| 蒋 帅,李德志,廖霈之,吴 啸,田长航.配电网精细化拓扑运行状态DCNN在线辨识方法[J].电力需求侧管理,2024,26(5):36-42 |
| 配电网精细化拓扑运行状态DCNN在线辨识方法 |
| Deep convolutional neural network online identification method for detailed topological operating states of distribution network |
| 投稿时间：2024-04-21 修订日期：2024-07-05 |
| DOI：10. 3969 / j. issn. 1009-1831. 2024. 05. 006 |
| 中文关键词: 拓扑辨识 负荷预测 深度神经网络 状态估计 配电网 |
| 英文关键词: topology identification load forecasting deep neural network state estimation distribution network |
| 基金项目:国家重点研发计划（2022YFB4100404）；深圳市科技计划资助项目（KJZD20230923114259050） |
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| 中文摘要: |
| 构建以新能源为主体的新型电力系统是实现碳达峰、碳中和目标的重要手段。新型电力系统中新能源将成为主力电源,高渗透率接入的新能源将深刻改变电力系统的形态、特性和机理。提出了一种结合潮流方程和深度神经网络的融合方法求解与量测值最优匹配的拓扑和线路参数估计方法,通过分析海量信息数据,透过数据关系探究电网运行规律,用于配电网精细化拓扑辨识及线路参数估计。首先,利用线性回归方法对拓扑和线路参数进行初步估计,得到初步辨识参数,并对初始辨识参数进行降噪处理；然后,基于深度神经网络对量测数据进行特征筛选,将筛选出的特征类别与相应的拓扑结构一一对应,构建训练数据集,进行离线训练,最终得到训练后的模型,从而得到精准的拓扑结构。最后,在IEEE 33节点配电网中进行了仿真验证,证明了该方法的有效性和较强的工程实用性。 |
| 英文摘要: |
| Building a new power system with new energy as the main body is an important means to achieve the goal of carbon peak and carbon neutrality. The new energy in new type power system will become the main power source, and the new energy with high penetration will profoundly change the form, characteristics and mechanism of the power system. A fusion method combining power flow equation and deep neural network is proposed to solve the topology and line parameter estimation method that best matches the measured value. By analyzing massive information data, the operation law of the power network is explored through data relations, which is used for fine topology identification and line parameter estimation of the distribution network. Firstly, the topology and line parameters are estimated by linear regression method, and the initial identification parameters are obtained, and the initial identification parameters are denoised. Then, feature screening is performed on the measured data based on the deep neural network, and the selected feature categories are one-to-one corresponding to the corresponding topology structure. Training data sets are constructed, and offline training is conducted, and the trained model is finally obtained, thus obtaining the accurate topology structure. Finally, the simulation results are carried out in IEEE 33-node distribution network, which proves the effectiveness and strong engineering practicability of the proposed method. |
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