作者机构：
[Tu, Yingang; Pei, Xiangyu] School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha;410114, China;[Zhou, Wandi; Li, Pengzhi; Shan, Yunhai] State Key Laboratory of Advanced Power Transmission Technology, State Grid Smart Grid Research Institute Co., Ltd., Beijing;102209, China;[Tu, Yingang; Pei, Xiangyu] 410114, China
通讯机构：
[Pei, X.] S;School of Electrical & Information Engineering, China
关键词：
commutation method;controllable active oscillation circuit;DC interruption;hybrid high voltage DC circuit breaker;negative-voltage commutation;VSC-based DC grid
摘要：
In aircraft feature detection, the difficulty of acquiring Synthetic Aperture Radar (SAR) images leads to the scarcity of some types of aircraft samples, and the high privacy makes the personal sample set have the characteristics of data silos. Existing data enhancement methods can alleviate the problem of data scarcity through feature reuse, but they are still powerless for data that are not involved in local training. To solve this problem, a new federated learning framework was proposed to solve the problem of data scarcity and data silos through multi-client joint training and model aggregation. The commonly used federal average algorithm is not effective for aircraft detection with unbalanced samples, so a federal distribution average deviation (FedDAD) algorithm, which is more suitable for aircraft detection in SAR images, was designed. Based on label distribution and client model quality, the contribution ratio of each client parameter is adaptively adjusted to optimize the global model. Client models trained through federated cooperation have an advantage in detecting aircraft with unknown scenarios or attitudes while remaining sensitive to local datasets. Based on the YOLOv5s algorithm, the feasibility of federated learning was verified on SAR image aircraft detection datasets and the portability of the FedDAD algorithm on public datasets. In tests based on the YOLOv5s algorithm, FedDAD outperformed FedAvg's mAP0.5-0.95 on the total test set of two SAR image aircraft detection and far outperformed the local centralized training model.
关键词：
Powerformer;stator faults;differential protection;capacitive current compensation
摘要：
A Powerformer is a special type of generator whose peak capacitive current is approximately 30 times larger than that of conventional generators with the same rated apparent power. Powerformer stator fault relays may be misoperated by using conventional differential protection strategies, thus degrading network reliability. Therefore, a novel differential protection scheme for a Powerformer is proposed in this paper. First, the nonlinear distribution laws of the Powerformer stator winding capacitance and induced electromotive force (EMF) are analyzed. Next, an equivalent distributed parameter circuit model of the Powerformer considering cable-windings with graded insulation is established and verified by using FEM-based analysis. Furthermore, to eliminate the influence of the capacitive current on the differential protection, the capacitive current is analyzed, and a novel differential protection scheme considering capacitive current compensation is proposed. A simulation model is established by using MATLAB and PSCAD, and extensive simulation studies verify the effectiveness of the proposed protection approach.
期刊：
IEEE Transactions on Artificial Intelligence,2023年:1-15 ISSN：2691-4581
作者机构：
[Wanneng Wu] School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, China;[Songyun Deng; Kunlin Zou; Hai Qin; Lekai Cheng] College of Electrical and Information Engineering, Hunan University, Changsha, China;[Qiaokang Liang] National Engineering Laboratory for Robot Vision Perception and Control, College of Electrical and Information Engineering, Hunan University, Changsha, China
摘要：
Recognition and early warning of plant diseases is one of the keys to agricultural disaster prevention and mitigation. Deep learning-based image recognition methods give us a new idea for plant disease identification. Due to the harsh conditions in agricultural environment, recent research has focused on exploring ways to lightweight the recognition model for deployment on low-power devices. In this paper, we propose an efficient and feature-guided real-time plant disease recognition model with a multi-classifier architecture, specifically designed for low-power devices. By comparing with other advanced methods, our model reaches the state-of-the-art in the combined metrics of recognition accuracy, the number of parameters and inference speed. First, we propose AMI-NanoNet based on RoofLine theory to significantly reduce the number of parameters and computational complexity. This model can achieve 99.8343% accuracy on PlantVillage by using a Feature-Guided Curriculum Learning (FGCL) with stepwise training strategy. Moreover, we design another training strategy suitable for lightweight ensemble models. Based on this strategy, our model only needs to integrate the classifiers at the end of the network to achieve 99.8708% identification accuracy, and it hardly increases the number of operations and parameters of the network. Extensive evaluations on this dataset demonstrate the effectiveness of our ensemble learning method. Furthermore, we tested our proposed methods on another dataset from other domains to validate its applicability to different scenarios. Overall, our research provides a basis for rapid and intelligent identification of plant diseases.
通讯机构：
[Fudong Li] S;School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
Ultra-short-term prediction;Global horizontal irradiance;Sky images;Time series decomposition;Sparrow search algorithm
摘要：
The development and utilization of solar energy has become an important strategic decision for the sustainable development of many countries. Short-term variations in solar irradiation have an impact on the safety and stability of photovoltaic and solar thermal power plants, therefore, the development and accuracy of solar irradiance prediction models have received much attention. This paper proposes a short-term irradiance prediction model based on mixed intelligent optimization algorithm and deep learning algorithm that integrates features of various forms of information. First, the sequence containing the picture attributes as well as the color and texture characteristics are recovered from ground-based cloud images, historical irradiance and meteorological feature information is decomposed and reconstructed by singular spectrum analysis (SSA). Secondly, the bidirectional short term to long term memory (BiLSTM) network is optimized for model training and finally assessment using an enhanced chaotic sparrow search method. The technique exceeds benchmark methods and a number of sophisticated individual algorithms in forecasting ultra-short-term global horizontal irradiance (GHI), according to experimental data, while also offering extremely high accuracy and resilience.
摘要：
Joint uncertainties and state estimation of a class of linearly coupled hyperbolic partial differential equation systems in the presence of unstructured and structured uncertainties are studied in this article. For unstructured uncertainties that are completely unknown, by employing Takagi-Sugeno fuzzy logic systems to approximate the unstructured uncertainties, a novel adaptive fuzzy boundary observer is developed to estimate both unknown system states as well as unknown weights in the fuzzy logic system, and the estimation errors are ultimately bounded. Therein, in the design of the proposed observer, a set of swapping filters and an infinite dimensional backstepping technique are combined. On the other hand, for structured uncertainties that can be described in a concrete parameterized form, the proposed method can easily achieve the exact estimation of weights and states to their true values. The rigorous proof is provided to show that the ultimately bounded estimation errors for the case of unstructured uncertainties and the exponential convergent estimation errors for the case of structured uncertainties can be realized. Finally, three illustrative simulations are carried out to show the feasibility and effectiveness of the developed methods in this article.
关键词：
VSC-based DC grids;DCCB;DC interruption;current commutation;controllable oscillating
摘要：
The DC circuit breaker (DCCB) is one of the key devices to ensure the stable operation of VSC-based DC grids. Compared to solid-state and hybrid DC circuit breakers, mechanical DC circuit breakers have the advantages of low on-state losses and good economics. However, existing mechanical DC circuit breakers still suffer from problems such as high voltage level of pre-charging system, inability to achieve continuous interruption, and poor economics when applied to high voltage levels. To address these problems, a novel controllable oscillating homopolar coupling DC Circuit Breaker (COHC-DCCB) is proposed in this paper. Firstly, the mathematical model of the controllable oscillating circuit is constructed, and its oscillation mechanism is analyzed. Then, the operation principle of the proposed COHC-DCCB is described. Finally, the effectiveness and feasibility of the proposed COHC-DCCB is verified by extensive simulations with PSCAD/EMTDC.
