Partial discharge localization in power transformer tanks using machine learning methods.

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SVM) to more complex approaches like convolutional neural networks (CNN). Multiple case studies are considered, each with different attributes, including sensor position, frequency content of the PD signal, and size of the transformer tank. The paper focuses on predicting the PD location in three-dimensional space using single-sensor electric field measurements. Various aspects of each method are analyzed, such as the input signal, core methodology, correlation coefficient between the predicted location and the actual location, and root mean square error (RMSE). These features are discussed and compared across the different methods. The results indicate that the CNN model exhibits superior performance in terms of location accuracy among the methods considered.

Improving robotic hand control via adaptive Fuzzy-PI controller using classification of EMG signals.

Robotic or prosthetic organs are designed to have the maximum similarity to human organs. This paper aims to improve robotic hand control via an adaptive Fuzzy-PI controller using EMG signals. The data is collected from the FDS and FPL muscles of the forearm of five individuals who performed eight movements. Then, appropriate filters are used to eliminate the noise of the signals, and MAV, VAR, and SE features are extracted. Based on MAV and VAR, classification is carried out using DA, KNN, and SVM. With an average accuracy, specificity, and sensitivity of 90.69%, 94.64%, and 62.10%, SVM is a better choice for movement detection. Following the movement detection by SVM, an appropriate reference signal is sent to the controller. The reference signal is the angle change of the fingers during the movement. All the eight gestures are modeled in a new way through these angles. The adaptive fuzzy-PI controller is used to control a robotic hand model with fifteen degrees of freedom. It has the advantages of learning from human experiences and adapting to environmental changes. The performance of the controller is evaluated in two ways. One is the comparison of the fuzzy-PI with the PI by RMSE. The average RMSE for eight movements using the fuzzy-PI is 1.6067, and for the PI, 5.0082. These results show that the fuzzy-PI controller performs better than the PI. Another new evaluation way presented in this paper is comparing the EMG signal features with the robotic hand movement signal features in terms of RMSE. The small RMSE values indicate that the EMG signal and robotic hand movement data features are significantly similar. Therefore, it can be concluded that the robotic hand controlled by the proposed controller is notably identical to the human hand.

Application of Response Surface Methodology and Box-Behnken Design for the Optimization of the Stability of Fibrous Dispersion Used in Drilling and Completion Operations.

Fibers are extensively used as a fluid additive in the oil and gas industry to improve hole-cleaning performance, control fluid filtration loss, and enhance hydraulic fracturing effectiveness. Generally, a small amount of fiber is dispersed in the base fluid to achieve the desired results without increasing the viscosity of the base fluid. Nevertheless, sustaining a uniform fiber dispersion can be challenging under wellbore conditions, which is essential for fibers' functionality. Consequently, a better understanding of fiber suspension or stability in base fluids is necessary for their efficient utilization in drilling and completion operations. In this study, response surface methodology (RSM) and box-behnken design (BBD) are used to investigate the stability of fiber in polymeric base suspensions, including carboxy methyl cellulose (CMC), polyacrylamide (PAM), and xanthan gum (XG). The BBD of three factors was selected to observe the influence of polymer concentration, fiber concentration, and temperature on fibrous suspension stability, with three levels of design factors (low, mid, and high) and two fiber aspect ratios (3 and 12 mm fibers). The base fluid polymer concentration ranged from 1 to 8 vol %, fiber concentration ranged from 0.01 to 0.08 wt %, and the temperature was varied from 25 to 80 °C. The stability measurements were analyzed using Minitab, subsequently, evaluating the factors' impact and interactions and determining the optimum conditions for the stability of the fibrous suspensions. The results predicted by the developed model were in good agreement with the experimental results R 2 ≥ 0.91-0.99. The sensitivity analysis showed that base fluid polymer concentration is the most significant factor affecting fibrous suspension stability. At high polymer concentrations, fiber concentration and temperature effects are minimal, while the temperature effect on the stability was observed at low concentrations (e.g., low suspension viscosities). The fiber aspect ratio indirectly affects system stability. Long fibers have a better tendency to entangle and form a structured network, which in turn hinders the buoyancy that induces individual fiber migration. On the contrary, short fibers do not form a network, allowing them to easily migrate to the surface and agglomerate at the top layer (unstable region). Optimization results revealed that suspensions with viscosities above 50 mPa·s are sufficient to maintain the stability of the suspensions at ambient (25 °C) and elevated (80 °C) temperatures.

An experimental validation of partial discharge localization using electromagnetic time reversal.

The localization of partial discharge (PD) sources is of importance for the monitoring and maintenance of power transformers. Time difference of arrival (TDoA) based methods are widely adopted in the literature for the localization of PDs. Recently, time reversal (TR) was suggested as an efficient means to locate PD sources. As opposed to TDoA, which needs at least 4 sensors, TR is able to locate PD sources in power transformers with only one sensor. Moreover, it needs neither line-of-sight wave propagation from the PD sources to the sensor nor time synchronization. In this study, we present for the first time an experimental demonstration of the ability of the TR process to locate PD sources. A typical TR process includes three steps: (1) recording the PD-emitted field by a sensor, (2) time reversing and back injecting the signal into the medium, (3) using a proper criterion to obtain the focusing point which corresponds to the location of the PD source. In this work, we present a laboratory setup in which steps one and two are performed experimentally, both in the frequency and in the time domain. The obtained peak electric field value is used as a criterion in the third step. It is found that the accuracy of the proposed method is better than 2.5 cm in a transformer tank model with dimensions 73 × 73 × 103 cm3. The effects of the presence of scatterers such as transformer windings are also investigated experimentally and found not to affect the location accuracy of the method.

