An Artificial Intelligence Framework for On-Line Transient Stability Assessment of Power Systems
An Artificial Intelligence Framework for On-Line Transient Stability Assessment of Power Systems
复制标题
电力系统在线暂态稳定评估的人工智能框架
DOI：
10.1109/59.193853
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发表时间：
1989
期刊：
影响因子：
--
通讯作者：
M. Ribbens
中科院分区：
文献类型：
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作者：
L. Wehenkel;T. Cutsem;M. Ribbens
电力系统的暂态稳定评估（TSA）有两个目标：一是评估系统承受重大突发事故的能力，二是在需要时提出补救措施，即提高这种能力的方法。第一个目标是分析方面的问题，第二个目标是控制方面的问题。目前，在线暂态稳定评估仍然是一个完全未解决的问题。实际上，现有的两大类方法（时域方法和直接方法）都无法满足分析方面的在线要求，也完全不适合处理控制方面的问题。我们引入的方法旨在通过利用预先离线构建的决策规则来解决上述在线问题。为此，开发了一种归纳推理方法，能够以二叉树的形式提供决策规则，表达电力系统的静态故障前运行条件与其承受假定扰动的鲁棒性之间的关系。本文专注于后一个问题，它是最困难的任务，也是整个方法的核心。所提出的归纳推理（II）方法属于一个特定的基于实例的机器学习家族。它源自昆兰[1]的ID3算法，并针对我们的问题进行了调整，其中实例由数值（潮流和稳定性）程序[2,3]提供。根据该方法，决策树（DT）是基于预先分析的学习集（LS）构建的，学习集由状态或运行点（OPs）组成。
Transient stability assessment (TSA) of a power system pursues a twofold objective: first to appraise the system's capability to withstand major contingencies, and second to suggest remedial actions, i.e. means to enhance this capability, whenever needed. The first objective is the concern of analysis, the second is a matter of control. For the time being, the on-line TSA is still a totally open question. Indeed, none of the existing two broad classes of methods (the time domain and the direct methods) are able to meet the on-line requirements of the analysis aspects, nor are they in the least appropriate to tackle control aspects. The methodology we are introducing aims at solving the above stated on-line problem by making use of decision rules, preconstructed off-line. To this end, an inductive inference method is developed, able to provide decision rules in the form of binary trees expressing relationships between static, pre-fault operating conditions of a power system and its robustness to withstand assumed disturbances. This paper concentrates on this latter problem, which is the most difficult task, and also the kernel of the overall methodology. The proposed inductive inference (II) method pertains to a particular family of Machine Learning from examples. It derives from ID3 by Quinlan [1], tailored to our problem, where the examples are provided by numeric (load flow and stability) programs [2, 3]. According to the method, a decision tree (DT) is built on the basis of a preanalyzed learning set (LS), composed of states or operating points (OPs).