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基于教学学习优化算法的功率因数补偿及云数据记录器监测系统

Power Factor Compensation Using Teaching Learning Based Optimization and Monitoring System by Cloud Data Logger.

作者信息

Cano Ortega Antonio, Sánchez Sutil Francisco Jose, De la Casa Hernández Jesús

机构信息

Department of Electrical Engineering, University of Jaen, 23071 EPS Jaen, Spain.

出版信息

Sensors (Basel). 2019 May 10;19(9):2172. doi: 10.3390/s19092172.

DOI:10.3390/s19092172
PMID:31083377
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6539470/
Abstract

The main objective of this paper is to compensate power factor using teaching learning based optimization (TLBO), determine the capacitor bank optimization (CBO) algorithm, and monitor a system in real-time using cloud data logging (CDL). Implemented Power Factor Compensation and Monitoring System (PFCMS) calculates the optimal capacitor combination to improve power factor of the installation by measure of voltage, current, and active power. CBO algorithm determines the best solution of capacitor values to install, by applying TLBO in different phases of the algorithm. Electrical variables acquired by the sensors and the variables calculated are stored in CDL using Google Sheets (GS) to monitor and analyse the installation by means of a TLBO algorithm implemented in PFCMS, that optimizes the compensation power factor of installation and determining which capacitors are connected in real time. Moreover, the optimization of the power factor in facilities means economic and energy savings, as well as the improvement of the quality of the operation of the installation.

摘要

本文的主要目标是使用基于教学学习的优化算法（TLBO）来补偿功率因数，确定电容器组优化（CBO）算法，并使用云数据记录（CDL）对系统进行实时监测。所实现的功率因数补偿与监测系统（PFCMS）通过测量电压、电流和有功功率来计算最佳电容器组合，以提高装置的功率因数。CBO算法通过在算法的不同阶段应用TLBO来确定要安装的电容器值的最佳解决方案。传感器采集的电气变量和计算出的变量使用谷歌表格（GS）存储在CDL中，以便通过PFCMS中实现的TLBO算法对装置进行监测和分析，该算法可优化装置的补偿功率因数并实时确定连接哪些电容器。此外，设施中功率因数的优化意味着经济和能源的节省，以及装置运行质量的提高。

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