Estimation of transformer parameters from nameplate data by imperialist competitive and gravitational search algorithms (original) (raw)
Related papers
Estimation of the Equivalent Circuit Parameters in Transformers Using Evolutionary Algorithms
Mathematical and Computational Applications
The conventional methods of parameter estimation in transformers, such as the open-circuit and short-circuit tests, are not always available, especially when the transformer is already in operation and its disconnection is impossible. Therefore, alternative (non-interruptive) methods of parameter estimation have become of great importance. In this work, no-interruption, transformer equivalent circuit parameter estimation is presented using the following metaheuristic optimization methods: the genetic algorithm (GA), particle swarm optimization (PSO) and the gravitational search algorithm (GSA). These algorithms provide a maximum average error of 12%, which is twice as better as results found in the literature for estimation of the equivalent circuit parameters in transformers at a frequency of 50 Hz. This demonstrates that the proposed GA, PSO and GSA metaheuristic optimization methods can be applied to estimate the equivalent circuit parameters of single-phase distribution and powe...
Transient model parameters identification of transformer based on PSO algorithm
2013 3rd International Conference on Electric Power and Energy Conversion Systems, 2013
In this paper a novel model for analyzing the transient state of the distribution transformers is proposed. The presented model is as simple that the simulation process can be conducted so fast and easy and also its application in the form of a two port element in power network is possible. By considering the complexity of the analytical methods, Particles Swarm Optimization (PSO) algorithm is applied for estimation of the parameters of the transient model of the transformers. In order to do that, related tests were performed on a 2.5 MV A and 6300/420 V distribution transformers, after that, the desired parameters were estimated by the implemented PSO algorithm. Finally, by comparing the experimental and the estimated values, the reliability of the PSO algorithm in this case was evaluated. Also, a comparison between the obtained values in this research and the results of Genetic Algorithm (GA) and the analytical method were carried out. The result reveals the more capabilities and accuracies of the PSO algorithm.
Estimating Power Transformer High Frequency Model Parameters Using Frequency Response Analysis
IEEE Transactions on Power Delivery, 2019
Frequency response analysis (FRA) has become a widely accepted technique by worldwide utilities to detect winding and core deformations within power transformers. The main drawback of this technique is its reliance on the personnel level of expertise more than standard or automated codes. To establish reliable FRA interpretation codes, accurate high frequency transformer model that can emulate the frequency characteristics of real transformers in a wide frequency range is essential. The model can be used to investigate the impact of various winding and core deformations on the transformer FRA signature. The transformer equivalent high frequency electric circuit parameters can be calculated based on design data, which are rarely available, especially for old transformers. As such, this paper presents an artificial intelligence technique to estimate these parameters from the transformer FRA signature. The robustness of the proposed technique is assessed through its application on three, 3-phase power transformers of different ratings, sizes, and winding structures to estimate their high frequency electric circuit parameters during normal and fault conditions. Results show that the proposed technique can estimate equivalent circuit parameters with high accuracy and helps interpret the FRA signature based on the numerical changes of these parameters. The main advantage of this approach is the physical meaning of the model parameters facilitates reliable identification of various faults and hence aids in establishing reliable interpretation codes for transformer FRA signatures.
Identification of transient model parameters of transformer using genetic algorithm
2010
In this paper, a black box model has been proposed in order to analyze transformer transient behaviors. This model is capable to reveal the transformer impedance (or admittance) characteristics up to 1.2 MHz observed from transformer terminals under various connections. Also, this model is powerful to display the characteristic of traveling wave toward consumer. In this contribution, a 6300/420V, 2500KVA transformer is used to investigate. The parameters of represented model have been computed by use of the practical measurements and also genetic algorithm method. Comparisons between the calculated and the measured results are satisfactory and confirm the good precision of the represented method in order to estimate model parameters.
Transformer Core Parameter Identification Using Frequency Response Analysis
2010
Abstract We present a novel model-based approach for parameter identification of a laminated core, such as magnetic permeability and electrical conductivity, of power transformers on the basis of frequency response analysis (FRA) measurements. The method establishes a transformer core model using the duality principle between magnetic and electrical circuits for parameter identification with genetic algorithms.
