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引用次数: 5
摘要
本文采用基于能量函数的势能边界面法(PEBS)计算了动态ATC。为了在放松管制的环境下有效利用电力系统,快速准确地评估最大可用输电能力(ATC)是非常重要的。用时域仿真方法进行暂态稳定评估是一个耗时的过程。讨论了一种新的动态偶然性筛选方法,选择临界偶然性进行动态空中交通管制的计算,以减少计算时间。有关空中交通管制的资料将持续实时更新,并透过开放存取同步资讯系统(OASIS)提供予市场参与者。在ATC的基础上,独立系统操作者(ISO)对交易进行评估。因此,必须快速准确地计算ATC。本文使用两种不同的神经网络,即i)反向传播算法(BPA)和ii)径向基函数(RBF)神经网络,对实时应用的ATC进行了计算。在WSCC 3机9总线系统和New England 10机39总线系统上对这两种方法进行了测试,并与传统的基于能量函数的PEBS方法进行了比较。
Dynamic Available Transfer Capability (DATC) Computation using Intelligent Techniques
In this paper dynamic ATC has been calculated using energy function based potential energy boundary surface (PEBS) method. For the effective use of power system under the deregulated environment, it is important to make a fast and accurate evaluation of the maximum available transfer capability (ATC). Transient stability assessment by time domain simulation method is a time consuming process. A novel dynamic contingency screening method is discussed and critical contingencies are selected for the computation of dynamic ATC in order to reduce the computational time. The information about the ATC is to be continuously updated in real-time and made available to the market participants through open access same time information system (OASIS). On the basis of ATC, independent system operator (ISO) evaluates the transaction. Thus the ATC must be computed fast and accurately. In this paper ATC has been computed for real time applications using two different neural networks viz., i) back propagation algorithm (BPA) and ii) radial basis function (RBF) neural network. These two methods are tested on WSCC 3 Machine 9 bus system and New England 10 machine 39 bus system and results are compared with the conventional energy function based PEBS method.