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A two-stage optimal scheduling method for active distribution networks considering uncertainty risk

ENERGY REPORTS 2021年 7卷 4633-4641页

作者： Ma, Hengrui Liu, Zhe Li, Min Wang, Bo Si, Yang Yang, Yan Mohamed, Mohamed A. Qinghai Univ Tus Inst Renewable Energy Xining Qinghai Peoples R China Guangxi Power Grid Co Ltd Nanning Guangxi Peoples R China Wuhan Text Univ Sch Math & Comp Wuhan Hubei Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan Hubei Peoples R China Minia Univ Fac Engn Elect Engn Dept Al Minya 61519 Egypt Fuzhou Univ Dept Elect Engn Fuzhou 350116 Peoples R China

The uncertainty of distributed energy generation and the uncertainty risk of system components failure are important means to ameliorate the economy and reliability of Active Distribution Network (ADN). In this paper, a two-stage optimal scheduling method for ADN considering uncertainty risk is proposed. Considering the uncertainty risk of renewable resources, load and electricity price, and the uncertainty risk of system component failure, the two-stage optimal dispatching process of ADN with energy storage system is investigated. Firstly, the first stage model is established to minimize the cost of operation of ADN under normal operation conditions, and then the second stage model is established to minimize the load reduction under emergency operation. Particle Swarm Optimization (pso) algorithm is proposed for model optimization. The simulation outcomes on IEEE-33 bus system simulation demonstrated high effectiveness of the proposed method. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Two-stage optimal scheduling model Distributed energy Uncertainty risk Active distribution network pso algorithm

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An ultra-low power hybrid 2nd order feed forward ΔΣ modulator design for implantable medical devices

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ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2021年第2期108卷 277-289页

作者： Laouej, Dalila Daoud, Houda Loulou, Mourad Univ Sfax Tunisia Natl Sch Engineers Sfax Lab Elect & Technol Informat LETI Sfax Tunisia

This paper presents a proposed ultra-low-power second order feedforward (FF) delta sigma (Delta Sigma) modulator using two different integrators structures for implantable biomedical devices in the MICS band. In fact, the first active integrator is designed using a proposed optimized bulk driven telescopic operational transconductance amplifier while the second passive one is designed using a MOS Parametric amplifier. The Delta Sigma modulator is designed for a signal bandwidth of 150 kHz with an oversampling ratio of 50. In fact, the proposed circuit is simulated using system-level model as well as device-level description for TSMC 0.18 mu m CMOS technology. In this circuit, an input signal of -1.93 dBFS magnitude and 68.66 kHz frequency was sampled at a frequency of 15 MHz. Device-level simulations results indicate that the designed Delta Sigma FF modulator achieves a signal to noise ratio (SNR) of 63 dB and a resolution of 10.18bits. It consumes only 3 mu W under a +/- 0.5 V supply voltage.

关键词： Delta-sigma modulator Bulk driven telescopic OTA pso algorithm MOS Parametric Integrator Implantable devices Low-power design TSMC 0.18 mu m CMOS technology

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Design and optimisation of die casting process for heavy-duty automatic transmission oil circuit board

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INTERNATIONAL JOURNAL OF CAST METALS RESEARCH 2021年第2期34卷 88-96页

作者： Wu, Huaichao Yang, Xuan Cao, Gang Zhao, Limei Yang, Lv Guizhou Univ Sch Mech Engn Guiyang Peoples R China

In this study, HPDC is introduced into the production of oil circuit board, aiming to solve forming defects and provide a complete design concept for the exploitation of die-casting process in large castings. First, the die-casting die was designed and its rationality was verified, but the unreasonable selection of injection process parameters caused casting non-conformance. Next, the experimental data of L-25 orthogonal array of Taguchi method were used as training samples and the porosity of the die-cast part was examined in relation to casting variable parameters. Specifically, the velocity, temperature and injection pressure were mainly investigated, while the mathematical model passed the test of accuracy. Finally, Particle Swarm Optimisation (pso) algorithm was used to optimise the model, achieving minimum porosity. The results have shown that the pso algorithm optimisation results are better than the one produced by the Taguchi method, providing a significant improvement over the pre-optimisation results.

