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A method has been developed for preparing load models for power flow and stability. The load modeling (LOADMOD) computer software transforms data on load class mix, composition, and characteristics into the from required for commonly-used power flow and transient stability simulation programs. Typical default data have been developed for load compo...
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... main goal of client-side caching is to reduce the request time for the client's web requests. Yet it may involve some drawbacks when the user's cached pages become stale, in order to solve this problem HTTP standards have some headers that help to reduce the probability of caching stale pages. Fig. 1 shows typical request and response HTTP headers, the relevant caching headers in the response are Date, Cache-Control, Expires, Last-Modified and ...
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... fig. 1 demonstrates, the main end points of web page path are the origin server which created the response and the client which receives the response. Along the way there are other nodes such as ISP servers and proxy servers. For these servers the problem of saving the bandwidth is an important one, and if the same page has been requested more than one time and gave the same response, it should be cached at the proxy server so that any client requests the same page, the A Design and Implementation Model for Web Caching Using Server "URL Rewriting" Mostafa E. Saleh, A. Abdel Nabi and A. Baith Mohamed A World Academy of Science, Engineering and Technology 36 2009 proxy doesn't have to waste additional bandwidth in contacting the origin server and getting the page once ...
Citations
... In analysis steps, where the maximum conversion error is more important than the direction, the absolute values of ε i and η i are used. To quantify the conversion error across a voltage range V 1 ...V N , the Mean Absolute Error (MAE) (21) and Mean Absolute Percentage Error (MAPE) (22) are used. MAE describes the mean magnitude of conversion error ε i and MAPE the mean absolute value of relative conversion error η i . ...
... The value of MAE (21) and MAPE (22) was calculated for each load model conversion using voltage range from 0.8 to 1.2 p.u. The same voltage range was used for load model conversion. ...
... Mean Square Error (MSE) (9) has been used in [18], typically the values were in the range 1·10 −5 ...10·10 −5 . Normalized Mean Square Error (NMSE) (10) has been used in [19] and [22]. Alternatively, Mean Absolute Error (MAE) (7) can be used [21]. ...
... Residential load represents the largest energy consumption and therefore the load model scale can change, starting from the transmission line power grid level to the home appliance level [6]. The load model that provides power flow and dynamic performance simulation is divided into two groups; static load model which depends on steady-state network representation and considers only voltage-dependent characteristics, such as power flow and dynamic load model which considers both voltage-dependent and frequencydependent variation of the load, such as dynamic stability [7,8]. Load modelling is necessary to evaluate residential DR at the distribution circuit level and to study customer behaviours [4,9]. ...
... where ℎ , is the DR signal, , is the set point temperature and ∆ is the dead band temperature of ±2℃. The amount of air conditioner power consumed in kW at a given interval, ℎ can be expressed as ℎ , = ℎ * ℎ (8) where ℎ is the status of the air conditioner, ℎ , =1 means air conditioner is switched on, ℎ , = 0 means the air conditioner is switched off and ℎ is the air conditioner rated power in kW. ...
Power consumption of household appliances has become a growing problem in recent years because of increasing load density in the residential sector. Improving the efficiency, reducing energy and use of building integrated renewable energy resources are the major key for home energy management. This paper focuses on the development of simulation models for two appliances, namely, an electric water heater (EWH) and air conditioning (AC) load for the purpose residential demand response (DR) applications. Residential DR refers to a program which offers incentives to homeowners who curtail their energy use during times of peak demand. EWH and AC have a great probability in executing residential DR programs because they consume more energy compare to other appliances and are frequently used on a daily basis. Load model designed according to operational and physical characteristics. Validations were made on the models against real data measurement and it is found to be an accurate model with mean average error of 0.0425 and mean square error of 0.3432 for EWH and mean average error of 0.1568 and mean square error of 0.3915 for AC respectively. Furthermore, the results give suggest and insight the need for control strategies to evaluate better performance in residential DR implementations.
... Each of them needs particular collections of data which can be so time consuming. These methods are components modeling method and measurement method [11]. ...
... As mentioned before, after selection of model, model' parameters must be calculated. The main challenge is determination of data analysis method123456789101112. In this paper, whereas there are so many recorded data and based on structure of selected model, an adaptive analysis method based on least square method is used to estimate model's parameters. ...
Loads in the power systems are one of the most important parts in the network studies which have been less focused because of shortages and lack of sufficient data. In this paper, an adaptive load modeling method for large scale power systems is proposed which is not only useful to stability and protection analysis, but also is suitable for long time dynamic simulations. So, direct measurement method and dynamic simulation of reactive power resources has been considered in this method. Least square method and Taboo search algorithm have been used to form the objective function of modeling problem and solve it respectively. comparison between simulation results and real data convincingly approves the accuracy of the mentioned method.
