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Algoritmos de Correção de Outliers para Curvas de Potência utilizando inteligência artificial

机译:基于人工智能的功率曲线的异常值校正算法

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One of the main problems of the data acquired by power utilities is the presence of outliers affecting the database measurements in the electrical system, damaging the analyzes of the distribution scenario. This work proposes a new module to complement the measurements made by the utilities. Two algorithms for outliers correction were developed using artificial intelligence techniques: fuzzy logic and artificial neural networks. The first technique, with a fuzzy approach, develops an inference system based on the variations of previous measurements to determine future variation. In the second algorithm developed using NN, the outliers were filled using a prediction model using 10 previous samples. To demonstrate the applicability of the developed methods, a case study is performed on a substation in a city of Paraíba.
机译:电力公司获取的数据的主要问题之一是离群值的存在会影响电气系统中的数据库测量,从而破坏配电方案的分析。这项工作提出了一个新的模块来补充公用事业公司进行的测量。使用人工智能技术开发了两种用于离群值校正的算法:模糊逻辑和人工神经网络。第一种技术采用模糊方法,基于先前测量值的变化来开发推理系统,以确定未来的变化。在使用NN开发的第二种算法中,使用预测模型填充了离群值,这些预测模型使用了10个先前的样本。为了证明所开发方法的适用性,在帕拉伊巴市的变电站中进行了案例研究。

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