Interval State Estimation Considering Randomness of Multiple Distributed Generations in Active Distribution Networks

Abstract

The high-permeability Distributed Generation (DG) was connected to the power grid, so that the state estimation of the active distribution network(ADN) needs to consider the uncertainty of the DG output. In this paper, an interval state estimation method for active distribution network considering the randomness of Wind Turbine and PV output is proposed. The method uses the Extreme Learning Machine (ELM) to model the randomness of Wind Turbines and PV output in the form of interval numbers, and to perform ultra-short-term prediction on Wind Turbines and PV output interval, and use the output interval as pseudo measurement, based on the particle swarm optimization(PSO) State estimation of the ADN. The results of IEEE-33 system verification show that the state estimation results obtained by PSO algorithm are more accurate than the traditional weighted least square (WLS); the state estimation result presents an interval form, which can provide the dispatcher with a more intuitive system state quantity upper and lower bound information. Introduction The intermittent power generation of high-permeability DG makes it necessary to consider more uncertain factors. The accuracy of the estimation results obtained by traditional methods can not meet the dispatching requirements[1]. Therefore, it is worthwhile to model reasonably the uncertainty of DG output and increase accuracy of AND state estimation. For the uncertain modeling of DG output, literature [2] regards DG output as the pseudomeasurement, ignoring the influence of DG output uncertainty. Document [3] treats DG as active power and reactive power injection nodes, but no specific physical model of DG is established. Literature [4] established a dynamic probability model of DG based on the difference in probability characteristics of DG at each time. However, the state estimation based on the probability distribution must obtain the detailed prior probability density function of each uncertainty in advance, carry out a large number of photovoltaic and wind power related data statistics, and make a prior assumption on the probability distribution of prediction errors, which results in a long time to solve the algorithm[5]. In addition, the probability density function of photovoltaic and wind power output is generally difficult to obtain, and it is only known in most cases. Upper and lower limits of power fluctuation [6]. Therefore, the interval number model can be used to describe the uncertainty problem in the state estimation model, so that it is not necessary to obtain the specific distribution of the parameters, and only need to pay attention to the upper and lower bounds of each uncertain variable, so the engineering application value is greater [7]. To solve these problems, an interval state estimation method considering the uncertainties of DG output is proposed in this paper. The example is verified by IEEE-33 system. Uncertainty Modeling of DG Output Photovoltaic Output Prediction Model The main factors affecting photovoltaic output are: light intensity, ambient temperature, and weather type. The PV output prediction model is shown in Fig. 1. The input of the model is the light intensity, ambient temperature, and weather type. Therefore, the input layer node is set to 3; the output , 2nd International Conference on Electrical and Electronic Engineering (EEE 2019) Copyright © 2019, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Engineering Research, volume 185