关键词：
integrated energy system;energy configuration optimization;coupling relationships;ICA-SAQGM ensemble
摘要：
As one representative smart energy infrastructure in smart cities, an integrated energy system (IES) consists of several types of energy sources, thus making more complicated coupling connections between the supply and demand sides than a power grid. This will impact when allocating different energy sources to ensure the appropriate energy utilization in the IES. With this motivation, an IES energy configuration optimization strategy based on a multi-model ensemble is proposed in this paper. Firstly, one coupling model is constructed to assess the underlying collaborative relationships between two sides for a renewable-energy-connected IES. Next, the independent component analysis (ICA) method is implemented for noise reduction in massive heterogeneous input databases, which can effectively improve the computing efficiency under such high-dimensional data conditions. Also, the self-adaptive quantum genetic model (SAQGM) is built for subsequent configuration optimization. Specifically, the quantum bit representation is incorporated to reduce computation complexity in multi-states scenarios, the double-chain formation of chromosomes is deployed to diminish the uncertainty when encoding, and the dynamic adaptation quantum gate is established to successively amend parameters. Finally, an empirical case study is conducted which can demonstrate the benefits of this strategy in terms of feasibility, efficiency, and economy.
摘要：
现有配电网故障定位方法严重依赖波头到达时间、固有频率值等单一特征量，在微弱故障条件下难以有效提取故障特征，故障定位精度低。对此，提出基于全景故障特征挖掘的配电网单端行波定位方法。首先，分析不同故障点行波传输过程，结合小波能量熵量化分析行波全景波形时频分布特性：行波全景波形各次波头频率分布包含了故障支路信息，典型波头到达时序包含了故障距离信息，行波全景波形与故障位置一一对应，即不同故障位置的行波全景波形具有唯一性；在此基础上，以行波全景波形作为输入样本，搭建以卷积神经网络（convolutional neural network,CNN）与长短期记忆（long short term memory,LSTM）神经网络为核心的深度学习混合模型，CNN提取各次波头频率分布特征，挖掘故障支路信息，LSTM提取典型波头时间关联特征，挖掘故障距离信息，在网络中设置双通道并联输出层，其中分类层输出故障支路，回归层输出故障距离，最终实现配电网故障精确定位。仿真结果表明：在含有大量微弱故障样本的测试集上，定位平均绝对误差为67m，在3kΩ故障条件下定位误差仅为111m。
期刊：
2023 IEEE 13th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER),2023年:939-944
作者机构：
[Wenjie Lin] Guangdong Jiya Precision Machinery Technology Co., Ltd, Foshan, China;[Zhuo Long] School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, China;Foshan Graduate School, Northeastern University, Foshan, China;College of Information and Engineering, Northeastern University, Shenyang, China;[Wenjie Chen] Lancheng Lab, Midea Group, Foshan, China
摘要：
In order to realize fault diagnosis of harmonic reducers in real industrial scenarios, a fault diagnosis method based on image visual information, i.e., SBS_FD is proposed. Firstly, Symmetrized Dot Pattern images are obtained based on vibration signals, and Scale Invariant Feature Transform method is used to extract image information, wherein the mappings between fault signals and visual information are achieved. Then the bag of visual word and space pyramid matching method are used to process the visual information. Finally, the support vector machine is used for fault diagnosis. Comprehensive experimental results show that the diagnostic accuracy of four fault states can be up to 98% at all speeds, using proposed SBS_FD method, illustrating superior performance over other machine learning methods.
摘要：
Due to the widespread use of renewable energy sources, lithium-ion batteries have developed rapidly because renewable energy sources, such as photovoltaics and wind, which are very much affected by the environment and their power output can be better leveled if lithium-ion batteries are used. Battery state of charge (SOC) characterizes the remaining battery power, while battery state of health (SOH) characterizes the battery life state, and they are key parameters to characterize the state of lithium-ion batteries. In terms of battery SOC estimation, this paper optimizes the extended Kalman filtering (EKF) algorithm weights to adjust the weights during high current bursts to obtain better SOC tracking performance and optimizes the back propagation (BP) neural network for SOH estimation to obtain better weights to further obtain more accurate battery SOH. The feasibility of the optimized algorithm is validated by the experimental platform.
通讯机构：
[Zhuo, C ] X;[Yu, K ] C;Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Natl Key Lab Disaster Prevent & Reduct Power Grid, Changsha 410114, Peoples R China.;Xiangjiang Lab, Changsha 410205, Peoples R China.;Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha 410114, Peoples R China.
关键词：
Distribution networks;Single-phase ground fault;Fault handling;Flexible grounding;Double closed-loop control
摘要：
A flexible grounding device of a distribution network achieves reliable arc suppression and protection of a singlephase grounding fault by flexibly adjusting the neutral point voltage. The existing current control methods cannot accurately obtain the reference current to achieve precise control. Therefore, a flexible grounding control strategy with proportional series inertia control is proposed for distribution networks. Taking the neutral point voltage as the control target, the difference from the neutral point reference voltage to the real-time feedback voltage is adjusted by the outer loop first-order inertia link to generate the inner loop reference current, and the error from the actual injection current to the reference current is corrected by a proportional controller. The neutral point voltage can be flexibly adjusted without measuring the system parameters to ground. The internal and external loop controller parameters are further optimized to enhance device control robustness. Simulation and experimental results show that the method offers good dynamic and steady-state performance and can suppress the fault voltage below the arcing voltage under various fault conditions to achieve the fast and reliable arc suppression of the grounding faults.
