Price prediction of power transformer materials based on CEEMD and GRU

The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.

Power Transformer Fault Diagnosis Using Random Forest and Optimized Kernel Extreme Learning Machine

Power transformer is one of the most crucial devices in power grid.It is significant to determine incipient faults of power transformers fast and accurately.Input features play critical roles in fault diagnosis accuracy.In order to further improve the fault diagnosis performance of power trans-formers,a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study.Firstly,the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration,gas ratio and energy-weighted dissolved gas analysis.Afterwards,a kernel extreme learning machine tuned by the Aquila optimization algorithm is implemented to adjust crucial parameters and select the optimal feature subsets.The diagnosis accuracy is used to assess the fault diagnosis capability of concerned feature subsets.Finally,the optimal feature subsets are applied to establish fault diagnosis model.According to the experimental results based on two public datasets and comparison with 5 conventional approaches,it can be seen that the average accuracy of the pro-posed method is up to 94.5%,which is superior to that of other conventional approaches.Fault diagnosis performances verify that the optimum feature subset obtained by the presented method can dramatically improve power transformers fault diagnosis accuracy.

Fault Diagnosis of Power Transformer Based on Improved ACGAN Under Imbalanced Data

The imbalance of dissolved gas analysis(DGA)data will lead to over-fitting,weak generalization and poor recognition performance for fault diagnosis models based on deep learning.To handle this problem,a novel transformer fault diagnosis method based on improved auxiliary classifier generative adversarial network(ACGAN)under imbalanced data is proposed in this paper,which meets both the requirements of balancing DGA data and supplying accurate diagnosis results.The generator combines one-dimensional convolutional neural networks(1D-CNN)and long short-term memories(LSTM),which can deeply extract the features from DGA samples and be greatly beneficial to ACGAN’s data balancing and fault diagnosis.The discriminator adopts multilayer perceptron networks(MLP),which prevents the discriminator from losing important features of DGA data when the network is too complex and the number of layers is too large.The experimental results suggest that the presented approach can effectively improve the adverse effects of DGA data imbalance on the deep learning models,enhance fault diagnosis performance and supply desirable diagnosis accuracy up to 99.46%.Furthermore,the comparison results indicate the fault diagnosis performance of the proposed approach is superior to that of other conventional methods.Therefore,the method presented in this study has excellent and reliable fault diagnosis performance for various unbalanced datasets.In addition,the proposed approach can also solve the problems of insufficient and imbalanced fault data in other practical application fields.

The Alpha Power Topp-Leone Distribution: Properties, Simulations and Applications

This paper presents an extended lifetime probability distribution based on the alpha power transformation. We refer to the proposed distribution as “the Alpha Power Topp-Leone (APTL) distribution”. Mathematical properties of the APTL distribution such as the density and cumulative distribution functions, survival and hazard rate functions, quantile function, median, moments and its relative measures, probability weighted moment, moment generating function, Renyi entropy, and the distribution of order statistics were derived. The method of maximum likelihood estimation was employed to estimate the unknown parameters of the APTL distribution. Finally, we used two real data sets obtained from the literature to illustrate the applicability of the APTL distribution in real-life data fitting.

Phase field model for electric-thermal coupled discharge breakdown of polyimide nanocomposites under high frequency electrical stress

In contrast to conventional transformers, power electronic transformers, as an integral component of new energy power system, are often subjected to high-frequency and transient electrical stresses, leading to heightened concerns regarding insulation failures. Meanwhile, the underlying mechanism behind discharge breakdown failure and nanofiller enhancement under high-frequency electrical stress remains unclear. An electric-thermal coupled discharge breakdown phase field model was constructed to study the evolution of the breakdown path in polyimide nanocomposite insulation subjected to high-frequency stress. The investigation focused on analyzing the effect of various factors, including frequency, temperature, and nanofiller shape, on the breakdown path of Polyimide(PI) composites. Additionally, it elucidated the enhancement mechanism of nano-modified composite insulation at the mesoscopic scale. The results indicated that with increasing frequency and temperature, the discharge breakdown path demonstrates accelerated development, accompanied by a gradual dominance of Joule heat energy. This enhancement is attributed to the dispersed electric field distribution and the hindering effect of the nanosheets. The research findings offer a theoretical foundation and methodological framework to inform the optimal design and performance management of new insulating materials utilized in high-frequency power equipment.

