A simple gated recurrent network for detection of power quality disturbances

Abstract

Abstract This paper presents a new concise deep learning–based sequence model to detect the power quality disturbances (PQD), which only uses original signals and does not require pre‐processing and complex artificial feature extraction process. A simple gated recurrent network (SGRN) with a new recurrent cell structure is developed, which consists of only two gates: forget gate and input gate, and two weight matrices. Compared with the standard Recurrent Neural Network (RNN) model, the training process of the proposed method is more stable and the prediction accuracy is higher. In addition, this special structure retains basic non‐linearity and long‐term memory, while enabling the simple gated recurrent network model to be superior to Long Short‐Term Memory (LSTM) Network and Gated Recurrent Unit (GRU) Network in terms of the number of parameters (i.e. memory cost) and detection speed. In the light of the experimental results, the simple gated recurrent network algorithm can achieve 99.07% detection accuracy, and contains only 18,959 parameters, which indicates that our proposed method is easier to deploy in resource‐constrained internet of things (IoT) micro‐controllers.

Keywords

• Microprocessors and microcomputers
• Microprocessor chips
• Data handling techniques
• Power supply quality and harmonics
• Power engineering computing
• Neural nets