基于大数据分析的10kV配网停电作业时长预测优化研究

传统的10kV配网作业停电时长预测主要依赖于前期现场勘查和工作负责人经验估算。随着配网作业手段的不断进步及用户供电可靠性、优质服务要求的不断提高,对10kV配网预安排停电预测的精细、准确程度也提出了较高的要求。针对目前停电时长预测与实际完成偏差仍然较大情况,本文提出了一种基于大数据分析的10kV配网停电作业时长预测方法。通过开展近三年配网停电事件分析,归纳整理影响预测准确度的不同因素,从标准化作业时长典型基准库、标准化作业时长优化调整机制、标准化作业时长后评估体系以及综合停电时长预测模型四个方面展开深入研究,优化提升综合停电作业时长准确预测能力,为停电作业预测偏差管控提供一个可参考、易操作的高效辅助工具。生产作业实际应用表明,这种基于大数据分析的10kV配网停电作业计划时长预测方法,可以较好的提升停电作业时长准确预测能力,降低可靠性预测偏差,发挥可靠性目标管控作用,助力停电信息发布等优质服务工作精准开展。     

一种基于随机森林的停电敏感模型

为深入全面地对不同用电群体用户进行分析,实现停电敏感用户的精准识别,制定针对性的风险防控策略,有效减少客户来电风险,本文提出一种基于随机森林的停电敏感模型,对客户停电的敏感程度进行划分,进而实现差异化地运营管理客户,为营销部、设备部、客服中心等部门提供有效数据支撑,助力电网营销管理.本文将随机森林模型引入停电敏感预测中...     

基于聚类算法和转换网络的光伏短期功率预测

精准的光伏功率预测对优化光伏电站的运行和管理以及提高光伏发电的效率具有重要的作用。本文提出了一种基于聚类算法和转换网络的光伏短期功率预测方法。该方法首先基于自编码器的无监督聚类算法对光伏短期功率数据进行了预处理,以降低光伏出力数据本身的不稳定性对功率预测的影响。之后,该方法使用具有自注意力机制和多头注意力机制的转换网络进行光伏短期功率的预测。转换网络由编码器和解码器组成。转换网络相比传统的循环神经网络(RNN)更善于挖掘时序之间的关系。注意力机制使得转换网络具有并行计算的能力,可以加快网络训练的速度。最后,在澳大利亚光伏功率与气象数据中心 (DKASC)的光伏数据集上验证了本文提出的光伏短期功率预测方法。实验结果表明,本文提出的方法具有令人满意的预测精度。     

计及光伏及风电并网的电力系统短期负荷预测

为提升光伏、风电等分布式能源大量接入电网后短期电力负荷的预测精度,促进电网消纳能力提升,本文对光伏出力及短期用电负荷采用小波——径向基函数(RBF)神经网络预测方法;对风力发电首先利用总体平均经验模态分解(EEMD)方法对其功率数据分解,再采用BP神经网络、RBF神经网络、小波神经网络、ELMAN神经网络四种神经网络预测方法进行预测,并用粒子群算法(PSO)和灰色关联度(GRA)修正。最后,利用等效负荷的概念,分析光伏、风力发电并网对于短期电力负荷预测的影响,并将三种模型有效结合,得到了考虑光伏及风力发电并网的电力系统短期负荷预测的等效负荷预测模型。实例分析表明,本文所提方法相较于其他方法在该预测项目上具有相对更高的预测精度。     

基于Elman神经网络的GNSS/INS全域高精度定位方法

针对当前智能网联汽车定位与导航系统无法接收全球导航卫星系统（GNSS）信号引起定位失效的问题，提出一种基于Elman神经网络的GNSS结合惯性导航系统（INS）的全域高精度定位方法。首先，采用神经网络方法，建立了基于Elman网络的GNSS/INS高精度定位训练模型和GNSS失效预测模型；然后，利用GNSS、INS和实时动态（RTK）等定位技术，设计了GNSS/INS高精度定位数据采集实验系统；最后，选取采集的有效实验数据进行了反向传播（BP）神经网络、级联BP（CFBP）神经网络、Elman神经网络的训练模型性能对比分析，并验证了基于Elman网络的GNSS失效预测模型。实验结果表明，所提方法训练误差指标均优于基于BP和CFBP神经网络的方法；在GNSS失效1 min、2 min、5 min时，基于预测模型的预测平均绝对误差（MAE）、方差（VAR）和均方根误差（RMSE）分别为18.88 cm、19.29 cm、58.83 cm，8.96、8.45、5.68和20.90、21.06、59.10，随着GNSS信号失效时长的增加，定位预测精度降低。     

