Feedback Linearization Controller for a Wind Energy Power System

Alrifai, Muthana T.; Zribi, Mohamed; Rayan, Mohamed

doi:10.3390/en9100771

Cited by 16 publications

(10 citation statements)

References 42 publications

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“…For instance, Alrifai et al [49] proposed a feedback linearization controller for a DFIG with a 3L-NPC-BTB. Xu et al [50] proposed a slip control strategy to regulate a PWM converter to control the output power.…”

Section: Reliability Analysismentioning

confidence: 99%

Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines

et al. 2017

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Electrical and electronic components are very important subcomponents in modern industrial wind turbines. Complex multimegawatt wind turbines are continuously being installed both onshore and offshore, continuously increasing the demand for sophisticated electronic and electrical components. In this work, most critical electrical and electronic components in industrial wind turbines have been identified and the applicability of appropriate condition monitoring processes simulated. A fault tree dynamic analysis has been carried out by binary decision diagrams to obtain the system failure probability over time and using different time increments to evaluate the system. This analysis allows critical electrical and electronic components of the converters to be identified in different conditions. The results can be used to develop a scheduled maintenance that improves the decision making and reduces the maintenance costs.

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Section: Reliability Analysismentioning

confidence: 99%

Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines

et al. 2017

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“…There has been a lot of progress in the non-linear control designing context in the past years. Most of these control techniques have been used to control the DFIG [47][48][49][50][51][52][53][54][55][56][57][58][59]. Some of the latest studies are recalled.…”

Section: Introductionmentioning

confidence: 99%

Asymptotic Output Tracked Artificial Immunity Controller for Eco-Maximum Power Point Tracking of Wind Turbine Driven by Doubly Fed Induction Generator

et al. 2018

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This paper aims to design a controller for a Doubly Fed Induction Generator (DFIG) targeting the Eco-Maximum Power Point Tracking (EMPPT) for environmental aspects. The proposed controller consists of two clusters, which are the novel Artificial Immunity sensorless Eco-Maximum Power Point Tracking (AI EMPPT) and the asymptotic non-linear control techniques. The main target of the AI EMPPT is to reduce the carbon dioxide emission by generating the maximum possible power from the renewable electrical energy resource, which is wind electrical power generation to replace the fossil-fuel conventional generation. To build the AI EMPPT, an Artificial Immunity System Estimator (AISE) based on artificial immunity technique and a Model Reference Adaptive System (MRAS) are used to estimate the DFIG rotor speed. Then, the AI EMPPT is applied to provide the reference electromagnetic torque signal. Subsequently, the reference electromagnetic torque interacts with the estimated generator speed, determined by the wind mechanical power, to supply the wind electrical power. The second cluster is the asymptotic non-linear control technique which proposes the reference signal tracking of the rotor direct and quadratic current, respectively. Thus, assigning specific zeros through feedback ensures the reproduction of an output that converges asymptotically to a required reference rotor current. For online operation, the Artificial Immunity Technique (AIT) is utilized to deal with the generated control reference signal. A proposal hardware implementation on Field Programmed Gate Array (FPGA) is also presented. The introduced approach was applied to a wind turbine generator driving a 3.7 kW load. MATLAB program was used to simulate and test the performance of the proposed control methods. The results to show the effectiveness of the proposed technique. The reduction in CO 2 emission was calculated.

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“…The encircling behaviour of prey can be described as following equations: (22) where t denotes the current generation, • Hunting prey…”

Section: • Encircling Preymentioning

confidence: 99%

“…The three lowest frequency oscillation modes are negative damping (Modes 7, 8 and 9 are in bold font in the table), so a damping controller should be installed to the system. The most suitable wide-area feedback signals of PODs In addition to normal operating condition, two other operating conditions corresponding to line 14-15 and line [21][22] outage are considered during the damping controller parameters optimization procedure.…”

Section: The Selection Of Wide-area Feedback Signals For Podsmentioning

confidence: 99%

“…Recently, several researches have proposed the possibility of using doubly-fed induction generator (DFIG)-based wind farms to improve damping performance and enhance the dynamic stability of wind power integrated power systems [14][15][16][17][18][19][20][21]. Many novel control schemes have been proposed for DFIGs to enable them to provide an auxiliary power system stabilization function or contribute to system voltage support during network faults [20][21][22][23][24][25]. What is more, wind farm POD design derived from the classical phase compensation principle of PSS has become a research hotspot [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous Robust Coordinated Damping Control of Power System Stabilizers (PSSs), Static Var Compensator (SVC) and Doubly-Fed Induction Generator Power Oscillation Dampers (DFIG PODs) in Multimachine Power Systems

Zuo

Shi

et al. 2017

Energies

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Abstract:The potential of utilizing doubly-fed induction generator (DFIG)-based wind farms to improve power system damping performance and to enhance small signal stability has been proposed by many researchers. However, the simultaneous coordinated tuning of a DFIG power oscillation damper (POD) with other damping controllers is rarely involved. A simultaneous robust coordinated multiple damping controller design strategy for a power system incorporating power system stabilizer (PSS), static var compensator (SVC) POD and DFIG POD is presented in this paper. This coordinated damping control design strategy is addressed as an eigenvalue-based optimization problem to increase the damping ratios of oscillation modes. Both local and inter-area electromechanical oscillation modes are intended in the optimization design process. Wide-area phasor measurement unit (PMU) signals, selected by the joint modal controllability/ observability index, are utilized as SVC and DFIG POD feedback modulation signals to suppress inter-area oscillation modes. The robustness of the proposed coordinated design strategy is achieved by simultaneously considering multiple power flow situations and operating conditions. The recently proposed Grey Wolf optimizer (GWO) algorithm is adopted to efficiently optimize the parameter values of multiple damping controllers. The feasibility and effectiveness of the proposed coordinated design strategy are demonstrated through frequency-domain eigenvalue analysis and nonlinear time-domain simulation studies in two modified benchmark test systems. Moreover, the dynamic response simulation results also validate the robustness of the recommended coordinated multiple damping controllers under various system operating conditions.

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Feedback Linearization Controller for a Wind Energy Power System

Cited by 16 publications

References 42 publications

Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines

Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines

Asymptotic Output Tracked Artificial Immunity Controller for Eco-Maximum Power Point Tracking of Wind Turbine Driven by Doubly Fed Induction Generator

Simultaneous Robust Coordinated Damping Control of Power System Stabilizers (PSSs), Static Var Compensator (SVC) and Doubly-Fed Induction Generator Power Oscillation Dampers (DFIG PODs) in Multimachine Power Systems

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