Passivity-based sliding-mode control design for optimal power extraction of a PMSG based variable speed wind turbine

Yang, Bo; Yu, Tao; Shu, Hongchun; Zhang, Yuming; Chen, Jian; Sang, Yiyan; Jiang, Lin

doi:10.1016/j.renene.2017.12.047

Cited by 260 publications

(122 citation statements)

References 41 publications

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“…The produced active power and reactive power of PMSG is regulated through the generator‐side VSC, while the grid‐side VSC attempts to deliver active power to the power grid through the DC link and to maintain the DC‐link voltage at the rated value. Two VSCs are controlled independently, while the dynamics of the PMSG and the power grid is fully decoupled via the DC link . As the MPPT mainly relies on the control of the generator‐side VSC, the dynamics of grid‐side VSC is ignored in this paper.…”

Section: Modelling Of Pmsg‐based Wind Energy Conversion Systemmentioning

confidence: 99%

“…The tip‐speed ratio λ of wind turbine is defined as

λ = \frac{ω_{m} R}{v_{wind}}

where ω m denotes the mechanical rotation speed of wind turbine, v wind represents the wind speed, and R is the blade radius of wind turbine, respectively. According to the wind turbine dynamics, the power coefficient C p ( λ , β ) can be described by

C_{p} ((),, λ, β) = c_{1} ((), \frac{c_{2}}{λ_{i}} - c_{3} β - c_{4}) e^{- \frac{c_{5}}{λ_{i}}}

with

\frac{1}{λ_{i}} = \frac{1}{λ + 0.08 β} - \frac{0.035}{β^{3} + 1}

where the coefficients c 1 to c 5 are selected as c 1 = 0.22, c 2 = 116, c 3 = 0.4, c 4 = 5, and c 5 = 12.5, respectively . In addition, β denotes the pitch angle.…”

Section: Modelling Of Pmsg‐based Wind Energy Conversion Systemmentioning

confidence: 99%

“…The dynamics of PMSG in the d‐q reference frames is written as

V_{d} = i_{d} R_{s} + L_{d} \frac{d i_{d}}{dt} - ω_{e} L_{q} i_{q}

V_{q} = i_{q} R_{s} + L_{q} \frac{d i_{q}}{dt} + ω_{e} ((), L_{d} i_{d} + K_{e})

T_{e} = p ([], ((), L_{d} - L_{q}) i_{d} i_{q} + i_{q} K_{e})

where V d and V q are the stator voltages in the d‐q axis, R s is the stator resistance, L d and L q are d‐q axis inductances, i d and i q are the currents in the d‐q axis, ω e = p ω m is the electrical rotation speed, K e is the permanent magnetic flux given by the magnets, and p is the number of pole pairs, respectively.…”

Section: Modelling Of Pmsg‐based Wind Energy Conversion Systemmentioning

confidence: 99%

“…The dynamics of mechanical shaft system and mechanical torque of PMSG is given by

J_{tot} \frac{d ω_{m}}{dt} = T_{m} - T_{e} - D ω_{m}

T_{m} = \frac{1}{2} italicρπ R^{5} \frac{C_{p} ((),, λ, β)}{λ^{3}} ω_{m}^{2}

where J tot is the total inertia of the drive train which equals to the summation of wind turbine inertia constant and generator inertia constant, D is the viscous damping coefficient, and T m is the mechanical torque of the wind turbine, respectively. In addition, active power is calculated as

P_{e} = T_{e} ω_{m}

with T e being the electromagnetic torque.…”

Section: Modelling Of Pmsg‐based Wind Energy Conversion Systemmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive fractional-order PID control of PMSG-based wind energy conversion system for MPPT using linear observers

Yang

Shu

et al. 2018

Int Trans Electr Energ Syst

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Summary This paper designs a novel adaptive fractional‐order PID (AFOPID) control of a permanent magnetic synchronous generator (PMSG)‐based wind energy conversion system, which attempts to extract the maximum wind power by using a linear perturbation observer. The combinatorial effect of generator nonlinearities and parameter uncertainties, unmodelled dynamics, and stochastic wind speed variation is aggregated into a perturbation, which is then estimated in real time by a linear extended‐state observer called high‐gain state and perturbation observer. Besides, the perturbation estimate is used as an auxiliary control signal which is fully compensated by a fractional‐order PID (FOPID) controller to achieve a globally robust control consistency, simple structure and high reliability, as well as an improved tracking performance compared to that of PID control. In addition, AFOPID does not require an accurate PMSG model while only the measurement of d‐axis current and mechanical rotation speed is required, in which parameter is optimally tuned by particle swarm optimization. Four case studies are carried out, including step change of wind speed, low‐turbulence stochastic wind speed, high‐turbulence stochastic wind speed, and generator parameter uncertainties, respectively. Simulation results verify the effectiveness and superiority of AFOPID compared to that of PID, FOPID, and nonlinear control.

