摘要
对于利用提高输电线路容量技术得到的在线监测数据,采用Bayes统计推断方法,根据实时运行状态数据和未来一段时间的气候参数实时估计和预测故障概率,并实现风险评估。使用马尔可夫链蒙特卡洛方法产生同边缘随机分布,用天气模型对序列进行计算,获得当前或未来一段时间导线温度的一个任意大小的仿真样本,根据该样本的频度值给出导线温度的概率累计函数或者其大于某阈值的概率。最后验证了该方法的正确性和可行性,并重点说明了需改进的问题。
The capacity data can be obtained by monitoring the transmission line using dynamic line rating（DLR） technique. The probability of malfunction is predicted and the risk assessment is realized utilizing the method of hayes statistics based on the real-time running state data and the climate parameter of pending time. The Markov chain Monte Carlo（MCMC） method is used to make chance distribution of boundary homomorphism; then the sequence is calculated to get a random line temperature＇s sample of the present or pending time using the synoptic model. According to the frequency value of the sample, the probability function of line temperature or its probability greater than a certain threshold is provided. The feasibility and validity of this method are verified, and the improving direction is emphasized at last.
出处
《广东电力》
2010年第3期7-11,共5页
Guangdong Electric Power
关键词
输电线路
BAYES统计
导线温度
风险评估
transmission line
bayes statistics
temperature of transmission line
risk assessment