Partial Discharge Localization Using Time Reversal: Application to Power Transformers.

In this work, we present a novel technique to locate partial discharge (PD) sources based on the concept of time reversal. The localization of the PD sources is of interest for numerous applications, including the monitoring of power transformers, Gas Insulated Substations, electric motors, super capacitors, or any other device or system that can suffer from PDs. To the best of the authors' knowledge, this is the first time that the concept of time reversal is applied to localize PD sources. Partial discharges emit both electromagnetic and acoustic waves. The proposed method can be used to localize PD sources using either electromagnetic or acoustic waves. As a proof of concept, we present only the results for the electromagnetic case. The proposed method consists of three general steps: (1) recording of the waves from the PD source(s) via proper sensor(s), (2) the time-reversal and back-propagation of the recorded signal(s) into the medium using numerical simulations, and (3) the localization of focal spots. We demonstrate that, unlike the conventional techniques based on the time difference of arrival, the proposed time reversal method can accurately localize PD sources using only one sensor. As a result, the proposed method is much more cost effective compared to existing techniques. The performance of the proposed method is tested considering practical scenarios in which none of the former developed methods can provide reasonable results. Moreover, the proposed method has the unique advantage of being able to locate multiple simultaneous PD sources and doing so with a single sensor. The efficiency of the method against the variation in the polarization of the PDs, their length, and against environmental noise is also investigated. Finally, the validity of the proposed procedure is tested against experimental observations.

Tumour shape-dependent microwave hyperthermia using a novel coaxial micro-cut slot antenna.

Given that the effectiveness of interstitial hyperthermia for cancer treatment is related to the temperature achieved during the ablation process, there is a need for an accurate understanding of the required temperature distribution which is affected by the physical shape and form of tumours. Although a maximum peak temperature value and minimum backward heating are desired, the temperature distribution needs to be not only high but also uniformly extended over a section instead of at one peak point, especially when a roughly oval-shaped tumour is aligned with the antenna. In this case, achieving a high temperature peak destroys only the central cancerous cells after the first minutes of ablation, leaving the cells on the side alive. In this paper, a complex model was extended for the study of the heat distribution of an antenna over a porous liver composed of blood, cancerous cells, and normal tissue. Three different types of antenna were analysed: single-slot, double-slot, and dipole-tip. A novel structure made up of the single-slot antenna with a micron cut, named the micro-cut slot (MCS) antenna, was proposed and analysed. Thanks to the new structure, high uniform temperature distribution with minimum backward heating was achieved. The extended model equations, which encompass a coupled nonlinear set of transient Maxwell's electromagnetic equations, extended Darcy-Brinkman equation, and local thermal non-equilibrium equations for porous medium approximation, were solved numerically using the novel alternating direction implicit, finite-difference time-domain approach. The results showed that each type of antenna could be useful if chosen according to the shape of the tumour. In comparison with previously used antennas, the MCS antenna presented a good combination of the required goals of achieving uniform high temperature distribution and minimum backward heating.

Single-Sensor Source Localization Using Electromagnetic Time Reversal and Deep Transfer Learning: Application to Lightning.

Electromagnetic Time Reversal (EMTR) has been used to locate different types of electromagnetic sources. We propose a novel technique based on the combination of EMTR and Machine Learning (ML) for source localization. We show for the first time that ML techniques can be used in conjunction with EMTR to reduce the required number of sensors to only one for the localization of electromagnetic sources in the presence of scatterers. In the EMTR part, we use 2D-FDTD method to generate 2D profiles of the vertical electric field as RGB images. Next, in the ML part, we take advantage of transfer learning techniques by using the pretrained VGG-19 Convolutional Neural Network (CNN) as the feature extractor tool. To the best of our knowledge, this is the first time that the knowledge of pretrained CNNs is applied to simulation-generated images. We demonstrate the skill of the developed methodology in localizing two kinds of electromagnetic sources, namely RF sources with a bandwidth of 0.1-10 MHz and lightning impulses. For the localization of lightning, based on the experimental recordings in the Säntis region, the new approach enables accurate 2D lightning localization using only one sensor, as opposed to current lightning location systems that need at least two sensors to operate.

Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance.

A survey on endoparasites and ectoparasites of stray cats from Mashhad (Iran) and association with risk factors.

As there appeared to be no data available on parasite infection of stray cats in the region and considering the potential threat of stray cats for animal and public health, the present study was carried out using biological samples and necropsy finding collected from cats captured in Mashhad city in the northeast of Iran. From a total 52 stray cats examined, 18 (34.6%) were male and 34 (65.4%) were female. Ten species of endoparasites including helminthes and protozoa and two species of ectoparasites were detected in the examined cats. There were two protozoa, five cestodes, three nematodes and two arthropods. Overall 46 cats (88.46%) have been infected with at least one of the parasites. The following parasites, with their respective prevalence, were found; Nematoda: Toxocara cati 28.84%, Toxocara leonina 7.69%, Physaloptera praeputialis 3.84%; Cestoda: Dipylidium caninum 23.08%, Mesocestoides lineatus 13.46%, Taenia taeniaformis 9.6%, Joyeuxiella echinorhyncoides 7.6% and Taenia hydatigena 1.92%; Protozoa: I. felis 23.7%, Haemobartonella felis 1.92%; Arthropoda: Ctenocephalides felis 1.92% and Cheyletiella blakei 1.92%. Based on our data, there was no significant difference in infection rate between male and female animals. However, the age of the cats were found to be an important risk factor associated with parasitic infection. Our results revealed that zoonotic agents, namely T. cati were present in stray cat colonies in the investigated area. In this respect, appropriate control measures should be taken and it is recommended to determine the most appropriate preventive methods.