Abstract
The fault prognostics of the photovoltaic (PV) power generation system is expected to be a significant challenge as more and more PV systems with increasingly large capacities continue to come into existence. The PV inverter is the core component of the PV system, and it is essential to develop approaches that accurately predict the occurrence of inverter faults to ensure the PV system’s safety. This paper proposes a fault prognostics method which makes full use of the similarities between inverter clusters. First, a feature space was constructed using the t-distributed stochastic neighbor embedding (t-SNE) algorithm. Then, the fast clustering algorithm was used to search the center inverter of each sampling time from the feature space. The status of the center inverter was adopted to establish the health baseline. Finally, the Gaussian mixture model was established with two data clusters based on the central inverter and the inverter to be predicted. The divergence of the two clusters could be used to predict the inverter’s fault. The performance of the proposed method was evaluated with real PV monitoring data. The experimental results showed that the proposed method successfully predicted the occurrence of an inverter fault 3 months in advance.
Highlights
With increasingly serious global environmental pollution and energy shortage, solar energy, as a renewable and pollution-free new energy source, has received extensive attention in recent years [1,2]
Status the status the inverter located the feature distribution center was chosen as thebaseline health baseline of theofinverter located in the in feature distribution center was chosen as the health of wholePV
The fault prognostics algorithm successfully predicted the occurrence of an inverter fault 3 months in advance
Summary
With increasingly serious global environmental pollution and energy shortage, solar energy, as a renewable and pollution-free new energy source, has received extensive attention in recent years [1,2]. To reduce the impact of environmental factors, we can make full use of the similarities between inverter clusters when the equipment health baseline is established. The core idea of this paper is to utilize the fast clustering algorithm to search the center inverter of each sampling time in the feature space and to construct the health baseline of the cluster directly using the initial status of the equipment to build the health baseline. Gu et al proposed a data preprocessing algorithm based on the t-SNE algorithm to reduce the dimensionality of the numerical weather prediction data related to wind farm operation. The Gaussian mixture model plays an important role in data cluster analysis and applied in fault diagnosis and uncertainty analysis on wind turbines [26,27,28].
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