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学者姓名：林俊杰
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Abstract ：
配电网同步相量测量单元（PMU）可以提供带有时标的高精度量测数据，配电网PMU线性状态估计高准确性和实时性的特点可以满足新型配电网的要求。该文重点研究了PMU配置成本与基于配电网PMU状态估计性能之间的权衡关系，提出了一种基于NSGA-II和熵权理想度排序法的多目标PMU优化配置方法，PMU优化配置的目标函数为最小化PMU配置成本和最小化PMU状态估计精度，约束条件考虑了PMU因发生意外事故而中断的情况，以应对因故障造成的量测不足的问题。基于NSGA-II算法和熵权理想度排序法，从Pareto解集中筛选出权衡多目标的最优解。算例表明，所提算法能得到权衡PMU配置成本和状态估计精度的最优解。
Keyword ：
NSGA-Ⅱ NSGA-Ⅱ PMU优化配置 PMU优化配置 熵权理想度排序法 熵权理想度排序法 状态估计 状态估计 配电网 配电网
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| GB/T 7714 | 陈浩宇 , 林俊杰 , 江昌旭 et al. 基于熵权理想度排序法的配电网PMU多目标优化配置 [J]. | 电网与清洁能源 , 2024 , 40 (08) : 36-45 . |
| MLA | 陈浩宇 et al. "基于熵权理想度排序法的配电网PMU多目标优化配置" . | 电网与清洁能源 40 . 08 (2024) : 36-45 . |
| APA | 陈浩宇 , 林俊杰 , 江昌旭 , 涂明权 , 林铮 , 卢玥君 . 基于熵权理想度排序法的配电网PMU多目标优化配置 . | 电网与清洁能源 , 2024 , 40 (08) , 36-45 . |
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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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在相量测量单元(PMU)配置数量不足以满足谐波状态估计的可观性条件时,可将电能质量监测装置(PQMD)作为数据补充源.该文针对多源量测数据存在的同步性差异和量测偏差等问题,提出一种融合PMU与PQMD量测数据的区间型抗差谐波状态估计方法.首先,根据PQMD 检测起始时刻不同步的特征,提出基于重叠度指标的 PMU 与 PQMD 量测数据融合方法;其次,采用量测变换从 PQMD 功率量测数据得到等效电流相量量测,构建区间型混合量测全集;再次,采用投影统计法和改进Huber权函数计算量测权重,对重叠度低且残差大的量测赋予较小的权重以抑制量测偏差的影响,并根据权重大小优选测点,得到非同步量测偏差最小的量测子集;最后,通过迭代重加权最小二乘法求解状态估计模型,在IEEE 30 系统验证了该文所提方法的可行性与有效性.
Keyword ：
区间算法 区间算法 同步相量量测 同步相量量测 多源量测数据融合 多源量测数据融合 电能质量监测装置 电能质量监测装置 谐波状态估计 谐波状态估计
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| GB/T 7714 | 陈艺煌 , 邵振国 , 林俊杰 et al. 融合多源量测数据的区间型抗差谐波状态估计 [J]. | 电工技术学报 , 2024 , 39 (23) : 7394-7405 . |
| MLA | 陈艺煌 et al. "融合多源量测数据的区间型抗差谐波状态估计" . | 电工技术学报 39 . 23 (2024) : 7394-7405 . |
| APA | 陈艺煌 , 邵振国 , 林俊杰 , 张嫣 , 陈飞雄 . 融合多源量测数据的区间型抗差谐波状态估计 . | 电工技术学报 , 2024 , 39 (23) , 7394-7405 . |
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Phasor measurement units (PMU) have the advantages of good synchronization, high resolution, direct phase angle measurement, etc. It is an important information source for realizing on-line real-time state perception of power systems. However, due to the influence of equipment failure, climate interference, communication problems and other factors, PMU data in the actual power grid are prone to data loss and anomalies, which will interfere with the subsequent advanced power grid applications based on PMU data, thereby affecting the reliability of power grid state perception and operation scheduling. Four kinds of low-quality data are summarized by analyzing the PMU data measured in the field, and the operating state of the system is identified by using mechanism analysis and correlation analysis methods. Then, combining the multi-view learning method with the power grid operation mechanism, a preliminary multi-view data reconstruction algorithm based on spatio-temporal information feature fusion is proposed to reconstruct the low-quality and missing PMU data. Finally, according to the characteristics of different running states of the system, the low quality data are identified by using different views to generate data, and an adaptive weighted missing data reconstruction method based on historical data is proposed. Simulation and measured data show that this method can effectively identify and reconstruct PMU low quality data in real time, which provides effective guarantee for the application of PMU data in power systems. © 2024 Chinese Society for Electrical Engineering. All rights reserved.
