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Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSA

SENSORS 2023年第15期23卷 6741-6741页

作者： Kong, Lingchao Liang, Hongtao Liu, Guozhu Liu, Shuo Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China

The internal structure of wind turbines is intricate and precise, although the challenging working conditions often give rise to various operational faults. This study aims to address the limitations of traditional machine learning algorithms in wind turbine fault detection and the imbalance of positive and negative samples in the fault detection dataset. To achieve the real-time detection of wind turbine group faults and to capture wind turbine fault state information, an enhanced ASL-CatBoost algorithm is proposed. Additionally, a crawling animal search algorithm that incorporates the Tent chaotic mapping and t-distribution mutation strategy is introduced to assess the sensitivity of the ASL-CatBoost algorithm toward hyperparameters and the difficulty of manual hyperparameter setting. The effectiveness of the proposed hyperparameter optimization strategy, termed the TtRSA algorithm, is demonstrated through a comparison of traditional intelligent optimization algorithms using 11 benchmark test functions. When applied to the hyperparameter optimization of the ASL-CatBoost algorithm, the TtRSA-ASL-CatBoost algorithm exhibits notable enhancements in accuracy, recall, and other performance measures compared with the ASL-CatBoost algorithm and other ensemble learning algorithms. The experimental results affirm that the proposed algorithm model improvement strategy effectively enhances the wind turbine fault detection classification recognition rate.

关键词： wind turbine CatBoost algorithm fault detection category imbalance intelligent optimization algorithm

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A novel mutual aid Salp Swarm algorithm for global optimization

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第17期35卷

作者： Zhang, Huanlong Feng, Yuxing Huang, Wanwei Zhang, Jie Zhang, Jianwei Zhengzhou Univ Light Ind Coll Elect & Informat Engn Zhengzhou 450002 Peoples R China Zhengzhou Univ Light Ind Coll Software Zhengzhou Peoples R China

Salp Swarm algorithm is a new intelligent optimization algorithm. Because of it is fewer control parameters and convenient operation, it has attracted the attention of researchers from all circles. However, due to the lack of complex iterative process, it has some disadvantages, such as low optimization precision and poor population diversity in the late iteration. To solve these problems of Salp Swarm algorithm, we proposed a Salp Swarm algorithm based on mutual learning mechanism. In this article, the improved Salp Swarm algorithm uses the iteration factor of tangent change to update the population position, which balances the global exploration and local development ability of the algorithm. At the same time, the introduction of mutual learning mechanism in the local development stage solves the problem of poor population diversity in the later iteration of Salp Swarm algorithm, and improves the convergence accuracy of the algorithm. Finally, 23 classical and CEC2014 benchmark functions are used to evaluate the effectiveness of the proposed algorithm. The experimental results show that the improved Salp Swarm algorithm has better optimization accuracy and stability compared with the algorithm of Salp Swarm, Moth Flame optimization, Grasshopper optimization, and Ant Lion optimization.

关键词： intelligent optimization algorithm mutual learning mechanism Salp Swarm algorithm tangent function

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Structural damage detection based on cross-correlation function with data fusion of various dynamic measurements

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JOURNAL OF SOUND AND VIBRATION 2022年 541卷

作者： Wang, Xiaojuan Chen, Feng Zhou, Hongyuan Ni, Pinghe Wang, Lihui Zhang, Jian Beijing Univ Technol Key Lab Urban Secur & Disaster Engn Minist Educ Beijing 100124 Peoples R China Beijing Inst Technol State Key Lab Explos Sci & Technol Beijing 100081 Peoples R China Jiangsu Univ Dept Mech & Engn Sci Zhenjiang 212013 Peoples R China

To take advantage of various types of dynamic measurement data for structural damage detection, an identification strategy based on cross-correlation function with data fusion of various dynamic measurements was proposed to improve the accuracy of damage identification in the present study. First of all, the cross-correlation functions among the easily acquired strain, acceleration, and displacement responses were theoretically derived when the structure was subjected to ambient excitations. Furthermore, an identification strategy was proposed for structural damage detection with the objective function of minimizing the difference between the measured and computed cross-correlation function under multiple unknown ambient excitations. In the proposed strategy, four optimization methods, namely, gradient search, genetic algorithm, particle swarm optimization, and the hybrid method of particle optimization method and gradient search were applied as the search engine to identify structural unknown damage index. Moreover, the performance of the proposed identification strategy was examined by the numerical studies on a two-dimensional and a three-dimensional truss as well as the experimental study on a cantilever beam. These results showed that the cross-correlation function among different types of vibration measurements could significantly improve the accuracy of the identification results, meanwhile, the proposed strategy exhibited excellent robustness to the measurement noise. In addition, the performance of the proposed strategy with different combinations of vibration data and the influence of reference data on the accuracy of damage identification results were further investigated.

