Transformer Research Articles

Simulation of accidental temperature and humidity in the pipeline rooms of the VVER NPP primary circuit

Simulation of accidental temperature and humidity in the pipeline rooms of the VVER NPP primary circuit

Development of Maintenance Management System for Real-Time Monitoring of Power Transformer to Improve Availability Performance: The Case of Tagamenda TANESCO Grid Substation

Power transformers are critical assets in TANESCO's grid substations, playing a vital role in ensuring a reliable and uninterrupted power supply. However, the growing challenges of ageing infrastructure, increasing energy demand, and reactive maintenance practices often lead to unplanned outages, higher operational costs, and reduced transformer availability. This dissertation focuses on the Development of a Maintenance Management System for Real-Time Monitoring of Power Transformers at TANESCO grid substations, with the goal of improving availability performance. The study explores the design and implementation of a Real-Time Monitoring Management System (RTMMS), leveraging advanced sensors, data analytics, and predictive maintenance techniques. The RTMMS continuously monitors critical parameters such as temperature, oil levels, dissolved gases, and load conditions, providing real-time insights into transformer health. By integrating predictive analytics, the system identifies potential faults early, enabling timely interventions and reducing downtime. Additionally, it supports data-driven maintenance planning, enhances operational reliability, and extends transformer lifespan. The proposed system addresses challenges such as high initial costs, data management complexity, and resistance to technological change through phased deployment, secure cloud solutions, and comprehensive training programs. The expected benefits include improved transformer availability, cost efficiency, enhanced grid sustainability, and a shift from reactive to proactive maintenance practices. This research underscores the transformative potential of real-time monitoring systems in modernizing maintenance management and aligns TANESCO with global best practices in energy sector innovation and operational excellence.

Improved Efficiency of Weakly Coupled Wireless High‐Power Transfer Systems by Loss‐Separation Strategy

ABSTRACTIn the equivalent T‐model of the loosely coupled transformers, the small mutual inductance can lead to higher conducting currents, which cause high losses in the primary circuit and significantly reduce the overall transfer efficiency under weak‐coupling states. To overcome this challenge, this paper proposes a strategy to separate the primary loss components from the weak‐coupling stage. In the proposed strategy, a gyrator in the form of a double‐resonance T‐block is added just before the weak‐coupling stage to improve the overall efficiency. Then, the strategy is realized by various topologies such as compensating circuits, added coils, isolated transformers, and integrated‐/split‐ inductors/coils. Also, the optimized designs and component selection for resonance and the mathematical derivation for optimal load resistances were investigated. ANSYS comparative analyses of the topologies are presented by considering practical aspects, including component designs, power flows, transfer efficiency, resonance frequency shifting, and optimal loads. Finally, the analyses were validated using the fabricated experimental setups and demonstrated an efficiency improvement of about 5% for a case with a coupling factor of 0.1. The proposed strategy offers suggestions for industrial designs of high‐power wireless battery charging systems using resonant inductive coils.

Current level, sources, and risk of human exposure to PAHs, PBDEs and PCBs in South American outdoor air: A critical review.

