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Smart Grid 2024
基于BP+Bi-LSTM的电力系统广域数据异常检测研究
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Abstract:
电力系统同步向量测量装置(PMU)产生的数据对电力系统规划和安全起着至关重要的作用。但PMU装置往往充满噪声，除去系统本身能够排除的干扰，仍旧会产生PMU数据丢失、精确度降低等问题。本文提出一种双向长短期记忆网络(Bi-LSTM)和BP神经网络相结合的解决方案。首先通过BP神经网络拥有强大的非线性成像技术和网络结构，针对不同情况改变其特殊性能结构；其次利用Bi-LSTM神经网络解决多变量问题，将这两者相互结合，BP+Bi-LSTM具备比BP神经网络模式更强大的非线性映射能力和泛化学习能力，大大提高数据分析应用范围。最后以新英格兰10机39节点为例，验证本文方法对不良数据检测可行性和正确性。
The data generated by the Phasor Measurement Unit (PMU) in power system plays a crucial role in power system planning and safety. However, PMU devices are often filled with noise, and even with interference that the system itself can eliminate, problems such as PMU data loss and reduced accuracy still occur. This paper proposes a solution that combines bi-directional long short-term memory network (Bi-LSTM) and BP neural network. Firstly, BP neural network has powerful nonlinear imaging technology and network structure, and its special performance structure can be changed according to different situations; Secondly, using Bi-LSTM neural network to solve multivariate problems, combining the two, BP+Bi-LSTM has stronger nonlinear mapping ability and generalization learning ability than BP neural network mode, greatly improving the application scope of data analysis; Finally, taking the example of 10 machines and 39 nodes in New England, the feasibility and correctness of our method for detecting bad data are verified.
| [1] | Guan, Z., Ji, T., Qian, X., Ma, Y. and Hong, X. (2017) A Survey on Big Data Pre-Processing. 2017 5th Intl Conf on Applied Computing and Information Technology/4th Intl Conf on Computational Science/Intelligence and Applied Informatics/2nd Intl Conf on Big Data, Cloud Computing, Data Science (ACIT-CSII-BCD), Hamamatsu, 9-13 July 2017, 241-247. https://doi.org/10.1109/acit-csii-bcd.2017.49 |
| [2] | 李刚, 焦谱, 文福拴, 宋雨, 尚金成, 何洋. 基于偏序约简的智能电网大数据预处理方法[J]. 电力系统自动化, 2016, 40(7): 98-106. |
| [3] | 王德文, 杨力平. 智能电网大数据流式处理方法与状态监测异常检测[J]. 电力系统自动化, 2016, 40(14): 122-128. |
| [4] | 李临风, 饶丹, 樊瑞, 张恒, 王军, 罗华煜, 刘拯, 徐广辉. 基于双向LSTM和注意力机制的输电线路故障判别方法[J]. 广东电力, 2022, 35(11): 91-98. |
| [5] | Alghamdi, T.A. and Javaid, N. (2022) A Survey of Preprocessing Methods Used for Analysis of Big Data Originated from Smart Grids. IEEE Access, 10, 29149-29171. https://doi.org/10.1109/access.2022.3157941 |
| [6] | Bilal, M., Ali, G., Iqbal, M.W., Anwar, M., Malik, M.S.A. and Kadir, R.A. (2021) Auto-Prep: Efficient and Robust Automated Data Preprocessing Pipeline. IEEE Access, 10, 107764-107784. https://doi.org/10.1109/ACCESS.2022.3198662 |
| [7] | Furukawa, T. and Doi, A. (2005) A Study of Data Reduction Method with Data Accuracy for Triangle Data. 11th International Conference on Parallel and Distributed Systems, Fukuoka, 20-22 July 2005, 210-213. https://doi.org/10.1109/ICPADS.2005.54 |
| [8] | 李永光, 张彦军, 李长军, 汪凯威. 智能电网建设中云计算大数据处理技术的运用[J]. 通信电源技术, 2018, 35(10): 145-146. |
| [9] | 杨智伟, 刘灏, 毕天姝, 杨奇逊. 基于长短期记忆网络的PMU不良数据检测方法[J]. 电力系统保护与控制, 2020, 48(7): 1-9. |
| [10] | Li, J. and Chen, H. (2022) Research on Abnormal Data Analysis and Data Processing Method Based on Network Traffic. 2022 International Conference on Computation, Big-Data and Engineering (ICCBE), Yunlin, 27-29 May 2022, 18-21. https://doi.org/10.1109/ICCBE56101.2022.9888172 |
| [11] | 刘灏, 朱世佳, 毕天姝. 基于局部离群因子的PMU连续坏数据检测方法[J]. 电力系统自动化, 2022, 46(1): 25-32. |
| [12] | Yang, Z., Liu, H., Bi, T. and Yang, Q. (2020) Bad Data Detection Algorithm for PMU Based on Spectral Clustering. Journal of Modern Power Systems and Clean Energy, 8, 473-483. https://doi.org/10.35833/mpce.2019.000457 |
| [13] | Su, L., Yao, Y., Li, N., Liu, J., Lu, Z. and Liu, B. (2018) Hierarchical Clustering Based Network Traffic Data Reduction for Improving Suspicious Flow Detection. 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/12th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), New York, 1-3 August 2018, 744-753. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00108 |