关键词： Oil circuit board die casting process pso algorithm parameter optimisation

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Range Free Localization for Three Dimensional Wireless Sensor Networks Using Multi Objective Particle Swarm Optimization

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WIRELESS PERSONAL COMMUNICATIONS 2021年第2期117卷 901-921页

作者： Kanwar, Vivek Kumar, Ashok Natl Inst Technol Dept Elect & Commun Engn Hamirpur India

Accurate and fast localization of randomly deployed sensor nodes is needed for many applications in wireless sensor networks. Localization also benefits in recognizing the geographically area where an event took place. There is no meaning of any event information without the knowledge of its location coordinates. DV-Hop is one of the main range free localization technique, which estimates the position of nodes using distance vector. Particle swarm optimization is suitable for the localization issues because of its fast computing speed and high precision. To further reduce the positioning error, the traditional DV-Hop localization algorithm based on single objective optimization algorithm is converted into a multi objective optimization algorithm. In our proposed scheme, we have considered six different single objective functions and three different multi objective functions. In this paper, a multi objective particle swarm optimization based DV-Hop localization is proposed in 3-dimensional wireless sensor networks. The proposed functions has been evaluated on the basis of computation time, average localization error and localization error variance. The simulation results show that our proposed multi objective function performs better as compared to traditional single objective function.

关键词： Wireless sensor network Localization Range free 3D DV-Hop Multi objectives pso algorithm

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ANFIS soft sensing model of SMB chromatographic separation process based on new adaptive population evolution particle swarm optimization algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第6期41卷 6755-6780页

作者： Wang, Dan Wang, Jie-Sheng Wang, Shao-Yan Xing, Cheng Li, Xu-Dong Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Liaoning Peoples R China Univ Sci & Technol Liaoning Sch Chem Engn Anshan Liaoning Peoples R China

Aiming at predicting the purity of the extract and raffinate components in the simulated moving bed (SMB) chromatographic separation process, a soft-sensor modeling method was proposed by adoptig the hybrid learning algorithm based on an improved particle swarm optimization (pso) algorithm and the least means squares (LMS) method to optimize the adaptive neural fuzzy inference system (ANFIS) parameters. The hybrid learning algorithm includes a premise parameter learning phase and a conclusion parameter learning phase. In the premise parameter learning stage, the input data space division of the SMB chromatographic separation process and the initialization of the premise parameters are realized based on the fuzzy C-means (FCM) clustering algorithm. Then, the improved pso algorithm is used to calculate the excitation intensity and normalized excitation intensity of all the rules for each individual in the population. In the conclusion parameter learning phase, these linear parameters are identified by the LMS method. In order to improve population diversity and convergence accuracy, the population evolution rate function was defined. According to the relationship between population diversity, population fitness function and particle position change, a new adaptive population evolution particle swarm optimization (NAPEpso) algorithm was proposed. The inertia weight is adaptively adjusted according to the evolution of the population and the change of the particle position, thereby improving the diversity of the particle swarm and the ability of the algorithm to jump out of the local optimal solution. The simulation results show that the proposed soft-sensor model can effectively predict the key economic and technical indicators of the SMB chromatographic separation process so as to meet the real-time and efficient operation of the SMB chromatographic separation process.

