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5th International Conference on Power Generation Systems and Renewable Energy Technologies (PGSRET)

作者： AlKhafaji, Mohammed Ayad Uzun, Yunus Mazaya Univ Dept Elect Elect & Comp Engn Dhiqar Iraq Aksaray Univ Dept Elect Elect Engn Aksaray Turkey

ISBN: (纸本)9781728123011

This paper presents modeling and simulation for a pure electric vehicle with the propulsion chain. The PEV be composed of two electric motors to propulsion the vehicle rear wheels, voltage source inverter, a control system which controls both of motors speed and the electronic differential controller (EDC). Two synchronous reluctance motors represent the propulsion system to the EV. The Particle Swarm Optimization (pso) is used to specify the optimal parameters of the cascade controllers to control the electric motors. The specify driving route topology has designed and used to test vehicle validity under three different operation conditions. The vehicle system has simulated and tested in the Matlab/Simulink environment.

关键词： PEV EDC Driving route topology pso algorithm SVPWM Inverter

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An Analysis Method for Power System Transient Signal Based on Improved Prony algorithm and pso algorithm

An Analysis Method for Power System Transient Signal Based o...

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International Conference on Information Technology and Applications (ITA)

作者： Yang, Xuanfang Wang, Jialin Naval Univ Engn Coll Elect Engn Wuhan Peoples R China

ISBN: (纸本)9781479928767

An analysis method for power system transient signal is proposed in this paper to improve the analysis precision for the transient signal including harmonic, inter-harmonic and decaying DC component. First of all, the multiple wavelet method is adopted to cut down the noise, and the differential algorithm is used to wipe off the decaying DC component whereas to magnify high frequency components. Then the amount of frequency components of the signal and its parameters values are estimated by improved Prony algorithm. Finally, the electric power parameter optimization model is set up based on these estimate, and the pso algorithm is used to solve the model. The Matlab simulation results demonstrated that the algorithm achieved high accuracy and rapid convergence.

关键词： power system transient signal differential algorithm Prony algorithm pso algorithm

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A multiple heterogeneous kernel RVM approach for analog circuit fault prognostic

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第2期22卷 S3849-S3861页

作者： Zhang, Chaolong He, Yigang Yuan, Lifen Xiang, Sheng Anqing Normal Univ Sch Phys & Elect Engn Anqing 246011 Peoples R China Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Anhui Peoples R China Wuhan Univ Sch Elect Engn Wuhan 430072 Hubei Peoples R China

This paper presents a multiple heterogeneous kernel relevance vector machine (MHKRVM) approach for analog circuit fault prognostic. Compared to other kernel learning methods, multiple kernel learning method produces the optimal kernel function because many effective kernels' combination always generates better generalization performance. By using the multiple heterogeneous kernel learning method, the proposed MHKRVM method's kernel function holds its diversification. Meanwhile, the sparse weights of consisted basic kernels in the MHKRVM are helpful in prediction accuracy, and they are yielded through particle swarm optimization algorithm. Six fault prognostic cases are conducted to demonstrate the whole prognostic procedure, and prove that the presented MHKRVM can predict the trend of falling circuit elements' health degree trajectories closely and estimate the remaining useful performance of failing circuit elements accurately.

关键词： Analog circuits Fault prognostic Relevance vector machine Multiple heterogeneous kernel learning method pso algorithm

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Pilot tones design using particle swarm optimization for OFDM-IDMA system

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NEURAL COMPUTING & APPLICATIONS 2019年第9期31卷 5299-5308页

作者： Taspinar, Necmi Simsir, Sakir Erciyes Univ Fac Engn Dept Elect & Elect Engn TR-38039 Kayseri Turkey

Since the channel estimation performance is directly affected by the pilot positions, it has been a major task in multicarrier transmission schemes to adjust the pattern of pilot distribution for the purpose of minimizing the estimation errors. Therefore, in this paper, we utilize particle swarm optimization (pso) algorithm for pilot design process in orthogonal frequency division multiplexing-interleave division multiple access (OFDM-IDMA) system. The main contributions of the paper are: (1) increasing the performance of least squares (LS) algorithm used for channel estimation in OFDM-IDMA system by optimizing the pilot positions using pso algorithm, (2) instead of using MSE itself in which the matrix inversion process is required as the fitness function of pso, using the upper bound of mean square error (MSE) in order to decrease the system complexity, (3) using two types of channel models known as COST 207 Rural Area and COST 207 Typical Urban in the simulations to be able to support the reliability and stability of the proposed method in different conditions. In the simulations, it is observed from the bit error rate (BER) and MSE graphs that optimizing the placement of pilot tones using our proposed method demonstrates a superior performance compared to the other considered pilot placement strategies by providing a significant increase in the performance of LS algorithm.

