Diagnostics on Power Electronics Converters by Means of Autoregressive Modelling
Abstract
:1. Introduction
- estimate an AR model of the measured signal (vibration, current, etc.);
- extract suitable features from the estimated model (prediction error variance, model coefficients, spectral density, etc.) that allow healthy conditions to be discriminated from faulty ones.
2. System Characteristics
3. Fault Diagnosis via Autoregressive Modelling
3.1. Autoregressive Modelling
3.2. Fault Diagnosis Procedure
4. Results
4.1. Non-Ideal Behavior and Fault Scenarios
4.1.1. Asymmetric Dead Time
4.1.2. Switch Fault
4.2. AR-Based FD: H-Bridge Inverter with Resistive Load
- First, a reference AR model is estimated by using a portion of the healthy signal and the corresponding PSD is computed from (11).
- Then, the remaining part of the healthy data and the faulty data are segmented into overlapping frames of length samples. For each sequence, an AR model of order is estimated and the associated PSD is computed.
- Finally, each current PSD is compared with the reference one through the health indicator (12).
- The values corresponding to the four situations are different and, within each interval, the value of the indicator exhibits very small variations; this is consistent with the fact that the processed signal is due to the same fault type.
- Furthermore, the value of the spectral distance in the healthy case also remains almost constant and lower than in the other cases (by at least an order of magnitude), making the detection clear.
4.3. AR-Based FD: H-Bridge Inverter Supplying a WPT System
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Quantity | Symbol | Value |
---|---|---|
Coil resistance | R | |
Coil self-inductance | L | H |
Coils mutual inductance | M | 5 H |
Compensation capacitance | C | 280 nF |
Resonance frequency | 85 kHz | |
Load resistance | ||
Source voltage | 50 |
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Diversi, R.; Sandrolini, L.; Simonazzi, M.; Speciale, N.; Mariscotti, A. Diagnostics on Power Electronics Converters by Means of Autoregressive Modelling. Electronics 2024, 13, 3083. https://doi.org/10.3390/electronics13153083
Diversi R, Sandrolini L, Simonazzi M, Speciale N, Mariscotti A. Diagnostics on Power Electronics Converters by Means of Autoregressive Modelling. Electronics. 2024; 13(15):3083. https://doi.org/10.3390/electronics13153083
Chicago/Turabian StyleDiversi, Roberto, Leonardo Sandrolini, Mattia Simonazzi, Nicolò Speciale, and Andrea Mariscotti. 2024. "Diagnostics on Power Electronics Converters by Means of Autoregressive Modelling" Electronics 13, no. 15: 3083. https://doi.org/10.3390/electronics13153083