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Section: Modelling Of Pmsg‐based Wind Energy Conversion Systemmentioning

confidence: 99%

“…The tip‐speed ratio λ of wind turbine is defined as

λ = \frac{ω_{m} R}{v_{wind}}

C_{p} ((),, λ, β) = c_{1} ((), \frac{c_{2}}{λ_{i}} - c_{3} β - c_{4}) e^{- \frac{c_{5}}{λ_{i}}}

with

\frac{1}{λ_{i}} = \frac{1}{λ + 0.08 β} - \frac{0.035}{β^{3} + 1}

where the coefficients c 1 to c 5 are selected as c 1 = 0.22, c 2 = 116, c 3 = 0.4, c 4 = 5, and c 5 = 12.5, respectively . In addition, β denotes the pitch angle.…”

Section: Modelling Of Pmsg‐based Wind Energy Conversion Systemmentioning

confidence: 99%

“…The dynamics of PMSG in the d‐q reference frames is written as

V_{d} = i_{d} R_{s} + L_{d} \frac{d i_{d}}{dt} - ω_{e} L_{q} i_{q}

V_{q} = i_{q} R_{s} + L_{q} \frac{d i_{q}}{dt} + ω_{e} ((), L_{d} i_{d} + K_{e})

T_{e} = p ([], ((), L_{d} - L_{q}) i_{d} i_{q} + i_{q} K_{e})

Section: Modelling Of Pmsg‐based Wind Energy Conversion Systemmentioning

confidence: 99%

“…The dynamics of mechanical shaft system and mechanical torque of PMSG is given by

J_{tot} \frac{d ω_{m}}{dt} = T_{m} - T_{e} - D ω_{m}

T_{m} = \frac{1}{2} italicρπ R^{5} \frac{C_{p} ((),, λ, β)}{λ^{3}} ω_{m}^{2}

P_{e} = T_{e} ω_{m}

with T e being the electromagnetic torque.…”

Section: Modelling Of Pmsg‐based Wind Energy Conversion Systemmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive fractional-order PID control of PMSG-based wind energy conversion system for MPPT using linear observers

Yang

Shu

et al. 2018

Int Trans Electr Energ Syst

Self Cite

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show abstract

“…Traditional fossil fuels, which supply more than one-quarter of global electricity power, are widely believed to be the main cause of severe global warming, and sustainable energy, such as wind, solar, biomass, tidal, and geothermal, has become a promising solution for continuous fossil fuel depletion [1][2][3][4]. Nowadays, the cost of renewable energy power generation has decreased with the development of manufacturing and materials technology [5][6][7]. In China, the installed capacity of wind power was more than 168 GW in 2016, which was an increase of 13.8% over the previous year [8].…”

Section: Introductionmentioning

confidence: 99%

Voltage Distribution–Based Fault Location for Half-Wavelength Transmission Line with Large-Scale Wind Power Integration in China

et al. 2018

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Large-scale wind farms are generally far away from load centers, hence there is an urgent need for a large-capacity power transmission scheme for extremely long distances, such as half-wavelength transmission lines (HWTLs), which can usually span thousands of kilometers from large-scale wind farms to load centers. An accurate fault location method for HWTLs is needed to ensure safe and reliable operation. This paper presents the design of a modal voltage distribution-based asynchronous double-end fault location (MVD-ADFL) scheme, in which the phase voltages and currents are transformed to modal components through a Karenbauer transformation matrix. Then, the modal voltage distributions along transmission lines are calculated by voltage and current from double ends. Moreover, the minimums and intersection points of calculated modal voltages from double ends are defined as the fault location estimation. In order to identify incorrect fault location results and reduce calculation errors for the correct ones, air modal and earth modal voltage distributions are applied in the fault location estimations. Simulation results verify the effectiveness of the proposed approach under different fault resistances, distances, and types. Lastly, a real-time digital simulator (RTDS)-based hardware-in-the-loop (HIL) test is undertaken to validate the feasibility of implementing the proposed approach.

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Modified linear active disturbance rejection control for microgrid inverters: Design, analysis, and hardware implementation

Zeng

Huang

et al. 2019

Int Trans Electr Energ Syst

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Summary Microgrid inverters are characterized by nonlinearities, strong coupling, and switching flexibility, by which traditional proportional‐integral control is inadequate to realize a satisfactory control performance due to its local linearization. This paper attempts to design a modified linear active disturbance rejection control (MLADRC) for voltage control of microgrid inverters. Under the framework of extended state observer, voltage error derivative is obtained indirectly by the measurement of filter capacitor current, such that the observation performance of extended state observer could be significantly improved. Furthermore, a first‐order inertial link is employed in the perturbation loop to effectively suppress observation noises. A thorough frequency response analysis of MLADRC, linear active disturbance rejection control, and proportional‐integral control is undertaken, which shows that MLADRC owns superior disturbance rejection performance than that of others. Lastly, hardware experiment validates the effectiveness and implementation feasibility of MLADRC.

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Passivity-based sliding-mode control design for optimal power extraction of a PMSG based variable speed wind turbine

Cited by 260 publications

References 41 publications

Adaptive fractional-order PID control of PMSG-based wind energy conversion system for MPPT using linear observers

Adaptive fractional-order PID control of PMSG-based wind energy conversion system for MPPT using linear observers

Voltage Distribution–Based Fault Location for Half-Wavelength Transmission Line with Large-Scale Wind Power Integration in China

Modified linear active disturbance rejection control for microgrid inverters: Design, analysis, and hardware implementation

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