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学者姓名：林俊杰
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Abstract ：
With the rapid development of dual-carbon targets, many distributed power sources, represented by wind power and photovoltaics, are being connected to distribution networks. This will further exacerbate the intermittency and volatility of power output. Dynamic reconfiguration of active distribution networks constitutes a complex, high-dimensional, mixed-integer, nonlinear, and stochastic optimization problem. Traditional algorithms exhibit numerous shortcomings in addressing this issue. By integrating the advantages of both deep learning and reinforcement learning, the deep reinforcement learning algorithm is highly suitable for formulating dynamically reconfigurable strategies for active distribution networks, which are currently of great concern. This paper first summarizes the characteristics of the active distribution network of the new generation power system, and analyzes the progress and challenges of the current research on the dynamic reconfiguration of the active distribution network in mathematical models. Secondly, the coding method of the distribution network dynamic reconfiguration is discussed, and the deep reinforcement learning algorithm is systematically reviewed. Furthermore, the shortcomings of the existing algorithms in dealing with the dynamic reconfiguration of the active distribution network are analyzed, and the research status and advantages of the deep reinforcement learning algorithm in the dynamic reconfiguration of the active distribution network are summarized. Finally, the future research directions for the dynamic reconfiguration of active distribution networks are presented. © 2025 Science Press. All rights reserved.
Keyword ：
Active learning Active learning DC distribution systems DC distribution systems Deep learning Deep learning Deep reinforcement learning Deep reinforcement learning Learning algorithms Learning algorithms Network coding Network coding Power distribution networks Power distribution networks Reinforcement learning Reinforcement learning
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| GB/T 7714 | Jiang, Changxu , Guo, Chen , Liu, Chenxi et al. Review of Active Distribution Network Dynamic Reconfiguration Based on Deep Reinforcement Learning [J]. | High Voltage Engineering , 2025 , 51 (4) : 1801-1816 . |
| MLA | Jiang, Changxu et al. "Review of Active Distribution Network Dynamic Reconfiguration Based on Deep Reinforcement Learning" . | High Voltage Engineering 51 . 4 (2025) : 1801-1816 . |
| APA | Jiang, Changxu , Guo, Chen , Liu, Chenxi , Lin, Junjie , Shao, Zhenguo . Review of Active Distribution Network Dynamic Reconfiguration Based on Deep Reinforcement Learning . | High Voltage Engineering , 2025 , 51 (4) , 1801-1816 . |
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An enormous challenge for the harmonic state estimation of distribution networks is how to perceive the complex and varied dynamic harmonics in a higher resolution method. To solve this problem, this article proposes an interval dynamic harmonic high-resolution state estimation method for distribution networks based on multisource measurement data fusion. First, to obtain the typical high-resolution harmonic measurement information of distribution networks under the limited measurement devices, a selection method for the measurement sites of high-resolution power quality monitoring devices (PQMDs) is proposed based on the harmonic electrical distance. On this basis, a multisource data fusion method based on the time period inclusion index is proposed to establish hybrid interval measurement datasets. Second, to improve the efficiency of interval dynamic harmonic state estimation, the interval intermediate variables are introduced to construct the three-stage hybrid interval harmonic measurement equations. Finally, an interval dynamic harmonic high-resolution state estimation method is proposed based on the predictor-corrector method, the IGG-III robust interval Kalman filter (IGGIII-RIKF) is used as the predictor stage, and the forward-backward interval constraint propagation (FBICP) algorithm is used as the corrector stage to realize interval dynamic harmonic high-resolution state estimation. The effectiveness and feasibility of the proposed method have been demonstrated on the IEEE 33-bus system and the IEEE 118-bus system.
