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学者姓名：王怀远
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Abstract ：
Recently, data-driven methods have been widely assessed by researchers in the field of power system transient stability assessment (TSA). The differences in prediction difficulty among the samples are ignored by most previous studies. To address this problem, anchor loss (AL) is introduced, which can dynamically reshape loss values based on the prediction difficulty of samples. Thereby, easy samples are suppressed by reducing their loss values to avoid being paid too much attention when they are misclassified. Meanwhile, hard samples are emphasized by increasing their loss values, in order to be predicted correctly as much as possible. On basis of the AL, historical information in the model training process is considered. A novel loss function named historical information anchor loss (HIAL) is designed. The loss values can be adaptively rescaled according to the previous prediction results as well as the prediction difficulty of samples. Finally, the HIAL is combined with the deep brief network (DBN) and applied in the IEEE 39-bus system, and a realistic system is produced to verify its effectiveness. By incorporating prediction difficulty and historical training information, the accuracy (with a reduction in misjudgment rate exceeding 30%) and convergence speed of the TSA model can be significantly improved.
Keyword ：
anchor loss anchor loss deep belief network deep belief network historical training information historical training information prediction difficulty prediction difficulty transient stability assessment transient stability assessment
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| GB/T 7714 | Xu, Jie , Huang, Jing , Wang, Huaiyuan . Transient Stability Assessment Considering Prediction Difficulty and Historical Training Information [J]. | ELECTRONICS , 2025 , 14 (1) . |
| MLA | Xu, Jie 等. "Transient Stability Assessment Considering Prediction Difficulty and Historical Training Information" . | ELECTRONICS 14 . 1 (2025) . |
| APA | Xu, Jie , Huang, Jing , Wang, Huaiyuan . Transient Stability Assessment Considering Prediction Difficulty and Historical Training Information . | ELECTRONICS , 2025 , 14 (1) . |
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为解决电网实际故障样本与训练样本分布差异较大而使模型无法评估的问题,提出一种定向对抗迁移的评估模型.首先,建立以堆叠自编码器为基础的传统对抗迁移模型,通过训练样本和潜在样本间的对抗学习,使模型提取到样本的共同特征,提高了模型评估潜在故障的能力;然后,在传统对抗迁移模型的基础上加入一种定向对抗方法,有选择性地迁移训练样本,所提方法根据训练样本和潜在故障样本的相似度值更改不同训练样本在对抗训练中的权重,减小大差异样本对迁移训练的负面影响;在实际区域系统仿真算例中所提方法相较传统对抗迁移模型提高5.72%的准确率.测试结果表明所提方法能够有效提高模型的迁移能力和评估准确率.
Keyword ：
堆叠自编码器 堆叠自编码器 对抗机器学习 对抗机器学习 暂态稳定 暂态稳定 样本相似度度量 样本相似度度量 迁移学习 迁移学习
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| GB/T 7714 | 符益华 , 卢国强 , 王怀远 . 基于定向对抗迁移的暂态稳定评估模型 [J]. | 太阳能学报 , 2025 , 46 (2) : 226-234 . |
| MLA | 符益华 等. "基于定向对抗迁移的暂态稳定评估模型" . | 太阳能学报 46 . 2 (2025) : 226-234 . |
| APA | 符益华 , 卢国强 , 王怀远 . 基于定向对抗迁移的暂态稳定评估模型 . | 太阳能学报 , 2025 , 46 (2) , 226-234 . |
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在弱过电流能力的构网型变流器中,一般需要引入电流限幅以保证故障限流.然而,电流限幅触发后系统同步稳定性遭到削弱,现有研究鲜有对此问题进行综合考虑并加以解决.为此,本文首先利用相量图法揭示了故障电流特性;接着,基于功角特性曲线和扩展等面积准则分析了电流限幅对变流器同步稳定性的不利影响.由此,本文提出一种增强同步稳定的构网型变流器虚拟惯量限流方法,通过定量控制虚拟惯量同时解决了变流器的故障限流和同步稳定问题.最后,通过Matlab/Simulink仿真实验验证了理论分析的正确性和所提方法的有效性.
