Power Loss Prediction for Aging Characteristics and Condition Monitoring for Parallel-Connected Power Modules Using Nonlinear Autoregressive Neural Network

<table class="table-group" id="tab5"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">The degradation grade</td><td class="align_center">Maintenance measures</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">Normal</td><td class="align_center">Continue working</td></tr><tr><td class="align_left">Slight aging I</td><td class="align_center">Usual inspection or check</td></tr><tr><td class="align_left">Slight aging II</td><td class="align_center">Usual inspection or check</td></tr><tr><td class="align_left">Serious aging I</td><td class="align_center">Stop and usual maintenance</td></tr><tr><td class="align_left">Serious aging II</td><td class="align_center">Stop and change devices</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

Complexity

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Table 5

Table 5: Power Loss Prediction for Aging Characteristics and Condition Monitoring for Parallel-Connected Power Modules Using Nonlinear Autoregressive Neural Network 

Table 5 | Power Loss Prediction for Aging Characteristics and Condition Monitoring for Parallel-Connected Power Modules Using Nonlinear Autoregressive Neural Network 