SIMULATING METHOD OF MAGNETIZING INRUSH CURRENT OF POWER TRANSFORMERS USING CONCEPT OF INSTANTANEOUS POWER

In this paper,a new simulating method is presented,using only the normal magnetizing curve (B-H) of the transformer core material,its geometric dimensions,the no-load power loss data and the concept of instantaneous power. At the end of this paper the simulating calculation using EMTP has been also performed for the same transformer. The comparison shows that the two sets of results are very close to each other,and proves the correctness of the new method. The new method presented in this paper is helpful to verify the correctness of the power transformer design,analyze the behavior of the transformer protection under switching and study the new transformer protection principles.

Application of fuzzy analytic hierarchy process and neural network in power transformer risk assessment

In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(FAHP) and artificial neural network(ANN) from the perspective of accuracy and quickness is proposed.An analytic hierarchy process model for the transformer risk assessment is built by analysis of the risk factors affecting the transformer risk level and the weight relation of each risk factor in transformer risk calculation is analyzed by application of fuzzy consistency judgment matrix;with utilization of adaptive ability and nonlinear mapping ability of the ANN,the risk factors with large weights are used as input of neutral network,and thus intelligent quantitative assessment of transformer risk is realized.The simulation result shows that the proposed method increases the speed and accuracy of the risk assessment and can provide feasible decision basis for the transformer risk management and maintenance decisions.

A Discrete State Event Driven Simulation based Losses Analysis for Multi-terminal Megawatt Power Electronic Transformer

At present,power electronic transformers(PETs)have been widely used in power systems.With the increase of PET capacity to the megawatt level.the problem of increased losses need to be taken seriously.As an important indicator of power electronic device designing,losses have always been the focus of attention.At present,the losses are generally measured through experiments,but it takes a lot of time and is difficult to quantitatively analyze the internal distribution of PET losses.To solve the above problems,this article first qualitatively analyzes the losses of power electronic devices and proposes a loss calculation method based on pure simulation.This method uses the Discrete State Event Driven(DSED)modeling method to solve the problem of slow simulation speed of large-capacity power electronic devices and uses a loss calculation method that considers the operating conditions of the device to improve the calculation accuracy.For the PET prototype in this article,a losses model of the PET is established.The comparison of experimental and simulation results verifies the feasibility of the losses model.Then the losses composition of PET was analyzed to provide reference opinions for actual operation.It can help pre-analyze the losses distribution of PET,thereby providing a potential method for improving system efficiency.

Structured Microgrids (SμGs) and Flexible Electronic Large Power Transformers (FeLPTs)

Structured microgrids(SμGs)and Flexible electronic large power transformers(FeLPTs)are emerging as two essential technologies for renewable energy integration,flexible power transmission,and active control.SμGs provide the integration of renewable energy and storage to balance the energy demand and supply as needed for a given system design.FeLPT’s flexibility for processing,control,and re-configurability offers the capability for flexible transmission for effective flow control and enable SμGs connectivity while still keeping multiscale system level control.Early adaptors for combined heat and power have demonstrated significant economic benefits while reducing environmental foot prints.They bring tremendous benefits to utility companies also.With storage and active control capabilities,a 300-percent increase in bulk transmission and distribution lines are possible without having to increase capacity.SμGs and FeLPTs will also enable the utility industry to be better prepared for the emerging large increase in base load demand from electric transportation and data centers.This is a win-win-win situation for the consumer,the utilities(grid operators),and the environment.SμGs and FeLPTs provide value in power substation,energy surety,reliability,resiliency,and security.It is also shown that the initial cost associated with SμG and FeLPTs deployment can be easily offset with reduced operating cost,which in turn reduces the total life-cycle cost by 33%to 67%.

Power flow calculation for a distribution system with multi-port PETs:an improved AC-DC decoupling iterative method

Recently,power electronic transformers(PETs)have received widespread attention owing to their flexible networking,diverse operating modes,and abundant control objects.In this study,we established a steady-state model of PETs and applied it to the power flow calculation of AC-DC hybrid systems with PETs,considering the topology,power balance,loss,and control characteristics of multi-port PETs.To address new problems caused by the introduction of the PET port and control equations to the power flow calculation,this study proposes an iterative method of AC-DC mixed power flow decoupling based on step optimization,which can achieve AC-DC decoupling and effectively improve convergence.The results show that the proposed algorithm improves the iterative method and overcomes the overcorrection and initial value sensitivity problems of conventional iterative algorithms.

