基于状态转移抽样法的包含风电场的发电系统Well-being分析
发布时间：2019-01-18 08:55
【摘要】：基于状态转移抽样法对包含风电场的发电系统进行Well-being分析,提出了一种增强N-1准则,并基于此建立新的Well-being模型。在此Well-being模型基础上,以RST79系统为例,考虑风机故障模型以及尾流效应,分析风电场的接入容量以及不同风速模型对发电系统可靠性的影响。研究表明,增强N-1准则下的Well-being模型相对于传统N-1准则下的Well-being模型对系统健康状态标准要求更高,因此处于健康状态概率会有所下降。风电场的接入能有效改善系统的可靠性,但是风电场规模到达一定值后,其改善效果趋于饱和。不同风速模型下的Well-being概率指标相近,但是失负荷频率存在差别,ARMA模型最接近实际模型,而Weibull风速模型的失负荷频率与实际风速模型相差较大。
[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.
【作者单位】： 南京工程学院电力工程学院;
【基金】：江苏省高校自然科学研究项目(14KJD470004) 江苏省配电网智能技术与装备协同创新中心开放基金项目资助(XTCX201612) 江苏省大学生实践创新训练计划项目(201611276025Y) 南京工程学院大学生科技创新基金项目(TB20160408)
【分类号】：TM614
本文编号：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.
【作者单位】： 南京工程学院电力工程学院;
【基金】：江苏省高校自然科学研究项目(14KJD470004) 江苏省配电网智能技术与装备协同创新中心开放基金项目资助(XTCX201612) 江苏省大学生实践创新训练计划项目(201611276025Y) 南京工程学院大学生科技创新基金项目(TB20160408)
【分类号】：TM614
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，本文编号：2410531
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