Enhancing Power Quality in DFIG Wind Systems with FOPID Controller

Enhancing Power Quality in Doubly Fed Induction
Generator-Based Wind Energy Conversion Systems Using
FOPID Controller
M. Vijayalaxmi
*
and P. Thevamudhan
Department of Electrical and Electronics Engineering,
College of Engineering, Guindy, Anna University,
Chennai, Tamilnadu, India
*
[email protected]
Received 2 May 2025
Accepted 15 October 2025
Published 28 February 2026
A doubly fed induction generator (DFIG) is the basis of the large-scale wind energy conversion
system (WECS), which has become more and more popular in recent years due to its many
technical and ¯nancial advantages. The stability and dependability of power systems have been
adversely a®ected by the rapid integration of WECS with current power grids. To address this
challenge, this study proposes an innovative control strategy for DFIG-based WECS using a
fractional order proportional-integral-derivative (FOPID) controller. The proposed technique
combines the Ebola optimization search algorithm (EOSA) and spiking neural network (SNN),
which is termed the EOSA-SNN method. The primary objective of the proposed technique is to
reduce total harmonic distortion (THD), increase the performance of reactive and active power
regulation, and improve the performance of DFIG-driven WECS under variable wind condi-
tions. The FOPID controller is used to regulate the rotor-side converter for optimal control, the
EOSA is used to enhance the gain parameters of the FOPID controller, and the SNN is
employed to predict the most suitable controller factor adjustments in response to dynamic
wind and grid °uctuations. The proposed method is implemented and evaluated, and is com-
pared with existing methods on the MATLAB platform, such as di®erential evolution (DE),
salp swarm algorithm (SSA) and particle swarm optimization (PSO). The results demonstrate
superior performance, achieving a THD of 2.1%, settling time of 0.036 s, overshoot of 0.3%, rise
time of 0.011 s and a computational time of 3.41 s, providing a faster, more accurate and energy-
e±cient solution for e®ective control of DFIG-driven wind energy systems (WES).
Keywords: Doubly fed induction generator; wind turbine; rotor side converters (RSC);
maximum power point tracking (MPPT); grid-side converters; FOPID controller.
*
Corresponding author.
Journal of Circuits, Systems, and Computers
Vol. 35, No. 13 (2026) 2650060 (27 pages)
#
.
cWorld Scienti¯c Publishing Company
DOI: 10.1142/S021812662650060X
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1. Introduction
1.1. Background
Renewable generation through solar and wind generation systems (WGS) is
encouraged by rising electricity demand, global warming and excessive carbon
emissions.
1
Air pollution and severe climate change are lessened by these generation
systems.
2
As a result, there is a pressing need for clean, a®ordable, domestic and
trustworthy generation sources everywhere in the world.
3
Research has demon-
strated that wind power generation is the most cost-e®ective, reliable and e±cient
option. When it comes to wind generation systems, the variable speed doubly fed
induction generator (DFIG)-wind turbine (WT) is a very acceptable system that
contributes signi¯cantly to the green era of WGS.
4,5
The interconnected power
system experiences temporary stability problems when DFIG-WT is present.
6
Numerous factors, including the load, wind power penetration level, fault clearing
time and voltage sag, can impact transient stability.
7,8
Wind energy's widespread
use presents serious threats to grid stability and security. Additionally, it has an
impact on the integrated system's center of inertia, which can lead to frequency
instability.
9,10
Wind power's intermittency and variability are the root causes of power imbal-
ance issues.
11
A power imbalance is caused by dynamically changing load demands.
In addition, when a fault occurs, DFIGs are more sensitive.
12
In the event of a fault,
DFIG must be associated with the grid and meet reactive and active power
requirements.
13
To ensure dependable operation during transient events, DFIG-
based wind turbines (WTs) need to be out¯tted with sophisticated switchgear and
isolators.
14
As a result, the grid code requirements have established a number of
precise technical guidelines to assure consumer power quality (PQ) and grid stabil-
ity.
15
Grid codes
16,17
include fault ride-through (FRT) operation, power system
protection, °ickering, PQ, harmonic oscillations, reactive power management, active
power control (to adjust the voltage) and other critical components. During
disruptions in the power system, these wind power-generating units suddenly
disconnect and become unstable.
