Multimotor power balance control of belt conveyor under Internet of things technology

1.School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China; 2.School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, Chin

Impedance Characteristics and Harmonic Analysis of LCL-Type Grid-Connected Converter Cluster

This study addresses the output impedance model of the LCL-type grid-connected converter considering the dead-time effects and the digital control delay. The model shows that the digital control delay will affect the accuracy of the output impedance of the grid-connected converter, and the dead-time effects are only equivalent to superimposing a disturbance voltage on the original output impedance model. The derived output impedance model is verified by comparing it with the switching model in the PSIM simulation environment. The harmonic interaction between converter cluster and utility grid is modeled and analyzed based on the output impedance model. A combined harmonic suppression strategy is incorporated into every converter control scheme to suppress the harmonic interaction. Simulation results are presented to demonstrate the correctness of harmonic interaction analysis and the effectiveness of the proposed suppression strategy

Compressed sensing image processing algorithm of underground coal mine

In view of the problem that energy consumption of sensor nodes is fast and equipment life is cut caused by large amount of information transmission of wireless sensor network, a kind of compressed sensing image processing algorithm based on wavelet transform was put forward. The algorithm uses sym8 wavelet for image sparse and fragmental processing, and uses measurement matrix for adaptive sampling measurement, and finally reconstructs image through OMP algorithm and wavelet inverse transformation. The experimental results show that the proposed algorithm can obtain reconstructed image with high quality by lower sampling rate compared with traditional compressed sensing algorithm

Information model of coal mine safety production monitoring system based on OPC UA

There are many kinds of subsystems in the coal mine safety production monitoring system, and the types of equipments in the subsystems are multifarious, which leads to the low semantic completeness of data and the fragmentation of information interaction data caused by the heterogeneous information of equipment. At present, although the research of coal mine informatization construction has basically realized the network integration of each subsystem, the massive data obtained can not be effectively shared, and the integration analysis can not be carried out. In order to solve the above problems, this paper proposes an information model of coal mine safety production monitoring system based on OPC UA. According to the relevant information of coal mine safety production monitoring system and the general modeling rules of OPC UA information model, the mapping relationship between actual equipment of coal mine safety production monitoring system and information model is analyzed, and the overall structure of information model of coal mine safety production monitoring system is proposed. It is pointed out that when new functions need to be extended in coal mine safety production monitoring system, they can be extended in function set. When new equipment needs to be added to the subsystem, new components can be added to the equipment set to ensure the extensibility of the information model. Taking the information model of gas extraction monitoring system as an example, the information model of methane sensor is established by using UaModeler tool. After the information model is graphically designed, the XML description file is generated and imported into the address space of OPC UA server. Through the third-party client UaExpert connecting server to test the information model of the OPC UA, the results show that the information model can realize the mapping in the address space of the OPC UA according to the mapping rules, and access the server's address space through the OPC UA client. The attributes of any object in each coal mine safety production monitoring subsystems can be obtained, which verifies the feasibility of using the OPC UA information model to realize the information interconnection

Attitude Optimization Control of Unmanned Helicopter in Coal Mine Using Membrane Computing

Unmanned helicopter for mission inspection has good application value in intelligent coal mining, and attitude control is important. In this paper, membrane computing is introduced to realize attitude optimization control of an unmanned helicopter. First, we give the application scenarios of unmanned helicopters in coal mines. Secondly, we establish a dynamic model of an unmanned helicopter with environmental participation, and the attitude model of the helicopter is deduced based on this model. Further, the cellular membrane system suitable for the attitude model of an unmanned helicopter under the control parameters of environment mapping is constructed, and the cellular membrane controller based on the characteristics and operation rules of the membrane system is designed. The robust performance of the controller is proved theoretically, and by the semiphysical experiments, the performance of trajectory tracking is almost consistent and attitude angle control is less than ±1°, in the range of ±2° by wind disturbance. Compared with the linear feedback controller in the same experimental environment, the performance of the membrane controller is improved by nearly 0.4026 on average. It shows that the cellular membrane controller constructed has good effectiveness and robustness. This will provide a good application value for membrane computing in the field of accurate coal mining

Fault Diagnosis of Permanent Magnet Synchronous Motor of Coal Mine Belt Conveyor Based on Digital Twin and ISSA-RF

Permanent magnet synchronous motors (PMSMs) have been gradually used as the driving equipment of coal mine belt conveyors. To ensure safety and stability, it is necessary to carry out real-time and accurate fault diagnosis of PMSM. Therefore, a fault diagnosis method for PMSM based on digital twin and ISSA-RF (Improved Sparrow Search Algorithm Optimized Random Forest) is proposed. Firstly, the multi-strategy hybrid ISSA is used to solve the problems of uneven population distribution, insufficient population diversity, low convergence speed, etc. In addition, the fault diagnosis model of ISSA-RF permanent magnet synchronous motor is constructed based on the optimization of the number of Random Forest decision trees and that of features of each node by ISSA. Secondly, considering the operation mechanism and physical properties of PMSM, the relevant digital twin model is constructed and the real-time mapping of physical entity and virtual model is realized through data interactive transmission. Finally, the simulation and experimental results show that the fault diagnosis accuracy of ISSA-RF, 98.2%, is higher than those of Random Forest (RF), Sparrow Search Algorithm Optimized Random Forest (SSA-RF), BP neural network (BP) and Support Vector Machine (SVM), which verifies the feasibility and ability of the proposed method to realize fault diagnosis and 3D visual monitoring of PMSM together with the digital twin model