... Physically-based method, component-based method, Artificial Neural Network (ANN)-based method and hybrid method are four common approaches used for deriving the structure of the load [8]. Component based [9], [10] and measurement based load modeling [11], [12] are two main approaches used for identification of load parameters. ...
Real time accurate representation of electric load model is of great importance in power system simulation, control and stability studies. Composite load model made up of an induction motor in parallel with static load has been widely accepted as an appropriate structure for describing load behaviors, especially for the studies that deal with power system stability. Measurement-based composite load model is reviewed in this paper and a real time observer is proposed to estimate the load model parameters using the concept of multiple model estimation algorithm. Simulation results are provided for a composite load including IEEE type 6 motor to show the effectiveness of the proposed real time estimator. This paper is a step in the right direction toward developing accurate real time techniques for stability analysis.
... Physically-based method, component-based method, Artificial Neural Network (ANN)-based method and hybrid method are four common approaches used for deriving the structure of the load [8]. Component based [9], [10] and measurement based load modeling [11], [12] are two main approaches used for identification of load parameters. ...
Electric load modeling is of great importance in power system stability analysis and control. Composite load model including induction motor in parallel with static load has been widely accepted as an appropriate structure for describing load behaviors, especially for the studies that deal with power system stability. This paper proposes a nonlinear observer based on Extended Kalman Filter (EKF) to estimate the dominant parameters of the composite load model using real time measurements. The developed estimator can be implemented online and does not require a huge memory for recording load behavior data over a long time. The outcome of this paper makes the online dynamic stability analysis of power systems a step closer to reality. Simulation results are carried out to show the effectiveness of the proposed approach.
... applied in power system simulation will lead to analysis results with big differences, even completely in contradictory12. Thus, building the load model reflecting the true characteristic of load has been of great importance to power system operation and control345678910111213. So far, load modeling is still an unsolved problem and a very difficult one. ...
Load modeling is very important for power system dynamic analysis and control. The composite load model widely applied recently in the power system operation centre consists of the static load and the equivalent motor. Current practices in measurement-based load modeling identify all the parameters in this composite load model. However, it is not clear whether all these parameters could be identified from the measurements. This paper investigates the identifiability of the equivalent motor parameters in the composite load model. Trajectory sensitivity approach is applied first to find the motor parameters that have great effects on the measured active as well as reactive load dynamics. The analysis results show that the motor outputs have various sensitivities with respect to the parameters. Since the voltage disturbance, the active load and the reactive load dynamics are applied to identify the motor parameters, those parameters affecting the measurements to a great extent are observable, thus identifiable from the measurements; while those that have little effects on the motor outputs are unobservable from the measurements and consequently unidentifiable. The case studies verify the identifiability of the motor parameters.
... Among all the components in the power system, the load model is one of the least-known elements ; however, its great influences on the system stability and control have long been recognized and recorded in the literature [1]–[9], [12]–[15]. There exist two approaches to build the load model: the component-based method [16], [17] and the measurement-based method [6], [8], [10], [11]. The component-based method builds the load model from the information on dynamic behavior of each individual component, the load composition data, and the load mixture data. ...
The representation of load dynamic characteristics remains an area of great uncertainty and has become a limiting factor for power system analysis and control. The random nature of the load makes load modeling a very difficult problem, which becomes even more challenging when the field measurements increase and the recorded dataset becomes large. This paper proposes a novel concept of modeling load based on support vectors (SVs) of load data. A three-stage procedure to find SVs of the recorded load dataset is presented. Then the load model is built on the SVs. Although the model is derived from only a small subset of the original dataset, it has a strong generalization capability to describe dynamics of the whole dataset. However, the computational burden on the modeling process is much relieved since only a small subset of data is involved. The proposed method also answers the question on how data should be grouped and how many load models should be built as data are accumulated. This paper infers that, although the data space where the load varies seems indefinite and big, its characteristic can be captured and modeled in a much smaller subspace. The presented method is shown to be effective by the case study on Hushitai substation.
Parameter identification is the key technology in measurement-based load modeling. A hybrid learning algorithm is proposed to identify parameters for the aggregate load model (ZIP augmented with induction motor). The hybrid learning algorithm combines the genetic algorithm (GA) and the nonlinear Levenberg-Marquardt (L-M) algorithm. It takes advantages of the global search ability of GA and the local search ability of L-M algorithm, which is a more powerful search technique. The proposed algorithm is tested for load parameter identifications using both simulation data and field measurement data. Numerical results illustrate that the hybrid learning algorithm can improve the accuracy and reduce the computation time for load model parameter identifications.