Keyword ：
同步稳定性 同步稳定性 故障限流 故障限流 构网型变流器 构网型变流器 电流限幅 电流限幅 虚拟惯量 虚拟惯量
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| GB/T 7714 | 孙东伟 , 张旸 , 温步瀛 et al. 增强同步稳定的构网型变流器虚拟惯量限流方法 [J]. | 电气技术 , 2025 , 26 (4) : 1-6,60 . |
| MLA | 孙东伟 et al. "增强同步稳定的构网型变流器虚拟惯量限流方法" . | 电气技术 26 . 4 (2025) : 1-6,60 . |
| APA | 孙东伟 , 张旸 , 温步瀛 , 王怀远 . 增强同步稳定的构网型变流器虚拟惯量限流方法 . | 电气技术 , 2025 , 26 (4) , 1-6,60 . |
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针对模块化多电平换流器(modular multilevel converter,MMC)子模块开路故障诊断中固定时刻诊断方法及时性和准确性难以平衡的问题,提出基于集成学习的时序自适应MMC子模块开路故障诊断方法.首先,分析MMC子模块的开路故障特性,选择子模块的电容电压作为故障监测和定位的故障特征参量.然后,基于代价敏感法构建两个具有相反诊断倾向性的集成模型,分别得到确定样本和不确定样本.接着,将当前阶段不确定样本交由下一时刻的模型继续诊断,直至诊断出所有样本的结果.最后,通过实验验证所提出诊断方法的有效性.结果表明,该方法能在更短的诊断周波内显著提高模型的诊断性能.
Keyword ：
子模块开路故障 子模块开路故障 故障诊断 故障诊断 时序自适应 时序自适应 模块化多电平换流器 模块化多电平换流器 集成学习 集成学习
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| GB/T 7714 | 魏银图 , 张旸 , 温步瀛 et al. 基于集成学习的时序自适应MMC子模块开路故障诊断方法 [J]. | 福州大学学报（自然科学版） , 2025 , 53 (1) : 35-41 . |
| MLA | 魏银图 et al. "基于集成学习的时序自适应MMC子模块开路故障诊断方法" . | 福州大学学报（自然科学版） 53 . 1 (2025) : 35-41 . |
| APA | 魏银图 , 张旸 , 温步瀛 , 王怀远 . 基于集成学习的时序自适应MMC子模块开路故障诊断方法 . | 福州大学学报（自然科学版） , 2025 , 53 (1) , 35-41 . |
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High impedance faults (HIFs) are difficult to detect because of low amplitude of the current signal. The interference from switching cases also jeopardizes the reliability of HIF detection (HIFD). Moreover, a long detection time is more likely to cause accidents. To diagnose HIFs promptly and precisely, a time-adaptive (TA) HIFD model is proposed. Firstly, the zero-sequence current data of the faulty feeder are processed into a variable length training set to train gated recurrent unit (GRU) networks. Secondly, a cost-sensitive method employed to train two biased GRU models with contrary preference. Then the two models are combined into high reliability evaluation model. The predicted reliability depends on the consistency of predicted results of the two models. Reliable results are output, while the unreliable results are set aside. To prevent untimely detection, an equal GRU-based model is activated when reaching the time threshold. Delayed judgment improves accuracy of HIFD and reduces probability of harm caused by untimely detection. The performance of proposed method validated by simulated data, and tested in a realistic 10 kV distribution network experimental system. In the true type 10kV system, the TA HIFD model can achieve an accuracy of 96.73% with average detection time of 4.315ms.
Keyword ：
cost-sensitive cost-sensitive fault detection fault detection gated recurrent unit (GRU) gated recurrent unit (GRU) high impedance fault (HIF) high impedance fault (HIF) time-adaptive (TA) time-adaptive (TA) variable mode decomposition (VMD) variable mode decomposition (VMD)
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| GB/T 7714 | Lin, Jianxin , Lin, Xiwen , Wang, Huaiyuan et al. Time-adaptive High Impedance Fault Detection Model Based on Cost-sensitive Method [J]. | ELECTRIC POWER SYSTEMS RESEARCH , 2025 , 247 . |
| MLA | Lin, Jianxin et al. "Time-adaptive High Impedance Fault Detection Model Based on Cost-sensitive Method" . | ELECTRIC POWER SYSTEMS RESEARCH 247 (2025) . |
| APA | Lin, Jianxin , Lin, Xiwen , Wang, Huaiyuan , Guo, Moufa . Time-adaptive High Impedance Fault Detection Model Based on Cost-sensitive Method . | ELECTRIC POWER SYSTEMS RESEARCH , 2025 , 247 . |
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Accurate and fast transient stability assessment is helpful for grid operators to take effective emergency control actions after faults. The gradual deployment of the wide-area measurement system provides a basis for introducing machine learning into transient stability assessment (TSA). However, the application of the machine learning model is restricted by the imbalance of samples in actual systems. In this paper, a time-adaptive assessment model with imbalance correction based on the ratio of loss function values is built to realize accurate and fast TSA. First, a long short-term memory (LSTM)-based model whose optimization goal is to reduce the prediction loss at a fixed time step is established. Several LSTM-based models with different decision time values are integrated as a multiple LSTM (MLSTM) TSA model. It is found that the effect of imbalanced samples on model parameters can be quantified by the loss function values. Then, the ratio of loss function values of two classes is obtained by pre-training, by which the imbalance degree of data can be quantified. The correction coefficient is determined and used to retrain LSTMs to solve the evaluation tendency problem. Simulation results in an IEEE 39-bus system and an actual power system show the excellent performance of the proposed imbalance correction scheme and evaluation scheme. Compared with traditional methods, imbalance correction can be achieved with better results.
