Load estimation for load monitoring at distribution substations

This paper addresses the issues related to the real-time monitoring of loads at distribution substations. A method is proposed for estimating the measurements that become unavailable due to metering problems. To address the low redundancy associated with the load monitoring scheme, the method uses a regression-based model and makes use of the strong correlation between the loads that are geographically close to each other. The method was tested with actual field data. The results indicate that the method has the acceptable performance for measurement loss of up to a week. The average estimation error varies 2-7.5%, depending on which measurement is lost and how up-to-date the historical data is.     

Load modeling for wide area power system

The electrical loads have significant impacts on the dynamic performances of the wide area power system, and the dynamic behaviors of the power system can not be reproduced by the simulation system, unless the electrical load was modeled accurately. In the previous load modeling, the dynamics of the electrical load itself is of concern. Using such load models, the satisfied dynamic behaviors of the wide area power system can not be obtained by simulation. In this paper, a system-wide load modeling strategy is proposed. The electrical loads are firstly classified into a few categories using the component based load modeling method. According to the problems studied, the output variables of the power system are selected, and the objective function is constructed using the data from Wide Area Measurement System (WAMS). The trajectory sensitivities of the load model parameters with respect to the output variables are analyzed. Based on which, the key parameters playing important roles on dynamics of the power system are identified and included in further parameter estimation. The load models for the wide area power system are built simultaneously, and the system-wide load modeling method is implemented on the IEEE 39-bus system to evaluate its effectiveness.     

Non-Gaussian multivariate modeling of plug-in electric vehicles load demand

This paper proposes an organized stochastic methodology to model the power demand of plug-in electric vehicles (PEVs) which can be embedded into probabilistic distribution system planning. Time schedules as well as traveling and refueling information of a set of commuter vehicles in Tehran are utilized as the input dataset. In order to generate the required synthetic data, the correlation structure of the aforesaid random variables is taken into account using a multivariate student’s t function. Afterwards, a Monte Carlo based stochastic simulation is provided to extract the initial state-of-charge of batteries. Further, a non-Gaussian probabilistic decision making algorithm is developed that accurately infers whether the PEVs charging should take place every day or not. Then, through presenting a state transition model to describe the charging profile of a PEV battery, hourly demand distributions of the PEVs are derived. The obtained distributions can be used to generate the random samples required in probabilistic planning problems. Eventually, the extracted distributions are employed to estimate demand profile of a fleet that can be efficiently utilized in various applications.     

电力系统综合负荷建模平台的开发

开发了一个电力系统综合负荷建模软件平台.平台由原始数据库服务器、应用服务器和客户机3个层次构成，实现原始负荷特性数据管理、数据预处理与基于元件的负荷特性综合、模型结构选择与辨识建模以及动特性分类与综合三大功能.运用结构化软件设计思想，各功能模块相互独立;数据库服务器采用SQL Server 2000开发工具，操作界面采用Visual Basic6.0开发环境，功能模块采用Visual Basic6.0和Visual C＋＋6.0实现.平台以湖南电网综合负荷特性的调查统计数据和变电站现场采集的负荷特性数据为基础，实现了基于元件和基于量测2种途径的综合负荷建模，具有功能完善、设计新颖、运行可靠、操作简单、扩展性好的特点，能够方便地建立各典型用户、各用电行业和变电站的综合负荷模型.     

Optimal configuration analysis for acampus microgrid—a case study

The foremost issues of 21st century are challenging demand of electrical energy and to control the emission of Green House Gases (GHG) emissions. Renewable energy resources based sustainable microgrid emerges as one of the best feasible solution for future energy demand while considering zero carbon emission, fossil fuel independency, and enhanced reliability. In this paper, optimization and implementation of institutional based sustainable microgrid are discussed based on cost analysis, carbon emission, and availability of energy resources. Various microgrid topologies are considered for addressing the most ideal solution. The metrological data such as irradiance is acquired from solar satellite data of NASA (National Aero Space Agency) while the data for wind speed is taken from synergy enviro engineer’s site. HOMER® simulation tool is used for modelling and optimization purpose.     

