This paper develops a fully automatic tool for the denoising of partial discharge (PD) signals occurring in electrical power networks and recorded in on-site measurements. The proposed method is based on the spectral decomposition of the PD measured signal via the joint application of the short-time Fourier transform and the singular value decomposition. The estimated noiseless signal is reconstructed via a clever selection of the dominant contributions, which allows us to filter out the different spurious components, including the white noise and the discrete spectrum noise. The method offers a viable solution which can be easily integrated within the measurement apparatus, with unavoidable beneficial effects in the detection of important parameters of the signal for PD localization. The performance of the proposed tool is first demonstrated on a synthetic test signal and then it is applied to real measured data. A cross comparison of the proposed method and other state-of-the-art alternatives is included in the study.

Identifying the location of partial discharges (PDs) and providing clear information on insulation vulnerabilities in power systems can help utility companies to improve their maintenance efficiency. This paper presents a PD monitoring and locating system for medium-voltage switchgears using a set of transient earth voltage (TEV) detectors. A time-of-flight methodology was used to identify the switchgear affected by PD among a row of switchgears. A time sharing access mechanism, which was implemented by multiple high-frequency surface mounting relays integrated into each TEV detector, was applied to automatically identify the site of PD and minimize the number of expensive high-speed acquisition cards. The operation principle and implementation of the modular system were formulated, and the efficiency of the proposed system in identifying the switchgear with PD among three 10 kV metal-clad switchgears was confirmed experimentally.

This paper proposes an innovative partial discharge (PD) location technique for overhead electrical power distribution networks. It is aimed at improving the condition-based maintenance of the network. PD localization is carried out via an improved double-sided traveling-wave method. The method is driven by a hybrid detection technique, which integrates a pulse-based synchronization mechanism and a global positioning system (GPS). The proposed solution offers a number of benefits. It has the nice inherent feature of being immune to varying physical parameters of the transmission line, and it has been proven be offer improved accuracy with respect of the conventional GPS-based location methods. Also, an in-house designed portable and non-invasive test setup is presented and thoroughly discussed, thus demonstrating the feasibility of the proposed method. Moreover, an enhanced algorithm is embedded into the PD location system to improve robustness to high-level noise. Finally, the proposed tool relies on a well-established automatic procedure which requires neither parameter tuning nor any expert intervention. The features and strengths of the method are validated on a real case consisting of a 2125-m long 10-kV overhead covered conductor line.

Partial discharge (PD) diagnosis is a crucial technique for assessing the insulation condition of high-voltage (HV) cables. The double-sided travelling wave method is effective for online PD localization on HV cables. However, the PD localization accuracy is limited by three main factors: inaccurate time synchronization among sensors, imprecise pulse propagation velocity, and noise interference. To address these issues, a bidirectional pulse-injected time synchronization method is proposed to correct the timing bases of detectors and assess the pulse propagation velocity along cables independently of cable parameters. Furthermore, a denoising method combining the short-time Fourier transform (STFT) and variational mode decomposition (VMD) is designed to enhance the robustness of the system to high-intensity noise. Finally, an integrated PD detector with pulse injection and signal detection functions and switchable measurement ranges is designed to improve detection sensitivity. To validate the effectiveness of the proposed method, a portable system is developed, and experiments are conducted on both a 10 kV cable in the laboratory and a 110 kV cable in the field. The experimental results confirm that the proposed method enhances the accuracy of PD localization.

In this letter, a novel damped AC (DAC) generator is proposed and implemented, whose working principle is based on the seamless combination of the high voltage (HV) DC source and the HV solid switch by replacing the diodes in the voltage multiplier (VM) with controllable insulated gate bipolar translators (IGBTs). Both laboratory tests on a lumped capacitor and field tests on a 10kV MV cable were performed to prove the feasibility of the proposed techniques.

As low-cost Internet-of-Thing-based PD monitors for medium-voltage apparatuses in distribution power systems increase, developing an effective PD de-noising algorithm is crucial to improve their robustness to on-site noise. Yet, de-noising PD signals in the monitoring devices is challenging primarily due to three critical reasons, i.e., high-level field noises, uncertain PD waveform, and limited computing resources. This work describes an adaptive and efficient PD de-noising algorithm based on the improved spectral decomposition of the noisy PD signal. PD pulses are accurately extracted from the noisy signal by selecting the dominant components via a low-rank singular value decomposition of the time-frequency spectrogram of the signal, thus reducing the size of the involved matrices and the computational complexity. The performance of the proposed de-noising algorithm is first demonstrated on a synthetic PD signal and compared with state-of-the-art alternatives implemented on three embedded systems commonly used for PD monitoring. Finally, the strength and the effectiveness of the proposed approach are further validated on experimental data based on the measurement of Internet-of-Thing-based PD monitors for 35-kV switchgears.