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A novel Decoupled Trigonometric Saturated droop controller for power sharing in islanded low-voltage microgrids

ELECTRIC POWER systems RESEARCH 2019年 168卷 146-161页

作者： Bouzid, Allal El Moubarek Sicard, Pierre Chaoui, Hicham Cheriti, Ahmed Sechilariu, Manuela Guerrero, Josep M. Univ Quebec Trois Rivieres Grp Rech Elect Ind Trois Rivieres PQ G9A 5H7 Canada Carleton Univ Intelligent Robot & Energy Syst IRES Res Grp Dept Elect Ottawa ON K1S 5B6 Canada Univ Technol Compiegne Sorbonne Univ EA 7284 AVE CS 60319 F-60203 Compiegne France Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

This paper proposes a novel droop control based on Decoupled Trigonometric Saturated (DTS) controller for stable power sharing applied to meshed parallel inverter systems in islanded microgrids. The novel DTS control scheme is introduced to improve the power-sharing accuracy with a better stability and to provide a proper dynamic decoupling of active and reactive power in the presence of different impedances. Moreover, this method not only achieves the aforementioned decoupling;but also, guarantees both voltage and frequency stability. The theoretical concept of the proposed novel droop control strategy is presented in detail. The DTS controller is applied to a common AC bus microgrid structure and a meshed parallel inverter system structure in islanded microgrids with mainly inductive or resistive line impedances. An offline time-domain simulation is conducted in MATLAB (R)/SimPowersystems environment using RT-EVENTS toolbox from OPAL-RT to model the inverters. Resulting waveforms from a three-phase microgrid with four distributed generators are presented along with a comparison against the conventional droop control strategy and show the effectiveness of the proposed method in allocating both real and reactive power.

关键词： Microgrid Decoupled droop control distributed generation (DG) parallel operation Power sharing

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Concept of Energy Efficient ESP32 Chip for Industrial Wireless Sensor Network

Concept of Energy Efficient ESP32 Chip for Industrial Wirele...

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IEEE International Conference on Intelligent Engineering systems (INES)

作者： Emil Gatial Zoltán Balogh Ladislav Hluchý Department of Parallel and Distributed Computing Slovak Academy of Sciences Bratislava Slovakia Department of Parallel and Distributed Computing Slovak Academy of Sciences Bratislava Slovania

ISBN: (数字)9781728110592

ISBN: (纸本)9781728110608

This paper proposes a concept of low-cost industrial wireless sensor network integrating the power consumption efficient ESP32 chip. The main focus is given on best practices of various sensors integration into real-time Operating System (RTOS) of ESP32 devices. The work proposes an architecture for data collection, security and possibility of data analysis. Energy effectiveness is estimated for battery usage in different modes of the chip. The last part of this work presents the conclusions and guidelines for future work. Road map of a distributed modeling framework for plant-wide process monitoring is also introduced.

关键词： Temperature sensors Wireless sensor networks Monitoring Temperature measurement Batteries real-time systems

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parallel stochastic programming for energy storage management in smart grid with probabilistic renewable generation and load models

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IET RENEWABLE POWER GENERATION 2019年第5期13卷 774-784页

作者： Wang, Yue Liang, Hao Dinavahi, Venkata Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada

Renewable power generation combined with energy storage (ES) is expected to bring enormous economical and environmental benefits to the future smart grid. However, the ES management in smart grid is facing significant technical challenges due to the volatile nature of renewable energy sources and the buffering effect of ES units. The challenges are further complicated by the increasing size and complexity of the system, as well as the consideration of random usage patterns of electrical appliances by customers. To address these challenges, this study proposes a parallel decomposition method for large-scale stochastic programming in a distribution system with renewable energy sources and ES units. By leveraging nested decomposition, the problem can be converted into independent sub-problems with a series of time periods. In addition, the reformulated problem is fully parallel for speed up in execution. The performance of the proposed method is evaluated based on the IEEE 4-bus and 33-bus test distribution systems with real photovoltaic generation and electrical appliance usage data. The case study demonstrates that the proposed scheme can substantially reduce the system operation cost, with low computational complexity.

关键词： computational complexity distributed power generation energy storage renewable energy sources stochastic programming photovoltaic power systems smart power grids domestic appliances demand side management parallel stochastic programming energy storage management probabilistic renewable generation load models renewable power generation enormous economical benefits environmental benefits future smart grid ES management significant technical challenges volatile nature renewable energy sources ES units increasing size complexity random usage patterns parallel decomposition method large-scale stochastic programming distribution system 33-bus test distribution systems photovoltaic generation electrical appliance usage data

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RedSync: Reducing synchronization bandwidth for distributed deep learning training system

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JOURNAL OF parallel AND distributed COMPUTING 2019年 133卷 30-39页

