Distribution Transformers Research Articles

Real-time data exchange using web service with CIM for aging estimation of distribution transformers

Real-time data exchange using web service with CIM for aging estimation of distribution transformers

Overload Alarm Prediction in Power Distribution Transformers

The growing demand for electricity puts more strain on the grid, requiring automated and proactive strategies such as overload prediction to improve grid maintenance. However, the intermittent nature of power distribution loads makes the prediction more challenging. This paper proposes a novel framework for overload alarm prediction in distribution transformers, aimed at enhancing the reliability and efficiency of grid operations. Leveraging real-world smart meter data and machine learning techniques, the proposed system develops a classification model to predict overloads for distribution transformers. Due to resource constraints, a new strategy is adopted to assess the significance of alarms based on expert observations. Subsequently, a new approach is developed to imitate the experts, leading to an automated decision-making process using random forest. Ultimately, the transfer learning strategy is utilized to predict overload alarms for distribution transformers facing data scarcity in real-world applications. The proposed system demonstrates high accuracy of overload alarm predictions, paving the way for developing more proactive grid maintenance strategies.

Identification Winding Material of Distribution Transformer Based on Multi-Information Fusion

Identification Winding Material of Distribution Transformer Based on Multi-Information Fusion

Power Usage Monitoring and Overload Protection System for Distribution Transformers

Efficient energy manitoring and overload protection on distribution transformers are very much needed. This study aims to develop a power usage management system and overload protection integrated with Internet of Things (IoT) technology, with a specific target to monitor and manage power usage on distribution transformers in real time, as well as provide automatic protection against overload conditions. The methodology used in this study involves the design and implementation of hardware and software that supports wireless communication and data analysis. This system is also equipped with a power reading and protection mechanism that can automatically cut off the electricity flow when an overload detection occurs, in order to prevent damage to the transformer, it is expected that this system can increase the efficiency of power usage management and provide a quick response to overload conditions. The implementation of this system is expected to reduce operational costs and increase the reliability of electricity supply on the distribution network. Based on the tests that have been carried out, it has been proven that the system has worked well in maintaining important variables in the power usage management system and overload protection on the Internet of Things integrated distribution transformer.

Analyzing the influence of electric vehicle charging scheduling on distribution transformer lifespan

The ongoing growth in global electricity demand and rapid proliferation of electric vehicles (EVs) are posing challenges to the operation of the low voltage power networks and their components. This study aims to analyze the impacts of peak-load demand on the distribution transformers, considering mild and extreme combinations of electric energy demand and localized charging of plug-in EVs under their different penetration levels, hypothesizing that shifting the demand for charging throughout the day can reduce the aging of distribution transformers. The proposed method employs a fuzzy-logic-based tool, which incorporates the influence of ambient temperature, higher harmonics, reactive power compensation, reverse power flows, and overload. Three different charging/discharging modes have been considered: demand charging, off-peak charging, and synergy of off-peak charging and vehicle-to-house technology. The results indicate that off-peak charging emerges as 2.1 times more effective than demand charging in terms of the transformer's loss of life, particularly for ultimate EV penetration of 100 %. It is demonstrated that the utilization of charging scheduling for EVs should be prioritized to defer costly system upgrades and reinforcements, particularly when EV penetration exceeds 50 %.

Study on Mechanical Properties and Fatigue of Cold-rolled Steel Plate for Distribution Transformer

The oil tank steel plate of the distribution transformer is covered with welds, and the fatigue failure of the oil tank often occurs in the internal defects of fillet welds. Given the above problems, this paper puts forward the bearing capacity calculation formula of cold-rolled steel plate and carries on the force analysis of steel plate and the fatigue analysis of weld through finite elements. Based on the theory of plastic limit design, the ultimate flexural bearing capacity of the control section of steel plate members is proposed. The calculation formula of equivalent structural stress is given based on the calculation method of structural stress and linear elastic fracture mechanics, and the calculation method of fatigue life and fatigue cumulative damage is given. Then, the elastic-plastic equation is solved according to three criteria: Mises yield criterion, plastic flow criterion, and strengthening criterion. Based on the above numerical simulation analysis, the mechanical model of steel plate weld is constructed, the mesh is divided, the boundary conditions are restricted and the same load is applied. ABAQUS analysis results show that the stress distribution curve of the connector has a maximum value at X = 80 mm and X = 210 mm, that is, the top surface of the connector steel plate has an obvious stress concentration at 80 mm and 210 mm. When the length of the weld is not more than 60 times the size of the welding foot, the full section of the core plate will yield, and the maximum stress value of the weld element will not exceed the tensile strength value of the welding material. The error of the results calculated by the formula in this paper is mostly within 10%, and the rest is basically within 20%. Therefore, the bearing capacity formula of the steel plate in this paper has ideal calculation accuracy and can provide effective guidance for the design of distribution transformer oil tanks.

