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A two-stage transformer fault diagnosis method based multi-filter interactive feature selection integrated adaptive sparrow algorithm optimised support vector machine

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IET ELECTRIC POWER APPLICATIONS 2023年第3期17卷 341-357页

作者： Shi, Hanyu Chen, Mingxia Guilin Univ Technol Sch Mech & Control Engn Guilin 541006 Guangxi Peoples R China

The scarcity of samples and disunity of feature inputs hinder the enhancement of transformer fault diagnosis performance, and there are mutual influences between model construction and feature selection, which cannot only consider a single process. Therefore, this study proposes a novel two-stage transformer fault diagnosis strategy, which includes a multi-filter interactive feature selection method (MIFS) constructed, and a diagnosis model ASSA-SVM based on the adaptive sparrow algorithm (ASSA) optimised support vector machine (SVM). Firstly, the proposed MIFS incorporates ReliefF and mRMR to establish a comprehensive criterion ReliefF-mRMR for feature importance ranking, and then performs dimension-by-dimension input classifier interaction selection based on the ranking results to obtain the optimal feature subset. Secondly, ASSA was proposed to optimise the kernel parameters of SVM. A two-stage integration model MIFS-ASSA-SVM was developed. Finally, Experiments were conducted using real fault data, and the diagnostic performance of different feature inputs, optimisation algorithms and classifiers were compared. The results show that the proposed method performs well on parameter optimisation, can dynamically and interactively select feature subsets with few dimensions and good generalisation performance, its overall diagnosis accuracy reached 92.47%, and the diagnosis performance of each fault type has good performance in multiple evaluation metrics.

关键词： feature selection sparrow algorithm support vector machine transformer fault diagnosis

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Simulation Research on Spatial Positioning of Transformer Partial Discharge Sources Based on sparrow algorithm

World Scientific Research Journal

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World Scientific Research Journal 2022年第8期8卷 610-618页

作者： Dingdong Qian Wei Wang Ke Song

For the partial discharge of the transformer fault, the UHF sensor is greatly affected by electromagnetic interference and disturbance, and the conventional ultrasonic external detection method is also difficult to extract the direct wave signal because the ultrasonic signal is greatly attenuated through the outer wall of the transformer. At the inspection port of the transformer, this paper proposes a built‐in sensor arrangement, establishes a three‐phase winding model with COMSOL, and uses the finite element method to analyze the propagation law of ultrasonic waves in the transformer winding. Then an optimization equation of spatial positioning is established according to the effective time difference, and the positioning problem is solved by the sparrow Optimization (SSA) algorithm. The simulation results show that for the PD sources of the high and low voltage windings of each phase of the three‐phase transformer, the arrangement of two built‐in detection points in each inspection port can effectively locate the PD source, which provides a reference for the internal PD positioning of the field experiment transformer.

关键词： sparrow algorithm Partial discharge Ultrasonic positioning Sound source localization

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Optimization of terminal area arrival flight sorting based on an improved sparrow search algorithm

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SCIENCE PROGRESS 2024年第1期107卷 368504241238078页

作者： Zhao, Weixi Liang, Te Kunming Univ Sci & Technol Fac Civil Aviat & Aeronaut Kunming Peoples R China Beihang Univ Fac Instrumentat Sci & Optoelect Engn Beijing Peoples R China Kunming Univ Sci & Technol Fac Civil Aviat & Aeronaut Kunming 650500 Peoples R China

At present, airspace congestion and flight delays have become widespread concerns. This study aims to optimize the sequencing of arrival flights in the terminal area of multirunway airports. Considering the constraints of multiple runways, slant intervals and moving flight positions, this article establishes an optimization model for arrival flight sequencing in a multirunway airport terminal area. Accordingly, an improved sparrow search algorithm (ISSA) is proposed based on Chebyshev chaotic mapping, the golden sine strategy, and the variable neighborhood strategy. Through six basic test functions, the ISSA is compared with particle swarm optimization, the whale optimization algorithm, the genetic algorithm, and other algorithms to verify its superiority. Finally, two sets of instance data from Kunming Changshui Airport were used for experiments. The results show that the total delay times (TDTs) of small-scale flights (number of aircraft: 29) and large-scale flights (number of aircraft: 147) are 55.3% and 20.5% lower, respectively, than those of the first-come-first-served algorithm. The superiority of the ISSA designed in this article is verified, and it can significantly reduce the TDTs of arrival flights. It is suitable for optimizing arrival flights during peak hours at most airports. This approach provides theoretical support for optimizing the sorting of flights in terminal areas.

