Abstract
We present the first model-based parameter identification method in the power distribution network to successfully achieve parameter identification directly based on sequential model-based optimization. This method is building a model with a posteriori probability to optimize an objective function. Furthermore, to achieve an efficient exploration, three different acquisition functions, i.e., random search, tree-structured Parzen estimator approach, and simulated annealing, were proposed. We applied our three models and the conventional model-free method to the actual feeder data with no adjustment of the other conditions. The experiment shows that our method achieves at least 25% and 70% improvements in accuracy and convergence speed, respectively.
Highlights
In the power distribution network (PDN), reliable and accurate parameters are the basis of security analysis, system control, state estimation, line loss calculation, power flow calculation, protection setting, and fault analysis [1]
Because there is no real-time and accurate data of the line parameters, Ustd is the standard voltage value per unit used for the voltage data, and Ualgo is the high-voltage side value calculated with the identification parameter value
Parameter calculated by simulated annealing (SA), and Figure 13 shows the identification parameter calculated by tree-structured Parzen estimator approach (TPE)
Summary
In the power distribution network (PDN), reliable and accurate parameters are the basis of security analysis, system control, state estimation, line loss calculation, power flow calculation, protection setting, and fault analysis [1]. [3], it is difficult to avoid the differences in data between the distribution management system and the actual situation, which do not reflect the real-time operation status in PDN, resulting in poor parameter calculations for the distribution network. The parameter identification research for lines and transformers is more active in the area of the power transmission network (PTN) than the PDN. Some of these studies include the theoretical formula calculation method based on the self-geometric spacing method [4] and the identification method based on field measurements of the voltage, current, power, frequency, and other network parameters using electrical instruments. Compared with other main networks, the PDN covers a larger area, and its measurement conditions are unsatisfactory. erefore, the parameter identification methods used in the PTN may not be suitable for the PDN
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