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学者姓名：温步瀛
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Abstract ：
The uncertainty of source and load along with the wind power correlation can make the scheduling results of regional integrated energy system less credible. To solve this problem，taking the minimum comprehensive operation cost as the objective，a scheduling model of regional integrated energy system with multi-time scale is proposed. In the day-ahead stage，the two-stage robust optimization model considering wind power correlation is proposed，and the column-and-constraint generation method is used for the iterative solution. In the intra-day scheduling stage，the different response rates of cold，heat and power are considered，and the hierarchical rolling optimization model of cold，heat and power based on model predictive control is proposed to further eliminate the fluctuation of source and load power. The simulative results show that the robust optimization method considering the wind power correlation reduces conservatism and improves economy. The economic and stable operation of regional integrated energy system is realized by using model predictive control in the hierarchical optimization of cold，heat and power. © 2023 Electric Power Automation Equipment Press. All rights reserved.
Keyword ：
heat and power heat and power hierarchy of cold hierarchy of cold linear polyhedron set linear polyhedron set model predictive control model predictive control multi-time scale multi-time scale regional integrated energy system regional integrated energy system
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| GB/T 7714 | Chen, Z. , Wen, B. , Zhu, Z. . Multi-time scale optimal scheduling of regional integrated energy system considering wind power correlation; [计及风电相关性的区域综合能源系统多时间尺度优化调度] [J]. | Electric Power Automation Equipment , 2023 , 43 (8) : 25-32 . |
| MLA | Chen, Z. 等. "Multi-time scale optimal scheduling of regional integrated energy system considering wind power correlation; [计及风电相关性的区域综合能源系统多时间尺度优化调度]" . | Electric Power Automation Equipment 43 . 8 (2023) : 25-32 . |
| APA | Chen, Z. , Wen, B. , Zhu, Z. . Multi-time scale optimal scheduling of regional integrated energy system considering wind power correlation; [计及风电相关性的区域综合能源系统多时间尺度优化调度] . | Electric Power Automation Equipment , 2023 , 43 (8) , 25-32 . |
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在电力系统暂态稳定评估模型的更新过程中，针对与潜在故障相关性较小的故障样本影响迁移效果的问题，本研究从原始样本的特征量出发，发现其分布差异能反映故障之间的相关程度，由此提出考虑样本加权的迁移学习方法，进一步提高更新后评估模型的性能.首先，通过预先训练获得一个独立的域判别器，以此衡量训练模型的各故障样本相对于潜在故障的相似程度.其次，将量化后的分布差异通过密度比估计的方式进行转化，得到训练模型的各故障样本所赋予的权重大小.最后，将权重引入迁移学习更新评估模型的损失函数中，实现样本筛选.所提方法的有效性在IEEE-39节点系统和华东某区域的实际系统中均得到验证.
Keyword ：
密度比估计 密度比估计 暂态稳定评估 暂态稳定评估 样本加权 样本加权 模型更新 模型更新 迁移学习 迁移学习
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| GB/T 7714 | 方熙 , 王怀远 , 党然 et al. 考虑样本加权的迁移学习暂态稳定评估模型更新方法 [J]. | 福州大学学报(自然科学版) , 2023 , 51 (06) : 777-783 . |
| MLA | 方熙 et al. "考虑样本加权的迁移学习暂态稳定评估模型更新方法" . | 福州大学学报(自然科学版) 51 . 06 (2023) : 777-783 . |
| APA | 方熙 , 王怀远 , 党然 , 温步瀛 . 考虑样本加权的迁移学习暂态稳定评估模型更新方法 . | 福州大学学报(自然科学版) , 2023 , 51 (06) , 777-783 . |
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随着模块化多电平换流器(MMC)应用范围越来越广泛,其子模块的开路故障诊断方法成为研究热点.MMC的故障样本少,正常样本多,冗余子模块过多.针对此问题,本文提出基于支持向量机(SVM)的MMC子模块开路故障诊断方法,判断子模块故障发生在区内还是区外,以实现故障子模块的检测和定位.针对 MMC 子模块开路故障特征,选取子模块电容电压作为样本特征,分析子模块故障对 A、B、C相样本的影响,通过赋予 A、B、C相正常样本不同的权重系数,提高故障识别的准确率.最后,搭建MMC仿真模型,证明了所提方法的有效性.
