Transient protection has the advantage of ultra-fast operation. However, traditional transient protection is susceptible to the influences of transitional resistance and the initial fault angle, resulting in issues with sensitivity and reliability under weak fault conditions. To address these issues, a new method of transient voltage protection is proposed by mining the equipment configuration information of the bus and line, as well as the frequency characteristics of the line boundary. To solve the problem that single-terminal transient line protection cannot distinguish between line fault and bus fault and the fault of adjacent line head end, a new fault feature is constructed by introducing a difference of instantaneous amplitude of the high-frequency signals between line voltage and bus voltage. To solve the problem that transient protection is susceptible to the influence of fault condition attributes, the fusion of fault characteristics with fault condition attributes results in synthetic fault characteristics, which contain fault attribute information. This approach overcame the adverse effects of fault attributes on transient protection and enhances protection reliability. Transient voltage high-frequency instantaneous amplitudes are obtained and integrated using the Variational Mode Decomposition Hilbert Transform (VMD-HT) to increase the stability of high-frequency fault signal detection. This algorithm solved the original problem of 42.2% cross-data, 41.6% false operations, and 8.9% refusal to operate. It protects both the full length of a transmission line and a busbar at the same time, with twice protection zone comparing with traditional protection. It is simpler, more reliable and more economical. Extensive ATP-Draw simulation experiments have validated the accuracy and reliability of the proposed algorithm.
In addressing the challenges of analyzing seismic response data for high-speed railroads, this research introduces a hybrid prediction model combining convolutional neural networks (CNN) and long short-term memory networks (LSTM). The model's novelty lies in its ability to significantly improve the precision of fiber grating monitoring for high-speed railroads. Employing quasi-distributed fiber optic gratings, seven grating monitoring points were strategically placed on each fiber to capture responses of the track plate, rail, base plate, and beam during seismic activities. Using data from peripheral gratings, the model predicts the central point's response. A continuous feature map, formed via a time-sliding window from the rail's acquisition location, undergoes initial feature extraction with CNN. These features are then sequenced for the LSTM network, culminating in prediction. Empirical results validate the model's efficacy, with an RMSE of 0.3753, MAE of 0.2968, and a R 2 of 0.9371, underscoring its potential in earthquake response analysis for rail infrastructures.
中程固定翼无人机在起降阶段高精度定位,要求实时计算无人机下滑道与固定基准站之间高精度的相对定位基线,以引导无人机精确起降.基于全球卫星导航系统(GNSS)实时动态载波相位差分(RTK)技术、无线链路数传电台技术,设计了固定基准站无人机起降引导系统.起降引导系统在静态零基线、动态飞行条件下,RTK引导系统与固定基准站之间相对定位精度达到水平方向≤0.03 m(1σ),高程方向≤0.05 m(1σ).试验结果表明,无人机起降引导系统达到了厘米级定位精度,满足中程固定翼无人机在起降阶段对高精度定位的要求.
对河流"分区、分类、分级"的精细化管理是推进水污染综合治理与水质保护的最佳管理办法.渌水作为湖南省创建的省级样板河,是湘赣边区域合作示范区流域综合治理典范,是一个边界完整的典型城市河流小流域.本研究通过计算整合渌水流域现状排污负荷数据,以水质分析模拟程序(water quality analysis simulation program,WASP)为工具,建立渌水水质模拟预测模型.以控制单元为载体,基于最大日污染负荷计划,计算分析流域各河段的污染负荷削减分配精细化管理方案.通过模型情景分析,考察上游来水水质超标对本流域的影响和在全流域生活污染治理工作落实完成后的水质改善情况.
商品销量受多种因素影响,这种影响难以用公式准确表达.为了获得更精准的预测结果,针对商品销量预测的高度复杂性和非线性,提出了基于混沌RBF神经网络的商品销量预测模型.该模型基于电商市场商品销售历史数据,使用李雅普诺夫指数算法评估商品销售数据混沌序列特性,并通过对数据库相结构的空间优化重构,利用RBF神经网络技术对优化重构后的数据进行训练归纳.同时,通过混沌方法计算网络的连接权值和高斯函数径向基中心,实现了RBF神经网络的优化.在Matlab平台上进行了仿真实验,结果表明,该混沌优化RBF神经网络商品销售预测模型具有较高的精度和较快的速度.
