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Traveling wave inversion method of power line faults

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年 149卷

作者： Xia, Yixiang Li, Zewen Wang, Shuai Sun, Jian Guo, Xinyu Changsha Univ Sci & Technol Elect & Informat Engn Dept Changsha Peoples R China

In substations, the fault signal at the measuring point includes a variety of signal aliasing. This is partly due to the short length of the substation lines and the exitance of multiple types of equipment that cause the reflection and refraction of the traveling waves. Compared with the incident wave, the distortion in the fault signal is often significant hence interfering with the fault identification and location. To address this issue, we propose an inversion method of the incident wave. To do this, we first investigate the impact of multiple outlets of the substation bus, equivalent stray capacitance of the bus to the ground, transformers, line trap, and other types of equipment on traveling wave transmission. Modeling the aliasing effect of the traveling wave measuring point signal we then propose a fault signal filtering algorithm based on the improved Empirical Mode Decomposition (EMD) and Fast Fourier Transform (FFT). Using this algorithm we then formulate an incident wave inversion model based on substation component parameters. The component parameters in the equivalent circuit model of substation are then accurately identified to achieve an accurate inversion of the incident waves. Identification of parameters is based on an optimization model of substation component parameters based on the particle swarm optimization algorithm (PSO). Our results show that compared with the measured mixed wave signal, the fault characteristics of the inverted incident waves are more accurate, hence improving the fault location accuracy.

关键词： Waveform inversion Incident wave Substation particle swarm optimization algorithm

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Prediction of electricity consumption based on GM(1,Nr) model in Jiangsu province, China

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ENERGY 2023年 262卷

作者： Du, Xiaoyi Wu, Dongdong Yan, Yabo Jiangnan Univ Sch Business Wuxi 214122 Jiangsu Peoples R China Nankai Univ Coll Tourism & Serv Management Tianjin 300350 Peoples R China

In this paper, GM(1,N-r) model is established to improve the traditional GM(1,N) model from three aspects: (1) transforming the original sequence to satisfy the modeling conditions with particle swarm optimization algorithm;(2) introducing grey incidence analysis to obtain the grey incidence ranking and carrying out stepwise test for significant variables to determine the number of variables;and (3) predicting the related factor sequence through the improved GM(1,1) model. Empirical analysis shows that the proposed GM(1,N-r) model has remarkable good prediction performance compared with the traditional grey forecasting model. It is also demonstrated that the extraction of influencing factors can significantly improve the prediction effectiveness, especially when pursuing the best fitting effect on small sample data. The findings indicate that the electricity consumptions of Jiangsu Province in the next several years will be at a high level and keep rising, with a predicted value of 9712.48 billion kilowatt-hours in 2030. The findings can help the government and energy related institutions to develop management policies on energy demand, and the proposed model can also be extended for the application in other regions.

关键词： Electricity consumptions forecasting Grey forecasting model GM(1,N-r) model Grey incidence analysis particle swarm optimization algorithm

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Quantitative Identification Method for Glass Panel Defects Using Microwave Detection Based on the CSAPSO-BP Neural Network

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SENSORS 2023年第3期23卷

作者： Fang, Jun Deng, Zhiyang Tu, Jun Song, Xiaochun Hubei Univ Technol Sch Mech Engn Wuhan 430068 Peoples R China Hubei Key Lab Modern Mfg Qual Engn Wuhan 430068 Peoples R China

To address the problem of the quantitative identification of glass panel surface defects, a new method combining the chaotic simulated annealing particle swarm algorithm (CSAPSO) and the BP neural network is proposed for the quantitative evaluation of microwave detection signals of glass panel defects. First, the parameters of the particle swarm optimization (PSO) algorithm are dynamically assigned using chaos theory to improve the global search capability of the PSO. Then, the CSAPSO-BP neural network model is constructed, and the return loss and phase of the microwave detection echo signal of glass panel defects are extracted as the input feature quantity of the network, from which the intrinsic connection between input and output is found through network training and testing to achieve the prediction of the depth and width of glass panel surface defects. The results show that the CSAPSO-BP network model can more accurately characterize the defect geometry of glass panels than the PSO-BP network model.

