Three-phase Power Transformer Research Articles

Power transformer fault diagnosis using dynamic multiscale graph modeling and M2SGCN network based on statistical fusion

Power equipment fault diagnostics hold significant importance for the stability of power grid systems. In pursuit of this objective, this paper proposes a fault diagnosis method that utilizes dynamic multiscale graph (DMG) modeling and the multiscale multi-stream GCN(M2SGCN) network, incorporating statistical fusion. Specifically, a novel DMG modeling method is proposed to derive visibility graph data and horizontal visibility graph data from vibration signals across multiple scales. Next, a comprehensive neural network architecture named M2SGCN is established to learn global and local features simultaneously, providing a more precise representation. Subsequently, a Dempster Shafer evidence theory statistical fusion technique combined with an adaptive threshold model (DSTFusion) is utilized to integrate primary decision results for enhanced fault diagnosis accuracy. In addition, two datasets obtained from single-phase and three-phase power transformers are analyzed to demonstrate the evolution process. When compared to state-of-the-art indicators such as accuracy, precision, recall, and F1 scores, the method proposed excels in multiple aspects, successfully detecting fault states before their occurrence and achieving outstanding performance.

Моделирование ЭДС магнитных трансформаторов тока для токовой защиты трехфазного силового трансформатора

One of the most common damage to the windings of three-phase transformers are loop closures. Traditional current protections have low sensitivity to them, and gas protections are not able to protect «dry» transformers. Unconventional protections on magnetic current transformers are devoid of these disadvantages. However, the implementation of such protections is impossible without calculating the MTT EMF in an arbitrary transformer operation mode, the magnitude of which is determined by the magnitude of the axial component of the magnetic field scattering of the transformer coils. Their mathematical modeling is a difficult task and does not allow taking into account the nonlinearity of this transformer. This can be avoided if the ELCUT software package is used to simulate this axial component of the magnetic scattering field of the transformer windings. Based on its use, a method is proposed for modeling the effective value of the EMF induced in the MTT windings located in the places proposed in the considered protection device of a linear three-phase transformer using the ELCUT software package. The adequacy of the results of modeling the magnetic fields of scattering of transformer windings is assessed by comparing the measured and calculated MTT EMF. Recommendations for taking into account the nonlinearity of this transformer are given.

ASPECTS OF IMPLEMENTATION OF DISTRIBUTED GENERATION SOURCES IN THE ELECTRICITY SUPPLY SYSTEM OF ENTERPRISES OF THE AGRICULTURAL COMPLEX

Today, the agro-industrial complex of Ukraine determines the socio-economic development of the state, and is an integrated production and economic system, uniting a number of agricultural, industrial, scientific-production and educational sectors aimed at obtaining, transporting, storing, processing and selling agricultural products. However, at the current stage of development of the agro-industrial complex of Ukraine, a significant obstacle to ensuring the appropriate level of competitiveness of enterprises and their products on the foreign market is the high level of energy consumption. A separate problem is the complete dependence on the electric power system, which mainly uses traditional fuel resources and has a number of disadvantages, which creates risks for the effective functioning of a strategically important sector of the state economy and substantiates the relevance of scientific research aimed at improving the optimization of the structure of energy consumption and energy supply, in that including due to the introduction of elements of alternative energy at enterprises of the agro-industrial complex of Ukraine. Thus, by implementing DRG and RES, agro-industrial complex enterprises increase their capacities, which contributes to the attraction of investments and increases their competitiveness. This state of affairs leads to a review of the issue of the active introduction of RES into the electricity supply structures of agricultural enterprises, which is associated with negative effects on the electricity supply systems of electricity consumers. But the analysis of previous studies shows that with a certain combination of disturbances on the same and different harmonic components of energy processes of different types of generators, mutual compensation or mutual reinforcement of one or another harmonic component in loads may occur [1]. In addition, three-phase power transformers with a "star-zero-delta" group of connections are widely used in the power supply systems of agro-industrial complex enterprises to power electric machines and other powerful consumers. In this case, the supply voltage from distribution networks of 6-10 kV is supplied to the transformer windings connected by a delta, and consumers are connected to the low side of 0.4 kV, the windings of which are connected in star with neutral. However, recently, quite often generating power is installed on the low side of the transformer in the power supply system. These can be photovoltaic plants, biogas power plants or diesel generator plants, which began to be installed en masse after the power supply crisis associated with Russian aggression. The paper examines the advantages, problems and prospects of DRG based on renewable energy sources at Ukrainian agro-industrial complex enterprises, which proves its economic feasibility in power supply systems. The proposed approach is that the introduction of DRG based on renewable energy into the power supply system of agro-industrial complex enterprises contributes to reducing the level of energy dependence of these enterprises and is one of the factors of increasing the competitiveness of manufactured products on the domestic and foreign markets due to the reduction of the cost of their production. The introduction of RES into the structures of the centralized power supply system of agricultural enterprises increases the reliability of power supply systems by providing electricity from two independent power sources. In addition, the amount of electricity received from the power supply company is significantly reduced. The use of artificial intelligence technology in the construction of control systems for the combined power supply systems of agro-industrial complex enterprises will increase the quality of electric energy, optimal use of RES and reduce abnormal modes of operation of the electrical complex of agro-industrial complex enterprises as a whole.