This study provides comprehensive overview of the current level, sources and human exposure risk to hazardous polycyclic aromatic hydrocarbons (PAHs), polybrominated diphenyl ethers (PBDEs), and polychlorinated biphenyls (PCBs) in South American outdoor air. Research documents were obtainable for only 6 countries within the target period (2014 - 2024). For all contaminants, urban concentrations exceeded that of rural/remote locations. PAHs were extensively reported with concentration reaching 1100 ∑16PAHs/m3 in Southwest of Buenos Aires province, Argentina. The health risk data also exceeded the threshold level in several locations. The profiles and seasonal fluctuations across all studies were widely influenced by the prevalent local/domestic sources. Biomass combustion (particularly of sugar cane/agricultural wastes and wood/coal for residential heating), vehicular emission, and industrial emission were accounted for most PAH sources. Regulations targeting biomass combustion for improved air quality seem not to currently have significant impacts on current PAH levels. PBDEs were widely reported within 0.3 to 55 pg ∑4-14BDE/m3, albeit high concentrations were documented in Concepción Bay, Chile (maximum = 1100 pg ∑4BDE/m3) and Córdoba, Argentina (maximum = 120 pg ∑4BDE/m3). Most notable source of PBDEs is solid municipal wastes. Similar to other global studies, BDE-47, 99 and 209 dominated the congeners reported. PCBs were reported with the highest concentrations measured in Córdoba, Argentina (maximum = 1700 pg ∑30PCBs/m3), but data remain limited in other important locations such as São Paulo, Brazil. Sources of PCBs were broadly associated with solid wastes, electric transformers, and re-volatilisation from polluted environment. PAHs, PCBs and PBDEs were all within average to top global concentrations. This study underscores potential rise in atmospheric level of the target contaminants without sustainable regulatory structure and the need for continuous monitoring of these contaminants as a measure of policy impacts. We provide sustainable recommendations.

Innovative Capacitive Wireless Power System for Machines and Devices

This article deals with the design, development, and analysis of a wireless power transfer system prototype based on capacitive coupling. The system is intended for the continuous charging of a suspended mine drivetrain. It consists of a resonant inverter, primary and secondary matching circuits, a suitable capacitive coupler, and a rectifier with a load, ultimately serving as a battery charger for a mobile energy storage device. The system successfully achieved the target output voltage of 320 V and a charger output power of 2 kW at an operating frequency of 300 kHz. Additionally, the total system efficiency was at the level of 60%, ensuring that the RMS voltages on passive components remained below 3 kV.

DGA-Based Fault Diagnosis Using Self-Organizing Neural Networks with Incremental Learning

Power transformers are vital components of electrical power systems, ensuring reliable and efficient energy transfer between high-voltage transmission and low-voltage distribution networks. However, they are prone to various faults, such as insulation breakdowns, winding deformations, partial discharges, and short circuits, which can disrupt electrical service, incur significant economic losses, and pose safety risks. Traditional fault diagnosis methods, including visual inspection, dissolved gas analysis (DGA), and thermal imaging, face challenges such as subjectivity, intermittent data collection, and reliance on expert interpretation. To address these limitations, this paper proposes a novel distributed approach for multi-fault diagnosis of power transformers based on a self-organizing neural network combined with data augmentation and incremental learning techniques. The proposed framework addresses critical challenges, including data quality issues, computational complexity, and the need for real-time adaptability. Data cleaning and preprocessing techniques improve the reliability of input data, while data augmentation generates synthetic samples to mitigate data imbalance and enhance the recognition of rare fault patterns. A two-stage classification model integrates unsupervised and supervised learning, with k-means clustering applied in the first stage for initial fault categorization, followed by a self-organizing neural network in the second stage for refined fault diagnosis. The self-organizing neural network dynamically suppresses inactive nodes and optimizes its training parameter set, reducing computational complexity without sacrificing accuracy. Additionally, incremental learning enables the model to continuously adapt to new fault scenarios without modifying its architecture, ensuring real-time performance and adaptability across diverse operational conditions. Experimental validation demonstrates the effectiveness of the proposed method in achieving accurate, efficient, and adaptive fault diagnosis for power transformers, outperforming traditional and conventional machine learning approaches. This work provides a robust framework for integrating advanced machine learning techniques into power system monitoring, paving the way for automated, real-time, and reliable transformer fault diagnosis systems.

