Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Finite control set model prediction control method for energy storage converter

A model predictive control, limited control set technology, applied in the direction of irreversible DC power input to AC power output, AC network circuits, electrical components, etc., can solve the problem of increasing system cost and complexity, increasing the number of sensors, and limiting applications and other problems to achieve the effect of reducing the number of traversal optimization, reducing complexity and cost, and reducing computing time

Active Publication Date: 2021-01-05
SHANGHAI MARITIME UNIVERSITY
View PDF1 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The traditional finite control set model predictive control needs to collect three variables of converter side current, capacitor voltage and grid current, and then select the optimal vector according to the cost function. The increase in the number of sensors greatly increases the cost and complexity of the system
On the other hand, the traditional finite control set model predictive control is applied to T-shaped three-level circuits, which needs to traverse 27 vectors, which takes too long, which limits the application of this method

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Finite control set model prediction control method for energy storage converter
  • Finite control set model prediction control method for energy storage converter
  • Finite control set model prediction control method for energy storage converter

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0077] The finite control set model predictive control method of the energy storage converter proposed by the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. Advantages and features of the present invention will be apparent from the following description and claims. It should be noted that the drawings are all in a very simplified form and use imprecise ratios, which are only used to facilitate and clearly assist the purpose of illustrating the embodiments of the present invention.

[0078] The energy storage converter used in the embodiment of the present invention is a T-type three-level energy storage converter. Such as figure 1 As shown, the energy storage converter includes a battery pack, a T-shaped three-level circuit, an LCL filter, and a grid connected in sequence. The battery pack is a DC power supply, and its output voltage is U dc , two voltage-dividing capacitors are connected i...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides a finite control set model prediction control method for an energy storage converter, and the method comprises the following steps: sampling a converter side current and a powergrid voltage, and estimating a capacitor voltage and a power grid current according to a state variable estimation method; approximately calculating a voltage vector of the energy storage converter according to the sampled power grid voltage so as to preliminarily screen an output voltage vector of the energy storage converter from a finite control set; and constructing a prediction model and a cost function according to the estimated capacitor voltage and the power grid current, taking the voltage vector with the minimum value of the cost function in the preliminarily screened output voltagevectors of the energy storage converter as the optimal output voltage vector of the energy storage converter, and activating the optimal output voltage vector in the next period. The method can reduce the number of sensors, reduces the cost, improves the reliability of a system, reduces the complexity of an algorithm, greatly reduces the calculation time of an energy storage converter control algorithm, and improves the work efficiency.

Description

technical field [0001] The invention relates to the technical field of bidirectional grid-connected power electronic conversion equipment for new energy electric energy, in particular to a simplified finite control set model predictive control method for a T-type three-level energy storage converter. Background technique [0002] The energy storage converter is one of the key components in the battery energy storage system. It is the interface component between the battery and the AC grid, and can realize the bidirectional transmission of energy from DC to AC. Finite control set model predictive control is a typical nonlinear control algorithm, which has been widely used in the industry. This control method is different from the traditional PI control. It predicts the grid-connected current in the next cycle based on the model, and then selects the optimal control amount according to the prediction results. The physical meaning is clear and the performance is excellent. [...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): H02J3/32H02J3/38H02M7/48
CPCH02J3/32H02J3/381H02M7/48H02J2203/20H02J2300/20
Inventor 高宁张冰涛吴卫民陈昊李波
Owner SHANGHAI MARITIME UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products