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Prediction method of pipe joint opening-closing deformation of immersed tunnel based on singular spectrum analysis and SSA-SVR

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APPLIED OCEAN REsearch 2023年 135卷

作者： Li, Ke Zhang, Zhongzhe Guo, Hongyan Li, Wenfeng Yan, Yu China Merchants Chongqing Commun Technol Res & Des Chongqing 400067 Peoples R China Chongqing Jiaotong Univ Sch Civil Engn Chongqing 400074 Peoples R China Hong Kong Zhuhai Macao Bridge Author Zhuhai 440400 Guangdong Peoples R China

In order to predict the joint opening-closing deformation of immersed tunnel in operation, a comprehensive prediction model combining time series decomposition, least square method, sparrow search algorithm (SSA) and support vector regression (SVR) is proposed, which is verified by the monitoring data of E13-E14 and E17 E18 joints opening-closing deformation of immersed tunnel in Hong Kong-Zhuhai-Macao Bridge. Firstly, on the basis of summarizing and analyzing the characteristics of joint opening-closing deformation, the monitoring sequence of joint opening-closing is decomposed into trend component, periodic component and residual component by using time series decomposition singular spectrum analysis method, which clearly shows the composition of each component. Secondly, the least square method is used to fit and predict the trend compo-nent;Combining the commonly used RBF kernel function, Sigmoid kernel function and Ploy kernel function with SVR respectively, the SSA algorithm is used to globally optimize the penalty factors and kernel function pa-rameters of the three models, and the optimized three coupling models are used to predict the periodic component and residual component. The results show that the prediction effect of the SVR model optimized by SSA algorithm is better than that of the traditional SVR model, The prediction result of SSA-SVR model con-structed by RBF kernel function is the best. Finally, the time series addition model is applied to superimpose the optimal predicted values to obtain the predicted opening-closing amount. The mean square errors of the total predicted values of the opening-closing amount of the two joints are 0.1900mm and 0.2291mm respectively, the average errors are 0.1587mm and 0.2156mm respectively, and the correlation coefficients are 0.9792 and 0.9695 respectively. A satisfactory prediction effect is obtained, which proves the reliability and accuracy of this method, and provides a new way for the prediction of the open

关键词： Immersed tunnel Opening -closing deformation prediction Singular spectrum analysis sparrow search algorithm Support vector regression Pipe joint

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Light intensity optimization of optical fiber stress sensor based on SSA-LSTM model

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FRONTIERS IN ENERGY REsearch 2022年 10卷

作者： Yu, Dakuan Qiao, Xueguang Wang, Xiangyu Northwestern Polytech Univ Sch Phys Sci & Technol Xian Peoples R China Northwest Univ Sch Phys Xian Peoples R China

In order to further improve the measurement range and accuracy of optical fiber stress sensor based on the interference between rising vortex beam and plane wave beam, a new stress demodulation model is designed. This model proposes a method to optimize the long-term and short-term memory network (LSTM) model by using sparrow search algorithm (SSA), extract the main characteristics of the influence of various variables on optical fiber stress sensor, and fit the relationship between sensor stress and beam phase difference. This method is an attempt of the deep learning model LSTM in the study of stress mediation model. There are very few related studies, and it is very necessary to fill this gap. In the experiment, the SSA-LSTM neural network is trained by using the data of stress and phase difference measured by the optical fiber stress sensor. The test results show that the mean error of SSA-LSTM neural network is less than that of LSTM neural network, which shows that the combination of SSA-LSTM model and optical fiber stress sensor can make its measurement accuracy higher, The algorithm can more effectively reduce the influence of the surrounding environment and the influence of the light source fluctuation on the measurement range and accuracy of the optical fiber sensor, and has good practical application value. It is proved that the deep learning LSTM neural network has good application value in the light intensity optimization of optical fiber stress sensor.