作者： Fang, Jiarui Fu, Haohuan Yang, Guangwen Hsieh, Cho-Jui Tsinghua Univ Dept Comp Sci Beijing Peoples R China Tsinghua Univ Dept Earth Syst Sci Beijing Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Natl Supercomp Ctr Wuxi Wuxi Jiangsu Peoples R China Univ Calif Los Angeles Comp Sci Los Angeles CA USA

Data parallelism has become a dominant method to scale Deep Neural Network (DNN) training across multiple nodes. Since the bandwidth requirement of synchronizing the gradients of the local model can be a bottleneck for large-scale distributed training, compressing communication traffic has gained widespread attention recently. Among several recent proposed compression algorithms, Residual Gradient Compression (RGC) is one of the most successful approaches-it can significantly compress the transmitting message size (0.1% of the gradient size) of each node and still achieve correct accuracy and the same convergence speed. However, the literature on compressing deep networks focuses almost exclusively on achieving good theoretical compression rate, while the efficiency of RGC in real implementation has been less investigated. In this paper, we develop an RGC method that is able to reduce the end-to-end training time on real-world multi-GPU systems. Our proposed RGC system design called RedSync, introduces a set of optimizations to reduce communication bandwidth while introducing limited overhead. We examine the performance of RedSync on two different multiple GPU platforms, including 128 GPUs of a supercomputer and an 8-GPU server. Our test cases include image classification on Cifar10 and ImageNet, and language modeling tasks on Penn Treebank and Wiki2 datasets. For DNNs featured with high communication to computation ratio, which has long been considered with poor scalability, RedSync shows significant performance improvement. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Deep learning Data parallelism Graphics processing unit (GPU) Gradient compression Sparsification Quantization MPI

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A distributed data management system to support large-scale data analysis

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JOURNAL OF systems AND SOFTWARE 2019年 148卷 105-115页

作者： Emara, Tamer Z. Huang, Joshua Zhexue Shenzhen Univ Coll Comp Sci & Software Engn Big Data Inst Shenzhen 518060 Guangdong Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Guangdong Peoples R China

distributed data management is a key technology to enable efficient massive data processing and analysis in cluster-computing environments. Specifically, in environments where the data volumes are beyond the system capabilities, big data files are required to be summarized by representative samples with the same statistical properties as the whole dataset. This paper proposes a big data management system (BDMS) based on distributed random sample data blocks. It presents a high-level architecture design of the BDMS which extends the current distributed file systems. This system offers certain functionalities for block-level management such as statistically-aware data partitioning, data blocks organization, and data blocks selection. This paper also presents a round-random partitioning scheme to represent a big dataset as a set of non-overlapping data blocks;each block is a random sample of the whole dataset. Based on the presented scheme, two algorithms are introduced as an implementation strategy to convert the HDFS blocks of a big file into a set of random sample data blocks which is also stored in HDFS. The experimental results show that the execution time of partitioning operation is acceptable in the real applications because this operation is only performed once on each input data file. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Big data distributed and parallel processing Random sample partition Randomness Data management

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Analyzing Simulation Model Profile Data to Assist Synthetic Model Generation 23

Analyzing Simulation Model Profile Data to Assist Synthetic ...

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23rd IEEE/ACM International Symposium on distributed Simulation and real time Applications (DS-RT)

作者： Kane, Sean Gupta, Sounak Wilsey, Philip A. Univ Cincinnati Dept EECS Cincinnati OH 45221 USA eBay Inc 2025 Hamilton Ave San Jose CA 95125 USA

ISBN: (纸本)9781728129235

Synthetic workloads are commonly used to exercise simulation tools for performance, performance tuning, and scalability studies. Sometimes these workloads are simple streams of test data following various distributions and in other cases these workloads are generated by more complex, configurable systems. An example of the former is a stream of input events at different arrival rates that might be used to test the performance of an event queue data structure. An example of the latter is the PHOLD simulation model that is often used to contrast the performance implications of different design solutions in a parallel simulation engine. One of the key challenges for synthetic workloads is the question of setting the parameters so that the workload properly reflects the behavior of actual workloads. This paper collects profile data from multiple real-world discrete-event simulation models in multiple configurations and sizes from the ROSS and WARPED2 repositories. A principle focus of this paper is the capture and reporting of profiling data to understand event granularities and event profile data to assist in the configuration of synthetic discrete event model generators.

关键词： Discrete Event Simulation Profiling simulation models parallel discrete event simulation PHOLD Synthetic workload generation

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Analysis of Negative Influence of Harmonic Circulation between parallel STATCOMs and Suppression Method 10

Analysis of Negative Influence of Harmonic Circulation betwe...

