結合貝葉斯推理與ART2wNF網(wǎng)絡的風力發(fā)電機組偏航系統(tǒng)的控制策略
發(fā)布時間：2018-11-08 11:05
【摘要】：隨著社會經(jīng)濟的發(fā)展,世界各國的能源矛盾日益突出。鑒于風能具有安全、清潔、充裕,穩(wěn)定等特點,加大對風能的利用將有效地緩解能源危機和減少環(huán)境污染。而發(fā)展風力發(fā)電是利用風能的最主要的形式,其控制系統(tǒng)的好壞直接影響風力發(fā)電機組的效率和使用壽命,其中風電的偏航系統(tǒng)是實現(xiàn)最大化捕獲風能和避免偏航機艙頻繁轉動的關鍵組成部分,這使得風力發(fā)電機組偏航系統(tǒng)的有效控制變得尤為重要。文章在詳細分析了風力發(fā)電的偏航系統(tǒng)的工作原理和控制技術的基礎上,提出了將結合了貝葉斯推理的ART2wNF(Adaptive resonance theory with neoteny feature)網(wǎng)絡與風向標控制及爬山算法相結合的偏航控制策略。針對風的隨機性,在風向服從正態(tài)分布的模型下,仿真得到了風向的樣本數(shù)據(jù),建立了基于最小二乘擬合下的風速模型,濾去了風信號中的噪聲數(shù)據(jù),為實現(xiàn)ART2wNF網(wǎng)絡對風信號的聚類做了數(shù)據(jù)的預處理。由于ART2wNF網(wǎng)絡在對樣本進行自組織學習和聚類時,其警戒值是固定不變的,而警戒值的高低直接影響類別數(shù)的多少,為了實現(xiàn)ART2wNF網(wǎng)絡警戒值的自動調(diào)節(jié),文章應用了貝葉斯分類器的原理,在風向的正態(tài)分布模型下,計算出新的風向樣本服從上一批樣本分布的概率,以此后驗概率作為警戒值的調(diào)節(jié)基準,設計了基于貝葉斯推理的ART2wNF網(wǎng)絡警戒值的調(diào)整機制,提高了風信號樣本的聚類效果,為解決風向在小的變化范圍內(nèi)出現(xiàn)集中風能的偏航問題奠定了基礎。通過具有幼態(tài)延續(xù)特征的ART2wNF網(wǎng)絡對經(jīng)過預處理的風向數(shù)據(jù)進行自組織學習和聚類,結合風向標控制和爬山算法對ART2wNF網(wǎng)絡的警戒值參數(shù)進行調(diào)節(jié),得到了聚類后每個樣本的聚類中心,即偏航位置,完成了自動偏航。文章通過在Matlab中搭建仿真模型模擬風力發(fā)電機組的偏航系統(tǒng),仿真驗證了文章提出的結合貝葉斯推理與ART2wNF網(wǎng)絡的風力發(fā)電機組偏航系統(tǒng)控制策略的可行性和有效性,其不僅能有效地解決當風向在小的變化范圍內(nèi)(比如正負15°)出現(xiàn)集中風能的偏航問題,還能有效地避免偏航電機的頻繁轉動,對提高風能的利用率和風力發(fā)電機組的使用壽命有著重要的意義。
[Abstract]:With the development of society and economy, the energy contradiction is becoming more and more prominent. Since wind energy is safe, clean, abundant and stable, increasing the use of wind energy will effectively alleviate the energy crisis and reduce environmental pollution. The development of wind power generation is the most important form of using wind energy, and the quality of its control system directly affects the efficiency and service life of wind turbines. The yaw system of wind power is the key component to maximize the capture of wind energy and avoid the frequent rotation of yaw engine room, which makes the effective control of wind turbine yaw system become more and more important. Based on the detailed analysis of the working principle and control technology of the yaw system of wind power generation, A yaw control strategy which combines Bayesian reasoning with ART2wNF (Adaptive resonance theory with neoteny feature) network and wind vane control and mountain climbing algorithm is proposed. According to the randomness of wind, under the model of normal distribution of wind direction clothing, the wind direction sample data are obtained by simulation, and the wind speed model based on least square fitting is established to filter out the noise data in wind signal. In order to realize the clustering of wind signal in ART2wNF network, the data preprocessing is done. Because the alert value of ART2wNF network is fixed when the samples are self-organized learning and clustering, and the level of alert value directly affects the number of categories, in order to realize the automatic adjustment of ART2wNF network alert value, This paper applies the principle of Bayesian classifier, under the normal distribution model of wind direction, calculates the probability of the distribution of new wind direction samples from the last batch of samples. Based on Bayesian reasoning, the adjustment mechanism of ART2wNF network warning value is designed, which improves the clustering effect of wind signal samples, and lays a foundation for solving the yaw problem of concentrated wind energy in a small variation range of wind direction. The preprocessed wind direction data are self-organized and clustered by the ART2wNF network with the characteristics of juvenile continuation, and the alert parameters of the ART2wNF network are adjusted by combining the wind vane control and the mountain-climbing algorithm. After clustering, the clustering center of each sample, that is, yaw position, is obtained, and the automatic yawing is completed. By building a simulation model in Matlab to simulate the yaw system of wind turbine, the feasibility and effectiveness of the proposed control strategy of wind turbine yaw system based on Bayesian reasoning and ART2wNF network are verified by simulation. It can not only effectively solve the yaw problem of concentrated wind energy when the wind direction is in a small range (for example, positive or negative 15 擄), but also effectively avoid the frequent rotation of the yaw motor. It is of great significance to improve the utilization rate of wind energy and the service life of wind turbine.
