本文關(guān)鍵詞： 不良數(shù)據(jù)辨識 數(shù)據(jù)存儲 回歸分析預測模型 相關(guān)性分析建模 關(guān)聯(lián)規(guī)則　出處：《電力系統(tǒng)保護與控制》2017年23期 　論文類型：期刊論文

【摘要】：隨著電力系統(tǒng)的快速發(fā)展,使得電網(wǎng)需要對海量、異構(gòu)和多態(tài)的數(shù)據(jù)進行分析與辨識。傳統(tǒng)的不良數(shù)據(jù)辨識方法辨識效率較低,且不能夠高效率利用已知的全部數(shù)據(jù)信息。為解決此問題,提出了一種基于負荷預測與關(guān)聯(lián)規(guī)則修正的不良數(shù)據(jù)辨識方法。根據(jù)數(shù)據(jù)量之間的內(nèi)在聯(lián)系,給出了一種三維矩陣的數(shù)據(jù)存儲方法。建立基于回歸分析法的預測模型與基于灰色關(guān)聯(lián)的相關(guān)性分析模型,分析節(jié)點注入功率與溫度之間的變化關(guān)系,并采用關(guān)聯(lián)規(guī)則與特殊斷面修正法對預測值進行修正,進而完成對注入功率的辨識。在此基礎(chǔ)上,再通過基爾霍夫定律與殘差辨識法完成對支路潮流數(shù)據(jù)的辨識工作。最后應用實際系統(tǒng)的仿真算例證明了該方法能夠在克服殘差污染和殘差淹沒現(xiàn)象的前提下準確辨識出全部的不良數(shù)據(jù)。
[Abstract]:With the rapid development of power system, the power network needs to analyze and identify the massive, heterogeneous and polymorphic data. In order to solve this problem, a bad data identification method based on load forecasting and association rule correction is proposed. In this paper, a data storage method of 3D matrix is presented, and a prediction model based on regression analysis and a correlation analysis model based on grey correlation are established to analyze the relationship between node injection power and temperature. The prediction value is corrected by association rules and special section correction method, and then the injection power identification is completed. On this basis. Through Kirchhoff's law and residual identification method, the identification of branch power flow data is completed. Finally, the simulation example of practical system is used to prove that this method can overcome residual pollution and residual submergence phenomenon. Identify all the bad data.
【作者單位】： 國網(wǎng)北京市電力公司;國網(wǎng)滄州供電公司;燕山大學電力電子節(jié)能及傳動控制河北省重點實驗室;
【基金】：國家自然科學基金(61374098) 教育部高等學校博士學科點專項科研基金(20131333110017)~~
【分類號】：TM711
【正文快照】： 3.燕山大學電力電子節(jié)能及傳動控制河北省重點實驗室,河北秦皇島066004)This work is supported by National Natural Science Foundation of China(No.61374098)and Research Fund for theDoctoral Program of Higher Education of China(No.20131333110017).電力系統(tǒng)規(guī)模與

【參考文獻】

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本文編號：1471897