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学者姓名：王怀远
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Abstract ：
To deal with the problem of the increasing operating loss of a distribution network caused by the disorderly large-scale charging of electric vehicles (EVs), a charging optimization strategy guided by the dynamic tariff mechanism of a subregion is proposed. The dynamic electricity price mechanism is to establish different dynamic electricity prices according to the load characteristics in different regions to optimize EV charging in the corresponding regions. The dynamic electricity price model taking into account the total charging power of the charging station is established in the commercial area, and the dynamic electricity price model taking into account wind and photovoltaic power output is adopted in residential and office areas. A charging benefit coefficient model is proposed to improve the charging time satisfaction of users in residential and office areas. Finally, the simulation results on the IEEE33-node system show that the EV charging optimization strategy proposed can not only guarantee the interests of the car owners, but also reduce network loss, improve the voltage quality of the distribution network, promote wind and photovoltaic power consumption, and enhance the economy of the distribution network. © 2024 Power System Protection and Control Press. All rights reserved.
Keyword ：
Charging (batteries) Charging (batteries) Dynamics Dynamics Electric loads Electric loads Electric losses Electric losses Electric power system protection Electric power system protection Electric vehicles Electric vehicles Energy efficiency Energy efficiency Energy utilization Energy utilization Housing Housing Power quality Power quality
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| GB/T 7714 | Deng, Yanhui , Li, Jian , Lu, Guoqiang et al. Charging optimization strategy of electric vehicles guided by the dynamic tariff mechanism of a subregion [J]. | Power System Protection and Control , 2024 , 52 (7) : 33-44 . |
| MLA | Deng, Yanhui et al. "Charging optimization strategy of electric vehicles guided by the dynamic tariff mechanism of a subregion" . | Power System Protection and Control 52 . 7 (2024) : 33-44 . |
| APA | Deng, Yanhui , Li, Jian , Lu, Guoqiang , Wang, Huaiyuan . Charging optimization strategy of electric vehicles guided by the dynamic tariff mechanism of a subregion . | Power System Protection and Control , 2024 , 52 (7) , 33-44 . |
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为了解决电力系统中样本数量和质量不平衡造成的暂态稳定评估偏差问题，从评估模型的训练过程出发，通过预训练模型获得样本对模型参数修正的梯度范数，引入梯度范数均值比量化样本的不平衡程度，相较于先验信息，梯度范数均值比综合考虑了样本数量与样本质量的不平衡，并提出基于代价敏感法的不平衡修正方法，利用该方法改善模型的评估倾向性，以实现较好的修正效果。IEEE39节点系统和华东电网系统的仿真结果验证了所提方法的有效性。
Keyword ：
不平衡样本 不平衡样本 代价敏感 代价敏感 堆叠稀疏自编码器 堆叠稀疏自编码器 暂态稳定评估 暂态稳定评估 梯度范数 梯度范数 深度学习 深度学习
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| GB/T 7714 | 胡力涛 , 王怀远 , 党然 et al. 基于梯度范数的暂态稳定评估模型的不平衡修正方法 [J]. | 电力自动化设备 , 2024 , 44 (04) : 158-163,177 . |
| MLA | 胡力涛 et al. "基于梯度范数的暂态稳定评估模型的不平衡修正方法" . | 电力自动化设备 44 . 04 (2024) : 158-163,177 . |
| APA | 胡力涛 , 王怀远 , 党然 , 童浩轩 , 张旸 . 基于梯度范数的暂态稳定评估模型的不平衡修正方法 . | 电力自动化设备 , 2024 , 44 (04) , 158-163,177 . |
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为应对大规模电动汽车无序充电引起的配电网运行损耗增加问题，提出一种分区域动态电价机制引导的电动汽车(electric vehicle,EV)充电优化策略。该动态电价机制是根据不同区域内的负荷特点建立不同的动态电价，从而优化对应区域的EV充电。其中商业区建立计及充电站充电总功率的动态电价模型，居民区和办公区采用计及风光出力的动态电价模型。同时，提出充电效益系数模型以提升在居民区和办公区用户的充电时间满意度。最后，在IEEE33节点系统上进行仿真验证。结果表明，所提出的基于分区域动态电价机制的EV充电优化策略能够在保证车主利益的同时，降低网损、提高配网电压质量、促进风光消纳以及提升配网的经济性。
Keyword ：
充电效益系数 充电效益系数 动态电价机制 动态电价机制 新能源消纳 新能源消纳 电动汽车 电动汽车
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| GB/T 7714 | 邓衍辉 , 李剑 , 卢国强 et al. 考虑分区域动态电价机制引导的电动汽车充电优化策略 [J]. | 电力系统保护与控制 , 2024 , 52 (07) : 33-44 . |
| MLA | 邓衍辉 et al. "考虑分区域动态电价机制引导的电动汽车充电优化策略" . | 电力系统保护与控制 52 . 07 (2024) : 33-44 . |
| APA | 邓衍辉 , 李剑 , 卢国强 , 王怀远 . 考虑分区域动态电价机制引导的电动汽车充电优化策略 . | 电力系统保护与控制 , 2024 , 52 (07) , 33-44 . |
