Abstract
In this paper, a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM) and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.
Highlights
self-organizing map (SOM) are an excellent candidate when it is necessary to provide an accurate model of a multivariate distribution of data, and the nonlinear map towards the output space allows us to introduce a number of very useful tools for data analysis, such as the measurement of cell occupancy that has been proposed in this work
The proposed model has been trained on the training set as specified in Table 3, and we discuss the outcome of the testing stage
In order to evaluate the ability of the proposed Key Performance Indicator (KPI) to detect anomalous working conditions, we show in black the normalized number of the true faulty instances Nf ault that were registered on each day
Summary
Analytical monitoring systems have been installed worldwide to timely detect possible malfunctions through the assessment of PV system performance [2,3,4,5,6,7,8,9,10]. Due to the abundance of relevant data, and the difficulty in modeling many complex aspects of PV plants, statistical methods based on data mining and machine learning algorithms are recently emerging as a very promising approach both for fault prediction and early detection. The recent development of key enabling technologies and paradigms, most notably Internet-of-Things (IoT)-environments and machine learning algorithms to handle massive quantities of data, have been recently applied to monitoring the functioning of PV systems. Similar strategies have been presented in works that tackle wind farms, see [20,21] with the objective of identifying equipment level failures, while in this case fewer works can be found for the counterpart for PV plants [22]
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