2019 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第9页

GA-Aided Power Flow Management in a Multi-Vector Energy System 基于遗传算法的多矢量能量系统潮流管理

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002943

Xiangping Chen, W. Cao, Lei Xing

{"title":"GA-Aided Power Flow Management in a Multi-Vector Energy System","authors":"Xiangping Chen, W. Cao, Lei Xing","doi":"10.1109/SSCI44817.2019.9002943","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002943","url":null,"abstract":"Utilization of renewable energy (e.g. wind, solar, bio-energy) is high on the governmental agenda globally. In order to tackle energy poverty and increase energy efficiency in energy systems, a hybrid energy system including wind, hydrogen and fuel cells is proposed to supplement to the main power grid. Wind energy is firstly converted into electrical energy while part of the generated electricity is used for water electrolysis to generate hydrogen for energy storage. Hydrogen is used by fuel cells to convert to electricity when electrical energy demand peaks. An analytical model is developed to coordinate the operation of the system involving energy conversion between hydrogen, electrical and mechanical forms. The proposed system is primarily designed to meet the electrical demand of a rural village while the energy storage system can meet the discrepancy between intermittent renewable energy supplies and fluctuated energy demands so as to improve the system efficiency. Genetic Algorithm (GA) is used as an optimization strategy to determine the operational scheme for the multi-vector energy system. In this work, case studies are carried out based on actual measurement data. The test results have confirmed the effectiveness of the proposed methodology and maximizing the wind energy consumption locally. This is an alternative to battery energy storage and can be widely used in wind-rich rural areas.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"7 5 1","pages":"3172-3176"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77407774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Collaborative System Identification via Consensus-Based novel PI-like Parameter Estimator 基于共识的新型类pi参数估计的协同系统辨识

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002750

Tushar Garg, S. Roy

{"title":"Collaborative System Identification via Consensus-Based novel PI-like Parameter Estimator","authors":"Tushar Garg, S. Roy","doi":"10.1109/SSCI44817.2019.9002750","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002750","url":null,"abstract":"This work proposes a consensus-based novel PI-like parameter estimator for collaborative system identification. Conventional online parameter estimation algorithms, which are used for system identification, require a restrictive condition of persistence of excitation (PE) for the estimates to converge to the true parameters. Some recent works have shown that collaborative system identification using multiple agents can relax the PE condition to a milder condition of collective persistence of excitation (C-PE) for parameter convergence. The C-PE condition implies that the PE condition is cooperatively satisfied by all the agents through sharing information between neighbors using a connected graph architecture, where each individual agent does not require to satisfy the PE condition separately. The proposed work designs a novel collaborative parameter estimator dynamics, which with the help of integral-like component ensures parameter convergence under a further slackened condition; coined as collective Initial Excitation (C-IE). The C-IE condition is an extension of the concept of initial excitation (IE), which is recently proposed in the context of parameter estimation in adaptive control. It has been already established that IE condition is significantly less restrictive than PE. The current work generalizes the concept of IE in a multi-agent settings, where information sharing through connected graph guarantees consensus parameter convergence under the C-IE condition. It can be argued that C-IE condition is milder than all of the other above mentioned conditions of PE, C-PE and IE. Simulation results further validate the efficacy of the proposed estimation algorithm.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"4 1","pages":"1285-1291"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77643044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Ensemble of Semi-Parametric Models for IoT Fog Modeling 物联网雾建模的半参数模型集成

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003089

Tony Jan, Saeid Iranmanesh, A. Sajeev

引用次数: 0

Structured Iterative Hard Thresholding for Categorical and Mixed Data Types 分类和混合数据类型的结构化迭代硬阈值

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002948

Thy Nguyen, Tayo Obafemi-Ajayi

引用次数: 0

Shallow Network Training With Dynamic Sample Weights Decay - a Potential Function Approximator for Reinforcement Learning 基于动态样本权值衰减的浅网络训练——一种用于强化学习的势函数逼近器

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003124

Leo Ghignone, M. Barlow

引用次数: 1

Analysis of Grain Condition in Improved Granary Based on Grey Prediction Algorithm 基于灰色预测算法的改进型粮仓粮食状况分析

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003036

Huichao Zhang, Guangyuan Zhao, X. Qin

引用次数: 0

Generation of Human-Like Movements Based on Environmental Features 基于环境特征的类人运动生成

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002822

A. Zonta, S. Smit, M. Hoogendoorn, A. Eiben

引用次数: 2

Weighted Two-dimensional Otsu Threshold Approached for Image Segmentation 加权二维Otsu阈值图像分割方法

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002689

Liyu Lin, Shuanqiang Yang

引用次数: 3

Generation of Artificial FO-contours of Emotional Speech with Generative Adversarial Networks 基于生成对抗网络的情绪语音人工fo轮廓生成

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002917

Shumpei Matsuoka, Yao Jiang, A. Sasou

引用次数: 0

Deep Recurrent Neural Networks for Nonlinear System Identification 用于非线性系统辨识的深度递归神经网络

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003133

Max Schüssler, T. Münker, O. Nelles

引用次数: 6