AIoT-Based Visual Anomaly Detection in Photovoltaic Sequence Data via Sequence Learning

Qian Wei (), Hongjun Sun, Jingjing Fan, Guojun Li and Zhiguang Zhou
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Qian Wei: Department of Intelligent Science and Technology, College of Artificial Intelligence, China University of Petroleum, Beijing 102249, China
Hongjun Sun: Department of Intelligent Science and Technology, College of Artificial Intelligence, China University of Petroleum, Beijing 102249, China
Jingjing Fan: Kunlun Digital Technology Co., Ltd., Beijing 102206, China
Guojun Li: Big Data Visualization and Human Computer Collaborative Intelligent Laboratory, School of Media and Design, Hangzhou Dianzi University, Hangzhou 310018, China
Zhiguang Zhou: Big Data Visualization and Human Computer Collaborative Intelligent Laboratory, School of Media and Design, Hangzhou Dianzi University, Hangzhou 310018, China

Energies, 2024, vol. 17, issue 21, 1-20

Abstract: Anomaly detection is a common analytical task aimed at identifying rare cases that differ from the majority of typical cases in a dataset. In the management of photovoltaic (PV) power generation systems, it is essential for electric power companies to effectively detect anomalies in PV sequence data, as this helps operators and experts understand and interpret anomalies within PV arrays when making response decisions. However, traditional methods that rely on manual labor and regular data collection are difficult to monitor in real time, resulting in delays in fault detection and localization. Traditional machine learning algorithms are slow and cumbersome in processing data, which affects the operational safety of PV plants. In this paper, we propose a visual analytic approach for detecting and exploring anomalous sequences in a PV sequence dataset via sequence learning. We first compare the sequences with their reconstructions through an unsupervised anomaly detection algorithm (Long Short-Term Memory) based on AutoEncoders to identify anomalies. To further enhance the accuracy of anomaly detection, we integrate the artificial intelligence of things (AIoT) technology with a strict time synchronization data collection and real-time processing algorithm. This integration ensures that data from multiple sensors are synchronized and processed in real time. Then, we analyze the characteristics of the anomalies based on the visual comparison of different PV sequences and explore the potential correlation factors to analyze the possible causes of the anomalies. Case studies based on authentic enterprise datasets demonstrate the effectiveness of our method in the anomaly detection and exploration of PV sequence data.

Keywords: photovoltaic sequence data; sequence learning; artificial intelligence of things; anomaly detection; visual exploration (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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