Abstract
The analysis of partial discharge (PD) signals has been identified as a standard diagnostic tool for monitoring the condition of different electrical apparatuses. This study proposes an approach to detecting PD patterns in gas-insulated switchgear (GIS) using a long short-term memory (LSTM) recurrent neural network (RNN). The proposed method uses phase-resolved PD (PRPD) signals as input, extracts low-level features, and finally, classifies faults in GIS. In the proposed method, LSTM networks can learn temporal dependencies directly from PRPD signals. Most existing models use support vector machines (SVMs) and mainly focus on improving feature representation and extraction manually to analyze PRPD signals. However, the proposed model captures important temporal features with the help of its low-level feature extraction capability from raw inputs. It outperforms conventional SVMs and achieves 96.74% classification accuracy for PRPDs in GIS.
Highlights
Power systems are rapidly growing in popularity because of increasing power demands, and the reliability of the power grid is critical to stable power system operation
Artificial cells for the modeling of partial discharge (PD) and an external ultra-high frequency (UHF) sensor were installed in the 345 kV gas-insulated switchgear (GIS) chamber
The cavity-backed patch antenna for the external UHF sensor and an amplifier with a gain of 45 dB and a signal bandwidth ranging from 500 MHz to 1.5 GHz were used for PD detection
Summary
Power systems are rapidly growing in popularity because of increasing power demands, and the reliability of the power grid is critical to stable power system operation. Energies 2018, 11, 1202 maximum amplitude, or the average amplitude in each phase [19,20,21,25,27,28] From these features, fault types are classified by many methods, including a knowledge-based fuzzy logic analysis [26]. The performance of the proposed LSTM RNN model is verified with conventional ANNs and SVMs. The proposed method yields highly accurate results even for the PRPD data observed in a very short time. The rest of the paper is organized as follows: we discuss PRPDs and noise experiments for a GIS, Section 3 presents the proposed LSTM RNN model, a performance evaluation is presented, and Section 5 concludes the study while discussing future research topics The rest of the paper is organized as follows: we discuss PRPDs and noise experiments for a GIS in Section 2, Section 3 presents the proposed LSTM RNN model, a performance evaluation is presented in Section 4, and Section 5 concludes the study while discussing future research topics
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