18
Increasing FRT capability is one of the main
objectives of grid codes. FRT capabilities can be divided into three primary cate-
gories: low-voltage, zero-voltage and high-voltage. As a result, research on PQ issues
and improvement has gained attention.
19,20
1.2. Literature review
Numerous research works in the literature were available depending on the control of
the DFIG-related wind energy conversion system (WECS) by utilizing various
systems and features. Some of them are given below.
Elkholy et al.
21
developed the best controller parameters for a grid-connected
WECS according to DFIG, which was found using the bonobo optimizer (BO).
By adjusting controller factors to reduce torque ripple and maximum power point
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tracking (MPPT) of WT-DFIG, the recommended optimal performance of WECS
was created. Proportional-integral (PI) factors of the grid side and rotor side con-
verters (RSCs), the DC bus's capacitance and voltage and the values of an LC ¯lter
with a delta connection for the rotor side converter (RSC) and an LC ¯lter for the
grid side converter (GSC) were the optimization factors.
Bossou¯ et al.
22
introduced a rooted tree optimization (RTO) algorithm-based
optimization method and a nonlinear adaptive back-stepping control method applied
to a DFIG-driven wind system. The Lyapunov nonlinear technique served as the
foundation for the backstepping control strategy, which ensured system stability.
In order to make the suggested system resistant to parametric variation, it was ¯rst
applied to the two converters and then enhanced with estimators.
Zhong et al.
23
suggested the DFIG's sliding mode observer (SMO) rotor current
state space equation. Then, by using the PSO algorithm to optimize the constructed
¯tness function and suggesting a new event-triggered mechanism (NETM) for the
designed SMO, the ideal SMO parameters were identi¯ed. In order to verify the
e®ectiveness of the suggested method, defects in the DFIG for wind power systems
were ¯nally found utilizing the sequence of residuals among the rotor current output
values and the sliding mode observations. The work was innovative in that it com-
bined the sliding mode reaching law to create a NETM, which was then incorporated
into the SMO design.
Ouari and Belkhier
24
presented for the DFIG wind turbine system (WTS), robust
predictive control according to direct torque control space vector modulation
(DTC-SVM), to increase the e±ciency of energy capture. In order to account for the
aerodynamic torque as an unknown perturbation, a new cost function was presented
for the predictive speed regulator. The technique aimed to improve dynamic
performance, such as reference tracking, disturbance rejection and robustness to
parameter changes. Two advantages of employing the DTC-SVM technique were a
constant switching frequency and decreased torque-°ux ripples.
Dembri et al.
25
illustrated that to keep the output power of the investigated
DFIG-driven WTs at the rated value under dynamic wind conditions, the design
parameters of the fractional-order fuzzy proportional-integral with derivative
(FOFPID) regulator were optimized using the social spider optimization (SSO)
technique. Combining the fractional-order controller's and fuzzy intelligent reg-
ulator's capabilities, the suggested FOFPID controller enhances DFIG current
management while permitting independent active and reactive power regulation.
The technique was incorporated into the DVC strategy of the DFIG's RSC and took
the place of the conventional PI regulator in the internal current loops.
Sreenivasulu and Hussain
26
recommended a WECS that uses electronic con-
verters to convert power back-to-back. The controllers' ability to keep the system
operating at its maximum power point was evaluated after an adaptive MPPT
algorithm was put into place. The goals of cascaded control loops and the design
process methodology were covered in detail one by one.
Power Quality in DFIG-Based Wind Systems
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Ahmadnejad et al.
27
o®ered to avoid the frequency second dip by increasing the
system frequency with wind farms and regaining the WT rotor's speed with the
DFIG. In the design, the new power reference was integrated into the MPPT
characteristic as a function of two parameters, the changes in system frequency and
the WT rotor speed, while accounting for the torque limit, after the disturbance and
system frequency decrease have been identi¯ed. The frequency change parameter
rises, and the rotor speed parameter falls during frequency support.
