PROJECT TITLE :

GPU-Accelerated Parallel Coevolutionary Algorithm for Parameters Identification and Temperature Monitoring in Permanent Magnet Synchronous Machines

ABSTRACT:

A hierarchical fast parallel co-evolutionary immune particle swarm optimization (PSO) algorithm, accelerated by graphics processing unit (GPU) technique (G-PCIPSO), is proposed for multiparameter identification and temperature monitoring of permanent magnet synchronous machines (PMSM). It is composed of 2 levels and is developed primarily based on compute unified device design (CUDA). In G-PCIPSO, the antibodies (Abs) of upper level memory are selected from the lower level swarms and improved by immune clonal-selection operator. The search information exchanges between swarms using the memory-based mostly sharing mechanism. Moreover, an immune vaccine-enhanced operator is proposed to steer the Pbests particles to unexplored areas. Optimized parallel implementations of G-PCIPSO algorithm is developed on GPU using CUDA, that considerably quickens the search process. Finally, the proposed algorithm is applied to multiple parameters identification and temperature monitoring of PMSM. It will track parameter variation and achieve temperature monitoring on-line effectively. Compared with a CPU-based serial execution, the computational efficiency is greatly enhanced by GPU-accelerated parallel computing technique.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE :GPU-Accelerated High-Throughput Online Stream Data Processing - 2018ABSTRACT:The Single Instruction Multiple Data (SIMD) architecture of Graphic Processing Units (GPUs) makes them perfect for parallel processing
PROJECT TITLE : Depth Reconstruction From Sparse Samples: Representation, Algorithm, and Sampling - 2015 ABSTRACT: The fast development of 3D technology and computer vision applications has motivated a thrust of methodologies
PROJECT TITLE : GPU-Accelerated Parallel Sparse LU Factorization Method for Fast Circuit Analysis - 2016 ABSTRACT: Lower higher (LU) factorization for sparse matrices is the foremost necessary computing step for circuit simulation
PROJECT TITLE :Hall-Effect Sensor Fault Detection, Identification, and Compensation in Brushless DC DrivesABSTRACT:During this paper, binary Hall-effect sensor faults are investigated in rectangular-current-fed brushless dc (BLDC)
PROJECT TITLE :Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing SystemsABSTRACT:High-throughput genotyping technologies (like SNP-arrays) allow the rapid assortment of up to a couple million genetic

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry