Active neutral point clamped multi-level four-quadrant elevator driving system and control method

Abstract

The invention discloses an active neutral point clamped multi-level four-quadrant elevator driving system and a control method. The driving system comprises an active neutral point clamped four-quadrant frequency converter and a motor for driving an elevator transmission system. The control method of the invention selects a switching state with a minimum cost function as a switching state of a next switching period by predicting and comparing system states corresponding to various possible switching states of the frequency converter. The elevator driving system of the invention has the advantages of bidirectional power transmission capability, good power grid side waveform, high power factor, high efficiency and energy conservation. The corresponding control method is simple and can overcome switching time delay, and the system has parameter adaptation function and strong robustness.

Description

Technical field [0001] The invention relates to an active midpoint clamping type multilevel four-quadrant elevator drive system and control method, belonging to an active midpoint clamping type multilevel four-quadrant elevator drive system based on chaotic parameter estimation switch traversal predictive control Technical field. Background technique [0002] At present, the energy saving and consumption reduction of elevators has attracted great attention from the industry. The traditional elevator drive system adopts the diode uncontrolled rectification method, so the drive system only has one-way power transmission capability. The energy of the motor cannot be fed back to the grid during the deceleration and braking process of the elevator, but is wasted through energy-consuming components such as resistors. In the practical application of elevator energy saving, energy feedback energy saving requires that the mechanical energy generated during elevator movement be converted ...

AI Technical Summary

Problems solved by technology

But predictive control needs to depend on the parameters of the system, and the system parameters often change with the environment.

However, there are still some technical problems in parameter estimation methods based on model reference adaptation, extended Kalman, fuzzy and neural networks: 1. It is easier to design estimation methods and convergence criteria for certain parameters in the system while keeping other parameters constant. 2. It is difficult to optimize the control parameters in the estimation process, and it is easy to fall into local optimization instead of global optimization with traditional optimization methods such as gradient method ; 3. The estimation method is complex and the amount of calculation is large

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