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Study on hydroturbine power trend prediction based on machine learning

ENERGY REPORTS 2023年 10卷 1996-2005页

作者： Huang, Xiaoping Lu, Qiu Zhou, Huamao Huang, Wenzhe Wang, Shoufen Guilin Univ Technol Guilin 541004 Guangxi Peoples R China Jiangxi Agr Univ Coll Engn Nanchang Jiangxi Peoples R China Shanghai Univ Elect Power Shanghai Peoples R China

The application of computational fluid dynamics combined with 3D modeling of the hydraulic model was discussed in this paper. Through the complex calculation of the internal low field, dynamic simulation was used to predict the characteristics of water energy index. Based on the collection of a large number of different types of turbine operation data in different working conditions, the improved multi-layer neural network power prediction model based on the adaptive inverse normalization strategy and the improved grey wolf algorithm were used to optimize the least squares support vector machine power prediction model. Rlue activation function was used in the improved multi-layer neural network model uses to improve the computation speed and prevent gradient disappearance. Combined with L2 regularization to prevent data overfitting, Adam gradient optimizer was applied to achieve fast convergence of network output. The simulation results shown that the method can realize the high precision prediction of turbine output power. & COPY;2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Hydroturbine power prediction Adaptive anti-normalization improved grey wolf optimization algorithm Least squares support vector machine Machine learning

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Unanticipated fault diagnosis of large telescopes based on IGWO-iForest 8

Unanticipated fault diagnosis of large telescopes based on I...

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Conference on Software and Cyberinfrastructure for Astronomy VIII

作者： Liu, Ruiqiang Xu, Lingzhe Deng, Zhuangzhuang Li, Yun Pan, Cong Yang, Shihai Chinese Acad Sci Nanjing Inst Astron Opt & Technol Nanjing 210042 Jiangsu Peoples R China Nanjing Inst Astron Opt & Technol CAS Key Lab Astron Opt & Technol Nanjing 210042 Jiangsu Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781510675261;9781510675254

A method for unanticipated fault diagnosis based on IGWO-iForest (improved grey wolf Optimizer-Isolation Forest) is proposed to address various unpredictable problems faced by large telescopes in extreme environments. First, the random forest feature selection algorithm is used to identify the features of the original dataset and eliminate redundant features. Secondly, the differential evolution strategy is introduced into the GWO (grey wolf Optimizer) to improve the local search efficiency and accuracy, and the Levy flight strategy is introduced into the GWO to improve the global search ability of the algorithm. Then, the improved IGWO is used to optimize the parameters of the iForest model. Finally, the performance of the model is verified through data collected from a fault diagnosis and self-healing hardware-in-the-loop simulation platform. The experimental results show that the IGWO-iForest algorithm achieves a fault diagnosis accuracy of 99.1%, which demonstrates its higher sensitivity to a small number of unanticipated fault data compared with other anomaly detection algorithms, proving the effectiveness of this method in accurately diagnosing unanticipated faults in telescopes.

关键词： large telescope unanticipated failure isolated forest improved grey wolf optimization algorithm feature selection algorithm

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Fault Diagnosis of Rotating Equipment Bearings Based on VMD-MSGWO-SVM 3

Fault Diagnosis of Rotating Equipment Bearings Based on VMD-...

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3rd Conference on Fully Actuated System Theory and Applications (FASTA)

作者： Deng, Jibao Liu, Yimin Li, Zixuan Wuhan Maintenance & Installat Petrochem Engn Co L Wuhan 430082 Peoples R China Wuhan Univ Sci & Technol Acad Informat Sci & Engn Wuhan 430081 Peoples R China

ISBN: (纸本)9798350373707;9798350373691

In this paper, the bearing fault problem of rotating equipment is studied by using support vector machine (SVM) model classification method. In order to solve the problem of low accuracy of using SVM to detect the fault for bearing, the method of variational mode decomposition (VMD) is introduced to extract features from original vibration data and the intelligent optimization algorithm of MSGWO is introduced to optimize parameters of SVM, which can improve fault diagnosis performance of classification system. A variety of comparative experiments are carried out by using FEMTO-ST bearing data set which is open source on the internet. The results show that the proposed algorithm has high prediction accuracy and is superior to the present methods in classification.

