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2020 Workshop on Communication Networks and Power Systems (WCNPS)最新文献

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Transient Voltages on Lightning Protection System with Stratified Soils and Damage Conditions 层状土壤防雷系统暂态电压及破坏条件
Pub Date : 2020-11-12 DOI: 10.1109/WCNPS50723.2020.9263716
W. L. D. de Azevedo, J. L. A. D’Annibale, A. D. de Araújo, J. Filho
Lightning Protection System (LPS) must provide a low-impedance path through its grounding system when a lightning strikes at the top of buildings. This grounding system is composed by combination of vertical (VE) and horizontal electrodes (HE) and a precise computation of its impedance must consider a stratified soil. Additionally, due to corrosion and electromagnetic forces, the electrode may present ruptures on connectors or welded parts. These factors affect the transient voltages along the grounding grid. In this paper, transient currents and voltages are investigated for a real LPS, subjected to a lightning strike, whose grounding system is buried in a homogeneous and stratified soil. In each condition, the grounding system is analysed under whole and damage state. In this study, a lumped approach for the VE and HE are obtained by the Vector Fitting technique and by the electromagnetic radiation theory (ERT), respectively. Results show that transient currents and voltages are affected by a rupture in the grounding electrodes; significant differences are more pronounced in high resistive homogeneous soil which may impact on the safety of people surrounding the LPS area.
当建筑物顶部发生雷击时,防雷系统(LPS)必须通过其接地系统提供低阻抗通道。该接地系统由垂直电极和水平电极组合而成,其阻抗的精确计算必须考虑分层土壤。此外,由于腐蚀和电磁力,电极可能会在连接器或焊接部件上出现破裂。这些因素影响着接地网沿线的暂态电压。本文研究了接地系统埋于均匀层状土壤中的真实LPS在雷击下的瞬态电流和电压。在每种情况下,对接地系统进行了整体状态和损坏状态的分析。本文分别采用向量拟合技术和电磁辐射理论(ERT)对VE和HE进行了集总处理。结果表明,接地电极的断裂会影响暂态电流和电压;在高电阻均质土壤中,显著差异更为明显,这可能会影响LPS区域周围人员的安全。
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引用次数: 0
Sensor fusion of camera and LiDAR raw data for vehicle detection 融合摄像头和激光雷达原始数据的车辆检测
Pub Date : 2020-11-12 DOI: 10.1109/WCNPS50723.2020.9263724
Gokulesh Danapal, Giovanni A. Santos, J. P. J. D. Da Costa, B. Praciano, Gabriel P. M. Pinheiro
Autonomous vehicles are expected to save almost half-million lives between 2035 to 2045. Moreover, since 90% of the accidents are caused by humans, 9% by weather and road conditions, and only 1% by vehicular failures, autonomous vehicles will provide much safer traffic, drastically decreasing the number of accidents. To perceive the surrounding objects and environment, autonomous vehicles depend on their sensor systems such as cameras, LiDARs, radars, and sonars. Traditionally, decision fusion is performed, implying into first individually processing each sensor’s data and then combining the processed information of the different sensors. In contrast to the traditional decision fusion of the processed information from each sensor, the raw data fusion extracts information from all sensors’ raw data providing higher reliability and accuracy in terms of object and environment perception. This paper proposes an improved sensor fusion framework based on You Only Look Once (YOLO) that jointly processes the raw data from cameras and LiDARs. To validate our framework, the dataset of the Karlsruhe Institute of Technology (KITTI) in partnership with Toyota Technical University generated using two cameras, and a Velodyne laser scanner is considered. The proposed raw data fusion framework outperforms the traditional decision fusion framework with a gain of 5% in terms of vehicle detection performance.
预计在2035年至2045年间,自动驾驶汽车将拯救近50万人的生命。此外,由于90%的事故是由人类造成的,9%是由天气和道路状况造成的,只有1%是由车辆故障造成的,因此自动驾驶汽车将提供更安全的交通,大大减少事故数量。为了感知周围的物体和环境,自动驾驶汽车依赖于它们的传感器系统,如摄像头、激光雷达、雷达和声纳。传统的决策融合是先单独处理每个传感器的数据,然后将不同传感器的处理信息结合起来。与传统的每个传感器处理信息的决策融合相比,原始数据融合从所有传感器的原始数据中提取信息,在物体和环境感知方面提供更高的可靠性和准确性。本文提出了一种改进的基于YOLO (You Only Look Once)的传感器融合框架,该框架联合处理来自相机和激光雷达的原始数据。为了验证我们的框架,卡尔斯鲁厄理工学院(KITTI)与丰田技术大学合作的数据集使用两台摄像机和一台Velodyne激光扫描仪生成。提出的原始数据融合框架在车辆检测性能方面优于传统决策融合框架,提高了5%。
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引用次数: 1
Management of an Electrical Storage System for Joint Energy Arbitrage and Improvement of Voltage Profile 联合能源套利与电压分布改善的蓄电系统管理
Pub Date : 2020-11-12 DOI: 10.1109/WCNPS50723.2020.9263720
Jonas V. de Souza, Felipe M. dos S. Monteiro, R. B. Otto, Mauricio Biczkowski, E. Asada
Due to the increasing inclusion of renewable energy sources in the Distribution System (DS), the interest in Energy Storage Systems (ESSs) connected to the network and how to justify the investment has grown. By its attractive features such as fast response and decreasing price, the ESS can be used in various scenarios in the electrical system. Among them, we highlight the profit from the purchase and sale of electricity and the improvement of the voltage profile. The objective of this work is to evaluate the operation of the storage system by using the Multi-objective Evolutionary Particle Swarm Optimization (MEPSO) to perform energy arbitrage and jointly improve the voltage profile of the network. The MEPSO is used to find a set of operational decisions to buy or store energy using the prices of the Day-Ahead Market (DAM) and, within these decisions, to operate the ESS during the Real-Time Market (RTM) hours. The results are promising and evidence that, through the proposed methodology, it is possible to perform energy arbitrage with the improvement of the voltage profile and a low number of charging/discharging cycles.
