本文關(guān)鍵詞： 風(fēng)電出力特性 風(fēng)速功率曲線 超短期風(fēng)電預(yù)測(cè) 時(shí)間序列法 智能算法 組合預(yù)測(cè) 自適應(yīng)神經(jīng)模糊推理系統(tǒng) 慣性控制　出處：《西南交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著風(fēng)電在電力系統(tǒng)中的滲透率越來越高,其固有的不確定性給電網(wǎng)安全經(jīng)濟(jì)運(yùn)行帶來嚴(yán)峻挑戰(zhàn)。對(duì)風(fēng)電出力概率特性進(jìn)行統(tǒng)計(jì)分析,實(shí)現(xiàn)高精度的超短期風(fēng)電預(yù)測(cè)能為電力系統(tǒng)運(yùn)行管理人員提供應(yīng)對(duì)風(fēng)電不確定性的基礎(chǔ)條件。風(fēng)電預(yù)測(cè)精度越高,電網(wǎng)接納風(fēng)電的能力越強(qiáng)。另一方面,越來越多的風(fēng)電機(jī)組代替?zhèn)鹘y(tǒng)的同步電機(jī),使系統(tǒng)頻率響應(yīng)特性持續(xù)惡化,因此風(fēng)電機(jī)組參與系統(tǒng)調(diào)頻在工業(yè)界和學(xué)術(shù)界均得到廣泛重視。本文基于四川地區(qū)某實(shí)際風(fēng)電場(chǎng)的實(shí)測(cè)數(shù)據(jù),對(duì)風(fēng)電出力特性、超短期風(fēng)電預(yù)測(cè)和慣性控制進(jìn)行研究,詳細(xì)內(nèi)容如下:1、對(duì)該風(fēng)電場(chǎng)年出力分布和年風(fēng)速分布進(jìn)行統(tǒng)計(jì)分析,對(duì)比15min、30min和60min時(shí)間尺度下的出力波動(dòng)特性;統(tǒng)計(jì)分析該風(fēng)電場(chǎng)風(fēng)電機(jī)組之間出力的相關(guān)性和互補(bǔ)性;分析單臺(tái)風(fēng)機(jī)在同一風(fēng)速下輸出功率的寬范圍分布現(xiàn)象,提出了一種基于最優(yōu)平滑階數(shù)的風(fēng)速功率曲線建模策略,以最優(yōu)平滑階數(shù)處理原始風(fēng)速得到輸入風(fēng)速?gòu)亩L(fēng)速功率曲線模型,并與已有方法中以原始風(fēng)速為輸入建立的風(fēng)速功率曲線模型進(jìn)行精度對(duì)比分析;2、提出了一種基于BP神經(jīng)網(wǎng)絡(luò)的長(zhǎng)時(shí)間尺度缺失風(fēng)電功率數(shù)據(jù)的補(bǔ)齊方法,以缺失數(shù)據(jù)時(shí)間段內(nèi)另7臺(tái)風(fēng)機(jī)的實(shí)測(cè)風(fēng)電功率數(shù)據(jù)為BP神經(jīng)網(wǎng)絡(luò)的輸入,得到待補(bǔ)齊風(fēng)電機(jī)組的功率數(shù)據(jù),并與常用的相鄰風(fēng)機(jī)法進(jìn)行精度對(duì)比分析;3、深入研究基于歷史功率數(shù)據(jù)的超短期風(fēng)電預(yù)測(cè),實(shí)現(xiàn)了持續(xù)法、ARMA、ARIMA這三種時(shí)間序列法;在考慮風(fēng)電功率序列波動(dòng)特性的基礎(chǔ)上提出了一種改進(jìn)的持續(xù)法,其精度較持續(xù)法有一定程度的提升;實(shí)現(xiàn)了 BP神經(jīng)網(wǎng)絡(luò)、RBF神經(jīng)網(wǎng)絡(luò)、SVM、PSO-SVM四種智能算法。對(duì)上述預(yù)測(cè)方法在不同季度以及不同預(yù)測(cè)步長(zhǎng)情況下的預(yù)測(cè)結(jié)果進(jìn)行對(duì)比分析。選擇時(shí)間序列方法中總體精度最好的ARMA方法和智能算法中總體精度最好的PSO-SVM為子預(yù)測(cè)方法,利用ANFIS組合上述兩個(gè)子預(yù)測(cè)方法從而得到最終的風(fēng)電預(yù)測(cè)結(jié)果,并將組合法的預(yù)測(cè)精度與兩個(gè)子預(yù)測(cè)方法進(jìn)行對(duì)比分析;4、分析了暫態(tài)過程中風(fēng)速波動(dòng)對(duì)風(fēng)機(jī)參與系統(tǒng)調(diào)頻的影響,進(jìn)而提出了一種基于超短期風(fēng)速預(yù)測(cè)的慣性控制策略�；谖磥�10s平均風(fēng)速的預(yù)測(cè)值來設(shè)計(jì)ROCOF和droop控制環(huán)的增益且每10s更新一次增益。在仿真系統(tǒng)中,基于多種來自實(shí)測(cè)數(shù)據(jù)的風(fēng)速波動(dòng)情況,開展了包括切機(jī)和負(fù)荷躍升等擾動(dòng)的算例研究,對(duì)比分析了基于超短期風(fēng)速預(yù)測(cè)的慣性控制策略與恒定增益的慣性控制策略的控制效果。
[Abstract]:With the increasing permeability of wind power in power system, its inherent uncertainty brings severe challenges to the safe and economic operation of power grid. The realization of ultra-short-term wind power prediction with high accuracy can provide basic conditions for power system operation managers to deal with the uncertainty of wind power. The higher the precision of wind power prediction, the stronger the power grid's ability to accept wind power. More and more wind turbines are replacing the traditional synchronous motors, which make the frequency response characteristics of the system deteriorate continuously. Therefore, the participation of wind turbine in FM system has been paid more and more attention in industry and academia. Based on the measured data of a practical wind farm in Sichuan area, the characteristics of wind power generation, prediction