Parameter Estimation of Single Phase Core Type Transformer Using Bacterial Foraging Algorithm

Padma, S.; Subramanian, S.

doi:10.4236/eng.2010.211115

Cited by 13 publications

(21 citation statements)

References 14 publications

(14 reference statements)

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“…Besides, precise calibration of these professional instruments is also needed, which complicates the measurement process. Reference [21] proposed using numerical algorithms to achieve parameter extraction and parameter identification of the transformer. Besides, some numerical methods for transformer evaluation in [9,22] have been proposed to design wire-wound transformers.…”

Section: Previous Evaluation Methodsmentioning

confidence: 99%

Section: Selection Of Resistor Rmentioning

confidence: 99%

“…From (21), it can be found that the dynamic CM capacitance C Q is not only determined by the structure capacitance C 0 and the terminal connection of winding, but also determined by the voltage potential difference V d between the primary ground and the secondary ground. The essential reason for this phenomenon is that V d changes the voltage potential distribution of the secondary winding and the resultant electric charges induced in the secondary side can also be changed.…”

Section: Ground Potential Differencementioning

confidence: 99%

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A General Transformer Evaluation Method for Common-Mode Noise Behavior

Chen

Lin

2019

Energies

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In isolated power converters, the transformer is a key part of voltage transformation and isolation. Since common-mode (CM) noise is rather difficult to suppress compared with different-mode (DM) noise, more and more scholars are paying attention to the characteristics of CM noise, especially in high-frequency CM noise behaviors. CM noise can be further divided into conducted CM noise and radiated CM noise, and the main focus of this paper is on conducted CM noise. The CM coupling capacitance of the transformer is one of the main contributors of CM noise, which has been verified in many previous studies. Hence, eliminating the CM noise in a transformer coupling path can significantly lower the whole CM noise level of the converter. Professional conducted electromagnetic interference (EMI) testing instruments are quite expensive. In this paper, a general transformer evaluation technique for CM noise behavior is proposed. Only a signal generator and oscilloscope can achieve transformer CM noise behavior evaluation. PCB planar flyback transformers are designed, and a series of noise spectrums and voltage waveforms can verify the effectiveness of the proposed transformer evaluation method. The flyback adapter porotype can pass the EMI standard limited line EN55022 class B by the proposed evaluation method.

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Section: Previous Evaluation Methodsmentioning

confidence: 99%

Section: Selection Of Resistor Rmentioning

confidence: 99%

Section: Ground Potential Differencementioning

confidence: 99%

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A General Transformer Evaluation Method for Common-Mode Noise Behavior

Chen

Lin

2019

Energies

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“…For that reason, a few different views on transformer parameters estimation can be found in the available literature [6][7][8][9][10][11][12][13][14]. Some of them include transformer's nonlinear behavior [6][7][8], whereas others deal with traditional (Steinmetz) transformer equivalent circuit [9][10][11][12][13][14]. Since transformers and induction machines have similar equivalent circuit, some studies investigate parameter estimation of both induction machine and transformer [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…The main advantage of the optimization techniques, based on the evolutionary algorithms is that the form of the cost (or goal) function can be defined arbitrary. This is reason why these techniques are also very popular for transformer parameters estimation [9][10][11][12][13][14][15][16]. They include Practical Swarm Optimization (PSO) [9,15,16], Genetic Algorithm (GA) [9,13], Imperialist Competitive Algorithm (ICA) [10], Gravitational Search Algorithm (GSA) [10], Bacterial Foraging Algorithm (BFA) [11,12] and Artificial Bee Colony Algorithm (ABCA) [14] and can be applied on transformer nameplate data or on the measured load data, without disconnection of the transformer from the power system.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Equivalent Circuit Parameters of Single-Phase Transformer by Using Chaotic Optimization Approach

et al. 2019

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This paper deals with parameter estimation of single-phase transformer equivalent circuit by using Chaotic Optimization Approach (COA). Unknown transformer equivalent circuit parameters need to be accurately estimated for the best possible matching between the measured and the estimated transformer output characteristics (for example, output power—load resistance characteristic). Unlike literature approaches which apply different estimation techniques and are based either on the nameplate data or the load data obtained from experiments, in this paper the use of COA is evaluated on both types of input data. For two single-phase transformers, different with respect to machine power and voltage levels, the COA-based parameter estimation is compared to various literature techniques as well as to classical method based on open-circuit and short-circuit tests. The results show that COA outperforms other approaches in terms of average error between the measured and the estimated values of the primary current, secondary current and secondary voltage at full load, or between the measured and the estimated output characteristics. The effectiveness of COA is additionally confirmed through its application on laboratory 2kVA, 220 V/110 V, 50 Hz single-phase transformer.

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Characterization of electrical 1-phase transformer parameters with guaranteed hotspot temperature and aging using an improved dwarf mongoose optimizer

Rizk‐Allah

El‐Fergany

Gouda

et al. 2023

Neural Comput & Applic

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Parameters identification of Electric Power Transformer (EPT) models is significant for the steady and consistent operation of the power systems. The nonlinear and multimodal natures of EPT models make it challenging to optimally estimate the EPT’s parameters. Therefore, this work presents an improved Dwarf Mongoose Optimization Algorithm (IDMOA) to identify unknown parameters of the EPT model (1-phase transformer) and to appraise transformer aging trend under hottest temperatures. The IDMOA employs a population of solutions to get as much information as possible within the search space through generating different solution’ vectors. Furthermore, the Nelder–Mead Simplex method is incorporated to efficiently promote the neighborhood searching with the aim to find a high-quality solution during the iterative process. At initial stage, power transformer electrical equivalent extraction parameters are expressed in terms of the fitness function and its corresponding operating inequality restrictions. In this sense, the sum of absolute errors (SAEs) among numerous factors from nameplate data of transformers is to be minimized. The proposed IDMOA is demonstrated on two transformer ratings as 4 kVA and 15 kVA, respectively. Moreover, the outcomes of the IDMOA are compared with other recent challenging optimization methods. It can be realized that the lowest minimum values of SAEs compared to the others which are 3.3512e−2 and 1.1200e−5 for 15 kVA and 4 kVA cases, respectively. For more assessment for the proposed optimizer, the extracted parameters are utilized to evaluate the transformer aging considering the transformer hottest temperature compared with effect of the actual parameters following the IEEE Std C57.91 procedures. It is proved that the results are guaranteed, and the transformer per unit nominal life is 1.00 at less than 110 °C as per the later-mentioned standard.

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Parameter Estimation of Single Phase Core Type Transformer Using Bacterial Foraging Algorithm

Cited by 13 publications

References 14 publications

A General Transformer Evaluation Method for Common-Mode Noise Behavior

A General Transformer Evaluation Method for Common-Mode Noise Behavior

Estimation of Equivalent Circuit Parameters of Single-Phase Transformer by Using Chaotic Optimization Approach

Characterization of electrical 1-phase transformer parameters with guaranteed hotspot temperature and aging using an improved dwarf mongoose optimizer

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