关键词： Structural damage detection Multiple unknown ambient excitations Cross -correlation function Data fusion intelligent optimization algorithm

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Framework for multivariate carbon price forecasting: A novel hybrid model

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024年 369卷 122275页

作者： Zhang, Xuankai Zong, Ying Du, Pei Wang, Shubin Wang, Jianzhou Jiangnan Univ Sch Business Wuxi 214122 Peoples R China Xian Univ Posts & Telecommun Sch Econ & Management Xian 710061 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

The complex characteristics of volatility and non-linearity of carbon price pose a serious challenge to accurately predict carbon price. Therefore, this study proposes a new hybrid model for multivariate carbon price forecasting, including feature selection, deep learning, intelligent optimization algorithms, model combination and evaluation indicators. First, this study collects and organizes the historical carbon price series of Hubei and Shanghai as well as the influencing factors in five dimensions including structured and unstructured data, totaling twenty variables. Second, data dimensionality reduction is performed and input variables are obtained using the least absolute shrinkage and selection operator, followed by the introduction of nine advanced deep learning models to predict carbon price and compare the prediction effects. Then, through the combination of models, three models with the best performance are combined with Pelican optimization algorithm to construct a hybrid forecasting model. Finally, the experimental results show that the developed forecasting model outperforms other comparation models in terms of prediction accuracy, stability and statistical hypothesis testing, and exhibits excellent prediction performance. Furthermore, this study also applies the developed model to European carbon market price prediction and uses the Hubei carbon market as an example for quantitative trading simulation, and the empirical results further verify its robust prediction performance and investment application value. In conclusion, the proposed hybrid prediction model can not only provide high-precision carbon market price prediction for the government and corporate decision makers, but also help investors optimize their trading strategies and improve their returns.

关键词： Carbon price prediction Hybrid forecasting model Deep learning intelligent optimization algorithm

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Wind speed prediction and reconstruction based on improved grey wolf optimization algorithm and deep learning networks

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 114卷

作者： Zhu, Anfeng Zhao, Qiancheng Yang, Tianlong Zhou, Ling Zeng, Bing Hunan Univ Sci & Technol Engn Res Ctr Hunan Prov Min & Utilizat Wind Turbin Xiangtan 411201 Peoples R China XEMC Wind Power Co Ltd Xiangtan 411102 Peoples R China

The random and intermittent of wind speeds can affect the secure and stable functioning of wind turbines (WTs). To enhance the security and stability of WTs, accurate and effective wind speed prediction, and abnormal wind speed reconstruction are essential. This study proposes a wind speed prediction and reconstruction approach that combines an improved grey wolf optimization algorithm with an adaptive search strategy (SAGWO) and long- and short-term memory network (LSTM). Firstly, the wind speed data collected by the anemometer is analyzed to determine the corresponding correlation threshold and to select WTs with high similarity. Then, the wind speed data of highly similar WTs are taken as input variables and the LSTM is optimized using the grey wolf optimization algorithm with an adaptive search strategy to enhance the convergence speed and prediction ability. Finally, the SAGWO-LSTM model is built to reconstruct the wind speed data with added noise. The experimental outcomes indicate that the developed approach has a favorable performance concerning wind speed prediction and reconstruction compared to the baseline model.

