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Optimal Configuration of Energy Storage System via MPC considering High Density Photovoltaics 3

Optimal Configuration of Energy Storage System via MPC consi...

引用

3rd IEEE International Electrical and Energy conference (CIEEC)

作者： Wu, Renguang Wang, Yunge Li, Rong Chen, Donghai Wang, Gang Xu, Yuan Gu, Zhongxian Yang, Jincheng Xiangshan Power Ind Co LTD Ningbo Peoples R China State Grid Zhejiang Xiangshan Cty Power Supply Co Ningbo Peoples R China Hohai Univ Design & Res Inst Co LTD Nanjing Peoples R China

ISBN: (纸本)9781728116754

In order to suppress the fluctuation of high density photovoltaic (PV) system, enhance the acceptance capability of the distribution network for PV power generation, and improve the utilization of solar energy, a side energy storage system with high density PV power distribution network will be built to coordinate and optimize the control, which can obtain better economic benefits and system stability. Also, an optimal allocation method based on model predictive control (MPC) is proposed. The method is aimed at energy storage system in distribution network with high density photovoltaics (HDPV). Considering the uncertainty of PV power generation and load demand, energy storage systems can obtain the lowest cost in the whole life cycle. The validity and superiority of the optimal allocation model are verified by the analysis of typical daily dispatch optimization in different seasons and the comparison with energy-storage-free system.

关键词： high density photovoltaic model predictive control energy storage system optimal configuration

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Regional consensus for non-ANCBC systems with input saturation 3

Regional consensus for non-ANCBC systems with input saturati...

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3rd IEEE conference on control Technology and Applications (IEEE CCTA)

作者： Yu, Zengming Chen, Fei Lan, Weiyao Xiamen Univ Dept Automat Xiamen 361005 Fujian Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Hebei Peoples R China

ISBN: (纸本)9781728127675

The primary aim of this paper is to study the regional consensus problem for saturated multi-agent systems satisfying solely the stabilization assumption. For such kind of systems, it is recognized that global or semi-global consensus generally cannot be reached. Instead, this paper studies the problem of regional consensus where the initial states of agents lie within a convex set which is positively invariant over a fixed undirected communication network topology. The approach of convex hull representation is extended to deal with the actuator saturation for multi-agents systems. Some sufficient conditions are derived, where the domain of consensus is estimated by the level set of a quadratic function. It is shown that these conditions can be transformed into a collection of linear matrix inequalities (LMIs), based on which an optimization problem is formulated to obtain the possibly largest estimate of the domain of consensus. A numerical example is presented to illustrate the effectiveness of the proposed method.

关键词： Consensus multi-agent system saturation LMI network

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Model Predictive control Based Data-Driven Retrofit controller for network Systems 3

Model Predictive Control Based Data-Driven Retrofit Controll...

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3rd IEEE conference on control Technology and Applications (IEEE CCTA)

作者： Naharudinsyah, Ilham Ishizaki, Takayuki Kawaguchi, Takahiro Imura, Jun-ichi Tokyo Inst Technol Grad Sch Engn Dept Syst & Control Engn 2-12-1 Meguro Tokyo Japan

ISBN: (纸本)9781728127675

In this paper, we propose a data-driven retrofit controller design method based on the robust model predictive control. Retrofit control is a distributed design of decentralized controllers for a large-scale networked system. By using the retrofit control paradigm, the damping performance of the subsystem of interest can be improved by only relying on the local information of the subsystem. In this paper, we propose the extension of the existing method by utilizing the approximate model of the environment through the system identification approach. Then, by using the robust model predictive control framework, we formulate an optimization problem which produces a retrofit controller that complies to the constraints under the presence of the interconnection signal noise. We show that although the retrofit control framework only requires the local information, the approximate model of the environment will improve the damping performance even with a certain degree of inaccuracy. Lastly, the effectiveness and robustness of the proposed method are demonstrated through numerical simulation.

关键词： controllers

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Power System Emergency control to Improve Short-Term Voltage Stability Using Deep Reinforcement Learning Algorithm 3

Power System Emergency Control to Improve Short-Term Voltage...

