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学者姓名：王怀远
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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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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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To address the problem that the model unable to evaluate accurately when the distribution difference between the actual fault samples and the training samples, an evalution model based on directional adversarial transfer is proposed. Firstly，a traditional adversarial transfer model based on stacked auto-encoder is built. Through adversarial learning between training samples and potential samples，the model extracts the common features of the samples，thus improving the ability of the model to evaluate potential faults. Then，a directional adversarial method is added to the traditional adversarial transfer model to selectively transfer the training samples. The proposed method changes the weights of different training samples in the adversarial training according to the similarity values of training samples and potential fault samples，thus reducing the negative impact of large difference samples on transfer training. The proposed method improves the accuracy by 5.72% compared to the traditional adversarial transfer model in the real system simulation examples. The test results show that the proposed method can effectively improve the transferability and evaluation accuracy of the model. © 2025 Science Press. All rights reserved.
Keyword ：
adversarial machine learning adversarial machine learning sample similarity measure sample similarity measure stacked auto-encoder stacked auto-encoder transfer learning transfer learning transient stability transient stability
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| GB/T 7714 | Fu, Y. , Lu, G. , Wang, H. . TRANSIENT STABILITY ASSESSMENT MODEL BASED ON DIRECTIONAL ADVERSARIAL TRANSFER LEARNING; [基于定向对抗迁移的暂态稳定评估模型] [J]. | Acta Energiae Solaris Sinica , 2025 , 46 (2) : 226-234 . |
| MLA | Fu, Y. et al. "TRANSIENT STABILITY ASSESSMENT MODEL BASED ON DIRECTIONAL ADVERSARIAL TRANSFER LEARNING; [基于定向对抗迁移的暂态稳定评估模型]" . | Acta Energiae Solaris Sinica 46 . 2 (2025) : 226-234 . |
| APA | Fu, Y. , Lu, G. , Wang, H. . TRANSIENT STABILITY ASSESSMENT MODEL BASED ON DIRECTIONAL ADVERSARIAL TRANSFER LEARNING; [基于定向对抗迁移的暂态稳定评估模型] . | Acta Energiae Solaris Sinica , 2025 , 46 (2) , 226-234 . |
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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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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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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 et al. "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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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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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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针对传统基于电力系统时序数据的机器学习方法对系统深层耦合信息挖掘不充分的问题,将失稳模式作为先验知识引入机器学习,提出一种融合系统关键机组信息的暂态稳定性评估方法.该方法通过潮流追踪原理对线路计算各发电机潮流贡献度,得出系统关键机组权重.根据图像形态学原理,对相平面轨迹图像依照关键机组权重进行特征增强.在IEEE-39节点和IEEE-145节点系统下的仿真结果表明,所提方法较传统评估方法具有更好的评估性能,所构建的相平面图像样本较传统时序图像样本拥有更小的占用空间和更优的分类性能.
Keyword ：
失稳模式 失稳模式 暂态稳定性评估 暂态稳定性评估 深度学习 深度学习 潮流追踪 潮流追踪 相平面 相平面
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| GB/T 7714 | 范欣辰 , 王怀远 , 温步瀛 . 融合关键机组信息与相平面图像的暂态稳定评估方法 [J]. | 福州大学学报（自然科学版） , 2024 , 52 (5) : 544-551 . |
| MLA | 范欣辰 et al. "融合关键机组信息与相平面图像的暂态稳定评估方法" . | 福州大学学报（自然科学版） 52 . 5 (2024) : 544-551 . |
| APA | 范欣辰 , 王怀远 , 温步瀛 . 融合关键机组信息与相平面图像的暂态稳定评估方法 . | 福州大学学报（自然科学版） , 2024 , 52 (5) , 544-551 . |
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With the phasor measurement units (PMUs) being widely utilized in power systems, a large amount of data can be stored. If transient stability assessment (TSA) method based on the deep learning model is trained by this dataset, it requires high computation cost. Furthermore, the fact that unstable cases rarely occur would lead to an imbalanced dataset. Thus, power system transient stability status prediction has the bias problem caused by the imbalance of sample size and class importance. Faced with such a problem, a TSA model based on the sample selection method is proposed in this paper. Sample selection aims to optimize the training set to speed up the training process while improving the preference of the TSA model. The typical samples which can accurately express the spatial distribution of the raw dataset are selected by the proposed method. Primarily, based on the location of training samples in the feature space, the border samples are selected by trained support vector machine (SVM), and the edge samples are selected by the assistance of the approximated tangent hyperplane of a class surface. Then, the selected samples are input to stacked sparse autoencoder (SSAE) as the final classifier. Simulation results in the IEEE 39-bus system and the realistic regional power system of Eastern China show the high performance of the proposed method.
Keyword ：
deep learning deep learning sample imbalance sample imbalance sample selection sample selection smart grid smart grid transient stability assessment transient stability assessment
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| GB/T 7714 | Li, Yongbin , Wang, Yiting , Li, Jian et al. Transient Stability Assessment Model With Sample Selection Method Based on Spatial Distribution [J]. | COMPLEXITY , 2024 , 2024 . |
| MLA | Li, Yongbin et al. "Transient Stability Assessment Model With Sample Selection Method Based on Spatial Distribution" . | COMPLEXITY 2024 (2024) . |
| APA | Li, Yongbin , Wang, Yiting , Li, Jian , Zhao, Huanbei , Wang, Huaiyuan , Hu, Litao . Transient Stability Assessment Model With Sample Selection Method Based on Spatial Distribution . | COMPLEXITY , 2024 , 2024 . |
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