Analysis and Simulation of Fault Characteristics of Power Switch Failures in Distribution Electronic Power Transformers

Sang, Zixia; Mao, Chengxiong; Lu, Jiming; Wang, Dan

doi:10.3390/en6084246

Cited by 23 publications

(9 citation statements)

References 28 publications

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“…Power switches work under stress at different temperature and power ratings; they are essential part of converters. Full bridge DC-DC converters (FBDCDC) are used at medium to high power applications, we propose here a [5] . According to authors, short circuit fault detection and protection should be very fast and based upon gate driver circuit.…”

Section: Introductionmentioning

confidence: 99%

A model based approach for fault diagnosis in converter of photovoltaic systems

Chavan

Chavan²

2014

2014 IEEE Global Conference on Wireless Computing &Amp; Networking (GCWCN)

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This paper presents model based approach for diagnosis of open circuit power switch faults in full bridge DC-DC converter of a photovoltaic system. The control circuitry of the proposed converter monitors and regulates output current, whenever output current lowers, controller monitors current at test points and localizes faulty switch by comparing fault features. This minimizes efforts required for manual diagnosis of faulty switch. The faults will be detected and diagnosed automatically and displayed on LCD display. In this a model of DC-DC converter is prepared and simulated in SimPowerSystems toolset of MATLAB/Simulink and obtained results are presented.

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Section: Introductionmentioning

confidence: 99%

A model based approach for fault diagnosis in converter of photovoltaic systems

Chavan

Chavan²

2014

2014 IEEE Global Conference on Wireless Computing &Amp; Networking (GCWCN)

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“…Power transformers are important equipment in power systems; their operational conditions directly affect the security and stability of the power grid. A fault on a power transformer will result in power outage at the associated region, which may cascade to the power grid leading to a widespread blackout, causing great social and economic losses [1,2]. Therefore, it is necessary to investigate fault diagnosis technologies for power transformers.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Adam Based Deep Neural Network for Fault Diagnosis of Oil-Immersed Power Transformers

Wang

Zhang

et al. 2019

Energies

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This paper presents a Dynamic Adam and dropout based deep neural network (DADDNN) for fault diagnosis of oil-immersed power transformers. To solve the problem of incomplete extraction of hidden information with data driven, the gradient first-order moment estimate and second-order moment estimate are used to calculate the different learning rates for all parameters with stable gradient scaling. Meanwhile, the learning rate is dynamically attenuated according to the optimal interval. To prevent over-fitted, we exploit dropout technique to randomly reset some neurons and strengthen the information exchange between indirectly-linked neurons. Our proposed approach was utilized on four datasets to learn the faults diagnosis of oil-immersed power transformers. Besides, four benchmark cases in other fields were also utilized to illustrate its scalability. The simulation results show that the average diagnosis accuracies on the four datasets of our proposed method were 37.9%, 25.5%, 14.6%, 18.9%, and 11.2%, higher than international electro technical commission (IEC), Duval Triangle, stacked autoencoders (SAE), deep belief networks (DBN), and grid search support vector machines (GSSVM), respectively.Energies 2019, 12, 995 2 of 16 commonly used fault diagnosis methods. However, these traditional methods have limitations. They may not be able to provide an interpretation to every possible combination of various ratios and may have excessively absolute coding boundary. Due to the objective uncertainty of transformer fault itself and the boundaries of the subjective judgment, it is difficult to meet the requirements of engineering application with the above ratio methods.Since the transformer faults are complex and concealed, simple and crude methods have difficulty performing effective diagnosis. It is essential to explore the principles, methods and means from various disciplines that are helpful in the fault diagnosis of transformers. With the rapid development of computer science and the rise of machine learning, multiple intelligent approaches such as artificial neural network [17][18][19], support vector machine (SVM) [20][21][22], fuzzy theory [23][24][25], extreme learning machine [26], and Bayesian network [27] have been applied in practice. A smart fault diagnostic approach based on integrating five interpretation methods using neural networks is proposed in [28]. Ma et al. [29] presented an intelligent framework for transformer condition monitoring and assessment. Within their framework, different intelligent algorithms can be effectively deployed. Peimankar et al. [30] first used multi-objective particle swarm optimization (PSO) algorithm to select the best subset of features corresponding to each fault class of power transformers. Then, they used ensemble learning systems to classify actual faults of transformers. Sherif et al. [31] utilized the thermodynamic theory to evaluate the severity based on the energy associated with each transformer fault type. These intelligent methods remedy the disadvantages of t...

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“…On the one hand, a number of recent investigations have been done on the circuit topology design, and establishing the mathematical model and control strategy design of EPTs [13][14][15][16][17]. On the other hand, efforts are focusing on the applications of EPTs in areas where conventional power transformers are dominating, such as solar farms, wind farms, charge stations and smart grids [18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Electronic Power Transformer Control Strategy in Wind Energy Conversion Systems for Low Voltage Ride-through Capability Enhancement of Directly Driven Wind Turbines with Permanent Magnet Synchronous Generators (D-PMSGs)

Huang

Mao

et al. 2014

Energies

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This paper investigates the use of an Electronic Power Transformer (EPT) incorporated with an energy storage system to smooth the wind power fluctuations and enhance the low voltage ride-through (LVRT) capability of directly driven wind turbines with permanent magnet synchronous generators (D-PMSGs). The decoupled control schemes of the system, including the grid side converter control scheme, generator side converter control scheme and the control scheme of the energy storage system, are presented in detail. Under normal operating conditions, the energy storage system absorbs the high frequency component of the D-PMSG output power to smooth the wind power fluctuations. Under grid fault conditions, the energy storage system absorbs the redundant power, which could not be transferred to the grid by the EPT, to help the D-PMSG to ride through low voltage conditions. This coordinated control strategy is validated by simulation studies using MATLAB/Simulink. With the proposed control strategy, the output wind power quality is improved and the D-PMSG can ride through severe grid fault conditions.

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Analysis and Simulation of Fault Characteristics of Power Switch Failures in Distribution Electronic Power Transformers

Cited by 23 publications

References 28 publications

A model based approach for fault diagnosis in converter of photovoltaic systems

A model based approach for fault diagnosis in converter of photovoltaic systems

A Dynamic Adam Based Deep Neural Network for Fault Diagnosis of Oil-Immersed Power Transformers

Electronic Power Transformer Control Strategy in Wind Energy Conversion Systems for Low Voltage Ride-through Capability Enhancement of Directly Driven Wind Turbines with Permanent Magnet Synchronous Generators (D-PMSGs)

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