关键词： Wind turbine Anemometer Wind speed reconstruction intelligent optimization algorithm Deep learning networks

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Enhancing the reliability and accuracy of data-driven dynamic soft sensor based on selective dynamic partial least squares models

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CONTROL ENGINEERING PRACTICE 2022年 127卷

作者： Shao, Weiming Han, Wenxue Li, Yougao Ge, Zhiqiang Zhao, Dongya China Univ Petr East China Coll New Energ Dept Chemi Equipment & Control Engn Qingdao Peoples R China Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou Peoples R China

Data-driven soft sensors have been widely applied to a broad range of process industries for virtually sensing difficult-to-measure but of-great-concern variables. However, it is still nontrivial to develop dynamic soft sensors with satisfactory performance. A crucial obstacle lies in strong dependence on quality and representativity of collected data. In other words, the predictive accuracy of the developed soft sensor could be quite sensitive to both offline training data and online unseen data, leading to unreliable and degraded generalization performance. In order to deal with such a troublesome issue, this paper proposes a framework for developing reliable dynamic soft sensor called selective dynamic partial least squares (SDPLS). The SDPLS consists of two-stage operations. At the offline stage, aided by intelligent optimization algorithm a model library is established through constructing various DPLS models, each of which accounts for certain 'mode'. At the online stage, adaptive online model updating for adapting to the current working condition is carried out based on evaluating the performance of the stored individual models, where a correction scheme is also developed for bias elimination. Extensive case studies have been conducted based on a numerical example and two real-life industrial processes, and the results efficaciously demonstrate the effectiveness and promising application prospects of the proposed schemes.

关键词： Dynamic soft sensor Selective dynamic partial least squares Performance reliability Adaptive model updating intelligent optimization algorithm

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Prediction of ultimate bearing capacity of rock-socketed piles based on GWO-SVR algorithm

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STRUCTURES 2024年 61卷

作者： Sun, Zongjun Liu, Fei Han, Yingfeng Min, Rui Shandong Univ Sci & Technol Coll Civil Engn & Architecture Qingdao 266000 Peoples R China Qingdao Ruihan Technol Grp Co Ltd Qingdao 266000 Peoples R China Qingdao West Coast Traff Investment & Comstruct Co Qingdao 266000 Peoples R China

The application of rock-socketed pile in engineering is becoming ever more common. Finding the ultimate bearing capacity of rock-socketed piles with a high-performance ratio and precision is an important area of research. The rock-socketed piles underwent a field load test, and ABAQUS software was utilized to create a model of the interaction between the pile and the soil. For the geometric and physical parameters of the pile, soil, and bedrock, a total of eighteen pile-soil parameters were chosen. Reasonably constructed orthogonal and control variable tests are utilized, and a sensitivity analysis of the relevant factors is performed. Ten elements that significantly affect the ultimate bearing capacity of rock-socketed piles (with a probability less than 0.05) are selected as the input for the bearing capacity prediction of rock-socketed piles based on the test's consistent findings. The ultimate bearing capacity of rock-socketed piles is predicted using the support vector regression (SVR) model, and the absolute coefficient, mean absolute error (MAE), mean square error (MSE) and other indicators are chosen as the evaluation indices of the model prediction effect. The SVR model predicts an absolute coefficient of 0.7, which is an unsatisfactory prediction outcome. Consequently, the model is optimized via the nested grey wolf (GWO) algorithm. With a value of 0.995, the GWO-SVR model is more akin to 1 than the SVR model. There are 0.14 and 0.034 for the MAE and MSE, respectively. The models of (Artificial bee colony) ABCSVR, (Cuckoo search) CS-SVR, (Sparrow search algorithm)SSA-SVR, and (Particle swarm optimization) PSO-SVR are built. When comparing different assessment indicators, the GWO-SVR model is smaller than the MAE and MSE. When the prediction model is used on a real project, the engineering requirements can be satisfied by the prediction effect.

关键词： intelligent optimization algorithm Rock-socketed pile Ultimate bearing capacity Support vector machine Orthogonal test

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Determination of hydraulic conductivity and its spatial variability in the Jianghan Plain using a multi-format, multi-method approach

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JOURNAL OF HYDROLOGY 2021年 594卷 125917-125917页

作者： Xue, Peipei Wen, Zhang Zhao, Dejun Jakada, Hamza Liang, Xing China Univ Geosci Sch Environm Studies Wuhan 430074 Hubei Peoples R China Hubei Inst Hydrogeol & Engn Geol Jingzhou Hubei Peoples R China Baze Univ Dept Civil Engn Abuja Nigeria

