本文選題：Elman神經(jīng)網(wǎng)絡(luò) + 主成分分析法��； 參考：《蘭州交通大學(xué)》2017年碩士論文

【摘要】：風(fēng)力發(fā)電具有隨機(jī)性、間歇性等特點(diǎn),導(dǎo)致風(fēng)電場(chǎng)輸出電能產(chǎn)生較大的波動(dòng),直接接入電網(wǎng),會(huì)嚴(yán)重威脅電網(wǎng)的穩(wěn)定性、連續(xù)性和可調(diào)性。尤其是酒泉等超大規(guī)模風(fēng)電基地采用集中式并網(wǎng),進(jìn)一步放大了風(fēng)電波動(dòng)性對(duì)電網(wǎng)造成的劇烈沖擊,產(chǎn)生巨大的安全隱患。因此,針對(duì)風(fēng)電出力的波動(dòng)性,提出研究精確的風(fēng)電功率預(yù)測(cè)方法,對(duì)實(shí)現(xiàn)風(fēng)場(chǎng)發(fā)電量的高精度預(yù)測(cè)和安全、經(jīng)濟(jì)調(diào)度具有重要的實(shí)際價(jià)值。本文根據(jù)酒泉風(fēng)電基地的對(duì)風(fēng)電預(yù)報(bào)的現(xiàn)實(shí)需求,以酒泉風(fēng)電基地內(nèi)某風(fēng)電場(chǎng)及周邊測(cè)風(fēng)塔的實(shí)測(cè)數(shù)據(jù)為基礎(chǔ),采用新型自適應(yīng)果蠅算法優(yōu)化改進(jìn)的動(dòng)態(tài)Elman神經(jīng)網(wǎng)絡(luò)對(duì)風(fēng)電場(chǎng)進(jìn)行未來24h內(nèi)短期風(fēng)電功率預(yù)測(cè)研究。具體工作如下:首先根據(jù)對(duì)風(fēng)功率公式的分析確定本文采用的原始輸入變量,針對(duì)原始數(shù)據(jù)的采集、傳輸以及存儲(chǔ)的過程中導(dǎo)致的數(shù)據(jù)缺失、錯(cuò)誤等問題,對(duì)數(shù)據(jù)進(jìn)行完整性、合理性的檢測(cè),并對(duì)檢測(cè)出的異常數(shù)據(jù)進(jìn)行分類,并進(jìn)行均值填補(bǔ)和非線性回歸填補(bǔ)。根據(jù)對(duì)當(dāng)前甘肅地區(qū)各風(fēng)電場(chǎng)誤差數(shù)據(jù)分布和誤差產(chǎn)生原因的分析,選擇合適的誤差評(píng)價(jià)指標(biāo)對(duì)本文的預(yù)測(cè)效果進(jìn)行合理的評(píng)價(jià)。其次,針對(duì)Elman神經(jīng)網(wǎng)絡(luò)本身的梯度下降學(xué)習(xí)算法收斂速度慢、易陷入局部最優(yōu)的劣勢(shì),提出通過具有良好全局尋優(yōu)性能和計(jì)算性能的自適應(yīng)果蠅優(yōu)化算法(FOA)優(yōu)化Elman神經(jīng)網(wǎng)絡(luò)模型權(quán)值、閾值,建立改進(jìn)的FOA-Elman神經(jīng)網(wǎng)絡(luò)模型。最后,從提高功率預(yù)測(cè)精度的角度出發(fā),考慮到影響預(yù)測(cè)精度的因素中除了模型選擇、學(xué)習(xí)算法之外,輸入數(shù)據(jù)的有效性也是至關(guān)重要的,所以采用主成分分析法(PCA)對(duì)短期風(fēng)電功率預(yù)測(cè)模型的輸入特征分析處理,經(jīng)過對(duì)輸入數(shù)據(jù)的主成分分析,得到四種互不相關(guān)的主成分,使用處理完的四種主成分作為輸入變量代入改進(jìn)的FOA-Elman神經(jīng)網(wǎng)絡(luò)模型,建立PCA-FOA-Elman神經(jīng)網(wǎng)絡(luò)模型通過測(cè)試數(shù)據(jù)進(jìn)行預(yù)測(cè)分析。通過實(shí)驗(yàn)仿真,將PCA-FOA-Elman模型與基于自適應(yīng)果蠅算法的FOA-Elman神經(jīng)網(wǎng)絡(luò)模型以及改進(jìn)Elman神經(jīng)網(wǎng)絡(luò)模型的預(yù)測(cè)效果圖和預(yù)測(cè)誤差分別進(jìn)行比較分析。結(jié)果顯示:在短期風(fēng)電功率預(yù)測(cè)中,建立的PCA-FOA-Elman模型的預(yù)測(cè)效果相對(duì)于Elman神經(jīng)網(wǎng)絡(luò)模型平均絕對(duì)誤差降低了39.44%,均方根誤差降低了36.45%,為提高短期風(fēng)電功率預(yù)測(cè)精度提供了一種新思路,對(duì)實(shí)現(xiàn)風(fēng)電的可測(cè)、可控、可調(diào)具有重要意義。
[Abstract]:Wind power generation has the characteristics of randomness and intermittency, which leads to large fluctuation of wind farm output energy and direct access to power grid, which will seriously threaten the stability, continuity and tunability of power grid. In particular, Jiuquan and other super-large scale wind power bases use centralized grid-connected, which further amplifies the severe impact of wind power fluctuation on the power grid, resulting in huge safety risks. Therefore, in view of the fluctuation of wind power output, an accurate wind power prediction method is proposed, which is of great practical value to achieve high precision prediction and safety of wind power generation and economic dispatch. According to the actual demand of wind power forecast in Jiuquan wind power base, this