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ANN training parameters_
| Parameters | Values |
|---|---|
| Network type | Feedforward/backpropagation |
| Learning algorithm | Trainlm |
| Epochs | 1,000 |
| Convergence limit (Goal) | 1e−12 |
| Hidden layers | 10 |
| Input layers | 4 |
| Output layers | 3 |
Summary of THD content levels (%)_
| System | THD before filter (%) | THD after LC filter (%) |
|---|---|---|
| FCSM | 43.71 | 7.60 |
| DRPWM | 42.83 | 7.40 |
| Proposed method | 35.59 | 2.17 |
Techno-economic comparison between conventional RPWM and proposed ANN-RPWM controller_
| Cost component | Conventional RPWM system | Proposed ANN–RPWM system | Remarks/assumptions |
|---|---|---|---|
| DSP/microcontroller | Included | Same | No additional processor required |
| Power semiconductors (IGBTs/drivers) | 6 IGBTs + 3 drivers | Same | Unchanged hardware configuration |
| Sensors/feedback circuits | 3 V, 3 current sensors | Same | No modification needed |
| Software/algorithmic overhead | Baseline PWM control | +3% additional CPU load | ANN inference executed on the same DSP |
| Development/training effort | N/A | One-time offline training | Conducted using MATLAB on PC |
| Filter components (L, C) | LC filter | Same | Identical filter design used |
| Implementation/maintenance | Standard | Standard | No extra calibration required |
| Estimated total cost impact | 100% | ≈101% | <1% incremental difference |
Summary of harmonics and their relative magnitudes_
| Harmonic order | FCSM (dB) | DRPWM (dB) | ANN-RPWM (dB) | Reduction vs. DRPWM (%) |
|---|---|---|---|---|
| 5 | −36 | −40 | −46 | 43 |
| 7 | −38 | −42 | −47 | 37 |
| 11 | −40 | −43 | −48 | 35 |
| 13 | −42 | −44 | −49 | 35 |