基于狀態(tài)轉(zhuǎn)移抽樣法的包含風(fēng)電場(chǎng)的發(fā)電系統(tǒng)Well-being分析
發(fā)布時(shí)間:2019-01-18 08:55
【摘要】:基于狀態(tài)轉(zhuǎn)移抽樣法對(duì)包含風(fēng)電場(chǎng)的發(fā)電系統(tǒng)進(jìn)行Well-being分析,提出了一種增強(qiáng)N-1準(zhǔn)則,并基于此建立新的Well-being模型。在此Well-being模型基礎(chǔ)上,以RST79系統(tǒng)為例,考慮風(fēng)機(jī)故障模型以及尾流效應(yīng),分析風(fēng)電場(chǎng)的接入容量以及不同風(fēng)速模型對(duì)發(fā)電系統(tǒng)可靠性的影響。研究表明,增強(qiáng)N-1準(zhǔn)則下的Well-being模型相對(duì)于傳統(tǒng)N-1準(zhǔn)則下的Well-being模型對(duì)系統(tǒng)健康狀態(tài)標(biāo)準(zhǔn)要求更高,因此處于健康狀態(tài)概率會(huì)有所下降。風(fēng)電場(chǎng)的接入能有效改善系統(tǒng)的可靠性,但是風(fēng)電場(chǎng)規(guī)模到達(dá)一定值后,其改善效果趨于飽和。不同風(fēng)速模型下的Well-being概率指標(biāo)相近,但是失負(fù)荷頻率存在差別,ARMA模型最接近實(shí)際模型,而Weibull風(fēng)速模型的失負(fù)荷頻率與實(shí)際風(fēng)速模型相差較大。
[Abstract]:Based on the Well-being analysis of the power generation system including wind farm, an enhanced N-1 criterion is proposed and a new Well-being model is established based on the state transition sampling method. On the basis of this Well-being model, taking RST79 system as an example, considering fan fault model and wake effect, the influence of wind farm access capacity and different wind speed models on the reliability of power generation system is analyzed. The results show that the Well-being model under the enhanced N-1 criterion requires a higher standard of the system health state than the Well-being model under the traditional N-1 criterion, so the probability of being in the health state will decrease. The access of wind farm can effectively improve the reliability of the system, but when the scale of wind farm reaches a certain value, the improvement effect tends to saturation. The Well-being probability index of different wind speed models is similar, but the frequency of load loss is different. The ARMA model is the closest to the actual model, while the Weibull wind speed model has a big difference from the actual wind speed model.
【作者單位】: 南京工程學(xué)院電力工程學(xué)院;
【基金】:江蘇省高校自然科學(xué)研究項(xiàng)目(14KJD470004) 江蘇省配電網(wǎng)智能技術(shù)與裝備協(xié)同創(chuàng)新中心開放基金項(xiàng)目資助(XTCX201612) 江蘇省大學(xué)生實(shí)踐創(chuàng)新訓(xùn)練計(jì)劃項(xiàng)目(201611276025Y) 南京工程學(xué)院大學(xué)生科技創(chuàng)新基金項(xiàng)目(TB20160408)
【分類號(hào)】:TM614
本文編號(hào):2410531
[Abstract]:Based on the Well-being analysis of the power generation system including wind farm, an enhanced N-1 criterion is proposed and a new Well-being model is established based on the state transition sampling method. On the basis of this Well-being model, taking RST79 system as an example, considering fan fault model and wake effect, the influence of wind farm access capacity and different wind speed models on the reliability of power generation system is analyzed. The results show that the Well-being model under the enhanced N-1 criterion requires a higher standard of the system health state than the Well-being model under the traditional N-1 criterion, so the probability of being in the health state will decrease. The access of wind farm can effectively improve the reliability of the system, but when the scale of wind farm reaches a certain value, the improvement effect tends to saturation. The Well-being probability index of different wind speed models is similar, but the frequency of load loss is different. The ARMA model is the closest to the actual model, while the Weibull wind speed model has a big difference from the actual wind speed model.
【作者單位】: 南京工程學(xué)院電力工程學(xué)院;
【基金】:江蘇省高校自然科學(xué)研究項(xiàng)目(14KJD470004) 江蘇省配電網(wǎng)智能技術(shù)與裝備協(xié)同創(chuàng)新中心開放基金項(xiàng)目資助(XTCX201612) 江蘇省大學(xué)生實(shí)踐創(chuàng)新訓(xùn)練計(jì)劃項(xiàng)目(201611276025Y) 南京工程學(xué)院大學(xué)生科技創(chuàng)新基金項(xiàng)目(TB20160408)
【分類號(hào)】:TM614
【相似文獻(xiàn)】
相關(guān)期刊論文 前1條
1 鄧樸;皮顯松;馬世英;王青;張銳鋒;;高頻事故下潮流轉(zhuǎn)移性質(zhì)和調(diào)速器狀態(tài)轉(zhuǎn)移控制[J];電力系統(tǒng)自動(dòng)化;2014年11期
,本文編號(hào):2410531
本文鏈接:http://sikaile.net/kejilunwen/dianlidianqilunwen/2410531.html
最近更新
教材專著