关键词：
Power distribution system failure risks;Risk-early-warning;Spatiotemporal imbalanced data;AREcffr
摘要：
Power distribution systems are susceptible to external environmental disturbances. The early warning of po-tential fault risks in both spatial and temporal scales can assist in maintenance planning and overhaul scheduling for distribution systems, thus their overall reliability consequently. To achieve it, this paper proposes a self-adaptive prediction model for future failure risks in distribution systems, namely the association rules explo-ration with conditional filter and fitness regulation (AREcffr). In this approach, electrical attributes along with surrounding condition factors are both implemented as inputs. Then, to cope with the commonly-occurred imbalanced data distribution in both two scales when distinguishing risky factors, the conditional importance diagnostic threshold setting and importance diagnostic standard calculation methods set are designed. In that case, the included high-risk-low-probability (HRLP) time series and condition factors in the sparsely distributed input data can be taken into the assessment. Next, to conduct more reasonable measurements of risk levels for those selected risky components, a component importance measure (CIM)-based relative weight analysis model, which is according to the variation of total system risks caused by each risky component rather than its appearance frequency or database share, is established. Finally, an empirical research is presented, and the flexibility and advantages of this risk-early-warning model can be validated and demonstrated in consequence.
作者机构：
[曹一家; 赵一睿; 李勇] College of Electrical and Information Engineering, Hunan University, Changsha;410082, China;[蔡晔; 施星宇] College of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha;410114, China;[周东] Hunan Xiangneng Electric Power Co., Ltd., Changsha
通讯机构：
[Zhao, Y.] C;College of Electrical and Information Engineering, China
关键词：
Convolutional neural network;dynamic convolution;dynamic large kernel;dynamic small kernel;pansharpening
摘要：
Pansharpening is a spatial-spectral fusion technique that fuses low-resolution multispectral (MS) images with high-resolution panchromatic (PAN) images to get high-resolution MS images which are rich in spectral and spatial information. Some pansharpening methods based on dynamic convolution were proposed to improve the adaptivity and generalization of the fusion network. However, these methods either only focus on local small regions or generate dynamic filters with a complex network. Besides, the dynamic filters in the existing methods only convolve with MS or PAN images, resulting in the extracted details or spectral features are inadequate. In this letter, we propose a dynamic large-small kernel convolutional network. To obtain small scale features, we propose a dual dynamic small kernel (DDSK) module which consists of dynamic spatial small filter (DASF) and dynamic spectral small filter (DESF). The multiscale dynamic large kernel convolution (MDLC) module is designed to expand the receptive field for obtaining large scale features. Considering the differences between PAN and MS images, DASF and spatial MDLC modules are presented to extract the details of PAN image. Similarly, DESF and spectral MDLC modules are proposed to obtain the spectral features of MS images. The proposed method is evaluated on GaoFen-2 and WorldView-3 datasets, and our method shows good performance.
期刊：
JOURNAL OF FIRE SCIENCES,2023年42(1):22-62 ISSN：0734-9041
通讯作者：
Zhou, TJ
作者机构：
[Zhou, Tejun; Wu, Chuanping; Lu, Jiazheng; Zhou, TJ] State Key Lab Disaster Prevent & Mitigat Power Tra, Changsha 410007, Peoples R China.;[Zhou, Tejun] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha, Peoples R China.
通讯机构：
[Zhou, TJ ] S;State Key Lab Disaster Prevent & Mitigat Power Tra, Changsha 410007, Peoples R China.
关键词：
Wildfires near transmission line;helicopters;spray live insulators
摘要：
Journal of Fire Sciences, Volume 42, Issue 1, Page 22-62, January 2024. <br/>Regarding the problem that helicopters cannot spray live transmission conductors and wildfires directly under an insulator, we built a full-scale 500 kV insulator flashover test platform to simulate high-altitude helicopters spraying fire extinguishing agents. The chemical formulation, spray intensity, and fragmentation method of the fire extinguishing agents were varied. We simulated the breakdown characteristics of insulators when helicopters spray fire extinguishing agents, revealing the mechanism of high-altitude live fire extinguishment for high-spray-intensity and high-conductivity agents. Furthermore, an insulation performance verification test of a helicopter spraying live equipment at different flight speeds and altitudes was carried out, and the behavior of the fire extinguishing agents was divided into a five-zone diffusion law consisting of the (1) water column, (2) continuous water block, (3) semi-continuous water body, (4) large droplet particles, and (5) small droplet particles. We propose a spraying live transmission line method in which the helicopter flight height and speed jointly control the particle size of the fire extinguishing agent. When the particle size of the fire extinguishing agent at the terminal is controlled to 560–4000 μm, the insulation performance of the fire extinguishing agent can be effectively improved. During high-incidence periods of wildfires, such as the Spring Festival and Qingming Festival in 2023, on-site firefighting on the Hunan power grid was performed using helicopters to spray fire extinguishing agents from top to bottom through live transmission conductors to extinguish wildfire disasters directly below the transmission conductors. Neither transmission line flashovers nor power outages occurred when the fires were extinguished.