关键词： glass panel chaos simulated annealing algorithm particle swarm optimization algorithm microwave detection quantitative identification

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船用固体氧化物燃料电池与二冲程低速双燃料发动机集成动力系统的设计与优化

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哈尔滨工程大学学报（英文版） 2023年第4期22卷 837-849页

作者：曲金博冯永明吴云金朱元清邬斌扬肖忠旭 College of Power and Energy Engineering Harbin Engineering UniversityHarbin 150001China State Key Laboratory of Engines Tianjin UniversityTianjin 300072China

A combined system including a solid oxide fuel cell(SOFC)and an internal combustion engine(ICE)is proposed in this ***,a 0-D model of SOFC and a 1-D model of ICE are built as agent ***,parameter analysis of the system is conducted based on SOFC and ICE *** show that the number of cells,current density,and fuel utilization can influence SOFC and ***,a deep neural network is applied as a data-driven model to conduct optimized calculations efficiently,as achieved by the particle swarm optimization algorithm in this *** results demonstrate that the optimal system efficiency of 51.8%can be achieved from a 22.4%/77.6%SOFC-ICE power split at 6000 kW power ***,promising improvements in efficiency of 5.1%are achieved compared to the original ***,a simple economic analysis model,which shows that the payback period of the optimal system is 8.41 years,is proposed in this paper.

关键词： Combined system SOFC-ICE integrated cycle Data-driven model particle swarm optimization algorithm

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An Improved particle swarm optimization algorithm

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International Conference on Mechanical Engineering and Intelligent Systems (ICMEIS 2012)

作者： Jiang, Changyuan Zhao, Shuguang Guo, Lizheng Ji, Chuan Donghua Univ Coll Informat Sci & Technol Shanghai 201600 Peoples R China

ISBN: (纸本)9783037854693

Based on the analyzing inertia weight of the standard particle swarm optimization (PSO) algorithm, an improved PSO algorithm is presented. Convergence condition of PSO is obtained through solving and analyzing the differential equation. By the experiments of four Benchmark function, the results show the performance of S-PSO improved more clearly than the standard PSO and random inertia weight PSO. Theoretical analysis and simulation experiments show that the S-PSO is efficient and feasible.

关键词： particle swarm optimization algorithm inertia weight sinusoidal changing differential equation

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Spatial accessibility analysis and location optimization of emergency shelters in Deyang

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GEOMATICS NATURAL HAZARDS & RISK 2023年第1期14卷

作者： Zhang, Zuopei Hu, Yunfeng Lu, Wei Cao, Wei Gao, Xing Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing Peoples R China

The selection and planning of the location of emergency shelters have a crucial impact on the safety of residents and cities. In this paper, based on multivariate open geographic data, the Gaussian two-step floating catchment area method, K-means clustering, and particle swarm optimization algorithm methods are utilized to carry out a spatial accessibility analysis and location optimization of emergency shelters in Deyang City, Sichuan Province, China. The study shows that: (1) Deyang City's emergency shelters are higher than the government's relevant standard requirements in terms of major indicators such as single building area, total area and per capita shelter area. (2) The spatial distribution of emergency shelters in the study area is uneven and unreasonable, with accessibility from the urban center outwards, exhibiting a "high-low" distribution pattern. (3) The study suggests that 10 new emergency shelters can reduce the number of accessible blind areas by 43.31%. The study recommends that the assessment and construction of emergency shelter facilities in rural areas in China and globally should be emphasized. Reliable recommendations for improvement of emergency shelter planning in Deyang city are provided in the study results.

关键词： Spatial accessibility Gaussian two-step floating catchment area method K-means clustering particle swarm optimization algorithm location optimization

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Based on the Improved PSO-TPA-LSTM Model Chaotic Time Series Prediction

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ATMOSPHERE 2023年第11期14卷

作者： Cai, Zijian Feng, Guolin Wang, Qiguang Yangzhou Univ Coll Phys Sci & Technol Yangzhou 225002 Peoples R China China Meteorol Adm Natl Climate Ctr Lab Climate Studies Beijing 100081 Peoples R China China Meteorol Adm China Meteorol Adm Training Ctr Beijing 100081 Peoples R China

In order to enhance the prediction accuracy and computational efficiency of chaotic sequence data, issues such as gradient explosion and the long computation time of traditional methods need to be addressed. In this paper, an improved particle swarm optimization (PSO) algorithm and Long Short-Term Memory (LSTM) neural network are proposed for chaotic prediction. The temporal pattern attention mechanism (TPA) is introduced to extract the weights and key information of each input feature, ensuring the temporal nature of chaotic historical data. Additionally, the PSO algorithm is employed to optimize the hyperparameters (learning rate, number of iterations) of the LSTM network, resulting in an optimal model for chaotic data prediction. Finally, the validation is conducted using chaotic data generated from three different initial values of the Lorenz system. The root mean square error (RMSE) is reduced by 0.421, the mean absolute error (MAE) is reduced by 0.354, and the coefficient of determination (R2) is improved by 0.4. The proposed network demonstrates good adaptability to complex chaotic data, surpassing the accuracy of the LSTM and PSO-LSTM models, thereby achieving higher prediction accuracy.

