Transformer Manufacturers Research Articles

Perancangan Prototype Monitoring dan Akuisi Data Pengujian Noise Trafo Berbasis ESP32

The noise level on the transformer is very important to be considered, because it can cause problems if it does not meet the standards. Excessive noise can violate regulations set by the Ministry of Environment regarding noise limit thresholds, In addition, excessive noise also indicates an indication of a bad transformer construction and will result in the performance of the transformer itself. PT Trafoindo Prima Perkasa, as one of the largest and first transformer manufacturers in Indonesia, tests every transformer produced, including its noise level, The testing is conducted in the Quality Check division. However, testing that is still done manually often causes queue buildup in the production line, along with the increasing demand, especially from PLN, a faster testing process is needed but still meets the set standards. Therefore, a faster, safer, and more efficient testing method is needed to support a continuous production. Based on this requirement, the author designs a prototype monitoring system and data acquisition of ESP32-based transformer noise testing to help speed up and simplify the testing process, so that the finished product can be sent immediately without reducing the quality and standards that have been set.

Analysis and design of wire winding tension control system based on genetic algorithm and fuzzy PID

Wire winding is a key process in transformer manufacturing, and the accuracy of tension control directly affects the quality of the products. To achieve the requirement of constant tension winding of wire, the influence of roll diameter, speed, and other factors on the dynamic performance of tension is analyzed by establishing the dynamic model of unwinding and winding as well as the pendulum part. A control strategy combining proportional-integral-derivative (PID) control with fuzzy control is adopted, and the parameters of the fuzzy PID controller are optimized using an improved genetic algorithm. Simulation results demonstrate that the optimized fuzzy PID controller significantly reduces tension overshoot and effectively suppresses the tension fluctuation when the speed and roll diameter change, and the tension fluctuation is controlled within 2%. Finally, the feasibility of the optimized fuzzy PID controller in the tension control system is experimentally validated.

Atomic Layer Processing (ALP): Ubi es et Quo Vadis?

AbstractAtomic Layer Processing (ALP) techniques have transformed materials engineering by enabling atomic/molecular‐level control over composition, fidelity in structure replication, and properties. Tracing its origins to pioneering molecular layering and atomic layer deposition work in the mid‐20th century, this multifaceted field has remarkably diversified to include molecular layer deposition (MLD), atomic layer etching (ALE), area‐selective deposition (ASD), and vapor‐phase infiltration (VPI) processes. ALP is making great impacts across diverse disciplines – facilitating semiconductor miniaturization through ultrathin dielectric films, improving battery materials and engineering catalysts for energy applications, creating bioactive surfaces for advanced biomaterials, and promoting sustainable membranes for environmental remediation. As ALP techniques continue evolving through integration with additive manufacturing, machine learning, and in situ diagnostics, new frontiers in materials design are emerging, driven by the growing focus on environmental considerations like renewable precursors, energy‐efficient processes, and waste minimization. This perspective article examines ALP's historical development, highlights current state‐of‐the‐art applications across selected fields, and provides insights into the anticipated future trajectory, emerging application domains, and the pivotal role of academic‐industry‐research laboratory collaborations in catalyzing ALP innovations and facilitating its widespread adoption as a transformative manufacturing platform.

High-Fidelity Sensing Modality for Anomaly Detection in Inkjet Printing

Abstract Inkjet three-dimensional (3D) printing has emerged as a transformative manufacturing technique, finding applications in diverse fields such as biomedical, metal fabrication, and functional materials production. It involves precise deposition of materials onto a moving substrate through a nozzle, achieving submillimeter scale resolution. However, the dynamic nature of droplet deposition introduces uncertainties, challenging consistent quality control. Current process monitoring, relying on image-based techniques, is slow and limited, hindering real-time feedback in erratic droplet ejection. In response to these challenges, we present the zero-dimensional ultrafast sensing (0-DUS) system, a novel, cost-effective, in situ monitoring tool designed to assess the quality of drop-on-demand inkjet printing. The 0-DUS system leverages the sensitivity of the light-beam field interference effect to rapidly and precisely detect and analyze localized droplets. Two core technical advancements drive this innovation: first, the exploration of integral sensing of the computational light-beam field, which allows for efficient extraction of temporal and spatial information about droplet evolution, introducing a novel in situ sensing modality; second, the establishment of a robust mapping mechanism that aligns sensor data with image-based data, facilitating accurate estimation of droplet characteristics. We successfully implemented the 0-DUS system within a commercial inkjet printer and conducted a comparative analysis with ground truth data. Our experimental results demonstrate a detection accuracy exceeding 95%, even at elevated speeds, allowing for an impressive in situ certification throughput of up to 500 Hz. Consequently, our proposed 0-DUS system meets the stringent quality assurance requirements, thereby expanding the potential applications of inkjet printing across a wide spectrum of industrial sectors.

