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Improved Metaheuristic Optimization algorithm Applied to Hydrogen Fuel Cell and Photovoltaic Cell Parameter Extraction

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ENERGIES 2021年第3期14卷 619页

作者： Liu, En-Jui Hung, Yi-Hsuan Hong, Che-Wun Natl Tsing Hua Univ Dept Power Mech Engn Hsinchu 30013 Taiwan Natl Taiwan Normal Univ Undergrad Program Vehicle & Energy Engn Taipei 106 Taiwan

As carriers of green energy, proton exchange membrane fuel cells (PEMFCs) and photovoltaic (PV) cells are complex and nonlinear multivariate systems. For simulation analysis, optimization control, efficacy prediction, and fault diagnosis, it is crucial to rapidly and accurately establish reliability modules and extract parameters from the system modules. This study employed three types of particle swarm optimization (PSO) algorithms to find the optimal parameters of two energy models by minimizing the sum squared errors (SSE) and roots mean squared errors (RMSE). The three algorithms are inertia weight PSO, constriction PSO, and momentum PSO. The obtained calculation results of these three algorithms were compared with those obtained using algorithms from other relevant studies. This study revealed that the use of momentum PSO enables rapid convergence (under 30 convergence times) and the most accurate modeling and yields the most stable parameter extraction (SSE of PEMFC is 2.0656, RMSE of PV cells is 8.839 center dot 10(-4)). In summary, momentum PSO is the algorithm that is most suitable for system parameter identification with multiple dimensions and complex modules.

关键词： metaheuristic optimization algorithm particle swarm algorithm photovoltaic cell proton exchange membrane fuel cell

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Elevator dynamic monitoring and early warning system based on machine learning algorithm

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IET NETWORKS 2022年第0期

作者： Zhang, Shuai Yin, Qiangguo Wang, Jinlong Weifang Engn Vocat Coll Dept Informat Engn Weifang Shandong Peoples R China Weifang Engn Vocat Coll Dept Informat Engn Weifang 262500 Shandong Peoples R China

With the rapid progress of modern society, elevator, as a vertical means of transportation in modern high-rise buildings, has become an indispensable part of People's Daily life. Because the elevator is a kind of mechanical equipment with a very high frequency of use, the peak hour will reach 250 times per hour, and because the elevator in the daily operation of the running parts work many times, the operating system is complex, good quality. Different passengers lead to frequent elevator failures. The dynamic monitoring and early warning system is a system which, according to the characteristics of the elevator, monitors the change trend of risk factors in the elevator by collecting relevant information, and evaluates the degree of deviation of various risk states from the early warning line, so as to send early warning signals to the decision-making level and take pre-control countermeasures in advance. Machine learning is a multi-domain interdisciplinary discipline, which specialises in studying how computers simulate or realise human learning behaviours to acquire new knowledge or skills and reorganise existing knowledge structures to continuously improve their own performance. This study aims to monitor and warn the elevator dynamics. Machine learning algorithm is introduced in this work, and particle swarm optimisation algorithm is used to improve the model. The model is optimised, and the experimental comparison shows that the optimisation of model parameters can further improve the accuracy of elevator load prediction. Then, the deep network prediction model is constructed, and the number of network layers is determined through experiments. Firstly, a multi-layer network is trained in the training model, and this process can be used to extract the indicators of inducing factors, so as to avoid the difficult and time-consuming manual extraction of indicators, and improve the prediction accuracy of the system. After five experiments, the prediction errors of t

关键词： deep confidence network machine learning algorithm monitoring and early warning system particle swarm algorithm

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Research on Simulation Technology of Adaptive Inductive Signal Control for Urban Road Intersections Based on VISSIM

International Core Journal of Engineering

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International Core Journal of Engineering 2020年第11期6卷 146-151页

