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28 October 2022, Volume 56 Issue 10 Previous Issue   
New Type Power System and the Integrated Energy
Inertial Control Strategy for Wind Farm with Distributed Energy Storage System Based on Model Predictive Control
SHEN Yangwu, SONG Xingrong, LUO Ziren, SHEN Feifan, HUANG Sheng
2022, 56 (10):  1285-1293.  doi: 10.16183/j.cnki.jsjtu.2022.134
Abstract ( 193 )   HTML ( 747 )   PDF (1641KB) ( 116 )  

Distributed energy storage (DES) wind turbine is an effective means to solve the problem of system frequency stability caused by large-scale wind power connection. In this paper, an inertial control method for DES wind farms based on model predictive control (MPC) is proposed.First, the linearized prediction model of the DES wind farm is established. Then, on this basis, in combination with the control framework of MPC, an optimization model and strategy of MPC inertial control are proposed considering the cost of energy storage loss and the balanced change of wind turbine rotor speed,in order to achieve the balanced change of wind turbine rotor speed during inertia control. The simulation results show that the proposed control strategy can effectively coordinate the active power output of the wind power generation unit and the energy storage system unit in the DES wind turbine, reduce the cost of charging and discharging loss of the energy storage system, and ensure that the rotational speed of all wind turbines in the wind farm tends to be average during the inertial control period, avoiding the problem of wind turbines exiting frequency regulation due to excessive reduction of the rotational speed of wind turbines. The inertial control strategy of the DES wind farm is beneficial to improve the frequency stability of the power grid, which is of great significance to ensure the safe operation of the power grid.

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Peafowl Optimization Algorithm Based Bi-Level Multi-Objective Optimal Allocation of Energy Storage Systems in Distribution Network
YANG Bo, WANG Junting, YU Lei, CAO Pulin, SHU Hongchun, YU Tao
2022, 56 (10):  1294-1307.  doi: 10.16183/j.cnki.jsjtu.2021.371
Abstract ( 95 )   HTML ( 111 )   PDF (2519KB) ( 69 )  

Based on the relation between battery energy storage systems (BESSs) planning and operation, a multi-objective optimal allocation model that takes into account both economic and technical requirements is established, and a bi-level optimization structure is constructed to ensure effective planning and high-efficient operation of BESSs. In the inner layer, a peafowl optimization algorithm (POA) is employed to solve the BESSs charge-discharge operation strategy with the purpose of BESSs operation benefit maximization. In the outer layer, a multi-objective peafowl optimization algorithm (MOPOA) is devised to solve the Pareto solution set of BESSs siting and sizing scheme, which aims at minimizing BESSs cost, as well as voltage fluctuation and load fluctuation in distribution network. Furthermore, a typical scenario set is obtained via the clustering algorithm considering uncertain operating conditions. The simulation is performed based on the extended IEEE-33 bus system. The results show that the proposed algorithm achieves a trade-off between local search and global search, thus obtains a high-quality solution. It can obtain a more widely distributed and uniform Pareto front, which not only achieves the best investment benefit, but also improves voltage quality and power stability.

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Two-Stage Optimal Schedule of Offshore Wind-Power-Integrated Multi-Microgrid Considering Uncertain Power of Sources and Loads
LU Qiuyu, YU Zhen, YANG Yinguo, LI Li
2022, 56 (10):  1308-1316.  doi: 10.16183/j.cnki.jsjtu.2021.409
Abstract ( 71 )   HTML ( 124 )   PDF (1391KB) ( 36 )  

