Energy-shaping L2-gain controller for PMSG wind turbine to mitigate subsynchronous interaction

Li, Penghan; Xiong, Liansong; Ma, M. M.; Huang, Sunhua; Zhu, Zean; Wang, Ziqiang

doi:10.1016/j.ijepes.2021.107571

Cited by 34 publications

(10 citation statements)

References 44 publications

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“…The wind farm in this analysis has a total capacity of 85 MW constructed of 100 wind turbines of a prototype Gamesa G52/850, each has a capacity of 850 kW [34, 45]. The active power extracted from a wind farm (

{P}_{WT}

) can be formulated as follows [46]:

\begin{equation}{{{P}}}_{{{WT}}} = {{\ }}0.5{{\ \rho \pi }}{{{R}}}^2{{V}}_{{w}}^3{{{C}}}_{{p}}\left( {{{\lambda }},{{\beta }}} \right)\end{equation}

where ρ indicates the air density, R denotes the radius of the turbine blade,

{V}_w

indicates the wind speed and

{C}_p

stands for power coefficient which represents the portion of the overall wind power which is converted into useful output mechanical power [47, 48]. The power coefficient can be formulated according to the following relations [46]:

\begin{equation}{{{C}}}_{{p}}\left( {{{\lambda }},{{\beta }}} \right) = \frac{{\left( {{{{\lambda }}}_{{i}} - 0.022{{\ }}{{{\beta }}}^2{{\ }} - 5.6} \right){{{e}}}^{ - 0.17{{{\lambda }}}_{{i}}}{{\ }}}}{2}\end{equation}

$$\begin{equation}{{\lambda \ }} = \frac{{{{{\omega }}}_{{t}}{{\ R}}}}{{{{\ }}{{{V}}}_{{w}}}}{{\ }}{\rm{\ a...

…”

Section: System Paradigm Descriptionmentioning

confidence: 99%

“…where ρ indicates the air density, R denotes the radius of the turbine blade, V w indicates the wind speed and C p stands for power coefficient which represents the portion of the overall wind power which is converted into useful output mechanical power [47,48]. The power coefficient can be formulated according to the following relations [46]:…”

Section: Wind Power Plantmentioning

confidence: 99%

See 1 more Smart Citation

Manta ray foraging optimization algorithm‐based load frequency control for hybrid modern power systems

Sobhy

Hasanien

Abdelaziz

et al. 2023

IET Renewable Power Gen

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The regulation of the frequency and line power flow in interconnected power networks is considered to be a key aspect of load frequency control (LFC). This article broaches a modern power network composed of three interconnected control areas including traditional generation units taking into account non‐linearities, also renewable energy sources (RESs) and energy storage (ES) units are involved in the power grid paradigm. Two forms of RESs are included in the analysis, which are photovoltaic (PV) and wind power plants. In addition, the study framework involves three types of ES units, which are batteries of plug‐in electric vehicles (PEVs), flywheel energy storage system (FESS) and capacitive energy storage system (CESS). In this analysis, LFC is accomplished by the use of proportional‐integral‐derivative (PID) controllers in the system control loops. A recent optimization algorithm called Manta Ray Foraging optimization (MRFO) is employed to obtain the optimal gain configuration of the controllers. Real site measurements are imported to the RESs involved in the study aiming to examine the proposed control scheme under realistic conditions. Compared with other rival algorithms, the effectiveness of the MRFO‐based PID controller is validated. Simulation results confirm the efficacy of the proposed control scheme. The findings also ensure the role of ES units in optimizing the time‐domain responses. The main contributions of this paper are applying a new metaheuristic optimization algorithm to solve the LFC problem and introducing a new criteria for judging the system performance in compliance with the harmonic spectrum of the responses in the frequency domain. The results of the simulation are retrieved through a MATLAB model.

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{P}_{WT}

) can be formulated as follows [46]:

\begin{equation}{{{P}}}_{{{WT}}} = {{\ }}0.5{{\ \rho \pi }}{{{R}}}^2{{V}}_{{w}}^3{{{C}}}_{{p}}\left( {{{\lambda }},{{\beta }}} \right)\end{equation}

where ρ indicates the air density, R denotes the radius of the turbine blade,

{V}_w

indicates the wind speed and

{C}_p

\begin{equation}{{{C}}}_{{p}}\left( {{{\lambda }},{{\beta }}} \right) = \frac{{\left( {{{{\lambda }}}_{{i}} - 0.022{{\ }}{{{\beta }}}^2{{\ }} - 5.6} \right){{{e}}}^{ - 0.17{{{\lambda }}}_{{i}}}{{\ }}}}{2}\end{equation}

$$\begin{equation}{{\lambda \ }} = \frac{{{{{\omega }}}_{{t}}{{\ R}}}}{{{{\ }}{{{V}}}_{{w}}}}{{\ }}{\rm{\ a...