Time series modeling and filtering method of electric power load stochastic noise

Stochastic noises have a great adverse effect on the prediction accuracy of electric power load. Modeling online and filtering real-time can effectively improve measurement accuracy. Firstly, pretreating and inspecting statistically the electric power load data is essential to characterize the stochastic noise of electric power load. Then, set order for the time series model by Akaike information criterion (AIC) rule and acquire model coefficients to establish ARMA (2,1) model. Next, test the applicability of the established model. Finally, Kalman filter is adopted to process the electric power load data. Simulation results of total variance demonstrate that stochastic noise is obviously decreased after Kalman filtering based on ARMA (2,1) model. Besides, variance is reduced by two orders, and every coefficient of stochastic noise is reduced by one order. The filter method based on time series model does reduce stochastic noise of electric power load, and increase measurement accuracy     

Load torque identification in induction motor using neural networks technique

Induction motors are widely used in several industrial sectors. However, the dimensioning of induction motors is often inaccurate because, in most cases, the load behavior in the shaft is completely unknown. The proposal of this paper is to use artificial neural networks as a tool for dimensioning induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Simulation results are also presented to validate the proposed approach.     

Speech and noise power estimation using Gamma modeling

In speech enhancement, having an accurate estimation of the power of the speech and noise signals forming the noisy observation is critical, as it can highly affect the performance of the enhancement algorithm. A method is introduced in which the distributions of the power of the speech and noise periodograms are modeled using the Gamma distribution to extract their shape parameters. These shape parameters are later used in the observed noisy speech to estimate the power when forming speech and noise periodograms. This method results in more accurate and faster power estimation with respect to the well‐known minimum statistics power estimation algorithm and together with the maximum a posteriori speech enhancement algorithm exhibits good speech enhancement performance.     

Uncertainty analysis for bulk power systems reliability evaluation using Taylor series and nonparametric probability density estimation

The uncertainties in reliability evaluation model are fundamentally classified into aleatory and epistemic types. Aleatory uncertainty arises from the intrinsic randomness associated with a physical system, such as components stochastic failure and repair process. Epistemic uncertainty, on the other hand, results from an incomplete or inaccurate scientific understanding of the underlying process, such as component reliability parameters uncertainty. It’s significant for risk based decision to distinguish the two kinds of uncertainties and quantify their impacts on reliability analysis. In literatures, most of papers focused on aleatory uncertainty, and only a few of them discussed the epistemic uncertainty. This paper is aimed to address uncertainty analysis of reliability indices considering the randomness of reliability parameters. Two goals are achieved in this paper. Firstly, the reliability indices are approximated through Taylor series with high efficiency after component parameters change, and its accuracy is compared with rerunning reliability evaluation. Secondly, to uncover the uncertainty propagation from input reliability parameters level to reliability evaluation output level, two methods, i.e. Taylor series Approximation and Monte Carlo simulation combined with nonparametric probability density estimation are proposed. Results obtained for the RBTS and IEEE-RTS79 power systems are presented and the validity of the proposed methods is verified.     

大区互联系统稳定计算中负荷模型研究

针对我国大区电网互联后稳定计算中出现的新情况，通过采用不同负荷模型对我国3个大区互联系统重要输电断面稳定极限的计算比较，详细分析稳定计算中马达挂在不同电压等级、配电网不同模拟方法对马达特性及稳定计算结果的影响，指出我国有些网调长期采用的马达模型存在定子漏抗过大的问题，建议现阶段采用IEEE-6马达模型，或者采用国内提出并计算出的稳定特性和稳定水平与之比较接近的研究马达模型或综稳Ⅲ型马达模型。     

浅析变电站及配电网电容器容量的合理配置

论述了并联电力电容器在无功补偿设备容量不足的电网中所具有的提高电压质量和降低损失的双重作用。通过分析变电站和配电网的结构和负荷特点，对如何进行合理的无功补偿及经济合理配置电容器等问题加以探讨。     

Parameter estimation of a transformer with saturation using inrush measurements

This paper presents a method to compute the parameters of a transformer model with saturation using the voltage and current waveforms of an inrush test and a no-load test. The transformer is modeled with their electric and magnetic equivalent circuits and a single-valued function that characterizes its non-linear magnetic behavior. A 3-kVA single-phase transformer and a 5-kVA three-phase three-legged transformer have been tested in the laboratory. The method to obtain the parameters of the non-linear flux–current relation that characterize the saturation has been described in the paper. The analytical function used to adjust the experimental measurements fits them very well.     