【學位授予單位】：長沙理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM315
本文編號：2318283
[Abstract]:With the development of society and economy, the energy contradiction is becoming more and more prominent. Since wind energy is safe, clean, abundant and stable, increasing the use of wind energy will effectively alleviate the energy crisis and reduce environmental pollution. The development of wind power generation is the most important form of using wind energy, and the quality of its control system directly affects the efficiency and service life of wind turbines. The yaw system of wind power is the key component to maximize the capture of wind energy and avoid the frequent rotation of yaw engine room, which makes the effective control of wind turbine yaw system become more and more important. Based on the detailed analysis of the working principle and control technology of the yaw system of wind power generation, A yaw control strategy which combines Bayesian reasoning with ART2wNF (Adaptive resonance theory with neoteny feature) network and wind vane control and mountain climbing algorithm is proposed. According to the randomness of wind, under the model of normal distribution of wind direction clothing, the wind direction sample data are obtained by simulation, and the wind speed model based on least square fitting is established to filter out the noise data in wind signal. In order to realize the clustering of wind signal in ART2wNF network, the data preprocessing is done. Because the alert value of ART2wNF network is fixed when the samples are self-organized learning and clustering, and the level of alert value directly affects the number of categories, in order to realize the automatic adjustment of ART2wNF network alert value, This paper applies the principle of Bayesian classifier, under the normal distribution model of wind direction, calculates the probability of the distribution of new wind direction samples from the last batch of samples. Based on Bayesian reasoning, the adjustment mechanism of ART2wNF network warning value is designed, which improves the clustering effect of wind signal samples, and lays a foundation for solving the yaw problem of concentrated wind energy in a small variation range of wind direction. The preprocessed wind direction data are self-organized and clustered by the ART2wNF network with the characteristics of juvenile continuation, and the alert parameters of the ART2wNF network are adjusted by combining the wind vane control and the mountain-climbing algorithm. After clustering, the clustering center of each sample, that is, yaw position, is obtained, and the automatic yawing is completed. By building a simulation model in Matlab to simulate the yaw system of wind turbine, the feasibility and effectiveness of the proposed control strategy of wind turbine yaw system based on Bayesian reasoning and ART2wNF network are verified by simulation. It can not only effectively solve the yaw problem of concentrated wind energy when the wind direction is in a small range (for example, positive or negative 15 擄), but also effectively avoid the frequent rotation of the yaw motor. It is of great significance to improve the utilization rate of wind energy and the service life of wind turbine.
【學位授予單位】：長沙理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM315
【參考文獻】
相關期刊論文 前2條
1 喬川;李斯陽;;1.5MW雙饋風力發(fā)電機組偏航控制系統(tǒng)[J];控制工程;2011年S1期
2 朱亞俊;楊金明;;小型永磁風力發(fā)電系統(tǒng)的集成控制策略[J];通信電源技術;2010年04期
，本文編號：2318283
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