关键词： OFDM-IDMA Pilot tones design Channel estimation GA pso algorithm

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Performance analysis and optimization for coverage enhancement strategy of Narrow-band Internet of Things

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 101卷 434-443页

作者： Wang, Xiangming Yang, Dong Jian, Xin Chen, Min Guna, Joze Chongqing Univ Coll Microelect & Commun Engn Chongqing 400030 Peoples R China China Elect Technol Grp Corp 26th Inst Chongqing 400060 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Hubei Peoples R China Univ Ljubljana Lab Multimedia Fac Elect Engn Trzaska Cesta 25 Ljubljana 1000 Slovenia

Narrowband Internet of Things (NB-IoT) is a clean-slate wireless protocol proposed by the 3rd Generation Partnership Project intending for massive machine type communications. The general objectives of the NB-IoT include supporting massive connections, enhanced coverage, reduced cost and complexity, ultra-low power consumption, and flexible delay characteristics. To achieve the objective of 20dB enhanced coverage of NB-IoT, the concept of narrow-band modulation, coverage classes updating and adaptive repetition were introduced. To evaluate the performance of these new techniques, a Markov chain model with coverage classes as state variables was proposed to describe the dynamics of the coverage classes updating mechanism of NB-IoT. In addition, an optimization model minimizing average probability of access failure as well as average power consumption was formulated, with which the effects of preamble repetition number, system load and global maximum transmission number on the optimal configuration of maximum transmission number of each coverage class was analyzed. To solve the aforementioned optimization model, two algorithms, namely exhaustive search method combining constraint transform and particle swarm optimization (pso) algorithm with self-adaptive stochastic inertia weight were proposed. Numerical results show that the maximum transmission number of normal coverage and extended coverage have a great influence on the system performance and their value ranges should be set within [3,10] and 11,61 respectively;while the maximum transmission number of extreme coverage has little influence on the system performance, and its recommended value is 1 for smaller power consumption. The average power consumption of coverage classes updating mechanism with coverage classes rollback is about 61% lower than that of 3GPP proposed model. All these researches together provide good reference for scale deployment of NB-IoT. (C) 2019 Elsevier B.V. All rights reserved.

关键词： NB-IoT Random access Coverage enhancement Coverage classes Markov chain pso algorithm

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An improved radial basis function neural network control strategy-based maximum power point tracking controller for wind power generation system

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2019年第11期41卷 3158-3170页

作者： Sitharthan, R. Parthasarathy, T. Rani, S. Sheeba Ramya, K. C. Madanapalle Inst Technol & Sci Sch Elect & Elect Engn Madanapalle 517325 Andhra Pradesh India Sri Krishna Coll Engn & Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India

This literature presents an improved maximum power point tracking (MPPT) controller based on radial basis function neural network (RBFNN) control strategy to extract optimal power for wind power generation system. The proposed RBFNN controller is trained online using gradient descent algorithm and its network learning rate modification is carried out by the modified particle swarm optimization algorithm. The proposed MPPT controller uses optimal torque control methodology to extract optimal power available in the wind by upholding the generated torque at an optimal level. The most promising aspects of the proposed controller are that it not only extracts maximum available power from wind, but it also rapidly responses to the change in wind speeds and maintains converter with negligible converter losses. To evaluate the performance of the proposed Mpso-RBFNN-based MPPT controller, an extensive simulation study and experimental analysis is performed. The attained results confirm the enhanced performance of the proposed MPPT controller.

关键词： Wind turbine DFIG MPPT pso algorithm Optimal torque controller RBF neural network

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Effect of fin thickness, grooves depth, and fin attachment angle to the hot wall on maximum heat transfer reduction in a square enclosure

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2019年 136卷 473-490页

作者： Dindarloo, M. Rafie Payan, S. Univ Sistan & Baluchestan Dept Mech Engn Zahedan 98135161 Iran

This study investigates the effect of optimal thickness, position, and length of a thick fin, optimal groove depth, position, and length of a horizontally grooved thin fin, and optimal position and length of an obliquely fin (at +/- 45 degrees to the horizontal) attached to the hot wall of a square enclosure in reduction of free convection heat transfer through this enclosure. The results are analyzed for Rayleigh numbers of 10(4) and 10(6) and thermal conductivity ratios of 1, 10, and 1000. The continuity, momentum and energy equations of the enclosure are discretized by the finite volume method and then solved with SIMPLER algorithm. The particle swarm optimization (pso) algorithm is used to optimize the fin size and position and groove depth so that heat transfer through the enclosure is minimized. The results are compared to determine the effects of aforementioned fin characteristics on heat transfer reduction performance. The results obtained with Rayleigh numbers of 10(4) and 10(6) show that, as can be expected, for the fins with thermal conductivity ratios of 1 and 1000, the optimal thickness values are, respectively, the upper and lower bounds set by problem definition (defined geometry). But for the fin with thermal conductivity ratio of 10, the optimal thickness varies with Rayleigh number. The results also show that after widening the range of allowed thickness values, the optimal values no longer stick to the upper and lower bounds of that interval. It is also shown that at low thermal conductivity ratios, the fin with horizontal grooves has the same heat transfer reduction performance as a solid thick fin, but allows some degree of conservation in materials. Moreover, the results show that at all thermal conductivity ratios in Ra = 10(6), the heat transfer reduction performance of the obliquely fin is better than both of the straight thick fin and horizontally grooved fin. However, at all thermal conductivity ratios, the straight thick and obliquely fin

