PV systems (PVS) failures are presented, then different methods proposed in literature for FDD of PVS are reviewed and discussed; particularly faults occurring in PV arrays (PVA). Special attention is paid to methods that can accurately detect, localise and classify possible faults occurring in a PVA. The advantages and limits of FDD methods in terms of feasibility, complexity, cost-effectiveness and generalisation capability for large-scale integration are highlighted. Based on the reviewed papers, challenges and recommendations for future research direction are also provided.

Fault detection and diagnosis methods for photovoltaic systems: A review

With the exponential growth in global photovoltaic (PV) power capacity, protection of PV systems has gained prodigious importance in last few decades. Even with the use of standard protection devices in a PV system, faults occurring in a PV array may remain undetected. Inspired by the ever increasing demand for a reliable fault detection technique, several advanced techniques have been proposed in literature; especially in the last few years. Hence, this paper carries out an in depth analysis of various fault occurrences, protection challenges and ramifications due to undetected faults in PV systems. Furthermore, with a widespread literature, the paper critically reviews numerous fault detection algorithms/techniques available for PV systems which are proven to be effective and feasible to implement. The proposed study is not only limited to surveying the existing techniques but also analyzes the performance of each technique with an emphasis on its: 1) Approach, 2) Sensor requirements, 3) Ability to diagnose and localize faults, 4) Integration complexity, 5) Accuracy, 6) Applicability and 7) Implementation cost. Overall, this paper is envisioned to avail the researchers working in the field of PV systems with a valuable resource, which will assist them to enrich their research works.

A comprehensive review on protection challenges and fault diagnosis in PV systems

As any energy production system, photovoltaic (PV) installations have to be monitored to enhance system performances and to early detect failures for more reliability. There are several photovoltaic monitoring strategies based on the output of the plant and its nature. Monitoring can be performed locally on site or remotely. It measures production, focuses also on verification and follow-up of converter and communication devices' effective operation. Up to now, some faults diagnosis methods for PV components and systems have been developed. However, given the evolution of PV installations, more advanced monitoring techniques are continuously under investigation. In this paper, major photovoltaic system failures are addressed. Then techniques for photovoltaic monitoring proposed in recent literature are overviewed and analyzed to point out their differences, advantages and limits.

Fault detection and monitoring systems for photovoltaic installations: A review

Statistical fault detection in photovoltaic systems

Random forest based intelligent fault diagnosis for PV arrays using array voltage and string currents

An enhanced machine learning based approach for failures detection and diagnosis of PV systems

In this paper, a heuristic optimization approach based on Artificial Bee Colony (ABC) algorithm is applied to the extraction of the five electrical parameters of a photovoltaic (PV) module. The proposed approach has several interesting features such as no prior knowledge of the physical system and its convergence is not dependent on the initial conditions. The extracted parameters have been tested against several static IV characteristics of different PV modules from different manufacturers. In order to assess the effectiveness of the extracted parameters, a dynamic model based maximum power point has been used and compared to real measurements data of a grid connected system located in the Centre de Developpement des Energies Renouvelables (CDER) in Algiers. In addition, comparison of the proposed ABC algorithm with some well-known heuristic algorithms such as, Particle Swarm Optimization (PSO) and Differential Evolution (DE), has given better results in terms of local minimum avoidance and accuracy.

Parameters extraction of photovoltaic module for long-term prediction using artifical bee colony optimization

This paper proposes a simple method to detect and diagnose short circuits and open circuits faults in photovoltaic (PV) systems based on the evaluation of three coefficients. The proposed method consists fundamentally on two steps: an offline step based on a simulated model and an online step in which a comparison of the real measured coefficients against those obtained in the offline step is performed. The simulated model of the PV array has been validated using a real experimental data of a daily profile from a 3 kWp grid connected system installed at Algiers. The effectiveness of the proposed method has been evaluated based on PSIM™/Matlab™ Co-simulation approach of four operating cases: healthy operating case, one short circuit module in a string operating case, five short circuits modules in a string operating case, and finally a completely disconnected string operating case. Simulation results have demonstrated the ability of the proposed method to detect and diagnose short circuits and open circuits faulty operation under any meteorological conditions.

Elyes Garoudja

Papers

Statistical fault detection in photovoltaic systems

An enhanced machine learning based approach for failures detection and diagnosis of PV systems

Parameters extraction of photovoltaic module for long-term prediction using artifical bee colony optimization

Efficient fault detection and diagnosis procedure for photovoltaic systems

Investigation of the effects of thermal annealing on the structural, morphological and optical properties of nanostructured Mn doped ZnO thin films