Considering the high-randomness and the low-economic-benefit characteristics of the offshore wind-power-integrated multi-microgrid, a two-stage optimal scheduling method considering the uncertain power of source and load is proposed to improve the operation profits of offshore wind-power-integrated multi-microgrid. The proposed two-stage optimal scheduling method consists of a day-ahead stage and an hour-ahead stage. In the day-ahead stage, the proposed method is based on the forecast data of the wind power and the load demand, which considers the distribution characteristics of the prediction errors. A stochastic optimization model is established to determine the unit committee of the diesel generators and the state-of-charge of the battery storages, so as to maximize the expected daily operation income. A deterministic optimization model is established based on the decisions from the day-ahead optimization relying on the hour-ahead forecast data of the wind power output and load demand. By optimizing the power of the diesel generators, wind turbines and battery energy storages, the operation income of each hour is maximized. Finally, a simulation model is established to verify the proposed method based on the prediction data of sources and loads in wind-power-integrated multi-microgrid. The simulation results show that compared with the conventional schedule strategies, the proposed two-stage optimal scheduling method can achieve a higher income and a higher overall consumption rate of the wind power.

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Adaptive Virtual Inertial Control Strategy of Optical Storage and Distribution Network Based on TOPSIS Algorithm
YU Wei, YANG Huanhong, JIAO Wei, ZHOU Ze
2022, 56 (10):  1317-1324.  doi: 10.16183/j.cnki.jsjtu.2022.106
Abstract ( 56 )   HTML ( 77 )   PDF (1435KB) ( 102 )  

Aimed at the problem of the inertia power allocation due to different indexes when multiple optical storage units are running together, a cooperative control strategy of multiple optical storage units is proposed by using adaptive virtual inertia control as a means to improve power quality. According to the charging and discharging characteristics of the battery, the inertia provided by the system is adjusted. As high-frequency disturbance occurs in the system, the super capacitor is the first choice to provide inertia support. As low-frequency disturbance occurs in the system, the battery provides inertia power support, and the distance algorithm of the superior and inferior solutions is introduced. As cooperative control is performed, indicators such as the allowable power fluctuation range of the converter and the allowable power fluctuation range of the energy storage device are selected as the evaluation reference, the coordination among multi virtual synchronous generator (VSG) units in multi-index comprehensive evaluation is realized. Finally, an AC system with multi VSG units is built on the experiment platform, and the effectiveness of the proposed control strategy is verified.

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High Performance Capacitors Based on Graphene and Boron Nitride
WU Jing, TAN Haiyun, SHI Yuchao, HOU Weihong, TANG Ming
2022, 56 (10):  1325-1333.  doi: 10.16183/j.cnki.jsjtu.2021.188
Abstract ( 67 )   HTML ( 93 )   PDF (9999KB) ( 28 )  

Flexible all-solid-state supercapacitors (FASS) are energy supplies for wearable electronic devices and power devices. Graphene nanosheets have unique two-dimensional (2D) structures, strong mechanical properties, and an excellent electrical conductivity, which are widely used in paper-like flexible electrodes. The essential feature of the double-layer electric performance for the simple graphene nanosheet-based FASS restricts the improvement of their capacitive performance and practical applications. FASS based on the ultralarge graphene nanosheets and the ultrathin boron nitride (BN) nanosheets are investigated. The nacre-like structures could efficiently integrate both merits of pseudocapacitive BN nanoflakes and conducting graphene, thereby exhibiting an excellent electrochemical performance in FASS. After 5000 charge-discharge cycles, the highest areal specific capacitance of FASS reaches 325.4 mF/cm2, with a high capacity retention rate of about 86.2% and a high energy density of 22.8 W·h/kg (1 W·h=3.6 kJ) at a power density of 85.7 W/kg.

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Materials Science and Engineering
A Detection Method of Wire Feeding Speed Based on Filtering Algorithm of Distortion Signal
LE Jian, LIU Yichun, ZHANG Hua, CHEN Xiaoqi
2022, 56 (10):  1334-1340.  doi: 10.16183/j.cnki.jsjtu.2021.270
Abstract ( 54 )   HTML ( 77 )   PDF (20772KB) ( 27 )  

Wire feeding speed has an important effect on the welding quality. In order to realize robot intelligent welding, it is necessary to study the accurate detection method of the wire feeding speed. First, the working principle of the wire feeding speed detection is studied, thus the wire feeding speed online detection can be realized. Then, a kind of wire feeding speed detection system is designed, which wirelessly transmits the sensing signal of the welding wire to the welding robot. Finally, the detection method of the wire feeding speed based on the filtering algorithm of distortion sensing signal is studied, including the principle of no mutation of adjacent wire feeding speed sensing signal, the interference signal elimination algorithm for adjacent detection signal of multiple sensing signal loss without abrupt change, and the detection method of the wire feeding speed. The experimental results show that the main noise in the original wire feeding speed sensing signal can be eliminated by using the designed algorithm and system, and the accuracy of the wire feeding speed detection can be improved. In addition, the width of weld pass after robot welding can not be affected by the change of the welding current.

