Power Distribution Transformers Research Articles

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

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

Analysis and Basics of Improving the Process of Cutting Electrical Sheet Bundles with a High-Pressure Abrasive Water Jet.

Electrical steels are widely used in the electrical industry in the construction of many devices, e.g., power transformer cores and distribution transformers. An important parameter of electrical components that determines the efficiency of devices is energy loss during remagnetization. These losses are influenced, among other factors, by steel cutting processes. The common techniques for cutting electrical materials on industrial lines are mechanical cutting and laser cutting. High-pressure abrasive water jet (AWJ) cutting, unlike the technologies mentioned above, can ensure higher quality of the cut edge and limit the negative impact of the cutting process on the magnetic properties of sheet metal. However, the correct control of the process and the conditions of its implementation comprise a complex issue and require extensive scientific research. This work presents a new approach to cutting electric sheets, involving bundle cutting, which significantly increases the processing efficiency and the dimensional and shape accuracy of the cut details. The tests were carried out for bundles composed of a maximum of 30 sheets, ready to be joined in a stator and rotor in a motor. The influence of processing conditions on the quality of the cut edges of sheet metal, the width of the deformation zone, and the burr height were analyzed. The detailed analysis of the quality of the cut edges of electrical bundled sheets creates new possibilities for controlling the AWJ cutting process in order to obtain a product with the desired functional and operational properties.

Analysis of the Process and Results of High-Pressure Abrasive Water Jet Multilayer Cutting of Electrical Steel.

Electrical steels are magnetically soft materials and are widely used in the electrical industry for the construction of power transformer cores, distribution transformers, current transformers, and voltage transformers. An important parameter of electrical components, which determines the efficiency of devices, is energy loss during remagnetization. Energy losses are caused by eddy currents, hysteresis, and magnetic delay associated with the low quality of the cut edge after the cutting of steels, and material deformations and excessive stress concentration in the surrounding cutting zones. Common techniques for cutting electrical materials in industrial lines include mechanical cutting and laser cutting. Work has shown that mechanical cutting of electrical steel single layers results in the occurrence of large deformation zones, and in cutting processes with a high-pressure abrasive water jet (AWJ), significant uplifts of material and burrs at the bottom edges of sheets occur. The problem of increasing the cutting quality was solved through selecting the stream parameters for bundle cutting of electrical steels. It has been shown that in the process of cutting electrical sheet bundles, the height of burrs on the cut surface and the zone of plastic deformation are reduced. The work also presents comparison and analysis of characteristic features of the cut edge of electrical sheets obtained through high-pressure abrasive water jet and mechanical cutting processes. The influence of the type and processing parameters on the characteristic features of the material hysteresis loop was determined.

Optimization of transformer parameters at distribution and power levels with hybrid Grey wolf-whale optimization algorithm

Oil-type transformers (OTT) are used more than dry-type transformers, based on cost in the transmission and distribution of electrical energy. Therefore, this usage density increases the importance of cost in OTT. Weight is important in transformer cost. The weight of the transformers depends on the variable parameters of the weights of the core and windings, C (iron cross section conformity factor) and s (current density), respectively. In this study, unlike the previous heuristic optimization studies, an innovative and complementary optimum weight was obtained by using the Gray Wolf - Whale Optimization hybrid algorithm for both distribution type and power transformer type OTT. A weight reduction of 44% and approximately 14% in power transformers was achieved. It was determined that this decrease in weights provided the same reduction in OTT costs. The comparison test of the study was performed both with the values of other algorithms and statistically.

