本文選題：智能配電網(wǎng) + 饋線　； 參考：《哈爾濱工業(yè)大學(xué)》2014年碩士論文

【摘要】：智能配電網(wǎng)饋線負(fù)荷預(yù)測，可以為配電網(wǎng)電能調(diào)度提供決策信息，指導(dǎo)用戶智能用電，平抑負(fù)荷波動，對保證配電網(wǎng)安全、經(jīng)濟(jì)的運行具有重要意義。隨著我國配電網(wǎng)的小型分布式電源如風(fēng)電、光伏等應(yīng)用發(fā)展迅速，其所發(fā)電能受到風(fēng)速、日照強(qiáng)度等不確定因素的影響，具有顯著的隨機(jī)間歇性。這間歇性加劇配電網(wǎng)饋線上的負(fù)荷波動，干擾配電網(wǎng)的安全穩(wěn)定運行，對智能配電網(wǎng)饋線負(fù)荷預(yù)測帶來新的挑戰(zhàn)。 針對智能配電網(wǎng)饋線負(fù)荷與傳統(tǒng)饋線負(fù)荷具有不同的特點，按照負(fù)荷、發(fā)電來源將饋線負(fù)荷劃分為用戶負(fù)荷、分布式風(fēng)電負(fù)荷和光伏負(fù)荷等部分�？紤]分布式負(fù)荷的波動對饋線負(fù)荷預(yù)測的影響，參照饋線潮流方向?qū)⒎植际截?fù)荷定性為負(fù)負(fù)荷，并定義了饋線凈負(fù)荷的概念。 在負(fù)荷劃分的基礎(chǔ)上，對智能配電網(wǎng)饋線上的各類負(fù)荷進(jìn)行特征分析，確定各類負(fù)荷預(yù)測所需的數(shù)據(jù)類型，，分別采用C-模糊聚類與K-均值聚類的方法對用戶負(fù)荷和光伏負(fù)荷進(jìn)行聚類分析，建立各類饋線負(fù)荷模式。 根據(jù)各類饋線負(fù)荷的特性，分別提出合適的各類饋線負(fù)荷預(yù)測方法。建立基于魯棒回歸和改進(jìn)的Elman神經(jīng)網(wǎng)絡(luò)的饋線用戶負(fù)荷預(yù)測模型。建立基于小波去噪的ARMA時間序列的饋線風(fēng)電負(fù)荷預(yù)測模型。建立基于GRNN神經(jīng)網(wǎng)絡(luò)的饋線光伏負(fù)荷預(yù)測模型。確定基于饋線負(fù)荷重組的饋線凈負(fù)荷預(yù)測方法。根據(jù)平均相對誤差和VAR值的誤差評價指標(biāo)，評價所提出方法的預(yù)測精度，分析預(yù)測誤差風(fēng)險。 根據(jù)預(yù)測所需數(shù)據(jù)類型及其相互關(guān)系，設(shè)計預(yù)測系統(tǒng)后臺數(shù)據(jù)倉庫，建立基于數(shù)據(jù)挖掘的智能配電網(wǎng)饋線負(fù)荷預(yù)測系統(tǒng)。該系統(tǒng)具有負(fù)荷瀏覽分析、建立各類負(fù)荷模式、各類饋線負(fù)荷預(yù)測、饋線凈負(fù)荷預(yù)測和預(yù)測誤差分析等主要功能。最后以某含分布式電源的智能配電網(wǎng)饋線為例，根據(jù)該網(wǎng)歷史負(fù)荷數(shù)據(jù)，實現(xiàn)系統(tǒng)各功能，驗證所設(shè)計預(yù)測系統(tǒng)的有效性與實用性。 本文工作得到國家電網(wǎng)公司科技項目《智能配電網(wǎng)控制運行模擬仿真關(guān)鍵技術(shù)研究》(DZB17201200260)的資助。
[Abstract]:The intelligent distribution network feeder load forecasting can provide decision information for distribution network power dispatching, guide users to use electricity intelligently, suppress load fluctuation, and is of great significance to ensure distribution network safety and economic operation. With the rapid development of small distributed generation (DG) such as wind power and photovoltaic (PV) in China's distribution network, the power generation can be affected by uncertain factors such as wind speed, sunshine intensity and so on. This intermittency intensifies the load fluctuation on the feeder line of the distribution network, interferes with the safe and stable operation of the distribution network, and brings a new challenge to the intelligent distribution network feeder load forecasting, aiming at the different characteristics between the intelligent distribution network feeder load and the traditional feeder load. According to the load, the feeder load is divided into user load, distributed wind load and photovoltaic load. Considering the influence of distributed load fluctuation on feeder load forecasting, the distributed load is defined as negative load according to the direction of feeder power flow, and the concept of net load of feeder line is defined. Based on the characteristic analysis of various kinds of loads on the feeder line of intelligent distribution network, the data types needed for load forecasting are determined, and the methods of C- fuzzy clustering and K-means clustering are used to cluster the user load and photovoltaic load, respectively. According to the characteristics of various kinds of feeder load, the appropriate load forecasting methods are put forward. A feeder user load forecasting model based on robust regression and improved Elman neural network is established. An ARMA time series model for wind power load forecasting based on wavelet denoising is established. A feed-line photovoltaic load forecasting model based on GRNN neural network is established. The net load forecasting method based on feeder load recombination is determined. According to the average relative error and the error evaluation index of VAR value, the prediction accuracy of the proposed method is evaluated, and the prediction error risk is analyzed. According to the data types and their relationships, the backstage data warehouse of the forecasting system is designed. The intelligent distribution network feeder load forecasting system based on data mining is established. The system has the main functions of load browsing analysis, setting up all kinds of load modes, various kinds of feeder load forecasting, feeder net load forecasting and forecasting error analysis and so on. Finally, taking the feeder of a smart distribution network with distributed power as an example, according to the historical load data of the network, the functions of the system are realized. To verify the effectiveness and practicability of the designed prediction system, this paper is supported by the State Grid Corporation's Science and Technology Project "key Technology Research on Simulation of Intelligent Distribution Network Control Operation Simulation" (DZB17201200260).
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM76

