基于交叉熵理论的电力系统短期可靠性评估

发布时间：2018-06-07 07:42

本文选题：交叉熵 + 重采样　；参考：《浙江大学》2014年博士论文

【摘要】：智能电网的全面推进给电力系统的发展注入了生机与活力。对新型系统开展深入研究的同时,各种不确定因素相伴而生。系统可靠性评估对于预测、防控系统遭遇各种随机因素时发生大面积停电事故具有重要的意义。在复杂电力系统短期可靠性评估问题中,由于待考察的前置期通常较短,危害性较大的停电事故发生概率较低,导致传统蒙特卡洛方法采样效率极其低下,阻碍了其工程应用的进程。本文立足于电力系统可靠性评估领域中得以广泛应用的蒙特卡洛仿真方法,以信息学领域衍生的交叉熵理论为基础,分别针对非序贯蒙特卡洛和序贯蒙特卡洛两种典型方法进行了算法开发,提出了三种基于交叉熵理论的电力系统短期可靠性重要度采样评估方法,分别是： (1)考虑多状态采样问题的离散多状态电力系统非序贯重采样评估方法。 (2)考虑时齐马尔科夫过程的三段式序贯重采样短期可靠性评估方法。 (3)考虑非时齐马尔科夫过程的自适应序贯重采样短期可靠性评估方法。这些方法的共同之处是本质上均属于众多方差减小方法之一的重采样方法,并利用预采样样本进行畸变采样概率的迭代寻优。不同之处在于,根据所考虑的问题和仿真机理的不同,畸变采样概率的迭代寻优遵循不同的优化模型。通过在IEEE-RTS79和Roy Billinton Reliability Test System改造系统上进行测试,分别论证了所提算法的精度并分析了相较于传统蒙特卡洛方法的效率优势。为进一步阐述算法的应用价值,以考虑多状态采样问题的离散多状态非序贯重采样评估方法为工具,在本文所提出的改进well-being框架下实现了对考虑风电的系统旋转备用响应能力进行了评估。传统well-being框架体系在电力系统旋转备用充裕性评估问题中应用广泛,对波动性、间歇性较强的风电系统尤为适用,本文提出一种适用于旋转备用响应能力评估的新well-being框架。该框架体系考虑了风电间歇性、随机性的特点以及传统well-being框架体系的不足,提出用四状态体系代替三状态体系,通过在IEEE RTS-79改造系统的算例分析揭示了新well-being框架体系的价值。
[Abstract]:The overall advance of smart grid has injected vitality into the development of power system. At the same time, all kinds of uncertain factors come along with the further study of the new system. The evaluation of system reliability is of great significance for forecasting large area blackouts when the control system encounters various random factors. In the short-term reliability evaluation of complex power systems, the sampling efficiency of the traditional Monte Carlo method is extremely low due to the short pre-period to be investigated and the low probability of power outages, which are more harmful than others. It hinders the progress of its engineering application. Based on the Monte Carlo simulation method, which is widely used in the field of power system reliability evaluation, this paper is based on the cross-entropy theory derived from the field of informatics. In this paper, two typical methods of non-sequential Monte Carlo and sequential Monte Carlo are developed, and three methods based on cross-entropy theory are proposed to evaluate the short-term reliability importance of power system. 1) Non-sequential resampling evaluation method for discrete multi-state power systems considering multi-state sampling. 2) A three-stage sequential resampling method for short term reliability evaluation considering time-homogeneous Markov processes. An adaptive sequential resampling method for short term reliability evaluation considering non-time homogeneous Markov processes is proposed. The common point of these methods is that they all belong to the resampling method which is one of the many variance reduction methods in essence and the presampled samples are used for iterative optimization of the distorted sampling probability. The difference is that the iterative optimization of distortion sampling probability follows different optimization models according to the problem considered and the different simulation mechanism. By testing on IEEE-RTS79 and Roy Billinton Reliability Test System, the accuracy of the proposed algorithm is demonstrated and the efficiency advantages compared with the traditional Monte Carlo method are analyzed. In order to further expound the application value of the algorithm, the discrete multi-state non-sequential resampling evaluation method considering the multi-state sampling problem is used as a tool. Based on the improved well-being framework proposed in this paper, the capability of rotating standby response of wind power system is evaluated. The traditional well-being framework is widely used in the evaluation of power system rotation reserve adequacy, especially for wind power systems with strong volatility and intermittence. This paper presents a new well-being framework for the evaluation of rotational standby response ability. Considering the intermittent and stochastic characteristics of wind power and the shortcomings of the traditional well-being frame system, the four-state system is proposed to replace the three-state system, and the value of the new well-being frame system is revealed by the example analysis in the IEEE RTS-79 reconstruction system.
【学位授予单位】：浙江大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TM732

【参考文献】