Long Short-Term Memory Networks Based Automatic Feature Extraction for Photovoltaic Array Fault Diagnosis

Photovoltaic (PV) array fault diagnosis is important because it helps reduce energy and revenue losses to PV system operators. It also reduces fire hazards and electric shocks caused by PV array faults. As a result, many machine-learning-based fault diagnosis techniques have been proposed in recent times. Although the fault diagnosis accuracies associated with these techniques have been impressive, most machine learning algorithms rely on manual feature extraction, which is time consuming, expensive, and diagnostic expertise exacting. To address the problem of manual feature extraction, this paper proposes a new PV array fault diagnosis technique capable of automatically extracting features from raw data for PV array fault classification. The proposed technique utilizes long short-term memory networks, which is a deep learning algorithm, for feature extraction. The extracted features feed into a softmax regression classifier for fault diagnosis. The proposed technique exhibits high fault diagnosis accuracies on both noisy and noiseless data. In addition, the results of the proposed technique compare favorably with those of other techniques. It can, therefore, be inferred from the results that the proposed fault diagnosis technique offers an effective approach to automatically extract useful features from raw data and thus remove the need for the manual feature extraction.