Decentralized load frequency controller for a multi-area interconnected power system

This paper proposes a control scheme for the load frequency control (LFC) problem of multi-area power systems. These systems are treated as interconnected dynamical systems. In the design of the proposed controller, each local area network is overlapped with states representing the interconnections with the other local area networks in the global system. Then, a decentralized control scheme is developed as function of the local area state variables and those resulting from the overlapped states which represent an approximation of the interconnection variables. The proposed controller guarantees the asymptotic stability of the overall closed loop system.The simulation results indicate that the proposed control scheme works well. In addition, they show that the controlled system is robust to changes in the parameters of the power system and to bounded input disturbances acting on the system. Moreover, the simulation results show that the controlled system behaves well even when there is a maximum limit on the rate of change in power generation.     

基于信息扩散估计的负荷综合建模

为了满足电力系统先验仿真的要求,负荷模型需要概括不同时刻的负荷特性。针对负荷建模实测数据较少的情况,提出一种基于信息扩散估计的负荷综合建模方法。该方法首先利用负荷节点采集到的小样本单条扰动数据建立负荷模型,模型中仅辨识灵敏度大的参数以降低参数分散性的影响。然后基于信息扩散理论估计负荷模型参数的概率密度函数,利用K-S方法检验所得概率密度的正确性。最后根据此概率密度函数估计综合负荷模型的参数。该方法可以对现场实测的有限数据样本信息进行拓展,通过对样本信息的深度挖掘来估计模型参数的总体特征,使得所建负荷模型具有良好的泛化能力。     

电厂电气系统图形建模仿真

数学模型是电厂仿真的基础。该文将电厂电气系统中的每一设备或元件作为一模块并编写相应的算法存入算法库,通过图形建模方法对其进行组态,实现了对电厂电气系统的仿真。目前,该算法已应用于上海石化热电总厂的仿真系统中,仿真结果表明,该算法速度快且不存在收敛问题,仿真精度能够满足现场要求。     

Interval analysis applied to the maximum loading point of electric power systems considering load data uncertainties

This paper proposes a simple and efficient power flow method to calculate, in an interval manner, the main variables corresponding to the maximum loading point, under load data uncertainties. The resulting interval nonlinear system of equations is solved using Krawczyk method. The proposed methodology is implemented in the Matlab environment using the Intlab toolbox. Results are compared with those obtainable by Monte Carlo simulations. IEEE 30 bus system and a South-southeastern Brazilian network are used to validate the proposed methodology.     

一种综合改进型遗传算法及其在综合负荷建模中的应用

针对基本遗传算法本身存在收敛速度慢和易早熟的缺陷,提出一种综合改进型遗传算法并成功地应用于负荷建模。该改进遗传算法通过对初始种群的选择、最优个体的保留、自适应的交叉和变异率、早熟现象的防止策略等各方面进行综合的科学设计,能十分有效地克服早熟、避免近亲繁殖、明显提高收敛速度,并具有优良的自适应特性。基于现场实测负荷特性数据的负荷建模实践表明,所提出的综合改进型遗传算法对于加速收敛缩短辨识时间、提高模型拟合精度、克服模型参数的分散性均具有显著作用,是一种很适合于负荷建模的优秀优化算法。     

Method for dynamic behaviour control of electric power systems

A statistical method that determines quality indices is presented. These indices give the regression relations for the dynamic behaviour of an electric power system as a function of some EPS scheme and regime parameters. The regression relations obtained are simple and allow a rapid and precise analysis and synthesis. The method is applied to solve an example of a real problem system.     

The load forecasting technology in the electric power distribution system

The authors proposed a method for creating a prediction model for dynamically predicting the load curve in a power distribution system whose composition varied day by day, and then validated its effectiveness for comparisons with measured current values. In the results, the authors divided power distribution lines into interval increments using the contract power category composition ratio and then created a prediction model for various interval patterns based on the substantial dependence of the consumer load curve on contract type. Furthermore, when confirming the prediction precision using their model, the authors found that the absolute average error was 7.7 A, with 95% within 30 A. The relative average error rate was confirmed at 9.6%. Upon systematizing the current value prediction method and performing opinion surveys and evaluations of users running trials in the power distribution system operation business, the authors were able to confirm that the prediction precision of their method was sufficient for the system's needs. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 153(2): 14–27, 2005; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20146