本文關鍵詞： 風電出力特性 風速功率曲線 超短期風電預測 時間序列法 智能算法 組合預測 自適應神經模糊推理系統(tǒng) 慣性控制　出處：《西南交通大學》2017年碩士論文　論文類型：學位論文

【摘要】：隨著風電在電力系統(tǒng)中的滲透率越來越高,其固有的不確定性給電網安全經濟運行帶來嚴峻挑戰(zhàn)。對風電出力概率特性進行統(tǒng)計分析,實現(xiàn)高精度的超短期風電預測能為電力系統(tǒng)運行管理人員提供應對風電不確定性的基礎條件。風電預測精度越高,電網接納風電的能力越強。另一方面,越來越多的風電機組代替?zhèn)鹘y(tǒng)的同步電機,使系統(tǒng)頻率響應特性持續(xù)惡化,因此風電機組參與系統(tǒng)調頻在工業(yè)界和學術界均得到廣泛重視。本文基于四川地區(qū)某實際風電場的實測數(shù)據(jù),對風電出力特性、超短期風電預測和慣性控制進行研究,詳細內容如下:1、對該風電場年出力分布和年風速分布進行統(tǒng)計分析,對比15min、30min和60min時間尺度下的出力波動特性;統(tǒng)計分析該風電場風電機組之間出力的相關性和互補性;分析單臺風機在同一風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)的風速功率曲線建模策略,以最優(yōu)平滑階數(shù)處理原始風速得到輸入風速從而建立風速功率曲線模型,并與已有方法中以原始風速為輸入建立的風速功率曲線模型進行精度對比分析;2、提出了一種基于BP神經網絡的長時間尺度缺失風電功率數(shù)據(jù)的補齊方法,以缺失數(shù)據(jù)時間段內另7臺風機的實測風電功率數(shù)據(jù)為BP神經網絡的輸入,得到待補齊風電機組的功率數(shù)據(jù),并與常用的相鄰風機法進行精度對比分析;3、深入研究基于歷史功率數(shù)據(jù)的超短期風電預測,實現(xiàn)了持續(xù)法、ARMA、ARIMA這三種時間序列法;在考慮風電功率序列波動特性的基礎上提出了一種改進的持續(xù)法,其精度較持續(xù)法有一定程度的提升;實現(xiàn)了 BP神經網絡、RBF神經網絡、SVM、PSO-SVM四種智能算法。對上述預測方法在不同季度以及不同預測步長情況下的預測結果進行對比分析。選擇時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為子預測方法,利用ANFIS組合上述兩個子預測方法從而得到最終的風電預測結果,并將組合法的預測精度與兩個子預測方法進行對比分析;4、分析了暫態(tài)過程中風速波動對風機參與系統(tǒng)調頻的影響,進而提出了一種基于超短期風速預測的慣性控制策略�；谖磥�10s平均風速的預測值來設計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,基于多種來自實測數(shù)據(jù)的風速波動情況,開展了包括切機和負荷躍升等擾動的算例研究,對比分析了基于超短期風速預測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.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM614

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