基于混沌免疫粒子群优化和广义回归神经网络的回采工作面瓦斯涌出量预测模型

为提高回采工作面绝对瓦斯涌出量预测的精度和效率,提出了将混沌免疫粒子群优化(CIPSO)算法与广义回归神经网络(GRNN)相耦合的绝对瓦斯涌出量预测模型。该方法采用CIPSO对GRNN的光滑因子进行动态优化调整,减少了人为因素对GRNN网络输出结果的影响,并采用优化后的网络建立瓦斯涌出量预测模型。通过对某煤矿瓦斯涌出量数据的仿真实验结果表明:基于CIPSO-GRNN的回采工作面绝对瓦斯涌出量模型比BP神经网络、Elman网络预测模型具有更好的预测精度和收敛速度,证明了该方法的有效性和可行性。     

基于犹豫模糊矩阵与变异算子的变电设备故障检修仿真

为降低变电设备故障检修时的综合风险成本,提出基于犹豫模糊矩阵与变异算子的变电设备故障检修方法。设置犹豫模糊矩阵,提取变电设备振动信号特征,将特征值输入稳定的Hopfield神经网络,分类诊断变电设备的故障;通过基于变异算子的变电设备故障检修优化模型,构建目标为综合风险成本的函数,设置约束条件为电网停电次数为1次、传输功率不越限,获取符合检修目标和约束条件的检修最优方案。实验仿真结果显示：所提方法可优化变电设备故障检修方案,提升变电设备故障诊断效率,保证设备检修的停电次数为每月1次,降低电网综合风险成本。     

基于多源信息融合的架空输电线路覆冰预测模型研究

输电线路严重覆冰可能会导致输电线路的机械和电气性能急剧下降,威胁电力系统安全、稳定运行。线路覆冰预测技术是电网防冰、抗冰领域难点之一。本文以电网输电线路自然覆冰监测大数据为基础,进行数据异常处理、缺失值填补等预处理,提出一种基于覆冰拉力浮动区间的区间准确率评测方法。研究基于新型深度学习的数据驱动输电线路覆冰预测技术,构建了融合历史监测拉力、微气象数据及未来天气预报的拉力时序、一阶差分拉力时序覆冰预测模型,实现覆冰监测终端未来24小时逐小时的拉力准确预测,提前预知输电线路是否覆冰以及覆冰程度,有助于防冰、融冰决策,保证电力系统稳定安全运行。     

Adaptive transfer learning in deep neural networks: Wind power prediction using knowledge transfer from region to region and between different task domains

Transfer learning (TL) in deep neural networks is gaining importance because, in most of the applications, the labeling of data is costly and time consuming. Additionally, TL also provides an effective weight initialization strategy for deep neural networks. This paper introduces the idea of adaptive TL in deep neural networks (ATL‐DNN) for wind power prediction. Specifically, we show in case of wind power prediction that adaptive TL of the deep neural networks system can be adaptively modified as regards training on a different wind farm is concerned. The proposed ATL‐DNN technique is tested for short‐term wind power prediction, where continuously arriving information has to be exploited. Adaptive TL not only helps in providing good weight initialization, but also in utilizing the incoming data for effective learning. Additionally, the proposed ATL‐DNN technique is shown to transfer knowledge between different task domains (wind power to wind speed prediction) and from one region to another region. The simulation results show that the proposed ATL‐DNN technique achieves average values of 0.0637, 0.0986, and 0.0984 for the mean absolute error, root mean squared error, and standard deviation error, respectively.     

