文章摘要
刘敦楠,张潜,李霄彤,等.基于云模型与模糊Petri网的电力市场潜在危害行为识别[J].电力系统自动化,2019,43(2):25-33. DOI: 10.7500/AEPS20180405004.
LIU Dunnan,ZHANG Qian,LI Xiaotong, et al.Identification of Potential Harmful Behaviors in Electricity Market Based on Cloud Model and Fuzzy Petri Net[J].Automation of Electric Power Systems,2019,43(2):25-33. DOI: 10.7500/AEPS20180405004.
基于云模型与模糊Petri网的电力市场潜在危害行为识别
Identification of Potential Harmful Behaviors in Electricity Market Based on Cloud Model and Fuzzy Petri Net
DOI:10.7500/AEPS20180405004
关键词: 电力市场  市场监管  潜在危害行为  模式识别  云模型  模糊Petri网
KeyWords: electricity market  market supervision  potential harmful behavior  pattern identification  cloud model  fuzzy Petri net
上网日期:2018-09-17
基金项目:国家电网公司总部科技项目“我国电力市场运营成效动态评估监测体系及信用评价体系研究”
作者单位E-mail
刘敦楠 新能源电力系统国家重点实验室(华北电力大学), 北京市 102206  
张潜 新能源电力系统国家重点实验室(华北电力大学), 北京市 102206  
李霄彤 新能源电力系统国家重点实验室(华北电力大学), 北京市 102206 lixiaotong134@163.com 
顾宇桂 北京电力交易中心有限公司, 北京市 100031  
谭忠富 新能源电力系统国家重点实验室(华北电力大学), 北京市 102206  
摘要:
      针对电力市场中存在潜在危害性交易行为难以被识别、管控的问题,提出基于云模型与模糊Petri网的电力市场潜在危害行为识别方法,在交易过程中对潜在危害行为进行实时预警。将云模型与模糊Petri网应用于模式识别中,有效解决了潜在危害行为难以量化识别的问题,也克服了一般识别方法不易理解的缺陷。首先,设计潜在危害行为识别的一般框架与步骤;其次,阐述通过逆向云发生器与根据专家描述的两种特征云的构建方法;同时,设计潜在危害行为识别的模糊Petri网网络结构与识别算法;最后,以江西省月度竞价交易市场为背景,采用真实交易数据进行算例分析。算例分析结果表明:所设计的方法给出的结果与专家分析得出的结果以及实际的数据高度一致,验证了方法的有效性。
Abstract:
      To solve the problem of identification and control of potential harmful transactions in electricity market, a method for identifying potential harmful behaviors based on the cloud model and the fuzzy Petri net is proposed to realize real-time warning of potential hazards in the transaction process. The cloud model and the fuzzy Petri net are applied to pattern recognition, which effectively solves the problem that the potential hazard behavior is difficult to quantify, and overcomes the defects that the general recognition method is not easy to understand. Firstly, a general framework and procedure for identifying potential harmful behaviors is designed. Secondly, two methods of building feature cloud based on reverse cloud generator and expert description are described. Meanwhile, a fuzzy Petri net structure and recognition algorithm for potential harmful behaviors is designed. Finally, an example is made and analyzed by using the real transaction data of monthly auction market in Jiangxi Province, which shows the results given by the proposed method are highly consistent with the results obtained by artificial analysis and actual data.
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