GPU Implementation of DPSO-RE Algorithm for Parameters Identification of Surface PMSM Considering VSI Nonlinearity

Liu, Zhaohua; Wei, Hua‐Liang; Zhong, Qing-Chang; Liu, Kan; Li, Xiaohua

doi:10.1109/jestpe.2017.2690688

Cited by 36 publications

(12 citation statements)

References 37 publications

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“…After OPF applications, GPU usage in dynamic state estimations [191][192][193][194][195][196][197][198][199], power quality [202][203][204][205][206][207][208][209], and dynamic models [210][211][212][213][214][215] appear. Related to the dynamic state estimation of power systems, a lateral two-level dynamic state estimator based on the extended Kalman Filter method is implemented in a CPU-GPU platform [194].…”

Section: Dynamic State Estimation Power Quality and Dynamic Modelsmentioning

confidence: 99%

A Review of Parallel Heterogeneous Computing Algorithms in Power Systems

et al. 2021

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The power system expansion and the integration of technologies, such as renewable generation, distributed generation, high voltage direct current, and energy storage, have made power system simulation challenging in multiple applications. The current computing platforms employed for planning, operation, studies, visualization, and the analysis of power systems are reaching their operational limit since the complexity and size of modern power systems results in long simulation times and high computational demand. Time reductions in simulation and analysis lead to the better and further optimized performance of power systems. Heterogeneous computing—where different processing units interact—has shown that power system applications can take advantage of the unique strengths of each type of processing unit, such as central processing units, graphics processing units, and field-programmable gate arrays interacting in on-premise or cloud environments. Parallel Heterogeneous Computing appears as an alternative to reduce simulation times by optimizing multitask execution in parallel computing architectures with different processing units working together. This paper presents a review of Parallel Heterogeneous Computing techniques, how these techniques have been applied in a wide variety of power system applications, how they help reduce the computational time of modern power system simulation and analysis, and the current tendency regarding each application. We present a wide variety of approaches classified by technique and application.

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Section: Dynamic State Estimation Power Quality and Dynamic Modelsmentioning

confidence: 99%

A Review of Parallel Heterogeneous Computing Algorithms in Power Systems

et al. 2021

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“…Offline identification technology is a method suitable for intensive calculation, which is divided into experimental and computational according to its nature. Experimental methods include high frequency injection [7,8], current decay test (CDT) [9], standstill frequency response (SSFR) test [10,11], back EMF [12,13] etc.. Computational ones include evolutionary algorithms, such as particle swarm algorithm [14,15], neural network method [16,17] and so on. Offline parameter identification provides accurate initial value of generator parameters for system controller, but it requires expensive laboratory equipment, such as function generators, power amplifiers, and spectrum analysers.…”

Section: Introductionmentioning

confidence: 99%

Parameter identification of direct‐drive permanent magnet synchronous generator based on EDMPSO‐EKF

Xiao

Liao

Shi

et al. 2022

IET Renewable Power Gen

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Aiming at the problem that extended Kalman filter (EKF) is difficult to determine the appropriate system and measurement noise matrices in parameter identification of direct‐drive permanent magnet synchronous generator (D‐PMSG), a parameter identification method of D‐PMSG based on mean particle swarm optimization with extreme disturbance (EDMPSO)‐EKF is proposed. Firstly, by analysing the principle of EKF, a dual‐thread identification model is established, then the particle swarm optimization (PSO) algorithm is improved to jump out of the local optimum by adding extremum interference and taking average extremum, and a suitable fitness function is designed. Finally, the improved PSO algorithm is applied to the adaptive optimization of EKF system noise matrix and measurement noise matrix, and the system performs the parameter identification after obtaining the optimal noise matrix. The simulation and experiment results show that the proposed method has better convergence speed and can more accurately identify the parameters of stator resistance, inductance and flux linkage, and it has better identification accuracy and generalization ability than the traditional method.

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“…In view of the shortcomings of the above parameter identification methods, intelligent optimization algorithms with the advantages of low objective function requirements and high efficiency are widely used [15]. At present, scholars mostly use neural network [16,17], genetic algorithm [18], particle swarm algorithm [19,20], etc. In [16,17], the neural network identification method was used to identify the resistance and flux linkage parameters of PMSM.…”

Section: Introductionmentioning

confidence: 99%

Fast Backfire Double Annealing Particle Swarm Optimization Algorithm for Parameter Identification of Permanent Magnet Synchronous Motor

Wen¹,

Shi²,

Liao³

et al. 2021

PIER M

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When particle swarm optimization (PSO) is used to identify the parameters of permanent magnet synchronous motor (PMSM), the movement of particles is not selective, which makes the algorithm easy to fall into the local optimum, and the accuracy is poor. The simulated annealing particle swarm optimization (SAPSO) improves the accuracy and evolution speed, but SAPSO has redundant iteration problems. To solve these problems, a motor parameter identification method based on fast backfire double anneal particle swarm optimization (FBDAPSO) is proposed. By reducing the optimization time and quickly tempering and annealing the "misunderstood" difference, the motor adjustable model and fitness function are designed, and the number of iterations is constantly reset to achieve the effect of online identification. Under different working conditions, simulated and experimental results show that the proposed method can quickly and accurately identify the four parameters of the motor's stator, winding resistance, stator winding d-axis inductance, stator winding q-axis inductance, and permanent magnet flux linkage at the same time, compared with the traditional method of parameter identification, and it has better accuracy, rapidity, and robustness.

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GPU Implementation of DPSO-RE Algorithm for Parameters Identification of Surface PMSM Considering VSI Nonlinearity

Cited by 36 publications

References 37 publications

A Review of Parallel Heterogeneous Computing Algorithms in Power Systems

A Review of Parallel Heterogeneous Computing Algorithms in Power Systems

Parameter identification of direct‐drive permanent magnet synchronous generator based on EDMPSO‐EKF

Fast Backfire Double Annealing Particle Swarm Optimization Algorithm for Parameter Identification of Permanent Magnet Synchronous Motor

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