关键词： SVM Rotating Equipment Bearings Fault VMD improved grey wolf optimization algorithm

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Improving voltage profile of unbalanced Low-Voltage distribution networks via optimal placement and operation of distributed energy storage systems

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IET RENEWABLE POWER GENERATION 2022年第13期16卷 2748-2761页

作者： Li, Yingliang Cai, Heming Xian Shiyou Univ Sch Elect Engn Xian 710065 Peoples R China

Distributed energy storage system (DESS) has flexible operating characteristics, and DESSs can be properly configured to effectively serve the voltage regulation of the active distribution network. The existing voltage regulation-oriented DESSs optimization configuration studies are usually based on the balanced network model to analyze the impact of energy storage operation characteristics on the system voltage, but the distribution network is actually three-phase unbalanced. To this end, a method for the sequence optimization of DESSs in unbalanced distribution networks based on voltage sensitivity analysis is proposed, and the optimal configuration of DESSs in unbalanced distribution networks is studied from the perspective of improving the network voltage performance. Firstly, the DESSs placement is determined based on the proposed comprehensive voltage sensitivity analysis method;secondly, taking into account the economy of system operation, optimize the capacity of DESSs with the goal of minimizing the investment and operation and maintenance cost of DESSs, and the model solved by the improved grey wolf optimization algorithm. Finally, numerical simulations are analyzed by two unbalanced distribution testing networks. The results show that the proposed method can effectively improve the voltage profile of the unbalanced distribution network while ensuring the economy of system operation.

关键词： investment optimisation numerical simulations balanced network model sensitivity analysis energy storage power distribution economics distributed energy storage system improved grey wolf optimization algorithm system operation economy power distribution control operation characteristics voltage control voltage sensitivity analysis method unbalanced low-voltage distribution networks numerical analysis voltage regulation-oriented DESS unbalanced distribution testing networks

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Bearing Fault Detection Based on improved GWO to Optimize Multi-Stable Stochastic Resonance Parameters 35

Bearing Fault Detection Based on Improved GWO to Optimize Mu...

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35th Chinese Control and Decision Conference (CCDC)

作者： Huang, Weichao Zhang, Ganggang Xian Univ Technol Fac Automat & Informat Engn Xian Peoples R China

ISBN: (纸本)9798350334722

For the problem that the traditional stochastic resonance system cannot adaptively adjust the system parameters in the bearing fault signal detection. In this paper, a bearing fault signal detection method based on improved gray wolf algorithm is proposed for adaptive optimization of multi-stable stochastic resonance parameters Firstly, the best point set theory was used to initialize the grey wolf population to improve the diversity of the population. An exponential convergence factor is introduced to balance the global search and local development of the algorithm, and an adaptive position update strategy is introduced to improve the accuracy of the algorithm. Then, based on the multi-stable stochastic resonance system model, the structure parameters of the multi-stable stochastic resonance were optimized by adaptive improvement of the grey wolf algorithm, so as to enhance the fault signals and realize the detection of bearing fault signals. The experimental results shown that the proposed method has fast convergence speed, high output signal-to-noise ratio of the multi-stable stochastic resonance system, and accurate and efficient fault detection performance. The method lays a foundation for the application of adaptive multi-stable stochastic resonance in engineering practice.

关键词： Stochastic Resonance improved grey wolf optimization algorithm Bearing Failure Signal

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UCAVs Collaborative Target Assignment Based on improved GWO algorithm 35

UCAVs Collaborative Target Assignment Based on Improved GWO ...