Demand management and pricing in renewable integrated plug-in electric vehicle charging station using reinforcement learning

ABSTRACT The increasing adaption of Plug-in Electric Vehicles (PEVs) has increased the power demand for charging. Renewable generation can significantly help to meet this demand. The uncertainty of renewable generation and randomness in PEV charging makes the demand management of charging stations challenging. To address these challenges, this study proposes a novel demand management pricing strategy for renewable integrated charging stations. The proposed strategy uses reinforcement learning for charging coordination of PEVs. The renewable generation is estimated using weather data and accordingly PEVs are scheduled for demand management. The strategy decides the PEV power price based on real-time price, renewable tariff and PEV load. The strategy uses the bidirectional power flow with battery degradation cost. The distribution transformer operating cost and loading constraints are included to resemble the real-time environment. The PEV randomness and uncertainty of renewable generations are incorporated in the study. The results of the numerical case study show that the proposed strategy can manage the charging station load efficiently. The proposed strategy has optimized the cost of charging, discharging and charging station profit and increased the service capability of the charging station.

Comparison between the models of potential storage and simulation of the demand for electricity transformer (400/11) in Diyala General Company

Storage models are usually used to determine the optimal order quantity and reorder point by obtaining the lowest total cost of storage to balance the amount of production and the amount of demand for materials, but sometimes these models can not be applied, so the use of simulation is resorted to where accurate and flexible results can be relied upon, to achieve the goal of the study, which is to improve the company's storage system. This research dealt with two cases of the probabilistic storage model, where the first model was applied to the monthly demand data on one of the products of Diyala General Company, which is the distribution transformer (400|11) for the years 2019,2020,2021. The second is the probabilistic storage model by simulating demand and comparing the two models, whichever is better to reach optimal. Where the distribution of demand was found to find the average and standard deviation and apply the model to obtain the indicators of the models. Using the MATLAB program, the statistical analysis of the data was carried out and the indicators of the model were obtained.

Vibration Position Detection of Robot Arm Based on Feature Extraction of 3D Lidar.

In the process of construction, pouring and vibrating concrete on existing reinforced structures is a necessary process. This paper presents an automatic vibration position detecting method based on the feature extraction of 3D lidar point clouds. Compared with the image-based method, this method has better anti-interference performance to light with reduced computational consumption. First, lidar scans are used to capture multiple frames of local steel bar point clouds. Then, the clouds are stitched by Normal Distribution Transform (NDT) for preliminary matching and Iterative Closest Point (ICP) for fine-matching. The Graph-Based Optimization (g2o) method further refines the precision of the 3D registration. Afterwards, the 3D point clouds are projected into a 2D image. Finally, the locations of concrete vibration points and concrete casting points are discerned through point cloud and image processing technologies. Experiments demonstrate that the proposed automatic method outperforms ICP and NDT algorithms, reducing the mean square error (MSE) by 11.5% and 11.37%, respectively. The maximum discrepancies in identifying concrete vibration points and concrete casting points are 0.059 ± 0.031 m and 0.089 ± 0.0493 m, respectively, fulfilling the requirement for concrete vibration detection.

Densification, deformation, and delamination during co‐sintering process of metal–ceramic laminates

AbstractToday, there is a high demand and increasing trend for multifunctional composite materials and components due to the combination of a wide range of favorable properties. However, undesired deformation leading to delamination, curvature, cracks, or even complete fracture frequently occurs during the co‐sintering process of metal–ceramic laminates (MCLs). To solve these issues, experimental work highly relies on the time‐consuming trial‐and‐error principle. This work aims to develop a thermo‐mechanical‐metallurgical model capable of predicting the densification, deformation, and delamination of MCLs during the co‐sintering process. It is found that the developed model successfully considers the inhomogeneous relative density distribution and phase transformation of the steel tape. In addition, a thin interlayer formed by a mixed slurry in the MCLs is modeled as cohesive elements to simulate the delamination process. The developed model is systematically validated by the experimental measurements and observations in terms of densification, deformation, and delamination during the co‐sintering process of the investigated laminate variants.

