Distribution Transformers Research Articles

Generalized Poisson random variable: its distributional properties and actuarial applications

Abstract Generalized Poisson (GP) distribution was introduced in Consul & Jain ((1973). Technometrics, 15(4), 791–799.). Since then it has found various applications in actuarial science and other areas. In this paper, we focus on the distributional properties of GP and its related distributions. In particular, we study the distributional properties of distributions in the $\mathcal{H}$ family, which includes GP and generalized negative binomial distributions as special cases. We demonstrate that the moment and size-biased transformations of distributions within the $\mathcal{H}$ family remain in the same family, which significantly extends the results presented in Ambagaspitiya & Balakrishnan ((1994). ASTINBulletin: the Journal of the IAA, 24(2), 255–263.) and Ambagaspitiya ((1995). Insurance Mathematics and Economics, 2(16), 107–127.). Such findings enable us to provide recursive formulas for evaluating risk measures, such as Value-at-Risk and conditional tail expectation of the compound GP distributions. In addition, we show that the risk measures can be calculated by making use of transform methods, such as fast Fourier transform. In fact, the transformation method showed a remarkable time advantage over the recursive method. We numerically compare the risk measures of the compound sums when the primary distributions are Poisson and GP. The results illustrate the model risk for the loss frequency distribution.

ERIRMS Evaluation of the Reliability of IoT-Aided Remote Monitoring Systems of Low-Voltage Overhead Transmission Lines.

Researchers have studied instances of power line technical failures, the significant rise in the energy loss index in the line connecting the distribution transformer and consumer meters, and the inability to control unauthorized line connections. New, innovative, and scientific approaches are required to address these issues while enhancing the reliability and efficiency of electricity supply. This study evaluates the reliability of Internet of Things (IoT)-aided remote monitoring systems specifically designed for a low-voltage overhead transmission line. Many methods of analysis and comparison have been employed to examine the reliability of wireless sensor devices used in real-time remote monitoring. A reliability model was developed to evaluate the reliability of the monitoring system in various situations. Based on the developed models, it was found that the reliability indicators of the proposed monitoring system were 98% in 1 month. In addition, it has been proven that the reliability of the system remains high even when an optional sensor in the network fails. This study investigates various IoT technologies, their integration into monitoring systems, and their effectiveness in enhancing the reliability and efficiency of electrical transmission infrastructure. The analysis includes data from field deployments, case studies, and simulations to assess performance metrics, such as accuracy, latency, and fault detection capabilities.

Simulation and experimental analysis of dynamic thermal rise relaxation characteristics for dry-type distribution transformer

Abstract The temperature distribution characteristics of dry-type distribution transformers reflect their overall performance and quality. Distribution transformer daily load changes dramatically, its temperature fluctuates within a certain range. However, most of the research results on transformer temperature rise focus on the steady-state temperature characteristics of transformers, and lack of research related to dynamic temperature rise characteristics. This paper carries out a multi-physics coupling simulation and experimental verification of the dynamic temperature rise relaxation characteristics. The simulation uses the “electromagnetic-flow-thermal” multi-physics coupling analysis method to simulate the steady-state temperature rise characteristics and dynamic temperature rise relaxation characteristics of windings under different load rates. Then, this paper also explores the correlation between the dynamic temperature rise relaxation characteristics and load rate at the typical positions of high-voltage windings and low-voltage windings. The results show that the temperature rise of the winding shows the law of increasing the saturation index function with the increase of the load application time at any load rate. A quantitative characterization model of dynamic temperature rise relaxation behavior at different positions of transformer windings is constructed. It is found that the steady-state temperature rise value of different parts of the winding increases regularly as a power function with the increase of load rate, while the constant value of dynamic temperature rise relaxation time in different parts of the winding decreases as a power function with the increase of load rate. The verification results of the quantitative characterization model of dynamic temperature rise relaxation behavior show that the maximum error of the measured and theoretical steady-state temperature rise value is only 1.56 K, and the maximum error of the constant value of dynamic temperature rise relaxation time is only 0.082 h. These findings provide valuable insights into the performance and quality of dry-type distribution transformers, and can help improve their design and operation for better efficiency and safety.

