| Title | Waveform Vector Embedding for Incipient Fault Detection in Distribution Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Wang, Yahui, Cui, Qiushi, Tang, Xinlu, Li, Dongdong, Chen, Tao |
| Conference Name | 2021 IEEE Sustainable Power and Energy Conference (iSPEC) |
| Keywords | compositionality, Conferences, Dictionaries, electrical fault detection, expandability, fault detection, incipient fault, Logistics, machine learning, Power system protection, Power systems, pubcrawl, Resiliency, Support vector machines, waveform dictionary, waveform vector |
| Abstract | Incipient faults are faults at their initial stages and occur before permanent faults occur. It is very important to detect incipient faults timely and accurately for the safe and stable operation of the power system. At present, most of the detection methods for incipient faults are designed for the detection of a single device's incipient fault, but a unified detection for multiple devices cannot be achieved. In order to increase the fault detection capability and enable detection expandability, this paper proposes a waveform vector embedding (WVE) method to embed incipient fault waveforms of different devices into waveform vectors. Then, we utilize the waveform vectors and formulate them into a waveform dictionary. To improve the efficiency of embedding the waveform signature into the learning process, we build a loss function that prevents overflow and overfitting of softmax function during when learning power system waveforms. We use the real data collected from an IEEE Power & Energy Society technical report to verify the feasibility of this method. For the result verification, we compare the superiority of this method with Logistic Regression and Support Vector Machine in different scenarios. |
| DOI | 10.1109/iSPEC53008.2021.9735765 |
| Citation Key | wang_waveform_2021 |
Waveform Vector Embedding for Incipient Fault Detection in Distribution Systems