基于灰色模型的电网技改规划可行性自动化分析系统

传统的电网技改规划系统存在电网设备陈旧、线路损坏等问题,电网系统的可靠性低,为此,设计基于灰色模型的电网技改规划可行性自动化分析系统.将灰色模型作为基本模型,结合余弦距离的思想,对电网运行过程中的数据进行预测.根据数据预测结果,对系统的运行效益和运行风险进行分析;通过电网运行效益分析结果、运行风险分析结果以及数据预测结果,组建基于灰色模型的电网技改规划可行性自动化分析系统,实现对整个电网系统运行方式的控制,使其能够安全、稳定的环境下运行.仿真实验结果表明,所设计系统能够快速、准确实现电网技改规划可行性自动化分析.     

基于CEEMDAN-SAFA-LSSVM的短期风功率预测

随着电网中风电渗透率的逐年提高,对其出力进行精确预测是保障电网可靠运行的技术措施之一.建立了基于CEEMDAN-SAFA-LSSVM短期风功率组合预测模型.采用完全集合经验模态分解(CEEMDAN)将原始风功率序列分解成特征互异的各个本征模态分量,对分解产生的本征模态分量进行相空间重构,然后根据得到的新模态分量建立相应的最小二乘支持向量机(LSSVM)预测模型.针对LSSVM模型的预测精度易受参数选择的影响,提出萤火虫算法(SAFA)优化LSSVM模型的参数,解决了 LSSVM参数寻优效率低的问题.算例分析表明CEEMDAN-SAFA-LSSVM模型在风功率预测中具有较高的预测精度和预测效率.     

智能电网环境下的电力调度安全运行监控方法研究

为了解决智能电网电力调度监控力度弱、运行安全差的问题,采用方案设计出一种电力调度安全监控网络,提高电网运行中的安全性;通过设计电力调度系统,总体把握电力运行过程,实现调度指令集中控制;建立DOS运行模型,对电力输送路径进行更新,为电力输送提供安全保证;建立安全监控网络,通过在运行节点设立监控设备,对电力运行实时监控,随时传达设备运行状态;采用生成对抗网络(ACGAN)算法,根据设立的电力安全标准,结果实现电力调度的安全优化设计;最后根据实验报表将电力调度安全等级分为三级,安全性达到Ⅲ级,允许并网;通过对比分析发现本研究安全指标达到90％以上;电力调度中波动频率在40～60 Hz,整体稳定性较好;从而验证了该设计方法的优越性,证实了本研究的可行性.     

基于TRACEr失误辨识方法的电网调度人因风险认知分析

辨识和分析电网调度中的人为失误对于防范和控制人因风险、保障电网安全稳定运行至关重要。人为失误预测和确认方法在复杂系统的设计、评估和运行中已得到了广泛应用。结合电网调度业务知识和认知心理学模型,对认知差错追溯和预测技术(Technique for Ret-rospective and Predictive Analysis of Cognitive Errors,TRACEr)进行了分析和改进,研究了其在电网调度人为风险分析中的应用。运用提出的人为失误分析方法,对电网调度中的人因风险案例进行了分析,结果表明,基于TRACEr失误辨识方法能较为全面地分析电网调度员的人为失误,并为失误的补救和防范提供有效的改进措施。     

电网调控一体化运行电力设备危险点控制仿真

针对传统电力设备危险点控制方法存在检测结果不准确、控制效果较差的问题,提出一种新的电网调控一体化运行电力设备危险点控制方法。通过数据挖掘获取电网调控一体化运行电力设备危险点数据特征输入量,对电力设备危险点进行检测。依据检测的电网调控一体化运行电力设备危险点数据,通过建立自回归模型对未来危险点进行预测,根据预测模型计算令性能指标达到最优的所有控制增量,采用误差信息加权法对误差进行计算,调整控制模型,获取经校正后的电网调控一体化运行电力设备危险点控制模型,完成电网调控一体化运行电力设备危险点控制。实验结果表明,所提方法的控制效果较好,对电力设备危险点检测结果准确,与实际检测结果具有很好的拟合度,保证了电力设备的安全运行。     

