考虑人为因素的基于隐马尔科夫的设备强迫停运率模型

为研究人为因素对电力系统可靠性的影响,提出了一种考虑人为因素的基于隐马尔科夫(HMM)的设备强迫停运率模型及其人为因素判别模型。通过分析人为因素与设备修复的关系,将人因场景分为三种情况,对传统的设备修复模型进行优化。进而根据HMM模型的特点,建立HMM设备强迫停运模型。运用前向算法,建立人为因素判别模型。通过实际算例验证了所提出的强迫停运率模型的适用性和人因判别模型的正确性。判别设备强迫停运情况下的人为因素,有针对性的采取措施,减少设备强迫停运时间,提高电力系统可靠性。

Equipment forced outage rate model based on HMM considering human factors

To study the influence of human factors on power system reliability, this paper proposes a forced outage rate model based on Hidden Markov Model (HMM) which considers human factors and its human factor discriminant model. By analyzing the relationship between human factors and equipment repair, it divides the human factors scene into three types and optimizes the traditional equipment repair model. According to features of HMM model, the forced outage rate of HMM equipment is established. By using forward algorithm, it establishes human factors discriminant model. Practical examples validate the applicability of the forced outage rate model proposed in this paper and the correctness of human factors discriminant model. Human factors of the forced outage are distinguished, and targeted measures are taken to reduce equipment forced outage time and improve power system reliability. This work is supported by Special Science Foundation of Shaanxi Educational Committee (No. 16JK2174) and Science and Research Foundation of Xi''an Aeronautical University (No. 2017KY1222).