Keyword ：
子模块开路故障 子模块开路故障 支持向量机(SVM) 支持向量机(SVM) 故障诊断 故障诊断 样本差异化 样本差异化 模块化多电平换流器(MMC) 模块化多电平换流器(MMC)
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| GB/T 7714 | 魏银图 , 张旸 , 温步瀛 et al. 基于支持向量机的模块化多电平换流器子模块开路故障诊断方法 [J]. | 电气技术 , 2023 , 24 (10) : 1-7 . |
| MLA | 魏银图 et al. "基于支持向量机的模块化多电平换流器子模块开路故障诊断方法" . | 电气技术 24 . 10 (2023) : 1-7 . |
| APA | 魏银图 , 张旸 , 温步瀛 , 王怀远 . 基于支持向量机的模块化多电平换流器子模块开路故障诊断方法 . | 电气技术 , 2023 , 24 (10) , 1-7 . |
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为解决源荷不确定性和风电相关性导致区域综合能源系统调度结果可信度低的问题,以综合运行成本最小为目标,提出一种计及多时间尺度的区域综合能源系统调度模型.在日前阶段,提出计及风电相关性的两阶段鲁棒优化模型,使用列和约束生成法进行迭代求解.日内调度阶段考虑了冷热电响应速率的不同,提出基于模型预测控制的冷热电分层滚动优化模型,进一步消除源荷功率波动.仿真结果表明:计及风电相关性的鲁棒优化方法降低了保守性,提高了经济性;在冷热电分层优化时使用模型预测控制,实现了区域综合能源系统的经济及稳定运行.
Keyword ：
冷热电分层 冷热电分层 区域综合能源系统 区域综合能源系统 多时间尺度 多时间尺度 模型预测控制 模型预测控制 线性多面体集合 线性多面体集合
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| GB/T 7714 | 陈志颖 , 温步瀛 , 朱振山 . 计及风电相关性的区域综合能源系统多时间尺度优化调度 [J]. | 电力自动化设备 , 2023 , 43 (8) : 25-32 . |
| MLA | 陈志颖 et al. "计及风电相关性的区域综合能源系统多时间尺度优化调度" . | 电力自动化设备 43 . 8 (2023) : 25-32 . |
| APA | 陈志颖 , 温步瀛 , 朱振山 . 计及风电相关性的区域综合能源系统多时间尺度优化调度 . | 电力自动化设备 , 2023 , 43 (8) , 25-32 . |
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电力系统受到大扰动后会进入紧急运行状态，必须及时采取紧急控制措施使系统恢复稳定运行。切机控制是维护系统稳定最有效且最常用的控制措施。针对传统基于策略表的控制方法在实际应用中存在故障不匹配的问题，提出了一种基于深度强化学习的电力系统暂态稳定切机控制决策方法。首先，引入深度确定性策略梯度(DDPG)算法，结合等面积定则，对算法各要素重新设计。其次，建立基于DDPG算法的切机控制决策模型。最后，利用PSA-BPA软件和Pycharm软件搭建单机-无穷大系统和IEEE39节点系统切机控制仿真模型，通过算例验证了所提方法的有效性。
Keyword ：
切机控制 切机控制 暂态稳定 暂态稳定 深度强化学习 深度强化学习 深度确定性策略梯度 深度确定性策略梯度
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| GB/T 7714 | 卢恒光 , 林碧琳 , 温步瀛 . 基于深度强化学习的切机控制策略研究 [J]. | 电器与能效管理技术 , 2023 , 6 (03) : 11-15,68 . |
| MLA | 卢恒光 et al. "基于深度强化学习的切机控制策略研究" . | 电器与能效管理技术 6 . 03 (2023) : 11-15,68 . |
| APA | 卢恒光 , 林碧琳 , 温步瀛 . 基于深度强化学习的切机控制策略研究 . | 电器与能效管理技术 , 2023 , 6 (03) , 11-15,68 . |
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Frequency prediction after a disturbance is devoted to providing a decision-making foundation to power system emergency control. In practice, the quantity of utilized variables is limited by the dimensionality of the physical model. Meanwhile, the accuracy of cognitive results is affected by the modeling precision. Owing to the model simplification, the computation efficiency of model-driven methods is improved, but the accuracy is sacrificed. In this paper, a prediction model combining the improved system frequency response (ISFR) model and long shortterm memory (LSTM) network is proposed to overcome this problem. Firstly, the ISFR model is employed to generate features representing system dynamic characteristics. Combined with the features provided by the ISFR model, the system operating features are applied to construct the training set for the deep learning network. Then, the LSTM network is introduced and trained to fit mapping relationship between multi-dimensional input features and system frequency response, thereby improving the overall accuracy of the integrated model. Finally, the simulation verification of the proposed model is performed in the IEEE 39-bus system and a realistic regional system. The simulation results demonstrate that the proposed model has better performance than that of traditional models.
