Abstract:

The basis of industrial and power plants are electrical machinery, whose operation mostly depends on the condition of stator insulation. Maintaining dependability, lowering downtime, and maximizing operational efficiency depend on accurate assessment of stator insulation. Current diagnostic techniques are unable of delivering real condition monitoring under various operating settings and lack sensitivity toward early-stage insulation defects. This work proposes a novel method based on Partial Discharge Measurement and Analysis (PDM-A) techniques to get beyond these restrictions. Using advanced signal processing and partial discharge event pattern analysis, the novel method allows one to identify early insulation breakdown. Early intervention made by the method makes predictive maintenance and longer machine lifetime feasible. Experimental validation shows that the approach proposed in this study significantly improves detection accuracy and dependability, thereby improving the evaluation of insulation condition. Usually, this approach increases machine performance and results in more efficient maintenance plans for electrical machine systems.