作者： Dejia Liu

Under the background that traffic control technology tends to be intelligent, this paper studies the adaptive induction control logic rules for urban road intersections, and uses Visual Basic to connect to the COM interface to develop the micro-traffic simulation software VISSIM to build a single-point intersection. Adapt to the signal control simulation platform. By comparing and analyzing the simulation evaluation results of timing control and induction control, the operation efficiency of the intersection under adaptive induction control is not as ideal as that of timing control. On this basis, this paper designs a calibration program for the adaptive induction control phase switching constraint conditions. Taking the VISSIM simulation output result as the target, the particle swarm intelligent optimization algorithm is used to iteratively optimize and find the optimal parameters. The adjusted adaptive signal control scheme has obvious advantages over the timing control scheme.

关键词： VISSIM COM interface Adaptive control particle swarm algorithm

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Analysis of Active Suspension Control Based on Improved Fuzzy Neural Network PID

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WORLD ELECTRIC VEHICLE JOURNAL 2022年第12期13卷 226页

作者： Li, Mei Li, Jiapeng Li, Guisheng Xu, Jie Hainan Univ Mech & Elect Engn Coll Haikou 570100 Peoples R China

To improve the comfort and smoothness of vehicle driving and reduce the vehicle vibration caused by uneven road surface. In this paper, a new active suspension control strategy is pro-posed by combining a fuzzy neural network and a proportional-integral-derivative (PID) controller, taking body acceleration as the main optimization target and adjusting the parameters of the PID controller in real time. Meanwhile, a fuzzy neural network parameter optimization algorithm combining a particle swarm optimization algorithm and gradient descent method is proposed to realize offline optimization and online fine-tuning of fuzzy neural network parameters. Finally, the active suspension model of a 2-degree-of-freedom 1/4 vehicle is established using MATLAB/Simulink, and the proposed control scheme is verified through simulation studies. The results show that the active suspension system with a particle swarm-optimized fuzzy neural network control method improves the spring mass acceleration, dynamic deflection of suspension, and dynamic tire deformation by 30.4%, 17.8%, and 15.5%, respectively, compared with the passive suspension. In addition, there are also 14.6%, 12.1%, and 11.2% performance improvements, respectively, compared to the PID-controlled active suspension system. These results indicate that the control strategy proposed in this paper can improve the vehicle driving performance and can support the design and development of active suspension systems.

关键词： fuzzy neural network particle swarm algorithm PID control active suspension MATLAB Simulink simulation

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OPTIMAL REGULATION MODEL OF INTEGRATED ENERGY SYSTEM CONTAINING HYDROGEN STORAGE CONSIDERING ELECTROTHERMAL COUPLING

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INTERNATIONAL JOURNAL OF POWER AND ENERGY SYSTEMS 2023年第10期43卷 1页

作者： Wei, Xuan China Elect Power Res Inst Beijing Key Lab Demand Side Multi Energy Carriers Beijing 100192 Peoples R China Nanchang Univ Sch Informat Engn Nanchang 330031 Peoples R China

As an important way to improve energy efficiency, IES plays a key role in promoting sustainable development of energy. In order to optimise the regulation effect of hydrogen storage IES, from the perspective of electrothermal coupling, the optimisation regulation model of hydrogen storage IES was constructed, and its effectiveness was tested. The results show that under this control strategy, the consumption of wind power, photovoltaic power, and hydrogen power generation is maintained in the range of 2,500-3,500 kWh, 1,500-2,500 kWh, and 3,500-4,500 kWh, respectively. Compared with the control strategy without considering the electric heating coupling, the coal consumption per unit time under this method is reduced by 40-80 J. This shows that the regulation method has improved the consumption of clean energy, greatly reduced coal consumption and economic costs, improved the utilisation rate of clean energy, optimised the regulation effect of IES, and is conducive to promoting the development of renewable energy.