Machine Learning for Additive Manufacturing of Functionally Graded Materials.

Additive Manufacturing (AM) is a transformative manufacturing technology enabling direct fabrication of complex parts layer-by-layer from 3D modeling data. Among AM applications, the fabrication of Functionally Graded Materials (FGMs) has significant importance due to the potential to enhance component performance across several industries. FGMs are manufactured with a gradient composition transition between dissimilar materials, enabling the design of new materials with location-dependent mechanical and physical properties. This study presents a comprehensive review of published literature pertaining to the implementation of Machine Learning (ML) techniques in AM, with an emphasis on ML-based methods for optimizing FGMs fabrication processes. Through an extensive survey of the literature, this review article explores the role of ML in addressing the inherent challenges in FGMs fabrication and encompasses parameter optimization, defect detection, and real-time monitoring. The article also provides a discussion of future research directions and challenges in employing ML-based methods in the AM fabrication of FGMs.

Assessment of Corporate Carbon Footprint and Energy Analysis of Transformer Industry

Transformers are primarily key components in power transmission and distribution systems. In the electrical industry, transformers are becoming increasingly important to increase energy efficiency and reduce environmental impact. In the process from the production to the use of transformers, various strategies and technologies are adopted to reduce the carbon footprint. To achieve decarbonization targets and a future with sustainable energy, ongoing efforts to reduce the carbon footprint of transformers need to continue. Therefore, this study aims to calculate the carbon footprint (CF) of Beta Energy in the Adana Province of Türkiye. A comprehensive inventory is being created to determine and monitor the greenhouse gas emissions of Beta Energy, a transformer manufacturer. This inventory includes direct and indirect greenhouse gas emissions from all of the company’s activities. The findings show that in 2023, the total CF of Beta Energy is equal to 1,799,482.72 tons of CO2-eq and considering the total of 6044 transformers sold in 2023, results in 297 tons of CO2-eq/transformer per product. The results show that the transformer manufacturing industry has a high carbon footprint because it is an energy-intensive process. The areas where the most carbon emissions occur in transformer production are revealed by CF hot spot analysis in this study. To minimize both current and future greenhouse gas emissions during transformer production, the measures to be taken during the R&D, production, transportation, and service stages are revealed. This study aims to establish a foundation for Beta Energy’s efforts to reduce greenhouse gas emissions by managing them effectively.

Design and Extrusion-Based 3D Printing of Continuous Fiber Composites: Status, Challenges, and Opportunities.

This perspective article summarizes recent advancements in extrusion-based 3D printing of continuous fiber-reinforced polymers (CFRPs). It focuses on manufacturing techniques and computational design methodologies. While fused deposition modeling has been the primary method for printing thermoplastic CFRPs, recent innovations have enabled the printing of thermoset CFRPs using direct ink writing or similar techniques. These printing processes are also integrated with robotic arms to dramatically enhance manufacturing capabilities. Additionally, there has been notable progress in enhancing computational design methodologies to simultaneously optimize fiber distribution and topology of 3D printed CFRPs. The article also discusses future directions aimed at improving mechanical properties, scalability, multifunctionality, and predictability in CFRP 3D printing, which offer valuable perspectives for the development of this transformative manufacturing approach.

