Application of Spectral Kurtosis to Characterize Amplitude Variability in Power Systems’ Harmonics
The highly-changing concept of Power Quality (PQ) needs to be continuously reformulated due to the new schemas of the power grid or Smart Grid (SG). In general, the spectral content is characterized by their averaged or extreme values. However, new PQ events may consist of large variations in amplit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-01-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/12/1/194 |
| _version_ | 1811184870145130496 |
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| author | Jose-María Sierra-Fernández Sarah Rönnberg Juan-José González de la Rosa Math H. J. Bollen José-Carlos Palomares-Salas |
| author_facet | Jose-María Sierra-Fernández Sarah Rönnberg Juan-José González de la Rosa Math H. J. Bollen José-Carlos Palomares-Salas |
| author_sort | Jose-María Sierra-Fernández |
| collection | DOAJ |
| description | The highly-changing concept of Power Quality (PQ) needs to be continuously reformulated due to the new schemas of the power grid or Smart Grid (SG). In general, the spectral content is characterized by their averaged or extreme values. However, new PQ events may consist of large variations in amplitude that occur in a short time or small variations in amplitude that take place continuously. Thus, the former second-order techniques are not suitable to monitor the dynamics of the power spectrum. In this work, a strategy based on Spectral Kurtosis (SK) is introduced to detect frequency components with a constant amplitude trend, which accounts for amplitude values’ dispersion related to the mean value of that spectral component. SK has been proven to measure frequency components that follow a constant amplitude trend. Two practical real-life cases have been considered: electric current time-series from an arc furnace and the power grid voltage supply. Both cases confirm that the more concentrated the amplitude values are around the mean value, the lower the SK values are. All this confirms SK as an effective tool for evaluating frequency components with a constant amplitude trend, being able to provide information beyond maximum variation around the mean value and giving a progressive index of value dispersion around the mean amplitude value, for each frequency component. |
| first_indexed | 2024-04-11T13:19:42Z |
| format | Article |
| id | doaj.art-dba2a708f87b4c68b4a1649ccabb7fed |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T13:19:42Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-dba2a708f87b4c68b4a1649ccabb7fed2022-12-22T04:22:15ZengMDPI AGEnergies1996-10732019-01-0112119410.3390/en12010194en12010194Application of Spectral Kurtosis to Characterize Amplitude Variability in Power Systems’ HarmonicsJose-María Sierra-Fernández0Sarah Rönnberg1Juan-José González de la Rosa2Math H. J. Bollen3José-Carlos Palomares-Salas4PAIDI-TIC-168, Computational Instrumentation, University of Cádiz, Av. Ramón Puyol, 11202 Algeciras, SpainElectric Power Engineering, Luleå University of Technology, Forskargatan 1, 931 87 Skellefteå, SwedenPAIDI-TIC-168, Computational Instrumentation, University of Cádiz, Av. Ramón Puyol, 11202 Algeciras, SpainElectric Power Engineering, Luleå University of Technology, Forskargatan 1, 931 87 Skellefteå, SwedenPAIDI-TIC-168, Computational Instrumentation, University of Cádiz, Av. Ramón Puyol, 11202 Algeciras, SpainThe highly-changing concept of Power Quality (PQ) needs to be continuously reformulated due to the new schemas of the power grid or Smart Grid (SG). In general, the spectral content is characterized by their averaged or extreme values. However, new PQ events may consist of large variations in amplitude that occur in a short time or small variations in amplitude that take place continuously. Thus, the former second-order techniques are not suitable to monitor the dynamics of the power spectrum. In this work, a strategy based on Spectral Kurtosis (SK) is introduced to detect frequency components with a constant amplitude trend, which accounts for amplitude values’ dispersion related to the mean value of that spectral component. SK has been proven to measure frequency components that follow a constant amplitude trend. Two practical real-life cases have been considered: electric current time-series from an arc furnace and the power grid voltage supply. Both cases confirm that the more concentrated the amplitude values are around the mean value, the lower the SK values are. All this confirms SK as an effective tool for evaluating frequency components with a constant amplitude trend, being able to provide information beyond maximum variation around the mean value and giving a progressive index of value dispersion around the mean amplitude value, for each frequency component.http://www.mdpi.com/1996-1073/12/1/194harmonicsconstant amplitude trendfourth-order statisticsdetectionspectral kurtosis |
| spellingShingle | Jose-María Sierra-Fernández Sarah Rönnberg Juan-José González de la Rosa Math H. J. Bollen José-Carlos Palomares-Salas Application of Spectral Kurtosis to Characterize Amplitude Variability in Power Systems’ Harmonics Energies harmonics constant amplitude trend fourth-order statistics detection spectral kurtosis |
| title | Application of Spectral Kurtosis to Characterize Amplitude Variability in Power Systems’ Harmonics |
| title_full | Application of Spectral Kurtosis to Characterize Amplitude Variability in Power Systems’ Harmonics |
| title_fullStr | Application of Spectral Kurtosis to Characterize Amplitude Variability in Power Systems’ Harmonics |
| title_full_unstemmed | Application of Spectral Kurtosis to Characterize Amplitude Variability in Power Systems’ Harmonics |
| title_short | Application of Spectral Kurtosis to Characterize Amplitude Variability in Power Systems’ Harmonics |
| title_sort | application of spectral kurtosis to characterize amplitude variability in power systems harmonics |
| topic | harmonics constant amplitude trend fourth-order statistics detection spectral kurtosis |
| url | http://www.mdpi.com/1996-1073/12/1/194 |
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