CAJ | 학술논문

为减少数据缓存成本，提高负荷数据在配电网规划设计、智能分析等领域的可用性，充分有效地对大规模、混杂、不精确的监测或采集负荷数据进行在线清洗，保证每个周期的时序数据得到一致的偏差检测和精确修复，在分析不同类型异常负荷数据产生原因和分布特点的基础上，提出一种面向大规模配电网负荷数据的在线清洗与修复方法，包括基于密度的负荷数据流异常辨识方法和基于协同过滤推荐算法的负荷数据修复方法。为突破配电网负荷大数据在线分析性能瓶颈，还在Hadoop平台上给出相应的分布式并行解决方案，通过使用实际配电网运行中的负荷数据进行验证，结果表明所提算法和框架能够有效预处理配电网负荷数据，具有实际应用价值。
위감소수거완존성본，제고부하수거재배전망규화설계、지능분석등영역적가용성，충분유효지대대규모、혼잡、불정학적감측혹채집부하수거진행재선청세，보증매개주기적시서수거득도일치적편차검측화정학수복，재분석불동류형이상부하수거산생원인화분포특점적기출상，제출일충면향대규모배전망부하수거적재선청세여수복방법，포괄기우밀도적부하수거류이상변식방법화기우협동과려추천산법적부하수거수복방법。위돌파배전망부하대수거재선분석성능병경，환재Hadoop평태상급출상응적분포식병행해결방안，통과사용실제배전망운행중적부하수거진행험증，결과표명소제산법화광가능구유효예처리배전망부하수거，구유실제응용개치。
In order to improve data availability in field of distribution network planning and intelligence analysis with reduced data cache cost, effectively analyze large-scale, mixed and inaccurately monitored or collected load data online, and to ensure consistent deviation detection and accurate repair for time series data in each cycle, an online data cleaning and repair method for large-scale distribution network load data is proposed based on analysis of different types of abnormal load causes and distribution features, including abnormal load steam identification method on density and data repair method on collaborative filtering recommendation algorithm. To break through bottlenecks in online data analysis performance for distribution network load, parallel solution on Hadoop platform is given. Verified with actual distribution network operation data, result shows that the proposed algorithm and frame could get effective data preprocessing and yield favorable significance in practice and research.