Keyword ：
data reconstruction data reconstruction low quality data low quality data multi-view-based learning method multi-view-based learning method phasor measurement unit data phasor measurement unit data system running status identification system running status identification
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| GB/T 7714 | Lin, J. , Tu, M. , Zhu, L. et al. PMU Voltage Data Reconstruction Method Based on Spatiotemporal Multi-view Learning Algorithm; [基于时空多视图学习算法的PMU 电压数据重构方法] [J]. | Proceedings of the Chinese Society of Electrical Engineering , 2024 , 44 (24) : 9533-9545 . |
| MLA | Lin, J. et al. "PMU Voltage Data Reconstruction Method Based on Spatiotemporal Multi-view Learning Algorithm; [基于时空多视图学习算法的PMU 电压数据重构方法]" . | Proceedings of the Chinese Society of Electrical Engineering 44 . 24 (2024) : 9533-9545 . |
| APA | Lin, J. , Tu, M. , Zhu, L. , Song, W. , Lu, C. . PMU Voltage Data Reconstruction Method Based on Spatiotemporal Multi-view Learning Algorithm; [基于时空多视图学习算法的PMU 电压数据重构方法] . | Proceedings of the Chinese Society of Electrical Engineering , 2024 , 44 (24) , 9533-9545 . |
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Due to the influence of equipment failure, extreme weather and other factors, the actual power grid is prone to various abnormal events. In order to detect the occurrence of abnormal events in the power grid, timely processing can ensure the safe and stable operation of the power system. In this paper, an innovative method of system anomaly event detection based on random matrix theory is proposed: Firstly, a grid zoning method based on ripple diffusion is proposed; Further, the sample covariance matrix is constructed for each partition based on M-P law of stochastic matrix theory. Finally, by extracting the feature of the sample covariance matrix, the detection index is generated based on the sliding T-test method, and the abnormal event occurrence time and abnormal region are detected. Simulation results show that the proposed algorithm is effective and accurate. © 2024 IEEE.
Keyword ：
abnormal events abnormal events grid zoning grid zoning random matrix theory random matrix theory sliding T-test sliding T-test
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| GB/T 7714 | Tu, M. , Lin, J. , Chen, H. . Abnormal event occurrence time and region detection method based on random matrix theory [未知]. |
| MLA | Tu, M. et al. "Abnormal event occurrence time and region detection method based on random matrix theory" [未知]. |
| APA | Tu, M. , Lin, J. , Chen, H. . Abnormal event occurrence time and region detection method based on random matrix theory [未知]. |
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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. © 2024 Elsevier Ltd
Keyword ：
Deep reinforcement learning Deep reinforcement learning 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 and 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 and 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 and Networks , 2024 , 40 . |
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当前能源转型背景下，发展综合能源系统是实现“碳达峰、碳中和”以及新型电力系统建设目标的重要途径。针对含多元化灵活资源的电热耦合系统，提出考虑“源-荷-储”协同的电热综合能源系统实验平台构建方案。设计考虑热电联产机组、电制热装置、蓄热罐以及电/热需求响应协同优化调度的实验案例，探讨实验平台在基础教学和拓展科研方面的用途。实验平台的建设为电气工程专业学生参与项目训练和创新实践类活动提供了重要的实践平台，有助于激发科研人员的探索性、创新性思维，促进理论研究和实验仿真的有机结合与良性循环，为综合能源系统领域的研究提供有力支撑。
Keyword ：
实验平台 实验平台 源荷储协同 源荷储协同 电热需求响应 电热需求响应 综合能源系统 综合能源系统
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| GB/T 7714 | 张亚超 , 朱蜀 , 林俊杰 . 基于源荷储灵活资源协同的电热综合能源系统实验平台 [J]. | 实验室研究与探索 , 2024 , 43 (07) : 69-75 . |
