INTRODUCTION
Electrical power is produced, transmitted, and distributed through
intricate networks known as synchronous power systems. They are built to
deliver dependable and effective power to users while maintaining a
steady frequency and voltage [1]. Adaptive controllers in
synchronous power systems can substantially enhance industrial
applications by improving power use efficiency, reliability, and
cost-effectiveness [2].
Most power systems are highly dynamic, nonlinear systems. Uses of the
fixed parameter linear control theory and the linearized power system
model are used in conventional PSS [3]. The fixed parameter
controller is unable to maintain the dynamic stability of the power
system when there is a slight disturbance in the operational point
changes. The dynamic stability of a synchronous power system can be
increased by adapting a control system that employs an adaptive
controller with output feedback. The power system operates fundamentally
as a set of connected oscillators when there are load fluctuations and
parameter changes. The damper or amortized effects can attenuate higher
frequency oscillations. However, damper windings have little effect on
the lower frequency oscillations, and these modes are linked to dynamic
instability. The transmission of electricity is commonly limited by
oscillation of modest magnitude and low frequency in the range of 0.5Hz
to 2.5Hz. Additionally, it is presumed that the voltage regulator adds
negative damping when the load is increased. However, there is still
hope for solving the problems with the dynamic electric power system
[4].
Newer ideas and approaches to problem-solving are always available in
response to difficult new problems. Supplementary Stabilizing Signals
(SSSs), produced artificially using supplementary excitation controls,
are generally provided to increase system damping [5]. A Power
System Stabilizer (PSS) is a network that generates PSSs during
low-frequency oscillations.
The implementation of adaptive controllers in synchronous power systems
can significantly enhance the efficacy, reliability, and
cost-effectiveness of power management in manufacturing industries. The
uninterrupted power supply that these controllers ensure is essential
for industries with continuous operations, such as manufacturing, data
centers, and healthcare, as they enhance dynamic stability. They
facilitate the adoption of sustainable energy solutions by businesses,
thereby reducing operational costs and carbon footprints, by stabilizing
the integration of renewable energy and managing demand variations
[6]. The lifespan of equipment is also enhanced by adaptive
controllers, which reduce wear and strain on machinery by mitigating
oscillations and disturbances. Moreover, their capacity to manage
nonlinearities and adjust to system changes directly affects
productivity and profitability by reducing outage and maintenance
requirements. In general, they offer a comprehensive framework that
enables industries to optimize power consumption, improve operational
resilience, and comply with contemporary energy strategies [7].
According to reports, the ideal controllers are particularly successful
at damping machine oscillations. However, the fixed parameter
controllers are unable to maintain the dynamic stability of the power
system when the operating condition changes due to a disturbance. For
these controllers to deliver the appropriate performances, their
parameters must be adjusted. The extra damping torque modulation
introduces the additional damping signal through the excitation system
[8]. Thus, adaptive controls have been suggested to enhance the
dynamic characteristics of the system over a wide range of operating
points. Power system engineers have identified two basic methods for
adaptive control: model reference adaptive control (MRAC) and
self-tuning adaptive control (STAC).
In the STAC scheme, system parameters are identified online using
parameter identification techniques like the recursive least squares
method, and these parameter estimates are then included in the control
strategy [9]. The MRAC scheme’s control strategy includes a
reference model that displays the desired system response. The
controller settings are updated to have the system output converge to
the model output using the difference between the output of the real
system and that of the reference model [10].
Although STAC or MRAC-based adaptive PSSs have been reported to be
effective, their underlying assumptions and associated nonlinearities
raise many fundamental issues whose real-world resolutions may
complicate the control structure. In practice, it can be challenging to
apply the parameter estimator design in STAC and the right choice of
reference model in MRAC [11].
According to reports, an existing control method can be retrofitted with
the MCS algorithm based on Popov’s hyper-stability theory, considerably
enhancing closed-loop robustness [12]. The MCS algorithm has already
been used to dampen machine oscillations in a multi-machine power system
with a decentralized control strategy. In power systems, the adaptive
controller is frequently employed as an additional controller in
addition to a traditional fixed parameter controller. The additional
adaptive controller is used to improve the dynamic stability as well as
return the system to normal operating conditions following a disruption.
Conventional controllers govern the standard voltage and frequency
modifications [13]. Here, a novel design strategy for a higher-order
power system based on the MCS algorithm for the supplementary adaptive
PSS is put forth. The effectiveness of this strategy has been examined
using a numerical example of a Single Machine Infinite Bus (SMIB) power
system with control equipment. The simulation findings demonstrate that
the MCS-based adaptive controller performs well with a range of tiny
disturbances and system parameter modifications [14].
An adaptive controller modifies the control parameters in response to
system output using output feedback. This indicates that even in the
presence of disturbance, the controller can respond to changes in the
system and maintain stability. The power system has identified two basic
methods of adaptive control [15]. Overall, an output-feedback
adaptive controller is an effective tool for enhancing the dynamic
stability of synchronous power systems. Even in the presence of varying
circumstances and disturbances, it can assist ensure that the system
stays dependable and stable.
An adaptive control system adapts the parameters of the controller to
changes in the parameters or structure of the controlled system in such
a way that the entire system maintains optimal behavior according to the
given criteria, independent of any changes that might have occurred
[16].
The goal of adaptive control is to develop a control algorithm that can
automatically adjust its parameters to optimize the system’s
performance. Power System Stabilizers (PSSs) have been used for many
years to increase system damping and enhance power systems’ dynamic
stability.