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首页> 外文期刊>Electric power systems research >Signal quality based power output prediction of a real distribution transformer station using M5P model tree
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Signal quality based power output prediction of a real distribution transformer station using M5P model tree

机译：使用M5P模型树的实际配电变压器站基于信号质量的功率输出预测

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摘要

Forecasting the output power of transformer stations is a prominent analysis of power systems, which is key aspect of load balancing. Moreover, power forecasting may lead to maximized energy efficiency in the electrical grid. In this study, a fully operational real distribution transformer station which is resident in Istanbul is investigated. Transformer's electrical characteristics which are strictly related with signal quality, are recorded every fifteen minute for four months, are employed to predict power output of the transformer. As a signal quality concern, a local electrical distribution company must supply stable and distortion free signal waveforms to the consumers where frequency-voltage stability, reduced THD values are satisfied. In order to estimate power output of the transformer, these parameters are subjected to well-known machine learning (ML) methods and consequently most effective methods are selected. In this paper M5P tree algorithm is promoted for total active power estimation of the analyzed transformer station. The M5P results have been compared with the recorded results using Root Mean Square Error (RMSE) and Correlation Coefficient (R) as 78.55 and 0.986, respectively. According to the results, the M5P model is performed quite efficiently in power estimation for the output power of distribution transformer station.
机译：预测变电站的输出功率是电力系统的重要分析，这是负载平衡的关键方面。而且，功率预测可以导致电网中的能量效率最大化。在这项研究中，研究了一个位于伊斯坦布尔的完全运作的实际配电变电站。与信号质量严格相关的变压器电气特性，每十五分钟记录一次，持续四个月，用于预测变压器的功率输出。作为信号质量问题，当地的配电公司必须向满足频率-电压稳定性和降低的THD值的用户提供稳定且无失真的信号波形。为了估计变压器的功率输出，这些参数要经过众所周知的机器学习（ML）方法，因此选择了最有效的方法。本文提出了M5P树算法，用于分析变电站的总有功功率估算。使用均方根误差（RMSE）和相关系数（R）分别将M5P结果与记录结果进行了比较，分别为78.55和0.986。根据结果​​，M5P模型在配电变压器站输出功率的功率估算中非常有效地执行。

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