Acoustic inspection is a valuable technique that can detect early stage defects in equipment, thereby facilitating predictive maintenance. In recent times, ultrasonic sensors have made detecting partial discharges through acoustic sensing increasingly feasible. However, interpreting the acoustic signals can pose challenges, as it requires extensive expertise and knowledge of equipment configuration. To address this issue, a technique based on zero-crossing rate and fundamental frequency estimation has been proposed to standardize insulator diagnosis. In an experiment involving a database of 72 raw acoustic signals with frequencies ranging from 0 to 128 kHz, various types of pollution and defects were introduced to a chain of insulators. By employing the proposed technique, the occurrence of partial discharges can be detected and classified according to type, such as corona or surface discharges. This advanced approach to diagnosis simplifies the process while providing valuable insights into the severity of observed phenomena in the field.

In this work, a new methodology is proposed for the online and non-invasive extraction of partial discharge (PD) pulses from raw measurement data obtained using a simplified setup. This method enables the creation of sub-windows with optimized size, each containing a single candidate PD pulse. The proposed approach integrates mathematical morphological filtering (MMF) with kurtosis, a first-order Savitzky-Golay smoothing filter, the Otsu method for thresholding, and a specific technique to associate each sub-window with the phase angle of the applied voltage waveform, enabling the construction of phase-resolved PD (PRPD) patterns. The methodology was validated against a commercial PD detection device adhering to the IEC (International Electrotechnical Commission) standard. Experimental results demonstrated that the proposed method, utilizing an off-the-shelf 8-bit resolution data acquisition system and a low-cost high-frequency current transformer (HFCT) sensor, effectively diagnoses and characterizes PD activity in high-voltage equipment, such as surge arresters and instrument transformers, even in noisy environments. It was able to characterize PD activity using only a few cycles of the applied voltage waveform and identify low amplitude PD pulses with low signal-to-noise ratio signals. Other contribution of this work is the diagnosis and fault signature obtained from a real surge arrester (SA) with a nominal voltage of 192 kV, corroborated by destructive disassembly and internal inspection of the tested equipment. This work provides a cost-effective and accurate tool for real-time PD monitoring, which can be embedded in hardware for continuous evaluation of electrical equipment integrity.

Reliable detection of partial discharges (PD) is essential for condition monitoring of high-voltage electrical equipment. In applications based on high-frequency current transformer (HFCT) sensors, the construction of phase-resolved partial discharge (PRPD) diagrams depends on the accurate discrimination of pulses in signals often contaminated by noise and interference. Traditional signal processing methods present limitations regarding parameterization, scalability, and automation, particularly in continuous monitoring scenarios. This work proposes a new automated pipeline for detecting time windows containing PD pulses from time-domain raw signals acquired with an off-the-shelf 8-bit data acquisition system coupled with an HFCT sensor, integrating deep-learning-based detection with the construction of PRPD diagrams. An aspect that characterizes the originality of this work lies in the fact that a hybrid convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) model are employed to classify temporal segments without the use of fixed statistical thresholds or heuristic rules. The identified windows are used to construct PRPD diagrams, explicitly preserving the phase and amplitude information associated with each pulse. In the test set, composed of acquisitions in a high-voltage (HV) laboratory involving equipment taken out of operation, the model achieved an accuracy of 97.49%, with a recall greater than 97% for the positive class and an area under the curve (AUC) of 0.993. The amplitude estimate derived from the maximum absolute value of the pulse is consistent with baseline reference results and conventional measurements in accordance with the IEC 60270 standard, providing quantitative and qualitative validation of the constructed PRPD patterns. The results indicate that the proposed original method increases the level of automation, preserves physical interpretability, and shows strong potential for application in intelligent continuous monitoring systems for HV equipment.