A Harmonic and Interharmonic Detection Method for Power Systems Based on Enhanced SVD and the Prony Algorithm
To address the problem of harmonic pollution in power systems, a harmonic and interharmonic detection method based on the adaptive order and the dominant factor algorithms is proposed. The proposed method greatly improves the accuracy and precision of harmonic detection, overcoming the notorious pro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/13/7558 |
| _version_ | 1827735255349460992 |
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| author | Junsong Gong Sanjun Liu |
| author_facet | Junsong Gong Sanjun Liu |
| author_sort | Junsong Gong |
| collection | DOAJ |
| description | To address the problem of harmonic pollution in power systems, a harmonic and interharmonic detection method based on the adaptive order and the dominant factor algorithms is proposed. The proposed method greatly improves the accuracy and precision of harmonic detection, overcoming the notorious problem of high sensitivity to noise of the traditional Prony algorithm that often leads to unsatisfactory detection results. In the proposed method, the “adaptive order determination” algorithm is first used to determine the optimal order of Singular Value Decomposition (SVD) denoising, resulting in a more accurate distinction between signal and noise components. Then, signal reconstruction is carried out to effectively remove noise components to enhance the denoising ability of SVD. This mitigates the Prony algorithm’s high sensitivity to noise and greatly reduces the amplitude of false components in the fitting results. Finally, the dominant factor algorithm is applied to accurately screen out the non-false components in Prony’s fitting results. Simulation results show that the proposed method can effectively reduce signal noise in different noise environments with noise intensities ranging from 5 dB to 30 dB, achieving an average signal-to-noise ratio improvement of around 20 dB. Meanwhile, the identification and screening results of harmonic and interharmonic components in the signal are accurate and reliable, with detection errors in amplitude, frequency, and phase at around 0.5%, 0.01%, and 0.5%, respectively. Overall, the proposed method is well suited for detecting harmonics and interharmonics in power systems under various noise environments. |
| first_indexed | 2024-03-11T01:47:30Z |
| format | Article |
| id | doaj.art-d9f3a00e9b4844279f113a41abe0dc70 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:47:30Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d9f3a00e9b4844279f113a41abe0dc702023-11-18T16:08:10ZengMDPI AGApplied Sciences2076-34172023-06-011313755810.3390/app13137558A Harmonic and Interharmonic Detection Method for Power Systems Based on Enhanced SVD and the Prony AlgorithmJunsong Gong0Sanjun Liu1College of Intelligent Science and Engineering, Hubei Minzu University, Enshi 445000, ChinaCollege of Intelligent Science and Engineering, Hubei Minzu University, Enshi 445000, ChinaTo address the problem of harmonic pollution in power systems, a harmonic and interharmonic detection method based on the adaptive order and the dominant factor algorithms is proposed. The proposed method greatly improves the accuracy and precision of harmonic detection, overcoming the notorious problem of high sensitivity to noise of the traditional Prony algorithm that often leads to unsatisfactory detection results. In the proposed method, the “adaptive order determination” algorithm is first used to determine the optimal order of Singular Value Decomposition (SVD) denoising, resulting in a more accurate distinction between signal and noise components. Then, signal reconstruction is carried out to effectively remove noise components to enhance the denoising ability of SVD. This mitigates the Prony algorithm’s high sensitivity to noise and greatly reduces the amplitude of false components in the fitting results. Finally, the dominant factor algorithm is applied to accurately screen out the non-false components in Prony’s fitting results. Simulation results show that the proposed method can effectively reduce signal noise in different noise environments with noise intensities ranging from 5 dB to 30 dB, achieving an average signal-to-noise ratio improvement of around 20 dB. Meanwhile, the identification and screening results of harmonic and interharmonic components in the signal are accurate and reliable, with detection errors in amplitude, frequency, and phase at around 0.5%, 0.01%, and 0.5%, respectively. Overall, the proposed method is well suited for detecting harmonics and interharmonics in power systems under various noise environments.https://www.mdpi.com/2076-3417/13/13/7558harmonic detectionSingular Value Decomposition (SVD)signal denoisingProny algorithm |
| spellingShingle | Junsong Gong Sanjun Liu A Harmonic and Interharmonic Detection Method for Power Systems Based on Enhanced SVD and the Prony Algorithm Applied Sciences harmonic detection Singular Value Decomposition (SVD) signal denoising Prony algorithm |
| title | A Harmonic and Interharmonic Detection Method for Power Systems Based on Enhanced SVD and the Prony Algorithm |
| title_full | A Harmonic and Interharmonic Detection Method for Power Systems Based on Enhanced SVD and the Prony Algorithm |
| title_fullStr | A Harmonic and Interharmonic Detection Method for Power Systems Based on Enhanced SVD and the Prony Algorithm |
| title_full_unstemmed | A Harmonic and Interharmonic Detection Method for Power Systems Based on Enhanced SVD and the Prony Algorithm |
| title_short | A Harmonic and Interharmonic Detection Method for Power Systems Based on Enhanced SVD and the Prony Algorithm |
| title_sort | harmonic and interharmonic detection method for power systems based on enhanced svd and the prony algorithm |
| topic | harmonic detection Singular Value Decomposition (SVD) signal denoising Prony algorithm |
| url | https://www.mdpi.com/2076-3417/13/13/7558 |
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