Keyword ：
Current measurement Current measurement Dynamic harmonic state estimation Dynamic harmonic state estimation Electric variables measurement Electric variables measurement Harmonic analysis Harmonic analysis high-resolution high-resolution interval approach interval approach Measurement uncertainty Measurement uncertainty multisource measurement data fusion multisource measurement data fusion Phasor measurement units Phasor measurement units Power measurement Power measurement power quality power quality Power system dynamics Power system dynamics Power system harmonics Power system harmonics State estimation State estimation Time measurement Time measurement
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| GB/T 7714 | Zhu, Tiechao , Shao, Zhenguo , Lin, Junjie et al. Interval Dynamic Harmonic High-Resolution State Estimation for Distribution Networks Based on Multisource Measurement Data Fusion [J]. | IEEE SENSORS JOURNAL , 2025 , 25 (4) : 6682-6697 . |
| MLA | Zhu, Tiechao et al. "Interval Dynamic Harmonic High-Resolution State Estimation for Distribution Networks Based on Multisource Measurement Data Fusion" . | IEEE SENSORS JOURNAL 25 . 4 (2025) : 6682-6697 . |
| APA | Zhu, Tiechao , Shao, Zhenguo , Lin, Junjie , Zhang, Yan , Chen, Feixiong . Interval Dynamic Harmonic High-Resolution State Estimation for Distribution Networks Based on Multisource Measurement Data Fusion . | IEEE SENSORS JOURNAL , 2025 , 25 (4) , 6682-6697 . |
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随着双碳目标的快速发展,大量以风电、光伏为代表的分布式电源接入配电网,这将进一步加剧电源出力的间歇性与波动性.主动配电网动态重构属于一个复杂的高维混合整数非线性随机优化问题,传统算法在解决该问题的过程中存在着诸多不足之处.而深度强化学习算法结合了深度学习与强化学习的优势,非常适用于制定当前备受关注的主动配电网动态重构策略.该文首先对新型电力系统主动配电网特征进行总结,并对当前主动配电网动态重构研究在构建数学模型方面所取得的进展以及所面临的挑战进行了深入分析.其次,对配电网动态重构编码方式进行了探讨,并对深度强化学习算法进行了系统性地综述.进而,重点分析了现有算法在处理主动配电网动态重构时的不足之处,并对深度强化学习算法在主动配电网动态重构方面的研究现状与优势进行了总结与概括.最后,对主动配电网动态重构的未来研究方向进行了展望.
Keyword ：
主动配电网 主动配电网 人工智能 人工智能 动态重构 动态重构 机器学习 机器学习 深度强化学习 深度强化学习 编码方式 编码方式
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| GB/T 7714 | 江昌旭 , 郭辰 , 刘晨曦 et al. 基于深度强化学习的主动配电网动态重构综述 [J]. | 高电压技术 , 2025 , 51 (4) : 1801-1816,中插16-中插20 . |
| MLA | 江昌旭 et al. "基于深度强化学习的主动配电网动态重构综述" . | 高电压技术 51 . 4 (2025) : 1801-1816,中插16-中插20 . |
| APA | 江昌旭 , 郭辰 , 刘晨曦 , 林俊杰 , 邵振国 . 基于深度强化学习的主动配电网动态重构综述 . | 高电压技术 , 2025 , 51 (4) , 1801-1816,中插16-中插20 . |
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The power supply capability serves as a pivotal element in the operational evaluation of subway traction power supply system. The results can offer critical insights for the formulation of preliminary operational strategies and train scheduling plans. This study, grounded in the national standards of the subway industry, identifies key indicators for the evaluation of power supply capability and subsequently designs a comprehensive evaluation framework for the power supply capability of subway traction power supply system, considering the boundaries of each indicator. Moreover, by integrating the power supply capability of the subway traction power supply system with the train departure intervals, a robust method for evaluating power supply capability and a procedure for computing the maximum power supply capability are established. This facilitates the real-time evaluation of power supply capability under various operational plans of the subway traction power supply system and the determination of the maximum power supply capability. The method proposed in this paper can visually and accurately display the situation of the system, providing a reference for the evaluation of subway operation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Subways Subways
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| GB/T 7714 | Wang, Xinwu , Lin, Junjie , Zhang, Baichi et al. A Methodology for the Evaluation of the Power Supply Capability of Subway Traction Power Supply System [C] . 2025 : 219-230 . |
| MLA | Wang, Xinwu et al. "A Methodology for the Evaluation of the Power Supply Capability of Subway Traction Power Supply System" . (2025) : 219-230 . |
| APA | Wang, Xinwu , Lin, Junjie , Zhang, Baichi , Yang, Minghao , Hong, Hongbin , Chen, Bingbing . A Methodology for the Evaluation of the Power Supply Capability of Subway Traction Power Supply System . (2025) : 219-230 . |
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Emergency control is essential for maintaining the stability of power systems, serving as a key defense mechanism against the destabilization and cascading failures triggered by faults. Under-voltage load shedding is a popular and effective approach for emergency control. However, with the increasing complexity and scale of power systems and the rise in uncertainty factors, traditional approaches struggle with computation speed, accuracy, and scalability issues. Deep reinforcement learning holds significant potential for the power system decision-making problems. However, existing deep reinforcement learning algorithms have limitations in effectively leveraging diverse operational features, which affects the reliability and efficiency of emergency control strategies. This paper presents an innovative approach for real-time emergency voltage control strategies for transient stability enhancement through the integration of edge-graph convolutional networks with reinforcement learning. This method transforms the traditional emergency control optimization problem into a sequential decision-making process. By utilizing the edge-graph convolutional neural network, it efficiently extracts critical information on the correlation between the power system operation status and node branch information, as well as the uncertainty factors involved. Moreover, the clipped double Q-learning, delayed policy update, and target policy smoothing are introduced to effectively solve the issues of overestimation and abnormal sensitivity to hyperparameters in the deep deterministic policy gradient algorithm. The effectiveness of the proposed method in emergency control decision-making is verified by the IEEE 39-bus system and the IEEE 118-bus system.