基于云模型的电力系统负荷组合预测

电力系统负荷预测的精确度决定着电网安全稳定、高效的运行.最小二乘支持向量机(LSSVM)被广泛应用电力系统负荷预测上,然而该方法在处理不确定性问题上有很多不足之处.为了更精确的选择核函数的参数,处理不确定性因素,提高短期负荷预测的精度,提出了一种将云模型、粒子群优化(PSO)和LSSVM相结合的组合模型.首先通过对各影响因子的不确定性分析,按不确定性高低将各影响因子分别应用Cloud-LSSVM和PSO-LSSVM进行预测,然后通过组合模型的加权计算的得到最终预测值.最后,通过仿真对比证明该模型能更好的处理不确定性,从而提高电力系统短期负荷预测精度.     

基于分布式平台的低压电网负荷监测模型研究

由于城市用电规模逐渐加大，要求有能适应现代城市需求的电网监测方法对电网进行全天候的安全监测。采用了电压结合频率的电路控制技术，以此为基础建立了低压逆变器模型，对电网模型的年负荷数值进行了预测。对比了改进前后2种控制策略下微电源电压稳定性，得到了基于改进后压力流量-电压频率(PQ-Vf)控制策略和采用电网逆变器建立的低压微电网模型。分析了负荷频率和电压幅值，得出该模型具有频率负荷震荡幅度小、电源电压稳定的特点。分布式平台建立的低压电网负荷监测模型适用性强。建立的低压电网监测系统为各类用电系统的安全管理提供了理论支撑，为电力企业的性能测试提供借鉴。     

基于神经网络的负荷预测仿真研究

提高电力负荷预测精度有利于电力部门的安全生产,有利于合理安排电网运行方式和机组的检修计划,有利于系统的合理规划和经济运行。为了提高短期负荷预测的精度,把自相关函数的概念应用到反向传播(Back Propogation,BP)神经网络输入变量选择中,通过MATLAB仿真软件建立负荷预测模型。最后对某电力系统1d的负荷进行预测,仿真结果验证了该模型的可行性和有效性。     

CS算法优化VMD-BiLSTM-AM的光伏功率预测

针对光伏发电功率的波动性与随机性对调度部门的负荷预测以及电网安全运行带来的严峻挑战, 提出了一种基于变分模态分解(VMD)和布谷鸟搜索(CS)算法优化的双向长短期记忆网络(BiLSTM)光伏发电功率预测方法. 首先使用VMD将光伏功率序列分解成不同频率的子模态, 通过皮尔逊相关性分析确定影响各模态的关键气象因子. 其次分别构建注意力机制(AM)和BiLSTM混合的光伏发电功率预测模型, 利用CS算法获取网络最优的权重和阈值. 最后, 将不同模态的预测结果相叠加, 得到最终的预测结果. 通过对亚利桑那州地区光伏电站输出功率进行预测, 验证了所提模型的有效性.     

GM(1,1)模型在巡航导弹预测中的应用

为了对巡航导弹的距离进行预测,建立了GM(1,1)模型,详细介绍了建模和计算预测值的过程.采用"等维灰数递补动态预测"的预测方法,对预测模型的可行性进行了分析,提出了对巡航导弹预测建模的原则,并进行了示例分析,用预测值和实际值进行了误差检验,并对模型精度进行了分析.建模和计算结果表明,该模型和方法对巡航导弹的距离进行预测,计算精度高且运算速度快.     

