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首页> 外文期刊>Electrical engineering in Japan >Identification of System Characteristics of a Power System with Time Series Data. Identification of Frequency Fluctuation Characteristics of a Small-Scale Isolated System
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Identification of System Characteristics of a Power System with Time Series Data. Identification of Frequency Fluctuation Characteristics of a Small-Scale Isolated System

机译:使用时间序列数据识别电力系统的系统特性。小型隔离系统的频率波动特性识别

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摘要

Understanding actual characteristics of a power system with recorded time series data is of great importance, for example, in improving the performance of the system. Although system identification is a well-known technique to achieve this goal, its applicability to a certain system should be examined for the particular case because its accuracy highly depends on the inherent characteristics of the system. While many papers have discussed application of a system identification technique to a power system, few papers have examined its applicability to the actual data of a power system. This paper presents a new system identification method to estimate characteristics of a power system while using output of intermittent generators or fluctuating loads as an external disturbance. The method employs cross spectra and coherence as a key factor in the identification; it estimates a transfer function of a power system, contribution of observed disturbance to total disturbance, and so on. The method is applied to time series data of two model systems: simulation results and measured data of an isolated power system with diesel generators. The study gives satisfactory results; implication on the accuracy of the method is discussed through the sample studies.
机译:例如,了解具有记录的时间序列数据的电力系统的实际特性在提高系统性能方面非常重要。尽管系统识别是实现此目标的众所周知的技术,但应针对特定情况检查其对特定系统的适用性,因为其准确性在很大程度上取决于系统的固有特性。尽管许多论文讨论了系统识别技术在电力系统中的应用,但很少有论文研究其在电力系统实际数据中的适用性。本文提出了一种新的系统识别方法,该方法可以在使用间歇性发电机的输出或波动负载作为外部干扰的情况下估算电力系统的特性。该方法将交叉光谱和相干性作为识别的关键因素。它估计电力系统的传递函数,观察到的扰动对总扰动的贡献等。该方法适用于两个模型系统的时间序列数据:仿真结果和柴油发电机隔离电力系统的实测数据。该研究结果令人满意。通过样本研究讨论了该方法的准确性。

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