…”

Section: System Paradigm Descriptionmentioning

confidence: 99%

Section: Wind Power Plantmentioning

confidence: 99%

Manta ray foraging optimization algorithm‐based load frequency control for hybrid modern power systems

Sobhy

Hasanien

Abdelaziz

et al. 2023

IET Renewable Power Gen

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“…It is thus of great importance to provide a wide area monitoring of SSR parameters, including the magnitude, frequency and damping. The data are crucial for replicating SSR events, identifying the sources of SSR [5] and supporting the design of countermeasures, e.g., feedback-linearized sliding mode controller [6], energyshaping L2-gain controller [7], damping controller [8].…”

Section: Nomenclaturementioning

confidence: 99%

“…Accordingly, X c should be expressed by replacing the variable p in ( 6) with mf pr , as shown in (7):…”

Section: Synchrophasor Model Under Ssrmentioning

confidence: 99%

Application of Hankel Dynamic Mode Decomposition for Wide Area Monitoring of Subsynchronous Resonance

Wang¹,

Li²,

Fan³

et al. 2023

Energy Engineering

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In recent years, subsynchronous resonance (SSR) has frequently occurred in DFIG-connected series-compensated systems. For the analysis and prevention, it is of great importance to achieve wide area monitoring of the incident. This paper presents a Hankel dynamic mode decomposition (DMD) method to identify SSR parameters using synchrophasor data. The basic idea is to employ the DMD technique to explore the subspace of Hankel matrices constructed by synchrophasors. It is analytically demonstrated that the subspace of these Hankel matrices is a combination of fundamental and SSR modes. Therefore, the SSR parameters can be calculated once the modal parameter is extracted. Compared with the existing method, the presented work has better dynamic performances as it requires much less data. Thus, it is more suitable for practical cases in which the SSR characteristics are timevarying. The effectiveness and superiority of the proposed method have been verified by both simulations and field data.

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“…The grid connectivity study is an important requirement in the development stage of any wind farm project. In order to improve the dynamic performance of the control system of the stator-side MMC and grid-side MMC, improvement methods such as energy-shaping L2-gain [25], sliding mode controller [26], pole and placement, etc., can be applied to the proposed model. However, because our only concern in this study is grid connectivity, the control system that is applied to the stator-side MMC and the grid-side MMC can be a standard control model using a PI controller.…”

Section: Grid-side MMC Controllermentioning

confidence: 99%

Detailed and Average Models of a Grid-Connected MMC-Controlled Permanent Magnet Wind Turbine Generator

et al. 2022

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In this paper, a detailed model and an average model of an MMC (Modular Multilevel Converter)-controlled Permanent Magnet Synchronous Generator (PMSG)-based direct drive wind turbine are proposed. The models are used to analyze the steady-state and transient characteristics of the grid connectivity study of the wind turbine generator. Configuration of the electrical topology and the control scheme of the wind turbine generator for both models are comprehensively presented. In the detailed model, the MMC circuit is represented by power electronic IGBTs, with switching phenomena considered. Meanwhile, in the average model, the MMC circuit is simplified by using voltage source representation, hence the complexity of the MMC circuit and the simulation duration of the analysis can be reduced. Comparative analysis between the detailed and the simplified models is also investigated through simulation performed using PSCAD/EMTDC. The simulation results show that both models have a good controllability and dynamic stability under steady-state and transient conditions. The simulation results also confirm that the average model has adequate accuracy, and simulation time can be reduced significantly.

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Energy-shaping L2-gain controller for PMSG wind turbine to mitigate subsynchronous interaction

Cited by 34 publications

References 44 publications

Manta ray foraging optimization algorithm‐based load frequency control for hybrid modern power systems

Manta ray foraging optimization algorithm‐based load frequency control for hybrid modern power systems

Application of Hankel Dynamic Mode Decomposition for Wide Area Monitoring of Subsynchronous Resonance

Detailed and Average Models of a Grid-Connected MMC-Controlled Permanent Magnet Wind Turbine Generator

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