Welcome to Francis Academic Press

Academic Journal of Engineering and Technology Science, 2023, 6(7); doi: 10.25236/AJETS.2023.060703.

Spatial Distance-based Uniformity Evaluation Method for Power Flow Datasets

Author(s)

Meng Xianbo, Li Yalou, Wang Zigan, Hu Shanhua

Corresponding Author:
Meng Xianbo
Affiliation(s)

China Electric Power Research Institute, Haidian District, Beijing, China

Abstract

Research on the application of artificial intelligence (AI) methods in power grid analysis has been widely conducted. The power flow dataset required for the training of AI models is not uniformly distributed, and to improve the quality of the dataset, related research has generally applied tuning methods that tend to have a more uniform sample distribution, resulting in improved performance of AI models. However, the lack of research on methods to evaluate the uniformity of distribution of the dataset poses an obstacle to the judgment of the validity of uniformity adjustment and the analysis of the impact of uniformly distributed datasets on the performance of AI models. This paper proposes a method to evaluate the uniformity of power flow datasets by using the calculation of distances in the high-dimensional feature space of the flow datasets and plotting the minimum distance statistics as a way to evaluate the uniformity of the flow datasets. It was also tested on the power flow dataset of 36-node grid and evaluated separately for two uniformity levels, which can clearly represent the uniformity of distribution of the dataset in the high-dimensional feature space.

Keywords

power flow dataset; high-dimensional feature space; uniformity evaluation

Cite This Paper

Meng Xianbo, Li Yalou, Wang Zigan, Hu Shanhua. Spatial Distance-based Uniformity Evaluation Method for Power Flow Datasets. Academic Journal of Engineering and Technology Science (2023) Vol. 6, Issue 7: 11-17. https://doi.org/10.25236/AJETS.2023.060703.

References

[1] Wang Z, Zhou Y, Guo Q, and Sun H (2021). Transient Stability Assessment of Power System Considering Topological Change: a Message Passing Neural Network-based Approach. Proceedings of the CSEE 44.07: 2341-2350(in Chinese).

[2] Su T, Liu Y, Shen X, et al (2020). Deep Learning-driven Evolutionary Algorithm for Preventive Control of Power System Transient Stability[J] Proc. CSEE, 40.12. (in Chinese)

[3] Shi Z, Yao W, Zeng L, et al (2020). Convolutional neural network-based power system transient stability assessment and instability mode prediction[J]. Applied Energy, 263: 114586.

[4] Sun C, Shrivastava A, Singh S, et al (2017). Revisiting unreasonable effectiveness of data in deep learning era[C]. Proceedings of the IEEE international conference on computer vision. 843-852.

[5] Chen J., Chen Y., Tian F., Guo Z., and Li T. (2019). The Method of Sample Generation for Power Grid Simulation Based on LSTM. Proc. CSEE, 39, 4129-4134.(in Chinese)

[6] Tan B, Yang J, Lai Q, Xie P, Li J, and Xu J. (2019) Data augment method for power system transient stability assessment based on improved conditional generative adversarial network. Automation of Electric Power Systems 43.1: 149-157(in Chinese).

[7] Batista G E A P A, Prati R C, Monard M C (2004). A study of the behavior of several methods for balancing machine learning training data [J]. ACM SIGKDD Explorations Newsletter, 6(1): 20-29.

[8] Chawla N V, Bowyer K W, Hall L O, et al (2002). SMOTE: synthetic minority over-sampling technique [J]. Journal of artificial intelligence research, 16: 321-357.

[9] Zhang H, Cisse M, Dauphin Y N, et al (2017). Mixup: Beyond empirical risk minimization [J]. arXiv preprint arXiv:1710.09412.

[10] He H, Bai Y, Garcia E A, et al. ADASYN: Adaptive synthetic sampling approach for imbalanced learning [C]. 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence). IEEE, 2008: 1322-1328.

[11] Japkowicz N, Stephen S (2002). The class imbalance problem: A systematic study[J]. Intelligent data analysis, 6(5): 429-449.

[12] He H, Garcia E A (2009). Learning from imbalanced data[J]. IEEE Transactions on knowledge and data engineering, 21(9): 1263-1284.

[13] Zhang Y, Zhang H, Li C, and Pu T. (2021) Review on deep learning applications in power system frequency analysis and control. Proceedings of the CSEE 41.10: 3392-3406+3665(in Chinese).

[14] Dippe M A, Wold E H. (1985) Antialiasing through stochastic sampling [C]. Proceedings of the 12th annual conference on Computer graphics and interactive techniques. 69-78.

[15] Cook R L (1986). Stochastic sampling in computer graphics[J]. ACM Transactions on Graphics, 5(1): 51-72.

[16] Yuksel C. (2015) Sample elimination for generating Poisson disk sample sets[C]. Computer Graphics Forum: Vol. 34. Wiley Online Library, 25-32.

[17] Yan D M, Guo J W, Wang B, et al (2015). A Survey of Blue-Noise Sampling and Its Applications [J]. Journal of Computer Science and Technology, 30(3): 439-452.

[18] Meng X, Li Y, Shi D, et al (2022). A Method of Power Flow Database Generation Base on Weighted Sample Elimination Algorithm[J]. Frontiers in Energy Research, 10: 919842.