配電網(wǎng)空間負(fù)荷聚類及預(yù)測方法研究
發(fā)布時間：2018-07-22 16:01
【摘要】：空間負(fù)荷預(yù)測是配電網(wǎng)規(guī)劃的前提和基礎(chǔ),負(fù)荷預(yù)測的精準(zhǔn)性不僅影響電網(wǎng)的投資和后期運行,而且影響城市規(guī)劃方案的合理性。本文在認(rèn)真梳理和總結(jié)國內(nèi)外先進理論和研究方法的特點的基礎(chǔ)上,以提升空間負(fù)荷預(yù)測的精準(zhǔn)性、實用性和適用性為目標(biāo),主要開展了以下三方面的研究:基于日負(fù)荷曲線的負(fù)荷分類模式研究。運用數(shù)據(jù)挖掘中的聚類技術(shù)對電力系統(tǒng)日負(fù)荷曲線進行分析,提出一種基于特性指標(biāo)降維的日負(fù)荷曲線聚類方法——特性指標(biāo)聚類(Pattern Index Clustering,PIC),通過負(fù)荷率、日峰谷差率等6個日負(fù)荷特性指標(biāo)對日負(fù)荷曲線進行降維處理,利用基于聚類有效性修正的德爾菲方法配置各指標(biāo)權(quán)重,以加權(quán)歐式距離作為相似性判據(jù),對日負(fù)荷曲線進行聚類。算例分析結(jié)果表明所提方法運行時間短,魯棒性好,提高了負(fù)荷曲線聚類質(zhì)量,能直觀反映典型負(fù)荷曲線的特點。利用該方法提取的典型日負(fù)荷曲線可作為空間負(fù)荷預(yù)測調(diào)研樣本分類校驗的依據(jù),為調(diào)研樣本提質(zhì)奠定基礎(chǔ)�？紤]地域差異的空間負(fù)荷預(yù)測方法研究。針對基于智能算法的負(fù)荷密度指標(biāo)法對樣本依賴性強且在各地實際應(yīng)用困難的不足,提出一種考慮地域差異的配電網(wǎng)空間負(fù)荷聚類及一體化預(yù)測方法。該方法首先通過大量調(diào)研得到分布不同地區(qū)、分屬不同類型的負(fù)荷樣本及所處地區(qū)信息;然后利用基于日負(fù)荷曲線的負(fù)荷分類校驗及精選方法對所有調(diào)研樣本進行分類精選;再根據(jù)區(qū)域分類、負(fù)荷分類對精選樣本構(gòu)成的全樣本空間進行兩級劃分,得到分層級子樣本空間;最后根據(jù)待測地塊的屬性信息對子樣本空間進行匹配,選取與其最相似的子樣本空間作為訓(xùn)練樣本,構(gòu)建支持向量機模型預(yù)測各地塊的負(fù)荷密度,進而得到電力負(fù)荷的空間分布。工程實例分析表明了該方法的實用性和有效性�？臻g負(fù)荷預(yù)測指標(biāo)體系優(yōu)化研究。針對現(xiàn)有研究偏重對預(yù)測方法的理論創(chuàng)新和精度提升,缺乏對各地各類空間負(fù)荷分布規(guī)律研究的不足,提出一種基于聚類分析與非參數(shù)核密度估計的空間負(fù)荷分布規(guī)律研究方法。以浙江電網(wǎng)為例,對調(diào)研采集的空間負(fù)荷按城市發(fā)展類型、用地類型進行二級劃分后,利用基于非參數(shù)核密度估計方法提取各類樣本負(fù)荷密度的典型分布特征,結(jié)合實際對浙江11個城市的工業(yè)、商業(yè)、居住等多類空間負(fù)荷的分布規(guī)律進行分析研究,為配電網(wǎng)規(guī)劃提供可靠支撐。
[Abstract]:Spatial load forecasting is the premise and foundation of distribution network planning. The accuracy of load forecasting not only affects the investment and later operation of power grid, but also affects the rationality of urban planning scheme. On the basis of combing and summarizing the characteristics of advanced theories and research methods at home and abroad, this paper aims at improving the accuracy, practicability and applicability of spatial load forecasting. This paper mainly studies the following three aspects: load classification model based on daily load curve. The daily load curve of power system is analyzed by using the clustering technology in data mining, and a new clustering method of daily load curve based on reducing dimension of characteristic index, pattern Index clustering (PIC), is proposed, which is based on load rate. Six daily load characteristic indexes, such as daily peak-valley difference ratio, are used to reduce the dimension of the daily load curve. The Delphi method based on clustering validity correction is used to configure the weights of each index, and the weighted Euclidean distance is used as the similarity criterion. The daily load curve is clustered. The results of example analysis show that the proposed method has the advantages of short running time and good robustness. It improves the clustering quality of load curves and can directly reflect the characteristics of typical load curves. The typical daily load curve extracted by this method can be used as the basis for the classification and verification of spatial load forecasting and investigation samples, which lays a foundation for improving the quality of the investigation samples. Research on spatial load forecasting method considering regional differences. In view of the shortage of intelligent algorithm based load density index method which is highly dependent on samples and difficult to be applied in various places, a method of spatial load clustering and integrated forecasting considering regional differences is proposed. In this method, firstly, a large number of load samples are obtained from different areas, which belong to different types of load samples, and then the load classification checking and selecting method based on daily load curve is used to classify and select all the investigation samples. Then according to regional classification and load classification, the whole sample space of selected samples is divided into two levels, and the sub-sample space is obtained. Finally, the sub-sample space is matched according to the attribute information of the plots to be measured. The most similar subsample space is chosen as the training sample, and the support vector machine model is constructed to predict the load density of each plot, and then the spatial distribution of power load is obtained. An engineering example shows the practicability and effectiveness of the method. Research on Optimization of Space load forecasting Index system. In view of the theoretical innovation and improvement of accuracy of the existing research on forecasting methods, there is a lack of research on the spatial load distribution law in various places. A research method of spatial load distribution based on clustering analysis and nonparametric kernel density estimation is proposed. Taking Zhejiang Power Grid as an example, the spatial load collected by investigation and acquisition is divided into two levels according to the type of urban development and the type of land, and the typical distribution characteristics of load density of all kinds of samples are extracted by using non-parametric kernel density estimation method. The distribution law of industrial, commercial, residential and other spatial loads in 11 cities of Zhejiang Province is analyzed and studied in order to provide reliable support for distribution network planning.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM715
