Application of Ensemble Learning with Mean Shift Clustering for Output Profile Classification and Anomaly Detection in Energy Production of Grid-Tied Photovoltaic System
Application of Ensemble Learning with Mean Shift Clustering for Output Profile Classification and Anomaly Detection in Energy Production of Grid-Tied Photovoltaic System
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集成学习与均值漂移聚类在并网光伏系统发电中的输出轮廓分类和异常检测中的应用
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发表时间：
2020
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通讯作者：
R. R. Vicerra
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作者：
Justin D. de Guia;Ronnie S. Concepcion;Hilario A. Calinao;Sandy C. Lauguico;E. Dadios;R. R. Vicerra
光伏能量管理系统中的故障检测与监控系统是实现光伏能量管理系统最佳性能的重要手段。有效的诊断系统涉及在给定天气条件下正确分析PV阵列的电参数。在这项研究中，均值漂移聚类应用于预分类和异常检测的时间序列数据的电气参数从并网逆变器，太阳辐照度。分类和异常检测是基于集成学习，其基本学习器是基于多层感知器。叠加集成用于能量生产剖面的分类，而装袋集成用于检测时间序列数据中的异常趋势。与单个分类器的准确率分别为85.25%、84.14%和63.4%相比，堆叠集成的准确率最高为94%。与单个自编码器相比，装袋集成自编码器在模型重建期间具有最低的均方误差。它在将异常点与正常数据点分类方面具有良好的性能，具有0.795的AUC值和0.71的F1得分，假设超参数为0.5。总体而言，集成学习器提高了分类和检测任务的性能。
Fault detection and monitoring system in photovoltaic (PV) energy management system is important in achieving its optimal performance. An effective diagnostic system involves correct analysis of electrical parameters of a PV array on a given weather condition. In the study, mean-shift clustering was applied for pre-classification and anomaly detection of time-series data of electrical parameters from grid-tied inverter, and solar-irradiance. Classification and anomaly detection applied is based in ensemble learning, where its base learners are based from multilayer perceptron. A stacking ensemble is used in classification of energy production profile while bagging ensemble is used detecting anomalous trend in time-series data. A stacking ensemble got a highest accuracy value of 94% compared to single classifiers which have accuracy value of 85.25%, 84.14%, and 63.4%, respectively. The bagging ensemble autoencoders have the lowest mean squared error during model reconstruction compared to single autoencoder. It has a fair performance in classifying anomaly points from normal datapoints, having an AUC value of 0.795 and F1-score of 0.71, given that the hyperparameter is 0.5. Overall, ensemble learners improve the performance in classification and detection tasks.