Data-Driven Parameter Estimation of Steady-State Load Models

被引：0

作者：

Tushar ^{[1
]}

Pandey, Shikhar ^{[1
]}

Srivastava, Anurag K. ^{[1
]}

Markham, Penn ^{[2
]}

Bhatt, Navin ^{[2
]}

Patel, Mahendra ^{[2
]}

机构：

[1] Washington State Univ, Pullman, WA 99163 USA

[2] Elect Power Res Inst, Knoxville, TN 37932 USA

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON POWER ELECTRONICS, DRIVES AND ENERGY SYSTEMS (PEDES) | 2016年

关键词：

Adaptive search-based algorithm; load models; least square (LS); recursive least square (RLS); ZIP model; parameter estimation;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Several techniques have been developed to estimate the load parameters in power systems. Most of the algorithms mainly focus on estimating the parameters for offline studies. With ongoing smart grid development, high-resolution data at fast rates are available to allow load modeling in real-time. This paper addresses the challenges in online estimation of the load parameters using Phasor Measurement Unit (PMU) data. A novel adaptive search-based algorithm to estimate load model parameters is presented here. In this paper, Z (constant impedance), I (constant current) and P (constant power), ZIP load model is derived. Simulation results for ZIP parameter estimation are presented using the IEEE 14bus system. Simulation results demonstrate the accurate estimation of the ZIP load model. ZIP parameters are also estimated using the industrial data and the voltage stability limits are computed using actual data to show the impact of ZIP parameters versus the traditional constant power load model.

引用

页数：5

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