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

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

The mitigation of environmental effects on clean-energy technology is an area of increasing interest. Photovoltaic (PV) modules have been widely used in small and large-scale applications for many years. However, they are not yet competitive with other electrical energy-generation technologies, especially in environments that suffer from dust, airborne particles, humidity and high ambient temperatures. This paper presents a review of the effect of climatic conditions on PV module performance, in particular, the effect of dust fouling. Research to date indicates that dust deposition has a considerable effect on PV module performance as it reduces the light transmissivity of the PV module surface cover. Studies on the ways in which dust is deposited on PV module surfaces are reviewed, as understanding this process is essential to develop effective mitigation approaches. Module performance is also adversely affected by high ambient temperature, humidity and lack of rainfall. The current review summarizes the past, current and promising future approaches towards mitigating environmental effects, in particular dust fouling. Electrostatic cleaning methods and micro/nanoscale surface functionalization methods both have the potential to counteract the negative effects of dust deposition, with the combination of the two methods showing special efficacy, particularly in arid regions.

The effect of environmental factors and dust accumulation on photovoltaic modules and dust-accumulation mitigation strategies

Comparing Mamdani Sugeno fuzzy logic and RBF ANN network for PV fault detection

Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system

Seven indicators variations for multiple PV array configurations under partial shading and faulty PV conditions

In this work, the authors present a new algorithm for detecting faults in grid-connected photovoltaic (GCPV) plant. There are few instances of statistical tools being deployed in the analysis of photovoltaic (PV) measured data. The main focus of this study is, therefore, to outline a PV fault detection algorithm that can diagnose faults on the DC side of the examined GCPV system based on the t-test statistical analysis method. For a given set of operational conditions, solar irradiance and module temperature, a number of attributes such as voltage and power ratio of the PV strings are measured using virtual instrumentation (VI) LabVIEW software. The results obtained indicate that the fault detection algorithm can detect accurately different types of faults such as, faulty PV module, faulty PV String, faulty Bypass diode and faulty maximum power point tracking unit. The proposed PV fault detection algorithm has been validated using 1.98 kWp PV plant installed at the University of Huddersfield, UK.

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Simultaneous fault detection algorithm for grid-connected photovoltaic plants

Hot spotting is a reliability problem in photovoltaic (PV) panels where a mismatched cell heats up significantly and degrades PV panel output-power performance. High PV cell temperature due to hot spotting can damage the cell encapsulate and lead to second breakdown, where both cause permanent damage to the PV panel. Therefore, the development of two hot-spot mitigation techniques is proposed using a simple and reliable method. PV hot spots in the examined PV system were inspected using the FLIR i5 thermal imaging camera. Multiple experiments have been tested during various environmental conditions, where the PV module $I - V$ curve was evaluated in each observed test to analyze the output-power performance before and after the activation of the proposed hot-spot mitigation techniques. One PV module affected by the hot spot was tested. The output power during high irradiance levels is increased to approximately 1.26 W after the activation of the first hot-spot mitigation technique. However, the second mitigation technique guarantees an increase in the power up to 3.97 W. An additional test has been examined during the partial shading condition. Both proposed techniques ensure a decrease in the shaded PV cell temperature; thus, there is an increase in PV output power.

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Output-Power Enhancement for Hot Spotted Polycrystalline Photovoltaic Solar Cells

This study analyses the impact of micro cracks on photovoltaic (PV) module output power performance and energy production. Electroluminescence imaging technique was used to detect micro cracks affecting PV modules. The experiment was carried out on ten different PV modules installed at the University of Huddersfield, United Kingdom. The examined PV modules which contain micro cracks shows large loss in the output power comparing with the theoretical output power predictions, where the maximum power loss is equal to 80.73%. LabVIEW software was used to simulate the theoretical output power of the examined PV modules under real time long term data measurements.

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Bruce Mehrdadi

Papers

Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system

Seven indicators variations for multiple PV array configurations under partial shading and faulty PV conditions

Simultaneous fault detection algorithm for grid-connected photovoltaic plants

Output-Power Enhancement for Hot Spotted Polycrystalline Photovoltaic Solar Cells

Effect of micro cracks on photovoltaic output power: case study based on real time long term data measurements