本文選題：智能電網(wǎng) + 健康評估��； 參考：《華北電力大學(xué)》2017年碩士論文

【摘要】：伴隨著數(shù)字信息化時代的快速發(fā)展,信息量也呈爆炸性增長態(tài)勢。當前信息通信技術(shù)與電力生產(chǎn)深度融合,對電力工業(yè)的價值貢獻已經(jīng)從量變轉(zhuǎn)變到質(zhì)變,其最鮮明的體現(xiàn)就是電力數(shù)據(jù)成為電力工業(yè)的核心資產(chǎn)。目前中國的電力系統(tǒng)已成為世界上最大規(guī)模的關(guān)系國計民生的電力網(wǎng)絡(luò)。電力設(shè)備的可靠性、高效運行與有效管理對電力系統(tǒng)的安全、穩(wěn)定變得愈來愈重要。如何從海量的電力設(shè)備監(jiān)測數(shù)據(jù)中快速挖掘和發(fā)現(xiàn)設(shè)備的健康狀態(tài)與缺陷信息,成為研究者和電力企業(yè)的重要關(guān)注點。智能電網(wǎng)中的眾多傳感器會實時地產(chǎn)生大量數(shù)據(jù)流,對新型流式數(shù)據(jù)的分析與處理,給設(shè)備的健康評估帶來了很大的挑戰(zhàn)。數(shù)據(jù)聚類方法是數(shù)據(jù)挖掘中的一種重要的數(shù)據(jù)處理技術(shù),許多研究人員提出了眾多具有代表性的聚類算法。然而,新型流式數(shù)據(jù)的出現(xiàn)使得這些經(jīng)典聚類算法不能直接運用,故而需要研究新的數(shù)據(jù)流分析與處理方法。云模型是將隨機性與模糊性相結(jié)合,通過特定的算法實現(xiàn)定性、定量間不確定轉(zhuǎn)換的一種模型。目前,該模型也受到眾多研究者關(guān)注,并成功應(yīng)用于許多領(lǐng)域�；谝陨蠁栴}本文探討了一種基于新型數(shù)據(jù)流聚類方法和云模型的設(shè)備健康評估方法。該方法包括離線處理和在線實時處理兩個模塊。離線處理模塊,首先基于設(shè)備的正常狀態(tài)的歷史運行數(shù)據(jù),運用聚類方法實現(xiàn)設(shè)備運行工況空間的劃分,并計算每種工況下的設(shè)備標準狀態(tài)組合高斯云;在線實時處理模塊,采用流式聚類算法對智能電網(wǎng)設(shè)備的實時數(shù)據(jù)流進行工況辨識,并針對每個聚簇采用微簇的方法獲取當前數(shù)據(jù)流的摘要信息,計算設(shè)備實時狀態(tài)的組合高斯云;之后計算實時狀態(tài)的組合高斯云與標準狀態(tài)組合高斯云的偏離值并將其作為設(shè)備的健康指數(shù);最后根據(jù)健康指數(shù)的大小對設(shè)備的健康狀態(tài)進行分級。文末通過實例驗證分析,利用風電機組的實時數(shù)據(jù)流,就本文所探討的方法進行該風電機組的健康評估。實驗結(jié)果說明該方法所得出的結(jié)論符合風電機組的實際運行情況,并能夠?qū)︼L電機組的健康惡化趨勢進行預(yù)警。
[Abstract]:With the rapid development of digital information age, the amount of information is also explosive growth trend.At present, the deep integration of information and communication technology and power production has changed the value contribution of power industry from quantitative change to qualitative change, the most obvious manifestation of which is that power data has become the core asset of power industry.At present, China's power system has become the world's largest power network related to the national economy and people's livelihood.The reliability, efficient operation and effective management of power equipment are becoming more and more important for the safety and stability of power system.How to quickly mine and discover the health status and defect information from massive monitoring data of power equipment has become an important concern of researchers and power enterprises.A large number of sensors in smart grid can generate a large number of data streams in real time. The analysis and processing of new flow data brings great challenges to the health assessment of equipment.Data clustering is an important data processing technology in data mining. Many researchers have proposed many representative clustering algorithms.However, due to the emergence of new flow data, these classical clustering algorithms can not be used directly, so it is necessary to study new data flow analysis and processing methods.Cloud model is a kind of model which combines randomness with fuzziness and realizes qualitative and quantitative uncertainty conversion through specific algorithms.At present, the model has been concerned by many researchers, and has been successfully applied in many fields.Based on the above problems, a new method of equipment health assessment based on new data stream clustering and cloud model is discussed.The method includes two modules: offline processing and online real-time processing.Off-line processing module, first of all, based on the normal state of the equipment historical operation data, the use of clustering method to achieve the division of equipment operating conditions space, and calculate the standard state of equipment under each condition of the combination of Gao Si cloud; online real-time processing module,The flow clustering algorithm is used to identify the real time data flow of smart grid equipment. For each cluster, the summary information of the current data flow is obtained, and the combined Gao Si cloud of the real time state of the equipment is calculated.Then the deviations of the combination Gao Si cloud in real time state and Gao Si cloud in standard state are calculated and used as the health index of the equipment. Finally, the health status of the equipment is classified according to the size of the health index.At the end of the paper, the method discussed in this paper is used to evaluate the health of the wind turbine unit by using the real time data stream of the wind turbine through an example verification and analysis.The experimental results show that the conclusions obtained by this method are in line with the actual operating conditions of wind turbines and can be used to warn the deterioration trend of wind turbines' health.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM76

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本文編號：1772496