Data Mining Practical Machine Learning Tools and Techniques

A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks

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

Three major catastrophic failures in photovoltaic (PV) arrays are ground faults, line-to-line faults, and arc faults. Although there have not been many such failures, recent fire events on April 5, 2009, in Bakersfield, CA, USA, and on April 16, 2011, in Mount Holly, NC, USA, suggest the need for improvements in present fault detection and mitigation techniques, as well as amendments to existing codes and standards to avoid such accidents. This review investigates the effect of faults on the operation of PV arrays and identifies limitations to existing detection and mitigation methods. A survey of state-of-the-art fault detection and mitigation technologies and commercially available products is also presented.

A Comprehensive Review of Catastrophic Faults in PV Arrays: Types, Detection, and Mitigation Techniques

Fault analysis in solar photovoltaic (PV) arrays is a fundamental task to protect PV modules from damage and to eliminate risks of safety hazards. This paper focuses on line–line faults in PV arrays that may be caused by short-circuit faults or double ground faults. The effect on fault current from a maximum-power-point tracking of a PV inverter is discussed and shown to, at times, prevent overcurrent protection devices (OCPDs) to operate properly. Furthermore, fault behavior of PV arrays is highly related to the fault location, fault impedance, irradiance level, and use of blocking diodes. Particularly, this paper examines the challenges to OCPD in a PV array brought by unique faults: One is a fault that occurs under low-irradiance conditions, and the other is a fault that occurs at night and evolves during “night-to-day” transition. In both circumstances, the faults might remain hidden in the PV system, no matter how irradiance changes afterward. These unique faults may subsequently lead to unexpected safety hazards, reduced system efficiency, and reduced reliability. A small-scale experimental PV system has been developed to further validate the conclusions.

Line–Line Fault Analysis and Protection Challenges in Solar Photovoltaic Arrays

Fault detection in solar photovoltaic (PV) arrays is an essential task for increasing reliability and safety in PV systems. Because of PV's nonlinear characteristics, a variety of faults may be difficult to detect by conventional protection devices, leading to safety issues and fire hazards in PV fields. To fill this protection gap, machine learning techniques have been proposed for fault detection based on measurements, such as PV array voltage, current, irradiance, and temperature. However, existing solutions usually use supervised learning models, which are trained by numerous labeled data (known as fault types) and therefore, have drawbacks: 1) the labeled PV data are difficult or expensive to obtain, 2) the trained model is not easy to update, and 3) the model is difficult to visualize. To solve these issues, this paper proposes a graph-based semi-supervised learning model only using a few labeled training data that are normalized for better visualization. The proposed model not only detects the fault, but also further identifies the possible fault type in order to expedite system recovery. Once the model is built, it can learn PV systems autonomously over time as weather changes. Both simulation and experimental results show the effective fault detection and classification of the proposed method.

Graph-Based Semi-supervised Learning for Fault Detection and Classification in Solar Photovoltaic Arrays

Because of the non-linear output characteristics of PV arrays, a variety of faults may be difficult to detect by conventional protection devices. To detect and classify these unnoticed faults, a fault detection and classification method has been proposed based on decision trees (DT). Readily available measurements in existing PV systems, such as PV array voltage, current, operating temperature and irradiance, are used as "attributes" in the training and test set. In experimental results, the trained DT models have shown high accuracy of fault detection and fault classification on the test set.

Decision tree-based fault detection and classification in solar photovoltaic arrays

A transient voltage surge suppression system incorporating a current protection device comprised of a voltage sensitive and a thermal sensitive device. The voltage sensitive device has a predetermined voltage rating and increases in temperature as voltage applied thereto exceeds its voltage rating. The thermal sensitive device is responsive to heating of the voltage sensitive device to break an electrical current path if an overvoltage condition sensed by the voltage sensitive device exceeds the voltage rating of the voltage sensitive device.

Circuit protection device

Solar photovoltaic (PV) arrays are unique power sources that may have uncleared fault current when utilizing conventional overcurrent protection devices. To monitor the PV operation and detect these unnoticed faults, outlier detection rules have been proposed for fault detection based on instantaneous PV string current. This paper discusses three rules in detail: 3-Sigma rule, Hampel identifier, and Boxplot rule. Unlike other methods, the proposed methods do not require weather measurement or efforts in model training. Our experimental results show that Hampel identifier and Boxplot rule may be recommended for PV fault detection. Furthermore, the proposed models become more reliable as the number of PV measurements increases. The developed methods may be integrated with PV monitoring system for real-time operation.

Jerry Mosesian

Papers

Line–Line Fault Analysis and Protection Challenges in Solar Photovoltaic Arrays

Graph-Based Semi-supervised Learning for Fault Detection and Classification in Solar Photovoltaic Arrays

Decision tree-based fault detection and classification in solar photovoltaic arrays

Circuit protection device

Outlier detection rules for fault detection in solar photovoltaic arrays