Tailored Algorithms for Anomaly Detection in Photovoltaic Systems

Abstract

The fastest-growing renewable source of energy is solar photovoltaic (PV) energy, which is likely to become the largest electricity source in the world by 2050. In order to be a viable alternative energy source, PV systems should maximise their efficiency and operate flawlessly. However, in practice, many PV systems do not operate at their full capacity due to several types of anomalies. We propose tailored algorithms for the detection of different PV system anomalies, including suboptimal orientation, daytime and sunrise/sunset shading, brief and sustained daytime zero-production, and low maximum production. Furthermore, we establish simple metrics to assess the severity of suboptimal orientation and daytime shading. The proposed detection algorithms were applied to a set of time-series of electricity production in Portugal, which are based on two periods with distinct weather conditions. Under favourable weather conditions, the algorithms successfully detected most of the time-series labelled with either daytime or sunrise/sunset shading, and with either sustained or brief daytime zero-production. There was a relatively low percentage of false positives, such that most of the anomaly detections were correct. As expected, the algorithms tend to be more robust under favourable rather than under adverse weather conditions. The proposed algorithms may prove to be useful not only to research specialists, but also to energy utilities and owners of small- and medium-sized PV systems, who may thereby effortlessly monitor their operation and performance.

Failure diagnosis and trend‐based performance losses routines for the detection and classification of incidents in large‐scale photovoltaic systems

“…and (b) nonzero production (due to snow coverage, degradation, soiling, etc.). 51 Nonzero production incidents due to failures were further categorized into three groups: (a) reduced current production class, (b) reduced voltage production class, and (c) reduced currentvoltage production class. More details are given in Section 2.5 and Table 1.…”

Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters

“…Another example of anomaly detection based on SM data is energy production by photovoltaic panels. Detected anomalies include zero daytime production, low maximum production, shading of panels during the day, dawn and dusk impacts on panels, suboptimal panel orientation [9], cloudy days, snowfall, or inverter failure [10]. Among other events, SM data analysis makes it possible to detect abnormal consumer behavior, faulty equipment, and room occupancy [11] and even assess unemployment [12].…”

An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array

“…The proper diagnosis is crucial to avoid any loss of efficiency, safeguard the system, and guarantee service continuity. The failures detected in a PV system are classified into three categories according to the source of the default (Figure 1): internal, external, and ageing effects [1,3,4]. Internal PV faults originate from the PV system and include all component failures (PV arrays, cables, converters, protections, batteries, inverters) [4].…”