在电力市场不断放开和新能源并网需求的持续刺激下,当代电力系统的结构正在发生快速变化.传统的基于单个元件可靠性参数推算面向系统或用户可靠性指标的方法,由于需要大量的历史统计和相对稳定的系统结构作为前提条件,难以有效实施和应用于评估结构快速发展的系统.提出一种基于BP神经网络的电力系统可靠性关联因素灵敏度计算方法.该方法通过定义指标和其驱动因素之间特定的"神经元链路",借助连续导数法则,推导了给定BP神经网络模型下的指标和单一驱动因素之间的解析关系表达式.在此基础上,创建了指标和其所有驱动因素之间考虑所有"神经元链路"的灵敏度计算方法.以用户年均停电小时数(TOH)指标为例,通过实际数据的仿真实验表明,该方法可有效地将其驱动因素区分为有利因素和不利因素,并能根据对TOH的灵敏度贡献,将同性质驱动因素的灵敏度重要程度进行量化排序.%Progressively incentivized by the deregulation of electricity market and injection requirement of renewable energy generations into power grids, configurations of contemporary power systems are under fast evolving. As a consequence, the traditional power system reliability evaluation methods, which basically require sufficient statistical samples from relatively stable system configuration, are difficult, if not impossible, to validate effective when applied to evaluating a fast evolving system. On the basis of a given back propagation neural artificial network, this paper proposes a methodology of computing sensitivity of a certain index with respect to an involved factor. The proposed method, by establishing a concept of "neuron link" a priori, derives the analytical expression of sensitivity of an index with respect to a driving factor, and then derives the sensitivity of any index with respect to any driving factor with full consideration of all the neuron links. Through case studies based on field records and taking the index of total customer outage hours (TOH) for instance, it is validated that the method is able to correctly classify any driving factor into either the beneficial or the detrimental, and is also able to quantitatively rank the indices falling into an identical category as per their sensitivity contributions to the TOH.
展开▼
