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31st Chinese Control And Decision Conference (CCDC)

作者： Chen, Xiangyu Liu, Jinliang Sun, Xiuli Univ Jinan Sch Elect Engn Jinan 250022 Peoples R China

ISBN: (纸本)9781728101057

In this paper, we desire to update the traditional PID controller of vertical roller mill PID controller to have a better performance in temperature adjustment. The open extent of cold blast sliding valve is our controlled device, and we design a fractional order proportion integration differentiation (FOPID) controller with Particle Swarm Optimization (pso) algorithm to control it. This algorithm is an assistant tool to achieve online updating the parameters of the FOPID controller. For starters, the pseudo-partial derivative (PPD) parameters are estimated dynamically by the state observer. Secondly. three parameters of integer order PID controller,K-p,K-i and K-d, are determined by PPD parameter observer and gradient descent algorithm. Afterwards. pso algorithm is used to adjust and correct mu, lambda and online update them. Finally, the simulation results show that the controller has a good impact for the outlet temperature control, which is better than the traditional PID controller.

关键词： Roller mill Temperature control FOPID controller pso algorithm

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Parameter Optimization of Neural Network Model Based on Simulated Moving Bed

Parameter Optimization of Neural Network Model Based on Simu...

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Chinese Automation Congress (CAC)

作者： Li, Ling Dai, Chang Deng, Jia-Bi Yuan, De-Cheng Shenyang Inst Chem Technol Coll Informat Engn Shenyang Peoples R China

ISBN: (纸本)9781728140940

In recent years Simulated Moving Bed (SMB) chromatography separation technology has been used in petroleum chemicals, food processing and fine chemicals. In this paper, Bayesian regularization BP neural network algorithm is used to fit the input and output data of mechanism model of Simulated Moving Bed. Genetic algorithm and particle swarm algorithm are used to optimize the operating parameters and to find out the best concentration. The results are put into the mechanistic model to verify the point. The simulation results show that the error is small and these methods can solve the problem that the parameter optimization time is too long.

关键词： component formatting style styling insert (key words) Simulated Moving Bed Bayesian regularization BP neural network GA algorithm pso algorithm

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State of Charge Estimation of Lithium-ion Batteries Electrochemical Model with Extended Kalman Filter

State of Charge Estimation of Lithium-ion Batteries Electroc...

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54th Annual Meeting of the IEEE-Industry-Applications-Society (IEEE IAS)

作者： Liu, Yuntian Huangfu, Yigeng Ma, Rui Xu, Liangcai Zhao, Dongdong Wei, Jiang Northwest Polytech Univ Sch Automat Xian 710129 Peoples R China Datang Northwest Elect Power Test & Res Inst Xian 710065 Peoples R China

ISBN: (纸本)9781538645390

Lithium-ion batteries as a dominant green energy are widely used in electrical vehicles (EV) due to their unique advantages. The battery modeling, parameter identification and state estimation are always the emphases of research which complement each other in a battery management system (BMS). Compared to the mainstream of the current equivalent circuit (EC) models, the rigorous electrochemical model with high complexity and tight coupling is not suitable for on-line simulation in EV. In this paper, the state of charge (SOC) estimation using extended Kalman filter (EKF) algorithm is proposed based on the simplified electrochemical model-single particle (SP) model. The battery parameters identified by the particle swarm optimization (pso) algorithm show a higher accuracy, which can track the terminal voltage effectively. The SOC estimation results show that SP model with EKF algorithm is a computational method with a good performance of robust, accuracy and stability which can be used in energy management systems of EV.

关键词： Lithium-ion battery SP model SOC estimation pso algorithm EKF algorithm

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Improved grey wolf optimisation algorithms

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JOURNAL OF ENGINEERING-JOE 2020年第13期2020卷 615-619页

作者： Gao, Zheng-ming Zhao, Juan Hu, Yu-rong Jingchu Univ Technol Sch Comp Engn Jingmen Peoples R China Jingchu Univ Technol Sch Elect & Informat Engn Jingmen Peoples R China Jingchu Univ Technol Dept Sci & Technol Jingmen Peoples R China