Optimization Algorithm of Network Node Layout of Fresh Agricultural Products’ Online Consumption Behavior in E-Commerce Environment

In order to improve the efficiency of online consumption and supply chain efficiency of fresh agricultural products in the e-commerce environment, a network node layout optimization algorithm was designed. In the e-commerce environment, a network node deployment model has been constructed for the online consumption behavior of fresh agricultural products. A network transmission supply chain detection model was established through the rotation scheduling method. By combining distributed transformation protocols, the spatial layout characteristics of network nodes have been reorganized. Further introduction of a multi-coverage scheduling model, combined with sensor information fusion tracking technology, accurately detects the layout characteristics of network nodes. Based on these detection data, forwarding and adaptive control protocols were developed. Real-time control using the node traversal method was developed. Under the optimal node supply chain transmission mechanism, the scheduling of the network node layout has been achieved. Meanwhile, convolutional neural networks were utilized to dynamically adjust layout control to ensure convergence and stability. The simulation results show that the node forwarding ability of the network node layout design for online consumption behavior of fresh agricultural products in the e-commerce environment is good, which improves the output balance of the network supply chain transmission of online consumption behavior of fresh agricultural products in the e-commerce environment. In addition, the optimized delivery cost decreased by 20% compared to before optimization, delivery efficiency increased by 30%, and inventory turnover rate increased by 25%, confirming that the proposed method has good optimization performance.

Multi-Source Fusion Deformation-Monitoring Accuracy Calibration Method Based on a Normal Distribution Transform–Convolutional Neural Network–Self Attention Network

Multi-Source Fusion Deformation-Monitoring Accuracy Calibration Method Based on a Normal Distribution Transform–Convolutional Neural Network–Self Attention Network

Quasiparton distributions in massive QED2: Toward quantum computation

We analyze the quasiparton distributions of the lightest η′ meson in massive two-dimensional quantum electrodynamics (QED2) by exact diagonalization. The Hamiltonian and boost operators are mapped onto spin qubits in a spatial lattice with open boundary conditions. The lowest excited state in the exact diagonalization is shown to interpolate continuously between an anomalous η′ state at strong coupling, and a nonanomalous heavy meson at weak coupling, with a cusp at the critical point. The boosted η′ state follows relativistic kinematics but with large deviations in the luminal limit. The spatial quasiparton distribution function and amplitude for the η′ state are computed numerically for increasing rapidity both at strong and weak coupling, and compared to the exact light front results. The numerical results from the boosted form of the spatial parton distributions, compare fairly with the inverse Fourier transformation of the luminal parton distributions, derived in the lowest Fock space approximation. Our analysis points out some of the limitations facing the current lattice program for the parton distributions. Published by the American Physical Society 2024

Acoustic near field effects in distribution transformer noise measurement

This paper examines the near field effect, including the active and reactive acoustic field around a liquid-filled distribution transformer with heat-dissipating corrugated fins on the tank walls. The analysis was carried out on the basis of measurements of sound pressure and sound intensity levels along with acoustic maps including the phase correlation of acoustic waves. The acoustic field around the transformer was evaluated in the measurement planes showing normal direction radiation of up to 1,0 m distance. Discrete point measurements made with the PU (pressure - velocity) probe was used to obtain parameters such as particle velocity, active and reactive sound intensity as well as the transfer function associated with the reference microphone. The last parameter, the transfer function, allows to show the phase correlation between acoustic wave propagation, which contributes to a better understanding of acoustic near-field effects where energy circulates, creating a sound field that can have overestimated sound pressure and intensity levels.

Probability density prediction for carbon allowance prices based on TS2Vec and distribution Transformer

Carbon allowance price is an important tool to reduce carbon emissions and achieve carbon neutrality. It is necessary to establish a predictive model to provide accurate and reliable information to managers and participants in the carbon trading market. Therefore, a novel probability density prediction model, called TS2Vec-based distribution Transformer (TDT), is proposed. TDT consists of two stages: contrastive unsupervised pre-training and supervised training. In the contrastive unsupervised training stage, time series to vector (TS2Vec) is used to represent the dynamic trends and unique features of the data. Then, these representations are fed into the distribution Transformer (DT) to fit the hypothetical probability distribution. Experimental results show that the prediction results of the proposed TDT are more accurate and reliable than other benchmark models. In addition, our research indicates reliable probability density predictions provide enterprises with opportunities to control carbon emission costs and increase economic returns, thereby improving the competitiveness of enterprises and promoting carbon emission reduction.

