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电力大数据:2019,22(9):-
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基于Apriori算法的电厂辅助服务考核数据分析与应用
孟科技
(徐州华润电力有限公司)
Analysis and Application of Power Plant Auxiliary Service Assessment Data Based on Apriori Algorithms
MENG Keji
(China Resources PowerXUZHOU Co.,Ltd.)
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投稿时间:2019-06-04    修订日期:2019-07-02
中文摘要: 为了保障电力系统的供电安全性、稳定性和可靠性及维持电能的优质经济运行,需要发电企业、电网企业等具有一次调频、自动发电控制、调峰等服务。鉴于火电机组的性能差异性,可能在某一时刻不能满足部分辅助服务考核项目而被考核。调度根据规程对其基本辅助服务和有偿辅助服务相关内容进行考核和奖励。针对机组因考核被罚款的问题,本文采用Apriori算法对电厂侧的数据进行挖掘分析。Apriori算法作为大数据算法其中一种方法,它是一种基于递推算法思想算法。在候选集生成和情节的向下封闭检测的两阶段时,通过频繁项集的关联规则的挖掘并获取各考核项目的内在关系。通过对电厂辅助服务考核数据的关联性进行了分析和挖掘,数据挖掘得到信息为我们做相应的优化调整和减少考核等决策提供了参考依据。
Abstract:In order to ensure the security, stability and reliability of power supply and maintain the high quality and economic operation of power, power generation enterprises and power grid enterprises need to have primary frequency regulation, automatic generation control, peak shaving and other services. In view of the performance differences of thermal power units, it may not be able to meet some auxiliary service assessment items at a certain time and be assessed. The dispatch shall assess and reward the relevant contents of its basic auxiliary services and paid auxiliary services according to the regulations. Aiming at the problem of unit fined for assessment, this paper uses Apriori algorithm to mine and analyze the data on the power plant side. As one of the big data algorithms, Apriori algorithm is a recursive algorithm. In the two stages of candidate set generation and downward closed detection of scenarios, the intrinsic relationship of each assessment item is obtained by mining association rules of frequent itemsets. Through the analysis and mining of the relevance of the assessment data of auxiliary services in power plants, the information obtained from data mining provides a reference basis for us to make corresponding optimization adjustments and reduce assessment decisions.
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基金项目:中国南方电网有限责任公司科技项目(GZ2015-2-0047)
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