关键词： chaotic sequence particle swarm optimization algorithm time-mode attention mechanism long short-term memory Lorenz system

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Energy-efficient beamforming optimization for MISO communication based on reconfigurable intelligent surface

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PHYSICAL COMMUNICATION 2023年 57卷

作者： Tan, Fangqing Xu, Xu Chen, Hongbin Li, Shichao Guilin Univ Elect Technol Guangxi Key Lab Wireless Wideband Commun & Signal Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Informat & Telecommun Guilin 541004 Peoples R China

Reconfigurable intelligent surface (RIS), a planar metasurface consisting of a large number of low-cost reflecting elements, has received much attention due to its ability to improve both the spectrum and energy efficiency (EE) by reconfiguring the wireless propagation environment. In this paper, we propose a base station (BS) beamforming and RIS phase shift optimization technique that maximizes the EE of a RIS-aided multiple-input-single-output system. In particular, considering the system circuits' energy consumption, an EE maximization problem is formulated by jointly optimizing the active beamforming at the BS and the passive beamforming at the RIS, under the constraints of each user' rate requirement, the BS's maximal transmit power budget and unit-modulus constraint of the RIS phase shifts. Due to the coupling of optimization variables, this problem is a complex non-convex optimization problem, and it is challenging to solve it directly. To overcome this obstacle, we divide the problem into active and passive beamforming optimization subproblems. For the first subproblem, the active beamforming is given by the maximum ratio transmission optimal strategy. For the second subproblem, the optimal phase shift matrix at the RIS is obtained by exploiting sine cosine algorithm (SCA). Moreover, for this case where each reflection element's working state is controlled by a circuit switch, each reflection element's switch value is optimized with the aid of particle swarm optimization algorithm. Finally, numerical results verify the effectiveness of our proposed algorithm compared to other algorithms.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Reconfigurable intelligent surface Energy efficiency Beamforming Sine cosine algorithm particle swarm optimization algorithm

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A novel integrated hydrogen and natural gas liquefaction process utilizing a modified double mixed refrigerant process pre-cooling system

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APPLIED THERMAL ENGINEERING 2023年 224卷

作者： Geng, Jinliang Sun, Heng China Univ Petr Natl Engn Res Ctr Oil & Gas Pipeline Transportat S MOE Key Lab Petr Engn Beijing Key Lab Urban Oil & Gas Distribut Technol Beijing 102249 Peoples R China

The hydrogen liquefaction process is energy-intensive due to the lower liquefaction temperature. To improve the energy efficiency of the hydrogen liquefaction process, a novel integrated liquefaction process employing a modified double mixed refrigerant process pre-cooling is proposed and analyzed. The three-stage adiabatic ortho-para hydrogen conversion makes the process compact. Furthermore, the particle swarm optimization algorithm is utilized to find the optimal decision variables. The result reveals that the total specific energy consumption (SEC) of the process is 6.5921 kWh/kgLH2-LNG, which is 15.83% lower than the base case. The SECs for the production of LNG and LH2 are 0.2445 kWh/kgLNG and 6.3476 kWh/kgLH2. Moreover, the exergy analysis and thermodynamic evaluation reveal that the exergy efficiency, coefficient of performance, and figure of merit of the process are 49.26%, 0.2306, and 0.49, exhibiting high thermodynamic performance. Finally, the sensi-tivity analysis indicates that the evaporation pressure of the sub-cooling refrigerant has the greatest impact on the performance of the process. The proposed process has an important reference value in the design of the combined production of LH2 and LNG from industrial by-products and the H2 liquefaction process.

关键词： Integrated liquefaction process Mixed refrigerant Liquefied hydrogen Liquefied natural gas particle swarm optimization algorithm

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Hybrid Structured Artificial Network For Compressive Strength Prediction Of HPC Concrete

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淡江理工学刊 2023年第7期26卷 989-999页

作者： Liang Chen

This study aims to develop a surrogate artificial neural network-based technique for predicting the compressive strength of concrete, which is the most significant factor in the life service of concrete and its durability in civil construction projects of civil engineering. For this goal, a data set for high-performance concrete is gathered from the literature repository, which includes a different percentage of fly ash, silica fume, and superplasticizer in the mix designs. The data set is applied to train and validate the optimal structured artificial neural network optimized by innovative equilibrium optimization and particle swarm optimization algorithms. The results showed that the EOANN-I and PANN-I model with the R2 and RMSE values of 0.9889, 1.771, 0.9881, and 1.8813, stood at the first and second stage of the most capable models in the prediction of HPC compressive strength. Although the PANN-I model's complexity is lower than the EOANN-I model, the rate of its prediction accuracy covers its lack of complexity.

关键词： HPC concrete compressive strength artificial neural network equilibrium optimization algorithm particle swarm optimization algorithm

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