Fault diagnosis model for photovoltaic array using a dual-channels convolutional neural network with a feature selection structure. (15th November 2021)

• Record Type: Journal Article
• Authors: Lu, Xiaoyang; Lin, Peijie; Cheng, Shuying; Fang, Gengfa; He, Xiangjian; Chen, Zhicong; Wu, Lijun
• Is Part Of: Energy conversion and management. Volume 248(2021)
• Publication Date: 2021-11-15
• Subjects: PV array -- Fault diagnosis -- Dual-channel convolutional neural network -- Feature selection structure -- Transient characteristic analysis; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2021.114777
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: A DcCNN is used to extract fault features from PVA normalized current and voltage. A feature selection structure is proposed to evaluate extracted features. A penalty is developed to train the proposed model in an end-to-end way. Results show that the DcCNN can extract high-discriminative features. The DcCNN has superior performance for different faults even LLF under complex conditions. Abstract: The effective fault diagnosis algorithm for the DC side photovoltaic (PV) array of a PV system (PVS) plays an important role in the operation efficiency and safety for PV power plants. But for fault diagnosis models it may fail to diagnose PV array (PVA) faults without detailed and quite fine fault features, especially line-line faults (LLF) occurring in the PVS that works under complex working conditions like low irradiance conditions and LLF with fault impedance. To address these challenges, this paper proposes a fault diagnosis scheme to diagnose different PVA faults using a proposed Dual-channel Convolutional Neural Network (DcCNN), which is able to automatically extract features and weight these features for fault classification. The important and fine features from the current and voltage electrical time series graph (ETSG) are extracted respectively by DcCNN in a double input way. Then, a proposed feature selection structure (FSS) is designed to improve the proposed fault diagnosis model capacity for diagnosing PVA faults under various conditions, including LLF, partial shading condition (PSC) and open circuit faults (OCF). Comparing to manually designed features, FSS not only helps DcCNN extract important features from PVA current and voltage automatically but also evaluates extracted features for further classification of DcCNN. Moreover, in the training stage, a proposed penalty is applied on DcCNN to constrain FSS, resulting in its sparse weight distribution. A comprehensive experiment based on a laboratory roof grid connected PVS is conducted. The results demonstrate the superior performance of the proposed approach compared with other algorithms as it can extract high-discriminative features from PVA current and voltage for different PVA faults, which is also effective on diagnosing LLF under low irradiance conditions and LLF with fault impedance.