Keyword ：
Deep learning Deep learning Frequency response prediction Frequency response prediction Integrated model Integrated model Long short-term memory (LSTM) Long short-term memory (LSTM)
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| GB/T 7714 | Hu, Yongfei , Wang, Huaiyuan , Zhang, Yang et al. Frequency prediction model combining ISFR model and LSTM network [J]. | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2022 , 139 . |
| MLA | Hu, Yongfei et al. "Frequency prediction model combining ISFR model and LSTM network" . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 139 (2022) . |
| APA | Hu, Yongfei , Wang, Huaiyuan , Zhang, Yang , Wen, Buying . Frequency prediction model combining ISFR model and LSTM network . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2022 , 139 . |
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A reconfiguration strategy for distribution network with DG（Distributed Generation），SOP（Soft Open Point） and orderly access of EVs（Electric Vehicles） is proposed. The disordered charging model of EVs is simulated based on the travel habits of office workers，and the SOP model under the reconfiguration of distribution network is constructed. For the disordered charging load of the accessed EVs，Lagrange relaxation decentralized optimization algorithm and virtual electricity price are adopted for orderly scheduling of EVs. The minimum sum of network loss cost，SOP operation cost，curtailment cost of wind power and photovoltaic power and switch operation cost is taken as the objective function，the reconfiguration model of distribution network is converted to a mixed-integer second-order cone programming model by big-M method and the second-order cone relaxation，and CPLEX solver is adopted for solution. The simulative results of IEEE 33-bus standard system show that the operation economy of distribution network can be improved by using SOP instead of traditional switch in the dynamic reconfiguration of distribution network，and the voltage quality of distribution network can be improved by adopting the proposed orderly scheduling method for the optimization of EV charging. © 2022 Electric Power Automation Equipment Press. All rights reserved.
Keyword ：
distribution network reconfiguration distribution network reconfiguration electric vehicles electric vehicles Lagrange relaxation decentralized optimization Lagrange relaxation decentralized optimization soft open point soft open point virtual electricity price virtual electricity price
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| GB/T 7714 | Lin, W. , Zhu, Z. , Wen, B. . Dynamic reconfiguration of distribution network with electric vehicles and soft open point [含电动汽车和智能软开关的配电网动态重构] [J]. | Electric Power Automation Equipment , 2022 , 42 (10) : 202-209,217 . |
| MLA | Lin, W. et al. "Dynamic reconfiguration of distribution network with electric vehicles and soft open point [含电动汽车和智能软开关的配电网动态重构]" . | Electric Power Automation Equipment 42 . 10 (2022) : 202-209,217 . |
| APA | Lin, W. , Zhu, Z. , Wen, B. . Dynamic reconfiguration of distribution network with electric vehicles and soft open point [含电动汽车和智能软开关的配电网动态重构] . | Electric Power Automation Equipment , 2022 , 42 (10) , 202-209,217 . |
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In case of faults or severe disturbances, the power system will enter an emergency operation state. After the system instability is detected, oscillation and blackout will occur in the system if effective control measures are not taken in time. Generator tripping control (GTC) is the most effective emergency control measure. In view of the mismatch between the traditional GTC algorithm and the transient stability assessment method based on machine learning, a new real-time GTC method is needed. In this paper, a three-part control framework is designed for the GTC problem. The control agent is endowed with decision-making ability by interacting with the simulation environment in the offline pre-learning part. Then the trained agent is transplanted to the online application which can help system operators make decisions. Meanwhile, the agent is updated with real data to be better adapted to the actual system in the online learning part. A deep reinforcement learning algorithm, deep deterministic policy gradient (DDPG) is employed to train the control agent in this framework. A modified DDPG algorithm and the corresponding reward function are designed for the GTC problem. Convolution neural network (CNN) is added to the DDPG network, by which the training time of the agent is shortened and the generalization ability of the algorithm is improved. Trained with simulation data and real system experience, the control agent can determine control strategies timely according to the system operating conditions. Simulation results on the IEEE-39 bus system and the realistic regional power system of Eastern China show the effectiveness, generalizability, and timeliness of the decision algorithm.
