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Economic Dispatch with High Penetration of Wind Power Using Extreme Learning Machine Assisted Group Search Optimizer with Multiple Producers Considering Upside Potential and Downside Risk

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1459-1471页

作者： Yuanzheng Li Jingjing Huang Yun Liu Zhixian Ni Yu Shen Wei Hu Lei Wu School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and TechnologyWuhan 430074China School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China State Grid Hubei Power Electric Research Institute Wuhan 430077China Department of Electrical&Computer Engineering Stevens Institute of TechnologyHoboken USA IEEE

The power system with high penetration of wind power is gradually formed,and it would be difficult to determine the optimal economic dispatch(ED)solution in such an environment with significant *** paper proposes a multi-objective ED(MuOED)model,in which the expected generation cost(EGC),upside potential(USP),and downside risk(DSR)are simultaneously *** heterogeneous indices of upside potential and downside risk mean the potential economic gains and losses brought by high penetration of wind power,***,the MuOED model is formulated as a tri-objective optimization problem,which is related to uncertain multi-criteria decision-making against ***,the tri-objective optimization problem is solved by an extreme learning machine(ELM)assisted group search optimizer with multiple producers(GSOMP).Pareto solutions are obtained to reflect the trade-off among the expected generation cost,the upside potential,and the downside *** a fuzzy decision-making method is used to choose the final ED *** studies based on the Midwestern US power system verify the effectiveness of the proposed MuOED model and the developed optimization algorithm.

关键词： Economic dispatch(ED) wind power extreme learning machine optimization algorithm

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Deep Bi-LSTM with Binary Harris Hawkes algorithm-Based Heart Risk Level Prediction

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SN Computer Science 2024年第1期5卷 134页

作者： Prasanna, Kamepalli S. L. Challa, Nagendra Panini School of Computer Science and Engineering (SCOPE) VIT-AP University Andhra Pradesh Amaravati 522237 India

Heart disease has seriously threatened people's health in recent decades due to its prevalence and high mortality rate. Detecting heart disease through clinical features is a major challenge in today’s world. Machine Learning (ML) and Deep Learning (DL) are technological innovations now being effectively used in healthcare, disease prediction, and biomedical care. This paper proposes a model RL-DLBH for identifying the Risk Levels (RL), to select the best features using BHHO(BH) and a deep learning model based on a Deep bi-LSTM (DL) classifier for detecting heart diseases and determining their risk level. The method was created by first choosing the most important attributes from the dataset by using BHHO to determine whether the patient has heart disease and the level of heart disease risk in patients based on their clinical report, and then using a deep Bi-LSTM model as a classifier. The characteristics of heart disease are defined by the main risk factors. ST depression, the highest heart rate, cholesterol, and chest pain are all factors to consider. Class labels were assigned to the following risk levels: risk level 1, risk level 2, and risk level 3. Three distinct datasets namely Cleveland, Hungarian, and CH (Cleveland and Hungarian) are used in this work. The experimental results show that BHHO with Deep bi-LSTM performs well with a classification accuracy of 98.12% compared with the existing models. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Classification Deep Bi-LSTM Model Harris Hawks algorithm Heart disease optimization algorithm

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An adaptive hybrid atom search optimization with particle swarm optimization and its application to optimal no-load PID design of hydro-turbine governor

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2021年第5期8卷 1204-1233页

作者： Zhao, Weiguo Shi, Tiancong Wang, Liying Cao, Qingjiao Zhang, Hongfei Hebei Univ Engn Sch Water Conservancy & Hydropower Handan 056038 Hebei Peoples R China

One metaheuristic algorithm recently introduced is atom search optimization (ASO), inspired by the physical movement of atoms based on the molecular dynamics in nature. ASO displays a unique search ability by employing the interaction force from the potential energy and the constraint force. Despite some successful applications, it still suffers from a local optima stagnation and a low search efficiency. To alleviate these disadvantages, a new adaptive hybridized optimizer named AASOPSO is proposed. In this study, the individual and group cognitive components in particle swarm optimization (PSO) are integrated into ASO to accelerate the exploitation phase, and the acceleration coefficients are introduced to adaptively achieve a good balance between exploration and exploitation. Meanwhile, to improve the search performance of the algorithm, each individual atom possesses its own force constant, which is effectively and adaptively adjusted based on the feedback of the fitness of the atom in some sequential steps. The performance of AASOPSO is evaluated on two sets of benchmark functions compared to the other population-based optimizers to show its effectiveness. Additionally, AASOPSO is applied to the optimal no-load PID design of the hydro-turbine governor. The simulation results reveal that AASOPSO is more successful than its competitors in searching the global optimal PID parameters.

