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首页> 外文会议>International Conference on Power System Technology >Fast Amplitude Determination of Switching Overvoltage in Black-Start Plans Based on Gas Turbine Distributed Energy Supply System
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Fast Amplitude Determination of Switching Overvoltage in Black-Start Plans Based on Gas Turbine Distributed Energy Supply System

机译：基于燃气轮机分布式能源供应系统的黑启动计划中开关过电压的快速幅度确定

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Gas turbine distributed energy supply system (DESS) is a kind of important black-start unit in the future power system. This paper proposes a method of using Support Vector Machine (SVM) model for fast amplitude determination of transmission line switching overvoltage in the black-start plans based on Gas turbine distributed energy supply system. Black-start is the last line of defense for ensuring the reliability of power system. Hence black-start plays an important role both in the process of system recovery to ensure system security. During the process of making black-start plans of power system, it is necessary to verify the rationality of some technical issues by repeated modeling and simulation of different black-start plans, thus costing a lot of manpower and time. In recent years, distributed integrated energy supply system is greatly supported by government because of high efficiency and less pollution. Especially, gas turbine integrated energy supply system has excellent self-start and flexible adjustment ability, which can be considered as suitable black start unit. In this paper, firstly, the black-start scenarios are classified by the function and the type of the black-start units. Secondly, transmission line switching overvoltage involved in the process of black-start are modeled through PSCAD/EMTDC simulation software and analyzed by a large number of simulations. Thirdly, a support vector machine (SVM) model is established for fast amplitude determination of overvoltage in a black-start scenario. In this model, the selection of characteristic inputs in SVM method is analyzed in detail under the influence of important technical problems and the features of Gas turbine distributed energy supply system, and then the characteristic inputs are selected by orthogonal decomposition method. In the study case, artificial neural network (ANN) and support vector machine method are used for comparison, 200 samples are used in training set and more than 1400 samples are used in testing set, the error analysis shows that the support vector machine method is more effective than the artificial neural network method in the case of small training sample size. At last, an actual example analysis which considered the Guangzhou Higher Education Mega Center distributed energy station as black-start unit shows that the fast amplitude determination of switching overvoltage model can effectively reduce manpower and time.
机译：燃气轮机分布式能源供应系统（DESS）是未来电力系统中的一种重要的黑启动单位。提出了一种基于支持向量机（SVM）模型的黑启动计划快速确定输电线路开关过电压的方法。黑启动是确保电力系统可靠性的最后一道防线。因此，黑启动在确保系统安全的系统恢复过程中都起着重要作用。在制定电力系统黑启动计划的过程中，有必要通过对不同的黑启动计划进行重复建模和仿真来验证一些技术问题的合理性，从而花费大量的人力和时间。近年来，由于效率高，污染少，分布式综合能源供应系统得到了政府的大力支持。特别地，燃气轮机集成能源供应系统具有出色的自启动和灵活的调节能力，可以被认为是合适的黑启动单元。本文首先根据黑启动单元的功能和类型对黑启动场景进行了分类。其次，通过PSCAD / EMTDC仿真软件对黑启动过程中涉及的传输线开关过电压进行建模，并通过大量仿真进行分析。第三，建立支持向量机（SVM）模型，用于在黑启动情况下快速确定过电压的幅度。该模型在重要技术问题和燃气轮机分布式能源供应系统特点的影响下，对支持向量机方法中特征输入的选择进行了详细分析，然后采用正交分解法进行特征输入的选择。在研究案例中，使用人工神经网络（ANN）和支持向量机方法进行比较，训练集中使用200个样本，测试集中使用1400多个样本，误差分析表明，支持向量机方法为在训练样本量较小的情况下，此方法比人工神经网络方法更有效。最后，以广州高等教育超级中心分布式能源站为黑启动单元为例进行的实例分析表明，快速确定开关过电压模型的幅值可以有效地减少人力和时间。

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