关键词： SMB chromatographic separation soft sensing adaptive neural fuzzy inference system pso algorithm inertia weight

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A data-driven support system for the efficient schedule delay management of the ultra-high-voltage projects considering subjective risk preferences

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SUSTAINABLE CITIES AND SOCIETY 2023年 91卷

作者： Wu, Yunna He, Jiaming Liao, Yijia Tao, Yao Liu, Fangtong Zhou, Jianli North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China Xinjiang Univ Sch Econ & Management Urumqi 830046 Peoples R China Minist Educ Engn Res Ctr Northwest Energy Carbon Neutral ERCNE Urumqi 830046 Peoples R China Xinjiang Univ Strategy & Decis Making Res Ctr Xinjiang Energy Ca Urumqi 830046 Peoples R China

Renewable generation technologies are thriving due to sustainable transformation of the whole society, while there exists a general gap between large-scale renewable energy sources and loads in major cities which could be addressed by Ultra-high voltage (UHV) transmission projects. This paper proposes a data-driven support system for schedule delays of UHV projects. The system is composed of three modules. Firstly, general project schedule routines are interpreted by logistic curves. Classical earned values are measured by trapezoidal fuzzy numbers which are used for subjective judgement of project managers. These measures serve as the system input. Then, combined objective schedule delay and subjective severity judgement are analyzed by least square support vector regression. Enhanced particle swarm optimization is applied to determine optimal parameter combinations ac-cording to the structure of trained data. Next, delay prediction results combining subjective and objective in-formation are divided into three severity degrees by an enhanced K-means algorithm and finally, integrated prevention measures are raised for project managers. The proposed system is validated by a real case study and results indicate good performance and reliability. The system could be integrated into the daily workflow of project managers and constantly provides references that facilitate efficient schedule management.

关键词： Urban energy transition Sustainable cities Schedule delay Decision support model pso algorithm LSSVM model

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Parameter identification of the phenomenological model for magnetorheological fluid dampers using hierarchic enhanced particle swarm optimization

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2021年第3期35卷 875-887页

作者： Guo, Jin Li, Zhendong Zhang, Mengxuan Shijiazhuang Tiedao Univ Dept Civil Engn Shijiazhuang 050043 Hebei Peoples R China Shijiazhuang Tiedao Univ Struct Hlth Monitoring & Control Inst Shijiazhuang 050043 Hebei Peoples R China

The magnetorheological (MR) fluid damper, as a new generation of high-performance and intelligent vibration damping devices for engineering structures, has broad application prospects in structural vibration reduction. Nonlinear hysteresis is a complex phenomenon of MR dampers. In this paper, the phenomenological model is used to simulate the dynamics of MR dampers. To accurately and efficiently identify the parameters of the phenomenological model, the hierarchical enhanced particle swarm optimization (HEpso) algorithm is proposed. This algorithm applies a hierarchical optimization mechanism and introduces media particles to the enhanced pso (Epso) algorithm to improve the optimization process of parameter identification and enhance the performance of the pso algorithm without reducing the possibility of finding the optimal solution. Compared with the standard pso (Spso) algorithm and the Epso algorithm, the HEpso algorithm has a higher efficiency, accuracy and robustness in identifying a highly nonlinear hysteretic system problem.

关键词： MR damper Phenomenological model Parameter identification pso algorithm Hierarchical optimization mechanism

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Virtual Model Reduction-based Control Strategy of Planar Three-link Underactuated Manipulator with Middle Passive Joint

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2021年第1期19卷 29-39页

作者： Huang, Zixin Lai, Xuzhi Zhang, Pan Wang, Yawu Wu, Min China Univ Geosci Sch Automat 388 Lumo Rd Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China

This paper presents a position control strategy for a planar active-passive-active (APA) underactuated manipulator with second-order nonholonomic characteristics. According to the structural characteristics of the planar APA system, we divide the system into two parts: a planar virtual Pendubot (PVP) and a planar virtual Acrobot (PVA). For the PVP, we mainly fulfill the target angle of the first link, which is calculated through the geometry method, and make the system stable. In this stage, via keeping the states of the third link being zero, the system is reduced to the PVP. Meanwhile, we design an open-loop control law based on the nilpotent approximation (NA) model of the PVP to make the second link stable and the first link stabilize at its target angle. Then, the planar APA system is reduced to a PVA with all links' angular velocities being zero. For the PVA, we mainly realize the other two links' target angles obtained via the particle swarm optimization (pso) algorithm. Thus, the control objective of the planar APA system is achieved. Finally, above control strategy is verified by simulation results.