A Transformer Oil Temperature Prediction Method Based on Data-Driven and Multi-Model Fusion

A power transformer is an important part of the power system, and the oil temperature of the transformer is an important state parameter that reflects the operation state of the transformer. The accurate prediction of the oil temperature of the transformer can ensure the safe and stable operation of the transformer. Given the lack of a practical and effective data processing process and the problem that most of the current research is conducted on small-scale ideal datasets, this paper proposes a transformer oil temperature prediction method based on data-driven and multi-model fusion. The method first analyses and processes the actual transformer inspection data; it then uses the multi-model fusion method to model and predict the transformer oil temperature. The base model was trained using the machine learning method, and the secondary learning model was trained using the improved TSSA-BP neural network. The improved sparrow search algorithm (TSSA) was used to optimise the parameters of the BP neural network to improve the convergence accuracy and prediction performance of the model. The transformer data are classified according to cooling mode, operating voltage, and other indicators, and then eight groups of experimental datasets under different actual conditions are constructed for modelling and prediction. The experimental results show that the maximum root mean square error and the mean absolute percentage error of this method on different datasets are 1.0877 and 1.58%, and compared with other prediction methods, the prediction accuracy of this method is better than other methods, which verifies the practicability and feasibility of modelling and predicting for the actual transformer inspection data.

Research on the Carbon Footprint Accounting Method of Transformer’s Whole Life Cycle Under the Background of Double Carbon

In response to the existing gaps in the carbon footprint assessment framework for core electrical equipment transformers, which impedes power companies from effectively supporting low-carbon procurement of materials and products, this study proposes a novel evaluation method for transformer carbon footprints. This method comprehensively considers all stages of the transformer lifecycle, including manufacturing, transportation, installation, operation, and decommissioning. A review of mainstream carbon footprint accounting schemes, both domestic and international, is first presented, summarizing established accounting methods and calculation processes. The paper then introduces a novel, integrated carbon footprint accounting approach for transformers, covering the entire ‘cradle-to-grave’ lifecycle, along with an associated calculation model. This framework analyzes the carbon footprint composition across production, assembly, transportation, usage, and recycling stages for four commonly used, high-efficiency transformers at State Grid Xinjiang Electric Power Company, the carbon footprint of an oil-immersed 100 kVA/10 kV transformer is 2.353 × 106 kg CO2e, approximately half that of a conventional 100 kVA/10 kV transformer. Finally, the study provides recommendations for carbon reduction pathways for transformers, considering both functional substitution and technological carbon reduction strategies.

A dissolved Gases Analysis Method for Power Transformer Faults Diagnosis Based on the Observation of Subsets of Labelled Fault Data

A dissolved Gases Analysis Method for Power Transformer Faults Diagnosis Based on the Observation of Subsets of Labelled Fault Data

Diffusion Characteristics of Dissolved Gases in Oil Under Different Oil Flow Circulations

The prediction of dissolved gas concentrations in oil can provide crucial data for the assessment of power transformer conditions and early fault diagnosis. Current simulations mainly focus on the generation and accumulation of characteristic gases, lacking a global perspective on gas diffusion and dissolution. This study simulates the characteristic gases produced by typical faults at different flow rates. Using ANSYS 2022 R1 simulation software, a gas–liquid two-phase model is established to simulate the flow and diffusion of characteristic gases under fault conditions. Additionally, a fault-simulation gas production test platform was built based on a ±400 kV actual converter transformer. The experimental data show good consistency with the simulation trends. The results indicate that the diffusion of dissolved gases in oil is significantly affected by the oil flow velocity. At higher flow rates, the characteristic gases primarily move within the oil tank along with the oil circulation, leading to a faster rate of gas dissolution in oil and a shorter time to reach equilibrium within the tank. At lower flow rates, the diffusion of characteristic gases depends not only on oil flow circulation but also on self-diffusion driven by concentration gradients, resulting in a nonlinear change in gas concentration across various monitoring points.

