Distribution Transformers Research Articles

P-685 An artificial intelligence based medication regimen recommendation model for optimizing the treatment outcomes of controlled ovarian stimulation

Abstract Study question Can a machine learning (ML) algorithm suggest an optimal controlled ovarian stimulation (COS) protocol for maximizing the number of retrieved and mature (MII) oocytes? Summary answer Based on historical electronic medical record patient data, using ML algorithm based medication regimen recommendation model may increase the number of retrieved oocytes. What is known already The application of artificial intelligence in the field of reproductive medicine is mainly focused on the image analysis of gamete and embryo, and prediction of treatment outcomes. The research on the optimization of COS process is limited. Previous studies mainly focused on one or several types of COS protocols such as GnRH-a protocol and GnRH-A protocol. And most of them aimed to assist single-node decision-making during COS process such as optimal gonadotropins starting dose and trigger day. Study design, size, duration The data used for developing this model consists of 38,355 COS cycles from women who underwent their first IVF/ICSI treatment in a large fertility center between January 2008 to December 2022, of which 33,853 were used as the model training set and 4,502 were used as the test set. This dataset contains a subset of 8,382 cycles with information about MII oocytes. Participants/materials, setting, methods The goal of model was to recommend the optimal COS protocol based on optimizing COS outcomes. The similarity of patient’s characteristic was used as model input. XGBoost algorithm was constructed to calculate the similarity of COS outcomes. The accuracy was compared with the patient similarity calculation method of KNN+Euclidean distance and KNN+cosine distance using mean absolute error (MAE). The performance of different models in improving the average number of retrieved and MII oocytes was compared. Main results and the role of chance The patient similarity calculation method based on XGBoost had lowest MAE when predicting the number of retrieved oocytes (XGBoost vs KNN+Euclidean distance+k=500 vs KNN+Euclidean distance+k=1000 vs KNN+cosine distance+k=500 vs KNN+cosine distance+k=1000: 3.729 vs 3.810 vs 3.815 vs 3.785 vs 3.788). Including AMH could improve the accuracy of all recommendation models in predicting protocols and the number of retrieved oocytes. When the optimal number of MII oocytes is used as the protocol recommended target, the patient similarity calculation method based on XGBoost had lowest MAE (XGBoost vs KNN+Euclidean distance+k=500 vs KNN+Euclidean distance+k=1000 vs KNN+cosine distance+k=500 vs KNN+cosine distance+k=1000: 2.868 vs 3.397 vs 3.452 vs 3.356 vs 3.395). The COS medication regimen recommended by the patient similarity calculation method based on COS treatment outcomes has resulted in higher numbers of retrieved oocytes in patients compared to those who did not adopt recommendation (P < 0.01). The medication regimen recommended model based on KNN+Euclidean distance algorithm (k = 500) has the best performance in improving the average number of retrieved oocytes（adopt recommendation vs not adopt recommendation: 13.01±6.61 vs 10.09±6.67, P < 0.01）There was no statistical difference in the number of MII oocytes between patients receiving the recommendation and those not adopt recommendation. Limitations, reasons for caution The medication regimen recommendation model is based solely on the predicted number of retrieved and MII oocytes, not including adverse treatment outcomes such as ovarian hyperstimulation syndrome. Moreover, the database used to train and validate the model is from a single fertility center. Wider implications of the findings The recommendation model is helpful to improve the number of retrieved oocytes. Based on the recommended COS protocol, further mining the medication content, medication time distribution and medication sequence transformation from the similar patient set can assist clinicians to develop individualized COS medication regimen for infertile patients. Trial registration number not applicable

Error analysis and correction strategy for measuring oriented silicon steel by SST method

AbstractTo accurately determine the relationship between magnetic polarisation and magnetic field strength in oriented silicon steel, the magnetic properties, such as power loss, were measured under different magnetisation conditions. This facilitates for the precise selection of iron core materials for distribution transformers. The deviation rate of measurement results for different magnetisation conditions in oriented silicon steel with varying thicknesses was analysed using the SST method. Additionally, the finite element simulation of MagNet was employed to further investigate this relationship. The causes of measurement errors in oriented silicon steel by the SST method are thoroughly studied, with a focus on 0.18 mm oriented silicon steel as an example. It is found that the main reason for the measurement error is the deviation between the actual effective magnetic path length and the conventional effective magnetic path length, which is caused by the uneven distribution of magnetic polarisation in oriented silicon steel. The main reason for the difference of magnetisation conditions is the change in the actual effective magnetic path length, which is mainly affected by changes in permeability. Combined with the magnetic field distribution inside the oriented silicon steel, the distribution coefficient of magnetic polarisation can be calculated, the actual effective magnetic path length under each condition can be calculated, and then the measurement results of the SST method can be modified. This research is expected to significantly contribute to the practical applications of oriented silicon steel in engineering.

