The paper proposes a method of automatic detection of parameters of a distribution transformer (model, type, and power) from a distance, based on its low-frequency noise spectra. The spectra are registered by sensors and processed by a method based on evolutionary algorithms and machine learning. The method, as input data, uses the frequency spectra of sound pressure levels generated during operation by transformers in the real environment. The model also uses the background characteristic to take under consideration the changing working conditions of the transformers. The method searches for frequency intervals and its resolution using both a classic genetic algorithm and particle swarm optimization. The interval selection was verified using five state-of-the-art machine learning algorithms. The research was conducted on 16 different distribution transformers. As a result, a method was proposed that allows the detection of a specific transformer model, its type, and its power with an accuracy greater than 84%, 99%, and 87%, respectively. The proposed optimization process using the genetic algorithm increased the accuracy by up to 5%, at the same time reducing the input data set significantly (from 80% up to 98%). The machine learning algorithms were selected, which were proven efficient for this task.

This paper proposes a method of automatically detecting and classifying low frequency noise generated by power transformers using sensors and dedicated machine learning algorithms. The method applies the frequency spectra of sound pressure levels generated during operation by transformers in a real environment. The spectra frequency interval and its resolution are automatically optimized for the selected machine learning algorithm. Various machine learning algorithms, optimization techniques, and transformer types were researched: two indoor type transformers from Schneider Electric and two overhead type transformers manufactured by ABB. As a result, a method was proposed that provides a way in which inspections of working transformers (from background) and their type can be performed with an accuracy of over 97%, based on the generated low-frequency noise. The application of the proposed preprocessing stage increased the accuracy of this method by 10%. Additionally, machine learning algorithms were selected which offer robust solutions (with the highest accuracy) for noise classification.

Paper presents results of comparative analysis of infrasound noise generated by wind turbines of two types: asynchronous type REPOWER MM92 with power equal to 2 MW and synchronous type Vensys 62 with power equal to 1.2 MW. Frequency spectra of sound pressure levels generated during operation by both turbines for exemplary chosen wind speed values are depicted. Within the shown spectra the resonant frequencies have been indicated, for which sound pressure variations over time are shown. Based on the achieved frequency spectra it was stated that in general the asynchronous type turbine produces lower pressure levels, which are less stable over time, and indicates higher pressure values around the resonant frequencies as compared to the synchronous type turbine. Also it was stated that the asynchronous type turbine is more influenced by the wind conditions and generates higher pressure values by higher wind speeds then the synchronous type turbine. The main contribution of this paper lies in indication that the type of wind turbine generator has significant impact on the level of infrasound noise emitted to the environment.

Computer simulation models of water supply networks are commonly applied in the water industry. As part of the research works, results of which are presented in the paper, OFF-LINE and ON-LINE calibration of water supply network model parameters using two methods was carried out and compared. The network skeleton was developed in the Epanet software. For optimization two types of dependent variables were subjected: the pressure on the node and volume flow in the network section. The first calibration method regards to application of the genetic algorithm, which is a build in plugin - “Epanet Calibrator”. The second method was related to the use of function ga, which is implemented in the MATLAB toolbox Genetic Algorithm and Direct Search. The possibilities of application of these algorithms to solve the issue of optimizing the parameters of the created model of water supply network in both cases: OFF-LINE and ON-LINE calibration was examined. An analysis of the effectiveness of the considered algorithms for different values of configuration parameters was performed. Based on the achieved results it was stated that application of the ga algorithm gives higher correlation of the calibrated values to the empirical data.

The subject matter of the research work presented in this paper refers to the measurements of the values of the acoustic pressure levels (noise) occurring around a main feeding-point aerial substation adjacent to apartment buildings. The paper presents the values of the noise accompanying the particular power devices, mainly transformers, during their regular operation. The main aim of this research work was the comparison and assessment of the acoustic pressure values measured with the permissible values defined by environmental standards binding in Poland. The research analysis carried out proved that during a long-term operation of power appliances installed in substations the acoustic pressure level that they emit into the environment is not constant but is subject to changes. Thus the increase of the noise level above the permissible values can be the cause of violation of environmental standards. Due to a significant increase of people's awareness and readiness to claim their rights, the main consequence of violating the standards is a growing number of claims. Therefore it is imperative that the level of the acoustic pressure be monitored during routine tests, especially around high-power transformers.

Within the research work, the results of which are presented in this paper, a comparative analysis of the acoustic emission(AE) signals generated by multi‐source partial discharges (PDs) was carried out. The research investigations were carried out in a model system in which PDs were generated with two spark‐gaps. A spark‐gap in the surface system and a point‐plane spark‐gap were used due to the fact that these are the most commonly occurring PD forms in power transformers. The AE signals were registered with a contact transducer placed on the external part of the tub. The analysis in the time‐frequency domain using a short‐time Fourier transform(STFT) was carried out for the AE signals generated by PDs. The results of the analysis are shown in the form of two‐ and three‐dimensional spectrograms of the power spectral density and three‐dimensional spectrograms of the amplitude spectrum.