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In order to solve the problem of transient stability assessment deviation caused by the imbalance of power system sample quantity and quanlity, starting from the training process of assessment model, the gradient norm of samples to model parameters is obtained by the pre-training model, and the mean ratio of gradient norm is introduced to quantify the imbalance of samples. Compared with the prior information, the mean ratio of gradient norm comprehensively considers the imbalance between the sample quantity and quality. An imbalanced correction method based on the cost-sensitive method is proposed, which is used to improve the assessment preference of the model and realize a preferable correction effect. The simulative results of IEEE 39-bus system and East China Power System verify the effectiveness of the proposed method. © 2024 Electric Power Automation Equipment Press. All rights reserved.
Keyword ：
Deep learning Deep learning Power quality Power quality Signal encoding Signal encoding System stability System stability Transients Transients
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| GB/T 7714 | Hu, Litao , Wang, Huaiyuan , Dang, Ran et al. Imbalance correction method of transient stability assessment model based on gradient norm [J]. | Electric Power Automation Equipment , 2024 , 44 (4) : 156-163 and 177 . |
| MLA | Hu, Litao et al. "Imbalance correction method of transient stability assessment model based on gradient norm" . | Electric Power Automation Equipment 44 . 4 (2024) : 156-163 and 177 . |
| APA | Hu, Litao , Wang, Huaiyuan , Dang, Ran , Tong, Haoxuan , Zhang, Yang . Imbalance correction method of transient stability assessment model based on gradient norm . | Electric Power Automation Equipment , 2024 , 44 (4) , 156-163 and 177 . |
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含氢燃料电池的微电网在孤岛运行时，仅采用虚拟同步发电机(virtual synchronous generator, VSG)控制技术作为外环无法达到理想的控制效果。线性自抗扰控制(linear active disturbance rejection control, LADRC)的引入提高微电网系统的抗扰能力，但传统LADRC中的线性扩张状态观测器(linear extend state observer, LESO)只具备观测阶跃信号的能力，而非线性扰动需要系统能够对周期信号进行跟踪和抑制。因此，提出基于重复控制的改进LADRC策略。利用重复控制能够对周期输入信号和扰动零误差跟踪补偿的特性，将其引入LADRC的LESO中改善系统的观测性能。对比分析LESO和基于重复控制的LESO的误差传递函数伯德图，并通过MATLAB/Simulink仿真平台验证该策略对于抑制非线性扰动的优越性。
Keyword ：
扩张状态观测器 扩张状态观测器 氢燃料电池 氢燃料电池 自抗扰控制 自抗扰控制 虚拟同步发电机 虚拟同步发电机 重复控制 重复控制
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| GB/T 7714 | 张敏 , 温涛 , 王怀远 . 基于重复控制的改进电压谐波线性自抗扰控制研究 [J]. | 广东电力 , 2024 , 37 (05) : 1-9 . |
| MLA | 张敏 et al. "基于重复控制的改进电压谐波线性自抗扰控制研究" . | 广东电力 37 . 05 (2024) : 1-9 . |
| APA | 张敏 , 温涛 , 王怀远 . 基于重复控制的改进电压谐波线性自抗扰控制研究 . | 广东电力 , 2024 , 37 (05) , 1-9 . |
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In the transient stability assessment (TSA) problem of a power system, the limited data mining ability of common machine learning algorithms prevents further improvement of TSA model evaluation accuracy. To solve this problem, taking different instability patterns of power system data as the entry point, a TSA method based on adaptive capture of instability patterns is proposed. An adaptive stability judgment combination model is constructed, one which is composed of multiple sub-evaluation models and an instability pattern discrimination model. First, the original data set is classified according to the different instability patterns, and several sub-evaluation models are trained for different instability patterns. Then, the weight value of the instability pattern discrimination model output is used to integrate the sub-evaluation model, and the instability pattern of the input data is captured adaptively. Finally, a case study is performed on the IEEE39-bus system and the East China power grid system. Simulation results show that the proposed method can reduce the negative rate of unstable samples and further improve model evaluation accuracy, which verifies the effectiveness of the proposed method. © 2024 Power System Protection and Control Press. All rights reserved.
