A method for detecting malfunctions in PV solar panels based on electricity production monitoring (original) (raw)
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Energies
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The paper is focused on the energy performance of the photovoltaic systems constituted by several arrays. The main idea is to compare the statistical distributions of the energy dataset of the arrays. For small-medium-size photovoltaic plant, the environmental conditions affect equally all the arrays, so the comparative procedure is independent from the solar radiation and the cell temperature; therefore, it can also be applied to a photovoltaic plant not equipped by a weather station. If the procedure is iterated and new energy data are added at each new run, the analysis becomes cumulative and allows following the trend of some benchmarks. The methodology is based on an algorithm, which suggests the user, step by step, the suitable statistical tool to use. The first one is the Hartigan's dip test that is able to discriminate the unimodal distribution from the multimodal one. This stage is very important to decide whether a parametric test can be used or not, because the parametric tests-based on known distributions-are usually more performing than the nonparametric ones. The procedure is effective in detecting and locating abnormal operating conditions, before they become failures. A case study is proposed, based on a real operating photovoltaic plant. Three periods are separately analyzed: one month, six months, and one year.
Solar Energy, 2019
One of the greatest challenges in a photovoltaic solar power generation is to keep the designed photovoltaic systems working with the desired operating efficiency. Towards this goal, fault detection in photovoltaic plants is essential to guarantee their reliability, safety, and to maximize operating profitability and avoid expensive maintenance. In this context, a model-based anomaly detection approach is proposed for monitoring the DC side of photovoltaic systems and temporary shading. First, a model based on the one-diode model is constructed to mimic the characteristics of the monitored photovoltaic array. Then, a one-class Support Vector Machine (1SVM) procedure is applied to residuals from the simulation model for fault detection. The choice of 1SVM approach to quantify the dissimilarity between normal and abnormal features is motivated by its good capability to handle nonlinear features and do not make assumptions on the underlying data distribution. Experimental results over real data from a 9.54 kWp grid-connected plant in Algiers, show the superior detection efficiency of the proposed approach compared with other binary clustering schemes (i.e., K-means, Birch, mean-shift, expectation-maximization, and agglomerative clustering).
Faults detection and identification in PV array using kernel principal components analysis
international journal of energy and environmental engineering, 2021
The exponential growth of the photovoltaic system installations also requires an adequate maintenance and supervision system to ensure the continuity of service of the system. Conventional protection systems for electrical systems have shown their shortcomings for protecting photovoltaic systems. In this article, a statistical approach based on principal component analysis and its variants is used to detect and identify faults in a photovoltaic array. This involves analysing the variations of the data of the current-voltage and voltage-power characteristics. Subsequently, the calculation of the contributions is applied to the SPE index for the identification of faults. By employing the intermediate value theorem, six different operating states have been identified. The various results obtained first from the simulation model from the Simulink environment and then from a real system of 18 PV show that the kernel principal component analysis allows defect detection with a better precision.
Pertanika Journal of Science and Technology
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Statistical Methods for Degradation Estimation and Anomaly Detection in Photovoltaic Plants
Sensors, 2021
Photovoltaic (PV) plants typically suffer from a significant degradation in performance over time due to multiple factors. Operation and maintenance systems aim at increasing the efficiency and profitability of PV plants by analyzing the monitoring data and by applying data-driven methods for assessing the causes of such performance degradation. Two main classes of degradation exist, being it either gradual or a sudden anomaly in the PV system. This has motivated our work to develop and implement statistical methods that can reliably and accurately detect the performance issues in a cost-effective manner. In this paper, we introduce different approaches for both gradual degradation assessment and anomaly detection. Depending on the data available in the PV plant monitoring system, the appropriate method for each degradation class can be selected. The performance of the introduced methods is demonstrated on data from three different PV plants located in Slovenia and Italy monitored f...
Multivariate statistical monitoring of photovoltaic plant operation
Energy Conversion and Management, 2020
Detecting anomalies in a photovoltaic system play a core role in keeping the desired performance and meeting requirements and specication. For this propose, a simple and ecient monitoring methodology using principal component analysis model and multivariate monitoring schemes is designed to monitor PV systems. The principal component analysis model is used to generate residuals for anomaly detection. Then, the residuals are examined by computing the monitoring schemes (T 2 and square predicted error) for the purpose of fault detection. However, these conventional schemes are usually derived under the hypothesis of Gaussian distribution. Thus, the major aim of this paper is to bridge this gap by designing assumption-free principal component analysis-based schemes. Specically, a nonparametric approach using kernel density estimation is proposed to set thresholds for decision statistics and compared with the parametric counterparts. Real measurements from an actual 9.54 kWp grid-connected PV system are used to illustrate the performance of the studied methods. To evaluate the fault detection capabilities of the proposed approach, six case studies are investigated, one concerning a string fault, one involving a partial shading, and one concerning the loss of energy caused by inverter disconnections. Results testify the ecient performance of the proposed method in monitoring a PV system and its greater exibility when using nonparametric detection thresholds.