An effective discrete artificial bee colony for the rectangular cutting problem with guillotine in transformer manufacturing

This paper addresses a two-dimensional rectangular cutting problem with guillotine constraint, applied to the transformer industry. The problem requires a set of rectangular items to be cut from larger and variable-sized rectangles, known as bins. The goal is to minimize the number of bins used to cut all required items. An effective discrete artificial bee colony is proposed to solve the problem. The approach uses a decimal encoding method where each integer represents a type of bin or item, and decodes the list of integers by a constructive heuristic to generate cutting patterns with guillotine-cut constraint. In addition, a copy strategy is designed to improve the solution quality. The approach’s performances and the effectiveness of the strategy are evaluated by three datasets generated from real-world data. The experimental results show that the discrete artificial bee colony is superior to other competitive algorithms, and that the copy strategy can produce higher quality solutions.

Analysing and Computing the Impact of Geometric Asymmetric Coils on Transformer Stray Losses

Designing and manufacturing transformers often involves variations in heights and thicknesses of windings. However, such geometric asymmetry introduces a significant impact on the magnitude of stray transformer losses. This study examines the effects of asymmetric coils on the generation of stray losses within core clamps and transformer tank walls. A model has been introduced to ascertain the dispersion magnetic field’s value at a specific distance from the coil. The analysis extends to characterising the dispersion magnetic field reaching the tank walls by using electromagnetic simulation by a finite element method. It explores strategies to diminish stray losses, including the placement of magnetic shunts as protective shields for the tank walls. It delves into the efficacy of employing a transformer shell-type configuration to mitigate the magnetic dispersion field. The findings revealed that achieving greater symmetry in transformer coils can minimise stray losses. Specifically, the incorporation of magnetic shunts has the potential to reduce additional losses by 40%, while the adoption of a shell-type configuration alone can lead to a 14% reduction. This work provides valuable insights into optimising transformer designs, contributes a user-friendly tool for estimating additional tank losses, thereby enhancing the knowledge base for transformer manufacturers.

Numerical and experimental comparison of the no-load losses in the different grain-oriented electrical steel materials

One of the critical pieces of equipment for power transmission and distribution is the transformer. The operation of the transformers is essential for the power supply's continued stability and efficacy. With an increase in rated power, the transformer's manufacturing time, cost, and size all go up. The core loss of the transformer is still a big concern for transformer designers. Minimization of the core loss in the transformer is also important for transformer manufacturers. Accurate evaluation of the core loss is also important for optimizing the magnetic design of the transformer. Evaluating the no-load losses of the new magnetic materials also needs a reliable technique as conventional analytical methods are limited and difficult to implement. In this study, the use of different materials for manufacturing the core of the transformers is used to minimize and compare the no-load losses. In this work, different types of grain-oriented silicon steel and lamination thickness are used for core materials to evaluate and minimize the core losses using numerical analysis. The results of the two different materials are also verified by a no-load experiment test performed on the prototype transformers. Considering the finite element analysis results of the different materials, core losses were calculated as 66.35 % lower in the M-H0 material than in the reference M-6 material. To increase transformer efficiency, a material with low no-load losses can be used. The results of this work will give guidelines to the transformer designers for selecting core materials to improve the efficiency of the transformer.

Discernment of transformer oil stray gassing anomalies using machine learning classification techniques

This work examines the application of machine learning (ML) algorithms to evaluate dissolved gas analysis (DGA) data to quickly identify incipient faults in oil-immersed transformers (OITs). Transformers are pivotal equipment in the transmission and distribution of electrical power. The failure of a particular unit during service may interrupt a massive number of consumers and disrupt commercial activities in that area. Therefore, several monitoring techniques are proposed to ensure that the unit maintains an adequate level of functionality in addition to an extended useful lifespan. DGA is a technique commonly employed for monitoring the state of OITs. The understanding of DGA samples is conversely unsatisfactory from the perspective of evaluating incipient faults and relies mainly on the proficiency of test engineers. In the current work, a multi-classification model that is centered on ML algorithms is demonstrated to have a logical, precise, and perfect understanding of DGA. The proposed model is used to analyze 138 transformer oil (TO) samples that exhibited different stray gassing characteristics in various South African substations. The proposed model combines the design of four ML classifiers and enhances diagnosis accuracy and trust between the transformer manufacturer and power utility. Furthermore, case reports on transformer failure analysis using the proposed model, IEC 60599:2022, and Eskom (Specification—Ref: 240-75661431) standards are presented. In addition, a comparison analysis is conducted in this work against the conventional DGA approaches to validate the proposed model. The proposed model demonstrates the highest degree of accuracy of 87.7%, which was produced by Bagged Trees, followed by Fine KNN with 86.2%, and the third in rank is Quadratic SVM with 84.1%.