Keyword ：
Adversarial machine learning Adversarial machine learning Deep learning Deep learning Electric power system stability Electric power system stability Transient stability Transient stability
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| GB/T 7714 | Zhang, Shiyi , Wang, Huaiyuan , Li, Jian et al. Transient stability assessment method based on adaptive capture of instability patterns [J]. | Power System Protection and Control , 2024 , 52 (18) : 35-44 . |
| MLA | Zhang, Shiyi et al. "Transient stability assessment method based on adaptive capture of instability patterns" . | Power System Protection and Control 52 . 18 (2024) : 35-44 . |
| APA | Zhang, Shiyi , Wang, Huaiyuan , Li, Jian , Lu, Guoqiang . Transient stability assessment method based on adaptive capture of instability patterns . | Power System Protection and Control , 2024 , 52 (18) , 35-44 . |
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An updating method for data-driven based transient stability assessment model of power systems is proposed in this paper. To cope with potential faults in the real-time operation of power systems, the parameters of models can be modified online through the proposed method. Firstly, according to the long-short term memory (LSTM) network, transient stability assessment models are initially trained offline by expected faults. Then, the feature distribution differences between expected faults and potential faults are evaluated by the time-sequence maximum mean discrepancy (TMMD). Compared with the traditional maximum mean discrepancy (MMD), the proposed TMMD approach takes the temporal properties of transient stability into account, which can reflect the relationship between fault samples and time series better. Finally, the parameters of models are adjusted by transfer learning to reduce the feature distribution differences, by which the updated models can be more suitable for the different but related potential faults. Therefore, through the proposed model updating method, the extensibility of evaluation models is greatly enhanced, which is more in line with the practical conditions of power systems. In this paper, the effectiveness of the proposed method has been demonstrated by the evaluation results in the IEEE 39-bus system and a realistic power system.
Keyword ：
LSTM LSTM Model updating Model updating TMMD TMMD Transfer learning Transfer learning Transient stability assessment (TSA) Transient stability assessment (TSA)
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| GB/T 7714 | Yang, Mingkai , Li, Yongbin , Li, Jian et al. Transfer learning-based updating method of transient stability assessment model [J]. | ENERGY REPORTS , 2024 , 12 : 442-452 . |
| MLA | Yang, Mingkai et al. "Transfer learning-based updating method of transient stability assessment model" . | ENERGY REPORTS 12 (2024) : 442-452 . |
| APA | Yang, Mingkai , Li, Yongbin , Li, Jian , Lu, Guoqiang , Wang, Huaiyuan , Fu, Yihua et al. Transfer learning-based updating method of transient stability assessment model . | ENERGY REPORTS , 2024 , 12 , 442-452 . |
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在电力系统暂态稳定评估(transient stability assessment,TSA)问题中,普通机器学习算法数据挖掘能力的有限性阻碍了TSA模型评估精度的进一步提高.针对此问题,以电力系统数据的不同失稳模式为切入点,提出了基于失稳模式自适应捕捉的TSA方法,构建了由多个子评估模型和一个失稳模式判别模型组成的自适应判稳组合模型.首先,根据失稳模式的不同对原始数据集进行分类,分别训练多个针对不同失稳模式的子评估模型.然后,利用失稳模式判别模型输出的权重值对子评估模型进行集成,自适应完成对输入数据失稳模式的捕捉.最后,在IEEE39节点系统和华东电网系统中进行测试验证.仿真结果表明,所提方法在降低不稳定样本漏报率的同时进一步提高了模型评估精度,验证了该方法的有效性.