Effective and Local Constraint-Aware Load Shifting for Microgrid-Based Energy Communities

The rising energy demand, coupled with increased integration of distributed energy resources (DERs) and fluctuating renewable generation, underscores the need for effective load management within energy communities. This paper addresses these challenges by implementing effective, constraint-aware load shifting within microgrid-based energy communities. Specifically, the goal of this study is to flatten the electrical load profile of a High-Voltage (HV)/Medium-Voltage (MV) power transformer. The load of a central power transformer includes (a) the diverse, fluctuating electrical and thermal demands of buildings within the energy community and (b) the load of the area supplied by the substation excluding the energy community loads. To achieve a flattened load profile, we apply time shifting to both electrical and heating, ventilation, and air conditioning (HVAC) loads of the energy community, allowing for a redistribution of energy consumption over time. This approach entails shifting non-critical loads, particularly those related to HVAC and other building operations, to off-peak periods. The methodology considers critical operational constraints, such as maintaining occupant thermal comfort, ensuring compliance with building codes, and adhering to technical specifications of HVAC and electrical systems and microgrid organized energy communities. Detailed simulations were conducted to prove the effectiveness of this constraint-aware load-shifting approach.

Selecting analytical method of leakage inductance calculation for optimizing high-voltage test transformer design

AN ACTUAL PROBLEM in transformer design is to optimize its design, since it allows you to create a competitive transformer. One of the objective functions in optimization algorithms for high-voltage test transformers is the ratio of the leakage inductive reactance of the transformer, reduced to the secondary side, to the load capacitance. Since the objective functions in the optimization procedure must be found many times, it is advisable to use approximate analytical calculation methods to calculate the leakage inductance. THE PURPOSE of the article is to analyze the error of the traditional analytical method for calculating the leakage inductance of power transformers with a rectangular axial cross-sections of the windings, and to develop a refined analytical method that takes into account the main design feature of high-voltage test transformers, which consists in the trapezoidal shape of the secondary winding cross-section. The refined method takes into account the change in the number of turns in the radial direction while maintaining the basic assumptions of the traditional method. METHODS. The error is studied by comparing the calculation results using these methods with the results of a numerical calculation of leakage inductance based on a 3D magnetostatic field, which are accepted as accurate. RESULTS. It is shown that the calculation error using the method proposed in the article is significantly reduced compared to the error of the method developed for power transformers with rectangular winding cross-sections. The ranges of changes in the relative geometric parameters of the base transformer have been determined, in which the error of this calculation method does not exceed 10%. The TGI 50/100 transformer, whose rated power is 5 kVA, was chosen as the base transformer. The developed analytical method is recommended for high-voltage test transformers optimization algorithms.

Modeling of Measuring Transducers for Relay Protection Systems of Electrical Installations.

The process of establishing relay protection and automation (RPA) settings for electric power systems (EPSs) entails complex calculations of operating modes. Traditionally, these calculations are based on symmetrical components, which require the building of equivalent circuits of various sequences. This approach can lead to errors both when identifying the operating modes and when modeling the RPA devices. Proper modeling of measuring transformers (MTs), symmetrical component filters (SCFs), and circuits connected to them effectively solves this problem, enabling the configuration of relay protection and automation systems. The methods of modeling the EPS in phase coordinates are proposed to simultaneously determine the operating modes of high-voltage networks and secondary circuits connected to the current and voltage transformers. The MT and SCF models are developed to concurrently identify the operating modes of secondary wiring circuits and calculate the power flow in the controlled EPS segments. This method is effective in addressing practical problems related to the configuration of the relay protection and automation systems. It can also be used when establishing cyber-physical power systems. For a comprehensive check of the adequacy of the MT models, 140 modes of the electric power system were determined which corresponded to time-varying traction loads. Based on the results of calculating the complexes of currents and voltages at the MT terminals, parametric identification of the power transmission line was performed. Based on this, the model of this transmission line was adjusted; repeated modeling was carried out, and errors were calculated. The modeling results showed a high accuracy when calculating the modules and phases of voltages using the identified model. The average error value for current modules was 0.6%, and for angles, it was 0.26°.