SIMULATION OF NARROWBAND POWER LINE CARRIER COMMUNICATION FOR SMART GRID APPLICATIONS

For developing a reliable Power Line Carrier Communication (PLCC) system, it is extremely important to know the characteristics of different subsystems in PLCC and power lines. The circuit used to superimpose carrier signal from the transmitter with high potential power lines is termed as line tuners. The design and simulation of a capacitive type line tuner meeting IEC 60481:1974 and IEEE Std C93.4-2012 are included in this article. The wave trap allows the power signal to pass through but blocks the high-frequency carrier signal to reach terminal equipment which works at 50/60 Hertz. Three types of wave traps are studied as part of this paper: single frequency, dual frequency, and wideband wave traps. Impedance characteristics obtained in simulation show effective rejection of carrier signal as desired. The distribution power transformers are an inevitable part of the power line network which offers high attenuation to carrier signal as being inductive it functions as low pass filter. To overcome this problem a bypass circuit which could be incorporated in future power transformers is proposed in this article. The Resistance, Inductance, and Capacitance (RLC) parameter of the power line cable used in the simulation model are derived based on the experimental values observed on an actual 11 kV underground cable and thus replicates actual field conditions. A complete PLCC system with all the above components is presented in this article and can be used as a testbed by designers to validate the MODEM for communication in different topologies by modifying the elements according to actual field conditions.

Monitoring of three-phase distribution power transformer based on the Internet of Things (IoT) and SCADA

The three-phase distribution transfomer, equipment for stepping down the voltage from medium (20/11,5 kV) to low voltage network (400/231 V) with a constant power, is a type of the PT. PLN (Persero) assets which has a direct relationship with customers. The condition and the performance of transformer are affecting on how the continuity of the electricity distributed. Hence, the monitoring process of three-phase distribution transfomer condition and performance should be done. Some elements which have to be monitored such as voltage (ZMPT101B sensor), current (ACS712 30 A sensor), power, and transfomer load. Those elements could be included as an electrical indicator.And then the transfomer’s temperature (DS18B20 sensor) and the oil transfomer level (HC SR04 sensor) could be included as a mechanical indicator. All of the sensors are processed and programmed with Arduino Mega 2560 which has been connected directly into an additional modul called Ethernet shield and router. The results then emitted by WiFi into SCADA to be shown. The results shown by SCADA is the information whether transformer need to be maintened or not

Power Transformer Characterization and Design Optimization Environment

Power Transformers are essential part of all AC Power Grids. This work proposes a characterization and design optimization environment for Power Transformers to increase efficiency while decreasing core volume. It utilizes a Finite-Element State-Space models in conjunction with Artificial Neural Networks and Particle Swarm Optimization. A case study of a 42 MVA, 118/13.8kV distribution power transformer was conducted, and transformer performance characteristics were compared to test data for verification. Next, the optimization environment was used to decrease the transformer volume and improve its efficiency. In addition, the Taguchi orthogonal arrays method was used to reduce computational costs.

The Output Voltage Estimation of Power Transformer Integrated with a Three Phase T-Type Inverter

The issues related to integrating these systems into the grids continue to gain importance with the increasing use and importance of renewable energy sources. Therefore, the importance of power distribution transformers is increasing. Besides, these power distribution transformers are connected to the grid with power electronics circuits and inverters. Considering the modular inverter structures, ease of maintenance, and connection, three-level T-type inverters are chosen for this study. The secondary output voltage of the power transformer is estimated by using circuit parameters such as the dead time of the inverter circuit, PWM switching frequency, and modulation rate. Based on the finite element analysis analysis according to the selected parameters, 810 data are obtained with time-dependent parametric analysis. The adaptive neuro-fuzzy inference system model is constructed by considering the simulation data to estimate the secondary output of the power transformer of these parameters. In the training phase of the model, 648 randomly selected data from 810 data obtained by ANSYS-Electronics/Simplorer are used. The remaining 162 data are used in the testing process to measure system performance. As a result of the analysis made by ANFIS, the Root Mean Square Error (RMSE) error is found as 2.475%. Since the values obtained in the estimation process of the study are very close to the simulation values, the ANFIS method can be used as an estimation method that will give accurate results during the design phase.

Mitigating Electrical Disturbances with Hybrid Distribution Transformer

Hybrid transformers (HT) have the advantages of the conventional transformer, the regulatory abilities of power electronic converters, and reduce the impact of the grid. The impacts of the existing grid are voltage sag, voltage swell, harmonic distortion, and voltage unbalanced. The power electronic converter has a controllable advantage such as regulating the voltage and can transfer only a fraction of the power. The aim of the paper is to augment the conventional power distribution transformer with a partially rated power electronic module to enhance flexibility and introduce new features to the distribution transformer. In this paper, the proposed back-to-back converter included an active front rectifier and a modular multilevel converter (MMC) was simulated by MATLAB/Simulink software. The proposed back-to-back converter was used at the primary side of the distribution transformer to compensate for the voltage sag and swell issues. The simulation results were obtained under different conditions such as various supply voltages and various loads. Hence, the proposed system has the ability to regulate the output voltage under various conditions with ±10%.

