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

Lack of typical fault samples remains a huge challenge for intelligent fault diagnosis of gearbox. In this paper, a novel approach named deep transfer multi-wavelet auto-encoder is presented for gearbox intelligent fault diagnosis with few training samples. Firstly, new-type deep multi-wavelet auto-encoder is designed for learning important features of the collected vibration signals of gearbox. Secondly, high-quality auxiliary samples are selected based on similarity measure to well pre-train a source model sharing similar characteristics with the target domain. Thirdly, parameter knowledge acquired from the source model is transferred to target model using very few target training samples. Transfer diagnosis cases for different fault severities and compound faults of gearbox confirm the feasibility of the proposed approach even if the working conditions have significant changes.

Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples

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.

Mark Dales

Papers

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

The impact of cracks on photovoltaic power performance

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

Parallel fault detection algorithm for grid-connected photovoltaic plants