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Subject matter of this paper deals with issues of the environmental impact of infrasound noise associated with operation of low-power wind turbines. It is now the current and relevant problem for both the cognitive and utilitarian standpoint. There is now a considerable growth in the number of such installations in Poland, Europe and in the world, due to the possibility of locating them on existing building roofs in heavily urbanized areas, and without having to build additional supporting structures. As part of the study, measurements of low frequency acoustic signals generated during operation of micro turbine equipped with a rotor of vertical axis of rotation relative to the wind direction have been performed. Analyses were carried out in order to determine the impact of wind speed and selected meteorological parameters on the level of infrasound signals emitted by the test turbine. In addition, frequency analysis of the recorded signals was performed. Then, the obtained results were subjected to comparative analysis. In summary, the achieved results were referred to the limits of sound pressure intensity level of infrasound signals as specified for the working environment in accordance with the Polish Standard PN-Z-01338. [ABSTRACT FROM AUTHOR]

Referred pain/sensation provoked by trigger points suits the nociplastic pain criteria. There is a debate over whether trigger points are related to a peripheral phenomenon or central sensitization (CS) processes. Referred pain is considered a possible sign of CS, which occurs probably mainly due to the abnormal activity of the immune and autonomic nervous systems. To confirm abnormal autonomic reactivity within the referred pain zone of active trigger points, a new diagnostic tool, the Skorupska Protocol® (the SP test®), was applied. The test uses noxious stimulation (10 minutes of dry needling under infrared camera control) as a diagnostic tool to confirm abnormal autonomic nervous system activity. A response to the SP test® of healthy subjects with referred pain sensations provoked by latent trigger points (LTrPs) stimulation was not explored before. The study aims at examining if LTrPs can develop an autonomic response. Methods . Two groups of healthy subjects, (i) gluteus minimus LTrPs with referred pain ( n  = 20) and (ii) control ( n  = 27), were examined using the SP test®. Results . Abnormal autonomic activity within the referred pain zone was confirmed for all analyzed LTrPs subjects. 70% of control subjects had no feature of vasodilatation and others presented minor vasomotor fluctuations. The size of vasomotor reactivity within the referred pain zone was LTrPs 11.1 + 10.96% vs. control 0.8 + 0.6% ( p < 0.05). Conclusions . Noxious stimulation of latent TrPs induces abnormal autonomic nervous system activity within the referred pain zone. The observed phenomenon supports the concept of central nervous system involvement in the referred pain patomechanizm., Competing Interests: The authors declare that they have no conflicts of interest in this work., (Copyright © 2023 Elzbieta Skorupska et al.)

The issues reported in this article concern the development of methods applied for measurement, processing, and analysis of infrasound signals generated in association with the operation of wind farms. In particular, the discussion involves the results of the analysis using synchrosqueezed wavelet transforms of infrasound noise emitted by a 2 MW wind turbine that have been recorded during its operation in actual conditions. To record infrasound signals, a wireless measurement system was used, consisting of a base station and three synchronized mobile recording stations. To identify the wavelet structures with the highest ratio of energy, the synchrosqueezed wavelet transforms were used, and the courses of six time runs representing instantaneous frequencies were determined. Application of this approach enables the selection of energy-dominant waveforms from the time-frequency images, whose assessment can be performed mainly in terms of qualitative measures. Application of the synchrosqueezed wavelet transform is an effective tool for the purposes of detection and selection in the designated wavelet structures for the recorded infrasound dominant frequencies for which the carried energy ranges have the highest value.

The study reported in this paper is concerned with areas related to developing methods of measuring, processing and analyzing infrasound noise caused by operation of wind farms. The paper contains the results of the correlation analysis of infrasound signals generated by a wind turbine with a rated capacity of 2 MW recorded by three independent measurement setups comprising identical components and characterized by the same technical parameters. The measurements of infrasound signals utilized a dedicated measurement system called INFRA, which was developed and built by KFB ACOUSTICS Sp. z o.o. In particular, the scope of the paper includes the results of correlation analysis in the time domain, which was carried out using the autocovariance function separately for each of the three measuring setups. Moreover, the courses of the cross-correlation function were calculated separately for each of the potential combinations of infrasound range recorded by the three measuring setups. In the second stage, a correlation analysis of the recorded infrasound signals in the frequency domain was performed, using the coherence function. In the next step, infrasound signals recorded in three setups were subjected to time-frequency transformations. In this part, the waveforms of the scalograms were determined by means of continuous wavelet transform. Wavelet coherence waveforms were calculated in order to determine the level of the correlation of the obtained dependencies in the time-frequency domain. The summary contains the results derived from using correlation analysis methods in the time, frequency and time-frequency domains., Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

The principal objective of this study is to improve the diagnostics of power transformers, which are the key element of supplying electricity to consumers. On Load Tap Changer (OLTC), which is the object of research, the results of which are presented in this article, is one of the most important elements of these devices. The applied diagnostic method is the acoustic emission (AE) method, which has the main advantage over others, that it is considered as a non-destructive testing method. At present, there are many measuring devices and sensors used in the AE method, there are also some international standards, according to which, measurements should be performed. In the presented work, AE signals were measured in laboratory conditions with various OLTC defects being simulated. Five types of sensors were used for the measurement. The recorded signals were analyzed in the time and frequency domain and using discrete wavelet transformation. Based on the results obtained, sets of indicators were determined, which were used as features for an autonomous classification of the type of defect. Several types of learning algorithms from the group of supervised machine learning were considered in the research. The performance of individual classifiers was determined by several quality evaluation measures. As a result of the analyses, the type and characteristics of the most optimal algorithm to be used in the process of classification of the OLTC fault type were indicated, depending on the type of sensor with which AE signals were recorded.

Effective, accurate and adequately early detection of any potential defects in power transformers is still a challenging issue. As the acoustic method is known as one of the noninvasive and nondestructive testing methods, this paper proposes a new approach of the classification method for defect identification in power transformers based on the acoustic measurements. Typical application of acoustic emission (AE) method is the detection of partial discharges (PD); however, during PD measurements other defects may also be identified in the transformer. In this research, a database of various signal sources recorded during acoustic PD measurements in real-life power transformers over several years was gathered. Furthermore, all of the signals are divided into two groups (PD/other) and in the second step into eight classes of various defects. Based on these, selected classification models including machine learning algorithms were applied to training and validation. Energy patterns based on the discrete wavelet transform (DWT) were used as model inputs. As a result, the presented method allows one to identify with high accuracy, not only the selected kind of PD (1st step), but other kinds of faults or anomalies within the transformer being tested (2nd step). The proposed two-step classification method may be applied as a supplementary part of a technical condition assessment system or decision support system for management of power transformers.

This article presents the possibility of using a scintillation detector to detect partial discharges (PD) and presents the results of multi-variant studies of high-energy ionizing generated by PD in air. Based on the achieved results, it was stated that despite a high sensitivity of the applied detector, the accompanying electromagnetic radiation from the visible light, UV, and high-energy ionizing radiation can be recorded by both spectroscopes and a system commonly used to detect radiation. It is also important that the scintillation detector identifies a specific location where dangerous electrical discharges and where the E-M radiation energy that accompanies PD are generated. This provides a quick and non-invasive way to detect damage in insulation at an early stage when it is not visible from the outside. In places where different radiation detectors are often used due to safety regulations, such as power plants or nuclear laboratories, it is also possible to use a scintillation detector to identify that the recorded radiation comes from damaged insulation and is not the result of a failure.