本文選題：風(fēng)電機(jī)組 + SCADA數(shù)據(jù)　； 參考：《華北電力大學(xué)(北京)》2017年碩士論文

【摘要】：風(fēng)力發(fā)電在眾多新能源領(lǐng)域中以其技術(shù)和經(jīng)濟(jì)優(yōu)勢(shì)日益受到重視,然而高昂的運(yùn)行和維護(hù)成本嚴(yán)重制約著風(fēng)力發(fā)電行業(yè)的發(fā)展。風(fēng)電機(jī)組的維修方式已經(jīng)從最初的故障維修和預(yù)防性維修,發(fā)展到如今的預(yù)知性維修。準(zhǔn)確可靠的設(shè)備健康狀態(tài)評(píng)估是實(shí)施預(yù)知性維修方法的前提和基礎(chǔ)。針對(duì)風(fēng)電機(jī)組開展健康狀態(tài)評(píng)估,掌握風(fēng)機(jī)健康狀態(tài)衰退趨勢(shì),合理調(diào)整運(yùn)行并開展有針對(duì)性的維修決策,可以提高風(fēng)電機(jī)組運(yùn)行的安全性和可靠性。本文圍繞風(fēng)電機(jī)組運(yùn)行性能和健康狀態(tài)問題,開展了如下研究工作:1)輸出功率是風(fēng)電機(jī)組最具代表性的性能指標(biāo)之一,風(fēng)功率曲線也是機(jī)組發(fā)電能力最直觀的表述。在對(duì)風(fēng)電機(jī)組SCADA系統(tǒng)風(fēng)速、功率數(shù)據(jù)清洗和篩選的基礎(chǔ)上,通過描繪出機(jī)組正常工作狀態(tài)下的風(fēng)功率散點(diǎn)圖,采用比恩法建立風(fēng)電機(jī)組實(shí)際風(fēng)功率曲線;統(tǒng)計(jì)分析不同風(fēng)速區(qū)間的輸出功率,利用逆向云發(fā)生器建立不同風(fēng)速下的輸出功率云模型,得到不同機(jī)組的整體功率云;通過對(duì)比分析功率云的特征值,實(shí)現(xiàn)輸出功率大小、波動(dòng)范圍和離散程度的量化分析;同時(shí)計(jì)算風(fēng)速、功率相關(guān)系數(shù)反映和評(píng)價(jià)機(jī)組響應(yīng)的靈敏度。云模型的應(yīng)用,把機(jī)組狀態(tài)從定性評(píng)價(jià)拓展到定量評(píng)價(jià),從宏觀綜合評(píng)價(jià)深入到風(fēng)速區(qū)間段精準(zhǔn)評(píng)價(jià),提高風(fēng)電機(jī)組性能分析的準(zhǔn)確性和全面性。2)風(fēng)電機(jī)組運(yùn)行環(huán)境惡劣多變,故障機(jī)理復(fù)雜,健康狀態(tài)評(píng)估存在明顯的模糊性和隨機(jī)性。為解決當(dāng)前模糊評(píng)價(jià)隸屬函數(shù)的確定具有主觀性且未考慮隨機(jī)性的問題,提出了一種基于組合賦權(quán)和云模型的風(fēng)電機(jī)組健康狀態(tài)評(píng)估方法。在構(gòu)建健康狀態(tài)評(píng)估指標(biāo)體系及等級(jí)劃分基礎(chǔ)上,通過組合賦權(quán)法確定各指標(biāo)的權(quán)重,應(yīng)用云模型確定定量指標(biāo)對(duì)各狀態(tài)等級(jí)的隸屬度,采用優(yōu)化的模糊綜合評(píng)判方法評(píng)估風(fēng)電機(jī)組健康狀態(tài)。最后,通過實(shí)例分析驗(yàn)證了本文所提方法的有效性、可行性和準(zhǔn)確性。
[Abstract]:Wind power generation has been paid more and more attention in many new energy fields because of its technical and economic advantages. However, the development of wind power industry is seriously restricted by the high operating and maintenance costs. The maintenance mode of wind turbine has developed from original fault maintenance and preventive maintenance to predictive maintenance. Accurate and reliable assessment of equipment health condition is the premise and foundation of implementing predictive maintenance method. The safety and reliability of wind turbine operation can be improved by evaluating the health status of wind turbine, mastering the decline trend of fan health state, adjusting operation reasonably and carrying out pertinent maintenance decision. This paper focuses on the operational performance and health state of wind turbine units. The following research work is carried out: 1) output power is one of the most representative performance indexes of wind turbines, and the wind power curve is also the most intuitive expression of generating capacity of wind turbines. On the basis of cleaning and screening wind speed and power data of SCADA system of wind turbine, the wind power divergence plot under normal working condition of wind turbine is described, and the actual wind power curve of wind turbine unit is established by using Bian method. The output power of different wind speed range is analyzed statistically, the output power cloud model under different wind speed is established by using reverse cloud generator, the whole power cloud of different units is obtained, and the output power is realized by comparing and analyzing the eigenvalue of power cloud. At the same time, the wind speed and power correlation coefficient are calculated to reflect and evaluate the sensitivity of the unit response. The application of cloud model extends the unit status from qualitative evaluation to quantitative evaluation, from macroscopic comprehensive evaluation to accurate evaluation of wind speed interval, improves the accuracy and comprehensiveness of wind turbine performance analysis, and improves the poor operating environment of wind turbine. The fault mechanism is complex, and the assessment of health status has obvious fuzziness and randomness. In order to solve the problem that the determination of membership function of fuzzy evaluation is subjective and stochastic, a new method of wind turbine health assessment based on combination weight and cloud model is proposed. On the basis of constructing index system of health state evaluation and grading, the weight of each index is determined by combination weighting method, and the membership degree of quantitative index to each state grade is determined by cloud model. The optimal fuzzy comprehensive evaluation method is used to evaluate the health status of wind turbine units. Finally, the effectiveness, feasibility and accuracy of the proposed method are verified by an example.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM315

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