Tao Huang

Bio: Tao Huang is an academic researcher. The author has contributed to research in topics: Electronic circuit & Electronics. The author has an hindex of 1, co-authored 1 publications receiving 7 citations.

Power electronic circuit optimization method based on particle swarm algorithm

Abstract: The demand for automation design of power electronic circuits becomes higher and higher with the development of the power electronic technology. In the invention, a particle swarm algorithm is applied to the design and optimization of the power electronic circuits, and the invention mainly relates to the power electronics field and the intelligent computation field. In the optimization method, an optimization process is divided into two parts by a decoupling technology to respectively optimize the power transmission of the power electronic circuit and a feedback network. Meanwhile, a mutation operator is introduced into the particle swarm algorithm to increase diversity of the swarm and improve the optimization efficiency of the algorithm. The optimization design of a buck converter is taken as an example for testing, which proves that the optimization method is very effective.

Fractional-order self-adjusting control method for steam temperature of coal-fired boiler

Abstract: The invention discloses a fractional-order self-adjusting control method for steam temperature of a coal-fired boiler, particularly relates to a self-adjusting control method based on a fractional-order model, and belongs to the field of automatic control. The fractional-order self-adjusting control method for the steam temperature of the coal-fired boiler comprises the steps that a steam temperature signal and an adjusting variable signal at the moment t are sampled with a sampling interval T and are recorded into a historical database, the performance index of a control system is calculated, whether parameters of a controller need to be updated is determined, a data sequence for modeling is obtained from the historical database, the fractional-order mathematical model of the controlled system is obtained through optimization by means of a mature swarm intelligence algorithm, the parameters of the controller are optimized by means of the mature swarm intelligence algorithm, and finally output of the controller is calculated and is applied to a controlled object. The fractional-order self-adjusting control method for the steam temperature of the coal-fired boiler is suitable for digital control of a coal-fired boiler steam temperature system and has the advantages that the steps are explicit, the concept is clear, the robustness is high, a specific disturbance test does not need to be carried out on the controlled object, a pseudorandom sequence does not need to be added into a control signal, and the steam temperature adjusting quality can be improved obviously.

Soft switching convertor parameter optimization method and soft switching conversion circuit

Abstract: The invention relates to a soft switching convertor parameter optimization method and a soft switching conversion circuit. The method comprises the steps: obtaining an inductance value to be measured;according to converter data, solving a unary quadratic equation which is determined based on the converter data, the inductance value to be measured and a corresponding inductance current value; whena solved solution can meet the preset efficiency optimum condition, performing parameter optimization configuration on a soft switching converter according to the inductance value to be measured andthe corresponding inductance current value; when the solved solution cannot meet the efficiency optimum condition, updating the inductance value to be measured and returning to the step of calculatingaccording to the preset converter data and the inductance value to be measured to obtain the corresponding inductance current value. The soft switching converter has the minimum conduction loss and turn-off loss when the same power is transmitted and can obtain the highest energy conversion efficiency, and the energy conversion efficiency is high.

Power circuit component optimization method based on orthogonal learning particle swarm

Abstract: The invention discloses a power circuit component optimization method based on an orthogonal learning particle swarm, and belongs to the power electronic technology and the field of computational intelligence An orthogonal learning particle swarm optimization with a mutation strategy is used for carrying out optimization on an optimal component design of a power electronic circuit Firstly, a method of generating a new optimal learning object based on an orthogonal combination mode is designed, and is used for mining information of a historical optimal solution of a particle individual and information of a globally-optimal solution of a swarm in the orthogonal learning particle swarm optimization, and combining a learning object which can guide particles to develop in a better direction, secondly, a mutation operator which can improve diversity of the orthogonal learning particle swarm optimization is designed, and the defect that the orthogonal learning particle swarm optimization easily falls into local optimum is overcome All components of the power electronic circuit serve as variables needing to be optimized and are coded into individuals of the orthogonal learning particle swarm optimization, optimization is carried out on values of the components of the power electronic circuit through specific optimization processes such as update of the speed, update of the location, mutation operation and update of the optimal learning object of the orthogonal learning particle swarm optimization, and the power circuit component optimization method based on the orthogonal learning particle swarm has important application value in the existing large-scale circuit design and optimization field