关键词： Flight sequence optimization algorithm sparrow algorithm air traffic management

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BCSSA-VMD and ICOA-ELM based fault diagnosis method for analogue circuits

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ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2025年第2期123卷 1-22页

作者： You, Dazhang Liu, Shan Yuan, Ye Zhang, Yepeng Hubei Univ Technol Coll Mech Engn Wuhan 430068 Peoples R China Hubei Prov Key Lab Modern Mfg Qual Engn Wuhan 430068 Peoples R China

Analog circuits are an important component of integrated circuit systems, and circuit systems are the foundation for ensuring the normal operation of electronic devices. Therefore, it is necessary to efficiently diagnose and maintain faults in analog circuits. However, due to the tolerance, high nonlinearity, and susceptibility to environmental interference of analog circuit components, the development of related research on fault diagnosis has been hindered, and it cannot meet the current practical requirements for high safety and reliability of electronic devices. With the continuous increase in circuit scale and integration level, how to effectively and as much as possible extract more discriminative fault features is the key research direction of analog circuit fault diagnosis. Therefore, this article proposes a variational model decomposition (VMD) feature extraction method that combines Butterfly and Cauchy sparrow search algorithms (BCSSA) and relies on an improved crayfish optimization algorithm (COA) to optimize the Extreme Learning Machine (ELM). Decomposition (VMD) feature extraction method, and rely on Improved Crayfish Optimization algorithm (COA) Optimized Extreme Learning Machine (ELM) to complete the classification of faults. Firstly, the BCSSA algorithm is used to optimize the number of VMD decomposition modes K and the penalty factor alpha to achieve the optimal VMD decomposition of the original fault signal, obtain a series of Intrinsic Mode Function (IMF) and calculate its envelope entropy, determine the optimal IMF component by selecting the IMF component with the lowest envelope entropy., and calculate its time-domain parameter, then normalize and reduce the dimensionality to construct the vector that contains the characteristics of the fault. The normalized dimensionality reduction process constitutes the fault feature vector;secondly, the ICOA algorithm is introduced to optimize the ELM;Ultimately, the fault feature vector is fed into the ELM

关键词： Analogue circuits Fault diagnosis sparrow algorithm VMD algorithm Crayfish algorithm Extreme learning machine

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Improved lithium battery state of health estimation and enhanced adaptive capacity of innovative kernel extreme learning machine optimized by multi-strategy dung beetle algorithm

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IONICS 2025年第1期31卷 329-343页

作者： Mo, Daijiang Wang, Shunli Zhang, Mengyun Fan, Yongcun Wu, Wenjie Fernandez, Carlos Su, Qiyong Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Inner Mongolia Univ Technol Coll Elect Power Hohhot 010080 Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen Scotland Hunan Univ Sci & Engn Sch Informat Engn Yongzhou 425199 Peoples R China

Accurate estimation of the state of health (SOH) of lithium batteries is crucial to ensure the reliable and safe operation of lithium batteries. Aiming at the problems of low accuracy of extreme learning machine and poor mapping ability of conventional kernel function, this paper constructs a kernel extreme learning machine model and uses a multi-strategy improved dung beetle algorithm to find the optimal parameters. In this paper, for the poor estimation effect caused by the difficulty of adapting the conventional kernel function to nonlinear batteries, we design a cosine polynomial kernel function, which improves the linear divisibility of the data;in addition, for the global search, local development, and convergence improvement of the dung beetle algorithm, we introduce the optimal Latin hypercubic idea, the Cauchy variation strategy, and the sparrow alert mechanism, which successfully improve the parameter searching capability and sensitivity of the algorithm, respectively. We successfully improve the capability and sensitivity of the algorithm in parameter searching. We experimentally verify the reliability and validity of the proposed model, and the maximum root mean square error and the average absolute percentage error obtained in the test are not higher than 0.00753 and 0.00399, respectively, and the minimum fit is not lower than 0.9921, which reflects the high accuracy and strong adaptive ability of the model.

