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Distribution power system state estimation based on Gaussian mixture model-Neural network

机译：基于高斯混合模型 - 神经网络的分配电力系统状态估计

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摘要

The main problem in the state estimation of distribution power system is that there are many nodes but few measuring points such that they are unobservable. With the construction and development of distribution network, most measuring devices of distribution power system have covered all nodes, but uploading their measured values to the power dispatching center in real time will take up a lot of communication resources, and once the data cannot be uploaded due to network congestion and other problems, the state estimation will be impossible to calculate. In this paper, the load Gaussian mixture model is established, and the load model under different scenarios is constructed. Obtain the load data from the smart meter, train the load model, and upload the model parameters to the power dispatching center where the neural network is trained with data such as node injection power generated by each node load model. Finally, the trained neural network is used to calculate the voltage and amplitude of each node. When some measure data is missing, the measure data generated by the compound model of the node stored in the power dispatching center is used as pseudo-measure for state estimation. The smart meter will update the training model regularly according to the change of node load, which helps to improve the robustness of the system. Compared with the traditional method of using power prediction as a pseudo-measurement, this method has the advantages of fast computing speed, high computing accuracy, small consumption of communication resources and strong robustness.
机译：分销电力系统的状态估计中的主要问题是有许多节点，但测量点很少，使得它们是不可观察的。随着分销网络的构建和开发，大多数分销电力系统的测量设备都覆盖了所有节点，但实时将其测量值上载到功率调度中心将占用大量通信资源，一旦数据无法上传由于网络拥塞和其他问题，国家估计是不可能计算的。在本文中，建立了负载高斯混合模型，构建了不同场景下的负载模型。从智能仪表中获取负载数据，培训负载模型，并将模型参数上传到电源调度中心，其中神经网络接受了由每个节点负载模型产生的节点注入功率等数据培训。最后，培训的神经网络用于计算每个节点的电压和幅度。当缺少某些测量数据时，由存储在电源调度中心中的节点的化合物模型生成的测量数据用作状态估计的伪测量。智能电表将根据节点负载的变化定期更新培训模型，这有助于提高系统的稳健性。与传统方法相比，使用功率预测作为伪测量，计算速度快，计算精度高，通信资源的消耗量小，鲁棒性强的优点。

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