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3rd IEEE International Electrical and Energy conference (CIEEC)

作者： Jiang, C. X. Li, Zhigang Zheng, J. H. Wu, Q. H. South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China

ISBN: (纸本)9781728116754

This paper develops a novel power system emergency control scheme to improve short-term voltage stability (STVS) using deep reinforcement learning (DRL). The proximal policy optimization (PPO) algorithm is developed to adaptively update the load shedding strategy and the deep neural network parameters through continuously interacting with the environment. In order to assess the effects of the load shedding action, a reward function is designed by using a transient voltage severity index. Simulations are conducted on a modified New England 39-bus system, and the results illustrate that the DRL algorithm can obtain a better strategy in a variety of STVS scenarios.

关键词： deep reinforcement learning load shedding proximal policy optimization short-term voltage stability

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Analysis of Different controllers on SMPS Based Charging Stations 3

Analysis of Different Controllers on SMPS Based Charging Sta...

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3rd International conference on Recent Developments in control, Automation and Power Engineering (rdCAPE)

作者： Padmini, N. Prakash, Anupama Moulik, Bedatri Amity Univ Dept Elect & Elect Engn Noida Uttar Pradesh India

ISBN: (纸本)9781728120683

Within any electrical-electronic system, power supply assumes a unique role by, providing consistent and repeatable power to its circuit. In addition, it serves as a medium (defender) between the mains and the circuit. Switched Mode Power Supply (SMPS) is the most prevailing architecture for DC power supply in modern systems, primarily for its capability to handle variable loads. Apart from efficiency the size and weight of the power supplies is becoming a great area of concern for the Power Supply Designers. With constant research and advancement in the field of artificial intelligence, the control operation can be enhanced with the use of artificial intelligence control techniques. The SMPS which can be widely classified into isolated and non-isolated topologies. The PWM control of the power MOSFET in SMPS is enhanced with use of AI. In this thesis the different AI control techniques such as fuzzy control, artificial neural network control and particle swarming optimization are implemented and the results are compared.

关键词： SMPS AC-DC converters AI DC supply PWM control

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Modelling and Simulation of Reverse Osmosis System Using PSO-ANN Prediction Technique 3rd

Modelling and Simulation of Reverse Osmosis System Using PS...

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3rd International conference on Soft Computing: Theories and Applications, SoCTA 2018

作者： Mahadeva, Rajesh Manik, Gaurav Verma, Om Prakash Goel, Anubhav Kumar, Sanjeev Department of Polymer and Process Engineering Indian Institute of Technology Roorkee RoorkeeUttarakhand247667 India Deparment of Instrumentation and Control Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar JalandharPunjab144011 India

ISBN: (纸本)9789811507502

Nowadays, among various water treatment and desalination technologies such as reverse osmosis (RO), multi-effect distillation (MED), and multi-stage flash (MSF), RO is an appropriate and suitable technology in the world. It is extremely used technology (>60%) around the globe. It is quite popular in separation and filtering process, especially for drinking water services as well as industrial applications. Modelling and simulation of such plants are necessary for better analysis and understanding with minimum effort, energy, and time. It involves various machine learning techniques such as an artificial neural network (ANN), support vector machine (SVM). Among these techniques, ANN is one of the best and reliable techniques, which provides good results. ANN may be learned through numerous training algorithms such as back-propagation (BP), particle swarm optimization (PSO);PSO-ANN learning algorithm generated the optimal values of initial weights and biases and to train the network. In this article, experimental datasets of RO plants have been collected from the literature and the regression coefficient (R) along with minimum mean square error (MSE) are evaluated. Four input variables (temperature T (°C), pressure P (MPa), feed concentration C (Mg/L), and pH) and three output variables (water recovery (%), total dissolved solids (TDS) rejection (%), and specific energy consumption (SEC) (kWh/m3)) are considered for analysis. The simulated results observed better regression coefficients (R) (0.98557, 0.96016, and 0.97118) with minimum MSE (0.5502%, 0.9389%, and 1.5755%), respectively, corresponding to output variables of the RO plant. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Desalination

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Real-time optimization of Continuous Catalytic Reforming Process with Hybrid Model 3

Real-time Optimization of Continuous Catalytic Reforming Pro...