End-to-End Signal Classification in Signed Cumulative Distribution Transform Space.

This paper presents a new end-to-end signal classification method using the signed cumulative distribution transform (SCDT). We adopt a transport generative model to define the classification problem. We then make use of mathematical properties of the SCDT to render the problem easier in transform domain, and solve for the class of an unknown sample using a nearest local subspace (NLS) search algorithm in SCDT domain. Experiments show that the proposed method provides high accuracy classification results while being computationally cheap, data efficient, and robust to out-of-distribution samples with respect to the existing end-to-end classification methods. The implementation of the proposed method in Python language is integrated as a part of the software package PyTransKit [1].

An experimental and spectroscopic investigation on pongamia pinata as liquid dielectrics for rural micro grid under various load conditions

The transformer mineral oil is generally hydrocarbon-based and is not environmentally friendly, so it holds a significant share in environmental pollution. As an alternative to conventional transformer oil, plant-based insulation oil has been investigated globally in the past few decades. Even though vegetable oils are considered an environmentally viable alternative to mineral oil, the extensive utilization of vegetable oils could create a threat to Indian food security. The present work focuses on using Pongamia Pinnata oil (PPO) as a significant alternative to conventional transformer oil (CTO) in distribution transformers. The effectiveness of the proposed PPO is experimentally verified using thermal studies and electrical studies through a low-level distribution transformer of 1 kVA. A comparative analysis was carried out between the proposed and conventional oil regarding physical, chemical, and thermal properties. Also, scanning electron microscopy (SEM) and Energy Dispersive X-ray spectroscopy analysis have been carried out for fibreglass cloth insulation material with PPO and CTO to ensure the intrinsic structural strength of the insulation material. The structural strength ensures the bonding and life span of the insulation in the transformer. The cost–benefit analysis is also favourable for the proposed oil as a better green liquid dielectric for transformers.

Design Algorithm for and Validation of Winding Losses in Large-Capacity Medium-Frequency Distribution Transformers

Design Algorithm for and Validation of Winding Losses in Large-Capacity Medium-Frequency Distribution Transformers

Reconfigurable topology assessment for efficient allocation of photovoltaic generators in unbalanced power delivery networks

The three-phase power delivery systems are reliable and efficient means of delivering electric power to customer node points. However, the unequal distribution of loads over the three phases leads to unbalance voltage between phases, resulting in increased power losses in distribution transformers and lines. It is highly challenging to install photovoltaic (PV) generators in unbalanced power distribution networks (PDNs) to restore power supply efficiency. This paper proposes dynamic reconfiguration (DR) in unbalanced PDNs to foster efficient integration of PV generators and improve system performance. Since the demand for electricity at residential node points and the PV-generated power varies with the season, this study proposes a seasonal DR of the PDNs. A framework with multiple objectives has been proposed for the DR-coupled PV deployment problem. A novel value-adaptive weight-aggregated (VAWA) grey-wolf optimizer (GWO) has been synthesized to contribute to system performance enhancement. The proposed strategy was validated and established through comparative investigations on an Indian 19-bus and modified IEEE 123-bus unbalanced radial distribution systems (URDSs).

Distribution Transformer Capacity Planning Based On Load Characteristics According To Electric Power Requirements At The Mugarsari Campus, Siliwangi University

Siliwangi University is currently building a second campus in the Mugarsari Tasikmalaya area. With the construction of this second campus, it is necessary to plan the transformer capacity in accordance with the installed electrical power requirements. In terms of designing electrical installations and the need for electrical power to be installed, proper planning is needed. In this research, the distribution transformer capacity value planning has been carried out in accordance with the installed electrical power requirements by considering load characteristics, namely demand factors and diversity factors. The aim of this research is to determine the capacity of the distribution transformer along with an analysis of the distribution transformer loading so that it complies with PLN standards. The total installed load is 1,566 kVA. In this study, calculations were carried out based on load characteristics, namely diversity factors and needs factors . The distribution transformer capacity obtained by considering load characteristics is 1,220 kVA with a loading percentage of 81% . The results of the loading that have been carried out are still in accordance with the Electricity Planning Projections through the 2020 RUPTL.

