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%.