Reproducing Transformers’ Frequency Response from Finite Element Method (FEM) Simulation and Parameters Optimization

Frequency response analysis (FRA) is being employed worldwide as one of the main methods for the internal condition assessment of transformers due to its capability of detecting mechanical changes. Nonetheless, the objective interpretation of FRA measurements is still a challenge for the industry. This is mainly attributable to the lack of complete data from the same or similar units. A large database of FRA measurements can contribute to improving classification algorithms and lead to a more objective interpretation. Due to their destructive nature, mechanical deformations cannot be performed on real transformers to collect data from different scenarios. The use of simulation and laboratory transformer models is necessary. This research contribution is based on a new method using Finite Element Method simulation and a lumped element circuit to obtain FRA traces from a laboratory model at healthy and faulty states, along with an optimization method to improve capacitive parameters from estimated values. The results show that measured and simulated FRA traces are in good agreement. Furthermore, the faulty FRA traces were analyzed to obtain the characterization of faults based on the variation of the lumped element’s parameters. This supports the use of the proposed method in the generation of faulty frequency response traces and its further use in classifying and localizing faults in the transformer windings. The proposed approach is therefore tailored for generating a larger and unique database of FRA traces with industrial importance and academic significance

Dynamic modeling and performance evaluation of axial flux PMSG based wind turbine system with MPPT control

This research work develops dynamic model of a gearless small scale wind power generation system based on a direct driven single sided outer rotor AFPMSG with coreless armature winding. Dynamic modeling of the AFPMSG based wind turbine requires machine parameters. To this end, a 3D FEM model of the generator is developed and from magnetostatic and transient analysis of the FEM model, machine parameters are calculated and utilized in dynamic modeling of the system. A maximum power point tracking (MPPT)-based FOC control approach is used to obtain maximum power from the variable wind speed. The simulation results show the proper performance of the developed dynamic model of the AFPMSG, control approach and power generation system

Enhanced Motor Imagery-Based Eeg Classification Using A Discriminative Graph Fourier Subspace

Dealing with irregular domains, graph signal processing (GSP) has attracted much attention especially in brain imaging analysis. Motor imagery tasks are extensively utilized in brain-computer interface (BCI) systems that perform classification using features extracted from Electroencephalogram signals. In this paper, a GSP-based approach is presented for two-class motor imagery tasks classification. The proposed method exploits simultaneous diagonalization of two matrices that quantify the covariance structure of graph spectral representation of data from each class, providing a discriminative subspace where distinctive features are extracted from the data. The performance of the proposed method was evaluated on Dataset IVa from BCI Competition III. Experimental results show that the proposed method outperforms two state-of-the-art alternative methods

Dynamic modeling of wind turbine based axial flux permanent magnetic synchronous generator connected to the grid with switch reduced converter

This paper studies the power electronic converters for grid connection of axial flux permanent magnetic synchronous generators (AFPMSG) based variable speed wind turbine. In this paper, a new variable speed wind turbine with AFPMSG and Z-source inverter is proposed to improve number of switches and topology reliability. Besides, dynamic modeling of AFPMSG is presented to analyze grid connection of the proposed topology. The Z-source inverter controls maximum power point tracking (MPPT) and delivering power to the grid. Therefore other DC–DC chopper is not required to control the rectified output voltage of generator in view of MPPT. As a result, the proposed topology requires less power electronic switches and the suggested system is more reliable against short circuit. The ability of proposed energy conversion system with AFPMSG is validated with simulation results and experimental results using PCI-1716 data acquisition system

Concept design of a high-voltage electrostatic sanitizer to prevent spread of COVID-19 coronavirus

