Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current ...Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current and high-voltage conditions,there is a greater likelihood of failures.Consequently,anomaly detection of power electronic systems holds great significance,which is a task that properly-designed neural networks can well undertake,as proven in various scenarios.Transformer-like networks are promising for such application,yet with its structure initially designed for different tasks,features extracted by beginning layers are often lost,decreasing detection performance.Also,such data-driven methods typically require sufficient anomalous data for training,which could be difficult to obtain in practice.Therefore,to improve feature utilization while achieving efficient unsupervised learning,a novel model,Densely-connected Decoder Transformer(DDformer),is proposed for unsupervised anomaly detection of power electronic systems in this paper.First,efficient labelfree training is achieved based on the concept of autoencoder with recursive-free output.An encoder-decoder structure with densely-connected decoder is then adopted,merging features from all encoder layers to avoid possible loss of mined features while reducing training difficulty.Both simulation and real-world experiments are conducted to validate the capabilities of DDformer,and the average FDR has surpassed baseline models,reaching 89.39%,93.91%,95.98%in different experiment setups respectively.展开更多
Due to the increased penetration of multi-inverter distributed generation(DG)systems,different DG technologies,inverter control methods,and other inverter functions are challenging the capabilities of islanding detect...Due to the increased penetration of multi-inverter distributed generation(DG)systems,different DG technologies,inverter control methods,and other inverter functions are challenging the capabilities of islanding detection.In addition,multi-inverter networks connecting the distribution system point of common coupling(PCC)create islanding at paralleling inverters,which adds the vulnerability of islanding detection.Furthermore,available islanding detection must overcome more challenges from non-detection zones(NDZs)under reduced power mismatches.Therefore,in this study,a new method called parameter self-adapting active islanding detection was utilized to minimize the dilution effect and reduce NDZs in multi-inverter power systems.The method utilizes an active frequency drift(AFD)method and applies a positive feedback gain of adoption parameters,which significantly minimizes NDZs at parallel inverters.The simulation and experimental outcomes indicate that the proposed method can effectively weaken the dilution effect in multi-inverter networks connecting the distribution system PCC.展开更多
To detect spacecraft damage caused by hypervelocity impact,we propose an advanced spacecraft defect extraction algorithm based on infrared imaging detection.The Gaussian mixture model(GMM)is used to classify the tempe...To detect spacecraft damage caused by hypervelocity impact,we propose an advanced spacecraft defect extraction algorithm based on infrared imaging detection.The Gaussian mixture model(GMM)is used to classify the temperature change characteristics in the sampled data of the infrared video stream and reconstruct the image to obtain the infrared reconstructed image(IRRI)reflecting the defect characteristics.The designed segmentation objective function is used to ensure the effectiveness of image segmentation results for noise removal and detail preservation,while taking into account the complexity of IRRI(that is,the required trade-offs are different).A multi-objective optimization algorithm is introduced to achieve balance between detail preservation and noise removal,and a multi-objective evolutionary algorithm based on decomposition(MOEA/D)is used for optimization to ensure damage segmentation accuracy.Experimental results verify the effectiveness of the proposed algorithm.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 62303090,U2330206in part by the Postdoctoral Science Foundation of China under Grant 2023M740516+1 种基金in part by the Natural Science Foundation of Sichuan Province under Grant 2024NSFSC1480in part by the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current and high-voltage conditions,there is a greater likelihood of failures.Consequently,anomaly detection of power electronic systems holds great significance,which is a task that properly-designed neural networks can well undertake,as proven in various scenarios.Transformer-like networks are promising for such application,yet with its structure initially designed for different tasks,features extracted by beginning layers are often lost,decreasing detection performance.Also,such data-driven methods typically require sufficient anomalous data for training,which could be difficult to obtain in practice.Therefore,to improve feature utilization while achieving efficient unsupervised learning,a novel model,Densely-connected Decoder Transformer(DDformer),is proposed for unsupervised anomaly detection of power electronic systems in this paper.First,efficient labelfree training is achieved based on the concept of autoencoder with recursive-free output.An encoder-decoder structure with densely-connected decoder is then adopted,merging features from all encoder layers to avoid possible loss of mined features while reducing training difficulty.Both simulation and real-world experiments are conducted to validate the capabilities of DDformer,and the average FDR has surpassed baseline models,reaching 89.39%,93.91%,95.98%in different experiment setups respectively.
基金supported by the National Natural Science Foundation of China under Grant No.61671109.
文摘Due to the increased penetration of multi-inverter distributed generation(DG)systems,different DG technologies,inverter control methods,and other inverter functions are challenging the capabilities of islanding detection.In addition,multi-inverter networks connecting the distribution system point of common coupling(PCC)create islanding at paralleling inverters,which adds the vulnerability of islanding detection.Furthermore,available islanding detection must overcome more challenges from non-detection zones(NDZs)under reduced power mismatches.Therefore,in this study,a new method called parameter self-adapting active islanding detection was utilized to minimize the dilution effect and reduce NDZs in multi-inverter power systems.The method utilizes an active frequency drift(AFD)method and applies a positive feedback gain of adoption parameters,which significantly minimizes NDZs at parallel inverters.The simulation and experimental outcomes indicate that the proposed method can effectively weaken the dilution effect in multi-inverter networks connecting the distribution system PCC.
基金Project supported by the National Natural Science Foundation of China(No.61873305)the Applied Basic Research Program of Sichuan Province,China(Nos.2018JY0410and 2019YJ0199)。
文摘To detect spacecraft damage caused by hypervelocity impact,we propose an advanced spacecraft defect extraction algorithm based on infrared imaging detection.The Gaussian mixture model(GMM)is used to classify the temperature change characteristics in the sampled data of the infrared video stream and reconstruct the image to obtain the infrared reconstructed image(IRRI)reflecting the defect characteristics.The designed segmentation objective function is used to ensure the effectiveness of image segmentation results for noise removal and detail preservation,while taking into account the complexity of IRRI(that is,the required trade-offs are different).A multi-objective optimization algorithm is introduced to achieve balance between detail preservation and noise removal,and a multi-objective evolutionary algorithm based on decomposition(MOEA/D)is used for optimization to ensure damage segmentation accuracy.Experimental results verify the effectiveness of the proposed algorithm.