Three-Phase Transformer Optimization Based on the Multi-Objective Particle Swarm Optimization and Non-Dominated Sorting Genetic Algorithm-3 Hybrid Algorithm

The performance of transformers directly determines the reliability, stability, and economy of the power system. The methodologies of minimizing the transformer manufacturing cost under the premise of ensuring performance is of great significance. This paper presented an innovative multi-objective optimization model to analyze the relationship between design parameters and transformer indicators. In addition, the sensitive analysis is conducted to exploit the interaction relationships between design parameters and targets. The reliability of the model was demonstrated in 50 MVA/110 kV and 63 MVA/110 kV prototypes, compared with the actual material usage, short-circuit impedance, and load loss, and the maximum error is less than 7%. Due to this problem having many optimization objectives and the high dimension of variables, a two-stage algorithm called MOPSO-NSGA3 (multi-objective particle swarm optimization and non-dominated sorting genetic algorithm-3) is presented. MOPSO is used to find non-domain solutions within the search space in the first stage, and the solution will be used as prior knowledge to initialize the population in NSGA3. The result shows that this algorithm can be effectively used in multi-objective optimization tasks and best meets the requirements of transformer designs that minimize the short-circuit deviation, operating loss, and manufacturing costs.

Indoor Air Quality Assessment in the Office of the Transformer Manufacturing Factory in Selangor, Malaysia

Introduction: As humans spend 90% of their time inside, indoor air quality (IAQ) is critical for occupant health. The primary concern associated with low IAQ is its impact on employees’ health, comfort, and productivity. In accordance with the Industry Code of Practice on Indoor Air Quality 2010 (ICOP IAQ 2010), a ten-parameter assessment was conducted in the office of the transformer manufacturing factory in Selangor, Malaysia. Methods: The measured parameters are temperature, air movement, relative humidity, carbon dioxide (CO2), carbon monoxide (CO), formaldehyde (CH2O), particulate matter (PM10), total volatile organic compounds (TVOCs), total fungal count (TFC), and total bacterial count (TBC). This study employed both qualitative and quantitative approaches by distributing questionnaires (N = 42), and measuring the indoor air quality parameters with integrated equipment at selected stations and comparing them to the ICOP IAQ 2010 standard. Results: A majority of the measures, with the exception of air movement, CO2, and TBC, complied with the ICOP IAQ 2010 standards. The one-way ANOVA test showed that there were significant differences (p < 0.05) for the parameters of temperature, PM10, and TVOCs. The Chi-Square test revealed that sleepiness was a symptom of the sick building syndrome, affecting both male and female employees the most frequently and significantly. Conclusion: Air movement, CO2, and TBC values that did not meet ICOP IAQ 2010 requirements revealed poor IAQ at the study site, which could have a negative influence on the employees’ health. To cut down on air pollution, the improvement on the ventilation system should be done to reduce the risks to the employees’ health.

Additive Manufacturing of Inductors and Transformers by Hybrid Aerosol Jet Printing and Electrochemical Deposition

Aerosol jet printing (AJP) is an attractive additive manufacturing process for printed electronic applications due to its high resolution (< 10 µm), flexible stand-off distance, and support of both metal and dielectric materials. A slow deposition rate (< 1 µm per layer) and the relatively low conductivity achievable with commercially available nanoparticle inks (~40% bulk) present some of the current limitations associated with AJP. Electrochemical deposition methods including both electrodeposition and electroless deposition are widely used in the microelectronics industry. Dense, high conductivity layers several 10s of µm can be deposited with well-established plating processes. Metals including Cu and Ni which are challenging to deposit by AJP, can be easily/readily deposited using commercially available plating baths. A hybrid approach of seed layer deposition using AJP and electrochemical deposition of highly conductive copper is used to improve the overall conductance of the printed structures. [1] Here, we apply this combined approach to the manufacturing of transformer and inductor devices.We have successfully prepared a two-layer secondary inductor on top of a two-layer primary inductor to form a coreless flyback transformer (Figure 1(a)). These inductors were fabricated by AJP of an Ag seed layer, followed by electroless deposition and electrodeposition of Cu and Ni. UV-curable dielectric polymer layers were deposited by AJP to separate each inductor layer. The flyback transformer produced a gain of 75.3x with an input voltage of 17 V at 400 kHz resulting in an output voltage of 1250 V (Figure 1(b)). We also demonstrated the repeated stacking of 8 coil layers by AJP of Ag seed layers and electrodeposition of Cu and Ni (Figure 1(c)). These inductors had an average equivalent series resistance of 0.6 Ω and a 1.7 µH inductance at 100 kHz (Figure 1(d)). Finally, we will study the deposition of these inductor and transformer devices on magnetic substrates, characterizing changes in inductance and gain. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. SAND2022-17001 A