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

Simulation of Stray and Core Shielding Loss for Power Transformer Based on 2D/3D FEM

In recent years, the capacity has increased and the volume has been limited in the power transformers. The stray losses of the tanks and structural parts cannot be ignored in the load loss. The tanks and the structural parts are complex structures. It is difficult for transformer manufacturers to use analytical methods to calculate stray losses for each component in the power transformers. If the stray loss distribution of the components can be estimated, it will help to optimize the power transformer’s cost and performance. Therefore, this paper uses the finite element method to solve the stray loss of components in the time-varying magnetic field. At the same time, the core losses of the magnetic shielding are solved. In addition, the position of the tap is considered, and the proposed stray loss model has the temperature factor, which can improve the accuracy of the simulation results. Finally, the short circuit test experiment is carried out in the 350 MVA three-phase five-limb power transformer. The proposed models and material parameters are verified by the experimental results.

A Simulation Model of a Three-Phase Power Transformer with a Twisted Shell-Type Magnetic Circuit

A Simulation Model of a Three-Phase Power Transformer with a Twisted Shell-Type Magnetic Circuit

Application of Deep Learning LSTM in Online Power Prediction on Three-Phase Power Transformer

Electrical energy plays an important role in daily life, especially companies. The 3 Phase Power Transformer is one of the important electrical components that is very influential in distributing electrical energy to companies as is the case in PT. Semen Baturaja (Persero). 3-phase power transformers require attention because they are one of the components that are prone to interference, this interference can hinder the effectiveness of using electrical energy as a company support for employees to work. As one of the disturbances for 3-phase power transformers is overload or excessive power usage, overload can raise the temperature at the winding and reduce its service life. Artificial intelligence can be one of the keys to predict the use of power transformers in the future, especially deep learning by utilizing the LSTM algorithm. Optimal power prediction requires a lot of maximum input variables so that in this study, it not only adds an offline learning mode but adds a learning mode that can directly access the company's Power Quality Monitoring (PQM) website online with an average accuracy value of 86.62%.

An Approach to Performing Stability Analysis for Power Transformer Differential Protection: A Case Study

Differential protection normally detects short circuits and ground faults in the windings of a power transformer and its terminals. Inter-turn faults refer to flashovers among the electrodes that arise only in a similar type of physical winding. Inter-turn faults can be examined when the adequate sum of turns is served as short-circuited. In electrical protection, it is difficult to detect inter-turn faults. An inter-turn fault of small magnitude is based on the limited number of turns that resultantly provide a large quantity of current. Due to this reason, protection that comes from the differential scenario possesses a higher degree of sensitivity without causing unwanted operations during external faults. In this paper, a protection-based stability method is proposed whereby external voltages are applied at the low-voltage (LV) side of the transformer while keeping the high-voltage (HV) side short-circuited. This was conducted using a three-phase power transformer (rating: 100 MVA, 380 kV/13.8 kV) at SWCC Shoaiba Power Plant, Saudi Arabia. In this work, differential protection (87T) and high-impedance differential protection for HV-restricted earth faults (REFs) were verified by creating In-Zone and Out-Zone fault conditions to ensure current transformer (CT) circuits and tripping logic. All of the IEDs, protection, and control schemes involved were designed by ABB. This method verifies protection stability for power transformers by implementing differential protection (87T) and high-impedance restricted earth fault (64HV) schemes through creating In-Zone and Out-Zone fault conditions.

REDUCTION OF THE INRUSH CURRENTS OF THREE-PHASE POWER TRANSFORMERS

Inrush currents that occur during the energization of a three-phase power transformer can reach very high values and cause many problems in an power system. A mathematical model of a transformer has been developed for the investigation of this problem, taking into account the effects of saturation, hysteresis and remanent magnetic flux. An algorithm has been described for the determination of the optimal instant of energization, with the goal of reducing inrush currents. As a method for measuring remanent magnetic flux, integration of the secondary voltage during the previous deenergization is proposed. A circuit model has been prepared of a system for the controlled energization of a three-phase transformer in which the simulation results are confirmed by measurement.

