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.