关键词： Electrothermal hydrogen energy storage particle swarm algorithm IES

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Multi-objective reactive power optimisation approach for the isolated grid of new energy clusters connected to VSC-HVDC

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The Journal of Engineering 2017年第13期2017卷 1024-1028页

作者： Shengli Yin Linlin Wu Wei Song Xiao Wang North China Electric Power University China Lianchi District Baoding 071003 People's Republic of China Research Institute State Grid Jibei Electric Power Co. Ltd Xicheng District Beijing 100045 People's Republic of China

It is known that reactive power optimisation is of great significance to improve the electrical power quality. How the security, reliability, and economy of the system can be effectively improved by reactive power optimisation has attracted the attention of scholars. In this study, according to the characteristics of an isolated grid of the new energy clusters connected to voltage source converter-based high-voltage direct current, the multi-objective reactive power optimisation model, which takes the network loss, and the reactive power margin of new energy clusters into consideration, is established. In particular, the maximisation of reactive power margin of new energy clusters is chosen as one goal, thus the response speed of voltage control can be effectively improved, and the preventive control of transient voltage stability of new energy clusters will be realised. Afterwards, using the weighted method the multi-objective optimisation model can be transformed into a single-objective optimisation model and the particle swarm algorithm is utilised to obtain the optimal solution. Finally, the simulation results show that the reactive power optimisation approach proposed can effectively reduce the network loss of the system, increase the reactive power margin of the new energy clusters, and improve the quality of the system voltage.

关键词： reactive power control HVDC power convertors voltage control power grids power supply quality particle swarm optimisation power system stability power system transients energy clusters multiobjective optimisation model single-objective optimisation model reactive power margin multiobjective reactive power optimisation approach isolated grid electrical power quality multiobjective reactive power optimisation model VSC-HVDC voltage control transient voltage stability particle swarm algorithm

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Location-Routing Problem With Demand Range

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IEEE ACCESS 2019年 7卷 149142-149155页

作者： Yu, Vincent F. Jodiawan, Panca Ho, Yi-Hsuan Lin, Shih-Wei Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan Chang Gung Univ Dept Informat Management Taoyuan 333 Taiwan Linkou Chang Gung Mem Hosp Dept Neurol Taoyuan 333 Taiwan Ming Chi Univ Technol Dept Ind Engn & Management New Taipei 243 Taiwan

This research proposes a new variant of the location-routing problem (LRP) called LRP with Demand Range (LRPDR) by allowing flexibility in the delivery quantity. The goal of the LRPDR is to minimize the objective value calculated by the total cost minus the extra revenue. The total cost consists of the travelling cost of vehicles, the opening cost of the depots, and the activation cost of vehicles. This study proposes a new hybrid algorithm, SAPSO, that combines simulated annealing (SA) and particle swarm algorithm (PSO) for solving the LRPDR. Since this problem has not yet been studied in the literature, a mathematical model is proposed and solved by the Gurobi solver. The results obtained by Gurobi are then compared with those obtained by the proposed SAPSO algorithm. In addition, the performance of the proposed SAPSO algorithm is assessed by solving the LRP benchmark instances, and comparing the results with those of existing state-of-the-art algorithms for LRP. Based on the experimental results, the proposed SAPSO algorithm improves the performance of the basic SA algorithm and outperforms Gurobi. Moreover, the benefits of the LRPDR over LRP are presented in terms of total cost reduction.

关键词： Demand range hybrid algorithm location routing problem particle swarm algorithm simulated annealing

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Research on High Voltage Cable Condition Detection Technology based on Wireless Sensor Network

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第12期13卷 694-703页

作者： Zhao, Yang Liu, Qing Shang, Tong Shang, Yingqiang Xia, Rong Shao, Shuai State Grid Beijing Powercable Co Beijing 100022 Peoples R China China Elect Power Res Inst Ltd Wuhan Branch Wuhan 430079 Peoples R China State Grid Shandong Elect Power Co Ctr Jinan Power Supply Co Jinan 250012 Peoples R China