关键词： sparrow search algorithm long and short term memory network optical fiber stress sensor light intensity optimization SSA-LSTM

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A Two-Terminal Directional Protection Method for HVDC Transmission Lines of Current Fault Component Based on Improved VMD-Hilbert Transform

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ENERGIES 2023年第19期16卷

作者： Liu, Shuhao Han, Kunlun Li, Hongzheng Zhang, Tengyue Chen, Fengyuan Guangxi Univ Guangxi Key Lab Power Syst Optimizat & Energy Tech Nanning 530000 Peoples R China

The traveling wave protection of high voltage direct current (HVDC) transmission lines is susceptible to the influence of transition resistance. As a backup protection, current differential protection has absolute selectivity, but usually requires an increase in delay to avoid misoperation caused by distributed capacitance on the line, resulting in a longer action time. Based on this, a two-terminal directional protection method for HVDC transmission lines is proposed based on sparrow search algorithm (SSA)-Variational Mode Decomposition (VMD) and Hilbert phase difference. On the basis of analyzing the directional characteristics of the current fault component at both ends of the rectifier and inverter sides under different faults, SSA is first used to optimize the parameters of VMD. The residual components representing the direction of the current fault component at both ends are extracted through VMD, and then the Hilbert phase difference of the residual components at both ends is calculated to identify faults inside and outside the line area. In addition, fault pole selection can be achieved based on the ratio of the sum of multi-band Hilbert energy of single-terminal voltage fault components at the positive and negative poles. Simulation experiments have shown that the proposed protection scheme can quickly and effectively identify fault and has good tolerance to transition resistance and noise interference.

关键词： HVDC current and voltage fault components sparrow search algorithm Variational Mode Decomposition Hilbert transform fault identification

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A swarm intelligence approach for energy management of grid-connected microgrids with flexible load demand response

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INTERNATIONAL JOURNAL OF ENERGY REsearch 2022年第4期46卷 4301-4319页

作者： Singh, Arvind R. Ding, Lei Raju, Dhenuvakonda Koteswara Raghav, Lolla Phani Kumar, Rangu Seshu Shandong Univ Sch Elect Engn Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan Peoples R China Natl Inst Technol Dept Elect Engn Silchar India

Ever since its inception, the concept and application of demand-side response have continued to evolve and take a new shape in microgrid energy management. The application of demand response programs in the microgrid literature lacks the consideration of flexible price elasticity of different load categories. The realistic characterization of load-responsive models with a combination of both linear and nonlinear models is necessary to study the effect of demand response programs. To cover this research gap, the impact of price-based demand response programs on the optimal scheduling of microgrids is investigated in the presence of linear and nonlinear load models. The flexible elasticity model is adopted to characterize the actual behavior of customer responsiveness towards changes in electricity price. Five load models, namely linear, logarithmic, exponential, power, and hyperbolic, were derived for each price-based demand response program. Furthermore, the stochastic-based scenario modeling is considered to cope with the volatile renewable generation in the microgrid network. The recently reported swarm intelligence-based algorithm called the sparrow search method is intended to solve the proposed microgrid energy management issue for the first time in the literature. Fifteen case studies on the basis of distinct linear and nonlinear load scenarios have been carried out to assess the effectiveness of the methodology proposed. Finally, various techno-economic performance indices were evaluated for all case studies, and a priority-wise ranking is assigned based on the multi-criteria assessment technique.

关键词： comprehensive load-responsive model critical peak pricing energy management system flexible price elasticity microgrid real-time pricing sparrow search algorithm time of use

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Bearing fault diagnosis using transfer learning and optimized deep belief network

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MEASUREMENT SCIENCE AND TECHNOLOGY 2022年第6期33卷

作者： Zhao, Huimin Yang, Xiaoxu Chen, Baojie Chen, Huayue Deng, Wu Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China Haifeng Gen Aviat Technol Co Ltd Beijing Peoples R China China West Normal Univ Sch Comp Sci Nanchong 637002 Peoples R China

