Multi-source perceptual blind compensation inspection method for substation based on equipment’s visual blind area identification and saliency detection
-
Zhigang Xie
Abstract
In order to expand the detection range and ensure the operation stability, the substation multi-source perception blind compensation detection method based on equipment visual blind area recognition and significance detection is studied. Acoustic sensors are used to collect acoustic signals from visual blind areas of equipment. The characteristics of noise signal are identified by wavelet analysis and noise reduction. The supercomplex Fourier transform model was used to extract the important region in the device image, and the texture features of the region were detected by Gabor filter. The blind compensation detection feature vector is formed by integrating two multi-source sensing features. The detection model of support vector machine is input to complete the blind compensation detection of the substation. The experimental results show that the proposed method is effective for the sound signal feature recognition in the visual blind area and the texture feature detection in the significant area of the device image. The different operating states of each equipment detected by the multi-source sensing feature vector are more accurate, which can realize the purpose of the multi-source sensing blind compensation check of the substation and ensure the safe and stable operation of the substation.
Funding source: State Grid Gansu Electric Power Company Management science and technology project support
Award Identifier / Grant number: 522709220008
Acknowledgements
State Grid Gansu Electric Power Company Management science and technology project support. (No. 522709220008).
-
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
-
Research funding: None declared.
-
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Sun, Z, Liu, X, Xu, B, Zhang, S, Fu, C, Yang, G, et al.. Equipment failure detection method of substation based on tunnel robot. Procedia Comput Sci 2020;166:305–9. https://doi.org/10.1016/j.procs.2020.02.094.Search in Google Scholar
2. Wang, J, Yin, H. Failure rate prediction model of substation equipment based on Weibull distribution and time series analysis. IEEE Access 2019;2:85298–9.10.1109/ACCESS.2019.2926159Search in Google Scholar
3. Zheng, H, Sun, Y, Liu, X, Laurent, C, Li, J, Liu, Y. Infrared image detection of substation insulators using an improved fusion single shot multibox detector. IEEE Trans Power Deliv 2020;36:3351–9.10.1109/TPWRD.2020.3038880Search in Google Scholar
4. Xiao, Y, Luo, D, Jiang, Q, Hu, L, Li, C, Zhang, L. Thermal infrared image recognition method for high voltage equipment failure in substation based on temperature probability density. High Volt Eng 2022;48:307–18.Search in Google Scholar
5. Xu, Q, Huang, H, Zhou, C, Zhang, X. Research on real-time infrared image fault detection of substation high-voltage lead connectors based on improved YOLOv3 network. Electronics 2021;10:544. https://doi.org/10.3390/electronics10050544.Search in Google Scholar
6. Guo, M, Ma, Y, Yang, X, Mankin, R. Detection of damaged wheat kernels using an impact acoustic signal processing technique based on Gaussian modelling and an improved extreme learning machine algorithm. Biosyst Eng 2019;184:37–44. https://doi.org/10.1016/j.biosystemseng.2019.04.022.Search in Google Scholar
7. Koyama, S, Kan, O, Tagawa, N. Acoustic sensing method for an occlusion area with super-directional sound sources and multiple modulation signal. Jpn J Appl Phys 2021;60:SDDB09. https://doi.org/10.35848/1347-4065/abfebe.Search in Google Scholar
8. Hirakawa, K, Koike, K, Kanawaku, Y, Moriyama, T, Sato, N, Suzuki, T, et al.. Short-time Fourier transform of free induction decays for the analysis of serum using proton nuclear magnetic resonance. J Oleo Sci 2019;68:369–78. https://doi.org/10.5650/jos.ess18212.Search in Google Scholar PubMed
9. Liang, Y, Yan, S, He, M, Li, M, Cai, Y, Wang, Z, et al.. Generation of a double-ring perfect optical vortex by the Fourier transform of azimuthally polarized Bessel beams. Opt Lett 2019;44:1504–7. https://doi.org/10.1364/ol.44.001504.Search in Google Scholar PubMed
10. He, J, Guo, Y, Yuan, H. Ship target automatic detection based on hypercomplex Flourier transform saliency model in high spatial resolution remote-sensing images. Sensors 2020;20:1–15. https://doi.org/10.3390/s20092536.Search in Google Scholar PubMed PubMed Central
11. Zheng, Y, Tong, H, Zhao, T, Guo, X, Xu, H, Yang, R. Support vector machine classification combined with multimodal magnetic resonance imaging in detection of patients with schizophrenia. IET Image Process 2020;14:2610–5. https://doi.org/10.1049/iet-ipr.2019.1108.Search in Google Scholar
12. Molina, SR, Ruiz-Blanco, YB, Harms, M, Munch, J, Sanchez-Garcia, E. PPI-detect: a support vector machine model for sequence‐based prediction of protein–protein interactions. J Comput Chem 2019;40:1233–42.10.1002/jcc.25780Search in Google Scholar PubMed
13. Xu, Y, Zhang, F, Li, H, Zhao, L. Application of Haar wavelet transform in OFDM system. Comput Simulat 2019;36:140–144+170.Search in Google Scholar
14. Liu, F, Zhang, Y, Yildirim, T, Zhang, J. Erratum: signal denoising optimization based on a Hilbert-Huang transform-triple adaptable wavelet packet transform algorithm. EPL 2019;125:29901. https://doi.org/10.1209/0295-5075/125/29901.Search in Google Scholar