ARIMA-AdaBoost hybrid approach for product quality prediction in advanced transformer manufacturing

End product quality prediction is one of the key issues in smart manufacturing. Reliable evaluation and parameter optimization is needed to ensure their high-quality production outputs. This study develops a novel approach that integrates adaptive machine learning and nonlinear regression to accurately predict highly customized end product quality based on small sets of supply-chain data through digital transformation, especially for complex industrial machinery manufacturing. This study was conducted in collaboration with a major power transformer manufacturer and its supply chain partners. Using the supply chain’s key component real dataset, the qualities of end products are predicted using the adaptive model trained and validated with reliable accuracy. The power transformer key component parameter, i.e., core (or iron) loss, is incorporated as an input dataset for prediction model training and testing. The novel model integrates autoregressive integrated moving average (ARIMA) and adaptive boosting (AdaBoost) machine learning, called ARIMA-AdaBoost. Compared with the previous research, the experiment results have shown that the ARIMA-AdaBoost outperforms the simple AdaBoost and Long Short-Term Memory (LSTM)-AdaBoost for transformer quality predictions. The ARIMA-AdaBoost model outperforms the existing methodologies in terms of mean absolute percentage error (MAE) and root mean square error (RMSE) in real-data verification. The proposed approach benefits the manufacturers in overall production costs for predicting complex, expensive, and highly customized industrial product qualities and is adaptable to various industrial sectors.

Application of Transformer Oil and Biodegradable Ester Mixtures as Insulation for High-Voltage Equipment

Synthetic and natural esters as insulating fluids for high voltage equipment have been a focus of much research, especially in the last two decades. This is due to the fact that, unlike mineral oil, ester fluids are biodegradable, non-toxic, safe for the environment and human health, produced from renewable raw materials, and feature high fire resistance. Despite the fact that commercially available ester fluids having excellent insulating properties are offered in the market, both transformer manufacturers and operators are not yet ready for mass-scale transition to the use of biodegradable ester dielectric fluids. In addition, since there is a huge amount of electrical equipment filled with mineral oil, mass-scale replacement of this oil with ester dielectric fluids will be extremely expensive for power companies. A suitable way for making a shift from oil to esters can be partial replacement of mineral oil with ester fluids (for example, topping up during repairs) or complete replacement of mineral oil with biodegradable esters. Whatever of these options is implemented, there will be a mixture of two insulating fluids inside of the equipment. Therefore, specialists intensely study the characteristics and behavior of insulating mixtures based on mineral oil and other dielectric fluid (synthetic ester, natural ester or silicone fluid). The article presents a review of the latest advances in the investigations of mixtures consisting of mineral oil and other dielectric fluids, including the studies of their properties. Problems associated with the operation of equipment in the case of its filling with mixed liquids are discussed.

Investigating and calculating the temperature of hot-spot factor for transformers

This article explores the measurement of temperature in transient states, utilizing the principles of heat transfer and thermal-electrical metaphor. The study focuses on the nonlinear thermal resistances present in various locations within a distribution transformer, while taking into account variations in oil physical variables and temperature loss. Real-time data obtained from heat run tests on a 250-MVA-ONAF cooled unit, conducted by the transformer manufacturer, is used to verify the thermal designs. The observations are then compared to the loading framework of the IEC 60076-7:2005 system. The findings of this research provide a better understanding of temperature measurement in transient states, particularly in distribution transformers, and can be applied to the design and development of more efficient and reliable transformer systems.