paper bases on the measured data of a wind farm and its surrounding wind tower in Jiuquan wind power base. A new adaptive Drosophila algorithm is used to optimize and improve the dynamic Elman neural network to predict the short-term wind power of wind farm in the next 24 hours. The specific work is as follows: firstly, according to the analysis of wind power formula, the original input variables are determined, and the data integrity is carried out in the process of collecting, transmitting and storing the original data. The rationality of the detection, and the detection of abnormal data classification, and the means of filling and nonlinear regression filling. Based on the analysis of the distribution of the error data and the causes of the errors in the current wind farms in Gansu Province, a reasonable evaluation of the prediction effect of this paper is carried out by selecting the appropriate error evaluation indexes. Secondly, for the gradient descent learning algorithm of Elman neural network itself, the convergence speed is slow, and it is easy to fall into the disadvantage of local optimum. An improved Elman neural network model based on adaptive Drosophila optimization algorithm (FOAA) with good global optimization performance and computational performance is proposed to optimize the weights and thresholds of the Elman neural network model. Finally, from the point of view of improving the accuracy of power prediction, considering the factors that affect the prediction accuracy, besides model selection and learning algorithm, the validity of input data is also very important. So we use the principal component analysis (PCA) to analyze the input characteristics of the short-term wind power prediction model, and through the principal component analysis of the input data, we get four independent principal components. The four principal components are used as input variables to replace the improved FOA-Elman neural network model, and the PCA-FOA-Elman neural network model is established to predict and analyze by testing data. Through experimental simulation, the prediction effect diagram and prediction error of PCA-FOA-Elman model, FOA-Elman neural network model based on adaptive Drosophila algorithm and improved Elman neural network model are compared and analyzed respectively. The results show that in the short-term wind power prediction, Compared with the average absolute error of Elman neural network model, the prediction effect of the established PCA-FOA-Elman model is reduced by 39.44 and the root mean square error is reduced by 36.45, which provides a new way to improve the prediction accuracy of short-term wind power, and can be used to realize the measurable and controllable wind power. Adjustable is of great significance.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP183;TM614

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本文編號(hào)：1920494