基于大数据平台的点负荷近中期负荷预测及行业负荷增长特征分析

为保障电力部门对于台区内设备的维护,需要预测台区的负荷。因此供电部门就必须具备预测未来一年以至更长时间的台区负荷的能力,防止因负荷过载对变压器造成损坏,并保证城市的可靠供电。对台区负荷的预测难点在于对于城中村的预测,城中村流动人口多,产业类型复杂多样,受就业环境、经济发展的影响深,表现为负荷的变化相较于其他的台区随机性更强。鉴于此原因,我们以大数据平台为依托,进行单因素变量的预测,采用季节分解模型对历史用电负荷进行季节分解;然后分别用线性回归和自回归积分滑动平均模型（ARIMA）对季节分解出来的趋势和季节、残差成分进行预测,获得精度良好的负荷预测模型,最后选择两个特征鲜明的行业进行比较,分析其负荷增长特征。     

A hybrid neural network and ARIMA model for water quality time series prediction

Accurate predictions of time series data have motivated the researchers to develop innovative models for water resources management. Time series data often contain both linear and nonlinear patterns. Therefore, neither ARIMA nor neural networks can be adequate in modeling and predicting time series data. The ARIMA model cannot deal with nonlinear relationships while the neural network model alone is not able to handle both linear and nonlinear patterns equally well. In the present study, a hybrid ARIMA and neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks. The proposed approach consists of an ARIMA methodology and feed-forward, backpropagation network structure with an optimized conjugated training algorithm. The hybrid approach for time series prediction is tested using 108-month observations of water quality data, including water temperature, boron and dissolved oxygen, during 1996–2004 at Büyük Menderes river, Turkey. Specifically, the results from the hybrid model provide a robust modeling framework capable of capturing the nonlinear nature of the complex time series and thus producing more accurate predictions. The correlation coefficients between the hybrid model predicted values and observed data for boron, dissolved oxygen and water temperature are 0.902, 0.893, and 0.909, respectively, which are satisfactory in common model applications. Predicted water quality data from the hybrid model are compared with those from the ARIMA methodology and neural network architecture using the accuracy measures. Owing to its ability in recognizing time series patterns and nonlinear characteristics, the hybrid model provides much better accuracy over the ARIMA and neural network models for water quality predictions.     

Combining seasonal time series ARIMA method and neural networks with genetic algorithms for predicting the production value of the mechanical industry in Taiwan

Supplying industrial firms with an accurate method of forecasting the production value of the mechanical industry to facilitate decision makers in precise planning is highly desirable. Numerous methods, including the autoregressive integrated-moving average (ARIMA) model and artificial neural networks can make accurate forecasts based on historical data. The seasonal ARIMA (SARIMA) model and artificial neural networks can also handle data involving trends and seasonality. Although neural networks can make predictions, deciding the most appropriate input data, network structure and learning parameters are difficult. Therefore, this article presents a hybrid forecasting method that combines the SARIMA model and neural networks with genetic algorithms. Analytical results generated by the SARIMA model are inputted as the input data of a neural network. Subsequently, the number of neurons in the hidden layer and the number of learning parameters of the neural network architecture are globally optimized using genetic algorithms. This model is subsequently adopted to forecast seasonal time series data of the production value of the mechanical industry in Taiwan. The results presented here provide a valuable reference for decision makers in industry.     

基于ARIMA-ANN预测模型的能量感知路由算法

针对传统能量感知OLSR协议在减少传输功率消耗和均衡节点剩余能量之间不能兼顾的特点,提出了一种新型的基于剩余能量比例和传输功率消耗的OLSR路由协议OLSR_RC,它利用上述两方面的指标构造复合能量开销,并将其作为路由选择的度量值。在减小网络开销的同时,也防止了部分低电量节点的能量被快速耗尽,延长了网络的生存周期。此外,新路由还采用ARIMA-ANN组合能量预测模型对节点的剩余电量进行预测,降低了由于拓扑控制(TC)消息丢失对选择路由所造成的影响。这种新型路由协议在无线传感器网络领域有比较广阔的应用前景。     

