【摘要】：迅速積累的調(diào)度控制系統(tǒng)大數(shù)據(jù)為電網(wǎng)設(shè)備風(fēng)險分析提供了充足的條件,在分析調(diào)度控制系統(tǒng)大數(shù)據(jù)特性的基礎(chǔ)上,給出了具有普遍適用性的調(diào)度控制系統(tǒng)大數(shù)據(jù)分析的總體架構(gòu),并針對在電網(wǎng)風(fēng)險管控中的應(yīng)用,提出一種基于高風(fēng)險模式樹(HRT)的高風(fēng)險設(shè)備集挖掘方法。通過分析電力系統(tǒng)中設(shè)備的風(fēng)險誘發(fā)因素,定義了設(shè)備風(fēng)險影響度,用于量化設(shè)備發(fā)生告警或故障后對電網(wǎng)運(yùn)行的影響程度,并提出設(shè)備風(fēng)險影響度計算指標(biāo)體系,通過融合設(shè)備故障發(fā)生頻次,計算設(shè)備風(fēng)險值。以設(shè)備風(fēng)險值為目標(biāo)進(jìn)行高風(fēng)險設(shè)備集挖掘,通過構(gòu)建HRT,保留原始事務(wù)數(shù)據(jù)庫中各設(shè)備風(fēng)險值及設(shè)備風(fēng)險先驗(yàn)知識信息,根據(jù)HRT的路徑信息輸出滿足一定風(fēng)險閾值的高風(fēng)險設(shè)備集。以調(diào)度控制系統(tǒng)的海量歷史告警數(shù)據(jù)為基礎(chǔ)進(jìn)行了仿真,結(jié)果表明,HRT可以在告警數(shù)據(jù)中迅速挖掘出滿足條件的高風(fēng)險設(shè)備集,并且能夠反映出高風(fēng)險設(shè)備組合之間存在的潛在關(guān)聯(lián)性。
[Abstract]:Big data, a rapidly accumulating dispatching control system, provides sufficient conditions for the risk analysis of power network equipment. On the basis of analyzing the characteristics of the dispatching control system big data, In this paper, the general framework of big data analysis of dispatching control system with universal applicability is presented. Aiming at the application in power network risk control, a mining method of high-risk equipment set based on high risk pattern tree (HRT) is proposed. By analyzing the risk inducing factors of the equipment in the power system, the paper defines the risk influence degree of the equipment, which is used to quantify the influence degree of the equipment alarm or fault on the power network operation, and puts forward the calculating index system of the equipment risk influence degree. The risk value of the equipment is calculated by combining the frequency of fault occurrence of the equipment. Taking the equipment risk value as the target, the high risk equipment set mining is carried out, and the priori knowledge information of each device risk value and equipment risk in the original transaction database is retained by constructing HRT,. According to the path information of HRT, a set of high risk devices satisfying certain risk threshold is outputted. Based on the massive historical alarm data of the dispatching control system, the simulation results show that HRT can quickly mine the high risk equipment set satisfying the condition in the alarm data. And can reflect the potential correlation between the portfolio of high-risk equipment.
【作者單位】： 華北電力大學(xué)電氣與電子工程學(xué)院;北京國電通網(wǎng)絡(luò)技術(shù)有限公司;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(51507063) 國家電網(wǎng)公司科技項(xiàng)目(B34681150152)~~
【分類號】：TM73
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本文編號：2390836