基于GRNN全信息神经网络的超短期风速预测研究

为了更好地研究风功率预测,风速预测显得至关重要.国内神经网络文献均只表现出了短期风速预测,而对于超短期风速预测的神经网络数学模型却相对稀少.引入了GRNN神经网络,详细说明了该方法的超短期风速预测原理并建立了数学模型;为了使超短期风速预测精度有一个良好的对比性分析,将影响风电输出功率的各NWP(numerical weather prediection)信息(包括风速、风向、气温、气压)进行组合,以国内某风电场2014年5月份的各NWP数据进行算例分析,实验结果表明,GRNN全信息神经网络可以达到很好的预测精度,而且运算网络的稳定性甚优.     

Learning to predict ice accretion on electric power lines

Ice accretion on power transmission and distribution lines is one of the major causes of power grid outages in northern regions. While such icing events are rare, they are very costly. Thus, it would be useful to predict how much ice will accumulate. Many current ice accretion forecasting systems use precipitation-type prediction and physical ice accretion models. These systems are based on expert knowledge and experimentations. An alternative strategy is to learn the patterns of ice accretion based on observations of previous events. This paper presents two different forecasting systems that are obtained by applying the learning algorithm of Support Vector Machines to the outputs of a Numerical Weather Prediction model. The first forecasting system relies on an icing model, just as the previous algorithms do. The second system learns an effective forecasting model directly from meteorological features. We use a rich data set of eight different icing events (from 2002 to 2008) to empirically compare the performance of the various ice accretion forecasting systems. Several experiments are conducted to investigate the effectiveness of the forecasting algorithms. Results indicate that the proposed forecasting system is significantly more accurate than other state-of-the-art algorithms.     

Software development effort prediction of industrial projects applying a general regression neural network

An important factor for planning, budgeting and bidding a software project is prediction of the development effort required to complete it. This prediction can be obtained from models related to neural networks. The hypothesis of this research was the following: effort prediction accuracy of a general regression neural network (GRNN) model is statistically equal or better than that obtained by a statistical regression model, using data obtained from industrial environments. Each model was generated from a separate dataset obtained from the International Software Benchmarking Standards Group (ISBSG) software projects repository. Each of the two models was then validated using a new dataset from the same ISBSG repository. Results obtained from a variance analysis of accuracies of the models suggest that a GRNN could be an alternative for predicting development effort of software projects that have been developed in industrial environments.     

Prediction of transmission line overloading using intelligent technique

With the worldwide deregulation of power system, fast line flows or real power (MW) security assessment has become a challenging task for which fast and accurate prediction of line flows is essential. Since last few years, limit violation of voltage and line loading has been responsible for undesirable incidents of power system collapse leading to partial or even complete blackouts. Accurate prediction and alleviation of line overloads is the suitable corrective action to avoid network collapse. The control action strategies to limit the transmission line loading to the security limits are generation rescheduling/load shedding. In this paper, an intelligent technique based on cascade neural network (CNN) is presented for identification of the overloaded transmission lines in a power system and for prediction of overloading amount in the identified overloaded lines. The effectiveness of the proposed CNN based approach is demonstrated by identification and prediction of line overloading for different generation/loading conditions in IEEE 14-bus system. Once the cascade neural network is trained properly, it provides accurate and quick results for previously unseen loading scenarios during testing phase.     

Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction

Neural networks have been employed in a multitude of transportation engineering applications because of their powerful capabilities to replicate patterns in field data. Predictions are always subject to uncertainty arising from two sources: model structure and training data. For each prediction point, the former can be quantified by a confidence interval, whereas total prediction uncertainty can be represented by constructing a prediction interval. While confidence intervals are well known in the transportation engineering context, very little attention has been paid to construction of prediction intervals for neural networks. The proposed methodology in this paper provides a foundation for constructing prediction intervals for neural networks and quantifying the extent that each source of uncertainty contributes to total prediction uncertainty. The application of the proposed methodology to predict bus travel time over four bus route sections in Melbourne, Australia, leads to quantitative decomposition of total prediction uncertainty into the component sources. Overall, the results demonstrate the capability of the proposed method to provide robust prediction intervals.     