Reliability Assessment of Distribution Power Transformers Considering Load Transfer Capability

A number of reliability models based on stationary Markov processes were developed at Hydro One to evaluate the reliability of a group of similar distribution power transformers. Those models accounted for a number of factors that affected the reliability of the group. The factors included, in addition to the basic Markov model parameters, the redundancy and mobile unit transformer capability at each individual station. There was one factor namely the load transfer capability between the stations that was not considered in the reliability assessment. The objective of load transfer is to further reduce the impact of transformer outages on customers and therefore improve the overall reliability of the system. The policy of load transfer between distribution stations can be simple or complicated and that can have a significant impact on the overall system reliability. This paper describes a probabilistic method based on stationary Markov models for evaluating the impacts of load transfers on the reliability of distribution stations. An example of a real distribution system is used to illustrate the proposed method of assessment.

Diagnosing Disk-Space Variation in Distribution Power Transformer Windings Using Group Method of Data Handling Artificial Neural Networks

Monitoring centers in the smart grid exchange the collected data by sensors and smart meters to monitor the current conditions and performance of electric power components. Distribution Power Transformers (DPTs) have a key role in maintaining the integrity of power flow in the smart grid. Online monitoring of DPTs to detect possible faults can potentially increase the reliability of modern power systems. Mechanical defects of DPTs are the major issues in their proper operation that must be detected in their early stage of occurrence. One of the most effective solutions for diagnosing mechanical defects in DPTs is Frequency Response Analysis (FRA). In this study, an appropriate condition monitoring scheme for DPTs is developed to identify even minor winding defects. Disk-Space Variation (DSV), a common DPT windings fault, is applied to the 20 kV-winding of a 1.6 MVA DPT in various locations and with different severity. Their corresponding frequency responses are then computed, and all four components of the frequency responses, i.e., amplitude, argument, and real and imaginary parts, are evaluated. Different data-driven-based indices are implemented to extract appropriate feature vectors in the preprocessing stage. Group Method of Data Handling (GMDH) Artificial Neural Networks is proposed to assist monitoring centers in interpreting FRA signatures and identifying DPT defects at primary stages. GMDH has a data-dependent structure, which gives high flexibility to modeling nonlinear characteristics of FRA test results with different data sizes. It is demonstrated that the proposed approach is capable of accurately determining the fault location and fault severity. The proposed Artificial Intelligence (AI)-based approach is used to extract essential features from frequency response traces in order to detect the position and degree of Disk-Space Variation (DSV) in the DPT windings. The experimental results verify the effectiveness of the proposed methods in determining the severity and location of DSV defects.

Implementation of Extreme Learning Machine to Predict Distribution Power Transformer Lifetime

The estimated remaining life of transformers is the most critical factor in maintaining service continuity and quality assurance in distributing electrical energy to customers. Therefore, an accurate approach is needed to predict the remaining life of a distribution power transformer. In this paper, an approach to predict transformer lifetime is carried out by utilizing transformer current, voltage, and temperature. The resulting model estimates the transformer age through Extreme Learning Machine in order to extract the maximum information from the transformer. Tests were performed using the proposed and other classification methods on a 20 kV/380-220V transformer whose remaining life spans between 14 and 26 years. Results show that the proposed method yields the lowest minimum error outperforming the other methods with the selection feature MAE at 0.1076 and MSE at 0.0179. The results further validate that the proposed method can achieve accuracy better than the ones obtained from other transformer remaining service life prediction methods.