Impact of Selected Fourth Industrial Revolution Technologies on the Music Industry: An Exploration of the Pros and Cons

The fourth industrial revolution (4IR) is changing the creative industry. This article examines the impact of selected technologies within the 4IR—artificial intelligence as well as virtual and augmented reality as case examples—on various aspects of creative production, distribution, and consumption of creative art. Using the theory of knowledge organisation, the article deals with the principles and practices of arranging information and concepts. This qualitative literature review focused on systematically collecting, analysing and synthesising data from existing research to gain a deeper understanding of 4IR from a creative arts perspective. Specifically, the article analyses how 4IR tools influence artistic expression in music and the evolving role of the artist, in addition to exploring the transformation of musical art distribution and audience engagement through online platforms and immersive experiences. The impact of selected technologies on the art industry has both positive and negative consequences. Positive impacts include new forms of artistic creation and collaboration, composition with artificial intelligence, art installations with virtual reality and audience engagement, democratisation of art production and accessibility, opportunities for innovation and growth, new forms of creativity, and better traceability of works. On the downside, there are technological challenges, skills and labour development, disruption of traditional art markets and business models, ethical considerations and concerns about 4IR-generated music art and information overload. This research contributes to a deeper understanding of the complex interplay between technology and artistic practice in the 4IR. Keywords: Creative industry, music, fourth industrial revolution, Artificial intelligence, virtual and augmented reality

Automatic Load Sharing of Distribution Transformer (IOT)

Automatic Load Sharing of Distribution Transformer (IOT)

Design of a Load Monitoring System for a Distribution Transformer

Design of a Load Monitoring System for a Distribution Transformer

Fuzzy model of compensation for aging factors of distribution transformers

Fuzzy model of compensation for aging factors of distribution transformers

Distributed Vibration Monitoring System for 10 kV-400 kVA 3D Wound Core Transformer under Progressive Short-Circuit Impulses.

As large-scale, high-proportion, and efficient distribution transformers surge into the grids, anti-short circuit capability testing of transformer windings in efficient distribution seems necessary and prominent. To deeply explore the influence of progressively short-circuit shock impulses on the core winding deformation of efficient power transformers, a finite element theoretical model was built by referring to a three-phase three-winding 3D wound core transformer with a model of S20-MRL-400/10-NX2. The distributions of internal equivalent force and total deformation of the 3D wound core transformer along different paths under progressively short-circuit shock impulses varying from 60% to 120% were investigated. Results show that the equivalent stress and total deformation change rate reach their maximum as the short-circuit current increases from 60% to 80%, and the maximum and average variation rate for the equivalent stress reach 177.75% and 177.43%, while the maximum and average variation rate for the total deformation corresponds to 178.30% and 177.45%, respectively. Meanwhile, the maximum equivalent stress and maximum total deformation reach 29.81 MPa and 38.70 μm, respectively, as the applied short-circuit current increased to 120%. In light of the above observations, the optimization and deployment of wireless sensor nodes was suggested. Therefore, a distributed monitoring system was developed for acquiring the vibration status of the windings in a 3D wound core transformer, which is a beneficial supplement to the traditional short-circuit reactance detection methods for an efficient grid access spot-check of distribution transformers.

Evaluasi Ketidakseimbangan Beban Pada Penyulang Unicora

Load imbalance in electrical distribution transformers can lead to significant power losses and decreased efficiency in electrical distribution systems. This study focuses on the load imbalance problem at the Unicora feeder of PT PLN (Persero) ULP Singosari, which hinders optimal operational efficiency. The purpose of this research is to evaluate the extent of load imbalance, its impact on power losses, and the effectiveness of load balancing interventions in mitigating these losses. The methodology includes phase load measurements from February to September 2021, analysis of load imbalance, and power loss simulations using ETAP 12.6.0 software. The study's important findings indicate that the average load imbalance decreased from 34% to 21% after implementing load balancing interventions. Concurrently, power losses in the neutral conductor were reduced from 106.3 kW to 78.0 kW. ETAP simulations revealed a total power loss of 773.5 kW in the Unicora feeder, underscoring the significant impact of load imbalance on system efficiency. The implications of this research are crucial for operational practices at PT PLN (Persero) ULP Singosari. The findings demonstrate that effective load balancing can significantly enhance distribution efficiency, reduce operational costs, and improve the reliability of electricity provision. This study advocates for continuous monitoring and adaptive management of load distribution in electrical networks to maintain optimal performance and sustainability.