Experimental investigation on wireless integrated smart system for energy and water resource management in Indian smart cities

In the realm of India's smart cities, precise meter readings are essential for effectively managing domestic energy and water systems. Nevertheless, meter reading employing conventional techniques prove to be both costly and time-consuming, especially considering the vast user base and deficit of everyday consumption analysis. To cope with this challenge, the proposed solution introduces a novel unified wireless smart metering system for measuring energy and water usage. This prototype harnesses the smart, advanced metering technology to manage energy and water resources. The presence of data and remote control capabilities in smart distribution transformers offers distribution operators the chance to optimize system operation and control. The innovative system proposed utilizes smart metering technology and incorporates a distribution system self-healing mechanism against power outages, revolutionizing the way utilities manage water and energy consumption. The system not only offers real-time consumption statistics to the utility provider but also provides the flexibility to remotely control the system's turn-on and turn-off functions. The system effectively captures real-time data, transmits it via LoRa to Telegram application. The system automatically re-establishes remote connections in the event of disconnections caused by emergency conditions or non-payment of the bill, once the relevant issue has been resolved. Additionally, the system promptly notifies authorities on overload, over-temperature, and instances of electricity theft. The system proficiently generates bills based on the consumed data and seamlessly transmits the consumed units to the authorities. The innovative system proposed has proven its ability to communicate effectively, yielding results that align with the given circumstances.

Modeling local deformation, damage distribution, and phase transformation in zirconia particle-reinforced TRIP steel composites

This study investigates tensile deformation, damage analysis, and the effect of microtexture on a 10 % vol. zirconia particle-reinforced TRIP steel composite. In situ tensile tests and simulations were conducted, with sequential SEM images captured during tensile loading tests and electron backscatter diffraction (EBSD) providing initial crystal orientation data. SEM images were utilized in digital image correlation (DIC) analysis to calculate the local plastic strain distribution using VEDDAC software. Initial micrographs and EBSD data were transformed into geometry files for simulations, employing crystal plasticity with a dislocation density-based model and a newly proposed phase field approach-based ductile-brittle damage criterion for both austenite and ceramic particles. MATLAB's image processing functions were used to compare and validate experimentally observed damage instances with simulation predictions. The effect of microtexture combined with various physical quantities was explored using MTEX. The results show that the simulations accurately predict strain concentration around ceramic particles, with a higher strain observed in the middle region compared to the DIC observations. The simulated strain in the austenitic matrix is found to be 1.4 times higher than the DIC measurements. Quantitative analysis of damage pixels reveals that the proposed critical plastic strain value aligns closely with experimental results, showing an error of - 0.25 % compared to 1.121 % for a smaller strain case. This comprehensive analysis provides insight into the effect of microstructural attributes on material performance and behavior under tensile deformation, leading to a validated microstructural-informed damage-inclusive crystal plasticity model.

An Experimental Investigation on the Synthetic Ester Circulation for Drying Cellulose Insulation in Distribution Transformers

Water can cause damage to power transformers by accelerating aging processes, reducing the dielectric margin, decreasing the partial-discharge inception voltage, and increasing the risk of unexpected failures. Modern electrical companies utilize a variety of drying techniques but sometimes do not comprehend them, making drying less effective. To address these challenges, this study proposes the application of synthetic ester to dry distribution transformers because water dissolves better in the ester than other dielectric liquids. An improved laboratory model of transformer insulation was used for the investigation. This model dried the ester using a molecular filter and carefully selected adsorbed weight. Pressboard strip water content before and after drying was analyzed to determine the drying efficacy of the cellulose insulation. The water content was measured using the Karl-Fischer titration method. The investigation proved that the drying procedure worked. At an ester moisture level of 105-120 ppm and an insulation system temperature of 70°C, samples dried for 5 days showed above 1% water loss. The experimental investigation demonstrated the high efficiency of the proposed drying method for distribution transformers.

Antiaging Performance of Mixed Oil Insulation Developed Using Modified Neem Oil and Mineral Oil for Distribution Transformers

Antiaging Performance of Mixed Oil Insulation Developed Using Modified Neem Oil and Mineral Oil for Distribution Transformers

Reducing Mode Collapse With Monge-Kantorovich Optimal Transport for Generative Adversarial Networks.