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Prediction of Modulus of Composite Materials by BP Neural Network Optimized by Genetic Algorithm
WANG Zhuoxin, ZHAO Haitao, XIE Yuehan, REN Hantao, YUAN Mingqing, ZHANG Boming, CHEN Ji’an
2022, 56 (10):  1341-1348.  doi: 10.16183/j.cnki.jsjtu.2021.126
Abstract ( 68 )   HTML ( 113 )   PDF (2532KB) ( 45 )  

In order to reduce the cost of testing and shorten the design cycle, this paper studies the prediction method of the modulus of resin matrix composites based on the machine learning method. Using a new prediction method — the neural network in combination with the genetic algorithm (GA-ANN), the strength, the Poisson’s ratio, and the failure strain of the T800/epoxy composite material are used as three input variables of the back propagation (BP) neural network. Then, the optimal threshold and weight are obtained in the genetic algorithm (GA), which are assigned to the corresponding network parameters, and the BP neural network is updated for higher accuracy to predict the modulus of resin matrix composites. Under the same conditions, the Adam algorithm is used to predict. A comparison of these two methods fully proves the feasibility of the GA-ANN algorithm.

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Resistance Element Welding of Carbon Fiber Reinforced Thermoplastic Composites to High-Strength Steel
WANG Yecheng, LI Yang, ZHANG Di, YANG Yue, LUO Zhen
2022, 56 (10):  1349-1358.  doi: 10.16183/j.cnki.jsjtu.2021.271
Abstract ( 53 )   HTML ( 128 )   PDF (47715KB) ( 61 )  

The high strength joining of carbon fiber reinforced nylon 6 composites (CF/PA6) to TWIP980 steel was achieved by resistance element welding (REW). A 304 stainless steel rivet was used as an assistant element. The effect of welding current and welding time on the joint mechanical property was studied. Four joint failure modes with different strengths were identified, and the microstructures of joints, and the interfaces between CF/PA6 and the steel were analyzed. As the melting point and thermal conductivity of CF/PA6 are lower than those of the high-strength steel, it is prone to overheat and decompose during welding. While ensuring the formation of a certain size of weld nugget, avoiding or reducing the decomposition of CF/PA6 is the key to the successful implementation of CF/PA6 high-strength steel REW. By using a hard welding process such as high welding current and short welding time, high strength joints can be obtained while reducing the decomposition of CF/PA6. Based on the failure load of the joint, the weld lobe under the conditions of this study was determined. The process is sensitive to the change of welding time, and the allowable welding time range is narrow. The decomposition of CF/PA6 cannot be avoided completely even when the welding parameters in the weld lobe are employed. Therefore, it is necessary to conduct further research on the temperature field and the nugget formation mechanism of the REW process.

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Mechanical Engineering
Joint Optimization of Replacement and Spare Parts Ordering with Dual Sourcing
YE Hongqing, SU Huade, ZHENG Meimei, XIA Tangbin
2022, 56 (10):  1359-1367.  doi: 10.16183/j.cnki.jsjtu.2021.151
Abstract ( 41 )   HTML ( 87 )   PDF (1030KB) ( 15 )  

To optimize the joint decisions of equipment maintenance and spare parts ordering with dual sourcing, a joint policy is proposed for a multi-unit parallel manufacturing system. The degradation trajectories of components are described by the Poisson process. The joint decisions are modeled according to the Markov decision process. Based on components and inventory status, the transition probability of a system is formulated. The value iteration algorithm is applied to obtain the optimal maintenance and spare parts ordering policy to minimize the average total cost of the system. Furthermore, to shorten the computation time, a heuristic strategy is developed according to the principle of sequential optimization. Numerical experiments with a two-component system are conducted to analyze the decision difference between the heuristic strategy and the optimal strategy. The sensitivity analysis shows that compared with the optimal strategy, the heuristic strategy can reduce the computation time when the total cost increases by no more than 5%.