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3rd IEEE Advanced Information Management, Communicates, Electronic and Automation control conference, IMCEC 2019

作者： Lou, Haichuan Lin, Xueru Xie, Liulei Zhejiang SUPCON Software Co. Ltd. China Zhejiang SUPCON Technology Co. Ltd. China Polytechnic Institute Zhejiang University China

ISBN: (纸本)9781728105130

A real-time optimization method with the combination of simplified mechanism and deep belief neural network model for continuous catalytic reforming (CCR) process is presented this paper. The basic model of reforming reaction is established based on the simplified mechanism as well as the kinetic parameters are estimated with deep belief neural network(DBNN), and the objective functuon together the maximization of aromatics yield with the minimization of energy consumption is solved by the non-line optimizer. The hybrid mode, can not only reflect the physical characteristics of the CCR process, but also improve the model extrapolation ability, which is greatly guaranteed the efficiency and stability for the real-time optimization of CCR. Finally, the effectiveness of method proposed is validated through a case study on a industry CCR process. © 2019 IEEE.

关键词： Energy utilization

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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...

引用

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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Bi-level Proximal Policy optimization for Stochastic Coordination of EV Charging Load with Uncertain Wind Power 3

Bi-level Proximal Policy Optimization for Stochastic Coordin...

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3rd IEEE conference on control Technology and Applications (IEEE CCTA)

作者： Long, Teng Ma, Xiao-Teng Jia, Qing-Shan Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BN R Dept Automat Ctr Intelligent & Networked Syst CFINS Beijing 100084 Peoples R China

ISBN: (纸本)9781728127675

Energy and environmental issues are one of the biggest technological challenges facing human society in the new century, electrical vehicle (EV) and wind power are good solutions for both ends of demand and supply. It is of great importance to schedule the EV charging load to match the uncertain wind power supply both for economic reasons and for sustainable development. However, many classical methods are hard to apply for solving this problem due to its huge state space, the high uncertainty and the complex dynamic evolution of the system. Proximal policy optimization (PPO) shows us a new way to solve large scale stochastic optimization problems. In this paper, we make the following major contributions. First, using the structure of different spatial scales of this problem, we formulate the system as a bi-level Markov decision process (MDP). EV aggregators (EVAs) are applied to gather EVs according to their parking regions. On the upper level, based on some brief state information, the independent system operator (ISO) only decides how much power to buy from the state grid, and how much power will be allocated to each EVA. On the lower level, each EVA will finally decide every EV's charging process based on the dispatched power and then the system state starts to shift. Second, we develop a bi-level proximal policy optimization (BPPO) algorithm to solve this bi-level MDP where the upper network and lower level network are interrelated. Third, the numerical results show that our algorithm outperforms three other algorithms on reducing the total charging cost.

关键词： Markov decision process proximal policy optimization electrical vehicle wind power smart grid

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Omnidirectional Transport System for Classification and Quality control using Artificial Vision 19

Omnidirectional Transport System for Classification and Qual...

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3rd International conference on Virtual and Augmented Reality Simulations (ICVARS)

作者： Barrionuevo Salazar, Erick Navas Escudero, Bryan Rea Minango, Sylvia Nathaly Univ Fuerzas Armadas ESPE Ave Gen Ruminahui S-N Sangolqui Ecuador

ISBN: (纸本)9781450365925

Classification and transport of material is a crucial stage in the manufacture of parts and represents a large portion of the lead time in a production line, which is why optimization is crucial. This work deals with an omnidirectional transport mechanism for classification and quality control of parts coming from rapid prototyping processes. Said mechanism is constituted by an artificial vision system, which will be responsible for taking the necessary information to perform the classification and quality control using a neural network;and, a matrix of omnidirectional wheels that allows the movement of the piece on the XY plane. The purpose of this investigation was to demonstrate that omnidirectional mechanisms can also be used to transport and classify parts within industrial processes, being another alternative of use to conventional systems. The system is able to classify three types of pieces of different forms, sizes and perspectives with high reliability and speed;it also allows a better human-machine interaction due to a graphical interface where the performed processes are detailed.

关键词： Artificial vision conveyor omnidirectional transport system

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