System of monitoring parameters of distribution grid with corrective control based on neural network

A. Manin, D.B. Vyner
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Abstract

Background. As a rule, the control of compensating devices is carried out in the automatic control system with sensors of network parameters and control system included in a specific node of the electrical network. However, the general state of the electrical network in terms of reactive power flows is not considered. At present, static VAR compensators are mostly spread. They are designed on the principle of an indirect compensation system, which has several disadvantages. In this regard, to optimize reactive power flows and maintain the specified voltage values in the network nodes with an abruptly variable nature of reactive power consumption, it is necessary to stabilize the required network parameters and minimize the loss of electrical energy due to the flow of reactive power. Materials and methods. To improve the energy efficiency of corrective devices, it is proposed to use static VAR compensators based on magnetic valve elements. To generate control actions, an artificial neural network (ANN) module is introduced into the monitoring to predict the capacities of consumers. Such a neural network is based on an electrical network model described by the combined matrix method. The main processor generates control signals for corrective devices. Results. The authors have proposed to generate control signals for corrective devices by processing information received from remote voltage sensors and current sensors of the distribution grid. The proposed system for monitoring the distribution grid makes it possible to stabilize the required parameters of the network for consumers, to minimize the loss of electrical energy due to the flow of reactive power. Conclusions. The block of neural networks minimizes the emergency situations and accidents. The use of the static VAR compensators based on magnetic valve elements will additionally improve the energy efficiency of the distribution network monitoring system. The use of matrix analysis of network parameters in the distribution network of monitoring system to generate control signals for corrective devices allows optimizing networks in such a way as to minimize reactive power losses to select and install reactive power compensation devices and control them. The use of SVC based on magnetic valve elements as a corrective device improves the efficiency of reactive power compensation in networks with an abruptly variable nature of electrical energy consumption.
基于神经网络校正控制的配电网参数监测系统
背景。补偿装置的控制通常在自动控制系统中进行,网络参数传感器和控制系统包含在电网的特定节点中。然而,从无功潮流的角度来看,电网的一般状态没有被考虑。目前,静态无功补偿器被广泛应用。它们是根据间接补偿制度的原则设计的,这种制度有几个缺点。因此,为了优化无功潮流,并在无功消费突然变化的网络节点上保持规定的电压值,需要稳定所需的网络参数,使无功潮流带来的电能损失最小化。材料和方法。为了提高校正装置的能量效率,提出了基于电磁阀元件的静态无功补偿器。为了产生控制动作,在监控中引入人工神经网络模块来预测用户的容量。这种神经网络基于用组合矩阵法描述的电网络模型。主处理器为校正装置产生控制信号。结果。作者提出了通过处理从配电网的远程电压传感器和电流传感器接收到的信息来产生纠偏装置的控制信号。所提出的配电网监控系统可以为用户稳定所需的电网参数,从而最大限度地减少因无功功率流动而造成的电能损失。结论。神经网络块最小化紧急情况和事故。采用基于电磁阀元件的静态无功补偿器,将进一步提高配电网监控系统的能效。利用监测系统配电网中网络参数的矩阵分析,生成纠偏装置的控制信号,使电网以无功损耗最小的方式进行优化,选择和安装无功补偿装置并对其进行控制。采用基于电磁阀元件的SVC作为纠偏装置,提高了电力消耗突然变化的电网中无功补偿的效率。
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