Keyword ：
Convolutional Neural Network Convolutional Neural Network Deep Reinforcement Learning Deep Reinforcement Learning Emergency Control Emergency Control Generator Tripping Control Generator Tripping Control
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| GB/T 7714 | Lin, Bilin , Wang, Huaiyuan , Zhang, Yang et al. Real-time power system generator tripping control based on deep reinforcement learning [J]. | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2022 , 141 . |
| MLA | Lin, Bilin et al. "Real-time power system generator tripping control based on deep reinforcement learning" . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 141 (2022) . |
| APA | Lin, Bilin , Wang, Huaiyuan , Zhang, Yang , Wen, Buying . Real-time power system generator tripping control based on deep reinforcement learning . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2022 , 141 . |
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针对可再生能源消纳与发电市场的博弈问题,研究不同场景中风电的调度策略,提出基于改进NashQ算法的风电调度策略模型.首先,在市场上博弈环境下建立风电优化调度模型,计及风电上网的预测偏差考核惩罚、风力发电经济效益与环境效益,考虑可再生能源的弃电限制,在这一基础上,对比风电独立运行、风—光、风—储联合运行下的风电调度策略;其次,采用JS散度优化各个智能体的学习率,提高多智能体强化学习的收敛效率;最后,在Matlab中搭建电网模型进行分析,仿真结果验证:改进NashQ方法相较于NashQ、NETRL算法的收敛速度有明显提升,风—车联合运行模式在多主体博弈下有较好吸引力.
Keyword ：
多主体博弈 多主体博弈 多智能体强化学习 多智能体强化学习 改进NashQ 改进NashQ 电动汽车充电站 电动汽车充电站 风电调度 风电调度
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| GB/T 7714 | 郑海林 , 朱振山 , 温步瀛 et al. 多主体博弈下基于改进NashQ算法的风电场调度策略 [J]. | 电力科学与技术学报 , 2022 , 37 (6) : 62-72 . |
| MLA | 郑海林 et al. "多主体博弈下基于改进NashQ算法的风电场调度策略" . | 电力科学与技术学报 37 . 6 (2022) : 62-72 . |
| APA | 郑海林 , 朱振山 , 温步瀛 , 翁智敏 . 多主体博弈下基于改进NashQ算法的风电场调度策略 . | 电力科学与技术学报 , 2022 , 37 (6) , 62-72 . |
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提出含分布式能源、智能软开关(SOP)和电动汽车(EV)有序接入的配电网重构策略。基于上班族的出行习惯模拟EV的无序充电模型，并构建配电网重构下的SOP模型；对于接入的EV无序充电负荷，采用拉格朗日松弛分散式优化算法和虚拟电价进行EV的有序调度；以最小化网损费用，SOP运行费用，弃风、弃光费用与开关动作费用之和为目标函数，通过大M法和二阶锥松弛将配电网重构模型转化为混合整数二阶锥规划模型，采用CPLEX求解器进行求解。IEEE 33节点标准系统的仿真结果表明，在配电网动态重构中采用SOP代替传统开关能够提升配电网运行的经济性，同时采用所提有序调度方法优化EV充电可以改善配电网电压质量。
Keyword ：
拉格朗日松弛分散式优化 拉格朗日松弛分散式优化 智能软开关 智能软开关 电动汽车 电动汽车 虚拟电价 虚拟电价 配电网重构 配电网重构
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| GB/T 7714 | 林文键 , 朱振山 , 温步瀛 . 含电动汽车和智能软开关的配电网动态重构 [J]. | 电力自动化设备 , 2022 , 42 (10) : 202-209,217 . |
| MLA | 林文键 et al. "含电动汽车和智能软开关的配电网动态重构" . | 电力自动化设备 42 . 10 (2022) : 202-209,217 . |
| APA | 林文键 , 朱振山 , 温步瀛 . 含电动汽车和智能软开关的配电网动态重构 . | 电力自动化设备 , 2022 , 42 (10) , 202-209,217 . |
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