【摘要】：隨著現(xiàn)代電力系統(tǒng)建設(shè)規(guī)模的不斷擴大，全國電網(wǎng)互聯(lián)工程的逐步開展，越來越復(fù)雜的電網(wǎng)結(jié)構(gòu)和運行方式將給電力系統(tǒng)的安全運行和電能質(zhì)量帶來較大的威脅，向電力負荷預(yù)測的實時性和精確度提出了更大的挑戰(zhàn)。超短期負荷預(yù)測利用最新負荷信息，實時跟蹤電力系統(tǒng)負荷變化，是動態(tài)電網(wǎng)安全檢測、自動發(fā)電控制和緊急狀態(tài)處理等的基本前提和重要依據(jù)。快速、精確的預(yù)測結(jié)果能夠指導(dǎo)電力部門及時維護電網(wǎng)頻率平衡，保證電網(wǎng)運行的安全性和經(jīng)濟性。因此研究兼顧預(yù)測實時性和準確性的超短期負荷預(yù)測實用方法具有重要意義。 本文從負荷構(gòu)成的基本模型入手，研究了負荷特性及其與相關(guān)影響因素之間的關(guān)系；針對各影響因素，研究其對負荷變化的具體影響作用，結(jié)果表明：時間因素影響較大，使負荷呈現(xiàn)出明顯的周期性；天氣因素與負荷變化之間有一定的相關(guān)性；不確定性因素的影響使負荷表現(xiàn)出較強的波動性，其規(guī)律難以把握。根據(jù)不同的負荷變化規(guī)律，探討了相應(yīng)的預(yù)測思路和方法。 提出了基于改進數(shù)據(jù)流在線分割的超短期負荷預(yù)測模型：利用數(shù)據(jù)流實時處理技術(shù)進行超短期負荷預(yù)測，，其快速分段預(yù)測能力避免了重復(fù)建模，增強了實時性；靜態(tài)提取蘊含天氣因素和負荷周期特性作用的短期負荷預(yù)測結(jié)果，對分割點進行實時修正，有效地增加了歷史信息利用率，提高了分割點預(yù)測精度，同時保證了預(yù)測實時性；經(jīng)實際算例檢驗，結(jié)果表明該模型的預(yù)測準確性和實時性均優(yōu)于幾種常規(guī)超短期預(yù)測算法，解決了預(yù)測精度與預(yù)測速度相互制約的矛盾，同時降低了拐點預(yù)測誤差，并在天氣突變時也具有穩(wěn)定的適應(yīng)性。 進一步考慮負荷隨機波動分量的影響作用，建立基于小波包分析的超短期負荷預(yù)測方法：通過小波包分析對負荷隨機分量進一步分解，便于深入分析隨機分量特性；對分解后的各小波包空間信號進行單支重構(gòu)，根據(jù)各組負荷子序列分量特性，分別建立預(yù)測模型，并將各子序列分量預(yù)測值相加獲得負荷預(yù)測結(jié)果。經(jīng)算例分析表明，該算法具有較高的預(yù)測精度和穩(wěn)定的預(yù)測效果。
[Abstract]:With the continuous expansion of the scale of modern power system construction and the gradual development of the national power network interconnection project, the more and more complex power network structure and operation mode will bring a great threat to the safe operation and power quality of the power system. It presents a greater challenge to the real-time and accuracy of power load forecasting. The ultra-short-term load forecasting uses the latest load information to track the load change of the power system in real time, which is the basic premise and important basis of dynamic power network safety detection, automatic generation control and emergency processing. The fast and accurate prediction results can guide the power sector to maintain the power network frequency balance in time and ensure the security and economy of the power network operation. Therefore, it is of great significance to study the practical method of super-short-term load forecasting which takes into account the real-time and accuracy of forecasting. Starting with the basic model of load composition, this paper studies the load characteristics and the relationship between load characteristics and related factors. In view of the influence factors, the concrete effect of the factors on the load change is studied. The results show that the time factor has a great influence on the load, which makes the load appear obvious periodicity, and the weather factor has certain correlation with the load change. The influence of uncertain factors makes the load exhibit strong volatility, and its law is difficult to grasp. According to the different law of load change, the corresponding forecasting ideas and methods are discussed. The ultra-short-term load forecasting model based on the improved on-line segmentation of data flow is proposed. The fast segment forecasting ability avoids repeated modeling and enhances the real-time performance of ultra-short-term load forecasting by using data stream real-time processing technology. Static extraction of short-term load forecasting results containing weather factors and load cycle characteristics can effectively increase the utilization rate of historical information and improve the accuracy of forecasting points, and ensure the real-time prediction. The results of practical examples show that the prediction accuracy and real-time performance of the model are better than those of several conventional ultra-short term prediction algorithms, and the contradiction between prediction accuracy and prediction speed is solved, and the error of inflection point prediction is reduced at the same time. It also has a stable adaptability in the event of sudden change in the weather. Considering the influence of load random fluctuation component further, an ultra-short-term load forecasting method based on wavelet packet analysis is established: the random load component is further decomposed by wavelet packet analysis, which is convenient for further analysis of stochastic component characteristics; The decomposed wavelet packet spatial signal is reconstructed by single branch. According to the characteristics of each group of load sub-sequence components, the prediction model is established, and the load forecasting results are obtained by adding the predicted values of each sub-sequence component. An example shows that the algorithm has high prediction accuracy and stable prediction effect.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM715

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