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.
Highlights
The growth of renewable energy (RE) sources has been increasingly significant in the last decade.For instance, in 2018, without considering large hydro-power plants, an outstanding amount of 190 GW total new capacity installed was reached worldwide, being 55% of the total power capacity that was installed during that year [1]
We presented a Monitoring System (MS) that provides electrical and environmental variables measurements, allowing to record instantaneous and historical data and estimate parameters that are related to the plant performance
The complete system is installed in a 5 kW PV plant and it was validated when considering 16 days with faults in different conditions
Summary
The growth of renewable energy (RE) sources has been increasingly significant in the last decade.For instance, in 2018, without considering large hydro-power plants, an outstanding amount of 190 GW total new capacity installed was reached worldwide, being 55% of the total power capacity that was installed during that year [1]. In the context of this work, for instance, PV performance can be compromised due to the high exposure to different weather conditions, like soiling [2] and temperature [3], resulting in electrical and mechanical faults, like cracked cells and short-circuits [4]. With such high exposure, the need of methods to maintain performance, reduce revenue losses and downtime, and ensure rapid fault detection, classification, location, and mitigation in PV systems emerge [5]. Only the MS is not enough to completely solve the problem [8], since PV faults demand specific techniques to detect and classify them, using monitored data [9,10]
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