@article{Zhang_2023, title={Power Outage Fault Judgment Method Based on Power Outage Big Data}, volume={10}, url={https://publications.eai.eu/index.php/ew/article/view/3906}, DOI={10.4108/ew.3906}, abstractNote={<p class="ICST-abstracttext"><span lang="EN-GB">INTRODUCTION: With the deepening of the application of big data technology, the power sector attaches great importance to power outage judgment. However, many factors affect the judgment result of power outage, and the analysis process is very complicated, which can not achieve the corresponding accuracy.</span></p><p class="ICST-abstracttext"><span lang="EN-GB">OBJECTIVES: Aiming at the problem that it is impossible to accurately judge the result in judging power failure, a deep mining model of big data is proposed.</span></p><p class="ICST-abstracttext"><span lang="EN-GB">METHODS: Firstly, the research data set is established using power outage big data technology to ensure the results meet the requirements. Then, the power failure judgment data are classified using big data theory, and different judgment methods are selected. Using big data theory, the accuracy of power failure judgment is verified.</span></p><p class="ICST-abstracttext"><span lang="EN-GB">RESULTS: The deep mining model of big data can improve the accuracy of power failure judgment and shorten the judgment time of power failure under big data, and the overall result is better than the statistical method of power failure.</span></p><p class="ICST-abstracttext"><span lang="EN-GB">CONCLUSION: The deep mining model based on power outage big data proposed can accurately judge the power outage fault and shorten the analysis time.</span></p>}, journal={EAI Endorsed Transactions on Energy Web}, author={Zhang, Xinyang}, year={2023}, month={Jul.} }