A harmonic parameter estimation method based on Particle Swarm Optimizer with Natural Selection

Chen, Y.; Li, M. S.

doi:10.1109/ictrc.2015.7156458

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Many algorithms have been proposed for harmonic estimation to improve the power quality performance but till today it is still a challenge for accurate estimation. In this case [42] an adaptive filtering algorithm called Fast Transverse Recursive Least Square (FT-RLS) is applied for the first time for estimating harmonic parameters and [7] presents a novel method for solving the estimation of the amplitudes and phases of different harmonic components contained in a power electrical signal, based on Particle Swarm Optimizer with Natural Selection (PSONS). Harmonic estimation and compensation are important tasks for improving power quality indexes in electric systems.…”

Section: Introductionmentioning

confidence: 99%

Harmonic Estimation Based Support Vector Machine for Typical Power Systems

Özdemir¹,

Demirtaş²,

Aydın³

2016

NNW

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Precise wind energy potential assessment is vital for wind energy generation and planning and development of new wind power plants. This work proposes and evaluates a novel two-stage method for location-specific wind energy potential assessment. It combines accurate statistical modelling of annual wind direction distribution in a given location with supervised machine learning of efficient estimators that can approximate energy efficiency coefficients from the parameters of optimized statistical wind direction models. The statistical models are optimized using differential evolution and energy efficiency is approximated by evolutionary fuzzy rules.

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Section: Introductionmentioning

confidence: 99%

Harmonic Estimation Based Support Vector Machine for Typical Power Systems

Özdemir¹,

Demirtaş²,

Aydın³

2016

NNW

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“…Then constraint conditions of the optimization control are listed. Four nonlinear optimization algorithms with constraints, quasinewton Lagrangian multiplier method (QNLM), sequential quadratic programming (SQP) [14,15] adaptive genetic algorithms (AGA) [16,17], and particle swarm optimization with random weighting and natural selection (PSO-RN) [18,19], 2 Mathematical Problems in Engineering are introduced to solve the objective function. In Section 4, the effectiveness of OTCM is verified by simulation analysis.…”

Section: Introductionmentioning

confidence: 99%

Research on Optimized Torque‐Distribution Control Method for Front/Rear Axle Electric Wheel Loader

Yang

Wang

Gao

et al. 2017

Mathematical Problems in Engineering

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Optimized torque-distribution control method (OTCM) is a critical technology for front/rear axle electric wheel loader (FREWL) to improve the operation performance and energy efficiency. In the paper, a longitudinal dynamics model of FREWL is created. Based on the model, the objective functions are that the weighted sum of variance and mean of tire workload is minimal and the total motor efficiency is maximal. Four nonlinear constraint optimization algorithms, quasi-newton Lagrangian multiplier method, sequential quadratic programming, adaptive genetic algorithms, and particle swarm optimization with random weighting and natural selection, which have fast convergent rate and quick calculating speed, are used as solving solutions for objective function. The simulation results show that compared to no-control FREWL, controlled FREWL utilizes the adhesion ability better and slips less. It is obvious that controlled FREWL gains better operation performance and higher energy efficiency. The energy efficiency of FREWL in equipment transferring condition is increased by 13-29%. In addition, this paper discussed the applicability of OTCM and analyzed the reason for different simulation results of four algorithms.

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A Comparison of Four Memetic Particle Swarm Optimization Algorithms for Continuous Optimization

Zhang

Liu

et al. 2018

Lecture Notes in Electrical Engineering

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A harmonic parameter estimation method based on Particle Swarm Optimizer with Natural Selection

Cited by 3 publications

References 12 publications

Harmonic Estimation Based Support Vector Machine for Typical Power Systems

Harmonic Estimation Based Support Vector Machine for Typical Power Systems

Research on Optimized Torque‐Distribution Control Method for Front/Rear Axle Electric Wheel Loader

A Comparison of Four Memetic Particle Swarm Optimization Algorithms for Continuous Optimization

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