Applying artificial optimization methods for transformer model reduction of lumped parameter models

Sensitivity Analysis on Ladder Network Equivalent Circuit Parameters of Power Transformer

“…The algorithm starts with a population of solutions (usually randomly), then improves it through repetitive process of reproduction, mutation, crossover, inversion, and selection operators. A simple GA procedure can be written as follows [21]. The problem has 50 unknown parameters (21 of which are mutual inductances) and 42 constraints.…”

Model‐based exact technique to identify type and degree of eccentricity faults in induction motors

“…Then, the motor performance is simulated by applying the sampled voltages to the model of the motor at the same loading conditions and the corresponding output signals, from the model and the experiments, are compared, while a proper algorithm searches for the static and dynamic eccentricity degrees with the best agreement between the measurement and simulation output signals. Particle swarm optimisation (PSO) is used for searching purposes [28][29][30]. The proposed technique is supported by experimental tests on an 11-kW squirrel-cage induction motor under eccentric and healthy conditions.…”

“…global best approach, all the particles are mutual neighbours and each particle is influenced by the entire swarm, whereas in the second one, i.e. local best approach, each particle is influenced just by its immediate neighbours [29].…”

Detecting the location and severity of transformer winding deformation by a novel adaptive particle swarm optimization algorithm

“…Different stochastic approaches based on measured sweep frequency response such as multiobjective GA, bee colony algorithm, bacterial swarming algorithm (BSA), improved particle swarm optimization, chaos optimization algorithm, genetic algorithm, particle swarm optimization, and bacterial foraging are used for solving the APDM problem. In most of the researches, for simplifying the solution of the problem and decreasing the size of the search space, a model with a low number of similar sections is considered and the number of used frequency points is largely reduced . In this paper, it is demonstrated that decreasing the number of used frequency points besides utilizing a model with a low number of sections leads to undesirable errors and an inaccurate model.…”

“…In most of the researches, for simplifying the solution of the problem and decreasing the size of the search space, a model with a low number of similar sections is considered and the number of used frequency points is largely reduced. 19,[21][22][23][25][26][27] In this paper, it is demonstrated that decreasing the number of used frequency points besides utilizing a model with a low number of sections leads to undesirable errors and an inaccurate model. Therefore, instead of those assumptions, using a fast, powerful, and efficient method is vital for extracting the model.…”