Pre- and postnatal developmental exposure to the polychlorinated biphenyl mixture aroclor 1221 alters female rat pituitary gonadotropins and estrogen receptor alpha levels

Polychlorinated-biphenyls (PCBs) are industrial compounds, which were widely used in manufacturing of electrical parts and transformers. Despite being banned in 1979 due to human health concerns, they persist in the environment. In humans and experimental model systems, PCBs elicit toxicity in part by acting as endocrine-disrupting chemicals (EDCs). Aroclor 1221 (A1221) is a weakly estrogenic PCB mixture known to alter reproductive function in rodents. EDCs can impact hormone signaling at any level of the hypothalamic-pituitary-gonadal (HPG) axis, and we investigated the effects of A1221 exposure during the prenatal and postnatal developmental periods on pituitary hormone and steroid receptor expression in female rats. Examining offspring at 3 ages, postnatal day 8 (P8), P32 and P60, we found that prenatal exposure to A1221 increased P8 neonate pituitary luteinizing hormone beta (Lhb) mRNA and LHβ gonadotrope cell number while decreasing LH serum hormone concentration. No changes in pituitary hormone or hormone receptor gene expression were observed peri-puberty at P32. In reproductively mature rats at P60, we found pituitary follicle stimulating hormone beta (Fshb) mRNA levels increased by prenatal A1221 exposure with no corresponding alterations in FSH hormone or FSHβ expressing cell number. Estrogen receptor alpha (ERα) mRNA and protein levels were also increased at P60, but only following postnatal A1221 dosing. Together, these data illustrate that exposure to the PCB A1221, during critical developmental windows, alters pituitary gonadotropin hormone subunits and ERα levels in offspring at different phases of maturation, potentially impacting reproductive function in concert with other components of the HPG axis.

A Brief Review of the Impregnation Process with Dielectric Fluids of Cellulosic Materials Used in Electric Power Transformers

In the manufacturing of power transformers, the impregnation of the solid electric insulation systems (cellulosic materials) with a dielectric liquid is a key issue for increasing the breakdown voltage of the insulation, and this prevents the apparition of partial discharges that deteriorate the insulation system. After introducing the problem, this article presents the theory of impregnation and later carries out a bibliographical review. Traditionally, mineral oils have been used as the dielectric liquid in electrical transformers, but for environmental (low biodegradability) and safety (low ignition temperature) reasons, since the mid-1980s, their substitution with other ester-type fluids has been studied. However, these liquids have some drawbacks, including their higher viscosity (especially at low temperatures). This property, among other aspects, makes the impregnation of cellulosic materials, which is part of the transformer manufacturing process, difficult, and therefore this tends to lengthen the manufacturing times of these machines.

Energy consumption and carbon footprint of 3D printing in pharmaceutical manufacture

Achieving carbon neutrality is seen as an important goal in order to mitigate the effects of climate change, as carbon dioxide is a major greenhouse gas that contributes to global warming. Many countries, cities and organizations have set targets to become carbon neutral. The pharmaceutical sector is no exception, being a major contributor of carbon emissions (emitting approximately 55% more than the automotive sector for instance) and hence is in need of strategies to reduce its environmental impact. Three-dimensional (3D) printing is an advanced pharmaceutical fabrication technology that has the potential to replace traditional manufacturing tools. Being a new technology, the environmental impact of 3D printed medicines has not been investigated, which is a barrier to its uptake by the pharmaceutical industry. Here, the energy consumption (and carbon emission) of 3D printers is considered, focusing on technologies that have successfully been demonstrated to produce solid dosage forms. The energy consumption of 6 benchtop 3D printers was measured during standby mode and printing. On standby, energy consumption ranged from 0.03 to 0.17 kWh. The energy required for producing 10 printlets ranged from 0.06 to 3.08 kWh, with printers using high temperatures consuming more energy. Carbon emissions ranged between 11.60 and 112.16 g CO2 (eq) per 10 printlets, comparable with traditional tableting. Further analyses revealed that decreasing printing temperature was found to reduce the energy demand considerably, suggesting that developing formulations that are printable at lower temperatures can reduce CO2 emissions. The study delivers key initial insights into the environmental impact of a potentially transformative manufacturing technology and provides encouraging results in demonstrating that 3D printing can deliver quality medicines without being environmentally detrimental.