Mode collapse has been a persisting challenge in generative adversarial networks (GANs), and it directly affects the applications of GAN in many domains. Existing works that attempt to solve this problem have some serious limitations: models using optimal transport (OT) strategies (e.g., Wasserstein distance) lead to vanishing or exploding gradients; increasing the number of generators can cause several generators focusing on the same mode; and approaches that modify the loss also do not satisfactorily resolve mode collapse. In this article, we reduce mode collapse by formulating it as a Monge problem of OT map. We show that the Monge problem can be transformed to the distribution transformation problem in GAN, and a rectified affine neural network can be considered as a measurable function. In this way, we propose Monge GAN that uses this measurable function to transform the generated data distribution into the original data distribution. We utilize the Kantorovich formulation to obtain the OT cost, which is regarded as the OT distance between the two distributions. Finally, we conduct extensive experiments on both image and numerical datasets to validate our Monge GAN in reducing model collapse.

A Study on the Analysis of the Water Contents in Oil of Distribution Transformer Based on Field Data

A Study on the Analysis of the Water Contents in Oil of Distribution Transformer Based on Field Data

Optimized gap positions for improved leakage impedance in dry-type transformer design

The power and distribution transformers must be properly designed to operate in a variety of conditions and manufactured following all applicable international standards to provide a reliable energy supply to consumers. The transformer can fail if it is improperly designed, and it cannot withstand the short-circuit condition if the short-circuit current is not calculated properly. Short-circuit current can be easily calculated by evaluating the leakage impedance. Customers can also demand the transformer companies to increase or decrease the leakage impedance. This paper evaluates the effect of the gap's position in the four different sections of the high voltage winding on the leakage impedance of the two-winding transformer and proposes effective design options for minimizing the leakage impedance. The accuracy of the finite element models was also investigated using an experimental laboratory transformer model specially designed for optimizing the leakage impedance. The experimental and finite element methods results show that the leakage impedance can be changed to the desired value and limits and easily assessed by the finite element analysis technique. The results indicate that even with the same use of the winding and core material, there was a considerable difference in the leakage impedance for each of the conditions.

Efficient predefined time adaptive neural network for motor execution EEG signal classification based brain-computer interaction

Nowadays, Electroencephalogram (EEG) devices that do not require invasive procedures get more attraction. Brain-Computer Interface (BCI) systems use EEG analysis to identify users' mental states, cognitive shifts, and stimuli-induced reactions. The Motor Execution (ME) paradigm is a vital control paradigm that holds great significance in this framework. In this manuscript, an Efficient Predefined Time Adaptive Neural Network for EEG-Based Brain-Computer Interaction in Motor Execution Classification (EPTNN-BCI-EEG) is proposed. Initially, the input signals are collected from EEG Dataset. The input signals are preprocessed using Robust M-Type Error-State Kalman Filter (RMESKF) to remove the artifacts and baseline signals. Then, the pre-processed signals are given to Signed Cumulative Distribution Transform (SCDT) for feature extraction. SCDT is used to extract Spatial and temporal features. Afterward, Efficient Predefined Time Adaptive Neural Network (EPTNN) is used to classify the EEG gender and signal, such as Audio/Video and Male/Female. In General, EPTNN does not expose any adoption of optimization systems to determine optimal parameters to classify the EEG gender and signal. Hence, Bitwise Arithmetic Optimization Algorithm (BAOA) is proposed to optimize the EPTNN. The proposed EPTNN-BCI-EEG approach is implemented in MATLAB and the performance metrics, such as Recall, Accuracy, Root Mean Square Error (RMSE), F1-score, Specificity, ROC, Computation time are assessed. The performance of EPTNN-BCI-EEG approach provides 17.82 %, 28.95 %, and 19.2 % higher accuracy, 24.38 %, 34.53 %, and 18.78 % higher recall, 26.7 %, 24.2 %, and 32.7 % higher specificity when analysed with the existing methods.