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IEEE 10th International Symposium on Power Electronics for distributed Generation systems (PEDG)

作者： Chen, Jikai Wang, Peng Hu, Yang Shao, Hui Li, Guoqing Wang, Xiaozhe Qin, Jiangchao Northeast Elect Power Univ Sch Elect Engn Jilin Jilin Peoples R China McGill Univ Sch Elect Engn Montreal PQ Canada Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ USA

ISBN: (纸本)9781728124551

In this paper, the cascaded H-bridge STATCOM parallel system is taken as the research object to analyze the instability of STATCOM system caused by the interaction between parallel STATCOMs in wind farm afflux station. Firstly, the inherent relation of harmonic circulation and low-frequency disturbance will be revealed by CPS-PWM theory. Then, the output impedance and transfer function of STATCOM give an insight into the interaction between STATCOMs and low-frequency resonance, and the virtual impedance method is proposed to shape the resonant peak. Finally, the correctness of theoretical analysis is verified by real-time simulation.

关键词： cascaded H-bridge STATCOM harmonic current low-frequency disturbance virtual impedance

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real time Key Frame Extraction Through parallel Computation of Entropy Difference 18th

Real Time Key Frame Extraction Through Parallel Computation ...

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18th International Conference on Computer Information systems and Industrial Management

作者： Gautam, Nandita Das, Debdoot Khatua, Sunirmal Saha, Banani Univ Calcutta Comp Sci Dept Kolkata 700098 India

ISBN: (纸本)9783030289560;9783030289577

The advancement of image processing in the field of Artificial Intelligence has created various research prospects in the area of object detection, pattern recognition etc. Capturing real time video stream for multiple cameras within a region of interest has become a common phenomenon for an Intelligent Situation Awareness System. Video processing is an important application which is rapidly developing nowadays as an area of extensive research. Content retrieval as well as information collection from a video requires both syntactic and semantic analysis. For a large video data, some set of frames are used to represent the video content. These are identified as key frames. Several algorithms have been defined to extract key frames from a stored video file. The existing algorithms that have been defined for key frame extraction are based on sequential mode. This paper looks into the extraction of key frames for any real time video stream. The experimental results show that there is an effective reduction in the execution time to a huge extent in the case of distributed processing as compared to the sequential processing of frames. In this paper, we propose a distributed framework to regulate the speed of key frame generation for heterogeneous speed of incoming video.

关键词： real time streaming Key frames parallel computing Entropy

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Speed up genetic algorithms in the cloud using software containers

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FUTURE GENERATION COMPUTER systems-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 92卷 276-289页

作者： Salza, Pasquale Ferrucci, Filomena USI Univ Svizzera Italiana Fac Informat Lugano Switzerland Univ Salerno Dept Comp Sci Salerno Italy

Scalability issues might prevent Genetic Algorithms from being applied to real world problems. Exploiting parallelisation in the cloud might be an affordable approach to getting time efficient solutions that benefit of the appealing features of scalability, resource discovery, reliability, fault-tolerance and costeffectiveness. Nevertheless, parallel computation is very prone to cause considerable overhead for communication. Also, making Genetic Algorithms distributed in an on-demand fashion is not trivial. Aiming at keeping under control the communication cost and, at the same time, supporting developers in the construction and deployment of parallel Genetic Algorithms, we designed and implemented a novel approach to distribute Genetic Algorithms in the form of a cloud-based application. It is based on the master/slave model, exploiting software containers, their cloud orchestration and message queues. We also devised a conceptual workflow covering each cloud Genetic Algorithms distribution phase, from resources allocation to actual deployment and execution, in an engineered fashion. Then, the application performance has been evaluated using a benchmark problem. According to the performance and setup times results, it emerged that the cloud can be considered a compelling way of scaling Genetic Algorithms and an excellent alternative to other technologies strictly related to the physically owned hardware. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Genetic algorithms parallel genetic algorithms Cloud computing Software containers Software engineering

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Energy Access in Community Microgrids based on Decentralized real-time Pricing

Energy Access in Community Microgrids based on Decentralized...

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2019 IEEE Decentralized Energy Access Solutions workshop, DEAS 2019

作者： Jinsiwale, Rohit Divan, Deepak Georgia Institute of Technology Center for Distributed Energy Atlanta United States

ISBN: (纸本)9781728107950

Non-dispatchable sources like PV and their increased adoption in emerging markets have made it extremely challenging to stabilize these systems. Enormous variations in the supply-demand ratio on a daily basis, point to the need for real-time market structures. Today's pricing mechanisms such as Locational Marginal Prices (LMPs) and Distribution Locational Marginal Prices (DLMPs) are highly dependent on real-time knowledge of system topology, grid states and high-speed communication infrastructure. These mechanisms are not yet widely adopted and are extremely prone to failure in the event of damage to the communication infrastructure or inaccurate system models. A novel decentralized pricing architecture is presented here which relies on frequency-a ubiquitous signal, to establish real-time prices. This ensures that the real-time price can be observed at every node without any communication infrastructure, making it extremely resilient. The approach also succeeds in generating common rules for control and operation of inverter-based as well as conventional sources. The approach integrates the transactive and operational aspects of the grid and leverages supply-demand flexibility, while ensuring the lowest cost of operation. © 2019 IEEE.

关键词： Costs

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