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A novel adaptive model predictive controller for load frequency control of power systems integrated with DFIG wind turbines

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Abstract

With the rapid growth of renewable energy resources, wind energy system is getting more interest everywhere throughout the world. However, its extensive use in power systems prompts many power system dynamics and stability problems. Load variation and anomalous operating conditions prompt inconsistencies in frequency and planned power trades. These inconsistencies should be remedied by load frequency control. This paper introduces a novel frequency control system utilizing a mix of adaptive model predictive controller (AMPC) and recursive polynomial model estimator (RPME) integrated with double fed induction generator wind turbines. Inside each control duration, the RPME is identifying a discrete-time online autoregressive exogenous model. The latter is used through the AMPC to update the interior plant model in order to achieve a successful nonlinear control. The performance of the proposed system has been verified and contrasted with the conventional MPC system through a computer simulation-based MATLAB/SIMULINK. The simulation results demonstrated the superiority of the proposed system as for the conventional MPC system.

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Abbreviations

\(\frac{1}{s}\) :

Integral Laplace operator

\(T_{\text{g}}\) :

Governor time constant

T :

Turbine time constant

\(\Delta P_{\text{g}}\) :

Change in governor output

\(\Delta P_{\text{m}}\) :

Change in mechanical power

\(\Delta f\) :

Deviation in frequency

\(\Delta P_{\text{L}}\) :

Load change

\(\Delta P_{\text{c}}\) :

Supplementary control action

\(p,s\) :

Differential operators

i :

Uncontrolled single area

y :

System output

H :

Inertia equivalent constant

R :

Characteristic of speed droop

T e :

Electromagnetic torque

T m :

Mechanical torque

ω :

Rotational speed

ω opt :

Operating point of rotational speed

P e :

Active power of WT

H t :

Equivalent constant of inertia of WT

i qr :

q-axis components of rotor current

V qr :

q-axis components of rotor voltage

ω s :

Synchronous speed

L m :

Mutual inductance

L r :

Leakage inductances for the rotor

L s :

Leakage inductance for the stator

L rr :

Self-inductance for the rotor

L ss :

Self-inductance for the stator

R r :

Resistance of the rotor

R s :

Resistance of the stator

\(N_{1}\) :

Minimum prediction horizon

\(N_{2}\) :

Maximum prediction horizon

\(\beta \left( j \right),\;\lambda \left( j \right)\) :

Control weighting factors

A, B :

Polynomials in the backward shift operator q−1

\(n_{u}\) :

Inputs numbers

\(u_{i}\) :

ith input

\(nk_{i}\) :

ith input delay

\(e\left( t \right)\) :

White noise variance

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Authors and Affiliations

  1. Department of Electrical Engineering, Fuzhou University, Fuzhou, 350116, China

    Mohamed A. Mohamed & Tao Jin

  2. Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, 61519, Egypt

    Mohamed A. Mohamed & Ahmed A. Zaki Diab

  3. College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al Kharj, Kingdom of Saudi Arabia

    Hegazy Rezk

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  1. Mohamed A. Mohamed
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  2. Ahmed A. Zaki Diab
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  3. Hegazy Rezk
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  4. Tao Jin
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Correspondence to Mohamed A. Mohamed.

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Mohamed, M.A., Diab, A.A.Z., Rezk, H. et al. A novel adaptive model predictive controller for load frequency control of power systems integrated with DFIG wind turbines. Neural Comput & Applic 32, 7171–7181 (2020). https://doi.org/10.1007/s00521-019-04205-w

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