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.

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.

Power System Transients Analysis by Wavelet Transforms

In contrast to Fourier transform, wavelet transform is especially suitable for transient analysis because of its time frequency characteristics with automatically adjusted window lengths. Research shows that wavelet transform is one of the most powerful tools for power system transient analysis. The basic ideas of wavelet transform are presented in the paper together with several power system applications. It is clear that wavelet transform has some clear advantages over other transforms in detecting, analyzing, and identifying various types of power system transients.

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.

Optical fiber-based power and data delivery system for high voltage electronic current transformer

This paper describes a prototype power delivery system developed for high voltage electronic current transformer （ECT） that uses laser light to transfer power to and communicates with the primary converter. The design is based on optical-to-electrical power converters, solid-state diode lasers and optical fibers. Command signals are transmitted via the same up-fiber used to send power from secondary power supply to primary converter. The upward data transmission is completed during the brief interruption of power delivery without affecting steady power-supply. A simple comparator added to the primary converter can take the command data. Experimental results show that the fibers can provide reliable up-link for data transmission at 200 kb/s from the secondary to the primary converter. Based on the delivery system, the secondary converter can control three auxiliary channels to provide additional information. These monitoring channels are used in a time-multiplexing mode to provide information about the operation temperature, voltage and current at the remote unit for monitoring the ECT. This preventive maintenance or built-in test can increase reliability by giving early warning for necessary maintenance request.

利用MATLAB中Sim Power Systems模库时变压器模型的参数计算及其仿真结果比较

从MATLAB-S im Power System s中变压器模型仿真的角度,介绍了变压器模型的参数计算方法与给定,并结合仿真结果,阐明该文中参数给定方法的合理性.

Study Hybrid Compensation Cophase Traction Power Supply System Schemes for High-Speed and Heavy Haul Electrical Railway with V Connection Transformer

In order to solve negative phase sequence problem of V connection transformer in the high speed and heavy haul electrical railway of China, the hybrid compensative co-phase traction power supply system which based on passive and active compensation is proposed. Firstly, There construction and capacity distribution are analyzed, and the compensation current of active equipment is gave;Second, the feature of the hybrid compensative schemes are discussed. In the end, the related simulation results have confirmed the effectiveness of the compensation schemes in this paper.

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%.

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.

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.

Transient Stability Analysis of Power System Based on an Improved Neural Network

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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.

基于Transformer神经网络的变压器状态监测

电力变压器是电力系统的关键设备,为保障电力系统健康稳定运行,对电力变压器开展状态监测十分必要。提出基于Transformer神经网络的变压器状态监测方法,Transformer神经网络具有自注意力机制,能够挖掘不同特征维度之间的关联性,为变压器状态监测提供更可靠的决策能力。在进行变压器数据收集时,将采集到的数据集分为健康、亚健康、病态3个类别;之后采用原始数据、小波特征以及傅里叶特征融合的方式对数据进行预处理,增加特征维度;通过数据生成和Focal Loss的方法降低模型训练时数据不平衡带来影响,再将处理后的数据输入Transformer神经网络进行模型训练,最终利用训练好的模型预测变压器健康状态。与传统机器学习方法、卷积神经网络、长短时记忆网络相对比,所提方法预测精度有明显提升,能够准确的监测变压器设备状态,预测准确率能达到90%,是一种有效的变压器状态监测方法。

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.

Breakdown Voltage Prediction by Utilizing the Behavior of Natural Ester for Transformer Applications

This research investigates the dielectric performance of Natural Ester(NE)using the Partial Differential Equation(PDE)tool and analyzes dielectric performance using fuzzy logic.NE nowadays is found to replace Mineral Oil(MO)due to its extensive dielectric properties.Here,the heat-tolerant Natural Esters Olive oil(NE1),Sunflower oil(NE2),and Ricebran oil(NE3)are subjected to High Voltage AC(HVAC)under different electrodes configurations.The break-down voltage and leakage current of NE1,NE2,and NE3 under Point-Point(P-P),Sphere-Sphere(S-S),Plane-Plane(PL-PL),and Rod-Rod(R-R)are measured,and survival probability is presented.The electricfield distribution is analyzed using the Partial Differential Equation(PDE)tool.NE shows better HVAC with stand capacity under all the electrodes configuration,especially in the S-S shape geometry.The exponential function is developed for the oils under different elec-trode geometry;NE shows a higher survival probability.Likewise,the most influ-ential dielectric properties such as breakdown voltage,kinematic viscosity,and water content are used to develop a Mamdani-based control system model that combines two variables to produce the surface model.The surface model indi-cates that the NE subjected for investigation is less susceptible to moisture effect and higher kinematic viscosity.Based on the surface models of PDE and fuzzy,it is concluded that NE possesses a high survival rate since its breakdown voltage does not react to changes in water content.Hence the application of NE in the transformer application is highly safe and possesses extended life.

Fuzzy Fault Tree Analysis for Fault Diagnosis of Cannula Fault in Power Transformer

Being one of the most expensive components of an electrical power plant, the failures of a power transformer can result in serious power system issues. So fault diagnosis for power transformer is highly important to ensure an uninterrupted power supply. Due to information transmission mistakes as well as arisen errors while processing data in surveying and monitoring state information of transformer, uncertain and incomplete information may be produced. Based on these points, this paper presents an intelligent fault diagnosis method of power transformer using fuzzy fault tree analysis (FTA) and beta distribution for failure possibility estimation. By using the technique we proposed herein, the continuous attribute values are transformed into the fuzzy numbers to give a realistic estimate of failure possibility of a basic event in FTA. Further, it explains a new approach based on Euclidean distance between fuzzy numbers, to rank the basic events in accordance with their Fuzzy Importance Index.

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.