The grey wolf optimisation (GWO) algorithm has fewer numbers of variables and appears quite simple with outstanding capabilities in solving the problems, which are used to describe mathematically what human met in nature. However, it still has its capability to be improved in the convergence ratio, stability, and reduce the errors. And it is also easily trapped in local optimum and converged slowly approaching the end, which is just the same defect appearing in other meta-heuristic algorithms such as the bat algorithm (BA), the particle swarm optimisation (pso) algorithm, and the genetic algorithm (GA). Lots of improvements have been proposed before. In this paper, we propose an improved GWO algorithm inspired by the pso algorithm to fasten the convergence ratio and reduce the errors. Empirical work and verifications are carried out;and results show its better performance than the standard GWO algorithm and other well-known meta-heuristic algorithms

关键词： grey systems particle swarm optimisation genetic algorithms meta-heuristic algorithms bat algorithm particle swarm optimisation algorithm genetic algorithm improved GWO algorithm pso algorithm convergence ratio standard GWO algorithm improved grey wolf optimisation algorithm outstanding capabilities

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Loan Default Prediction Model Improvement through Comprehensive Preprocessing and Features Selection

Loan Default Prediction Model Improvement through Comprehens...

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International Arab Conference on Information Technology (ACIT)

作者： Al-qerem, Ahmad Al-Naymat, Ghazi Alhasan, Mays Zarqa Univ Comp Sci Dept Zarqa Jordan Princess Sumaya Univ Technol King Hussein Sch Comp Sci Amman Jordan

ISBN: (纸本)9781728130101

For financial institutions and the banking industry, it is very crucial to have predictive models for their financial activities, as they play a major role in risk management. Predicting loan default is one of the critical issues that they focus on, as huge revenue loss could be prevented by predicting customer's ability to pay back on time. In this paper, different classification methods (Naive Bayes, Decision Tree, and Random Forest) are being used for prediction, comprehensive different pre-processing techniques are being applied on the dataset, and three different feature extractions algorithms are used to enhance the accuracy and the performance. Results are compared using F1 accuracy measure, and an improvement of over 3% has been obtained.

关键词： Classification Pre-processing Prediction Features selection Generic algorithm pso algorithm Naive Bayes decision tree SVM Random Forest

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Design of low power discrete time FF ΔΣ modulator for biomedical application

Design of low power discrete time FF ΔΣ modulator for biom...

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IEEE International Conference on Design and Test of Integrated Micro and Nano-Systems (DTS)

作者： Laouej, Dalila Daoud, Houda Mallek, Jihene Loulou, Mourad Natl Engn Sch Sfax Informat Technol & Elect Lab Sfax Tunisia

ISBN: (纸本)9781728111285

High speed, low voltage delta-sigma (Delta Sigma) analog to digital converter (ADC) is required for low power medical applications. A second order discrete time (DT) feed forward (FF) Delta Sigma modulator with very low power consumption is presented in this paper. The OTA is the key building block and the most power-hungry circuit for Delta Sigma modulator design. Using TSMC 0.18 mu m CMOS technology, the telescopic OTA is optimized using the practical swarm optimization (pso) algorithm;it achieves a DC gain of 62dB, a GBW of 166.6MHz and it consumes only 35 mu W. The optimized FF Delta Sigma modulator has been implemented with an OSR of 50, a signal bandwidth of 0.5MHz and a sampling frequency of 50MHz. The designed modulator is suitable for low medical application in 2.4GHz ISM band for IEEE 802.15.1/Bluetooth standard. This circuit attains a peak SNR of 52dB and a resolution of 8,34bits. This topology consumes only 71 mu W under 1.8V supply voltage.

关键词： feed forward Delta Sigma modulator Telescopic OTA pso algorithm Bluetooth standard 2.4GHz ISM frequency band low power biomedical application

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Grey-box Parameter Identification for Drive-train System of Large-scale Wind Turbine 38

Grey-box Parameter Identification for Drive-train System of ...

