Application of Whale Optimization Technique for Evaluating the Performance of Wind-Driven PMSG Under Harsh Operating Events

Abstract

Permanent magnet synchronous generators (PMSGs) have become a promising alternative for wind energy generation systems (WEGSs) because of their optimal power extraction, complete controllability, and improved power quality. The growing penetration of PMSG-based WEGSs into the electrical power system has made its performance analysis an imperative field of research. In this paper, detailed mathematical models for a wind turbine, PMSG, power converters, control system, and grid model are used to study the dynamic behavior and operation of the PMSG-based WEGSs. Optimal torque control is used to operate the wind system at maximum power point tracking (MPPT); in addition, whale optimization algorithm-based PI controllers are utilized for the current control of the machine side converter. Furthermore, a hysteresis controller-based braking chopper system is utilized to improve the fault ride-through (FRT) capability and to keep DC-link voltage within its permissible limits. Two scenarios are studied to evaluate the transient and dynamic response of the system. The first scenario exposes the studied system to a regular grid condition (step-change and random variations in the wind speed profile), while the second scenario exposes it to an irregular grid operation (single-phase and three-phase faults). MATLAB/SIMULINK environment is used to validate the effectiveness and superiority of the proposed control schemes during the studied scenarios. The obtained simulation results assure the viability of the overall proposed system and control schemes in improving the power smoothing capability and dynamic response of the system parameters in addition to operating the wind system at MPPT and realizing the FRT capability. Moreover, keeping a constant DC-link voltage is one of the benefits that would result in increasing the life span of the power converters and reducing the amount of cut-off time of the whole system that may otherwise be caused by their destruction.

Appendices

Appendix 1

(Table 5)

Table 5 Parameters for simulated wind-driven PMSG (Mahmoud et al. 2020).

Appendix 2

(Table 6)

Table 6 BC resistance parameters (Mahmoud et al. 2020).

Appendix 3 Influence of inertia on electromagnetic torque (Mahmoud et al. 2021).

$${T}_{e}\pm {T}_{m}=f{\omega }_{m}+J{{\omega }^{.}}_{m}$$

(A.1)

(−) and (+) Signs represent acceleration and deceleration modes, respectively.

Acceleration mode ($\Delta \omega /\Delta t>0$)

$${T}_{e}-{T}_{m}=f{\omega }_{m}+J{{\omega }^{.}}_{m}$$

(A.2)

In step-change $\Delta t\to 0$ very small value, so that,

$${T}_{e}\uparrow \uparrow \alpha J\uparrow \frac{d{\omega }_{m}}{dt\downarrow \downarrow }\alpha J\frac{\Delta {\omega }_{m}}{\Delta t}$$

(A.3)

Deceleration mode ($\Delta \omega /\Delta t<0$)

$${T}_{e}+{T}_{m}=f{\omega }_{m}+J{{\omega }^{.}}_{m}$$

(A.4)

And,

$${T}_{e}\downarrow \downarrow \alpha J\uparrow \frac{d{\omega }_{m}}{dt\downarrow \downarrow }\alpha J\frac{\Delta {\omega }_{m}}{\Delta t}$$

(A.5)

where,

$$\Delta \omega ={\omega }_{new}-{\omega }_{old}$$