of wind power and inertial control are studied in this paper. The detailed contents are as follows: 1. The annual output force distribution and the annual wind speed distribution of the wind farm are statistically analyzed, and the fluctuation characteristics of the output force are compared under the time scale of 15 min or 30 min and 60 min respectively, and the correlation and complementarity of the output force among the wind farm units are analyzed statistically. Based on the analysis of the wide range distribution of the output power of a single typhoon under the same wind speed, a modeling strategy of wind speed power curve based on the optimal smoothing order is proposed. The input wind speed is obtained by processing the original wind speed with the optimal smoothing order, and the wind speed power curve model is established. The accuracy of the wind speed power curve model based on the original wind speed input is compared with that of the existing methods, and a new method based on BP neural network is proposed to correct the wind power data. Taking the measured wind power data of the other 7 typhoon turbines in the missing data period as the input of BP neural network, the power data of the wind turbine to be compensated are obtained. By comparing and analyzing the accuracy of the conventional adjacent fan method, the ultra-short-term wind power prediction based on historical power data is studied in depth, and the three time series methods, ARMA-ARIMA, are realized. On the basis of considering the fluctuation characteristics of wind power series, an improved persistence method is proposed, the accuracy of which is improved to a certain extent. Four intelligent algorithms of BP neural network and RBF neural network are implemented. The prediction results of the above prediction methods in different seasons and different prediction steps are compared and analyzed. The overall accuracy of the time series method is selected. The best ARMA method and the best overall precision of the intelligent algorithm are PSO-SVM subprediction methods. The final wind power prediction results are obtained by combining the above two sub-prediction methods with ANFIS. The prediction accuracy of the combined method and the two sub-prediction methods are compared and analyzed. The influence of the wind speed fluctuation on the frequency modulation of the fan participating in the system is analyzed in the transient process. Furthermore, an inertial control strategy based on ultra-short-term wind speed prediction is proposed. The gain of ROCOF and droop control loops is designed based on the predicted value of the average wind speed of 10 s in the future and the gain is updated every 10 s. Based on the fluctuation of wind speed from measured data, a numerical example of disturbance, such as cutting machine and load jump, is carried out. The control effects of inertial control strategy based on ultrashort wind speed prediction and constant gain inertial control strategy are compared and analyzed.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM614

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