Research on power electronic transformer applied in AC/DC hybrid distribution networks

The AC/DC hybrid distribution network is one of the trends in distribution network development, which poses great challenges to the traditional distribution transformer. In this paper, a new topology suitable for AC/DC hybrid distribution network is put forward according to the demands of power grid, with advantages of accepting DG and DC loads, while clearing DC fault by blocking the clamping double sub-module(CDSM) of input stage. Then, this paper shows the typical structure of AC/DC distribution network that is hand in hand. Based on the new topology, this paper designs the control and modulation strategies of each stage, where the outer loop controller of input stage is emphasized for its twocontrol mode. At last, the rationality of new topology and the validity of control strategies are verified by the steady and dynamic state simulation. At the same time, the simulation results highlight the role of PET in energy regulation.

Influence analysis of local heat source on internal temperature distribution of power transformer

Oil immersed power transformer is the main electrical equipment in power system.Its operation reliability has an important impact on the safe operation of power system.In the process of production,installation and operation,its insulation structure may be damaged,resulting in partial discharge and even breakdown inside the transformer.In this paper,S9-M-100/10 oil immersed distribution transformer is taken as the research object,and the distribution laws of electromagnetic field and temperature field in transformer under normal operation,inter turn short circuit and inter layer short circuit are simulated and analyzed.The simulation results show that under normal conditions,the temperatures at the oil gap between the transformer core and the high and low voltage windings and the middle position of the high-voltage winding are high.When there are inter turn and inter layer short circuit faults,the electromagnetic loss of the fault part of the transformer increases,and the temperature rises suddenly.The influence of the two faults on the internal temperature field of the transformer is different,and the influence of the inter turn short circuit fault on the temperature nearby is obvious.The analysis results can provide reference for the thermal fault interpretation and fault classification of transformer.

Insulation System of Power Transformers Behavior Comparison of Mineral Oils and Natural Ester Dielectric Fluids

Paper deals with a comparison of selected properties of several vegetable oil representatives along their accelerated thermal ageing at the temperature of 90 ℃. These properties are compared to two widely used and commercially available mineral transformer oils. A combined insulating system （an oil-paper system） was created with the usage of mentioned oils for measurement purposes. Dissipation factor, capacity and volume resistance are characteristics measured along a thermal ageing of the oil-paper systems. Infrared spectroscopy was used as an additional method. After 1,000 hours of ageing, the dissipation factor of all systems based on vegetable oils did not exceed the value of 0.015. The volume resistance of systems containing mineral oils was approx, twice as high as the volume resistance of those with vegetable oils. The capacity on the other hand was slightly lower in the case of mineral oils application. An experiment also showed that the paper combined with the vegetable oil dries more quickly than in combination with the mineral oil. Infrared spectroscopy has not shown any expressive changes in the chemical structure of aU tested oils yet （up to 1,000 hours of ageing）.

Detection of Partial Discharge in Power Transformers Using Rogowski Coil and Multiresolution Analysis

Partial discharge detection in power transformers is discussed using a new approach that exploit the broad band of the Rogowski coils and the potential of two signal processing tools： discrete wavelet transform and empirical mode decomposition. Detecting and analyzing incipient activities of partial discharge can provide useful information to diagnostics and prognostics about transformer insulation. So, partial discharge signals embedded in the electric current at ground conductor are measured using the Rogowski coil. These signals are submitted to noise suppression and the partial discharges waveforms are extracted through different ways： using discrete wavelet transform and using empirical mode decomposition. The comparison of these two methods show that the extraction with discrete wavelet transform results in a faster and simpler algorithm than the empirical mode decomposition. But this one produces more precise waveforms due its adaptive characteristic.