Real-Time Monitoring of Oil Temperature in Distribution Power Transformer by Using Internet of Things

In Malaysia, on-site technical personnel manually inspect power transformers. Some vital condition indicators, such as oil and winding temperatures, are not monitored in real-time. This condition can be hazardous if the transformer gets overheated. Overheating can cause mechanical deformation and insulation degradation if not monitored regularly. Thus, an online monitoring system that meets industry standards is needed to enhance power transformer monitoring and troubleshooting. In this research, the Internet of Things (IoT) based data acquisition (DAQ) system was deployed for real-time oil temperature monitoring and inspection to detect incipient faults in power transformers early. This IoT-based DAQ system was connected to the substation remote terminal unit (RTU) to update real-time data on each power transformer. The long-range (LoRa) technology is proposed for the system to transmit temperature, current, and voltage from the power transformers. The data transmission from the oil temperature indicator (OTI), network server, and database was monitored and compared. It is observed that the temperature data was transferred from the network server to the database without any transmission delay. The average deviation from the two experiments was 0.006 and 0.003, respectively, compared to the manual reading from the OTI scale meter with a digital reading by the proposed DAQ system. For testing purposes, the alert module in this system would notify technical personnel if the temperature exceeded +40°C in the power transformers. The proposed system can be used to assist with the upgrade and maintenance of the existing power transformer.

Comparison of Heuristic Approaches in Weight Optimization of Different Power Levels Transformers

ABSTRACT In this study, the weight of distribution type transformers and power transformers, which are used extensively in the industry, is optimized. A heuristic approach that uses the Firefly algorithm, Ant Colony algorithm, and the Bee algorithm to determine the weight of transformers is described. Optimization of the iron weight components effective in the weight of a transformer to be produced in multi-type is provided. It is aimed to reduce the transformer cost, increase the life cycle of the transformer, and increase the efficiency by obtaining the optimum current density (s) and iron section (C) values which are the variable parameters used in the iron weight calculation of the transformer. In the study, it is determined that heuristic optimization algorithms, which provide more effective and efficient values than traditional approaches, give better results. These popular algorithms, in the weight parameters of 50 and 1000 kVA transformer powers, can obtain a lower weight value of 9.6% with the weight obtained by the Bee algorithm, 7.4% by the Ant Colony algorithm and 11% by the firefly algorithm than the weight obtained through the classical method. According to the heuristic optimization results, it shown that optimum 50 and 1000 kVA transformer weights can be determined and less costly transformer designs can be made.

Ester-based Dielectric Fluid for Power Transformers: Design and Test Experience under the GreenEst Project

Ester-based dielectric fluids have now been on the market for several decades, providing fire-safe and environmentally friendly alternatives to mineral oils, which have traditionally been used in transformers and other electrical equipment. This opens the door to innovation in power transformers. However, the use of esters-based dielectrics in power transformers is still very limited, especially for the higher voltage levels. The usage of these esters-based dielectrics in higher voltage power transformers is not yet consensual. this work present results with the use of natural esters in power distribution transformers. Tests carried out on mineral oil and natural ester oil found that the ester-based dielectric can withstand higher voltage thresholds for AC and Impulses tests, mainly within the specs of destructive tests, e.g., the natural ester was able to withstand a 185kV impulse without registering dielectric rupture while the natural oil registered a dielectric rupture with a 160kV impulse. Heating and mechanical tests demonstrated that ester-based dielectric oils for power transformers lead to a flow reduction between 16,8% and 18,2% in the cooling system that was design for mineral oils but they achieve a higher heat transfer coefficient, between 0,5% to 5% depending on the location of measurement.

Power Transformer Voltages Classification with Acoustic Signal in Various Noisy Environments.

Checking the stable supply voltage of a power distribution transformer in operation is an important issue to prevent mechanical failure. The acoustic signal of the transformer contains sufficient information to analyze the transformer conditions. However, since transformers are often exposed to a variety of noise environments, acoustic signal-based methods should be designed to be robust against these various noises to provide high accuracy. In this study, we propose a method to classify the over-, normal-, and under-voltage levels supplied to the transformer using the acoustic signal of the transformer operating in various noise environments. The acoustic signal of the transformer was converted into a Mel Spectrogram (MS), and used to classify the voltage levels. The classification model was designed based on the U-Net encoder layers to extract and express the important features from the acoustic signal. The proposed approach was used for its robustness against both the known and unknown noise by using the noise rejection method with U-Net and the ensemble model with three datasets. In the experimental environments, the testbeds were constructed using an oil-immersed power distribution transformer with a capacity of 150 kVA. Based on the experimental results, we confirm that the proposed method can improve the classification accuracy of the voltage levels from 72 to 88 and to 94% (baseline to noise rejection and to noise rejection + ensemble), respectively, in various noisy environments.

