To compete with other types of electrical machines as well as other manufacturers, the performance characteristics of axial-flux permanent-magnet (AFPM) machines should be improved. Reduction of the higher harmonic components from the induced back electromotive force is preferred during design process of an electrical machine. To achieve an almost sinusoidal induced phase voltage, a sinusoidal air-gap flux density distribution should be produced. Furthermore, in order to easy startup and also less noisy performance of electrical machines, the cogging torque of the machine should be reduced. In this paper, the surface-mounted AFPM machines equipped with magnets with different outlines are investigated and the machine performances are computed via 3-D finite-element analysis. According to the obtained results, magnets with sinusoidal outline offer better performance characteristics compared with cylindrical and sector-like magnets.

Measuring the furan concentration in transformer oil is one of the most common methods of monitoring and evaluating insulation paper and estimating its degree of polymerization. Here, methods for measuring furan concentration, especially ultraviolet-visible spectroscopy method, are reviewed on the old transformers oil. The results show that the excessive turbidity of old oils and noise at low wavelengths are the main disadvantages of this method, which reduces the measurements accuracy. Following solutions are proposed to overcome these disadvantages: a) Diluting insulation oil with hexane to reduce absorption intensity, b) using average absorption at a given wavelength to reduce noise impact. These solutions are applied to the oil samples of 10 old distribution transformer. It is observed that the correlation between 2-FAL concentration and average absorption intensity in 190-600 nm wavelength range is equal to R² = 0.98. A mathematical equation for calculating 2-FAL concentration based on average absorption is presented. It has been shown that to reduce the test time, a 190-400 nm range is the optimal measurement wavelength range. The results show that the average difference between the values of degree of polymerization in the proposed method and the direct measurement method is equal to 77.5.

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.

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.

Transformer outages significantly impact the reliability and cost efficiency of power systems. Studies indicate that approximately 30% of transformer failures stem from issues with on-load tap changers (OLTC), crucial components in transformer operation. Therefore, continuous monitoring of OLTCs is essential to enhance transformer serviceability. In this study, we employ a vibro-acoustic signal analysis-based monitoring system to assess the condition of OLTCs. This system has been operational since 2016 on three single-phase autotransformers within the Hydro-Québec network, continuously measuring vibration signals from their OLTCs. Notably, these transformers are equipped with sister OLTC units, and the system also records temperature and other pertinent parameters. To detect anomalies in OLTCs and analyze the generated vibration signals, we utilize a convolutional variational autoencoder (CVAE), trained individually for each transformer family. This approach allows us to map the signal envelope into a two-dimensional latent space using the encoder component of the CVAE, facilitating visual investigation and analysis. The decoder component reconstructs the original input from data in the latent space. We evaluate several thresholds based on reconstruction errors to detect anomalies, achieving optimal thresholds for each family. This results in anomaly detection rates of 4%, 5%, and 2%, respectively, when tested on data from within the same family not used in the training phase. Furthermore, when tested on data from the other two families, the anomaly detection rates are 99%, 99%, and 100%, respectively. These findings underscore the methodology’s accuracy and effectiveness in identifying anomalies in OLTC operations and distinguishing between different transformer families. Consequently, it holds promise for preemptively identifying potential future anomalies.