Distribution Transformer Winding Fault Detection Based on Hybrid Wavelet‐CNN

This paper presents a new decision logic approach for protecting and distinguishing internal faults in power transformers. This method uses the feature extraction technique based on wavelet transform and artificial neural network. The proposed method is designed based on the difference between the energies of the wavelet transform coefficients produced by short circuit fault currents in a certain frequency band. First, the operation of the transformer under phase‐to‐ground and phase‐to‐phase short circuit faults was examined. Then, the resulting secondary winding current was transferred to the discrete wavelet transform. This method is used to analyze components of the signal at different scales. Based on the results, it has been shown that due to the good temporal and frequency characteristics of the wavelet transform, the features extracted by the wavelet transform have more distinct features than those extracted by the fast Fourier transform. As a result, the fault detection process was improved by integrating the obtained wavelet transform values into artificial intelligence models. This step allows the analysis process to be automated and faults to be identified more quickly and accurately. The proposed method is more efficient and faster and achieves a higher success rate than the traditional method.

Refined design of a DNA logic gate for implementing a DNA-based three-level circuit.

DNA computing circuits are favored by researchers because of their high density, high parallelism, and biocompatibility. However, compared with electronic circuits, current DNA circuits have significant errors in understanding the OFF state and logic "0". Nowadays, DNA circuits only have two input states: logic "0" and logic "1", where logic "0" also means the OFF state. Corresponding to an electronic circuit, it is more like an on-off switch than a logic circuit. To correct this conceptual confusion, we propose a three-level circuit. The circuit divides the input signal into three cases: "none", logic "0" and logic "1". In subsequent experiments, 34 input combinations of the primary AND gate, OR gate as well as secondary AND-OR and OR-AND cascade circuits were successfully implemented to perform the operation, which distinguished the OFF state and logic "0" correctly. Based on this, we successfully implemented a more complex voting operation with only 12 strands. We believe that our redefinition of the OFF state and logic "0" will promote tremendous developments in DNA computing circuits.

A new Six-Gas based fault prediction Graphical Technique in Dissolved Gas analysis for oil immersed Power Transformer

A new Six-Gas based fault prediction Graphical Technique in Dissolved Gas analysis for oil immersed Power Transformer

Design and validation of experimental test platform for field-circuit coupling wide-frequency characteristics model of power transformer

Design and validation of experimental test platform for field-circuit coupling wide-frequency characteristics model of power transformer

Integrated Digital Twins System for Oil Temperature Prediction of Power Transformer Based On Internet of Things

Integrated Digital Twins System for Oil Temperature Prediction of Power Transformer Based On Internet of Things

Transformer oil degradation detection system based on color scale analysis

The rise in power transformer load results in degradation of the condition of the transformer oil and ultimately a deficiency in the distribution of electrical energy. This degradation can be slowed down by reconditioning transformer oil based on oil color detection. This research aims to design, test and validate a transformer oil color testing system based on color sensor and microcontroller. To obtain an accurate system, tests were carried out on selecting the types of sensors, the color of the chamber walls, and the shapes of transformer oil sample vessel used. The oil color scale of the samples was determined visually according to the ASTMD1500, 2009 standard as a benchmark. The test results showed that the TCS3200 color sensor was able to detect the color of all transformer oil samples. White chamber wall and test tube as oil sample containers were chosen to increase system accuracy. Overall, the system is able to detect the color of transformer oil, convert to the ASTMD1500, 2009 standard transformer oil color scale, determine the condition of the transformer oil and conclude the level of transformer oil degradation according to CIGRE-761, 2019. Validation results showed the system had an accuracy level of 92.65%.

Influence of extreme temperatures on the lifespan of power transformers and cables in Benin’s electricity distribution network by 2035-2050: innovative approach for sustainable improvement of resilience

Influence of extreme temperatures on the lifespan of power transformers and cables in Benin’s electricity distribution network by 2035-2050: innovative approach for sustainable improvement of resilience