一种基于SARIMA-LSTM模型的电网主机负载预测方法

随着智能电网的不断发展,如何提高对信息设备运行状态的预测准确率以及设置适应数据变化的动态阈值区间是电网IT运维面临的巨大挑战。为了解决这些问题,提出了组合时间序列预测模型(SARIMA-LSTM),即在传统周期性ARIMA 模型(SARIMA)的基础上,引入深度学习领域的LSTM模型,并摒弃了过去精度低、效果差的误差拟合方法,使用误差自回归方法来补偿预测结果。该模型可以学习到传统ARIMA模型无法捕捉到的误差波动规律,解决其无法预测非线性数据的问题。实验结果表明,在实际预测电网内存负载数据时,与ARIMA模型和SAIRIMA模型相比,SARIMA-LSTM模型可以实现更高的预测精度。     

Predictive modeling for wastewater applications: Linear and nonlinear approaches

This study compares the multi-period predictive ability of linear ARIMA models to nonlinear time delay neural network models in water quality applications. Comparisons are made for a variety of artificially generated nonlinear ARIMA data sets that simulate the characteristics of wastewater process variables and watershed variables, as well as two real-world wastewater data sets. While the time delay neural network model was more accurate for the two real-world wastewater data sets, the neural networks were not always more accurate than linear ARIMA for the artificial nonlinear data sets. In some cases of the artificial nonlinear data, where multi-period predictions are made, the linear ARIMA model provides a more accurate result than the time delay neural network. This study suggests that researchers and practitioners should carefully consider the nature and intended use of water quality data if choosing between neural networks and other statistical methods for wastewater process control or watershed environmental quality management.     

Time-Series Forecast Modeling on High-Bandwidth Network Measurements

With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. We have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of the resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate time-series forecast model is developed with the Seasonal decomposition of Time series by Loess (STL) and the AutoRegressive Integrated Moving Average (ARIMA) on Simple Network Management Protocol (SNMP) path utilization measurement data. Compared with the traditional approach such as Box-Jenkins methodology to train the ARIMA model, our forecast model reduces computation time up to 92.6 %. It also shows resilience against abrupt network usage changes. Our forecast model conducts the large number of multi-step forecast, and the forecast errors are within the mean absolute deviation (MAD) of the monitored measurements.     

振荡型GM(1,1)幂模型及其应用

针对现实世界广泛存在的小样本振荡序列建模和预测问题,提出含有系统延迟和时变参数的振荡型GM(1,1)幂模型。给出最小二乘准则下的两级参数包计算公式,在此基础上构建非线性优化模型以寻求最佳幂指数和时间作用参数,以此识别原始数据所蕴含的振荡特征。将该模型应用于应急资源需求预测,并将建模结果与传统GM(1,1)幂模型、ARIMA和EMD-ARIMA方法进行比较,结果表明振荡型GM(1,1)幂模型具有较高的精度。     

Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming

The autoregressive integrated moving average (ARIMA), which is a conventional statistical method, is employed in many fields to construct models for forecasting time series. Although ARIMA can be adopted to obtain a highly accurate linear forecasting model, it cannot accurately forecast nonlinear time series. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but explaining the meaning of the hidden layers of ANN is difficult and, moreover, it does not yield a mathematical equation. This study proposes a hybrid forecasting model for nonlinear time series by combining ARIMA with genetic programming (GP) to improve upon both the ANN and the ARIMA forecasting models. Finally, some real data sets are adopted to demonstrate the effectiveness of the proposed forecasting model.     

Determination of OD cooling system parameters based on thermal modeling of power transformer winding

Power transformers are the main and one of the most expensive parts of electrical networks. Optimum design of power transformers and their cooling system requires the precise calculation of losses, hot spot temperature and position. In this paper, thermal behavior of winding in power transformers and its insulation system have been modeled and parameters affecting the transformer cooling have been determined. This study is based on the thermal modeling of disk winding. Using software programs, several cooling schemes have been simulated and the results show the effect of different geometrical parameters on cooling of the transformer winding. The exact position of hot spot and the heat loss of a model transformer has been obtained and compared with the results provided by the manufacturer. It is shown that inclusion of eddy current losses improves the prediction of the hot spot position, while ignoring this loss could lead to inaccurate prediction of the hot spot position and temperature.