Multi-objective design optimization of hermetically sealed core-type distribution transformer considering current harmonics of power grid using NSGA III

In this paper, the design optimization of hermetically sealed oil-immersed distribution transformers is studied. For the first time in the design of this type of transformer, harmonics of the power grid affecting the transformer design, along with all design variables, are included, and their boundaries suitable for the production line are determined. The design probability (search space) for a total of 21 design variables is 1.32×1025. The Brute Force method is not a time-efficient approach to finding the optimal result in this search space. Due to the mixed integer nonlinear programming (MINLP) nature of the transformer design problem, heuristic optimization algorithms are selected because of their shorter solution time and near-optimal results. Considering the principle of the “No Free Lunch” theorem, extensive evaluations were conducted on various heuristic algorithms to identify the most effective ones for addressing the transformer design problem. The most successful algorithms for solving the problem were identified after thorough evaluation, considering three distinct scenarios within a multi-objective optimization framework. These algorithms are namely MOEA/D, MOGWO, MOWOA, and NSGA III. In this study, an optimization tool is developed for the design optimization of hermetically sealed distribution transformers, covering a range from 50 kVA to 3150 kVA and encompassing all design variables. The developed optimization program is fully compatible with the production line and can be easily adjusted to meet all national and international standards, such as IEC and IEEE, as well as customer specifications.

A Chua’s Chaotic Chirp Spread-Spectrum Power Spectral Homogenization Strategy Based on Distribution Transformation

When utilizing high-dimensional chaotic signals for frequency modulation, achieving a uniformly distributed power spectrum is a challenging task. This paper addresses this challenge by proposing a power spectrum homogenization strategy based on distribution transformation. The strategy transforms the task of achieving a uniformly distributed power spectrum in frequency modulation of high-dimensional chaotic signals to solve and equalize the probability density function of the chaotic signals, thereby further enhancing the ability of high-dimensional chaotic signals to suppress electromagnetic interference. Firstly, the difficulty of obtaining a smooth probability density function of chaotic modulation signals is solved using the kernel density estimation algorithm. Then, a distribution transformation algorithm is proposed to convert non-uniformly distributed chaotic modulation signals into uniformly distributed chaotic modulation signals. By using uniformly distributed chaotic modulation signals for frequency modulation, the objective of power spectrum equalization is achieved. Finally, taking the Chua’s chaotic signal as an example, the effectiveness of the proposed strategy is verified using an experimental platform based on a digital signal processor-controlled active clamping flyback converter.

Lightning-induced voltages on low-voltage distribution lines

Special attention has been drawn to transients in low-voltage systems in recent years. As the surge withstand capabilities of low-voltage networks are much lower than those of medium-voltage lines, they are more susceptible to lightning-caused disturbances. One of the primary sources of overvoltages is related to indirect lightning strokes. Such overvoltages are highly dependent on the soil characteristics and generally tend to increase with the soil resistivity. Considering realistic conditions, this paper discusses the effects of different parameters on the phase-to-ground and phase-to-neutral lightning-induced voltages on the low-voltage side of a typical distribution transformer. In the case of high-resistivity soils or high ground resistance values, phase-to-ground voltages induced by indirect strokes can reach several tens of kilovolts. Phase-to-neutral voltages higher than 5 kV may be common if surge protective devices are not applied.

