This paper presents a procedure for evaluating self-capacitances and parasitic capacitors between physically adjacent turns in multiconductor systems-with a special focus on single-layer air-core inductors. To this end, the paper proposes the use of standard cells, which correspond to a minimal and basic turn, layer, or macrolevel arrangement of a winding in reference to its patterns of stored electrostatic energy. A standard cell embraces mathematical rules derived by means of a curve fitting approach through a set of finite element analysis (FEA) simulations. Besides, it is applied in a matching routine wherein self- and stray capacitances are evaluated according to the relative position of each turn, layer, or macrolevel arrangement along the length of the winding. Finally, the analytical technique is validated by comparing frequency-domain results obtained from an implemented SPICE model and laboratory measurements.

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.

Traditional islanding detection methods present improper action when the power mismatch between generation and load in the islanding system is small. The minimum power mismatch for correct islanding detection defines the limits os a Non-Detection Zone. This paper proposes an efficient method for islanding detection based on angular velocity harmonic patterns. To accomplish this, the measured frequency is decomposed by a Fourier Transform and applied to a Perceptron artificial neural network for pattern classification. The performance of the proposed method is evaluated by tests on a modified IEEE 34 node test system. Several islanding and non-islanding cases were simulated. The proposed method achived a performance of 88.16% in the classification of harmonic patterns with a small training set. Compared with the Under/Over frequency method, the proposed method represents a performance improvement of 17%.

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.