A survey of power system prediction based on deep learning

被引：0
|
作者：
Miao L. [1 ]
Li Q. [1 ,2 ]
Jiang Y. [1 ,2 ]
Cui J.-R. [1 ]
Wang Y.-X. [1 ]
机构：
[1] School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing
[2] Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing
来源：
Gongcheng Kexue Xuebao/Chinese Journal of Engineering | 2023年 / 45卷 / 04期
关键词：
deep learning; health state prediction; load forecasting; new energy power prediction; power system;
D O I：
10.13374/j.issn2095-9389.2021.12.21.006
中图分类号：
学科分类号：
摘要：
Power system is one of the largest and complex artificial engineering in the modern society. With the development of intelligence, digitization and long-distance technology, a large number of multi-source, multi-state and heterogeneous operational data have emerged. As a new trend direction of machine learning, deep learning has shown potential in data feature extraction and pattern recognition. Because of its excellent ability in data analysis and prediction, it is widely used in power system, which has a significant impact on optimizing power production planning, improving power production efficiency and energy utilization, and ensuring the smooth operation of the system influence. Based on massive quantities of data and by means of deep learning, it can better fit the nonlinear relationship between the factors affecting the subsequent operational state of the system, so as to further improve the prediction accuracy. Power system prediction includes load forecasting, new energy power prediction and state-of-health prediction. Power production planning can be optimized using load forecasting; thus, electrical energy can be finely dispatched. The capacity of new energy power consumption is improved through power prediction to reasonably use electrical energy. Potential equipment hazards can be timely found using power equipment health state prediction, thereby ensuring safe and smooth operation. First, in this paper, the characteristics and applicable scenarios of typical deep learning models are introduced, among them, deep belief network and stacked auto encoder belong to stack structure, so the structure is flexible and easy to expand, which is suitable for the modeling and feature extraction of unrelated data type; convolutional neural network shares convolution kernel internally to reduce the number of network parameters and is good at processing high-dimensional data type; recurrent neural network has feedforward and feedback connections, so it is suitable for processing sequence data with pre and post dependence. Second, the application frontiers of predictive power systems based on deep learning are reviewed, which include civil and industrial scenarios, photovoltaic and wind power, mechanical and non-mechanical equipment health state prediction. Finally, facing the challenges of power system in energy efficient allocation, high proportion of new energy power consumption, highly stable operation of power equipment and so on, the key problems and future development trends are presented. © 2023 Science Press. All rights reserved.
引用
收藏
页码：663 / 672
页数：9
相关论文
共 83 条
  • [11] Wang C, Kou P., Wind speed forecasts of multiple wind turbines in a wind farm based on integration model built by convolutional neural network and simple recurrent unit, Trans China Electrotech Soc, 35, 13, (2020)
  • [12] Gao W, Guo M F, Xu L B., Recognition method of internal overvoltage type for distribution network via improved CWD-CNN, Electr Mach Control, 24, 8, (2020)
  • [13] Li R, Sun F, Ding X, Et al., Ultra short-term load forecasting for user-level integrated energy system considering multi-energy spatio-temporal coupling, Power Syst Technol, 44, 11, (2020)
  • [14] Xu Z, Zhang G, Wang H M, Et al., Error compensation of collaborative robot dynamics based on deep recurrent neural network, Chin J Eng, 43, 7, (2021)
  • [15] Yang C, Jiang W X, Guo Z W., Time series data classification based on dual path CNN-RNN cascade network, IEEE Access, 7, (2019)
  • [16] Hochreiter S, Schmidhuber J., Long short-term memory, Neural Comput, 9, 8, (1997)
  • [17] Xia M, Shao H D, Ma X D, Et al., A stacked GRU-RNN-based approach for predicting renewable energy and electricity load for smart grid operation, IEEE Trans Ind Inform, 17, 10, (2021)
  • [18] Tan M, Yuan S P, Li S H, Et al., Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning, IEEE Trans Power Syst, 35, 4, (2020)
  • [19] Chen M R, Zeng G Q, Lu K D, Et al., A two-layer nonlinear combination method for short-term wind speed prediction based on ELM, ENN, and LSTM, IEEE Internet Things J, 6, 4, (2019)
  • [20] Hinton G E, Osindero S, Teh Y W., A fast learning algorithm for deep belief nets, Neural Comput, 18, 7, (2006)
123456789