In addition to public health measures, including social distancing, masking, cleaning, surface disinfection, etc., ventilation and air filtration can be a key component of a multi-pronged risk mitigation strategy against COVID-19 transmission indoors. Electrostatic precipitators (ESP) have already proved their high performance in fluid filtration, particularly in industrial applications, to control exhaust gas emissions and remove fine and superfine particles from the flowing gas, using high-voltage electrostatic fields and forces. In this contribution, a high-voltage electrostatic sanitizer (ESS), based on the electrostatic precipitation concept, is proposed as a supportive measure to reduce indoor air infection and prevent the spread of COVID-19 coronavirus. The finite element method (FEM) is used to model and simulate the proposed ESS, taking into account three main mechanisms involving in electrostatic sanitization, namely electrostatic field, airflow, and aerosol charging and tracing, which are mutually coupled to each other and occur simultaneously during the sanitization process. To consider the capability of the designed ESS in capturing superfine particles, functional parameters of the developed ESS, such as air velocity, electric potential, and space charge density, inside the ESS are investigated using the developed FEM model. Simulation results demonstrate the ability of the designed ESS in capturing aerosols containing coronavirus, precipitating suspended viral particles, and trapping them in oppositely charged electrode plates

Impact of perlator on the cooling liquid flow and hottest point temperature of superconducting windings in HTS transformer

The generated heat by the superconducting windings and the other parts such as current leads in transformer increases the hottest point temperature (HPT) and causes the high temperature superconducting (HTS) windings to quench. Due to the properties of superconducting windings, reducing the HPT is of critical importance for the stable operation of the HTS transformer. The cooling system of HTS transformers, not only provides the cryogenic temperature for the proper operation of the superconductors but also is responsible for dissipating the generated heat by the windings. In this paper, the effect of the angle of inlet pipes in cooling system was investigated. This was a simple and effective solution which increases the heat transfer in liquid nitrogen. It was shown that inlet angle has a significant effect on the flow turbulence and the windings temperature. The Perlator is used as a lattice sheet which is installed inside the inlet valve and increases the turbulence of inlet flow of liquid nitrogen to increase heat transfer and reduce HPT. The thermal analysis is obtained by finite element method using ANSYS Fluent software. The influence of changing the inlet pipe angle and different structures of Perlator on heat transfer was investigated

Identifying unmet information needs of advanced cancer patients in Iran: An in‐depth qualitative study

Abstract Background and Aims One of the main vital needs for self‐care in patients with advanced cancer is information need. Meeting this need has significant positive effects on improving their treatment and care. This study was conducted to identify the unmet information needs of patients with advanced cancer in Iran. Methods This exploratory study was performed from July to February 2021 in the Kerman University of Medical Sciences cancer treatment centers. Oncologists selected eligible patients by purposeful sampling method. Semistructured and in‐depth interviews were conducted with selected patients to collect data. Interviews continued until data saturation. Each interview was audio‐recorded and transcribed verbatim. Results In the interviews, 15 patients with advanced cancer ranging in age from 43 to 65 years participated. The most common type of cancer in women was breast (71.4%) and prostate (50%) in men. The two main categories of “types of unmet information needs” and “reasons for not meeting information needs” were extracted from the analysis of patient interviews, with six and four subcategories, respectively. Conclusion Cancer patients had a large number of unmet information needs. At the time of identifying the unmet information needs of cancer patients, the basic reasons for not meeting these needs should also be considered because cultural differences and social gaps in societies are inevitable

Improved monitoring and diagnosis of transformer solid insulation using pertinent chemical indicators

Transformers are generally considered to be the costliest assets in a power network. The lifetime of a transformer is mainly attributable to the condition of its solid insulation, which in turn is measured and described according to the degree of polymerization (DP) of the cellulose. Since the determination of the DP index is complex and time-consuming and requires the transformer to be taken out of service, utilities prefer indirect and non-invasive methods of determining the DP based on the byproduct of cellulose aging. This paper analyzes solid insulation degradation by measuring the furan concentration, recently introduced methanol, and dissolved gases like carbon oxides and hydrogen, in the insulating oil. A group of service-aged distribution transformers were selected for practical investigation based on oil samples and different kinds of tests. Based on the maintenance and planning strategy of the power utility and a weighted combination of measured chemical indicators, a neural network was also developed to categorize the state of the transformer in certain classes. The method proved to be able to improve the diagnostic capability of chemical indicators, thus providing power utilities with more reliable maintenance tools and avoiding catastrophic failure of transformers