State of the Art and Future Trends of Unconventional PD-Measurement at Power Transformers

This paper discusses the current state of the art of diagnostics at power transformers. A special focus is set on the UHF-PD-measurement （ultra-high-frequency partial discharge measurement） because at power transformers, this diagnostic method has become more important in recent years. The current state, basics and principles of operations, proceedings as well as advantages of PD-measurement methods are covered. Furthermore problems and proposed solutions are discussed. Bushings and tap changers are not discussed in detail. In many cases, one single diagnostic method does not have the ability to sufficiently evaluate a power transformer. Therefore, a variety of diagnostic methods came up over times, which are commonly used by now. To expand the evaluation opportunities of power transformers, science strives to develop new diagnostic methods as well as to improve the existing ones. Furthermore, environmentally friendly and hardly inflammable ester liquids are examined for the use at power transformers and PD-measurement at HVDC （high voltage direct current） converter transformers as well. Potential diagnostic options and respectively current developments and findings in the field of oil-paper-insulation systems are outlined conclusively.

Power Transformer Fault Diagnosis Using Fuzzy Reasoning Spiking Neural P Systems

This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.

Dual-Timescale Control for Power Electronic Zigzag Transformer

Power electronic zigzag transformer is an attractive solution for the flexible interconnection of smart distribution networks.It is constituted by slow-response and low-precision thyristor converters and fast-response and high-accuracy voltage source converters.This paper models its primary circuit and addresses its basic operation mechanism.Then a dual-timescale control scheme is investigated to realize the coordinated regulation of both types of converter.A simulation case is established in PSCAD containing interconnected mid-voltage distribution networks.Simulations with poor-and well-matched control timescales are both carried out.And accordingly,the power flow controllability under these conditions is compared.When the shorter control timescale is no more than tenth of the longer one,the power electronic zigzag transformer will operate with satisfying performances.

Unbalanced Load Simulation of Electronic Power Transformer in Distribution System

A control scheme of electronic power transformer (EPT) in a three-phase four-wire distribution system, which included an input section, an isolating section and an output section, was researched under unbalanced loads. The simple and appropriate control scheme was developed through analyzing the system requirements of the primary side and the load requirements of the secondary side. In the input section, a dual-loop control in synchronous rotating d-q coordinates was introduced, and in the output section, a dual-loop control based on instantaneous output voltage was used. Load characteristics of EPT were investigated by using Matlab/Simulink software. Simulation results showed that, with the proposed control scheme, the EPT has good performances and the sinusoidal input current and constant output voltage can be realized under both balanced and unbalanced loads.

Alpha Power-Kumaraswamy Distribution with An Application on Survival Times of Cancer Patients

The aim of the study is to obtain the alpha power Kumaraswamy(APK)distribution.Some main statistical properties of the APK distribution are investigated including survival,hazard rate and quantile functions,skewness,kurtosis,order statistics.The hazard rate function of the proposed distribution could be useful to model data sets with bathtub hazard rates.We provide a real data application and show that the APK distribution is better than the other compared distributions from the right-skewed data sets.

An Effective Detection of Inrush and Internal Faults in Power Transformers Using Bacterial Foraging Optimization Technique

Power transformers in transmission network are utilized for increasing or decreasing the voltage level. Power Transformers fail to connect directly to the consumers that result in the less load fluctuations. Power transformer operation under any abnormal condition decreases the lifetime of the transformer. Power Transformer protection from inrush and internal fault is critical issue in power system because the obstacle lies in the precise and swift distinction between them. Due to the limitation of heterogeneous resources, occurrence of fault poses severe problem. Providing an efficient mechanism to differentiate between faults (i.e. inrush and internal) is the key for efficient information flow. In this paper, the task of detecting inrush and internal fault in power transformers is formulated as an optimization problem which is solved by using Hyperbolic S-Transform Bacterial Foraging Optimization (HS-TBFO) technique. The Gaussian Frequency- based Hyperbolic S-Transform detects the faults at much earlier stage and therefore minimizes the computation cost by applying Cosine Hyperbolic S-Transform. Next, the Bacterial Foraging Optimization (BFO) technique has been proposed and has demonstrated the capability of identifying the maximum number of faults covered with minimum test cases and therefore improving the fault detection efficiency in a wise manner. The HS-TBFO technique is evaluated and validated in various simulation test cases to detect inrush and internal fault in a significant manner. This HS-TBFO technique is investigated based on three phase power transformer embedded in a power system fed from both ends. Results have confirmed that the HS-TBFO technique is capable of categorizing the inrush and internal faults by identifying maximum number of faults with minimum computation cost as compared to the state-of-the-art works.