| 引用本文: | 张汪洋,樊艳芳,侯俊杰,等.基于集成深度神经网络的配电网分布式状态估计方法[J].电力系统保护与控制,2024,52(3):128-140. |
| ZHANG Wangyang,FAN Yanfang,HOU Junjie,et al.Distribution network distributed state estimation method based on an integrated deep neural network[J].Power System Protection and Control,2024,52(3):128-140 |
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| 摘要: |
| 随着大量分布式能源的接入，配电系统的运行与控制方式愈加复杂。针对配电网状态估计方法面临分布式电源波动数据辨识困难、估计精度低、鲁棒性与估计时效性差等问题，提出一种基于集成深度神经网络的配电网分布式状态估计方法。首先，利用量测数据相关性检验的数据辨识技术识别不良数据和新能源波动数据。在此基础上，利用时域卷积网络(temporal convolutional network, TCN)-双向长短期记忆网络(bidirectional long short term memory, BILSTM)对不良数据进行修正。然后，建立集成深度神经网络(deep neural network, DNN)状态估计模型，采用最大相关-最小冗余(maximum relevance-minimum redundancy, MRMR)的方法优化训练样本，从而提高状态估计的精度和鲁棒性。最后，建立分布式集成深度神经网络模型，弥补了集中式状态估计速度慢的不足，从而提高状态估计效率。基于IEEE123配电网的算例分析表明，所提方法能更准确地辨识分布式电源波动数据和不良数据，同时提高状态估计的精度和效率，且具有较高的鲁棒性。 |
| 关键词: 状态估计 最大相关-最小冗余 分布式 集成深度神经网络 |
| DOI：10.19783/j.cnki.pspc.230867 |
| 分类号: |
| 基金项目:新疆维吾尔自治区自然科学基金项目资助(2022D01C365，2022D01C662)；2022天山英才培养计划项目资助(2022TSYCLJ0019) |
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| Distribution network distributed state estimation method based on an integrated deep neural network |
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ZHANG Wangyang, FAN Yanfang, HOU Junjie, SONG Yulu
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School of Electrical Engineering, Xinjiang University, Urumqi 830047, China
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| Abstract: |
| With the integration of a large number of distributed energy sources, the operation and control methods of distribution systems have become increasingly complex. In response to the problems faced by distribution network state estimation methods such as difficulty in identifying distributed power source fluctuation data, low estimation accuracy, poor robustness and estimation timeliness, a distribution network distributed state estimation method based on integrated deep neural networks is proposed. First, the data identification technique of measuring data correlation testing is used to identify bad data and new energy fluctuation data. From this, the bad data is corrected using a temporal convolutional network (TCN) - bidirectional long short term memory (BILSTM). Then, an integrated deep neural network (DNN) state estimation model is established, and the maximum relevance-minimum redundancy (MRMR) method is used to optimize the training samples, thereby improving accuracy and robustness. Finally, a distributed integrated DNN model is established to compensate for the slow speed of centralized state estimation and improve efficiency. The numerical analysis based on an IEEE123 distribution network shows that the proposed method can more accurately identify distributed power source fluctuation data and bad data, while improving the accuracy and efficiency of state estimation, and is very robust. |
| Key words: state estimation maximum relevance-minimum redundancy distributed integrated deep neural network |
