Power system equivalent inertia prediction based on EMD-SSA-LSTM and system partitioning
Pages 46 - 52
Abstract
Power system inertia level forecast in advance is a crucial need for the reliable functioning of power systems. A power system equivalent inertia prediction model based on the EMD-SSA-LSTM network with system partitioning is suggested in order to increase the accuracy of power system inertia prediction. Firstly, the system is partitioned using K-Medoide, and the dominant generator in each region is selected according to the partitioning basis, then the prediction model of the system operation and system equivalent inertia for the dominant generator, new energy generators, etc. is established, and the hyper-parameters of the LSTM algorithm are optimized by SSA through the EMD after the data processing, and finally, the Pearson correlation coefficients are matched with the appropriate prediction model. Finally, the prediction of the equivalent inertia of the power system is completed, and the results show that the prediction accuracy of EMD-SSA-LSTM based on the system partition is higher than that of other algorithms, its prediction accuracy is within 99%, which effectively improves the accuracy of the prediction of the equivalent inertia of the power system.
References
[1]
J.B. Hu, X.M. Yuan, S.J. Cheng. Multi-scale Switching Control of Power Electronic Grid-connected Equipment and Multi-scale Transient Problem of Power Electronic System [J]. Proceedings of the CSEE, 2019, 39(18): 5457-5467+5594.
[2]
Y. Zhao, H.M. Wang, L, Kang, Short-term power load forecasting based on time-convolutional networks [J]. Transaction of China Electrotechnical Society, 2022, 37(5): 1242-1251.
[3]
Cherkassky V, Ma Y. Practical selection of SVM parameters and noise estimation for SVM regression [J]. Neural Networks, 2004, 17(1): 113-126.
[4]
B. Zhang, Z. P. Wang, W. J. Ji, CNN-GRU short-term power load forecasting method based on attention mechanism [J]. Power System Technology, 2019, 43(12): 4370-4376.
[5]
Z. W. Zhang, L. Zhao, Y. Bian, Based on ELM and SVM electrical sentiment classification study [J]. Automation and instrumentation, 2023 (7) : 10-13. carroll nki. 1001-9227.2023.07.010.
[6]
Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications [J]. Neurocomputing, 2006, 70(1/3): 489-501.
[7]
Q. K. Sun, X. J. Wang, Y. Z. Zhang, Multivariate load forecasting for integrated energy systems based on LSTM and multi-task learning [J]. Automation of Electric Power Systems, 2021, 45(5):63-70.
[8]
Sundermeyer M, Ralf Schlüter, Ney H. LSTM Neural Networks for Language Modeling[C]//Interspeech. 2012.
[9]
CAO X, STEPHEN B, ABDULHADI I. F. Switching Markov Gaussian models for dynamic power system inertia estimation [J]. IEEE Transactions on Power Systems, 2016, 31(5): 3394-3403.
[10]
L. Zhang, H.T. Li, Z.Z. Xiong, Short term prediction method of power system inertia based on interpretable XGBoost [J]. Electric Power Construction, 2023, 44 (08): 22-30
[11]
H.D. Sun, B.C. Wang, W.F. Li, Study of inertia system for frequency response of high scale power electronic power system [J]. Proceedings of the CSEE, 2020, 40(16):13.
Index Terms
- Power system equivalent inertia prediction based on EMD-SSA-LSTM and system partitioning
Recommendations
Learning-aided predictor integration for system performance prediction
The integration of multiple predictors promises higher prediction accuracy than the accuracy that can be obtained with a single predictor. The challenge is how to select the best predictor at any given moment. Traditionally, multiple predictors are run ...
Research on the prediction of short time series based on EMD-LSTM
An algorithm based on EMD-LSTM (Empirical Mode Decision – Long Short Term Memory) is proposed for predicting short time series with uncertainty, rapid changes, and no following cycle. First, the algorithm eliminates the abnormal data; second, the ...
Adaptive prediction model of gas concentration based on EMD and GPR
CSSE '21: Proceedings of the 4th International Conference on Computer Science and Software EngineeringIn order to expand the function of safety monitoring and control system in coal mine, and realize the accurate real-time prediction of mine gas concentration, study the adaptive prediction method for gas concentration based on EMD and GPR. The raw gas ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 2023
1809 pages
ISBN:9798400708305
DOI:10.1145/3650400
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 April 2024
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
EITCE 2023
EITCE 2023: 2023 7th International Conference on Electronic Information Technology and Computer Engineering
October 20 - 22, 2023
Xiamen, China
Acceptance Rates
Overall Acceptance Rate 508 of 972 submissions, 52%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 7Total Downloads
- Downloads (Last 12 months)7
- Downloads (Last 6 weeks)1
Reflects downloads up to 09 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format