关键词： Lithium battery state of health Dung beetle optimization algorithm Cosine polynomial kernel function Kernel extreme learning machine sparrow algorithm

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Research on energy management strategy for incremental range electric vehicles integrating speed prediction

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SCIENCE PROGRESS 2025年第1期108卷 368504241308957页

作者： Wang, Wei Zhang, Kun Zhang, Qingming Wang, Xiaochun Jiangsu Univ Technol Sch Mech Engn 1801 Zhongwu Ave Changzhou 213001 Jiangsu Peoples R China

The main challenge facing current energy management strategies for extended-range electric vehicles is effectively balancing power demand and energy utilization to enhance fuel economy under complex and variable driving conditions. Therefore, to optimize the distribution between the two energy sources of extended-range electric vehicles and improve their fuel economy, this paper proposes an energy management strategy incorporating speed prediction. Firstly, the long short-term memory neural network speed prediction scheme is investigated, and its effectiveness under different cyclic conditions is verified. Secondly, the four hyperparameters of the long short-term memory neural network structure were optimized using the sparrow algorithm (SA) to further enhance the prediction accuracy of the long short-term memory speed prediction algorithm. After optimization, the mean square deviation and mean absolute error are reduced by 46.46% and 54.46%, respectively, compared with the pre-optimization period. Finally, an energy management strategy based on speed prediction was designed using the sparrow algorithm-long short-term memory model. The results show that the speed prediction-based energy management strategy reduces fuel consumption by 6.05% and 3.50% under the New European Driving Cycle and World Light Vehicle Test Cycle conditions, respectively, compared to the rule-based hybrid control strategy.

关键词： Extended-range electric vehicle vehicle speed forecasting LSTM neural network sparrow algorithm energy management

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Short-Term Electricity Price Forecast Based on SSA-SVM Model 2nd

Short-Term Electricity Price Forecast Based on SSA-SVM Model

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2nd International Conference on Advanced Intelligent Technologies (ICAIT)

作者： Duan, Zhenyu Liu, Tianyu Shanghai DianJi Univ Sch Elect Engn Shanghai 201306 Peoples R China

ISBN: (纸本)9789811697357;9789811697340

In the electricity market environment, price forecast has the characteristics of periodicity and unpredictability. Based on these characteristics, this paper proposes a sparrow-algorithm-optimized support vector machine (SSA-SVM) model to predict short-term electricity prices. The sparrow algorithm (SSA) has high convergence performance and local search ability, and the parameters of SVM are optimized, the SSA-SVM prediction model is established, and the electricity price data is predicted and analyzed, and then compared with the simulation results of the SSA-SVM model. Finally, the SSA-SVM method has a good effect, error, and convergence speed of the SSA-SVM model are improved.

关键词： Short-Term Electricity Price Forecast sparrow algorithm SSA-SVM Model Support Vector Machine

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Research on Intelligent Optimization Method of Prefabricated Component Layout 43

Research on Intelligent Optimization Method of Prefabricated...

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43rd Chinese Control Conference (CCC)

作者： Li, Dong Zeng, Guodong Zhu, Mintao Zhang, Lu Liu, Yu Shenyang Jianzhu Univ Sch Mech Engn Shenyang 110168 Peoples R China Shanghai Construct Grp Co Ltd Shanghai Peoples R China Shenyang Baide New Bldg Mat Co Ltd Shenyang Peoples R China