15. Abeysinghe, A, Fard, M, Jazar, R, Zambetta, F, Davy, J. Mel frequency cepstral coefficient temporal feature integration for classifying squeak and rattle noise. J Acoust Soc Am 2021;150:193–201. https://doi.org/10.1121/10.0005201.Search in Google Scholar PubMed
16. Ghaffar, M, Khan, US, Iqbal, J, Rashid, N, Hamza, A, Qureshi, W, et al.. Improving classification performance of four class FNIRS-BCI using Mel Frequency Cepstral Coefficients (MFCC). Infrared Phys Technol 2020;112:103589. https://doi.org/10.1016/j.infrared.2020.103589.Search in Google Scholar
17. Bruno, A, Gugliuzza, F, Pirrone, R, Ardizzone, E. A multi-scale colour and keypoint density-based approach for visual saliency detection. IEEE Access 2020;2:121330–43.10.1109/ACCESS.2020.3006700Search in Google Scholar
18. Chen, C, Wang, G, Peng, C, Zhang, X, Qin, H. Improved robust video saliency detection based on long-term spatial-temporal information. IEEE Trans Image Process 2019;23:1090–100. https://doi.org/10.1109/tip.2019.2934350.Search in Google Scholar PubMed
19. Chen, T, Gao, T, Zhao, X. Single sample description based on Gabor fusion. IET Image Process 2020;13:2840–9. https://doi.org/10.1049/iet-ipr.2018.6665.Search in Google Scholar
20. Deng, X, Jiang, P, Peng, X, Mi, C. An intelligent outlier detection method with one class support Tucker machine and genetic algorithm toward big sensor data in Internet of things. IEEE Trans Ind Electron 2019;66:4672–83. https://doi.org/10.1109/tie.2018.2860568.Search in Google Scholar
21. Wu, DA, Xz, A, Yz, C, Yi, LD, Xz, B, Hc, E, et al.. An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems; 2022.Search in Google Scholar
22. Deng, W, Liu, H, Xu, J, Zhao, H, Song, Y. An improved quantum-inspired differential evolution algorithm for deep belief network. IEEE Trans Instrum Meas 2020;10:7319–27. https://doi.org/10.1109/tim.2020.2983233.Search in Google Scholar
23. Wu, D. Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem. Appl Soft Comput 2020;100:1568–4946.10.1016/j.asoc.2020.106724Search in Google Scholar
24. Cai, X, Zha, H, Shang, S, Zhou, Y, Deng, W. An improved quantum-inspired cooperative co-evolution algorithm with muli-strategy and its application. Expert Syst Appl 2021;171:114629.1–13. https://doi.org/10.1016/j.eswa.2021.114629.Search in Google Scholar
25. Zhao, H, Zhang, P, Zhang, R, Yao, R, Deng, W. A novel performance trend prediction approach using ENBLS with GWO. Meas Sci Technol 2022;34:025018. https://doi.org/10.1088/1361-6501/ac9a61.Search in Google Scholar
26. Zhao, H, Yang, X, Chen, B, Chen, H, Deng, W. Bearing fault diagnosis using transfer learning and optimized deep belief network. Meas Sci Technol 2022;33:065009.1–15.10.1088/1361-6501/ac543aSearch in Google Scholar
27. Armi, L, Fekri-Ershad, S. Texture image classification based on improved local quinary patterns. Multimed Tool Appl 2019;78:18995–9018. https://doi.org/10.1007/s11042-019-7207-2.Search in Google Scholar
© 2023 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research Articles
- Harmonic power sharing control using adaptive virtual harmonic impedance in islanded microgrids
- Performance evaluation of seven level grid-tied PV inverter employs seven switches with the triple gain
- Transient thermal analysis of gas insulated switchgear modules using thermal network approach
- Multi-source perceptual blind compensation inspection method for substation based on equipment’s visual blind area identification and saliency detection
- Electric vehicle charging pile capacity planning based on normal distribution Monte Carlo sampling model
- Robust synergetic control of electric vehicle equipped with an improved load torque observer
- Techno-economic analysis of integrating battery energy storage systems in industrial buildings
- Enhanced sensitive phase alpha plane scheme against high resistance ground faults
- Improved adaptive micro-grid over current protection scheme considering false tripping
- Low voltage ride through control strategy for grid-tied solar photovoltaic inverter
- Study on the influence of dual-winding optimization design on the torque and suspension performance of bearingless motor
Articles in the same Issue
- Frontmatter
- Research Articles
- Harmonic power sharing control using adaptive virtual harmonic impedance in islanded microgrids
- Performance evaluation of seven level grid-tied PV inverter employs seven switches with the triple gain
- Transient thermal analysis of gas insulated switchgear modules using thermal network approach
- Multi-source perceptual blind compensation inspection method for substation based on equipment’s visual blind area identification and saliency detection
- Electric vehicle charging pile capacity planning based on normal distribution Monte Carlo sampling model
- Robust synergetic control of electric vehicle equipped with an improved load torque observer
- Techno-economic analysis of integrating battery energy storage systems in industrial buildings
- Enhanced sensitive phase alpha plane scheme against high resistance ground faults
- Improved adaptive micro-grid over current protection scheme considering false tripping
- Low voltage ride through control strategy for grid-tied solar photovoltaic inverter
- Study on the influence of dual-winding optimization design on the torque and suspension performance of bearingless motor