Keyword ：
Deep reinforcement learning Deep reinforcement learning Edge graph convolutional network Edge graph convolutional network Emergency control Emergency control Transient stability Transient stability
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| GB/T 7714 | Jiang, Changxu , Liu, Chenxi , Yuan, Yujuan et al. Emergency voltage control strategy for power system transient stability enhancement based on edge graph convolutional network reinforcement learning [J]. | SUSTAINABLE ENERGY GRIDS & NETWORKS , 2024 , 40 . |
| MLA | Jiang, Changxu et al. "Emergency voltage control strategy for power system transient stability enhancement based on edge graph convolutional network reinforcement learning" . | SUSTAINABLE ENERGY GRIDS & NETWORKS 40 (2024) . |
| APA | Jiang, Changxu , Liu, Chenxi , Yuan, Yujuan , Lin, Junjie , Shao, Zhenguo , Guo, Chen et al. Emergency voltage control strategy for power system transient stability enhancement based on edge graph convolutional network reinforcement learning . | SUSTAINABLE ENERGY GRIDS & NETWORKS , 2024 , 40 . |
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Power system state estimation is a primary and major method for monitoring power grids in real time. Massive synchrophasor data contains temporal correlations and spatial characteristics based on the physical constraints of the power system. The spectral-domain convolution method based on the graph Fourier transform is used to construct a multilayer graph convolution neural network model to predict the short-term states of a power system, including the latest state, when the power system is in the quasi-steady state. Combining the advantages of linear state estimation, a forecasting-aided state estimation method that can take advantage of predicted values and phase measurement units is designed to obtain the real-time state. Furthermore, predicted innovations analysis method are proposed to identify system mutations and bad data. Enough simulation tests validate that the proposed method can accurately estimate the real-time state of a power system.
Keyword ：
Convolution Convolution Graph convolution neural network (NN) Graph convolution neural network (NN) Kalman filters Kalman filters phase measurement units phase measurement units Phasor measurement units Phasor measurement units Power measurement Power measurement Power system dynamics Power system dynamics power system forecasting-aided state estimation (FASE) power system forecasting-aided state estimation (FASE) Power systems Power systems State estimation State estimation synchrophasors synchrophasors
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| GB/T 7714 | Lin, Junjie , Tu, Mingquan , Hong, Hongbin et al. Spatiotemporal Graph Convolutional Neural Network-Based Forecasting-Aided State Estimation Using Synchrophasors [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (9) : 16171-16183 . |
| MLA | Lin, Junjie et al. "Spatiotemporal Graph Convolutional Neural Network-Based Forecasting-Aided State Estimation Using Synchrophasors" . | IEEE INTERNET OF THINGS JOURNAL 11 . 9 (2024) : 16171-16183 . |
| APA | Lin, Junjie , Tu, Mingquan , Hong, Hongbin , Lu, Chao , Song, Wenchao . Spatiotemporal Graph Convolutional Neural Network-Based Forecasting-Aided State Estimation Using Synchrophasors . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (9) , 16171-16183 . |
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Harmonic state estimation is a key part of power system operation management, which can be used to monitor the harmonic in the power grid and provide an important reference for the stable operation of the power grid. At present, the number of phasor measurement unit (PMU) configurations are difficult to satisfy the observability requirements of state estimation. It is necessary to adapt power quality monitoring device (PQMD) data to improve the redundancy of measurement and make the harmonic state estimation possible. However, the non-synchronized monitoring data characteristics of PQMD make the fused measurement data still have deviations, which will lead to a large error in harmonic estimation state. Fusing PMU and PQMD measurement data and minimizing the asynchronous measurement bias of PQMD measurement data, as well as suppressing the influence of this measurement bias in state estimation, will provide a more effective means for grid harmonic analysis. Therefore, the paper proposes an interval robust harmonic state estimation method based on PMU and PQMD measurement data fusion. Firstly, the detection period of PQMD is selected with the overlap index, and the reference period is selected with the maximum overlap as the target. The selected reference period is used as the measurement buffer of PMU, and the PQMD measurement data in this period is fused to form an interval mixed measurement set. The harmonic power measurements of PQMD are converted into equivalent harmonic current phasor measurement by the measurement transformation strategy, which is updated with state quantity in iterative solution. Secondly, the projection statistical method is used to calculate the initial weight of the measurement, and