結(jié)合貝葉斯推理與ART2wNF網(wǎng)絡(luò)的風(fēng)力發(fā)電機(jī)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(jì)的發(fā)展,世界各國的能源矛盾日益突出。鑒于風(fēng)能具有安全、清潔、充裕,穩(wěn)定等特點,加大對風(fēng)能的利用將有效地緩解能源危機(jī)和減少環(huán)境污染。而發(fā)展風(fēng)力發(fā)電是利用風(fēng)能的最主要的形式,其控制系統(tǒng)的好壞直接影響風(fēng)力發(fā)電機(jī)組的效率和使用壽命,其中風(fēng)電的偏航系統(tǒng)是實現(xiàn)最大化捕獲風(fēng)能和避免偏航機(jī)艙頻繁轉(zhuǎn)動的關(guān)鍵組成部分,這使得風(fēng)力發(fā)電機(jī)組偏航系統(tǒng)的有效控制變得尤為重要。文章在詳細(xì)分析了風(fēng)力發(fā)電的偏航系統(tǒng)的工作原理和控制技術(shù)的基礎(chǔ)上,提出了將結(jié)合了貝葉斯推理的ART2wNF(Adaptive resonance theory with neoteny feature)網(wǎng)絡(luò)與風(fēng)向標(biāo)控制及爬山算法相結(jié)合的偏航控制策略。針對風(fēng)的隨機(jī)性,在風(fēng)向服從正態(tài)分布的模型下,仿真得到了風(fēng)向的樣本數(shù)據(jù),建立了基于最小二乘擬合下的風(fēng)速模型,濾去了風(fēng)信號中的噪聲數(shù)據(jù),為實現(xiàn)ART2wNF網(wǎng)絡(luò)對風(fēng)信號的聚類做了數(shù)據(jù)的預(yù)處理。由于ART2wNF網(wǎng)絡(luò)在對樣本進(jìn)行自組織學(xué)習(xí)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(luò)警戒值的自動調(diào)節(jié),文章應(yīng)用了貝葉斯分類器的原理,在風(fēng)向的正態(tài)分布模型下,計算出新的風(fēng)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(zhǔn),設(shè)計了基于貝葉斯推理的ART2wNF網(wǎng)絡(luò)警戒值的調(diào)整機(jī)制,提高了風(fēng)信號樣本的聚類效果,為解決風(fēng)向在小的變化范圍內(nèi)出現(xiàn)集中風(fēng)能的偏航問題奠定了基礎(chǔ)。通過具有幼態(tài)延續(xù)特征的ART2wNF網(wǎng)絡(luò)對經(jīng)過預(yù)處理的風(fēng)向數(shù)據(jù)進(jìn)行自組織學(xué)習(xí)和聚類,結(jié)合風(fēng)向標(biāo)控制和爬山算法對ART2wNF網(wǎng)絡(luò)的警戒值參數(shù)進(jìn)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(fēng)力發(fā)電機(jī)組的偏航系統(tǒng),仿真驗證了文章提出的結(jié)合貝葉斯推理與ART2wNF網(wǎng)絡(luò)的風(fēng)力發(fā)電機(jī)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(dāng)風(fēng)向在小的變化范圍內(nèi)(比如正負(fù)15°)出現(xiàn)集中風(fēng)能的偏航問題,還能有效地避免偏航電機(jī)的頻繁轉(zhuǎn)動,對提高風(fēng)能的利用率和風(fēng)力發(fā)電機(jī)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(xué)位授予單位】：長沙理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)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(xué)位授予單位】：長沙理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM315
【參考文獻(xiàn)】
相關(guān)期刊論文 前2條
1 喬川;李斯陽;;1.5MW雙饋風(fēng)力發(fā)電機(jī)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(fēng)力發(fā)電系統(tǒng)的集成控制策略[J];通信電源技術(shù);2010年04期
，本文編號：2318283
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