Water turbine parameter identification method based on self-adaptive chaotic and differential evolution particle swarm optimization

Abstract

The invention discloses a water turbine parameter identification method based on self-adaptive chaotic and differential evolution particle swarm optimization. The water turbine parameter identification method is characterized by comprising the following steps of firstly, determining a nonlinear mode of a water turbine; secondly, acquiring frequency step test data; thirdly, determining a fitness function of the self-adaptive chaotic and differential evolution particle swarm optimization; fourthly, setting a basic parameter of an identification algorithm; fifthly, calculating a fitness function value of particles and an individual extreme value of the particles in a swarm as well as a global extreme value of the swarm and updating the speed and the position of the particles; sixthly, carrying out premature judgment, if the premature is judged, carrying out differential mutation, transposition, selection and other operations to avoid local optimization; seventhly, checking whether the algorithm meets end conditions or not, if so, outputting an optimal solution, and otherwise, self-adaptively changing an inertia factor and executing the fifth step to the seventh step again. According to the water turbine parameter identification method disclosed by the invention, a water hammer time constant of the water turbine is identified, and the algorithm is high in convergence speed and convergence precision; in addition, test data of the water turbine at any load level can be utilized, so that the test cost is effectively reduced.

Description

technical field [0001] The invention relates to a water turbine parameter identification method based on self-adaptive chaos and differential evolution particle swarm algorithm, which belongs to the field of electric system parameter identification. Background technique [0002] For hydro-generator units, the hydro-turbine and its speed control system, as an important part of the generating unit, not only undertake the important tasks of starting and stopping the unit, adjusting frequency, and adjusting active power, but also the primary frequency modulation, secondary frequency modulation, and automatic power generation control. (AGC), its precise mathematical model is not only of great value for improving the efficient operation of power plants on the power side, but also has a major impact on the design, planning, and stability analysis of power systems under the new situation. [0003] In the traditional power system analysis, the hydro turbine model under ideal conditio...

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Problems solved by technology

The establishment of an accurate mathematical model also depends on the identification of model parameters. The commonly used parameter identification methods, such as least squares method, matrix bundle, TLS-ESPRIT algorithm, etc., are only applicable to linear models, and the effect of parameter identification on nonlinear systems is poor.

Intelligent algorithms such as particle swarm optimization can identify nonlinear turbine models very well, [Huang Qingsong, Xu Guangwen. Custom Modeling and Application of Hydraulic Turbine Speed ​​Control System [J]. Electric Power System Automation, 2012,36(16):115-117 .]【He Changsheng. Simulation and parameter identification of hydraulic turbine regulation system [D]. Xi'an University of Technology, 2009.】But it is easy to fall into local optimum, resulting in large error of results

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