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第三十八届中国控制会议

作者： Chenyang Pan Yunhua Xi Yang Hu School of Control and Computer Engineering North China Electric Power University

ISBN: (纸本)9781728123295;9789881563972

Due to the resource limitation of fossil energies and the environmental requirements, wind energy has gained increasing attention in virtue of its richness in resources and cleanliness, and wind power generation technology has achieved rapid development. The modeling accuracy of wind turbines has a great influence on the dynamic analysis and control of wind turbines. Thus, this paper studies the dominant mechanism model of the drive-train system and analyzes its transfer function under the two-mass structure which is a non-self-balancing object. Then, two parameter identification methods via the recursive least squares(RLS) algorithm and the intelligent parameter identification algorithm with the particle swarm optimization(pso)are compared. Furthermore, the grey-box state-space model of the drive-train system is constructed and the identification steps are systematically presented. In order to validate the effectiveness of the two methods, the identification processes are carried out based on the simulation data of a 5 MW wind turbine from FAST under different wind conditions. The identification results confirm the feasibilities of the proposed methods and prove that intelligent parameter identification is capable of acquiring better identification performances and less dependent on the selection and preprocessing of identified data.

关键词： Wind turbine drive-train system parameter identification RLS algorithm pso algorithm

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Research on The Location and Capacity of The Distributed Generation in Distribution Network 3

Research on The Location and Capacity of The Distributed Gen...

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3rd International Conference on Data Science and Business Analytics (ICDSBA)

作者： Li, Yuhang Nari Technol State Grid Elect Power Res Inst Co L Nari Tech Power Distribut & Rural Electrificat Br Nanjing Peoples R China

ISBN: (纸本)9781728146447

Distributed Generation plays an important role in promoting sustainable economic and social development. It has a huge contribution to optimizing energy structure, coping with climate change and protecting ecological environment. The way for selecting and fixing the capacity, therefor, is a very important part of the implementation process. Based on chaotic search and particle swarm optimization, this paper implements an economical and practical method for distributed power supply location and capacity by analyzing grid loss. Through the analysis of the example, the new algorithm, which is superior to the original particle swarm optimization algorithm in calculation accuracy, convergence velocity and optimization ability, provides a theoretical base for multi-objective planning of distributed generation supply.

关键词： component: distributed generation pso algorithm chaos search location and capacity

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RBF Neural Network Based Fast Converging Terminal Global Sliding Mode Control of Aero-engine 9

RBF Neural Network Based Fast Converging Terminal Global Sli...

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9th International Conference on Information Science and Technology (ICIST)

作者： Zhang, Yu Xie, Shousheng Ren, Litong Wang, Lei Air Force Engn Univ PLA Aviat Engn Coll Xian Peoples R China

ISBN: (纸本)9781728121062

This paper focuses on the sliding mode control of aero-engine control system with system uncertainties and disturbances. A terminal sliding mode controller with variable exponential parameter has been proposed together with a global sliding surface. It has the advantages of both terminal global sliding control and global sliding mode control scheme, that is, both finite-time high speed convergence and global robustness can be realized. The exponential parameter, which decides the contradictory relation between convergence speed and chattering amplitude, has been optimized by adaptive pso algorithm. Then to obtain the full-envelop controller, RBF neural network has been used to obtain the nonlinear mapping relation between different working conditions and the variable exponential parameter. Simulation results indicate that the proposed controller has great dynamic performance and can effectively suppress chattering.

关键词： Aero-engine terminal sliding mode global sliding surface pso algorithm RBF neural network

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Comparative study of Controller Optimisation for CSTR using Particle Swarm Optimization Technique 5

Comparative study of Controller Optimisation for CSTR using ...

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5th International Conference on Electrical Energy Systems (ICEES)

作者： Durgadevi, S. Sundari, K. Thirupura Raaghavi, D. Akshaya, R. S. Sri Sairam Engn Coll Dept Elect & Instrumentat Engn Chennai 600044 Tamil Nadu India

ISBN: (纸本)9781538692486

The Continuous Stirred Tank Reactor (CSTR) plays a main role in all chemical process industries for *** found in wide applications where the industries allow liquid, gas and solid reactions, with continuous agitation and series configuration for concentration and temperature. Hence to attain specific concentration, the full strength of solution is mixed with desired proportions of water. This is usually carried out with the help of conventional PI &PID controllers. The main objective of the proposed work is to compare and analyze the conventional controllers used in CSTR with a soft computing technique tuned PI&PID controllers for various error criteria. The conventional controllers have difficulty in dealing with problems that appear in complex non-linear processes. In order to tackle this problem and improve the dynamic response of CSTR, the reactor performance is analyzed with soft computing technique known as Particle swarm Optimization (pso).The control objective to maintain the CSTR at steady state operating point in terms of less settling time and reduced % overshoot.

关键词： CSTR conventional PI & PID controller pso algorithm error criteria

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