Passive Reactive Power Compensators for Improving the Sustainability of Three-Phase, Four-Wire Sinusoidal Systems Supplied by Unbalanced Voltages

Compensation of reactive power is necessary in power systems due to economical, energetic, and environmental reasons. Reactive power increases energy power losses and carbon dioxide emissions in distribution lines and power transformers. However, capacitor banks used in most industrial applications do not significantly reduce energy losses in lines and transformers when supply voltages and loads are unbalanced and therefore do not fully improve the sustainability of distribution networks. This fact is explained in this paper using positive-, negative-, and zero-sequence reactive power components in three-phase, four-wire sinusoidal power systems supplied with unbalanced voltages. Likewise, several devices have also been developed for the compensation of the total reactive power and, specifically, for each of its components in these power systems. Comparing the effectiveness of these reactive compensators and other well-known passive compensators as capacitor banks on the sustainability improvement of the electrical installation of an actual industry, reductions between 20% and 100% in energy losses and carbon dioxide emissions, caused by circulation of reactive currents in transformer and lines, can be expected depending on the type of compensator used.

An Approach to Steady-State Power Transformer Modeling Considering Direct Current Resistance Test Measurements.

Measurements obtained in transformer tests are routinely used for computing associated steady-state model parameters, which can then be used for load flow simulation and other modeling applications. The short circuit and open circuit tests are most commonly performed with this purpose, allowing estimation of series and parallel branch transformer parameters. In this study, an extended model is proposed which does not employ the usually assumed cantilever circuit approximation and explicitly accounts for transformer connection resistance. An estimation of the proposed model parameters is enabled by usage of additional measurements yielded by the direct current (DC) resistance test. The proposed approach is validated by means of an experiment carried out on a real distribution power transformer, whose results demonstrate that the proposed model and parameter computation approach effectively decompose total transformer resistance into winding and contact components. Furthermore, the numerical results show that contact resistance is not negligible especially for low voltage windings, which reinforces the usefulness of the proposed model in providing detailed modeling of transformer resistances.

Survey on the Advancements of Dielectric Fluids and Experiment Studies for Distribution Power Transformers

The main objective of this work is to summarize the most commonly used dielectric fluids in the power distribution transformers, as well as to discuss what are the latest and the rationale behind those trends. The favorable and unfavorable reasons for any choice behind each of those dielectric fluids will be discussed. Additionally, this work also advances the power distribution transformers health index most commonly used to assess the condition of the transformer.

Grid Impact Assessment of Centralized and Decentralized Photovoltaic-Based Distribution Generation: A Case Study of Power Distribution Network with High Renewable Energy Penetration

This paper presents a grid impact assessment of a 5 MWp photovoltaic-based distribution unit on a 33 kV/23 MVA power distribution network with high penetration of renewable energy generation. The adapted network has an average load demand of 23 MVA, with a 3 MWp centralized PV system, and a number of decentralized PV systems of a capacity of 2 MWp. A grid impact assessment is done to an additional 5 MWp of PV generation as a centralized system as well as a number of decentralized systems. Power flow analysis is conducted to the grid considering different generation loading scenarios in order to study grid performance including active and reactive power flow, voltage profiles, distribution power transformers loading, transmission lines ampacity levels, and active and reactive power losses. On the other hand, the distribution of the decentralized systems is done optimally considering power distribution transformer loading and available area using the geographical information system. Finally, an economic analysis is done for both cases. Results showed that grid performance is better considering decentralized PV systems, whereas the active power losses are reduced by 13.43% and the reactive power losses are reduced by 14.48%. Moreover, the voltage of buses improved as compared to the centralized system. However, the decentralized PV systems were found to affect the power quality negatively more than the centralized system. As for the economic analysis, the decentralized PV system option is found slightly less profitable than the centralized system, whereas the simple payback period is 9 and 7 years, respectively. However, decentralized PV systems are recommended considering the technical implications of the centralized PV system.