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35th Chinese Control and Decision Conference (CCDC)

作者： Huang, Hanqiao Jiang, Zijian Dong, Yue Weng, Weiye Bi, Tengfei Shen, Yue Northwestern Polytech Univ Unmanned Syst Res Inst Xian Shanxi Peoples R China Northwestern Polytech Univ Sch Mech Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9798350334722

With increasing complexity of the operational environment, aiming at the target assignment problem of the manned/unmanned cooperative air-to-ground attack mission, the collaborative target assignment model of UCAVs is built up through analyzing enemy threat coefficient and Unmanned Combat Aerial Vehicles' (UCAVs) damage effectiveness. An improved grey wolf optimization (GWO) algorithm is proposed to improve quality and speed of the solution, which uses triploid grey wolf gene strategy to increase initial population diversity, improves the convergence speed by weighted combination location updating strategy and introduces nonlinear factor as well as different mutation operators to achieve the balance between global search and local search. By simulation experiments, the feasibility and superiority of the algorithm are verified. And compared with GWO algorithm, the improved GWO algorithm's optimization process is faster and more accurate, which can well adapt to the complex battlefield environment and meet the requirements of target assignment in the dynamic combat process.

关键词： Unmanned Combat Air Vehicles collaborative target assignment target threat assessment improved grey wolf optimization algorithm

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Green and efficient-oriented human-robot hybrid partial destructive disassembly line balancing problem from non-disassemblability of components and noise pollution

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2024年 90卷

作者： Guo, Lei Zhang, Zeqiang Wu, Tengfei Zhang, Yu Zeng, Yanqing Xie, Xinlan Southwest Jiaotong Univ Sch Mech Engn Technol & Equipment Rail Transit Operat & Maintena Chengdu 610031 Peoples R China Southwest Jiaotong Univ Tangshan Inst Tangshan 063000 Peoples R China

Current research on the disassembly line balancing problem ignores the influence of non-disassemblability of components. And this problem can lead to failure of the disassembly task, which can seriously affect the disassembly efficiency. This study integrates destructive operation into the human-robot disassembly line while considering noise. First, a mixed integer programming model is established for human-robot hybrid partial destructive disassembly line balancing problem to accurately obtain the number of stations, smoothness index, costs and negative impact of noise pollution on workers. Then, an improved grey wolf optimization algorithm is proposed for the NP-hard characteristic of problem. A three-layer encoding and two-stage decoding strategy is designed to constrain the uniqueness of the solution, considering the noise constraints, and the different disassembly times of the human-robot. A disturbance factor is also designed to prevent local optimality, which enhances the performance of the proposed algorithm. Different cases are also used to verify the correctness and superiority of the proposed method. Finally, an engine case is used to validate the practicality of the proposed method. The results of the comparison of the different disassembly schemes show that: (1) The proposed algorithm outperforms the three classical Swarm Intelligence methods and other eleven algorithms in the disassembly line balancing problem. (2) The human-robot hybrid partial destructive disassembly line can effectively avoid the problem of task failure, and the smoothing index is reduced by 12.27 % compared with the original scheme. Disassembly costs increased by 1.28 %, but this was minimal compared to line-wide smooth running and worker health. (3) The human-robot hybrid disassembly line is more appropriate to solve the actual production process compared to worker disassembly and robot disassembly, and has a greater advantage in solving the actual disassembly line balance problem

关键词： Remanufacturing Human -robot hybrid partial destructive disassembly Mixed-integer programming model Non-disassemblability of components Noise pollution improved grey wolf optimization algorithm

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An Operational Mechanism for Prosumers to Participate in Real-Time Demand Response 9

An Operational Mechanism for Prosumers to Participate in Rea...

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9th International Forum on Electrical Engineering and Automation (IFEEA)

作者： Tan, Zhukui Liu, Bin Wu, Yufeng Zhang, Junwei Tang, Saiqiu Xu, Yutao Guizhou Power Grid Co Ltd Elect Power Res Inst Guiyang Guizhou Peoples R China South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China

ISBN: (纸本)9781665464215

As the integrator of prosumers' resources, prosumer aggregator realizes its own economic optimization through multiple interactions with the interest demands of different prosumers at the lower level. In view of the above interaction process, an operational mechanism for prosumers to participate in real-time demand response is proposed based on the master-slave game, in which aggregators aim to maximize its economic benefits and prosumers aim to minimize the cost of electricity consumption. the upper aggregator optimizes the electricity price information according to the power demand / supply information reported by the lower level;The lower level prosumers adjust the internal resource operation plan according to the electricity price information released by the upper level, and modify the overall power demand / supply information. In addition, the improved grey wolf optimization algorithm based on a nonlinear adjustment of the convergence factor (IGWO-NL) is used to solve the Nash equilibrium of the game model. Simulation results show that the proposed model can effectively reduce the peak valley difference of the overall load while optimizing the economy of each subject.

