Considering the quality of the output voltage, the distributed power supply needs to have a certain power quality control ability when the unbalanced fault occurs. In response to this requirement, this paper proposes ...
详细信息
ISBN:
(纸本)9798350382570;9798350382563
Considering the quality of the output voltage, the distributed power supply needs to have a certain power quality control ability when the unbalanced fault occurs. In response to this requirement, this paper proposes a parallel system. An inverter is placed at the common coupling point of the main circuit and the power grid as an external device for fault ride-through when a serious fault drop. During normal operation, the parallel inverter does not operate, and the main circuit inverter outputs active power. Under asymmetric fault conditions, the parallel inverter injects reactive power to the common coupling point to limit AC current and grid voltage support. Firstly, the power model of the inverter under asymmetric conditions is analyzed in the first part. Then, the power quality management and capacity optimization strategy of the parallel system are described. Finally, the waveform results show the correctness and superiority of the capacity optimization strategy.
The rapidly advancing fields of machine learning and mathematical modeling, greatly enhanced by the recent growth in artificial intelligence, are the focus of this special issue. This issue compiles extensively revise...
详细信息
The rapidly advancing fields of machine learning and mathematical modeling, greatly enhanced by the recent growth in artificial intelligence, are the focus of this special issue. This issue compiles extensively revised and improved versions of the top papers from the workshop on Mathematical Modeling and Problem Solving at PDPTA'23, the 29th internationalconference on parallel and distributed Processing Techniques and Applications. Covering fundamental research in matrix operations and heuristic searches to real-world applications in computer vision and drug discovery, the issue underscores the crucial role of supercomputing and parallel and distributedcomputing infrastructure in research. Featuring nine key studies, this issue pushes forward computational technologies in mathematical modeling, refines techniques for analyzing images and time-series data, and introduces new methods in pharmaceutical and materials science, making significant contributions to these areas.
Large-scale deployment of Internet of Things (IoT) devices provides efficient data collection and control capabilities in the smart grid, while edge computing plays a key role in increasing the speed of data processin...
详细信息
The large-scale penetration of distributed photovoltaic (PV) power generation systems has brought new challenges to the topology identification and detection of traditional distribution networks. This article mainly s...
详细信息
ISBN:
(纸本)9798350375145;9798350375138
The large-scale penetration of distributed photovoltaic (PV) power generation systems has brought new challenges to the topology identification and detection of traditional distribution networks. This article mainly studies the topology identification technology (TIT) of distributed PV low-voltage (LV) distribution network lines, aiming to design a topology identification method that can adapt to the dynamic changes of the power grid, have large-scale capacity, and improve system accuracy under the same conditions. At the same time, the data processing speed has been improved. This article first constructs a system model, including node model, edge model, and parameter model. It mathematically represents the topology structure of the power grid using graph theory and designs a topology recognition algorithm based on optimization techniques and state estimation. This algorithm is used to solve the distributed characteristics of power grid topology recognition, the difficulty of data collection, the dynamic diversity of power grid structure, the uncertainty of equipment parameters, the high computational complexity of data processing, and the communication constraints of power grid topology recognition. This algorithm adopts modern programming languages and parallelcomputing frameworks, making it easy to implement efficiently. The results on the simulation platform show that the highest recall rate for 22 test cases is 93.8%, and the response time for test cases is 425 ms to 980 ms, providing a fast response to the information space of the grid.
Loss sensitivity in electricity systems refers to the responsiveness of power losses in electrical transmission and distribution networks to various influencing factors. This concept is critical for optimizing the eff...
详细信息
ISBN:
(纸本)9798350377477;9798350377460
Loss sensitivity in electricity systems refers to the responsiveness of power losses in electrical transmission and distribution networks to various influencing factors. This concept is critical for optimizing the efficiency and reliability of power delivery from generation sources to end consumers. Especially in the electricity market trading process, network losses, although only a small part of the total cost, have a significant impact on each specific transaction and grid user. Therefore, in-depth understanding and analysis of the impact of generation transactions on the grid network loss can provide a basis for the power purchase plan for the grid to provide reference for the grid network loss management, which is of great significance. The current mainstream calculation and analysis method for network loss sensitivity is the marginal loss coefficient method, which determines the network loss share of each bus by analyzing the impact of bus injected power on the total network loss changes, but the nature of its serial calculation cannot properly solve the balance between real-time analysis of network loss optimization and accuracy, and with the increase in system size, this problem has gradually become a major obstacle to the realization of real-time analysis. This paper presents a novel parallelcomputing method based on graph computing for determining the sensitivity of system network loss and node-injected power. Graph computing is a parallelcomputing method that stores, computes, and analyzes the information in a system by constructing a topological graph model and based on it. The method alleviates the computational burden associated with calculating network loss sensitivity as the scale of the power system expands by leveraging the parallelcomputing capabilities of graph databases. Furthermore, it facilitates the graphical calculation and analysis of network loss sensitivity through the utilization of graph storage.
We consider a sparse matrix-matrix multiplication (SpGEMM) setting where one matrix is square and the other is tall and skinny. This special variant, TS-SpGEMM, has important applications in multi-source breadth-first...