Comparative Analysis of Deep Learning Methods for Fault Avoidance and Predicting Demand in Electrical Distribution

In recent years, the distribution network in Chile has undergone various modifications to meet new demands and integrate new technologies. However, these improvements often do not last as long as expected due to inaccurate forecasting, resulting in frequent equipment changes and service interruptions. These issues affect project investment, unsold energy, and penalties for poor quality of supply. Understanding the electricity market, especially in distribution, is crucial and requires linking technical quality standards with service quality factors, such as the frequency and duration of interruptions, to understand their impact on regulated distribution to customers. In this context, a comparative study will be carried out between Long Short-Term Memory (LSTM) and transformer architectures, with the aim of improving the sizing of distribution transformers and preventing failures when determining the nominal power of the transformer to be installed. Variables such as voltages and operating currents of transformers installed between 2020 and 2021 in the Valparaíso region, Chile, along with the type and number of connected customers, maximum and minimum temperatures of the sectors of interest, and seasonality considerations will be used. The compilation of previous studies and the identification of key variables will help to propose solutions based on error percentages to optimise the accuracy of transformer sizing.

Assessment of Electric Vehicle Charging On Distribution Transformer Aging, Aswan City as a Case Study

Assessment of Electric Vehicle Charging On Distribution Transformer Aging, Aswan City as a Case Study

Detuned reactor for harmonic reduction on 20 kv distribution network Pocut Baren feeder in Banda Aceh

Abstract Nonlinear loads in distribution network have adverse impacts in the form of harmonic distortion. When the harmonic frequency is close to resonance, the harmonic will be amplified and overloading capacitor bank. Here detuned reactor is inserted in series to avoid from that risk. In this study, the impact of detuned reactor on suppressing the harmonics and power factor improvement are investigated, evaluated and analyzed. The Pocut Baren feeder is chosen as the case of the study. It is a radial distribution network with 28 distribution transformers where the starting point is connected to substation transformers and capacitor banks. Simulation is used to verify the efficacy of detuned reactor. The results shown that detuned reactor is able to reduce voltage harmonics and improve power factor across the distribution line. Although the efficacy wane at the distribution end, it can be compensated from adjusting the detuned reactor value to meet the 5% total voltage harmonic distortion (THDv) threshold at the end of the line. Simulation results confirmed that detuned reactor is able to reduce harmonics from 12% to meet the threshold. Power factors have also been improved beyond 0,9 from previously arround 0,8.

Evaluation of the Electrical Power Transformer Fins Design Technology: Numerical Analysis and Experimental Validation

The study includes numerical analysis and experimental verification on an electrical power distribution transformer (250 kVA, 11 kW, Oil Natural Air Natural). ANSYS Fluent R3 2019 software was used to develop the numerical simulation model. The validity of the numerical model was confirmed by comparing the results of the numerical model and experimental data. The study aims to improve the efficiency and performance of electrical power distribution transformers by proposing a design that reduces the temperature of the transformer while maintaining its traditional size. Numerically, the effect of fin geometry on the temperature and density of transformer oil was studied. Four different fin designs were proposed and compared with the traditional design. According to the results, all proposed designs contributed to improving the cooling performance of the transformer compared to the traditional design. Design A is similar to the traditional transformer design, with the only modification being manipulation of the fin length, and achieves an average oil temperature reduction of 4 K. Design B showed the smallest temperature drop of the four designs, with a 3 K drop. Designs C and D include ventilation channels that match the shape of the fin, providing distinct design differences. The difference between both designs relied on the fact that for design C, the orthogonally of fin plates was retained. On the other hand, in design D, skewing of fin plates was introduced. Design D proved to be the most effective in reducing the average oil temperature, being reduced by 10 K. On the other hand, Design C reduced the average oil temperature by 7 K.

PENYEIMBANGAN BEBAN TRANSFORMATOR DISTRIBUSI PCC-007 UNTUK MENGURANGI RUGI-RUGI JARINGAN PADA TEGANGAN MENENGAH 20KV

The distribution system of electrical power networks serves as a means to distribute electrical energy to both industrial and household consumers. With the increasing population growth, the number of electrical energy users or consumers also increases, potentially impacting the loading on each distribution transformer. If the loading on each transformer increases, it can lead to overload, load imbalance, power losses, and damage to the transformer. Therefore, to ensure the proper operation of distribution transformers, PLN (a state-owned electricity company in Indonesia) has set a standard for the loading on distribution transformers, which should not exceed 80%. This research aims to measure the loading on distribution transformers and analyze the occurrence of power losses caused by load imbalance. Measurements are conducted using a clamp meter and earth tester to obtain current and voltage values as well as the resistance value of grounding. Subsequently, calculations are performed for transformer loading, load imbalance, and power losses on the feeder.