关键词： component Prosumer Aggregator Electricity price demand response Multi-Agent Stackelberg game improved grey wolf optimization algorithm

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Scheduling optimization of ship plane block flow line considering dual resource constraints

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SCIENTIFIC REPORTS 2024年第1期14卷 1-22页

作者： Li, Jinghua Lin, Pengfei Wu, Xiaoyuan Song, Dening Yang, Boxin Zhou, Lei Harbin Engn Univ Coll Mech & Elect Engn Harbin Peoples R China Harbin Engn Univ Coll Shipbldg Engn Harbin Peoples R China Shanghai Waigaoqiao Shipbldg Co Ltd Shanghai Peoples R China

A well-designed scheduling plan that meets the practical constraints of the workshop is crucial for enhancing production efficiency in ship plane block assembly. Unlike traditional flow line scheduling problems, the scheduling optimization problem for ship plane block flow line involves dual resource constraints, including work teams and spare parts supply limitations. This can be seen as a Dual Resource Constrained Blocked Flow Shop Scheduling Problem (DRCBFSP). This paper presents a scheduling optimization method for this kind of problem to minimize the maximum completion time. To address the dual constraints, chromosomes are encoded as a two-dimensional array composed of positive integers representing the assembly order of blocks and the allocation of work teams. An improved grey wolf optimization algorithm (IGWO) is proposed to solve the problem, and the Rank Order Value (ROV) rule is used to transform the discrete scheduling solution with the continuous individual position vector. The IGWO algorithm also incorporates nonlinear search factors, dynamic inertia weight factors, and Gaussian mutation perturbation strategies to enhance its development and exploration capabilities. The experimental results suggest that the mathematical model and the IGWO algorithm established in this paper can effectively solve the DRCBFSP encountered in ship block building.

关键词： Scheduling problem Ship plane block flow line Dual resource constraints optimization improved grey wolf optimization algorithm

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improved fuzzy neural network control for the clamping force of Camellia fruit picking manipulator

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MECHANICS & INDUSTRY 2023年 24卷

作者： Fan, Ziyan Li, Lijun Liao, Kai Gao, Zicheng Li, Yuhang Xie, Hao Cent South Univ Forestry & Technol Coll Mech & Elect Engn Changsha 410000 Peoples R China

During the operation of the vibrating mechanism, the push-shaking camellia fruit picking manipulator needs to ensure a constant force output of the clamping hydraulic motor in order to make sure that the camellia fruit tree trunk wouldn't loosen or damage, which may affect its later growth, during the picking process. In this regard, this paper derived the state space model of the valve-controlled clamping hydraulic motor system of the push-shaking camellia fruit picking manipulator, and the fuzzy wavelet neural network (FWNN) was designed on the basis of the traditional incremental PID control principle and the parameters of the neural network were optimized by the improved grey wolf optimizer (GWO). And then, the control system was simulated with the MATLAB/Simulink software without and with external interference, and compared and analyzed it with traditional PID controller and fuzzy PID (FPID) controller. The results show that the traditional PID controller and the FPID control have slow response and poor robustness, while the improved fuzzy wavelet neural network PID (IFWNN PID) controller possesses the characteristics of fast response and strong robustness, which can well meet the requirement of the constant clamping force of hydraulic motors. Finally, the field clamping test was carried out on the picking manipulator. The results show that the manipulator controlled by the IFWNN PID controller shortens the clamping time by 20.0% and reduces the clamping damage by 13.6% compared with the PID controller, which is verified that the designed controller can meet the clamping operation requirements of the camellia fruit picking machine.

关键词： Camellia fruit picking manipulator clamping force PID control fuzzy wavelet neural network improved grey wolf optimization algorithm

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