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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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A grey wolf optimization algorithm for solving partial destructive disassembly line balancing problem consider feasibility evaluation and noise pollution

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ADVANCED ENGINEERING INFORMATICS 2024年 60卷

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

Partial destructive disassembly (PDD) is a crucial tool for enhancing efficiency and reducing disassembly costs when recycling complex end-of-life (EOL) products. Evaluating the feasibility of PDD is crucial for determining its implementation. However, the existing partial destructive disassembly line balancing (PDDLB) problem prioritizes profitability and randomly selects components for destructive, disregarding the feasibility of PDD and the noise pollution resulting from destructive operations. To address this concern, this paper introduces the integration of feasibility evaluation and noise considerations into the PDDLB problem for the first time. Subsequently, a mathematical model is established for the PDDLB problem, aiming to minimize disassembly costs, achieve smoothness balance, minimize the number of stations, and mitigate the adverse effects of noise. Then, an improved grey wolf optimization algorithm (IGWOA) considering feasibility evaluation and noise limitations is developed. It non-linearizes the convergence factor to enhance accuracy in the search for optimality, introduces a Le ' vy flight operation to further expand the search solution space, and incorporates an interference factor to prevent local optimality. In addition, the superiority of the proposed algorithm in solving different cases is verified by comparing five algorithms for the 25-task example and seven algorithms for the 52-task example in the latest literature. Finally, the proposed method is applied to a real engine case. The obtained results are compared with several classical Swarm Intelligence methods. The comparison results show that IGWOA has advantages due to other group intelligence methods in solving small, medium, and large-scale example. Moreover, the disassembly cost is comparatively low.

关键词： Partial destructive disassembly line improved grey wolf optimization algorithm Noise pollution Feasibility evaluation Swarm Intelligence methods

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Machine learning and LSSVR model optimization for gasification process prediction

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第6期7卷 5991-6018页

作者： Cong, Wei Xijing Univ Sch Comp Sci Xian 710123 Shaanxi Peoples R China

Gasification stands as a transformative thermochemical process, ingeniously converting carbon-rich substances like methane (CH4) and a spectrum of hydrocarbons, including ethylene (C2Hn), into a versatile synthesis gas (syngas). This dynamic blend predominantly comprises hydrogen (H2) and carbon monoxide (CO), presenting a potent feedstock for diverse industrial applications. In recent years, the focus on sustainable energy has intensified due to concerns about climate change, energy security, and dwindling fossil fuel reserves. Biomass energy has emerged as a promising alternative, offering the potential for a global circular economy and carbon neutrality, thanks to its abundant resources and reliable energy production. This article introduces two hybrid models that combine Least Square Support Vector Regression (LSSVR) with Dwarf Mongoose optimization (DMO) and the improved grey wolf optimization algorithm (IGWO). These models utilize nearby biomass data to predict the elemental compositions of CH4 and C2Hn. The assessment of both individual and hybrid models has demonstrated that integrating LSSVR with these optimizers significantly improves the accuracy of CH4 and C2Hn predictions. According to the findings, the LSDM model emerges as the top performer for predicting both CH4 and C2Hn, achieving impressive R2 values of 0.988 and 0.985, respectively. Moreover, the minimal RMSE values of 0.367 and 0.184 for CH4 and C2Hn predictions respectively affirm the precision of the LSDM model, rendering it a suitable option for practical real-world applications. Accurate predictions enable the design of systems that efficiently convert a wide range of feedstocks into valuable syngas, which can be employed to generate heat, electricity, fuels, and chemicals. By understanding and optimizing gasification processes, it becomes possible to minimize emissions of pollutants, reduce waste, and mitigate greenhouse gas emissions through carbon capture and utilization technologies.

关键词： Biomass gasification Least square support vector regression Dwarf mongoose optimization improved grey wolf optimization algorithm Machine learning

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An intelligent fault identification method of rolling bearings based on SVM optimized by improved GWO

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2019年第1期7卷 289-303页

作者： Huang, Haisong Fan, Qingsong Wei, Jianan Huang, Dong Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang Guizhou Peoples R China

To improve the accuracy and the recognition efficiency of a bearing fault diagnosis, a fault diagnosis method based upon the improved grey wolf optimization (IGWO) algorithm and support vector machine (SVM) is proposed in the following manners. First, the data are pre-processed by using the set ensemble empirical mode decomposition (EEMD), Shannon wavelet packet entropy (SWPE), and principal component analysis (PCA). Next, the idea of updating the host bird nest by the cuckoo search (CS) optimization algorithm is introduced into the grey wolf optimization (GWO) algorithm to obtain the IGWO algorithm. Then, the SVM is optimized by the IGWO algorithm to obtain optimal parameters for a new diagnostic model. This model improves the problem where the algorithm easily to falls into a local optimum. The learning ability and the generalization ability of the SVM are also enhanced. Finally, the effectiveness of the optimization model is tested by two different bearing data sets. The results show that compared to the genetic algorithm (GA), particle swarm optimization (PSO) and GWO algorithm optimization, the IGWO algorithm can be more accurate and efficient when diagnosing bearings.

关键词： improved grey wolf optimization algorithm support vector machine parameter optimization bearing fault diagnosis

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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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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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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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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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Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期110卷 104713-104713页

作者： Jin, Zhenzhen He, Deqiang Wei, Zexian Guangxi Univ Coll Mech Engn Guangxi Key Lab Mfg Syst & Adv Mfg Technol Nanning 530004 Peoples R China

The vibration signal of the axle box bearing of the train is affected by the track excitation and the random noise of the environment. The vibration signal is nonlinear and non-stationary, and the signal characteristics of the early fault are weak and easy to be submerged, which leads to the low accuracy of the weak fault diagnosis of the bearing. To solve this problem, a weak fault diagnosis method for train axle box bearing based on parameter optimization Variational Mode Decomposition (VMD) and improved Deep Belief Network (DBN) is proposed. Firstly, the nonlinear convergence factor, Levy flight theory and greedy algorithm optimization theory are introduced into the grey wolf optimization algorithm (GWO), and an improved GWO algorithm based on hybrid strategy is proposed to improve the performance of the algorithm and solve the local optimal problem of the algorithm. Secondly, the improved GWO is applied to optimize the VMD parameters, which is used for signal decomposition. And the fault feature information of modal components with maximum correlation coefficient is extracted by multi-scale scatter entropy. Finally, the improved GWO algorithm is applied to optimize the parameters of the DBN to solve the parameter setting problem, and the optimized DBN is used as a pattern recognition algorithm for weak fault diagnosis of bearings. Through experimental comparison and analysis, the proposed method can effectively solve the problem of weak fault diagnosis of axle box bearings, and has high diagnostic accuracy.

关键词： improved grey wolf optimization algorithm Parameter optimization Feature extraction Optimized Deep Belief Network improved grey wolf optimization algorithm Parameter optimization Feature extraction Optimized Deep Belief Network

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