Keyword ：
Cost-sensitive Cost-sensitive imbalance correction imbalance correction Indexes Indexes LSTM LSTM Machine learning Machine learning Power system stability Power system stability Stability criteria Stability criteria Support vector machines Support vector machines Training Training Transient analysis Transient analysis transient stability transient stability
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| GB/T 7714 | Chen, Qifan , Wang, Huaiyuan , Lin, Nan . Imbalance Correction Based on the Ratio of Loss Function Values for Transient Stability Assessment [J]. | CSEE JOURNAL OF POWER AND ENERGY SYSTEMS , 2025 , 11 (2) : 838-849 . |
| MLA | Chen, Qifan et al. "Imbalance Correction Based on the Ratio of Loss Function Values for Transient Stability Assessment" . | CSEE JOURNAL OF POWER AND ENERGY SYSTEMS 11 . 2 (2025) : 838-849 . |
| APA | Chen, Qifan , Wang, Huaiyuan , Lin, Nan . Imbalance Correction Based on the Ratio of Loss Function Values for Transient Stability Assessment . | CSEE JOURNAL OF POWER AND ENERGY SYSTEMS , 2025 , 11 (2) , 838-849 . |
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The measurement data of power systems are often mixed with a lot of noise due to the interference of the external environment. In order to eliminate the effect of noise, it is significant to denoise the noisy data to obtain the real state measurements. In order to deal with the problem of insufficient interpretability in existing data-driven denoising methods, a hybrid physical-data driven denoising model (PDDM) based on the stacked denoising autoencoder (SDAE) is proposed. First, the previous knowledge is extracted from the physical model of the generator. Physical constraints are designed based on the inherent relationships between rotor angle, angular frequency, and power. Second, based on SDAE deep-learning (DL) model, physical constraints are embedded into the loss function to guide the training of a neural network. The derivatives of denoised data are leveraged in anticipation of satisfying the differential-algebraic equations. The physical process is directly approximated by the neural network in this method, making the outputs satisfy the physical laws. The reliability and interpretability of the denoising results are improved. Meanwhile, the dependence on datasets is reduced by virtue of the hybrid physical-data driven mode. The robustness is still maintained. Finally, it is verified in the 39-bus New England system and a realistic regional power system. The real noisy data are also taken into account in testing to verify its extensibility. The test results show that the method proposed can achieve a satisfactory effect in both denoising accuracy and generalization capability.
