Search

Load model is one of the most important element power system simulation and control. However, quantitatively analyzing uncertainty of key parameter in load model is difficult, due to the high computation cost of the traditional uncertainty analysis methods. Through Probabilistic Collocation Method (PCM), the quantitative uncertainty analysis of load models in dynamic stability is studied. Based on the measurements of artificial three-phase short circuit test in 2005 in North East Power Grid of China, this paper analyzes and compares the uncertainties of the composite load model and static load model, by using second-order PCM approximation function. Cases study indicates that PCM is suitable and feasible for uncertainty analysis in dynamics of power system.

It has been well recognized that the load model has great effects on the power system analysis and control. However, it is also widely known that the load modeling is a quite difficult problem due to its special properties such as time-variant characteristics of the load. In this paper, the measurement-based load modeling approach and multi-curve identification technique are applied in Hushitai power station in Northeast China. In order to build practical load models that can describe the actual load characteristics without changing the model parameters too often, cluster analysis is used to classify the loads. Two main kinds of eigenvectors used in cluster analysis are also compared. Finally, based on the load classification, a new aggregation method is proposed to build load models with good generalization capability. Case studies show the efficiency of the proposed method.

Load modeling is very important to power system operation and control. Measurement-based load modeling has been widely practiced in recent years. Mathematically, measurement-based load modeling problem are closely related to the parameter identification area. Consequently, an efficient optimization method is needed to derive the load model parameters based on the feedback of estimation errors between the measurements and model outputs. This paper reports our work on applying genetic algorithms on measurement-based load modeling research. Due to its robustness to the initial guesses on the load model parameters, genetic algorithms are very suitable for load model parameter identification. Two cases including both the real measurement in a power station and the digital simulation are studied in the paper. For comparison purpose, the classical nonlinear least square estimation method is also applied to find the load model parameters. The simulated outputs from the load model confirm the efficiency of genetic algorithms in measurement-based load modeling analysis. Future work will focus on fastening the converging speed of the genetic algorithms, and/or utilizing more efficient evolutionary computation methods.

Summary form only given. The accuracy of the load model has great effects on power system stability analysis and control. Based on our practice in China on modeling load from field measurements, this paper systematically develops a measurement-based composite load model. Principles guiding the load modeling practice are discussed based on detailed analysis on stochastic characteristics of modeling procedure. The structure of the measurement-based composite load model is presented. A multi-curve identification technique is described to derive parameters. The generalization capability of thus built load model is also investigated in the paper. Two cases are studied to illustrate the accuracy of the developed load model on describing the load dynamic characteristics in the actual power system.