system is a commonly employed method for regulating the electricity generated by a wind turbine. Many

researchers have observed developments in the pitch control field during the last few decades. Traditional PID

controllers have the drawback of being slow or imprecise when wind and pitch angles suddenly change. These

drawbacks can be solved with artificial intelligent algorithms. However, the algorithms' design and implementation

are highly complex. A new pitch-regulated variable-speed control strategy for wind turbines to address their

nonlinear properties is presented. To manage the pitch system's control mechanisms with disturbances, this

Received: March 9, 2023. Revised: November 26, 2023. Accepted: December 13, 2023. Published: December 31, 2023.

energy is first transformed from kinetic energy to

electrical energy using a wind turbine generator, [1],

[2], [3].

energy, similar to a fuel plant; half of the electricity

produced by the wind turbines is generated during

around 15% of their operating duration, [5].

Nowadays, wind turbines represent major significance

inside microgrids as energy generation sources. They

can be operated at both fixed and variable speeds.

Many research studies have been conducted on wind

power and wind turbines, [6], [7], [8].

Figure 1 illustrates the structure of WT. The

transformer sends the Electricity to the grid by the

generator, [9].

Non-torque loads are applied as input and output

to the wind turbine, mainly on both the generator's

and blade's sides. These non-torque loads impact the

WT drive train's mechanical loads and stresses, [10].

Fig. 1: Wind turbine components, [9]

The utilization of fuzzy logic as a technique of the

hybrid plug-in electric automobiles. The simulation

outcomes demonstrate that this approach exhibits

greater efficacy in adjusting system parameters when

compared to PID control, [12].

The efficacy of the standalone incorporated

system of renewable energy, comprised of solar

photovoltaic cells, WT, and fuel cells, this

improvement was attained through the execution of a

how to get the greatest output power of these systems

at various wind speeds.

First, this paper will describe a proposed simple

optimum (SO-PID) for pitch angle control of variable-

speed WT. The results attained by a simple optimum

(SO-PID) controller have been compared with those

obtained using a fuzzy-adaptive-PID controller, FLC,

and PID controller by changing the input by unit step

and unit sine wave to determine the fastest response at

Optimum PID (SO PID). A PC system simulation and

its results are compared with PID, Fuzzy Logic

Controller (FLC), and Fuzzy-adaptive-PID control,

which are demonstrated in Section 5. Section 6

concludes this study.

The WT's maximum power extraction is as follows in

equ (1).

The coefficient of power, denoting the proportion

of power generated by wind energy, can be calculated

using two parameters.

maintained at a minimal value less than the rated wind

velocities. It can be modified to safeguard the turbine

the overall power accessible from the wind, as stated

by the physicist Betz (Betz Limit), [19].

time constant and transfer function.

The transfer function that is required is denoted by

can be specified. Table 1 shows the initial wind

turbine parameters, [23].

As shown in Figure 5, the mechanical drive-train

model is illustrated, [24]. The mathematical modeling

of the drive train is represented by

By applying Newton’s second law,

from the combination of the generator shaft speed and

controller, the proposed design formula for simple

Fig. 6: closed-loop system with PID controller

The controller is represented by the following

equations:

The process transfer function

󰇛   󰇜  (19)

Matching the Equation (24)'s coefficient to

equation (25), which it corresponds to.

The MDOF controller comprises two controllers,

one intended for fine-tuning and the other for

accommodating a wide range of errors. The multi-

degree of freedom controller (MDOF) is generated

through the combination of two controllers, where the

weights of the controllers differ based on the error at

each value of the controller input. The MDOF

controller underwent a two-step design process. The

first step is to devise two distinct controllers, one of

proposed is denoted in per-unit values as follows:

Controller output = Output1*(Error) +

where O/P1 is denoted to the controller output for

a wide-range controller (using large T in the design

formula) and O/P2 is denoted to the controller output

for the fine-tuning controller (using large T in the

design formula).

The two controllers were incorporated into the

system, each assigned with adaptive weights: one for

linear relationship between the controller weights and

A robust controller must meet specified requirements

in terms of design, implementation, and performance.

Simplified design & adaptation techniques are

preferred for the controller, which entails simple

process modeling (traditional control) or input-output

testing (modern control). The design must be

accomplished through the implementation of a simple

performance. The results of the suggested controller

demonstrate that an optimal response can be attained

using a simple optimal PID controller. Figure 16

illustrates the unit step response of pitch angle with

(SO-PID). The response is done with the lowest rise

Table 3. Comparison response of various controller