关键词： Nilpotent approximation planar underactuated manipulator position control pso algorithm second-order nonholonomic characteristics

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Optimal Capacity and Cost Analysis of Battery Energy Storage System in Standalone Microgrid Considering Battery Lifetime

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BATTERIES-BASEL 2023年第2期9卷 76-76页

作者： Wongdet, Pinit Boonraksa, Terapong Boonraksa, Promphak Pinthurat, Watcharakorn Marungsri, Boonruang Hredzak, Branislav Suranaree Univ Technol Sch Elect Engn Nakhon Ratchasima 30000 Thailand Rajamangala Univ Technol Rattanakosin Sch Elect Engn Nakhon Pathom 73170 Thailand Rajamangala Univ Technol Suvarnabhumi Fac Engn & Architecture Sch Elect Engn Nonthaburi 11000 Thailand Univ New South Wales Sch Elect Engn & Telecommun Sydney 2052 Australia

In standalone microgrids, the Battery Energy Storage System (BESS) is a popular energy storage technology. Because of renewable energy generation sources such as PV and Wind Turbine (WT), the output power of a microgrid varies greatly, which can reduce the BESS lifetime. Because the BESS has a limited lifespan and is the most expensive component in a microgrid, frequent replacement significantly increases a project's operating costs. This paper proposes a capacity optimization method as well as a cost analysis that takes the BESS lifetime into account. The weighted Wh throughput method is used in this paper to estimate the BESS lifetime. Furthermore, the well-known Particle Swarm Optimization (pso) algorithm is employed to maximize battery capacity while minimizing the total net present value. According to simulation results, the optimal adjusting factor of 1.761 yields the lowest total net present value of US$200,653. The optimal capacity of the BESS can significantly reduce the net present value of total operation costs throughout the project by extending its lifetime. When applied to larger power systems, the proposed strategy can further reduce total costs.

关键词： standalone microgrid renewable energy battery lifetime capacity optimization pso algorithm

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The High-Efficiency Design Method for Capacitive MEMS Accelerometer

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MICROMACHINES 2023年第10期14卷 1891-1891页

作者： Liu, Wen Zhao, Tianlong He, Zhiyuan Ye, Jingze Gong, Shaotong Wang, Xianglong Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China Shandong Univ State Key Lab Crystal Mat Jinan 250100 Peoples R China

In this research, a high-efficiency design method of the capacitive MEMS accelerometer is proposed. As the MEMS accelerometer has high precision and a compact structure, much research has been carried out, which mainly focused on the structural design and materials selection. To overcome the inconvenience and inaccuracy of the traditional design method, an orthogonal design and the particle swarm optimization (pso) algorithm are introduced to improve the design efficiency. The whole process includes a finite element method (FEM) simulation, high-efficiency design, and verification. Through the theoretical analysis, the working mechanism of capacitive MEMS accelerometer is clear. Based on the comparison among the sweep calculation results of these parameters in the FEM software, four representative structural parameters are selected for further study, and they are le, nf, lf and wPM, respectively. le and lf are the length of the sensing electrode and fixed electrode on the right. nf is the number of electrode pairs, and wPM is the width of the mass block. Then, in order to reduce computation, an orthogonal design is adopted and finally, 81 experimental groups are produced. Sensitivity SV and mass Ma are defined as evaluation parameters, and structural parameters of experimental groups are imported into the FEM software to obtain the corresponding calculation results. These simulation data are imported into neural networks with the pso algorithm. For a comprehensively accurate examination, three cases are used to verify our design method, and every case endows the performance parameters with different weights and expected values. The corresponding structural parameters of each case are given out after 24 iterations. Finally, the maximum calculation errors of SV and Ma are 1.2941% and 0.1335%, respectively, proving the feasibility of the high-efficiency design method.

关键词： capacitive MEMS accelerometer high-efficiency design method FEM orthogonal design pso algorithm

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