Modeling of direct AC/AC hybrid distribution transformer based on relaxed two port network theory

Traditional distribution transformers have shortcomings such as poor dynamic characteristics and unreasonable voltage compensation. It is difficult for them to meet the demand for power quality with the large-scale integration of new energy into the grid. Hybrid distribution transformers (HDTs) are an important means of addressing these issues. They have good flexibility in voltage regulation and huge development potential. To analyze the operating status and losses of an HDT, this study constructs a simplified equivalent impedance model for an HDT. First, the operating principle, characteristics, and voltage regulation range of a direct AC/AC HDT were analyzed. Second, based on the state–space averaging method, we obtain the HDT single-phase average circuit topology and construct an HDT relaxed two-port network model. Third, considering the operating losses of the transformer, Thevenin equivalent parameters of an HDT were derived. Then, a simplified equivalent impedance model for an HDT and the corresponding control strategies are presented. Finally, we verify the correctness and rationality of the proposed theory by constructing an HDT simulation model.

Distribution transformer mechanical faults diagnosis method incorporating cross-domain feature extraction and recognition of unknown-type faults

The complexity and uncertainty of vibration signals from distribution transformers pose significant challenges for diagnosing mechanical faults. To address this, this paper proposes a novel fault diagnosis model for distribution transformers, which combines a cross-domain fusion multi-scale convolutional autoencoder (CFMS-CAE) with an open-set domain adaptation classifier (OSDA-C). Specifically, in order to extract more comprehensive features, a convolutional autoencoder (CAE) model based on multi-output objectives is constructed to extract the time-frequency domain characteristics of transformer vibration signals. Multiple-scale convolutional layers are incorporated into the convolutional autoencoder to enable multi-range feature extraction. Additionally, parameter optimization is achieved using the crayfish optimization algorithm (COA). Subsequently, an open-set domain adaptation module is integrated into the convolutional neural network classifier to establish boundaries for each category and facilitate the identification of transformer fault categories, including unknown-type faults. The experimental results demonstrate that the proposed method is effective for fault identification in both dry-type and oil-immersed transformers, with average accuracy reaching 99.35% and 99.62%, respectively. For unknown-type faults, the accuracy also achieved 100% and 97.5%, respectively.

Adaptive Multi-Sensor Fusion Localization Method Based on Filtering

High-precision positioning is a fundamental requirement for autonomous vehicles. However, the accuracy of single-sensor positioning technology can be compromised in complex scenarios due to inherent limitations. To address this issue, we propose an adaptive multi-sensor fusion localization method based on the error-state Kalman filter. By incorporating a tightly coupled laser inertial odometer that utilizes the Normal Distribution Transform (NDT), we constructed a multi-level fuzzy evaluation model for posture transformation states. This model assesses the reliability of Global Navigation Satellite System (GNSS) data and the laser inertial odometer when GNSS signals are disrupted, prioritizing data with higher reliability for posture updates. Real vehicle tests demonstrate that our proposed positioning method satisfactorily meets the positioning accuracy and robustness requirements for autonomous driving vehicles in complex environments.

Operationalizing the Circular Economy—A Longitudinal Study on Sustained Circular Action

Circularity is becoming increasingly important for Distribution System Operators (DSOs) as their infrastructure ages and needs replacement, alongside compliance with stringent environmental regulations like the Corporate Sustainability Reporting Directive (CSRD). However, implementing circular economy (CE) practices is challenging due to the fragmented nature of the current CE landscape and its limited interaction with practical application. A longitudinal case study at the Dutch DSO, Liander, focusing on the circularity of distribution transformers, was conducted to generate prescriptive knowledge on operationalizing circularity. This resulted in the formulation of six design propositions for circular action that suggest to (1) initiate small-scale circularity experiments; (2) involve technical and strategic experts; (3) synergize circularity with more urgent, primary goals; (4) translate circular initiatives bottom-up and top-down; (5) collaborate with other DSOs; and (6) create multidisciplinary teams. The propositions suggest to situationally select interventions and build upon the outcomes of previous interventions in order to incrementally contribute to circular change. Other DSOs could use these propositions to optimize their strategy toward circular action. Additionally, the findings contribute to advancing scientific knowledge to implementable actions in order to initiate and sustain circular change.