Bearing is an important component in mechanical equipment. Its main function is to support the rotating mechanical body and reduce the friction coefficient and axial load. In the actual operating environment, the bearings are affected by complex working conditions and other factors. Therefore, it is very difficult to effectively obtain data that meets the conditions of independent and identical distribution of training data and test data, which result in unsatisfactory fault diagnosis results. As a transfer learning method, joint distribution adaptive (JDA) can effectively solve the learning problem of inconsistent distribution of training data and test data. In this paper, a new bearing fault diagnosis method based on JDA and deep belief network (DBN) with improved sparrow search algorithm (CWTSSA), namely JACADN is proposed. In the JACADN, the JDA is employed to carry out feature transfer between the source domain samples and target domain samples, that is, the source domain samples and target domain samples are mapped into the same feature space by the kernel function. Then the maximum mean difference is used as the metric to reduce the joint distribution difference between the samples in the two domains. Aiming at the parameter selection of the DBN, an improved sparrow search algorithm (CWTSSA) with global optimization ability is used to optimize the parameters of the DBN in order to construct an optimized DBN model. The obtained source domain samples and target domain samples are divided into training set and test set, which are input the optimized DBN to construct a bearing fault diagnosis model for improving the diagnosis accuracy. The effectiveness of the proposed method is verified by vibration data of QPZZ-II rotating machinery. The experimental results show that the proposed JACADN method can effectively improve the fault diagnosis accuracy of rolling bearings under variable operating conditions.

关键词： bearing fault diagnosis joint distribution adaptive deep belief network sparrow search algorithm

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An Advance Control of Grid Integrated Wind Turbine Driven DFIG-Battery System with Grid Power Shaping Under Gust Wind Variation

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IETE JOURNAL OF REsearch 2024年第3期70卷 3030-3051页

作者： Ranjan, Alok Behera, Manoja Kumar Saikia, Lalit Chandra Natl Inst Technol Silchar Silchar 788010 Assam India

There has been an increase in wind power penetration into the utility grid. The wind speed variations exhibit undesirable oscillations that influence power quality and system reliability. Therefore, the grid operation is challenging in this scenario. Battery energy storage system (BESS) smooths out wind power production and reduces fluctuations. However BESSs low power density and high capacity requirements to compensate for fluctuation limit its implementation for high-power applications. This paper introduces a wind energy conversion system (WECS) based on a doubly fed induction generator (DFIG) incorporating a vanadium redox flow battery (VRFB) in the DC-link, which supports grid power smoothing and power balance even under varying wind speed. Real-site wind data was used to develop a technique for designing the VRFB. The rotor side converter (RSC) employs an off-maximum power point tracking to keep the VRFB operation within the limit and deliver consistent power to the grid. An improved phase lock loop control is designed for DFIG that suppresses DC offset at the input and allows RSC and grid side converter (GSC) to perform appropriately even under grid disturbances. The GSC control is designed with an improved DC-link voltage control by adopting an anti-windup fractional-order proportional-integral (AWFOPI) controller, which can improve transient response. A sparrow search algorithm is adopted to tune the AWFOPI controller. Matlab Simulink is used to simulate the system. Test results show that the system performs well under various operating conditions and is satisfactory. It has also been tested on the OPAL-RT real-time simulator test bench.

关键词： Doubly fed induction generator flow battery fractional order control improved PLL power smoothing sparrow search algorithm

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Power system short-term voltage stability assessment based on improved CatBoost with consideration of model confidence

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ENERGY SCIENCE & ENGINEERING 2023年第2期11卷 783-795页

作者： Zhu, Ruijin Ciren, Gesang Tang, Bo Gong, Xuejiao Tibet Agr & Anim Husb Univ Elect Engn Coll Nyingchi Peoples R China State Grid Tibet Elect Power Co Ltd Ultra High Voltage Branch Lhasa Peoples R China Tibet Agr & Anim Husb Univ Elect Engn Coll Nyingchi 860000 Peoples R China

With the intensive commissioning of high voltage direct current, transient voltage problems have become increasingly prominent, which seriously threatens the safe and stable operation of the power system. On the basis of cascaded CatBoost (CasCatBoost) and sparrow search algorithm (SSA), a novel temporal-adaptive data-driven method for short-term voltage stability (STVS) assessment is proposed in this paper. First, normalized mutual information feature selection is employed for important feature selection to reduce the computational burden. Taking the important features as inputs, the CasCatBoost model is constructed to mine STVS informatization. On the basis of CatBoost and cascaded structure, CasCatBoost can enhance the assessment accuracy without sacrificing the assessment earliness. Furthermore, the SSA is embedded into the offline phases of CasCatBoost to determine the optimal confidence threshold. Extensive numerical tests are conducted on the improved New England 39-bus system, and the simulation results demonstrate that the proposed method outperforms other representative machine learning algorithms and exhibits excellent reliability, rapidity, and applicability.