development and progress of modern society cannot be achieved without the support of electric power resources, and at present, electric power is the most important energy source to promote social development and maintain human life. As a key unit under the power distribution and transmission system, the high-voltage cable of the power grid will undertake the task of supplying power resources to the whole power grid. Therefore, based on the transmission line fault diagnosis framework of Wireless Sensor Networks (WSN), a high-voltage cable path condition monitoring scheme using LoRa technology is proposed. Three high-voltage cable condition monitoring periods are proposed according to the difference of high-voltage cable fault rate, and the delay and energy consumption of the high-voltage cable monitoring system are optimized by multi-objective particle swarm algorithm reality. The experimental results show that the proposed high-voltage cable detection technology can switch the working mode according to different environments, and the data communication packet loss rate is less than 5%, while the detection platform has excellent delay performance and energy saving effect. The high-voltage cable status detection solution can effectively solve the problem of blind high-voltage cable channels in high mountain areas of China. The research content has important reference value for the detection of China's power grid circuit system.

关键词： Wireless sensor high-voltage cable fault diagnosis particle swarm algorithm

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Hyperspectral identification of travertine state in Huanglong by the PSO-BPNN method

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GEOCARTO INTERNATIONAL 2024年第1期39卷

作者： Xu, Menghui Wang, Weihong Cai, Jialun Dai, Qunwei Fan, Jing Li, Sicheng Southwest Univ Sci & Technol Sch Environm & Resources Mianyang 621000 Peoples R China Natl Remote Sensing Ctr China Mianyang S&T City Div Mianyang Peoples R China Southwest Univ Sci & Technol Tianfu Inst Res & Innovat Chengdu Peoples R China

The Huanglong Scenic and Historic Interest Area in China, a UNESCO World Heritage Site, is famous for its large-scale, diverse, intricately structured, and brightly colored surface travertine landscapes. However, severe degradation of the Huanglong travertine formations, such as blackening and algae erosion, has occurred in recent years, necessitating monitoring and identification. We collected hyperspectral reflectance data of the travertine formations in different states and bare ground using a ground-based hyperspectral radiometer (PSR-2500) from ASD company. After conducting a correlation analysis between the hyperspectral reflectance data and the travertine formations, we identified healthy travertine formations, blackened travertine formations, travertine formations affected by algae erosion, and bare ground. The Siamese network method was employed to generate data labels, and the spectral features of the travertine formations were extracted by combining the sensitive bands with pre-processed and reduced data. The PSO-BPNN classifier was developed by optimizing the back propagation neural network (BPNN) using the particle swarm optimization algorithm (PSO). To verify the effectiveness of PSO-BPNN in accurately distinguishing different states of travertine formations, we compared its performance with that of BPNN using three performance indices. Finally, the proposed method was applied to the real-world hyperspectral image data collected by the Micro-Hyperspectral imaging instrument to classify the travertine formations in different states and bare ground. The test set demonstrated good overall performance, with an average overall accuracy (OA) of 0.93, F1-score of 0.92, and Kappa coefficient of 0.97.

关键词： Travertine hyperspectral spectral feature extraction particle swarm algorithm back propagation neural network

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Network Intrusion Prediction Model based on RBF Features Classification

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INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS 2016年第4期10卷 241-248页

作者： Wang Xing-zhu Hunan Univ Arts & Sci Furong Coll Changde 415000 Hunan Peoples R China

According to the relationship between feature subset and parameters of RBF neural network, in order to improve the intrusion detection accuracy, it proposed an improved particle swarm optimization neural network of network intrusion detection model. Network feature subset and parameters of RBF neural network were regarded as a particle, through collaboration and information exchange between particles to find the optimal feature subset and parameters of RBF neural network, so as to establish the optimal network intrusion detection model, and using KDD Cup 99 data sets to carry out simulation experiment. The simulation results showed that, IPSO-RBF neural network reduced the feature dimensions, and the better parameters of RBF neural network was obtained then, which is a kind of network intrusion detection model with high detection accuracy and high speed.

关键词： Network intrusion particle swarm algorithm Neural network Feature selection

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