Pitch Angle Adaptive PI Controller Based on Heuristics Optimization Algorithms

Shawqran, Ahmed M.; El-Marhomy, Abdallah; Attia, Mahmoud A.

doi:10.1007/978-3-030-69717-4_63

Cited by 6 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The HSA algorithm is applied firstly, and the system optimum solution is determined. Then, the lower and the upper boundaries of the GA parameters are settled with a ±10 % tolerance of this optimum solution [ 30 ].…”

Section: Techniques Of Optimization Algorithmsmentioning

confidence: 99%

“…Figure 12 shows the applied profile of winter wind speed to the wind turbine. The initializations of the API are carried out by using HSA-GA and HSA-TLBO [ 30 ], while the AFOPI initializations are carried out by using HSA-EO and the gains of the PID are tuned with the HSA optimization algorithm [ 31 ]. The parameters of the AFOPI, API, and traditional PID gains are listed in Table 2 .…”

Section: Validation Of the Afopi Controllermentioning

confidence: 99%

See 1 more Smart Citation

Enhancement of wind energy conversion system performance using adaptive fractional order PI blade angle controller

Shawqran

El-Marhomy

Attia

et al. 2021

Heliyon

Self Cite

View full text Add to dashboard Cite

Wind energy is considered as one of the rapidest rising renewable energy systems. Thus, in this paper the wind energy performance is enhanced through using a new adaptive fractional order PI (AFOPI) blade angle controller. The AFOPI controller is based on the fractional calculus that assigns both the integrator order and the fractional gain. The initialization of the controller parameters and the integrator order are optimized using the Harmony search algorithm (HSA) hybrid Equilibrium optimization algorithm (EO). Then, the controller gains (K p ; K i ) are auto-tuned. The validation of the new proposed controller is carried out through comparison with the traditional PID and the Adaptive PI controllers under normal and fault conditions. The fractional adaptive PI improved the wind turbine's electrical and mechanical behaviors. The adaptive fractional order PI controller has been subjected to other high variation wind speed profiles to prove its robustness. The controller showed robustness to the variations in wind speed profile and the nonlinearity of the system. Also, the proposed controller (AFOPI) assured continuous wind power generation under these sharp variations. Moreover, the active power statistical analysis of the AFOPI showed increase in energy captured of around 25 %, and reduction in the standard deviation and root mean square error of around 10% compared to the other controllers.

show abstract

Section: Techniques Of Optimization Algorithmsmentioning

confidence: 99%

Section: Validation Of the Afopi Controllermentioning

confidence: 99%