Control Algorithm of Permanent Magnet Direct Drive Belt Conveyor System for Mining Based on Reduced Order Model

2021 ◽  
Vol 35 (14) ◽  
Author(s):  
Jiehui Liu ◽  
Hao Qin ◽  
Guimei Wang ◽  
Haichao Zhao
Keyword(s):  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motor ◽  
Reduced Order Model ◽  
Direct Drive ◽  
Belt Conveyor ◽  
Order Model ◽  
Conveyor System ◽  
Reduced Order ◽  
Predictive Controller ◽  
Drive Belt

Over the decades, permanent magnet synchronous motor (PMSM) has been widely used in coal mine production. In this paper, an optimized neural network predictive controller (NNPC) of permanent magnet direct drive belt conveyor system (BCS) for mining based on reduced order model (ROM) is established. First, in order to establish the full order model of the permanent magnet direct drive BCS, CEMA is used for dynamic analysis, and the dynamic equation of the permanent magnet direct drive BCS is established. Second, the Proper Orthogonal Decomposition (POD) method is used to reduce the order in this paper. Finally, the NNPC of permanent magnet direct drive BCS based on the ROM is proposed. The simulation result shows that the order of BCS model is effectively reduced by the POD method. The NNPC based on the ROM has a good performance in the control of permanent magnet direct drive BCS, and the error between of the full order model and the ROM is 0.19[Formula: see text]m/s.

Power Consumption Model of Permanent-Magnet Direct-Drive Belt Conveyor System Based on GACO–BP

2022 ◽  
Author(s):  
Xuyang Han ◽  
Guimei Wang ◽  
Jiehui Liu ◽  
Lijie Yang ◽  
Pingge Zhang
Keyword(s):  
Power Consumption ◽  
Permanent Magnet ◽  
Total Power ◽  
Direct Drive ◽  
Belt Conveyor ◽  
Conveyor System ◽  
Belt Speed ◽  
Speed Regulation ◽  
Drive Belt ◽  
Consumption Model

Permanent-magnet direct-drive belt conveyors (PMDDBCs) rotate at high speed most of the time, resulting in a large number of invalid energy consumption. To realize the speed regulation of PMDDBC, it is necessary to clarify the relationship between the belt speed, coal quantity of the conveyor and total power of the system. Based on the BP neural network, this paper establishes the power consumption model of PMDDBC, which is related to coal quantity, belt speed and total power. Furthermore, an improved hybrid algorithm (GACO) that combines the advantages of genetic algorithm (GA) and ant colony optimization (ACO) is proposed to optimize the BP power consumption model. The GACO–BP power consumption model is obtained. The original power consumption model is compared with the GACO–BP power consumption model through experiments. Results demonstrate that the GACO–BP power consumption model reduces various prediction errors, while the optimization ability, prediction accuracy and convergence speed are significantly enhanced. It provides a reliable speed regulation basis for the permanent-magnet direct-drive belt conveyor system and also provides a theoretical reference for energy savings and consumption reduction in the coal industry.

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