ISBN: (纸本)9798350366907;9789887581581

To address the issue of the low utilization rate of mold platforms in the production line of precast concrete components, a method combining an improved sparrow algorithm with the lowest horizontal line algorithm is proposed. Firstly, a layout model for precast concrete components considering the reinforcement process is established. Subsequently, the improved sparrow algorithm is employed to optimize the production sequence of components, and the lowest horizontal line algorithm is utilized to determine the optimized layout positions of the components. By introducing a cubic chaotic sequence to initialize the sparrow population, setting a similarity mutation operator, and applying the cauchy mutation to similar individuals, the sparrow algorithm is enhanced. This enhancement ensures a more even distribution of the sparrow population in the solution space, thereby strengthening the global optimization capability of the sparrow algorithm. Finally, the effectiveness of the proposed method is validated through simulation tests.

关键词： precast concrete component lowest horizontal line algorithm sparrow algorithm layout optimization similarity mutation operator

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Temperature Prediction for Ultra-Low Temperature Measurement System Based on Improved Extreme Learning Machine algorithm 3

Temperature Prediction for Ultra-Low Temperature Measurement...

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3rd International Conference on Electronic Information Engineering and Computer Communication, EIECC 2023

作者： Zhang, Zhenyu Dong, Xiang Xue, Yufei Jiang, Man Cao, Huibin School of Electrical Engineering and Automation Anhui University Anhui Hefei230601 China Institutes of Physical Science and Information Technology Anhui University Anhui Hefei230601 China Hefei Institutes of Physical Science Chinese Academy of Sciences Anhui Hefei230601 China

ISBN: (纸本)9798350359961

Temperature monitoring is essential to ensure the operational safety of fusion devices in the ultra-low temperature liquid helium region. However, the unavoidable errors caused by environmental effects severely hinder the accuracy of its temperature measurement. Considering that traditional methods are not accurate enough to predict the temperature at low temperatures, for the first time, this work proposes an algorithm based on the SSA-PSO-ELM network for constructing a prediction model of the cryogenic measurement system. By this method, the initial number of hidden layer nodes is first randomly generated, then the network is trained with weight thresholds, and finally a quadratic optimization search is performed to determine the best network prediction model. Compared with the traditional algorithm, our algorithm effectively reduces the prediction error, improve the accuracy, and realize more accurate temperature monitoring in ultra-low temperature environment. © 2023 IEEE.

关键词： Extreme Learning Machine Particle swarm arithmetic sparrow algorithm Temperature prediction Ultra-low temperature measurement system

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Image Segmentation Method With Improved GA Optimization of Two-Dimensional Maximum Entropy

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IEEE ACCESS 2024年 12卷 183279-183292页

作者： Wang, Jin Tan, Yanli Bo, Xiaoning Li, Guoqin Taiyuan Inst Technol Dept Elect Engn Taiyuan 030008 Peoples R China Univ Teknol MARA UiTM Coll Comp Informat & Math Shah Alam 40450 Selangor Malaysia

Image segmentation is the cornerstone that determines the effectiveness of image processing, but traditional image segmentation methods have issues such as long computation time, low recognition accuracy, and poor anti-interference ability. To address this issue, research improves the genetic algorithm using adaptive hybridization and adaptive mutation probability, and combines it with the Bat algorithm to optimize the local optimization problem of the image. The sparrow algorithm is utilized to optimize the two-dimensional maximum entropy of the image, and the nonlinear inertia weight factor is brought to optimize the local search ability. The Levy flight constant is used to overcome the local optimization problem. The experiment findings indicate that the optimized algorithm improves the similarity of medical image features by an average of 11.2%, reduces segmentation accuracy by 2.6% under noise interference compared to other algorithms, and has an average peak signal-to-noise ratio 0.96 higher than other algorithms. From this, the improved algorithm greatly raises the similarity of segmented image features, has stronger resistance to noise interference than other algorithms, and significantly improves the recognition accuracy of different parts of the image. The improved algorithm provides a reference for subsequent image processing research.

关键词： Image segmentation Accuracy Genetic algorithms Image recognition Image processing Mathematical models Entropy Biomedical imaging Probability PSNR Genetic algorithm adaptive mutation probability sparrow algorithm image segmentation two-dimensional maximum entropy

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