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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of fault section estimation in electric power systems, undertaken at a control center level and using information about the operation of protection relays and circuit breakers. The developed methodology should be used after the occurrence of contingencies with definitive disconnections, and before beginning the process of network restoration. Due to the absence of an analytic formulation, the problem calls for the use of artificial-intelligence techniques, such as neural networks and expert systems. Neural networks are employed to model the protection systems, dealing with the uncertainties involved with relay and circuit-breaker operation messages. An expert system is used to complement the results provided by the neural networks, considering the network topology. The results show that the developed methodology is applicable to real large-scale power systems. In addition, it is capable of noise suppression in relay and circuit-breaker trip messages, treats multiple faults naturally, and infers a solution even in cases when remote backup protection action occurs. [ABSTRACT FROM AUTHOR]

This paper presents a neural system intended to aid the control center operator in the task of fault section estimation. Its analysis is based on information about the operation of protection devices and circuit breakers. In order to allow the diagnosis task, the protection system philosophy of busbars, transmission lines, and transformers are modeled with the use of two types of neural networks: the general regression neural network and the multilayer perceptron neural network. The tool described in this paper can be applied to real bulk power systems and is able to deal with topological changes without having to retrain the neural net- works. [ABSTRACT FROM AUTHOR]