Parameter Estimation Techniques for Transformer Systems

Accurate estimation of transformer parameters is fundamental to the reliable design, control and maintenance of power systems. Traditional approaches rely on analytical calculations derived from open-circuit and short-circuit tests to extract equivalent circuit parameters such as winding resistances, leakage reactances and core losses. In recent years, however, the complexity of modern transformer designs and the need for real-time diagnostics have driven the adoption of optimisation‐based and data‐driven methods. Metaheuristic algorithms—including swarm-inspired, evolutionary and nature-inspired strategies—have shown great promise in navigating the multimodal, high-dimensional landscapes characteristic of parameter identification tasks. Hybrid frameworks that combine deterministic sensitivity analysis with stochastic search have further enhanced convergence speed and robustness, enabling simultaneous minimisation of multiple criteria such as manufacturing cost, energy efficiency and electromagnetic performance. These advances support more precise modelling, facilitate condition monitoring and underpin digital twin implementations that can predict ageing and optimise maintenance schedules.

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Parameter Estimation Techniques for Transformer Systems publication trend

The graph below shows the total number of articles in parameter estimation techniques for transformer systems across all publications each year (not limited to Nature Index journals).

Technical terms

Equivalent circuit parameters: Resistances, reactances and core-loss elements representing transformer performance in a simplified electrical model.

Metaheuristic optimisation: Algorithmic paradigms using stochastic, nature-inspired rules to explore and exploit complex solution spaces.

Fitness function: Quantitative measure of the discrepancy between observed measurements and model outputs to guide optimisation.

Multi-objective optimisation: Simultaneous minimisation or maximisation of conflicting objectives, yielding a set of Pareto-optimal solutions.

Open-circuit and short-circuit tests: Standard procedures that respectively characterise core losses and winding impedances under controlled conditions.

References

1. Identification of Transformer Parameters Using Dandelion Algorithm. Applied System Innovation (2024).
2. Improved Tasmanian devil optimization algorithm for parameter identification of electric transformers. Neural Computing and Applications (2023).
3. Three-Phase Transformer Optimization Based on the Multi-Objective Particle Swarm Optimization and Non-Dominated Sorting Genetic Algorithm-3 Hybrid Algorithm. Energies (2023).