由于可再生能源越来越多地纳入配电系统(DS),对与电网连接的储能系统(ess)的兴趣以及如何证明投资的合理性已经增长。由于其响应速度快、价格低廉等特点,可应用于电力系统的各种场合。其中,我们强调了购电和售电的利润和电压剖面的改善。本研究的目的是利用多目标进化粒子群优化(MEPSO)进行能量套利,共同改善电网的电压分布,从而评估储能系统的运行情况。MEPSO用于找到一组使用日前市场(DAM)价格购买或存储能源的操作决策,并在这些决策的范围内,在实时市场(RTM)时间内运行ESS。结果是有希望的,并且证据表明,通过提出的方法,可以通过改善电压分布和减少充电/放电循环次数来进行能源套利。
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引用次数: 0
Analysis of the Frequency Dependent Lumped Parameter Transmission Line Model 频率相关的集总参数传输线模型分析
Pub Date : 2020-11-12 DOI: 10.1109/WCNPS50723.2020.9263744
J. Colqui, L. Timaná, P. Caballero, Sérgio Kurokawa
An accurate transmission line model must consider the frequency-dependence of the longitudinal parameters. In this context, the lumped parameter model (LPM), despite its well-known limitations, had been largely used to compute transient responses in power systems. To improve its accuracy, the frequency dependence of the longitudinal parameters had been included in the LPM by using fitting techniques and equivalent circuits. However, this representation had been described only for open-circuit tests. In this paper, we present the generic state space model of the frequency dependent LPM. We investigate how the number of line sections and the quality of the fitting affect the performance and accuracy of the frequency-dependent LPM, both in the time domain and in the frequency domain. Results shows that the frequency dependent LPM is accurate when compared to the JMarti line model.
一个精确的传输线模型必须考虑纵向参数的频率依赖性。在这种背景下,集总参数模型(LPM)尽管有其众所周知的局限性,但已被广泛用于计算电力系统的暂态响应。为了提高LPM的精度,利用拟合技术和等效电路将纵向参数的频率依赖关系纳入LPM中。然而,这种表述只适用于开路试验。本文给出了频率相关LPM的一般状态空间模型。我们研究了线段的数量和拟合的质量如何影响频率相关LPM在时域和频域的性能和精度。结果表明,与JMarti线模型相比,频率相关的LPM是准确的。
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引用次数: 0
A Power Transformer Event Classification Technique Based on Support Vector Machine 基于支持向量机的电力变压器事件分类技术
Pub Date : 2020-11-12 DOI: 10.1109/WCNPS50723.2020.9263773
L. D. Simões, B. L. Souza, H. J. Costa, R. P. de Medeiros, V. S. Orivaldo, F. Costa
Currently, different artificial intelligence techniques have been applied in power transformer protection purposes to discriminate internal faults from inrush currents and other disturbances. This paper proposes the application of a support vector machine (SVM) algorithm, for distinguishing among internal faults, external faults, and transformer energizations in a power transformer. The event classifier is enabled through a disturbance detector, hence receiving as input features the first post-fault boundary wavelet differential energies, which are processed by the classifier during the training and set stages. Simulations of a 100 MVA rated power transformer using the Alternative Transients Program (ATP) were carried out. Hence considering a wide variety of the fault parameters, a performance analysis of the SVM classifier regarding the overall accuracy and the time of operation in discriminating the events was accomplished, and promising results were achieved.
目前,不同的人工智能技术已应用于电力变压器保护目的,以区分内部故障与涌流和其他干扰。本文提出了一种支持向量机(SVM)算法,用于电力变压器内部故障、外部故障和变压器断电的识别。事件分类器通过扰动检测器使能,因此接收作为输入特征的第一个后故障边界小波微分能量,由分类器在训练和设置阶段进行处理。利用备选瞬变程序(ATP)对100 MVA额定功率变压器进行了仿真。因此,在考虑多种故障参数的情况下,对SVM分类器在事件识别方面的总体准确率和运行时间进行了性能分析,并取得了令人满意的结果。
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引用次数: 2
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2020 Workshop on Communication Networks and Power Systems (WCNPS)
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