RTDGPS Accuracy Improvement Using PSO-LSWSVM and Low-Cost GPS Receivers

Refan, Mohammad Hossein; Dameshghi, Adel; Kamarzarrin, Mehrnoosh

doi:10.1007/s11277-019-06848-3

Cited by 2 publications

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“…To solve this problem, the LS-SVM technique is used so that linear equations are applied instead of Quadratic Programming (QP). To improve the LS-SVM performance, essential parameters of the SVM and kernel function are obtained by the Particle Swarm Optimization (PSO) method (Dameshghi and Refan, 2019; Refan et al, 2014, 2016, 2020; Van Gestel et al, 2004). The power of approximation and training the kernel function is very important.…”

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confidence: 99%

A new intelligent fault diagnosis and prognosis method for wind turbine doubly-fed induction generator

et al. 2021

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Condition Monitoring and fault-prognosis approaches are typical methods to reduce the energy production cost and Wind Turbine downtime. In this paper, a new CM combinatory system and fault prognosis are proposed based on an adaptive threshold, feature-level fusion, and new degradation indicator and the CM operation is based on a new index Symptom of Degeneration crossing of an adaptive threshold. Also, a new adaptive threshold is proposed based on the fuzzy rules and WT operation point. Fault prognosis is conducted with the Least-Squares Support-Vector Machine method, and Particle Swarm Optimization is employed for the optimum selecting of the wavelet Kernel function and the SVM parameters. The proposed technique is compared with other methods and the simulation results illustrate the PSO-LS-SVM superiorities. The effectiveness of the proposed prognostic structure is evaluated using a WT test-rig prototype. The experimental results demonstrate that the Condition-Based Maintenance is improved by the proposed structure and the RUL is predicted before serious damage occurrences.

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