Common and differential mode of dc-bias in three-phase power transformers

Dc magnetization due to the geomagnetically induced currents (GICs) and HVDC (High Voltage Direct Current) systems may cause core saturation and result in serious destruction in the transformer performance as well as the power system stability. Based on susceptibility, transformers are classified into different groups. For instance, a three-phase, three-limb transformer is considered less vulnerable to effects of GIC compared to a single-phase or a three-phase, five-limb transformer. However, our study shows that such classifications do not apply to the dc magnetization caused by converter modulation. In this article, we introduce the concept of common mode and differential mode to distinguish dc-bias caused by different mechanisms. Main focus is given on differential mode dc current since it has rarely been reported in any literature. The differential mode dc current was demonstrated by system simulations of classic three-level voltage source converters as well as modular multilevel converter. Detailed experimental investigations were made on a three-phase, three-limb transformer, where the loss impact as well as reactive power consumption were studied. The test shows a significant difference in stray loss between the two modes in three-phase power transformers. Finally, we discuss the effect of delta winding on dc-bias of different modes.

An aspect of transformer inrush current

Paper analyzes a sparking phenomena that were observed on power transformers during their first few energizing. Sparks occurred on the flange between tank cover and tank side, in spite of the fact that a jumper (copper link) was used. The phenomenon was observed on transformers from different manufacturers, with various transformer ratings, three or five limb core. Paper proposes a simple electromagnetic model for analyzing the phenomena. Three-phase power transformer is modeled in finite element method (FEM) magnetic software. Simplified 3D transformer model is used in order to simulate well known core saturation during the inrush current event. Voltages between tank cover and tank side are calculated for simulated conditions. The same model is used for calculation of possible currents flowing between tank cover and tank side, showing that a value of several kA can be reached. In spite of the fact that the observed phenomenon is harmless mitigation measures are proposed. However, by doing nothing, sparking would disappear very soon.

New Hybrid Machine Learning Method for Detecting Faults in Three-Phase Power Transformers

A novel hybrid machine learning technique is proposed for the protection of three-phase power transformers in this study. Here, the developed model was tested across several types of current signal fault cases from different fault conditions and examined based on a laboratory-constructed transformer system, in which internal and external faults were created. The data gathered on signals were used to develop a novel hybrid model. A process for optimal feature identification was put forward, with machine learning classifiers being trained to classify faults. The methods used included orthogonal matching pursuit and discrete wavelet transform for extraction of statistical characteristics from unprocessed data. Following this, the bees algorithm (BA) was used to create an optimized subset, minimizing the amount of data needed and making the model more accurate. In order to distinguish normal operational conditions (inrush current) from faults, an optimized feature set was used as an input for three classification algorithms: the k-nearest neighbour, support vector machine, and artificial neural network. Training was conducted via k-fold cross-validation. Comparisons were made between the proposed approach and a comparable approach, which used the genetic algorithm (GA). This model was analysed based on specificity, accuracy, precision, recall, and F1 score. The findings from the experiment suggest that the model proposed here is suitable for fault identification in a range of conditions and faults.

Research on power transformer vibration and noise under DC bias condition

In the process of Ultra high voltage direct current (UHVDC) transmission, the direct current (DC) bias of power transformer is easily induced, which makes the transformer exciting current distorted, the ferromagnetic material saturated and the magnetic leakage increased, and then leads to the increase of core vibration and noise. Aiming at this problem, taking a 240 MVA, 330 kV three-phase five-column power transformer as an example, the coupling of the electromagnetic field, structural force field and acoustic field is studied, and the influence of DC bias on vibration and noise of power transformer core is analyzed in this paper. According to the magnetic density and electric density of transformer core under different magnetic bias degree, the structural force field is solved, and the displacement and surface acceleration of core are obtained, which can be as the excitation of sound field to determine the noise distribution of transformer. In order to avoid the natural frequencies which easily cause resonance, the modal analysis is needed to obtain the natural frequencies and modal modes of the core. The transformer noise under no-load and DC bias conditions of the prototype is tested experimentally and compared with the theoretical calculation, the results prove the accuracy of the simulation calculation method in this paper.

Improvement of power transformer differential protection through detection and exploitation of the negative sequence currents

Introduction. Power transformers are the most important and the most expensive equipment used in transport and distribution of electrical energy. Their failure results in huge economic losses. Despite the great advances in the design of power equipment in recent years, the feeble link in the chain remains the insulation weakness of coil turns of the power transformer. The novelty of the proposed research consists in the development of a new procedure for diagnosing and localizing the occurrence of turn to turn short-circuits in the windings of three-phase power transformer. The main problems of the current differential relay are short circuits of one or more turns of a transformer winding. Hence a new approach using' the amplitude comparison between the negative sequence currents' is developed and a digital discriminator internal / external fault is applied to discriminate turn to turn faults among the other ones. The proposed procedure is based on the exploitation of the negative sequence currents. The purpose of using this new procedure is to identify small faults inside power transformer coils and to distinguish inner faults from the outer faults by using an ameliorate circuit. The method used in this paper is a novel algorithm which based on the comparison between the negative sequence current amplitudes and to calculate the corresponding phase angle shifts. The performance of the proposed procedure has been confirmed by MATLAB/Simulink environment. The results of simulation reveal the efficiency of the suggested procedure, and indicate that this procedure can provide fast and sensitive approach for detecting low level turn-to-turn faults.