详细信息
ISBN:
(数字)9798350352917
ISBN:
(纸本)9798350352924;9798350352917
We consider a sparse matrix-matrix multiplication (SpGEMM) setting where one matrix is square and the other is tall and skinny. This special variant, TS-SpGEMM, has important applications in multi-source breadth-first search, influence maximization, sparse graph embedding, and algebraic multi-grid solvers. Unfortunately, popular distributed algorithms like sparse SUMMA deliver suboptimal performance for TS-SpGEMM. To address this limitation, we develop a novel distributed-memory algorithm tailored for TS-SpGEMM. Our approach employs customized 1D partitioning for all matrices involved and leverages sparsity-aware tiling for efficient data transfers. In addition, it minimizes communication overhead by incorporating both local and remote computations. On average, our TS-SpGEMM algorithm attains 5x performance gains over 2D and 3D SUMMA. Furthermore, we use our algorithm to implement multi-source breadth-first search and sparse graph embedding algorithms and demonstrate their scalability up to 512 Nodes (or 65,536 cores) on NERSC Perlmutter.
The ARBITRARY PATTERN FORMATION (APF) is widely studied in distributedcomputing for swarm robots. This paper deals with the APF problem in an infinite grid under an asynchronous scheduler. In [Bose K, Adhikary R, Kun...
详细信息
The ARBITRARY PATTERN FORMATION (APF) is widely studied in distributedcomputing for swarm robots. This paper deals with the APF problem in an infinite grid under an asynchronous scheduler. In [Bose K, Adhikary R, Kundu MK, et al. Arbitrary pattern formation on infinite grid by asynchronous oblivious robots. Theor Comput Sci. 2020;815:213-227], the authors proposed an algorithm for APF problem in OBLOT model under an asynchronous scheduler, but the proposed algorithm was neither time optimal nor move optimal. This work provides two algorithms that solve APF problem in an asynchronous scheduler. The first algorithm is move optimal considering OBLOT model and the second algorithm is move and time optimal considering the LUMI model, where each robot has one light having three distinct colours.
Affix-oriented metadata search is one of the essential fuzzy search capabilities that allow users to find data of interest in their voluminous data set with incomplete query conditions. With the recent transition towa...
详细信息
ISBN:
(纸本)9798350395679;9798350395662
Affix-oriented metadata search is one of the essential fuzzy search capabilities that allow users to find data of interest in their voluminous data set with incomplete query conditions. With the recent transition towards object-centric data management systems in the science community, there is a paramount need for the support of such features in distributed settings. However, existing metadata search solutions either do not support efficient affix-oriented metadata search or do not suit well in a distributed setting of object-centric data management systems. To bridge this gap, we introduce IDIOMS, a metadata search solution underpinned by a distributed metadata index, meticulously designed to enable high-performance affix-oriented metadata search for parallel object-centric storage. One of the standout features of IDIOMS is its efficiency in supporting four distinct types of highly demanded metadata queries. Furthermore, IDIOMS is flexibly catering to both independent and collective metadata search operations. Our experimental comparisons with SoMeta, a state-of-the-art metadata query method, demonstrate more than 400x performance boost for independent queries and up to 300x performance improvements for collective queries, while keeping a small index management overhead.
To accurately evaluate the patient's condition, medical workers usually need to register multiple pathological images of the lesion site samples. Using computer technology to assist in registration work can effect...
详细信息
ISBN:
(纸本)9798350391961;9798350391954
To accurately evaluate the patient's condition, medical workers usually need to register multiple pathological images of the lesion site samples. Using computer technology to assist in registration work can effectively improve the efficiency of doctors analyzing pathological images. One of the most advanced methods currently is the Virtual Alignment of Pathology Image Series method, which is a multi-staining digital pathology image registration method that combines global and local calculations. However, this method may encounter certain biases when processing images with significant angle differences. Through a detailed analysis of this method, this article proposes an improvement plan which optimizes the acquisition of non-rigid registration mask images, enabling the method to obtain mask images more reasonably and achieve better registration results for images with significant angle differences. This provides more accurate judgment basis and helps doctors diagnose and develop treatment plans more accurately.
Optimization problems are one of the main focus of scientific research. Their computational-intensive nature makes them prone to be parallelized with consistent improvements in performance. This paper sheds light on d...
详细信息
ISBN:
(纸本)9798350363074;9798350363081
Optimization problems are one of the main focus of scientific research. Their computational-intensive nature makes them prone to be parallelized with consistent improvements in performance. This paper sheds light on different parallel models for accelerating Karmarkar's Interior-point method. To do so, we assess parallelization strategies for individual operations within Karmarkar's algorithm using OpenMP, GPU acceleration with CUDA, and the recent parallel Standard C++ Linear Algebra library (PSTL) executing both GPU and CPU. Our different implementations yield interesting benchmark results that show the optimal approach for parallelizing interior point algorithms for general Linear Programming (LP) problems. In addition, we propose a more theoretical perspective of the parallelization of this algorithm, with a detailed study of our OpenMP implementation, showing the limits of optimizing the single operations.
暂无评论