Composite Harmonic Source Detection with Multi-Label Approach Using Advanced Fusion Method
Abstract
:1. Introduction
- The factors that generate harmonics in the new power system are complex. It is more difficult to grasp the harmonic generation mechanism and construct a harmonic source identification model comprehensively and accurately, and it is less practical and adaptable.
- The method based on mechanism analysis does not make full use of the massive data generated by intelligent monitoring equipment in the era of big data, and the fitting results cannot reflect all the information of the harmonic source [11].
- Some data are not easy to obtain, e.g., harmonic impedance, harmonic phase angle, and topology cannot be directly obtained from the monitoring device.
2. New Perspective on Harmonic Source Characteristic Analysis
2.1. Time-Frequency Characteristics of Harmonic Sources
2.2. TransR Knowledge Representation
3. Identification Method of Composite Harmonic Source Based on TTM
3.1. Harmonic Time-Frequency Data Representation Layer
3.2. Harmonic Feature Recognition Layer
3.3. Composite Harmonic Source Multi-Label Classification Layer
4. Analysis
4.1. Experimental Configuration and Evaluation Indicators
- Precision focuses on the recognition of binary positive samples. The multi-label classification problem can be regarded as a multiple-binary classification problem, and the evaluation index adopts the mean micro-precision, which is the quotient of the number of correctly predicted positive samples for all categories and the number of predicted positive samples for all categories. The formula [20] is:
- 2.
- Recall represents the coverage of binary positive sample prediction, and the multi-label classification metric is micro-recall. The formula [20] is:
- 3.
- F1 considers both accuracy and recall, which better represent the effectiveness of classification. In the multi-label classification problem, micro-F1 is used to represent, and the formula [20] is:
- 4.
- HL focuses on predicting incorrect labels, that is, directly comparing the predicted tags with the authentic labels bit by bit and calculating the quotient of the number of predicted error labels and the total number of marks. HL ranges from 0 to 1, with smaller HL indicating better prediction performance. An HL of 0 indicates that the prediction is right, while, conversely, it declares all errors. The formula [20] is:
4.2. Example Analysis of Measured Data
5. Simulation
6. Discussion
7. Conclusions
- TTM model integrating time-frequency feature extraction.
- 2.
- Feasibility verification applied to actual scenarios.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Reference | Method | Advantage | Shortcoming |
---|---|---|---|
[5] | Harmonic Power Method | Simple and easy to operate. | Prefer to qualitatively explore whether there are harmonic sources in the grid or on which side of the directional analysis. |
[6] | Harmonic Impedance Method | Able to consider the impedance characteristics of the system. | Requires accurate harmonic impedance information. |
[7,8,9] | Signal Processing Tech | Can handle nonlinear and time-varying signals, suitable for real-time applications. | Sensitive to noise and interference, more sensitive to parameter selection. |
[10] | Mathematical Statistics | Provides rigorous analysis and inference of data. | Strict assumptions about data distribution and may not be able to handle nonlinear relationships. |
Harmonic Order/th | Im | |||||||
---|---|---|---|---|---|---|---|---|
H1 | H2 | H3 | H4 | H5 | H6 | H7 | H8 | |
3 | 0.173 | 0.245 | 0.018 | 0.153 | 0.094 | 0.017 | 0.073 | 0.049 |
5 | 0.624 | 0.453 | 0.482 | 0.487 | 0.277 | 0.781 | 0.074 | 0.734 |
7 | 0.329 | 0.674 | 0.230 | 0.277 | 0.321 | 0.619 | 0.099 | 0.418 |
9 | 0.044 | 0.048 | 0.223 | 0.041 | 0.057 | 0.018 | 0.014 | 0.043 |
11 | 0.564 | 0.466 | 0.631 | 0.041 | 0.805 | 0.069 | 0.844 | 0.298 |
13 | 0.354 | 0.170 | 0.469 | 0.056 | 0.281 | 0.020 | 0.333 | 0.271 |
15 | 0.025 | 0.073 | 0.010 | 0.023 | 0.032 | 0.007 | 0.012 | 0.019 |
17 | 0.132 | 0.111 | 0.088 | 0.041 | 0.186 | 0.027 | 0.009 | 0.093 |
19 | 0.044 | 0.056 | 0.033 | 0.754 | 0.131 | 0.010 | 0.022 | 0.096 |
21 | 0.004 | 0.005 | 0.016 | 0.247 | 0.019 | 0.003 | 0.009 | 0.007 |
23 | 0.015 | 0.012 | 0.180 | 0.033 | 0.076 | 0.012 | 0.306 | 0.034 |
25 | 0.010 | 0.008 | 0.076 | 0.018 | 0.052 | 0.006 | 0.240 | 0.019 |
Learning Rate | Epoch | Batch Size | Sigmoid Threshold |
---|---|---|---|
0.0005 | 300 | 64 | 0.5 |
Harmonic | Micro-Precision | Micro-Recall | Micro-F1 | HL | ||||
---|---|---|---|---|---|---|---|---|
Time-Frequency | Frequency | Time-Frequency | Frequency | Time-Frequency | Frequency | Time-Frequency | Frequency | |
H1 | 0.996 | 0.999 | 0.999 | 0.899 | 0.997 | 0.946 | 0.002 | 0.021 |
H2 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.000 | 0.000 |
H3 | 0.992 | 0.967 | 0.998 | 0.986 | 0.995 | 0.976 | 0.001 | 0.003 |
H4 | 1.000 | 0.998 | 1.000 | 0.982 | 1.000 | 0.990 | 0.000 | 0.004 |
H5 | 1.000 | 0.985 | 0.998 | 0.889 | 0.999 | 0.935 | 0.001 | 0.023 |
H6 | 0.997 | 0.982 | 1.000 | 1.000 | 0.998 | 0.991 | 0.000 | 0.000 |
H7 | 0.999 | 0.898 | 0.989 | 0.971 | 0.994 | 0.933 | 0.003 | 0.006 |
H8 | 1.000 | 0.997 | 1.000 | 1.000 | 1.000 | 0.998 | 0.000 | 0.000 |
Avg | 0.998 | 0.978 | 0.998 | 0.966 | 0.998 | 0.971 | 0.001 | 0.007 |
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Sun, L.; Wang, H.; Qi, L.; Yan, J.; Jiang, M. Composite Harmonic Source Detection with Multi-Label Approach Using Advanced Fusion Method. Electronics 2024, 13, 1275. https://doi.org/10.3390/electronics13071275
Sun L, Wang H, Qi L, Yan J, Jiang M. Composite Harmonic Source Detection with Multi-Label Approach Using Advanced Fusion Method. Electronics. 2024; 13(7):1275. https://doi.org/10.3390/electronics13071275
Chicago/Turabian StyleSun, Lina, Hong Wang, Linhai Qi, Jiangyu Yan, and Meijing Jiang. 2024. "Composite Harmonic Source Detection with Multi-Label Approach Using Advanced Fusion Method" Electronics 13, no. 7: 1275. https://doi.org/10.3390/electronics13071275
APA StyleSun, L., Wang, H., Qi, L., Yan, J., & Jiang, M. (2024). Composite Harmonic Source Detection with Multi-Label Approach Using Advanced Fusion Method. Electronics, 13(7), 1275. https://doi.org/10.3390/electronics13071275