Purpose: Detection of deformation of devices in high voltage electricity transmission line systems is an important issue in terms of economy and reuse. This study is aimed to detect devices that are deformed or thought to have suffered due to environmental and electrical reasons. Design/methodology/approach: In this experimental study, it was ensured that the sound and deformed insulators used in energy transmission lines were determined by the analysis of the sounds obtained by using the impact method. Equal intensity impact was applied to the isolator using the pendulum and the resulting sound noise signal analyses were made using power spectral density (PSD), magnitude scalogram (MS), multitape power spectrum density (MPSD) and continuous wavelet transform (CWT) methods in the study. In the analysis results, the isolators that are not visible to the eye and have certain damage were successfully separated from the intact insulators. Especially, MPSD and CWT analysis results are quite satisfactory. Findings: Damage analysis of insulators used in electricity transmission lines has been made. A total of 40 insulators were examined in two categories in their group, both damaged and not damaged. Data collection system was established. The data obtained from the data collection system were analysed and compared using four analysis methods. PSD, MS, MPSD and CWT analyses were made in the study. All the analyses carried out generally contain features that distinguish damaged and undamaged insulators from each other, the most successful results are MS and CWT results. CWT results are very successful in terms of time and amplitude, and it has been proposed as a method that can be used to separate damaged and undamaged insulators. Originality/value: It can be suggested as a result of experimental tests that the results of CWT analysis can be used in the pulse noise method in isolators to be tested for reuse in electrical power transmission lines.

An electric power network can be evolved into smart grids, which are measured by providing energy efficiency and improving the available resources. With the development of software and hardware elements, the decision-making mechanism of existing smart grids is transformed into more robust uninterrupted and economical energy management systems. In this study, a cognitive-based algorithm using dynamic energy management flexibility, storage and energy management algorithm and cloud computing architecture is proposed. Using this approach, an uninterrupted and economical energy management system can be planned. In addition, the proposed approach provides the optimization of supply and demand sides.

The power system is one of the most susceptible systems to failures, which are most frequently caused by transmission line faults. Transmission line failures account for 85% of all power system malfunctions. However, over the last decade, numerous fault detection methods have been developed to ensure the reliability and stability of power systems. A hybrid detection method based on the idea of redundancy property is presented in this paper. Because the continuous wavelet transform itself does not extract fault features for small defects effectively, the stationary wavelet transform approach is employed to assist in their detection. As a result of its ability to decompose the signal into high- and low-frequency components, undecimated reconstruction by using the algebraic summation operation (ASO) is used. This approach creates redundancy, which is useful for the feature extraction of small defects and makes faulty parts more evident. The numerical value of the redundancy ratio’s contribution to the original signal is approximately equal to 36%. Following this method for redundant signal reconstruction, a continuous wavelet transform is used to extract the fault characteristic significantly easier in the time-scale (frequency) domain. Finally, the suggested technique has been demonstrated to be an efficient fault detection and identification tool for use in power systems. In fact, using this advanced signal processing technique will help with early fault detection, which is mainly about predictive maintenance. This application provides more reliable operation conditions.

AbstractCeramic materials are an indispensable part of our lives. Today, ceramic materials are mainly used in construction and kitchenware production. The fact that some deformations cannot be seen with the naked eye in the ceramic industry leads to a loss of time in the detection of deformations in the products. Delays that may occur in the elimination of deformations and in the planning of the production process cause the products with deformation to be excessive, which adversely affects the quality. In this study, a deep learning model based on acoustic noise data and transfer learning techniques was designed to detect cracks in ceramic plates. In order to create a data set, noise curves were obtained by applying the same magnitude impact to the ceramic experiment plates by impact pendulum. For experimental application, ceramic plates with three invisible cracks and one undamaged ceramic plate were used. The deep learning model was trained and tested for crack detection in ceramic plates by the data set obtained from the noise graphs. As a result, 99.50% accuracy was achieved with the deep learning model based on acoustic noise.