A Novel Enhanced Arithmetic Optimization Algorithm for Global Optimization

The arithmetic optimization algorithm (AOA) is based on the distribution character of the dominant arithmetic operators and imitates addition ( $A$ ), subtraction ( $S$ ), multiplication ( $M$ ) and division ( $D$ ) to find the global optimal solution in the entire search space. However, the basic AOA has some drawbacks of premature convergence, easily falls into a local optimal value, slow convergence rate, and low calculation precision. To improve the overall optimization ability and overcome the drawbacks of the basic AOA, an enhanced AOA (EAOA) based on the Lévy variation and the differential sorting variation is proposed to solve the function optimization and the project optimization. The Lévy variation increases population diversity, broadens the optimization space, enhances the global search ability and improves the calculation precision. The differential sorting variation filters out the optimal search agent, avoids search stagnation, enhances the local search ability and accelerates the convergence rate. The EAOA realizes complementary advantages of the Lévy variation and the differential sorting variation to avoid falling into the local optimum and the premature convergence. The sixteen benchmark functions and five engineering design projects are applied to verify the effectiveness and feasibility of the EAOA. The EAOA is compared with other algorithms by minimizing the fitness value, such as artificial bee colony, ant line optimizer, cuckoo search, dragonfly algorithm, moth-flame optimization, sine cosine algorithm, water wave optimization and arithmetic optimization algorithm. The experimental results show that the overall optimization ability of the EAOA is superior to that of other algorithms, the EAOA can effectively balance the exploration and the exploitation to obtain the best solution. In addition, the EAOA has a faster convergence rate, higher calculation precision and stronger stability

YOLOv4-Tiny-Based Coal Gangue Image Recognition and FPGA Implementation

Nowadays, most of the deep learning coal gangue identification methods need to be performed on high-performance CPU or GPU hardware devices, which are inconvenient to use in complex underground coal mine environments due to their high power consumption, huge size, and significant heat generation. Aiming to resolve these problems, this paper proposes a coal gangue identification method based on YOLOv4-tiny and deploys it on the low-power hardware platform FPGA. First, the YOLOv4-tiny model is well trained on the computer platform, and the computation of the model is reduced through the 16-bit fixed-point quantization and the integration of a BN layer and convolution layer. Second, convolution and pooling IP kernels are designed on the FPGA platform to accelerate the computation of convolution and pooling, in which three optimization methods, including input and output channel parallelism, pipeline, and ping-pong operation, are used. Finally, the FPGA hardware system design of the whole algorithm is completed. The experimental results of the self-made coal gangue data set indicate that the precision of the algorithm proposed in this paper for coal gangue recognition on the FPGA platform are slightly lower than those of CPU and GPU, and the mAP value is 96.56%; the recognition speed of each image is 0.376 s, which is between those of CPU and GPU; the hardware power consumption of the FPGA platform is only 2.86 W; and the energy efficiency ratio is 10.42 and 3.47 times that of CPU and GPU, respectively

Multi-Attention Ghost Residual Fusion Network for Image Classification

In order to achieve high-efficiency and high-precision multi-image classification tasks, a multi-attention ghost residual fusion network (MAGR) is proposed. MAGR is formed by cascading basic feature extraction network (BFE), ghost residual mapping network (GRM) and image classification network (IC). The BFE uses spatial and channel attention mechanisms to help the MAGR extract low-level features of the input image in a targeted manner. The GRM is formed by cascading 4 multi-branch group convolutional ghost residual blocks (MGR-Blocks). Each MGR-Block is cascaded by a dimension reducer and several ghost residual sub-networks (GRSs). The GRS integrates ghost convolution and residual connection, and the use of ghost convolution can significantly reduce parameters and achieve high-efficient classification. The GRS is a parallel convolution structure with 32 branches, which ensures that GRM has enough width to extract advanced features and extract as much feature information as possible, so as to obtain high-precision classification. The IC completes the aggregation of high-dimensional channel feature information, and then achieves a significant improvement in the classification accuracy of MAGR, by fusing the effective channel attention mechanism, global average pooling and SoftMax layer. Simulation experiment shows that MAGR has excellent classification capability while achieving high efficiency and lightweight. Compare with VGG16, the parameters of MAGR on CIFAR-10 is reduced by 94.8% while the classification accuracy is increased by 1.18%. Compare with MobileNetV2, the parameters of MAGR on CIFAR-100 is reduced by 33.9% while the classification accuracy is increased by 15.6%