article · IEEE Access
The Coyote Optimization Algorithm has been applied to determine the operating parameters of single-phase and three-phase electric power transformers using data from manufacturer operation reports. The optimisation process evaluates the deviation between actual and estimated values as its primary objective function. To benchmark its performance, the technique was compared against two established methods: particle swarm optimisation and Jaya optimisation algorithms. In addition, experimental validation was conducted using a 1 kVA, 230/230 V single-phase transformer and a 4 kVA, 380/380 V three-phase transformer. Across these tests, the Coyote Optimization Algorithm demonstrated superior stability and accuracy compared with the alternative methods. The parameters estimated through this approach showed the closest agreement with experimentally measured values, thereby ensuring an accurate representation of transformer performance.
Accurate transformer parameters are essential for predicting performance and maintaining the stability of electrical grids. Determining these values directly can be complex, making estimation from standard manufacturer reports a valuable alternative. Providing higher parameter precision helps engineers and utilities simulate transformer behaviour with greater confidence, reducing uncertainty in power system operations.
This method could be integrated into engineering software tools used by electrical utilities and transformer manufacturers to model equipment performance from data sheets. The technology is applied and tested, having demonstrated experimental validation on small laboratory transformers of up to 4 kVA, though application to full-scale grid infrastructure is not indicated.
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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.
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DOI: 10.1109/access.2020.2978398
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