Simplified Identification Strategy of Load Model Based on Global Sensitivity Analysis

Abstract

Load modeling plays an important role in accessing and enhancing the dynamic stability of power systems. Though the Synthesis Load Model Considering Voltage Regulation of Distribution Network (π model) has high accuracy, its parameters are too many. In order to improve the identification efficiency and reduce the difficulty of identification, a simplified model identification strategy based on parameter sensitivity analysis is proposed. Firstly, based on the global sensitivity analysis, the sensitivity analysis of the model parameters is carried out to obtain the First Order Sensitivity Indices (FSI)and Total Sensitivity Indices (TSI). Secondly, the FSI and TSI of each parameter are analyzed, and the effect on the output of model of each parameter is determined by FSI. For less influential parameters, whether the parameter should be fixed as constant is determined by the value of TSI. The parameter whose TSI equal or approximately equal to zero should be fixed as a constant. Finally, the improved genetic algorithm is used to identify the parameter-simplified model, and the effectiveness of the simplified identification strategy is verified by comparing the fitting effects with the measured curve and the residual and the integrated parameter models.

Load component decomposition and online modelling based on small disturbance response characteristic matching

“…There are many methods to solve Equation (12), such as the improved genetic algorithm [5] and particle swarm optimization (PSO) algorithm [17]. Considering complex nonlinear characteristics for the load model and long solution time consumption of optimization algorithm, this study aims to realize a fast decomposition of the load model based on an ANN in the online environment.…”

“…where P agg,n and Q agg,n are the actual measured values of the node total active and reactive power, respectively, in group n.ΔP agg,n and ΔQ agg,n are the deviation values of the node total active and reactive power, respectively, in group n. When the constraint condition ( 6) is satisfied, the 2-norm of the total power deviation vector is minimized as an objective function, and the proportional coefficient vector is optimally solved, as shown: min ‖B − A ω‖ 2 (12) There are many methods to solve Equation (12), such as the improved genetic algorithm [5] and particle swarm optimization (PSO) algorithm [17]. Considering complex nonlinear characteristics for the load model and long solution time consumption of optimization algorithm, this study aims to realize a fast decomposition of the load model based on an ANN in the online environment.…”

“…where P agg (U ) is the normalized total active power-voltage response function of the node according to Equation (4), and p i (U ) is the unit model of the load category i calculated by Equation (5). For a rated voltage (U 0 =1.0 p.u.…”

“…After the data preprocessing using different filters according to various requirements, the support vector machine regression model is adopted to identify the load model parameters. Based on the global sensitivity analysis, the load model parameters with little influence on the system response are determined [5], and an improved genetic algorithm is applied to identify the other model parameters. Parameter identification is carried out in multiple fault scenarios [6], and chaos algorithm with global search advantage is integrated into whale algorithm to solve the optimal parameters, which improves the accuracy and generalization ability of load model parameters.…”

Novel method for power system synthesis load identification

“…With the development of computers and algorithms, various nonlinear intelligent optimization algorithms are studied [7]. A simplified model identification strategy based on parameter sensitivity analysis was proposed in the literature [8], and genetic algorithm was used for parameter identification. In literature [9], the crossover operation in a genetic algorithm was introduced into particle swarm optimization, which improved the convergence of traditional particle swarm optimization.…”