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.

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.

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.

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 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.

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）.

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.

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.

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.

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.

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.

Extracting Power Transformer Vibration Features by a Time-Scale-Frequency Analysis Method

In order to take advantage of the merits of WPT and HHT in feature extraction from vibration signals of power transformer, a time-scale-frequency analysis method is developed based on the combination of these two techniques. This method consists of two steps. First, the desirable wavelet packet nodes corresponding to characteristic frequency bands of power transformer are selected through a Correlation Degree Threshold Screening (CDTS) technique for reconstructing a time-domain signal that contains useful information of power transformer. Second, the HHT is then conducted on the reconstructed signal to track the instantaneous frequencies corresponding to natural characteristics of power transformer. Experimental results are provided by analyzing a real power transformer vibration signal. Compared with the features extracted by directly using HHT, the features obtained by the proposed method reveal clearer condition pattern of the transformer, which shows the potential of this method in condition monitoring of power transformer.

Large Power Transformer Fault Diagnosis and Prognostic Based on DBNC and D-S Evidence Theory

Power transformer is a core equipment of power system, which undertakes the important functions of power transmission and transformation, and its safe and stable operation has great significance to the normal operation of the whole power system. Due to the complex structure of the transformer, the use of single information for condition-based maintenance (CBM) has certain limitations, with the help of advanced sensor monitoring and information fusion technology, multi-source information is applied to the prognostic and health management (PHM) of power transformer, which is an important way to realize the CBM of power transformer. This paper presents a method which combine deep belief network classifier (DBNC) and D-S evidence theory, and it is applied to the PHM of the large power transformer. The experimental results show that the proposed method has a high correct rate of fault diagnosis for the power transformer with a large number of multi-source data.

Application of Voltage Response Method to Condition Assessment of Power Transformer

Power transformer is one of the most important equipment in the power system.Its operating condition affects the reliability of power supply directly.Therefore,in order to guarantee transformer operation safely and reliably,it is necessary to assess condition of power transformer accurately.Return voltage method based on voltage response measurements is still a new non-intrusive diagnosis method for internal insulation aging of transformer.In this paper the technique and application of return voltage measurement and some results of voltage response measurements of several transformers was introduced.Voltage response measurements were performed on various transformers with different voltage grades,various operating periods,different moisture contents and aging degrees on site.Derived moisture contents from return voltage measurement were compared with the corresponding moisture contents obtained from the analysis of oil samples.Based on on-site experiments and theoretical analysis,the criteria for insulation state of transformer are proposed.Moisture condition of transformer insulation can be determined by using return dominant time constant,and a good correlation between aging degree and the return voltage initial slopes of the aged transformers.Field test performed on several transformers,its interpretation of results are also presented,which proves that return voltage measurements can be used as a reliable tool for evaluating moisture content in transformer insulation.

Assessing Strategy of Power Transformers Insulation State Based on Part-division and Entropy Method

Failure mechanisms of power transformers are complex and uncertain; it is difficult to determine index weights of insulation state. Therefore, it is a challenge to acquire an accurate assessment of insulation state of power transformers. In this paper, an assessing strategy for transformer insulation is proposed base on part-division of transformer and a comprehensive weight determination method. An index system of transformer is established on the basis of part-division of transformer. Each index’s weight is consisted of two parts, the constant weight and the variable weight, which are determined by improved analytic hierarchy process (AHP) and entropy method respectively. Af- ter categorizing insulation state into four levels and standardizing assessing indexes, a Cauchy membership function is forged, and a fuzzy algorithm is employed to simulate the uncertainty of the insulation state. Finally, a confidence criterion is employed to perform part-division based condition assessment of transformer. Case studies reveal that the proposed assessing strategy method is effective, convenient, and practical; with the new strategy, potential failures of transformers can be forecasted and insulation state of transformer parts can also be as- sessed. Furthermore, the assessing results can be used to guide condition-based maintenance.

Power Transformer No-Load Loss Prediction with FEM Modeling and Building Factor Optimization

Estimation of power transformer no-load loss is a critical issue in the design of distribution transformers. Any deviation in estimation of the core losses during the design stage can lead to a financial penalty for the transformer manufacturer. In this paper an effective and novel method is proposed to determine all components of the iron core losses applying a combination of the empirical and numerical techniques. In this method at the first stage all computable components of the core losses are calculated, using Finite Element Method (FEM) modeling and analysis of the transformer iron core. This method takes into account magnetic sheets anisotropy, joint losses and stacking holes. Next, a Quadratic Programming (QP) optimization technique is employed to estimate the incomputable components of the core losses. This method provides a chance for improvement of the core loss estimation over the time when more measured data become available. The optimization process handles the singular deviations caused by different manufacturing machineries and labor during the transformer manufacturing and overhaul process. Therefore, application of this method enables different companies to obtain different results for the same designs and materials employed, using their historical data. Effectiveness of this method is verified by inspection of 54 full size distribution transformer measurement data.

Detection of Mechanical Deformation in Old Aged Power Transformer Using Cross Correlation Co-Efficient Analysis Method

Detection of minor faults in power transformer active part is essential because minor faults may develop and lead to major faults and finally irretrievable damages occur. Sweep Frequency Response Analysis (SFRA) is an effective low-voltage, off-line diagnostic tool used for finding out any possible winding displacement or mechanical deterioration inside the Transformer, due to large electromechanical forces occurring from the fault currents or due to Transformer transportation and relocation. In this method, the frequency response of a transformer is taken both at manufacturing industry and concern site. Then both the response is compared to predict the fault taken place in active part. But in old aged transformers, the primary reference response is unavailable. So Cross Correlation Co-Efficient (CCF) measurement technique can be a vital process for fault detection in these transformers. In this paper, theoretical background of SFRA technique has been elaborated and through several case studies, the effectiveness of CCF parameter for fault detection has been represented.

A Contribution to the Analysis of Frequency Response and Impedance Measurements to Evaluation and Diagnosis of Power Transformers

This paper has an objective to show a developed quantitative criterion,based in two mathematical variables that explicit the deviation degree of a normal situation,applying simultaneously data from terminal impedances and frequency response.Based in more than 100-measured equipment,of different applications(step-up transformer,transmission transformer,etc.,),for a period of 10 years,the work presents some examples of practical application of this methodology in Brazilian Electrical System.