Keyword ：
Accuracy Accuracy Data recovery Data recovery deep learning (DL) deep learning (DL) Generators Generators Noise Noise Noise measurement Noise measurement Noise reduction Noise reduction Phasor measurement units Phasor measurement units physics-informed neural networks (PINNs) physics-informed neural networks (PINNs) Pollution measurement Pollution measurement Power measurement Power measurement power system power system Power system stability Power system stability stacked denoising autoencoder (SDAE) stacked denoising autoencoder (SDAE) Training Training
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| GB/T 7714 | Wang, Huaiyuan , Zhang, Shiping , Liu, Baojin . Hybrid Physical-Data Driven Model for Denoising of Generator State Measurements [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
| MLA | Wang, Huaiyuan et al. "Hybrid Physical-Data Driven Model for Denoising of Generator State Measurements" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 74 (2025) . |
| APA | Wang, Huaiyuan , Zhang, Shiping , Liu, Baojin . Hybrid Physical-Data Driven Model for Denoising of Generator State Measurements . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
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Deep learning methods are widely adopted in power system transient stability assessment (TSA). However, the interpretability of the assessment results and the controllability of the assessment process hinder the further application of deep learning methods in practice. In this article, an instability pattern-guided model updating method is proposed to optimize the TSA model. Firstly, a TSA model based on Transformer encoder is proposed to explain and analyze the model's prediction through attention distribution. Secondly, an attention-guiding loss is employed to revise the assessment rules for specified instability patterns. The samples with specified instability patterns can be classified more accurately. Thirdly, an attention-keeping loss is employed to maintain the assessment rules for other samples and mitigate overfitting in the update. In addition, a representative dataset is introduced to reduce the update cost. The samples in the representative dataset are extracted from an original training set based on the attention distribution. The effectiveness of the proposed method is verified in the IEEE 39-bus system and the East China Power Grid system.
Keyword ：
Accuracy Accuracy Attention mechanism Attention mechanism Contingency management Contingency management controllability controllability Controllability Controllability deep learning deep learning interpretability interpretability model update model update Power system stability Power system stability Training Training Transformers Transformers Transient analysis Transient analysis transient stability assessment transient stability assessment
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| GB/T 7714 | Wang, Huaiyuan , Gao, Fajun , Chen, Qifan et al. Instability Pattern-Guided Model Updating Method for Data-Driven Transient Stability Assessment [J]. | IEEE TRANSACTIONS ON POWER SYSTEMS , 2025 , 40 (2) : 1214-1227 . |
| MLA | Wang, Huaiyuan et al. "Instability Pattern-Guided Model Updating Method for Data-Driven Transient Stability Assessment" . | IEEE TRANSACTIONS ON POWER SYSTEMS 40 . 2 (2025) : 1214-1227 . |
| APA | Wang, Huaiyuan , Gao, Fajun , Chen, Qifan , Bu, Siqi , Lei, Chao . Instability Pattern-Guided Model Updating Method for Data-Driven Transient Stability Assessment . | IEEE TRANSACTIONS ON POWER SYSTEMS , 2025 , 40 (2) , 1214-1227 . |
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For a variety of applications within power systems, the precision of data acquisition is of paramount importance. However, the actual data may be corrupted by noise in the process of measurement or transmission, and the accuracy of dynamic security assessment (DSA) will be affected. In light of the poor interpretability exhibited by traditional machine learning (ML) methods in denoising, a physics-informed denoising model (PIDM) for dynamic data recovery is proposed. The differential equations of physical models in power systems are employed to guide the training of PIDM. They are transformed into physical constraints and subsequently incorporated into the loss function of stacked denoising autoencoder (SDAE) to cleanse noisy data. By integrating the powerful learning capabilities of ML with the rigorous constraints of physical laws, the noisy data recovered by PIDM can better satisfy the dynamic equations. Consequently, a more pronounced denoising effect can be achieved. The improvement of the PIDM over common ML-based models is explored when dealing with the noisy data with varying degrees of interference or those of unexpected faults. The effectiveness is validated through simulation results in IEEE 39-bus system and the East China power grid. The results show that this method can reduce the total mean square error (MSE) of the recovery of noisy data to at least 65.27% of that of the traditional methods under the same conditions. In addition to demonstrating superior denoising performance, the generalization capability under diverse noise conditions is also deemed excellent.
Keyword ：
Data recovery Data recovery denoising and physics-informed method denoising and physics-informed method stacked denoising auto-encoder stacked denoising auto-encoder
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| GB/T 7714 | Li, Jian , Lu, Guoqiang , Li, Yongbin et al. Physics-Informed Denoising Model for Dynamic Data Recovery of Power Systems [J]. | IEEE ACCESS , 2025 , 13 : 12002-12013 . |
| MLA | Li, Jian et al. "Physics-Informed Denoising Model for Dynamic Data Recovery of Power Systems" . | IEEE ACCESS 13 (2025) : 12002-12013 . |
| APA | Li, Jian , Lu, Guoqiang , Li, Yongbin , Zhao, Dongning , Wang, Huaiyuan , Ouyang, Yucheng . Physics-Informed Denoising Model for Dynamic Data Recovery of Power Systems . | IEEE ACCESS , 2025 , 13 , 12002-12013 . |
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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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