关键词： cascaded CatBoost confidence threshold data-driven short-term voltage stability sparrow search algorithm

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Stall Warning algorithm of Axial Compressor Based on SSA-DBN

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电脑学刊 2022年第3期33卷 59-71页

作者： Xiao-Hong Qiu Jia-Li Chen Zi-Ying Ao

To solve the problem of stall warning for axial compressors, this paper proposes a stall warning algorithm based on the sparrow search algorithm, which optimizes the deep belief network. The deep belief network is trained by using deep learning to extract the FFT spectrum of compressor stall experiment data directly as the feature vector. To improve the accuracy of DBN classification, parameters of hidden layer nodes n and initial weights w of DBN were optimized by SSA algorithm, and stall warning algorithm model of SSA-DBN axial-flow compressor was established. The experimental results of the algorithms show that the stall data at each speed is at least 0.1~0.3s in advance for early warning. This method is 0.075~0.281s ahead of the various models from the past to the present, and noise is superimposed on the experimental data to verify the Robustness of the way, better surge warning margin performance, and engineering practicability.

关键词： deep belief network sparrow search algorithm parameter optimization SSA-DBN axial compressor

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Identification of red jujube varieties based on hyperspectral imaging technology combined with CARS-IRIV and SSA-SVM

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JOURNAL OF FOOD PROCESS ENGINEERING 2022年第10期45卷

作者： Wang, Simin Sun, Jun Fu, Lvhui Xu, Min Tang, Ningqiu Cao, Yan Yao, Kunshan Jing, Jianpeng Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China

To identify the varieties of red jujube rapidly and nondestructively, hyperspectral imaging (HSI) technology was applied in this article. Hyperspectral data of 480 samples with four different varieties were acquired in the range of 400.68-1001.61 nm. First, Savitzky-Golay and standard normal variable were utilized to process raw spectra. Afterward, a novel method combining competitive adaptive reweighted sampling and iterative retained information variable (CARS-IRIV) was proposed to select feature wavelengths. The support vector machine (SVM) modeling results indicated that CARS-IRIV had better information extraction performance and simplified the model. Finally, to further improve the accuracy, sparrow search algorithm (SSA) was adopted to optimize the parameters (c, g) of SVM. The results showed that SSA-SVM exhibited greater accuracy than other compared models, and the accuracy of training and test sets were 100% and 96.68%, respectively. It confirmed that HSI technology coupled with CARS-IRIV-SSA-SVM can effectively identify varieties of red jujube. Practical Applications The traditional ways of classifying red jujube varieties are destructive and laborious. Therefore, HSI technology was adopted to overcome the above shortcomings. In this article, the best identification performance was based on the CARS-IRIV-SSA-SVM model with an identification accuracy of 96.68%. This study is helpful for the identification of other agricultural product varieties by HSI technology.

关键词： CARS-IRIV HSI technology identification model red jujube varieties sparrow search algorithm

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Lithium-Ion Battery Life Prediction Method under Thermal Gradient Conditions

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ENERGIES 2023年第2期16卷

作者： Song, Dawei Wang, Shiqian Di, Li Zhang, Weijian Wang, Qian Wang, Jing V. State Grid Henan Elect Power Econ & Technol Res In Zhengzhou 450052 Peoples R China State Grid Henan Elect Power Co Internet Dept Zhengzhou 450052 Peoples R China Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

Thermal gradient is inevitable in a lithium-ion battery pack because of uneven heat generation and dissipation, which will affect battery aging. In this paper, an experimental platform for a battery cycle aging test is built that can simulate practical thermal gradient conditions. Experimental results indicate a high nonlinear degree of battery degradation. Considering the nonlinearity of Li-ion batteries aging, the extreme learning machine (ELM), which has good learning and fitting ability for highly nonlinear, highly nonstationary, and time-varying data, is adopted for prediction. A battery life prediction model based on the sparrow search algorithm (SSA) is proposed in this paper to optimize the random weights and bias of the ELM network and verified by experimental data. The results show that compared with traditional ELM and back-propagation neural networks, the prediction results of ELM optimized by SSA have lower mean absolute error percentages and root mean square errors, indicating that the SSA-ELM model has higher prediction accuracy and better stability and has obvious advantages in processing data with a high nonlinear degree.

关键词： thermal gradient capacity degradation life prediction extreme learning machine sparrow search algorithm

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