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 陳昌松;段善旭;殷進(jìn)軍;;基于神經(jīng)網(wǎng)絡(luò)的光伏陣列發(fā)電預(yù)測模型的設(shè)計[J];電工技術(shù)學(xué)報;2009年09期

2 萬國成;王漢華;;最小二乘法在配網(wǎng)饋線年度負(fù)荷預(yù)測中的應(yīng)用[J];電氣應(yīng)用;2008年09期

3 劉健,勾新鵬,徐精求,黨海鋒,董海鵬;基于區(qū)域負(fù)荷的配電網(wǎng)超短期負(fù)荷預(yù)測[J];電力系統(tǒng)自動化;2003年19期

4 王成山;肖朝霞;王守相;;微網(wǎng)綜合控制與分析[J];電力系統(tǒng)自動化;2008年07期

5 趙宏偉;吳濤濤;;基于分布式電源的微網(wǎng)技術(shù)[J];電力系統(tǒng)及其自動化學(xué)報;2008年01期

6 李勛;龔慶武;胡元潮;楊群瑛;梁文騰;;智能配電網(wǎng)體系探討[J];電力自動化設(shè)備;2011年08期

7 張文亮;劉壯志;王明俊;楊旭升;;智能電網(wǎng)的研究進(jìn)展及發(fā)展趨勢[J];電網(wǎng)技術(shù);2009年13期

8 胡學(xué)浩;;智能電網(wǎng)——未來電網(wǎng)的發(fā)展態(tài)勢[J];電網(wǎng)技術(shù);2009年14期

9 侯佑華;房大中;齊軍;李洪波;牛偉;楊濤;;大規(guī)模風(fēng)電入網(wǎng)的有功功率波動特性分析及發(fā)電計劃仿真[J];電網(wǎng)技術(shù);2010年05期

10 林弘宇;田世明;;智能電網(wǎng)條件下的智能小區(qū)關(guān)鍵技術(shù)[J];電網(wǎng)技術(shù);2011年12期

相關(guān)博士學(xué)位論文 前3條

1 李鵬;分布式發(fā)電微網(wǎng)系統(tǒng)暫態(tài)仿真方法研究[D];天津大學(xué);2010年

2 任海軍;基于智能計算的配電網(wǎng)負(fù)荷預(yù)測方法研究[D];重慶大學(xué);2010年

3 王飛;并網(wǎng)型光伏電站發(fā)電功率預(yù)測方法與系統(tǒng)[D];華北電力大學(xué);2013年

本文編號：2007344