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Intelligent Bearing Fault Diagnosis Based on Adaptive Deep Belief Network Under Variable Working Conditions
MA Hangyu, ZHOU Di, WEI Yujie, WU Wei, PAN Ershun
2022, 56 (10):  1368-1377.  doi: 10.16183/j.cnki.jsjtu.2021.161
Abstract ( 53 )   HTML ( 96 )   PDF (7932KB) ( 12 )  

In engineering, working environment and operating state are constantly changing, which decreases the correct rate of equipment fault diagnosis, resulting in the loss of time and cost. The structure of the deep belief network is investigated for the time-varying factors in the mechanical system. In combination with the signal decomposition technology of fixed learning step size, the original characteristics of the sensor data are retained. In addition, the deep key information of the signal is repeatedly extracted layer by layer. The data loss technology is integrated to optimize the network structure to avoid over-fitting problems. Further, considering the domain adaptive method in transfer learning, the memory characteristics of different levels of deep belief networks are solidified. Therefore, a domain adaptive deep belief network with shift-invariant features (SIF-DADBN) is proposed for rolling bearing fault diagnosis. By identifying the characteristic information of similar fault signals with variable working conditions, the accuracy and generalization of bearing intelligent fault diagnosis are both improved. Based on the public data set of rolling bearings, the average correct rate of the fault diagnosis technology is found to be as high as 95.65%. Compared with five other methods, the effectiveness and accuracy of SIF-DADBN under variable working conditions are verified.

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Loop Closure Detection Method of Laser SLAM Based on Global Feature Descriptor
HAN Chao, CHEN Min, HUANG Yuhao, ZHAO Minghui, DU Qiankun, LIANG Qinhua
2022, 56 (10):  1379-1387.  doi: 10.16183/j.cnki.jsjtu.2021.202
Abstract ( 53 )   HTML ( 64 )   PDF (5130KB) ( 18 )  

To solve the problem that localization error of the underground inspection system continues to accumulate over time, a loop closure detection algorithm based on point cloud global feature descriptor is proposed, which is suitable for laser simultaneous localization and mapping (SLAM). The feature vector of each point in point cloud is calculated by curvature, then the global feature descriptor of point cloud is constructed based on the angle distribution and scale distribution relationship between the feature vector and center point coordinate system. In addition, the pose transformation of two similar frames is calculated by feature point registration to improve computing efficiency. The proposed algorithm is verified by simulation experiments and open-source data experiments. The experimental results show that the proposed algorithm has a significant improvement in localization accuracy and real-time performance, which can effectively solve the problems of increased cumulative error and poor global consistency of the localization algorithm during long-term inspections.

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Dual Modular Soft Robot with Multi-Terrain Movement Ability
WANG Yuxuan, LIU Zhaoyu, WANG Jiangbei, FEI Yanqiong
2022, 56 (10):  1388-1396.  doi: 10.16183/j.cnki.jsjtu.2021.290
Abstract ( 88 )   HTML ( 78 )   PDF (32108KB) ( 56 )  