关键词： particle swarm optimization atom search optimization metaheuristic optimization algorithm hydro-turbine governor PID design

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Attitude Model Predictive Control with Actuator Saturation Using an Arc-Search Interior-Point Method

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2023年第4期46卷 726-733页

作者： Yang, Yaguang NASA Goddard Space Flight Ctr Miss Engineering & Syst Anal Div 8800 Greenbelt Rd Greenbelt MD 20771 USA

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Generating VR Meeting Rooms with Non-rectangular Floor Plans Using Cost optimization and Hard Constraints 19th

Generating VR Meeting Rooms with Non-rectangular Floor Plans...

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19th EuroXR International Conference (EuroXR)

作者： Tuemmers, Katja Kemper, Tobias Fuhrmann, Arnulph TH Koln Comp Graph Grp Cologne Germany World VR GmbH Cologne Germany

ISBN: (纸本)9783031162343;9783031162336

This paper describes a method to generate 3D meeting rooms for virtual reality (VR) applications using a greedy cost minimization. Our algorithm can create unique meeting rooms during runtime efficiently enough that its suitable for commonly used stand-alone consumer VR Headsets. First, it extracts information about the room, such as its volume and shape. Then it iteratively generates a layout by altering the furniture and subsequently evaluating it. Changes that lead to inferior layouts are reversed, and those that improve the layout are kept. The algorithm takes the functionality of furniture into consideration as well as design guidelines. In contrast to previous research, the algorithm focuses on non-rectangular rooms. For this purpose, we propose improved cost terms. Additionally, hard constraints were implemented at the end of the algorithm to enforce functional and aesthetic standards. To test our generated rooms we conducted a user study, comparing our proposed algorithm with previous work. Results of this study show that our algorithm generates rooms that were consistently preferred by users.

关键词： Immersive environments Furniture arrangement Content generation optimization algorithm Greedy cost minimization

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Optimal Configuration of Sensor Network for Structural Shape Reconstruction Based on Improved Jaya algorithm 43

Optimal Configuration of Sensor Network for Structural Shape...

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43rd Chinese Control Conference (CCC)

作者： Yang, Hong Jiao, Peng Chen, Leran Gao, Zhiyuan Zhang, Hesheng Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

In the research background of aerospace structural health monitoring, The optimal configuration of the strain sensor network used for shape reconstruction of flexible plate structures is studied. Based on the characteristics of the 3D curved surface reconstruction algorithm, the co-row and co-column, row and column spacing, and strain distribution are employed as the optimization criteria of sensor network optimization configuration and the reconstruction error of structural shape is used as the evaluation metric of optimization effect. In order to solve the problem that the traditional Jaya algorithm is prone to local optimums in the optimal configuration solution, an improved algorithm is proposed to design a comprehensive optimization of the number and location of sensor deployment in the sensor network. In addition, the simulation results show that the shape reconstruction effect of the plate structure is improved effectively through the optimal design of the sensor network configuration. Finally, the optimally configured sensor network is used to conduct shape reconstruction experiments on aluminum alloy plate, which verifies the effectiveness of the optimal configuration method and provides some theoretical basis for the optimal configuration of the sensor network for structural shape reconstruction.

关键词： Structural Shape Reconstruction Optimal Sensor Configuration Error of Reconstruction Improved Jaya optimization algorithm

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Short-term photovoltaic power prediction based on coyote algorithm optimized long-short-term memory network 24

Short-term photovoltaic power prediction based on coyote alg...

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International Conference on Power Electronics and Artificial Intelligence (PEAI)

作者： Mai, Jinjin Zhang, Xiaohong Shanghai Dianji Univ Shanghai 2000000 Lingang Peoples R China

ISBN: (纸本)9798400716638

Enhancing the accuracy of PV power prediction is crucial for guaranteeing secure scheduling and steady power system operation. This research proposes a coyote algorithm (COA) to optimize the prediction model of the long-short-term memory network (LSTM). Taking into full consideration of the five factors constraining the output power of PV, and taking PV power generation as the research object, the power generation efficiency under different weather is analyzed, and COA is used to optimize the parameters of the LSTM fully-connected layer, and establish a COA-LSTM combination model to predict the PV power, which has a better convergence speed and solving efficiency, and it can also avoid the local optimal solution effectively. Finally, based on the real-time data of a photovoltaic power station in Xinjiang, simulation is carried out, and the experimental results show that the COA-LSTM is more accurate in predicting the photovoltaic power than the LSTM.

