CAJ | 학술논문

运用基于大数据处理架构的Naive Bayes分类方法提出了暂态电能质量评估方法, 将数据来源扩展至电网运行监测数据、电力用户数据和公共信息数据等方面, 并将评估结果按严重程度分为暂态正常状态、短时电压暂降状态、短时深度电压暂降状态、短时电压失压状态. 基于MapReduce架构, 设计分布式Naive Bayes算法实现状态分类. 在分类器训练阶段, 对海量历史数据进行分布式学习, 周期性地生成评估规则库并部署到所有评估节点. 在状态评估阶段, 各评估节点基于流处理框架快速生成实时评估样本, 并根据当前规则库实时地得出评估结果. 试验结果表明, 所提出的基于大数据分析的暂态电能质量评估方法是可行, 在准确率和处理速度上都取得了较好的效果.
운용기우대수거처리가구적Naive Bayes분류방법제출료잠태전능질량평고방법, 장수거래원확전지전망운행감측수거、 전력용호수거화공공신식수거등방면, 병장평고결과안엄중정도분위잠태정상상태、 단시전압잠강상태、단시심도전압잠강상태、 단시전압실압상태. 기우MapReduce가구, 설계분포식Naive Bayes산법실현상태분류. 재분류기훈련계단, 대해량역사수거진행분포식학습, 주기성지생성평고규칙고병부서도소유평고절점. 재상태평고계단, 각평고절점기우류처리광가쾌속생성실시평고양본, 병근거당전규칙고실시지득출평고결과. 시험결과표명, 소제출적기우대수거분석적잠태전능질량평고방법시가행, 재준학솔화처리속도상도취득료교호적효과.
A transient power quality assessment method is proposed based on Naive Bayes classification in the architecture of big data processing. The data sources are extended to power grid monitoring data, power customer data and public data, and the assessment se-verities are classified into normal state, abnormal state, critical state, and failed state according to the results of Naive Bayes classifi-cation. A Naive Bayes classification method based on MapReduce to realize power quality assessment is designsed. In the classifier training phase, massive historical data are used as the distributed learning object, and assessment rules are generated periodically. In the state assessment phase, each assessment node updates the assessment rules generated by the training phase, generates real-time e-valuation of the samples from the stream processing framework, and evaluates the power quality state according to the current rule. Ex-periment results show that the transient power quality evaluation method based on big data analysis presented in this paper is feasible, and achieve good results both in classification accuracy and processing speed.