[Abstract]:Spatial load forecasting is the premise and foundation of distribution network planning. The accuracy of load forecasting not only affects the investment and later operation of power grid, but also affects the rationality of urban planning scheme. On the basis of combing and summarizing the characteristics of advanced theories and research methods at home and abroad, this paper aims at improving the accuracy, practicability and applicability of spatial load forecasting. This paper mainly studies the following three aspects: load classification model based on daily load curve. The daily load curve of power system is analyzed by using the clustering technology in data mining, and a new clustering method of daily load curve based on reducing dimension of characteristic index, pattern Index clustering (PIC), is proposed, which is based on load rate. Six daily load characteristic indexes, such as daily peak-valley difference ratio, are used to reduce the dimension of the daily load curve. The Delphi method based on clustering validity correction is used to configure the weights of each index, and the weighted Euclidean distance is used as the similarity criterion. The daily load curve is clustered. The results of example analysis show that the proposed method has the advantages of short running time and good robustness. It improves the clustering quality of load curves and can directly reflect the characteristics of typical load curves. The typical daily load curve extracted by this method can be used as the basis for the classification and verification of spatial load forecasting and investigation samples, which lays a foundation for improving the quality of the investigation samples. Research on spatial load forecasting method considering regional differences. In view of the shortage of intelligent algorithm based load density index method which is highly dependent on samples and difficult to be applied in various places, a method of spatial load clustering and integrated forecasting considering regional differences is proposed. In this method, firstly, a large number of load samples are obtained from different areas, which belong to different types of load samples, and then the load classification checking and selecting method based on daily load curve is used to classify and select all the investigation samples. Then according to regional classification and load classification, the whole sample space of selected samples is divided into two levels, and the sub-sample space is obtained. Finally, the sub-sample space is matched according to the attribute information of the plots to be measured. The most similar subsample space is chosen as the training sample, and the support vector machine model is constructed to predict the load density of each plot, and then the spatial distribution of power load is obtained. An engineering example shows the practicability and effectiveness of the method. Research on Optimization of Space load forecasting Index system. In view of the theoretical innovation and improvement of accuracy of the existing research on forecasting methods, there is a lack of research on the spatial load distribution law in various places. A research method of spatial load distribution based on clustering analysis and nonparametric kernel density estimation is proposed. Taking Zhejiang Power Grid as an example, the spatial load collected by investigation and acquisition is divided into two levels according to the type of urban development and the type of land, and the typical distribution characteristics of load density of all kinds of samples are extracted by using non-parametric kernel density estimation method. The distribution law of industrial, commercial, residential and other spatial loads in 11 cities of Zhejiang Province is analyzed and studied in order to provide reliable support for distribution network planning.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM715
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