基于过程神经元网络的动态预测模型及其应用

〗针对动态系统过程预测预报问题，提出了一种基于过程神经元网络的动态预测方法．过程神经元网络的输入/输出均可以是时变函数，其时空聚合运算和激励可同时反映时变输入信号的空间聚合作用和输入过程中的阶段时间累积效应．基于过程神经元网络的动态预测模型能同时满足对系统的非线性辨识和过程预测，在机制上对动态预测预报问题有较好的适应性．文中给出了基于函数基展开和梯度下降法的学习算法，以电力负荷预报为例验证了模型和算法的有效性．     

An approximation method of origin–destination flow traffic from link load counts

Traffic matrix (TM) is a key input of traffic engineering and network management. However, it is significantly difficult to attain TM directly, and so TM estimation is so far an interesting topic. Though many methods of TM estimation are proposed, TM is generally unavailable in the large-scale IP backbone networks and is difficult to be estimated accurately. This paper proposes a novel method of TM estimation in large-scale IP backbone networks, which is based on the generalized regression neural network (GRNN), called GRNN TM estimation (GRNNTME) method. Firstly, building on top of GRNN, we present a multi-input and multi-output model of large-scale TM estimation. Because of the powerful capability of learning and generalizing of GRNN, the output of our model can sufficiently capture the spatio-temporal correlations of TM. This ensures that the estimation of TM can accurately be attained. And then GRNNTME uses the procedure of data posttreating further to make the output of our model closer to real value. Finally, we use the real data from the Abilene Network to validate GRNNTME. Simulation results show that GRNNTME can perform well the accurate and fast estimation of TM, track its dynamics, and holds the stronger robustness and lower estimation errors.     

Integrated evolutionary neural network approach with distributed computing for congestion management

Electric supply industry is facing deregulation all over the world. Under deregulated power supply scenario, power transmission congestion has become more intensified and recurrent, as compared to conventional regulated power system. Congestion may lead to violation of voltage or transmission capacity limits, thus threatens the power system security and reliability. Also the growing congestion may lead to unanticipated divergent electricity pricing. Owing to these facts congestion management has become a crucial issue in the deregulated power system scenario.Fast and precise prediction of nodal congestion prices in real time deregulated/spot power market may enable market participants and system operators to keep pace with the congestion by taking preventive measures like transaction rescheduling, bids (both for supplying and consuming electricity) modification, regulated dispatch of electric power, etc. This paper proposes an integrated evolutionary neural network (ENN) approach to predict nodal congestion prices (NCPs) for congestion management in spot power market. Distributed computing is employed to tackle the heterogeneity of the data in the prediction of NCP values. Developed ENNs have been trained and tested under distributed computing environment, using a message passing paradigm. Proposed hybrid approach for NCP prediction is demonstrated on a 6-bus test power system with and without distributed computing. The proposed approach not only demonstrated the computing efficiency of the developed ENN model over the conventional optimal power flow method but also shows the time saving aspect of distributed computing.     

Short-term electric power load forecasting using feedforward neural networks

Abstract: This paper presents the results of a study on short‐term electric power load forecasting based on feedforward neural networks. The study investigates the design components that are critical in power load forecasting, which include the selection of the inputs and outputs from the data, the formation of the training and the testing sets, and the performance of the neural network models trained to forecast power load for the next hour and the next day. The experiments are used to identify the combination of the most significant parameters that can be used to form the inputs of the neural networks in order to reduce the prediction error. The prediction error is also reduced by predicting the difference between the power load of the next hour (day) and that of the present hour (day). This is a promising alternative to the commonly used approach of predicting the actual power load. The potential of the proposed method is revealed by its comparison with two existing approaches that utilize neural networks for electric power load forecasting.     

基于协作通信技术的无线再生中继网络中的功率最优分配

针对无线再生中继网络的系统性能与系统误帧率、中断概率等有关问题,以协作通信技术中的多用户协作分集协议MDP(Multi-user Diversity-cooperative Protocol)为基础,设计了新型的MDPTS(Multi-user Diversity-cooperative Protocol Transmission Scheme)传输方案,优化了信号传输过程,提升了信号传输效率与网络吞吐量。在此基础上以系统误帧率最小为目标,提出了在发射端和中继器间的功率分配方案,以误帧率下届为目标函数,通过拉格朗日法求得系统的功率分配最优解。仿真结果表明,与采用传统传输方案的网络相比,采用MDPTS传输方案的网络具有更高的分集增益和复用增益,因此系统性能也更好。