Control Monitoring Of The Distribution Grid Of The Workshop Based On The Method Of Combined Matrices

A. Manin, D. B. Vayner
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Abstract

The existing control systems for compensating installations do not take into account the general state of the power grid in terms of reactive power flows. The article proposes the monitoring system that processes data from a current and voltage sensor in a distribution grid, based on the processed data control actions are formed on corrective devices. Today high-speed static VAR compensators (SVC), built on the principle of indirect compensation, are most widely used. However, such compensators have a number of disadvantages, therefore, to improve the energy efficiency of the monitoring system, it is advisable to use SVC based on magnetic valve elements. To predict reactive power in the distribution grid, the artificial neural networks (ANN) module is used in the monitoring structure. To train the neural network, using the method of combined matrices, a distribution grid model is formed.The proposed monitoring system will make it possible to stabilize the parameters of the grid for consumers, to reduce losses from reactive power flows. The neural network module included in the main processor will reduce the development of emergency situations. The use of SVC based on magnetic valve elements will increase the energy efficiency of the monitoring system in distribution grids with a sharply variable nature of electricity consumption.
基于组合矩阵法的车间配电网控制监控
现有的补偿装置控制系统在无功潮流方面没有考虑到电网的一般状态。本文提出了一种对配电网中电流、电压传感器的数据进行处理的监测系统,并根据处理后的数据对纠偏装置形成控制动作。目前,基于间接补偿原理的高速静态无功补偿器(SVC)应用最为广泛。然而,这种补偿器有许多缺点,因此,为了提高监测系统的能效,建议采用基于电磁阀元件的SVC。为了预测配电网的无功功率,在监测结构中采用了人工神经网络模块。为了训练神经网络,采用组合矩阵的方法,建立了配电网模型。提出的监测系统将有可能为消费者稳定电网的参数,以减少无功功率流的损失。主处理器中包含的神经网络模块将减少突发情况的发展。采用基于电磁阀元件的SVC将提高配电网监测系统的能源效率,使配电网的电力消耗具有急剧变化的性质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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