Indonesian Journal of Electrical Engineering and Computer Science
The mismatch between the transformer and its model leads to deviation of the results during the study of the different abnormal phenomena. This paper presents an optimization technique using transformer nameplate data to minimize the difference in the estimation of the parameters between the model and the actual transformer data. The turbulent flow of water through a narrow path (TFWO) in a circular form technique is used for the optimization of the transformer parameters. The optimization algorithms are used in extracting the parameters of the different rating of transformers, this technique needs an objective function for performing the optimization process. Minimizing the sum of square error (SSE) is the objective function of the optimizer technique. The SSE function includes the summation of the square error for the primary current and secondary current and voltage referring to the primary. The proposed optimization transformer parameters evaluation based on the nameplate data i...
A novel methodology for transformer low-frequency model parameters identification
International Journal of Electrical Power & Energy Systems, 2013
This paper describes a novel methodology to estimate the parameters of a low-frequency model of a 3-phase transformer, by only using data from its frequency response. The described calculation procedure takes into account the magnetic coupling among different phases and allows their analysis separately, enabling the identification of a possible failure. This work describes the used core model, the procedure to obtain its parameters, and its application when interpreting the low frequency results of the Frequency Response Analysis (FRA) measurements.
IEEE Access
In this work, the Coyote Optimization Algorithm (COA) is implemented for estimating the parameters of single and three-phase power transformers. The estimation process is employed on the basis of the manufacturer's operation reports. The COA is assessed with the aid of the deviation between the actual and the estimated parameters as the main objective function. Further, the COA is compared with well-known optimization algorithms i.e. particle swarm and Jaya optimization algorithms. Moreover, experimental verifications are carried out on 4 kVA, 380/380 V, three-phase transformer and 1 kVA, 230/230 V, single-phase transformer. The obtained results prove the effectiveness and capability of the proposed COA. According to the obtained results, COA has the ability and stability to identify the accurate optimal parameters in case of both single phase and three phase transformers; thus accurate performance of the transformers is achieved. The estimated parameters using COA lead to the highest closeness to the experimental measured parameters that realizes the best agreements between the estimated parameters and the actual parameters compared with other optimization algorithms. INDEX TERMS Coyote, PSO, Jaya, single-phase transformer, transformer equivalent circuit.
Intelligent Modelling and Condition Assessment of Power Transformers
for valuable discussions on MTL models. Many thanks go to the Department of Electrical Engineering and Electronics in the University of Liverpool, for providing research facilities that made possible conducting this research. Financial support provided by the Center for International Programs of the Ministry of Education and Science of the Republic of Kazakhstan under the Presidential Bolashak Scholarship and the JSC Science Fund within the frame of the "Sharyktau" competition is immensely thanked and acknowledged. Most of all, I want to thank my mam and dad, my sister Zhanna and brother Askhat, whose love and support miles away was a constant source of encouragement, without which this research work would not have been complete. I am deeply grateful to my wife Dinara for her love, patience, invaluable support and understanding through the whole period of my study in Liverpool. She gave me our beautiful children, Gulnaz and Alibek, and is constantly making my life rich and meaningful every day we are together.
arXiv: Applications, 2017
This paper presents a set of validation metrics for transmission network parameters that is applicable in both creation of synthetic power system test cases and validation of existing models. Using actual data from two real-world power grids, statistical analyses are performed to extract some useful statistics on transformers and transmission lines electrical parameters including per unit reactance, MVA rating, and their X/R ratio. It is found that conversion of per unit reactance calculated on system common base to transformer own power base will significantly stabilize its range and remove the correlation between per unit X and MVA rating. This is fairly consistent for transformers with different voltage levels and sizes and can be utilized as a strong validation metric for synthetic models. It is found that transmission lines exhibit different statistical properties than transformers with different distribution and range for the parameters. In addition, statistical analysis shows...