Open AccessA simple gated recurrent network for detection of power quality disturbances
Author(s) -
Zu Xiangrong,
Wei Kai
Publication year - 2021
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12056
Subject(s) - recurrent neural network , computer science , simple (philosophy) , artificial intelligence , deep learning , long short term memory , artificial neural network , process (computing) , feature (linguistics) , pattern recognition (psychology) , philosophy , linguistics , epistemology , operating system
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.