关键词： Free convection Optimal fin thickness Grooved fin Oblique fin pso algorithm

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Network security threat detection technology based on Epso-BP algorithm

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EURASIP JOURNAL ON INFORMATION SECURITY 2024年第1期2024卷 6-6页

作者： Lan, Zhu Dazhou Vocat Coll Chinese Med Minist Basic Med Educ Dazhou 635000 Peoples R China

With the development of Internet technology, the large number of network nodes and dynamic structure makes network security detection more complex, which requires the use of a multi-layer feedforward neural network to build a security threat detection model to improve network security protection. Therefore, the entropy model is adopted to optimize the particle swarm algorithm to decode particles, and then the single-peak and multi-peak functions are used to test and compare the particle entropy and fitness values to optimize the weights and thresholds in the multi-layer feedforward neural network. Finally, Suspicious Network Event Recognition Dataset discovered by data mining is sampled and applied to the entropy model particle swarm optimization for training. The test results show that there are four functions for the optimal mean and standard deviation in this algorithm, with values of 5.712e - 02, 4.805e - 02, 4.914e - 01, 1.066e - 01, 1.577e - 01, 1.343e - 01, and 2.089e + 01, 5.926, respectively. Overall, the algorithm proposed in the study is the best. Finally, the detection rate of attack types is calculated. The multi-layer feedforward neural network algorithm is 83.80%, the particle swarm optimization neural network algorithm is 91.00%, and the entropy model particle swarm optimization algorithm is 95.00%. The experiment shows that the research model has high accuracy in detecting network security threats, which can provide technical support and theoretical assistance for network security protection.

关键词： Entropy model pso algorithm Inertia weight strategy Neural network Security threat detection

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A Fuzzy logic-based approach to verify the optimized parameters a gas turbine power plant by pso algorithm

A Fuzzy logic-based approach to verify the optimized paramet...

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13th Iranian Conference on Fuzzy Systems (IFSC)

作者： Alizadeh, Saeed Khosravi, Ahmad Khademi, Maryam Islamic Azad Univ South Tehran Branch Dept Comp Engn Tehran Iran Islamic Azad Univ South Tehran Branch Dept Energy Syst Engn Tehran Iran Islamic Azad Univ South Tehran Branch Dept Appl Math Tehran Iran

ISBN: (纸本)9781479912278;9781479912285

Exergoeconomic and thermoeconomic analysis are among powerful and effective tools to find the best solutions between the two objective functions that can be used by energy engineers, minimizing economic cost and maximizing exergetic efficiency. In this research, operating parameters of a gas turbine power plant that produces 140 MW of electricity is optimized by using exergoeconomic principles and pso algorithm. The comparison of results between the pso algorithm and the base model shows very good improvements, i.e. the fuel mass flow rate by optimized parameters is 11.24% lower than the base case. Also the exergetic efficiency is 6.55% more than the base case. The verification by fuzzy logic shows that total matching pso results and base case is 99.5%.

关键词： Fuzzy logic pso algorithm Exergy analysis Exergoeconomic optimization Gas turbine

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Analysis of Risk-Based Airport Passenger Classification with pso-BP Neural Network

Analysis of Risk-Based Airport Passenger Classification with...

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第三十九届中国控制会议

作者： Zhenwu Zhao Chenchen Zhang Dongdan Guo College of Economics Management Civil Aviation University of China Travelsky Technology Limited Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC

In order to reduce passengers’ waiting time, improve the quality of security inspection, minimize the cost of service resource and maximize the utilization of security resource, the index system of risk-based airport passenger classification was constructed to identify passenger risk levels. Firstly, the index system of risk-based airport passenger classification was initially constructed through literature review and expert consultation. Secondly, the index system of risk-based airport passenger classification is optimized with the help of relevant professionals’ questionnaires and SPSS22.0 software. Finally, BP neural network classifier is selected and which was optimized by pso algorithm. The classifier was trained in simulations and tested for classification effects by using the sample data of questionnaire survey. Test results show that the index system of risk-based airport passenger classification can be obtained to measure passenger risk degree from five aspects, natural condition, occupation status, economic condition, credit situation and flight condition. The effectiveness of the index system and the BP neural network classifier were verified by the classification results of 5 passengers through the classifier.

关键词： Risk-based airport passenger classification index system questionnaire survey pso algorithm BP neural network classifier

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