Aimed at the problems of limited application range, single movement scene, inability of climbing, and limited space movement of the multi-terrain movement robot, a novel dual module soft robot with the multi-terrain movement ability is proposed. Each soft module is composed of a four-chamber omnidirectional bending soft pneumatic actuator. By establishing the bending model of the omnidirectional bending soft actuator, the variation law of the omnidirectional bending soft actuator is analyzed. A new rotary movement mode is proposed, which enables the robot to move in a variety of complex environments in the rotary movement mode. A gait control method based on pulse width modulation (PWM) is proposed to make the robot realize the multi-terrain movement function more simply and quickly, and its feasibility is verified by experiments. The experimental results show that the dual modular soft robot based on the four-chamber omnidirectional bending soft actuator can climb vertically along circular pipes, square pipes, and irregular rods (human forearms), and the crawling speed can reach 11.7 mm / s. It can also move rapidly in complex terrain such as flat ground, artificial turf, rugged road surface, and slope, and the crawling speed can reach 14.0 mm/s, which overcomes the shortcomings of the existing pipe climbing robot and multi-terrain movement robot. The modular soft robot can move stably and quickly in a variety of terrain, and has a strong adaptability. It has a potential application value in pipeline detection and complex terrain detection.

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Adaptive Process Monitoring of Online Reduced Kernel Principal Component Analysis
GUO Jinyu, LI Wentao, LI Yuan
2022, 56 (10):  1397-1408.  doi: 10.16183/j.cnki.jsjtu.2021.084
Abstract ( 45 )   HTML ( 55 )   PDF (1945KB) ( 19 )  

In the case of dynamic systems, the traditional kernel principal component analysis (KPCA) method does not perform well. The moving window kernel principal component analysis method can adapt to the normal parameter drift of dynamic systems, but it needs a longer computation time when processing large number of samples. Therefore, an adaptive process monitoring method for online reduced kernel principal component analysis is proposed. In this method, a small training set is selected as the initial reduced set in a large number of samples for modeling, and the online real-time collected data are analyzed to judge whether the new sample is normal or not. If it is a normal sample, the method judges whether the sample is added to the reduced set, and updates the online KPCA model automatically when adding to the reduced set. The proposed method is applied to a numerical example and the Tennessee-Eastman (TE) process. The simulation results show that the proposed method is effective and feasible.

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Working Posture Generation Method for Virtual Human Based on Complete Reachable Region Analysis
ZHU Wenmin, LUO Xiaomeng, FAN Xiumin, ZHANG Lei, CAI Junqi
2022, 56 (10):  1409-1419.  doi: 10.16183/j.cnki.jsjtu.2021.304
Abstract ( 54 )   HTML ( 53 )   PDF (15284KB) ( 12 )  

Due to the large degree of freedom of the human body and the working environment, the generation of virtual human working posture during simulation is very complicated, which requires a lot of time and energy. To solve this problem, according to the structure and motion characteristics of human body, the templates of assembly actions for virtual human are constructed. Through theoretical analysis and calculations, the formulas of the reachable region of different action templates are deduced, and the complete reachable region of the virtual human is established, based on which, feasible assembly actions are screened out through reachability analysis. In combination with the existing multi-objective solution model of virtual human working posture, the automatic generation of virtual human working posture is realized. Based on the above research, this method is verified by examples.

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Uncertainty Quantitative Analysis of Subchannel Code Calculation of PSBT Void Distribution Benchmark
ZHANG Juntao, LIU Xiaojing, ZHANG Tengfei, CHAI Xiang
2022, 56 (10):  1420-1426.  doi: 10.16183/j.cnki.jsjtu.2021.068
Abstract ( 53 )   HTML ( 60 )   PDF (1810KB) ( 25 )  

In order to evaluate the accuracy and reliability of the subchannel code, it is necessary to quantitatively give the uncertainty of the calculation results. The uncertainty analysis is conducted by using the statistical method based on propagation of input uncertainties, and the uncertainty range of the subchannel code calculation results can be obtained quantitatively. Based on the assumption that the uncertainty of model parameters obeys normal distribution, the statistical method is used to determine the distribution of the uncertainty of model parameters to replace the traditional expert judgment. Through the calculation of the pressurized water reactor sub-channel and bundle tests (PSBT) benchmark, the ability of the subchannel code COBRA-IV to predict the experimental results is analyzed, and the uncertainty interval satisfying the tolerance limit of the calculation results is obtained. The results demonstrate that the experiment data is well enveloped by the obtained uncertainty bands and the model calibrated by the statistical mean value presents a good improvement of calculations.

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