A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

Abstract

Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.

A Low-Cost IoT System for Real-Time Monitoring of Climatic Variables and Photovoltaic Generation for Smart Grid Application

“…A system for fault detection in PV systems is presented in [ 31 ]. The National Instruments CompactRIO (cRIO) controller is used to obtain the solar irradiance and ambient temperature data from a weather station and the DC and AC voltage and current data from the PV system.…”

“…Storing data only locally on the system can create difficulties and a greater complexity when providing remote access. This form of storage is performed in [ 21 , 22 , 26 , 28 , 31 , 32 ].…”

A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic

“…In the given task to model the expected PV system behavior, the output data are one of the electrical parameters: current (I MPP ), voltage (U MPP ), or power (P MPP ) at the maximum power point. Implementing statistical or intelligent models to predict the expected electrical parameters has also been applied in [18,19] as a step in failure detection. For example, to predict the power, a recursive linear model was used in [19], having the measured irradiance and the previously predicted power as the input, while a neural network was used in [18] to predict the maximum output power given the open-circuit voltage, short-circuit current, and other weather data.…”

A Review of Conventional Fault Detection Techniques in Solar PV Systems and a Proposal of Long Range (LoRa) Wireless Sensor Network for Module Level Monitoring and Fault Diagnosis in Large Solar PV Farms

“…A system that automatically detects fault using IoT is proposed by authors in [20]. Current sensors are connected to the panel to obtain current data.…”