Comparison of Machine Learning Algorithms for the Prediction of Mechanical Stress in Three-Phase Power Transformer Winding Conductors

This research compares four machine learning techniques: linear regression, support vector regression, random forests, and artificial neural networks, with regard to the determination of mechanical stress in power transformer winding conductors due to three-phase electrical faults. The accuracy compared with finite element results was evaluated for each model. The input data were the transient electrical fault currents of power system equivalents with impedances from low to high values. The output data were the mechanical stress in the conductors located in the middle of the winding. To simplify the design, only one hyperparameter was varied on each machine learning technique. The random forests technique had the most accurate results. The highest errors were found for low-stress values, mainly due to the high difference between maximum and minimum stresses, which made the training of the machine learning models difficult. In the end, an accurate model that could be used in the continuous monitoring of mechanical stress was obtained.

A new method to incorporate three-phase power transformer model into distribution system load flow analysis

A new method to incorporate three-phase power transformer model into distribution system load flow analysis

A new method to incorporate three-phase power transformer model into distribution system load flow analysis

This paper proposes a new and simple method to incorporate three-phase power transformer model into distribution system load flow (DSLF) analysis. The objective of the present work is to find a robust and efficient technique for modeling and integrating power transformer in the DSLF analysis. The proposed transformer model is derived based on nodal admittance matrix and formulated by using the symmetrical component theory. Load flow formulation in terms of branch currents and nodal voltages is also proposed in this paper to enable integrating the model into the DSLF analysis. Singularity that makes the calculations in forward/backward sweep (FBS) algorithm is difficult to be carried out. It can be avoided in the method. The proposed model is verified by using the standard IEEE test system.

An improved technique for power transformer protection using fuzzy logic protective relaying

The three-phase power transformer in the transmission or distribution substations represents one of the essential devices on electric power networks. Losing this devise cause a disconnection of the power utility to a large number of electrical loads. The robust protection system must be designed to protect the device during abnormal operations. A complete protection system for a poly-phase power transformer for one of the Karbala transmission networks (East Karbala substation) is modeled and simulated, adopting a fuzzy logic protective relaying using MATLAB/SIMULINK environment. This study discusses fuzzy logic-based relaying for a power transformer safety, as well as internal faults that are clearly identified. Two principles of operation are used to protect the transformer; differential relay and overcurrent relay. The differential relay is proposed as the unit protection, while the overcurrent is backup protection. The proposed fuzzy logic controller (FLC) is used to detect abnormal operation; it is also modeled to organize the operation between unit and backup protection. The numerical results clarify that the proposed model can perform fast, rigorous, and authoritative protection for the transformer. Also, modeling of the protection mode decreases the complexity of designing various subsystem and combining them in one controller.

High-Efficiency Vibration Isolation for a Three-Phase Power Transformer by a Quasi-Zero-Stiffness Isolator

The vibrations generated by a three-phase power transformer reduce the comfort of residents and the service life of surrounding equipment. To resolve this tough issue, a quasi-zero-stiffness (QZS) isolator for the transformer is proposed. This paper is devoted to developing a QZS isolator in a simple way for engineering practices. The vertical springs are used to support the heavy weight of the transformer, while the oblique springs are employed to fulfill negative stiffness to neutralize the positive stiffness of the vertical spring. Hence, a combination of the vertical and oblique spring can yield high static but low dynamic stiffness, and the vibration isolation efficiency can be improved substantially. The dynamic analysis for the QZS vibration isolation system is conducted by the harmonic balance method, and the vibration isolation performance is estimated. Finally, the prototype of the QZS isolator is manufactured, and then the vibration isolation performance is tested comparing with the linear isolator under real power loading conditions. The experimental results show that the QZS isolator prominently outperforms the existing linear isolator. This is the first time to devise a QZS isolator for three-phase power transformers with heavy payloads in engineering practices.

Determination of the saturation curve of power transformers by processing transient measurements

The sudden energization of large power transformers may lead to the saturation of the magnetic core and induce resonant overvoltage on weak networks due to low frequency high module inrush currents. The magnetization curve of the transformer and the value of its air-core inductance play a key role in the constraints that can be applied to the equipment due to the switching. The method set out in that document allows to identify the saturation curve of three-phase power transformers from measurements recorded during switching transients.