The other models performed slightly better, giving about 92% accuracy. The random forest model classified PD signals with 90% accuracy. We then used the Classification Learner App to train and evaluate classification models using a variety of methods, including logistic regression, support vector machine classification, random forest classification, and ensemble learning. We extracted features from the signal data in MATLAB, using signal processing algorithms to calculate the time between signal peaks and other features commonly used in PD analysis (Figure 2). More recently, we began using machine learning to automate the classification of captured signals. To make the analysts’ job easier, we implemented denoising and other digital signal processing algorithms, but the process remained highly subjective-even highly trained analysts with years of experience could sometimes reach different conclusions about the same signal.
This process was both tedious and slow, and it sometimes produced inconsistent results and false positives. In the past, analysts processed the captured signals manually, looking for patterns that indicate the presence of PD. We follow industry-standard quality control for manufacturers’ PD testing in which a higher-than-normal voltage is applied to an underground cable and to a coupler with an analog-to-digital converter (ADC) that captures high-frequency time-series signals. These networks not only detect PD signals they also identify the approximate location of PD in the cable, the type of defect that produced it, and its severity (Figure 1). Because these signals are symptomatic of dielectric deterioration and eventually fault, detecting them early can head off unexpected cable failures and allow for repair before the failure occurs.Īt IMCORP, we use MATLAB ® to design and train deep learning networks that accelerate and automate the process of detecting and characterizing PD signals. When activated, PD produces high-frequency signals-typically, less than 100 millivolts in amplitude. Left undetected, cable defects can cause power outages and danger to the public.Īccording to IEEE, approximately 90% of failures in underground cable systems are associated with partial discharge (PD), a phenomenon that occurs when the electric field within the cable exceeds the ability of the dielectric insulation to withstand it. They are more expensive to repair, however, and failures are more difficult to pinpoint and to restore.
Underground power cable systems are less susceptible than overhead lines to windstorms, lightning, wildfires, ice storms, and other adverse weather events.
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