1.3. Research gap and motivation
The generic review of this research work displays that control of DFIG-based WECS is
an important and evolving area of research, critical for ensuring stable, reliable and
high-quality renewable energy integration into modern power grids. This application is
vital for maintaining grid stability, minimizing harmonic distortion and maximizing
energy capture from variable wind resources. However, achieving optimal dynamic
performance and robust control in such systems remains di±cult due to challenges like
wind intermittency, grid disturbances, nonlinear system dynamics and parameter
uncertainties. The development of a robust, adaptable and computationally e±cient
control strategy is a challenging task in dynamic and uncertain renewable energy
systems (RES). To address this, many researchers have proposed solutions using ad-
vanced techniques such as PSO, DE, SSA, Backstepping Control, SMO, FOFPID and
robust predictive control (RPC). However, the PSO, DE and SSA techniques su®er
from high computational overhead, static tuning and slow convergence, limiting their
real-time applicability. The Backstepping and SMO approaches are limited by model
dependency and implementation complexity, while the FOFPID and RPC approa-
ches, though improving transient response and robustness, typically focus on a single
performance objective and fail to simultaneously manage multiple constraints. These
methods, although promising, do not adequately focus on dynamic, real-time adap-
tation to °uctuating wind and grid conditions while balancing multiple objectives such
as PQ, stability and FRT capability. In the existing literature, only a few works have
attempted to integrate optimization algorithms with intelligent control frameworks
that can e®ectively handle these complexities. These drawbacks have motivated the
present research, which proposes a hybrid control framework based on FOPID tuning
that leverages the strengths of evolutionary optimization and neural-based prediction
to dynamically adjust controller factors, improve transient and steady-state perfor-
mance and ensure reliable operation of DFIG-based WECS under uncertain and
variable operating conditions.
1.4. Novelty
The novelty of this study lies in the design of a hybrid EOSA-SNN-driven FOPID
controller speci¯cally tailored to tackle the complex challenges of DFIG-based
wind energy systems (WES), including nonlinear dynamics, °uctuating wind speeds,
grid disturbances and multi-objective performance demands. In this dual-layer
M. Vijayalaxmi & P. Thevamudhan
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framework, the Ebola optimization search algorithm (EOSA) performs global opti-
mization of the FOPID gains, e±ciently exploring the multi-modal parameter space
to provide near-optimal baseline settings, while the spiking neural network (SNN)
captures temporal and dynamic patterns in system behavior to enable predictive
adjustment of these parameters under varying operating conditions. Individually,
EOSA ensures global optimality but lacks adaptability to rapid system changes,
whereas SNN provides adaptive prediction but cannot guarantee global optimality.
Their integration allows the controller to simultaneously achieve MPPT, reactive
and active power regulation, harmonic mitigation and enhanced transient response.
This synergy results in faster convergence, lower computational cost and superior
robustness compared to conventional and existing hybrid approaches. Unlike prior
methods, EOSA-SNN uniquely combines global search capability with predictive
temporal adaptation, o®ering a scienti¯cally justi¯ed, scalable and °exible solution
rather than a simple application of popular algorithms. The Comparative Analysis of
Existing Methods with the Proposed EOSA-SNN Approach is illustrated in Table 1.
1.5. Contribution
The primary contribution of this manuscript is described as follows:
.By integrating EOSA for global optimization with SNN for dynamic predictive
adjustment, the method ensures faster convergence, avoidance of local optima and
improved controller performance under variable wind and grid conditions com-
pared to standalone optimization approaches.
.The integration of predictive neural adjustment into the FOPID controller pro-
vides robust control of reactive and active power under °uctuating operating
conditions, a capability often overlooked in prior DFIG control studies.
.The approach minimizes total harmonic distortion (THD) while simultaneously
improving e±ciency and transient response, o®ering a holistic improvement in PQ
and system stability.
.Extensive validation on DFIG-based WES demonstrates that EOSA-SNN-based
FOPID control outperforms conventional metaheuristic-tuned FOPID controllers,
achieving the lowest THD, highest e±ciency and fastest settling time.
.The method is shown to adapt e®ectively across varying wind pro¯les, achieving
precise MPPT and stable operation under uncertain conditions, proving its suit-
ability for modern WES.
1.6. Organization
This paper is arranged as follows. Section 2outlines the con¯guration of the DFIG-
driven wind turbine (WT). The proposed EOSA-SNN is established in Sec. 3. Sec-
tion 4explains the outcomes and discussion. Section 5concludes.
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