Estimation of electrical transformer parameters with reference to saturation behavior using artificial hummingbird...

Area of Science:

• Electrical Engineering
• Optimization Algorithms
• Power Systems

Background:

• Accurate electrical transformer modeling is crucial for power system analysis and operation.
• Parameter extraction from nameplate data is essential for creating reliable transformer equivalent circuits.
• Existing optimization methods may not efficiently determine transformer parameters under various constraints.

Purpose of the Study:

• To propose an efficient tool for defining unknown electrical transformer parameters.
• To utilize the artificial hummingbird optimizer (AHO) for precise parameter extraction.
• To validate the AHO's performance against other optimization techniques.

Main Methods:

• Parameter extraction framed as an optimization problem with inequality constraints.
• Minimization of the sum of absolute errors (SAEs) using transformer nameplate data.
• Application of the artificial hummingbird optimizer (AHO) for parameter estimation.

Main Results:

• The AHO achieved the lowest SAE values compared to other optimizers for 4 kVA and 15 kVA transformers.
• The method successfully determined transformer parameters for maximum efficiency calculations.
• Extracted parameters enabled accurate simulation of transformer steady-state and inrush behaviors.

Conclusions:

• The artificial hummingbird optimizer (AHO) is an effective tool for accurate electrical transformer parameter identification.
• The AHO provides superior performance in minimizing errors compared to existing algorithms.
• Accurate parameter extraction facilitates reliable simulations of transformer operational dynamics.