In recent times, the groundwater in Jianghan Plain, Central China has come under severe stress from agricultural and industrial demand. The spatial variability of hydraulic conductivity (K) in the region is important for better and sustainable groundwater exploration and management. In this study, we determined the spatial distribution of K using a multi-method approach, i.e. analytic and numerical simulation as well as intelligent optimization methods (particle swarm optimization (PSO) and differential evolution (DE)) and geostatistics. Data from 55 multi-format pumping tests situated across the middle-confined aquifer (consisting of late Pleistocene (Qp(3)s) and middle Pleistocene (Qp(2)j)) were used. Results showed that for transient pumping test with constant rate by multiwells, PSO and DE were faster and more precise, especially DE. For transient pumping test with variable rate as well as transient pumping test with constant rate by single well, the numerical method fitted the drawdown better, while the PSO and DE had large deviations at the early stage of pumping. Overall, the values of K vary from 0.032 to 10.64 m/d within Qp(3)s and 0.33 to 36.24 m/d within Qp(2)j. The spatial variability model demonstrated that the K values in Qp(3)s are greater than those in Qp(2)j. The heterogeneity elucidates the sedimentary development of the basin. These methods and results can provide new insights for estimating the spatial distribution of K for regional groundwater resources assessment in similar sedimentary environments around the world.

关键词： Hydraulic conductivity intelligent optimization algorithm Geostatistics Sedimentation Jianghan Plain

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Optimal Scheduling of Regional Combined Heat and Power System Based on Improved MFO algorithm

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ENERGIES 2022年第9期15卷 3410页

作者： Wang, Fan Liao, Xiang Fang, Na Jiang, Zhiqiang Hubei Univ Technol Hubei Engn Res Ctr Safety Monitoring New Energy & Sch Elect & Elect Engn Wuhan 430068 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430068 Peoples R China

Due to the inflexibility of cogeneration power plants and the uncertainty of wind power production, the excess power of the distribution network brings challenges to the power grid operation. This paper introduced an improved moth-flame optimization algorithm to meet the challenge of energy complementary dispatching. The proposed algorithm adopts three effective strategies, namely inertia weight, unified initialization, and the spiral position update strategy, which maintains a strong global search ability and a potent compromise between global and local search. The effectiveness of the proposed method was evaluated by benchmark functions. Furthermore, the proposed method was applied to combine heat and power system operation problems and economic dispatch in light load and wind power unpredictability. In order to verify the robustness of the algorithm and solve the complex constraints of power systems under extreme conditions, three different cases had been discussed. The experimental findings indicate that the proposed algorithm shows better performances in terms of convergence speed, ability to escape from a local optimum solution, and population diversity maintenance under different complexity conditions of engineering problems.

关键词： combined heat and power plant moth-flame optimization intelligent optimization algorithm inertial weight integrated energy system

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Ship docking and undocking control with adaptive-mutation beetle swarm prediction algorithm

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OCEAN ENGINEERING 2022年 251卷 111021-111021页

作者： Wang, Le Li, Shijie Liu, Jialun Wu, Qing Negenborn, Rudy R. R. Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430063 Peoples R China Wuhan Univ Technol Intelligent Transportat Syst Res Ctr Wuhan 430063 Peoples R China Natl Engn Res Ctr Water Transport Safety Wuhan 430063 Peoples R China Delft Univ Technol Dept Maritime & Transport Technol NL 2628 CD Delft Netherlands

Autonomous docking and undocking control is an important part of intelligent ship motion control. In this study, the adaptive-mutation beetle swarm prediction (AMBS-P) algorithm is used to propose a control approach for autonomous docking and undocking. Firstly, this paper introduces the principle of the AMBSP algorithm, then the convergence is proved. Secondly, the "Tito-Neri"model ship is introduced as a case study, and the thrust allocation process is described. Finally, the effect of docking and undocking is verified in multiple scenarios starting from different angles. In the verification, first of all, when designing the docking and undocking controllers, the correctness of the algorithm and the practicality of the control are verified by whether there is ship drag or not. Secondly, by analyzing the parameters of the algorithm, the optimal parameters of it are determined and verified in the real environment. Thirdly, compared with typical proportion-integral-derivative (PID) algorithm and nonlinear model predictive control (NMPC) algorithm, the AMBS-P algorithm has better results for autonomous docking and undocking control, no matter in long-distance or short-distance. The research shows that the AMBS-P algorithm has a fast response and good effect for the ship autonomous docking and undocking, and does not rely too much on the system model.

关键词： Ship Docking Undocking intelligent optimization algorithm Predictive control

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