DATA ANOMALY DETECTION METHOD FOR PHOTOVOLTAIC INVERTER DATA BASED ON IMPROVED RESIDUAL NETWORK

Yao Senshan; Pang Chengxin

doi:10.19912/j.0254-0096.tynxb.2024-0006

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (4) : 322-330. DOI: 10.19912/j.0254-0096.tynxb.2024-0006

DATA ANOMALY DETECTION METHOD FOR PHOTOVOLTAIC INVERTER DATA BASED ON IMPROVED RESIDUAL NETWORK

Yao Senshan, Pang Chengxin

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Abstract

In order to solve the problem of PV module anomalies leading to PV inverter data anomalies, a PV inverter data anomaly detection method based on improved residual network （SCCB-ResNet50） is proposed. The method introduces a Markov transfer field to convert the PV power time series data into a two-dimensional image in order to increase the data feature points and thus improve the detection accuracy, and also extracts the data anomaly features using the improved residual network for data anomaly detection. The improved residual network introduces channel attention and spatial attention fusion mechanism in the residual network and uses improved SGD optimizer and cosine annealing learning rate reduction strategy to improve the data anomaly detection accuracy. The experimental results show that; the method achieves 95.8%, 81.5% and 96.0% in AUC, recall rdtio, and accuracy, respectively. Compared with other data anomaly detection methods such as LSTM, all the three evaluation indexes are effectively improved and have excellent data anomaly detection capability.

Key words

anomaly detection / photovoltaic inverter / fault analysis / machine learning / ResNet / Markov process

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Yao Senshan, Pang Chengxin. DATA ANOMALY DETECTION METHOD FOR PHOTOVOLTAIC INVERTER DATA BASED ON IMPROVED RESIDUAL NETWORK[J]. Acta Energiae Solaris Sinica. 2025, 46(4): 322-330 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0006

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