the overlap index is introduced into the Huber weight function to adjust the measurement weight. The measurement with low overlap and large residual is given a small weight to suppress the influence of measurement deviation, and further improve the robustness of the algorithm. Finally, the measurement points are preferring according to the weights, and the measurement subset with the least deviation of non-synchronous measurement is obtained. The harmonic state estimation model is solved by iterative reweighted least square method, and the harmonic state range of the whole network is obtained. The simulation results show that when the load fluctuation is 10% and the average overlap degree is 0.85, the estimated error of the proposed method is 1.92% in the upper bound and 3.24% in the lower bound. The error of phase angle upper bound estimation is 2.27%, and the error of lower bound estimation is 4.22%. When the overlap degree is reduced to 0.6, the average error of amplitude and phase angle of the proposed algorithm is less than 6%. When the level of load fluctuation increases to 40%, the average estimation error of state quantity is less than 5%. The following conclusions can be obtained through simulation analysis: (1) The overlap index is used to quantify the measurement deviation of PQMD to improve the weight matrix in the state estimation, which can effectively suppress the influence of the measurement deviation on state estimation. In addition, the interval mixed measurement subset is obtained by preferring the measurement points according to the weight coefficient, which can further reduce the non-synchronous measurement deviation of the measurement set, improve the reliability of the interval mixed measurement set and the accuracy of state estimation. (2) Converting the interval weight matrix and Jacobian matrix into a definite value can further reduce the conservatism of the solution interval. (3) The proposed algorithm can effectively reduce the estimation error under different measurement deviations and different load fluctuation sizes which has robustness. © 2024 China Machine Press. All rights reserved.
Keyword ：
Access control Access control Conformal mapping Conformal mapping Data fusion Data fusion Electric power measurement Electric power measurement Flow velocity Flow velocity Fourier series Fourier series Linear regression Linear regression Linear transformations Linear transformations Phase measurement Phase measurement Phasor measurement units Phasor measurement units Redundancy Redundancy Signal sampling Signal sampling Size determination Size determination Strain measurement Strain measurement Time difference of arrival Time difference of arrival Velocity measurement Velocity measurement Weighing Weighing
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| GB/T 7714 | Chen, Yihuang , Shao, Zhenguo , Lin, Junjie et al. Interval Harmonic Robust State Estimation Method Based on Multi-Source Measurement Data Fusion [J]. | Transactions of China Electrotechnical Society , 2024 , 39 (23) : 7394-7405 . |
| MLA | Chen, Yihuang et al. "Interval Harmonic Robust State Estimation Method Based on Multi-Source Measurement Data Fusion" . | Transactions of China Electrotechnical Society 39 . 23 (2024) : 7394-7405 . |
| APA | Chen, Yihuang , Shao, Zhenguo , Lin, Junjie , Zhang, Yan , Chen, Feixiong . Interval Harmonic Robust State Estimation Method Based on Multi-Source Measurement Data Fusion . | Transactions of China Electrotechnical Society , 2024 , 39 (23) , 7394-7405 . |
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The proliferation of distributed energy resources and the introduction of new loads in distribution networks present significant challenges for monitoring and operation. To satisfy the enhanced observability and controllability requirements of modern distribution networks, there is an increasing demand for advanced monitoring devices. Distribution Network Phasor Measurement Units (DPMUs) offer high-precision measurement data with precise timestamps, thereby improving both the accuracy and redundancy of measurements within the distribution network.This paper introduces an optimization model for the strategic placement of PMUs within distribution networks, leveraging node metric indices. The indices considered are node degree, spatiotemporal correlation, and node power ratio. The relative importance of these indices is determined using an improved entropy weight method, which quantifies the differentiation of nodes within the network. This method facilitates the prioritized placement of DPMUs at critical nodes. The proposed model also incorporates constraints such as the depth of unobservability and zero injection nodes. Utilizing a 0-1 integer programming algorithm, the model derives a multi-stage optimal placement scheme for PMU placement. This scheme evolves from incomplete observability to critical observability and ultimately to full redundancy. Importantly, this approach allows for the monitoring of key nodes within the distribution network and enhances measurement redundancy without necessitating an increase in the number of placements.