关键词： photovoltaic power generation long and short-term memory networks optimization algorithm coyote algorithm

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Holistic Efficiency Map Calculation of Variable Flux Machines Regarding Relative Magnetisation

Holistic Efficiency Map Calculation of Variable Flux Machine...

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27th International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)

作者： Kesten, Julius Mueller, Philipp Brodatzki, Matthias Doppelbauer, Martin Karlsruhe Inst Technol Inst Elect Engn Karlsruhe Germany Offenburg Univ Appl Sci Inst Sustainable Energy Syst Offenburg Germany

ISBN: (纸本)9798350387605;9798350387599

An algorithm for the integral calculation of efficiency maps of variable flux machines under consideration of the relative flux level is presented. The general idea and the algorithm logic are presented in detail and the efficiency map calculation is validated against a conventional gradient based efficiency map calculation algorithm. Instead of calculating efficiency maps for discrete flux levels and interpolating these maps afterwards, the algorithm presented here directly includes the relative magnet flux in the efficiency calculation. The presented approach uses torque and voltage surfaces in a plane of current, current angle and flux level. As results, the an exemplary efficiency map calculated with the algorithm is presented and the effect of different numbers of underlying data sets are discussed. Finally, a short outlook on practical applications is given.

关键词： variable flux machine optimization algorithm efficiency flux level

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Mission Planning for Minimal Orbital Conjunctions

Mission Planning for Minimal Orbital Conjunctions

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AIAA SciTech Forum

作者： Rockwood, Troy D. Jah, Moriba K. Cheng, Yang Mitre Corp Colorado Springs CO 80910 USA Univ Texas Austin Austin TX 78705 USA XAnalytix Syst LLC Dept Aerosp Engn & Engn Mech Clarence Ctr NY 14032 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

Selection of destination orbits during mission planning involves a number of factors. Mission goals, the economic factors of size, weight, and power, and the operational constraints imposed by on-orbit conjunctions each affect orbit selection decisions. The number of conjunctions that a satellite will experience dictates mission lifetime and operational efficiency. Minimizing the effect of conjunctions involves significant analytic effort. An optimization tool is presented for mission planning. It takes mission-derived parameters such as acceptable altitudes and inclinations, and finds orbits with the minimum number of conjunctions with resident space objects utilizing the space object catalog. Global optimization techniques and implementation details are presented.

关键词： Resident Space Object Satellites optimization algorithm Parallel Computing Architectures Orbital Capacity Sun Synchronous Orbit Space Environment Right Ascension Orbital Debris Propellant

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Sensitivity Analysis and Uncertainty Quantification for Crystal Plasticity Parameters of Ti-6Al-4V Alloy

Sensitivity Analysis and Uncertainty Quantification for Crys...

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AIAA SciTech Forum

作者： Elleithy, Mohamed Acar, Pinar Virginia Tech Dept Mech Engn Blacksburg VA 24061 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This study presents a comprehensive investigation into the crystal plasticity behavior of the dual-phase Ti-6Al-4V alloy through the utilization of Crystal Plasticity Finite Element (CPFE) modeling and subsequent calibration. Employing a rate-independent, single-crystal constitutive model, the calibration process integrates an interior-point optimization algorithm with experimental data, yielding precise predictions for crystal plasticity (CP) parameters. Uncertainty quantification (UQ) through Monte Carlo Sampling (MCS) method reveals the impact of CP parameters and their uncertainty on the alloy's elasto-plastic mechanical properties. Sensitivity analysis further elucidates the influence of slip-system specific parameters on the alloy's elastic and plastic behavior, emphasizing the role of initial slip resistance as a critical determinant in both regimes. The research findings contribute to a deeper understanding of CP modeling of Ti-6Al-4V alloy and its implications for materials design.

关键词： Sensitivity Analysis Uncertainty Quantification Plasticity Ti 6Al 4V Mechanical Properties optimization algorithm Critical Resolved Shear Stress Microstructure Finite Element Method Histograms

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