Analysis of Load Imbalance in Tarakan Distribution Transformer of PT PLN (Persero) Sukarami Customer Service Unit

Load imbalance in an electric power distribution system always occurs and the cause of this imbalance is unbalanced single-phase loads on low voltage network customers. As a result of this load imbalance, a current arises in the neutral of the transformer, which can cause power losses. The aim of the research is to analyse the load imbalance of the Distribution Transformer PC0044, PC0074, PC0088, PC0129, PC1319 on the Tarakan Feeder of PT PLN (Persero) Sukarami Customer Service Unit. The results of the research show that the highest peak load percentage occurs on the PC1319 Distribution Transformer, namely 93.0874%, and based on PLN ED Decree No. 0017.E/DIR/2014, transformer loading. The largest percentage of load imbalance occurs in the PC0044 Distribution Transformer, namely 38.0952%, and based on PLN ED Decree No. 0017.E/DIR/2014, the current imbalance between phases is included in the bad category because the load imbalance is ≥ 25%. The largest percentage of power losses due to neutral current in the transformer neutral conductor occurs in the PC0044 Distribution Transformer, namely 2.6737%, and based on PLN ED Decree No. 0017.E/DIR/2014, the neutral current TR is included in the good category because the power losses due to the neutral current in the transformer neutral conductor are <10%. The largest percentage of power losses due to neutral current flowing to the ground occurs in the PC0044 Distribution Transformer, namely 5.0784%.

Contribution to the use of palm kernel oil methyl esters as liquid bio-insulators in distribution transformers: Experimentation and simulation of heat transfer

This work examines the potential use of palm kernel oil methyl esters (PKOME) as liquid bio-insulators in distribution transformers. A mathematical model of heat transfer inside a transformer is developed and solved using COMSOL Multiphysics software. The results of the model are validated by experimental data, showing a temperature variation of 31–81.8 °C in the measurements and 31–82.5 °C in the simulation. This validation confirms the accuracy of the model in predicting the temperature distribution inside the PKOME-filled transformer, paving the way for their use in distribution transformers.

Distribution-on-distribution regression with Wasserstein metric: Multivariate Gaussian case

Distribution data refer to a data set in which each sample is represented as a probability distribution, a subject area that has received increasing interest in the field of statistics. Although several studies have developed distribution-to-distribution regression models for univariate variables, the multivariate scenario remains under-explored due to technical complexities. In this study, we introduce models for regression from one Gaussian distribution to another, using the Wasserstein metric. These models are constructed using the geometry of the Wasserstein space, which enables the transformation of Gaussian distributions into components of a linear matrix space. Owing to their linear regression frameworks, our models are intuitively understandable, and their implementation is simplified because of the optimal transport problem’s analytical solution between Gaussian distributions. We also explore a generalization of our models to encompass non-Gaussian scenarios. We establish the convergence rates of in-sample prediction errors for the empirical risk minimizations in our models. In comparative simulation experiments, our models demonstrate superior performance over a simpler alternative method that transforms Gaussian distributions into matrices. We present an application of our methodology using weather data for illustration purposes.

DATFNets-dynamic adaptive assigned transformer network for fire detection

Fires cause severe damage to the ecological environment and threaten human life and property. Although the traditional convolutional neural network method effectively detects large-area fires, it cannot capture small fires in complex areas through a limited receptive field. At the same time, fires can change at any time due to the influence of wind direction, which challenges fire prevention and control personnel. To solve these problems, a novel dynamic adaptive distribution transformer detection framework is proposed to help firefighters and researchers develop optimal fire management strategies. On the one hand, this framework embeds a context aggregation layer with a masking strategy in the feature extractor to improve the representation of low-level and salient features. The masking strategy can reduce irrelevant information and improve network generalization. On the other hand, designed a dynamic adaptive direction conversion function and sample allocation strategy to fully use adaptive point representation while achieving accurate positioning and classification of fires and screening out representative fire samples in complex backgrounds. In addition, to prevent the network from being limited to the local optimum and discrete points in the sample from causing severe interference to the overall performance, designed a weighted loss function with spatial constraints to optimize the network and penalize the discrete points in the sample. The mAP in the three baseline data sets of FireDets, WildFurgFires, and FireAndSmokes are 0.871, 0.909, and 0.955, respectively. The experimental results are significantly better than other detection methods, which proves that the proposed method has good robustness and detection performance.