A novel constrained skew-Gaussian filter and its application to maneuverable reentry vehicle tracking

The state of the system generally satisfies specific constraints imposed by material properties or physical laws, so the application of these constraints can improve the accuracy of state estimation. In this paper, a novel recursive filter referred as constrained high-degree cubature skew-Gaussian filter (CHCSGF) is proposed, which achieves soft-constrained state estimation by compressing the probability density of unconstrained states with constraint information. First, the probability density of the state under inequality soft constraints is modeled as a skew-Gaussian (SG) distribution, rather than truncated or single Gaussian distributions. Then, a recursive constrained SG filter is developed to handle inequality soft constraints in linear systems. Addressing nonlinear challenges, a 5th-degree spherical-radial cubature approximation method is presented to numerically calculate SG-weighted integrals for the nonlinear transformation of SG distribution. Finally, the CHCSGF algorithm is proposed using this method to tackle nonlinear filtering problems. The CHCSGF is applied to reentry trajectory tracking to improve estimation accuracy by dealing with heat flow, dynamic pressure and overload constraints during reentry flight. Simulation results demonstrate that the CHCSGF achieves higher estimation accuracy than unconstrained methods under nonlinear inequality soft constraints, and is robust to the constraints with a prior error. Compared to particle filter and moving horizon estimation, the computational complexity of CHCSGF is significantly reduced.

Power Distribution Transformer Maintenance Skills Required byElectrical Engineering Technology Students ofPolytechnics inNorth-East Nigeria

Electric power distribution industry in North-East, Nigeria is faced with inadequate number of power transformer maintenance personnel which has led to numerous abandonment of broken down power transformers. Transformer faults have been blamed for many power outages of long duration in many towns and communities in North-East, Nigeria.It is against this backdrop that thisstudy determined the power distribution transformer maintenance skills required by Electrical Engineering Technology students of polytechnics in North-East Nigeria. Three research questions and three hypotheses guided the study which employed a descriptive survey research design. The population of the study was 144 comprising of 135 Electrical Engineering Technology Lecturers, 3 Power Transformer Maintenance Technicians and 6 Electric Power Distribution Company-Based Supervisors. The entire population was used hence, there was no sampling. The instrument used for data collection was a structured questionnaire titled: Power Distribution Transformer Maintenance Skills Required Questionnaire (PDTMSRQ) developed by the researchers. The instrument was validated by three experts and a reliability of 0.74 was obtained using Cronbach Alpha reliability method. Mean statistic was used to answer the research questions while ANOVA was used to test the two null hypotheses of the study at 0.05 level of significance. The findings of the study revealed that the Electrical Engineering Technology students of polytechnics in North-East Nigeria required preventive, predictive and corrective maintenance skills. The study recommended the following: Lecturers should encourage the students’ skill acquisition process by exposing them to various maintenance activities in order for them to imbibe the different methods of power transformer maintenance; Students should be trained to recognize sounds and sights of faulty equipment in order to predict the parts or components that needed replacement or repairs.

Determination of Thermal Behavior from Core Flux Density with Image Processing Technique for Distribution Transformers

Determination of Thermal Behavior from Core Flux Density with Image Processing Technique for Distribution Transformers

Comparison Between Maintenance of Distribution Substations Using the Mobile Substation Unit (UGB) Method and Maintenance of Blackout Distribution Substations regarding Energy Not Served (ENS) which is not distributed to PLN UP3 Serpong

Electricity has become an important part of human life along with technological developments in this modern era. Economic progress, quality of life and the business world are very dependent on the electricity supply supplied by PT. PLN (Persero) UP3 Serpong. Electric power distribution is the final stage in the electric power delivery process. This is the process of distributing electric power from the electricity transmission system to electricity consumers. One of the main components in the electric power distribution system is the distribution substation. A distribution substation is an electrical installation device which contains disconnecting, connecting, safety and distribution transformer equipment to distribute electrical power according to consumer needs. Maintenance at distribution substations is carried out to support services to electricity consumers so that the substation equipment used is in reliable condition. and guaranteed continuity. Regular maintenance on distribution substations is important so that distribution substations do not experience damage and disruption while operating.

Optimization scheduling strategy for flexible interconnected distribution network considering transformer offline

Based on the flexible interconnection technology with intelligent soft open point (SOP), new opportunities have arisen to address issues such as complex power flow and uneven load rates in existing distribution networks. However, most existing research focuses on load balancing strategies and N-1 fault tolerance, without fully considering the impact of flexible access and offline of distribution transformers on the losses in the distribution network. Therefore, this paper analyzes the impact of structural adjustments to the distribution transformer interconnection system on the comprehensive losses of the distribution network, and concludes that under low-load conditions, the offline of lightly loaded transformers optimizes the comprehensive losses of the distribution network. However, the offline of lightly loaded transformers leads to an increase in the remaining transformer load rates in the distribution station areas. To address this shortfall, this paper proposes an optimization scheduling strategy for flexible interconnected distribution networks considering transformer offline, by simultaneously utilizing shared energy storage on the DC side to balance transformer load rates and minimize losses. Finally, case studies based on an IEEE 18-node interconnected system are conducted to validate the proposed approach under two different scenarios. The results demonstrate that the proposed control strategy outperforms existing strategies by optimizing the comprehensive losses of the system, thereby enhancing its operational economy and reliability.