Intelligent Prediction for Multi-dimensional Frequency Indicators Based on Regularized Greedy Forests

doi:10.12204/j.issn.1000-7229.2020.09.014

Abstract

Abstract:

In order to realize fast and precise perception of the frequency response performance of the power system under the background of massive anticipated faults, this paper proposes a method for predicting the multi-dimensional frequency indicators based on regularized greedy forests (RGF). This method establishes the non-linear mapping relationship between input features and multi-dimensional indicators through RGF. By optimizing global parameters and introducing three regularization mechanisms to the decision-making forest, the RGF can effectively represent complex functions and prevent overfitting. To ensure the performance of the model, the combinations of parameters are traversed by the grid search to find the best parameter of the constructed RGF model. Case studies on the modified IEEE RTS-79 system demonstrate the high precision, rapidity, and well generalization ability of the proposed method.

Key words: frequency, inertia, intelligent prediction, regularized greedy forests, grid search

CLC Number:

TM71

HUANG Mingzeng, WEN Yunfeng, WANG Ronghua, XU Weiting, LI Ting, GOU Jing, ZHAO Rongzhen. Intelligent Prediction for Multi-dimensional Frequency Indicators Based on Regularized Greedy Forests[J]. ELECTRIC POWER CONSTRUCTION, 2020, 41(9): 124-131.

Figures/Tables 13

Fig.1 A decision forest composed of three decision trees

Table 1 Input feature set

Fig.2 Model of frequency matrix prediction based on RGF

Fig.3 Flowchart of frequency matrix prediction

Fig.4 Modified IEEE RTS-79 system

Fig.5 The visualization of grid search

Table 2 Errors of the fnadir Hz

Fig.6 Error distribution of the fnadir

Fig.7 Error distribution of the fss

Fig.8 Error distribution of the RoCoFmax

Table 3 Errors of the fss Hz

Table 4 Error results of the RoCoFmaxHz/s

Table 5 Comparison of RGF and time-domain simulation in times

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