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Big Data technologies including Big Data management and utilization based on increasingly collected data from every component of the power grid are crucial for the successful deployment and monitoring of the system. This paper reviews the key technologies of Big Data management and intelligent machine learning methods for complex power systems. Based on a comprehensive study of power system and Big Data, several challenges are summarized to unlock the potential of Big Data technology in the application of smart grid. This paper proposed a modified and optimized structure of the Big Data processing platform according to the power data sources and different structures. Numerous open\u2010sourced Big Data analytical tools and software are integrated as modules of the analytic engine, and self\u2010developed advanced algorithms are also designed. The proposed framework comprises a data interface, a Big Data management, analytic engine as well as the applications, and display module. To fully investigate the proposed structure, three major applications are introduced: development of power grid topology and parallel computing using CIM files, high\u2010efficiency load\u2010shedding calculation, and power system transmission line tripping analysis using 3D visualization. The real\u2010system cases demonstrate the effectiveness and great potential of the Big Data platform; therefore, data resources can achieve their full potential value for strategies and decision\u2010making for smart grid. The proposed platform can provide a technical solution to the multidisciplinary cooperation of Big Data technology and smart grid monitoring.<\/jats:p>","DOI":"10.1155\/2018\/8496187","type":"journal-article","created":{"date-parts":[[2018,9,20]],"date-time":"2018-09-20T23:34:05Z","timestamp":1537486445000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2213-5489","authenticated-orcid":false,"given":"Yuanjun","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8580-534X","authenticated-orcid":false,"given":"Zhile","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengzhong","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinxing","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,9,20]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40565-015-0111-5"},{"key":"e_1_2_9_2_2","first-page":"1","article-title":"Integration of macro energy thinking and big data thinking part one big data and power big data","volume":"40","author":"Xue Y.","year":"2016","journal-title":"Automation of Electric Power Systems"},{"key":"e_1_2_9_3_2","first-page":"1","article-title":"Integration of macro energy thinking and big data thinking: part two applications and exploration","volume":"40","author":"Xue Y.","year":"2016","journal-title":"Automation of Electric Power Systems"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2014.10.007"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsg.2015.2445828"},{"key":"e_1_2_9_6_2","unstructured":"Chinese Society of Electrical Engineering Chinese white paper on the development of large power data 2013 1\u201310."},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2017.06.006"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2017.2723563"},{"key":"e_1_2_9_9_2","first-page":"527","article-title":"Big data analysis and parallel load forecasting of electric power user side","volume":"35","author":"Wang D.","year":"2015","journal-title":"Proceedings of the Csee"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2016.2549063"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TLA.2017.7827888"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.05.139"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2014.2359460"},{"key":"e_1_2_9_14_2","doi-asserted-by":"crossref","unstructured":"PlathottamS. 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