Keyword ：
失稳模式 失稳模式 暂态稳定评估 暂态稳定评估 深度学习 深度学习 自适应评估 自适应评估 集成学习 集成学习
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| GB/T 7714 | 张诗熠 , 王怀远 , 李剑 et al. 基于失稳模式自适应捕捉的暂态稳定评估方法 [J]. | 电力系统保护与控制 , 2024 , 52 (18) : 35-44 . |
| MLA | 张诗熠 et al. "基于失稳模式自适应捕捉的暂态稳定评估方法" . | 电力系统保护与控制 52 . 18 (2024) : 35-44 . |
| APA | 张诗熠 , 王怀远 , 李剑 , 卢国强 . 基于失稳模式自适应捕捉的暂态稳定评估方法 . | 电力系统保护与控制 , 2024 , 52 (18) , 35-44 . |
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Transient stability assessment (TSA) is usually considered as a challenging problem in power systems. During the measurement and transmission of real-time data in power grids, different degrees of noise may occur. The real noise is complex and hard to predict, so the current methods cannot output satisfied TSA results. Therefore, an adaptive denoising combined model (ADCM) is proposed in this paper. The ADCM can adaptively output the results of TSA in complex noise environment. Firstly, the stacked denoising auto-encoder based model trained by data with expected noise is proposed, which is called targeted denoising model (TDM). The TDM can output satisfied results for specific noisy data. Then, the ADCM composed of multiple TDMs and a noise recognition model is constructed. The recognition model is a regression model based on deep neural networks. When actual data are input, the recognition model can output weight values for TDMs. And when the actual noise is similar to the noise trained for the TDM, the weight obtained is great for this model. The final results are obtained by assigning the weights to the results of each TDM. The effectiveness of this method is verified by simulation results in IEEE 39-bus system and realistic system.
Keyword ：
Combined model Combined model Deep learning Deep learning Stacked denoising auto-encoder (SDAE) Stacked denoising auto-encoder (SDAE) Transient stability assessment (TSA) Transient stability assessment (TSA)
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| GB/T 7714 | Ouyang, Yucheng , Wang, Huaiyuan . Adaptive denoising combined model with SDAE for transient stability assessment [J]. | ELECTRIC POWER SYSTEMS RESEARCH , 2023 , 214 . |
| MLA | Ouyang, Yucheng et al. "Adaptive denoising combined model with SDAE for transient stability assessment" . | ELECTRIC POWER SYSTEMS RESEARCH 214 (2023) . |
| APA | Ouyang, Yucheng , Wang, Huaiyuan . Adaptive denoising combined model with SDAE for transient stability assessment . | ELECTRIC POWER SYSTEMS RESEARCH , 2023 , 214 . |
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Fast and reliable transient stability assessment (TSA) is significant for safe and stable power system operation. Deep learning provides a new tool for TSA. However, it is difficult to apply the TSA models based on deep learning practically because of their inexplicability. Therefore, an interpretable time-adaptive model based on a dual-stage attention mechanism and gated recurrent unit (GRU) is proposed for TSA. A feature attention block and a time attention block are included in the dual-stage mechanism to explain the TSA rules learned by the proposed TSA model. Meanwhile, interpretability is utilized to guide the optimization of the TSA model. Firstly, the measurements are input into the feature attention block to calculate feature attention factors. Then, the measurements weighted by the feature attention factors are input into a GRU block for further abstracting. The abstracted features are input into the time attention block to obtain time attention factors. Finally, the abstracted features weighted by the time attention factors are sent into fully connected layers for TSA. To achieve time-adaptive TSA, multiple channels are constructed to process the features at different decision moments. The performance of the proposed model is verified in the IEEE-39 bus system and a realistic regional system.
Keyword ：
attention mechanism attention mechanism Electrical engineering Electrical engineering gated recurrent unit (GRU) gated recurrent unit (GRU) interpretability interpretability Logic gates Logic gates Power system reliability Power system reliability Power system stability Power system stability Stability criteria Stability criteria Training Training Transient analysis Transient analysis Transient stability Transient stability
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| GB/T 7714 | Chen, Qifan , Lin, Nan , Bu, Siqi et al. Interpretable Time-Adaptive Transient Stability Assessment Based on Dual-Stage Attention Mechanism [J]. | IEEE TRANSACTIONS ON POWER SYSTEMS , 2023 , 38 (3) : 2776-2790 . |
| MLA | Chen, Qifan et al. "Interpretable Time-Adaptive Transient Stability Assessment Based on Dual-Stage Attention Mechanism" . | IEEE TRANSACTIONS ON POWER SYSTEMS 38 . 3 (2023) : 2776-2790 . |
| APA | Chen, Qifan , Lin, Nan , Bu, Siqi , Wang, Huaiyuan , Zhang, Baohui . Interpretable Time-Adaptive Transient Stability Assessment Based on Dual-Stage Attention Mechanism . | IEEE TRANSACTIONS ON POWER SYSTEMS , 2023 , 38 (3) , 2776-2790 . |
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