Keyword ：
Measurement Redundancy Measurement Redundancy Node metric Index Node metric Index Observability Observability Optimization Placement Optimization Placement PMU PMU
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| GB/T 7714 | Lin, Junjie , Chen, Haoyu , Jiang, Changxu et al. Multi-stage optimization placement of DPMUs based on node metric indices [J]. | SUSTAINABLE ENERGY GRIDS & NETWORKS , 2024 , 39 . |
| MLA | Lin, Junjie et al. "Multi-stage optimization placement of DPMUs based on node metric indices" . | SUSTAINABLE ENERGY GRIDS & NETWORKS 39 (2024) . |
| APA | Lin, Junjie , Chen, Haoyu , Jiang, Changxu , Han, Kunyu , Wei, Xinchi , Fang, Chen . Multi-stage optimization placement of DPMUs based on node metric indices . | SUSTAINABLE ENERGY GRIDS & NETWORKS , 2024 , 39 . |
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同步相量测量装置(phasor measurement unit,PMU)具有同步性好、分辨率高、相角直接可测等优点,是实现电力系统在线实时状态感知的重要信息源.然而,由于受到设备故障、气候干扰、通信问题等因素影响,实际电网中的PMU 数据容易出现数据缺失和异常等情况,这将干扰后续基于PMU数据的电网高级应用,进而影响电网状态感知和运行调度的可靠性.首先,通过分析现场实测的PMU数据,归纳出4种低质量数据情况,并且利用机理分析和相关性分析方法对系统运行状态进行辨识;然后,将多视图学习方法与电网运行机理相结合,提出基于时空信息特征融合的多视图数据初步重构算法,对PMU低质量和缺失数据进行重构;最后,结合系统不同运行状态特点,利用不同视图生成数据进行低质量数据的辨识,并提出一种基于历史数据的自适应加权的缺失数据重构方法.仿真和实测数据表明该方法能有效对 PMU 低质量数据进行辨识并实时重构生成,为 PMU数据在电力系统中的应用提供有效保障.
Keyword ：
低质量数据 低质量数据 同步相量量测数据 同步相量量测数据 多视图学习法 多视图学习法 数据重构 数据重构 系统运行状态辨识 系统运行状态辨识
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| GB/T 7714 | 林俊杰 , 涂明权 , 朱利鹏 et al. 基于时空多视图学习算法的PMU电压数据重构方法 [J]. | 中国电机工程学报 , 2024 , 44 (24) : 9533-9545,中插3 . |
| MLA | 林俊杰 et al. "基于时空多视图学习算法的PMU电压数据重构方法" . | 中国电机工程学报 44 . 24 (2024) : 9533-9545,中插3 . |
| APA | 林俊杰 , 涂明权 , 朱利鹏 , 宋文超 , 陆超 . 基于时空多视图学习算法的PMU电压数据重构方法 . | 中国电机工程学报 , 2024 , 44 (24) , 9533-9545,中插3 . |
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基于故障指示器(FI)和同步相量测量单元(PMU)两种量测设备的特点和优势,提出一种基于PMU和FI协同的配电网故障测距方法.首先,利用电力系统网络拓扑结构建立关系矩阵,根据FI的报警信息以及故障电流流向建立报警向量,由两者对应关系求解故障区段.其次,利用PMU配置节点量测数据与线路参数信息推算故障区段的节点电量数据.最后,对故障区段外电路进行等效,根据区段节点数据的完整性,选择单端阻抗法或双端阻抗法求解故障距离,实现故障精确定位.仿真试验表明,针对短路故障情况,所提方法基本不受故障位置、故障类型、过渡电阻的影响,具有较高的测距精度.
Keyword ：
同步相量测量单元 同步相量测量单元 故障区段定位 故障区段定位 故障指示器 故障指示器 故障测距 故障测距 配电网 配电网
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| GB/T 7714 | 张百驰 , 林俊杰 , 江昌旭 et al. 基于PMU和FI协同的配电网故障测距方法 [J]. | 太阳能学报 , 2024 , 45 (12) : 659-666 . |
| MLA | 张百驰 et al. "基于PMU和FI协同的配电网故障测距方法" . | 太阳能学报 45 . 12 (2024) : 659-666 . |
| APA | 张百驰 , 林俊杰 , 江昌旭 , 邵振国 , 方陈 , 魏新迟 . 基于PMU和FI协同的配电网故障测距方法 . | 太阳能学报 , 2024 , 45 (12) , 659-666 . |
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