| MLA | 张亚超 et al. "基于源荷储灵活资源协同的电热综合能源系统实验平台" . | 实验室研究与探索 43 . 07 (2024) : 69-75 . |
| APA | 张亚超 , 朱蜀 , 林俊杰 . 基于源荷储灵活资源协同的电热综合能源系统实验平台 . | 实验室研究与探索 , 2024 , 43 (07) , 69-75 . |
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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. © 2024 Elsevier Ltd
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, J. , Chen, H. , Jiang, C. et al. Multi-stage optimization placement of DPMUs based on node metric indices [J]. | Sustainable Energy, Grids and Networks , 2024 , 39 . |
| MLA | Lin, J. et al. "Multi-stage optimization placement of DPMUs based on node metric indices" . | Sustainable Energy, Grids and Networks 39 (2024) . |
| APA | Lin, J. , Chen, H. , Jiang, C. , Han, K. , Wei, X. , Fang, C. . Multi-stage optimization placement of DPMUs based on node metric indices . | Sustainable Energy, Grids and Networks , 2024 , 39 . |
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For data-driven dynamic stability assessment (DSA) in modern power grids, DSA models generally have to be learned from scratch when faced with new grids, resulting in high offline computational costs. To tackle this undesirable yet often overlooked problem, this work develops a light-weight framework for DSA-oriented stability knowledge transfer from off-the-shelf test systems to practical power grids. A scale-free system feature learner is proposed to characterize system-wide features of various systems in a unified manner. Given a real-world power grid for DSA, selective stability knowledge transfer is intelligently carried out by comparing system similarities between it and the available test systems. Afterward, DSA model fine-tuning is performed to make the transferred knowledge adapt well to practical DSA contexts. Numerical test results on a realistic system, i.e., the provincial GD Power Grid in China, verify the effectiveness of the proposed framework.
Keyword ：
autoencoder autoencoder deep learning deep learning dynamic stability assessment dynamic stability assessment time series time series transfer learning transfer learning transient stability transient stability
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| GB/T 7714 | Wen, Weijia , Ling, Xiao , Sui, Jianxin et al. Data-Driven Dynamic Stability Assessment in Large-Scale Power Grid Based on Deep Transfer Learning [J]. | ENERGIES , 2023 , 16 (3) . |
| MLA | Wen, Weijia et al. "Data-Driven Dynamic Stability Assessment in Large-Scale Power Grid Based on Deep Transfer Learning" . | ENERGIES 16 . 3 (2023) . |
| APA | Wen, Weijia , Ling, Xiao , Sui, Jianxin , Lin, Junjie . Data-Driven Dynamic Stability Assessment in Large-Scale Power Grid Based on Deep Transfer Learning . | ENERGIES , 2023 , 16 (3) . |
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针对大规模风电并网导致的电力系统运行风险和水电、风电出力的互补特性，提出一种计及风电出力不确定性的含流域梯级水电的水火风联合低碳调度模型。采用高斯混合模型-吉布斯采样法生成融入不同时间尺度关联特性的风电出力动态场景集。综合考虑水头、流量多重因素及多种复杂运行约束的影响，建立流域梯级水电站群耦合模型。在此基础上，构建以基准风电场景下机组发电、排放成本和风电动态场景下机组调整成本为整体优化目标的两阶段随机规划调度模型。以改进的IEEE-24节点系统为例进行仿真分析，验证了所提模型的有效性，从而为大规模水火风互补发电系统的联合优化调度提供了参考。
Keyword ：
两阶段随机规划 两阶段随机规划 梯级水电站 梯级水电站 水火风联合调度 水火风联合调度 风电动态场景 风电动态场景
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| GB/T 7714 | 林文彪 , 郑志文 , 张亚超 et al. 含流域梯级水电的水火风联合低碳调度模型 [J]. | 科学技术与工程 , 2023 , 23 (17) : 7378-7384 . |
| MLA | 林文彪 et al. "含流域梯级水电的水火风联合低碳调度模型" . | 科学技术与工程 23 . 17 (2023) : 7378-7384 . |
| APA | 林文彪 , 郑志文 , 张亚超 , 林俊杰 . 含流域梯级水电的水火风联合低碳调度模型 . | 科学技术与工程 , 2023 , 23 (17) , 7378-7384 . |
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