Wind-turbine collective-pitch control via a fuzzy predictive algorithm

Lasheen, Ahmed; El-Shafei, A.

doi:10.1016/j.renene.2015.10.030

Cited by 82 publications

(37 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Different researchers have developed MPPT algorithms for pitch control for each blade. The authors of [60][61][62] propose using three FLC. One FLC controls the rotor torque by means collective pitch angle and, other two FLC control to blade moment.…”

Section: Results: Mppt Techniques Reviewmentioning

confidence: 99%

Expert Control Systems for Maximum Power Point Tracking in a Wind Turbine with PMSG: State of the Art

et al. 2019

View full text Add to dashboard Cite

Wind power is a renewable energy source that has been developed in recent years. Large turbines are increasingly seen. The advantage of generating electrical power in this way is that it can be connected to the grid, making it an economical and easily available source of energy. The fundamental problem of a wind turbine is the randomness in a wide range of wind speeds that determine the electrical energy generated, as well as abrupt changes in wind speed that make the system unstable and unsafe. A conventional control system based on a mathematical model is effective with moderate disturbances, but slow with very large oscillations such as those produced by turbulence. To solve this problem, expert control systems (ECS) are proposed, which are based on human experience and an adequate management of stored information of each of its variables, providing a way to determine solutions. This revision of recent years, mentions the expert systems developed to obtain the point of maximum power generation in a wind turbine with permanent magnet synchronous generator (PMSG) and, therefore, offers a control solution that adapts to the specifications of any wind turbine.

show abstract

Section: Results: Mppt Techniques Reviewmentioning

confidence: 99%

Expert Control Systems for Maximum Power Point Tracking in a Wind Turbine with PMSG: State of the Art

et al. 2019

View full text Add to dashboard Cite

show abstract

“…For controller design purpose, different modeling approaches are used based on the desired control framework. For example, in Lasheen and Elshafei, a fuzzy Takagi‐Sugeno model is constructed and used for prediction in a model predictive controller. In many applications, a linear approximation of the wind turbine model around its nominal operating point is used (Novak et al and Yuan and Tang).…”

Section: Wind Turbine Modelingmentioning

confidence: 99%

Power regulation and control of wind turbines: LMI‐based output feedback approach

Bayat

Bahmani

2017

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This paper addresses the problem of power regulation and control of wind turbines using an output feedback scheme formulated as a well‐defined linear matrix inequality (LMI) problem. To this aim, first, a piecewise linear (PWL) model is constructed for the wind turbine system with the wind speed as a parameter. Then, using the achieved PWL model a set of output feedback controllers are formulated and solved as an LMI problem. To implement the designed controller in practice, 2 different approaches are proposed, ie, online and off‐line approaches. In the first approach (online), an exact optimal control law is obtained by online evaluation of the PWL model and then solving the associated LMI problem which leads to a relatively high computational complexity. To deal with this computational complexity, an alternative approximate approach is proposed which puts parts of online computations to off‐line precalculation with the cost of sub‐optimality. In this approach, the controller is predesigned for some predefined operating points and then the sub‐optimal controller is achieved by introducing a convex combination of precalculated controllers while guaranteeing the constraints satisfaction. Finally, the performance and characteristics of the proposed approaches are verified using simulation results.

show abstract

“…Optimization of the wind pitch controller for frequency control was designed [14][15][16][17] based on the genetic algorithm. A fuzzy logic controller was proposed for controlling the hybrid system [18][19][20], and a second type was based on artificial neural networks [21]. The particle swarm algorithm (PSO) is applied in [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Performance Enhancement of Micro Grid System with SMES Storage System Based on Mine Blast Optimization Algorithm

et al. 2019

View full text Add to dashboard Cite

Frequency control represents a critically significant issue for the enhancement of the dynamic performance of isolated micro grids. The micro grid system studied here was a wind-diesel system. A new and robust optimization technique called the mine blast algorithm (MBA) was designed for tuning the PID (proportional-integral-differential) gains of the blade pitch controller of the wind turbine side and the gains of the superconducting magnetic energy storage (SMES) controller. SMES was implemented to release and absorb active power quickly in order to achieve a balance between generation and load power, and thereby control system frequency. The minimization of frequency and output wind power deviations were considered as objective functions for the PID controller of the wind turbine, and the diesel frequency and power deviations were used as objective functions for optimizing the SMES controller gains. Different case studies were considered by applying disturbances in input wind, load power, and wind gust, and sensitivity analysis was conducted by applying harsh conditions with varying fluid coupling parameter of the wind-diesel hybrid system. The proposed MBA-SMES was compared with MBA (tuned PID pitch controller) and classical PI control systems in the Matlab environment. Simulation results showed that the MBA-SMES scheme damped the oscillations in the system output responses and improved the system performance by reducing the overshoot by 75% and 36% from classical and MBA-based systems, respectively, reduced the settling time by 45% compared to other systems, and set the final steady-state error of the frequency deviation to zero compared to other systems. The proposed scheme was extremely robust to disturbances and parameter variations.

show abstract

Wind-turbine collective-pitch control via a fuzzy predictive algorithm

Cited by 82 publications

References 22 publications

Expert Control Systems for Maximum Power Point Tracking in a Wind Turbine with PMSG: State of the Art

Expert Control Systems for Maximum Power Point Tracking in a Wind Turbine with PMSG: State of the Art

Power regulation and control of wind turbines: LMI‐based output feedback approach

Performance Enhancement of Micro Grid System with SMES Storage System Based on Mine Blast Optimization Algorithm

Contact Info

Product

Resources

About