Low-frequency electromechanical oscillations (0.1–2.5 Hz) threaten the stability of interconnected large power systems. Power system stabilizers (PSS) are the most cost-effective solution for enhancing damping via generator excitation system. Since the 1960s, numerous PSS design methods have been proposed, ranging from classical phase compensation to modern intelligent algorithms [1-3]. This paper provides a comprehensive review of these methods, systematically analyzing their principles, advantages, limitations, and applicability in modern power systems.

The traditional design of the PSS is based on the single-machine infinite bus (SMIB) system and the concept of damping torque. PSS always includes a wash-out filter to eliminate steady-state offset, a lead-lag compensator to address phase delay between the excitation and rotor dynamics, and a gain module to regulate the damping ratio [1-2].

PSS design methods can be categorized into three types: traditional phase compensation, modern control strategies, and intelligent computational techniques. Traditional methods rely on tuned lead-lag networks to compensate for phase lag at specific oscillation frequencies. They are simple, and easy to implement, as standardized in IEEE PSS1A and PSS2B [4]. However, since they typically focus on a single operating condition, they may provide poor damping when system conditions change. Furthermore, in large power systems, sequential tuning of individual PSS units cannot guarantee global optimal damping performance.

To overcome these limitations, modern control methods have been developed, including robust control (e.g., H∞ and μ-synthetic control), adaptive control (e.g., model-reference adaptive control and self-tuning controllers), and optimal control techniques (e.g., LQR and LQG) [5]. Although these methods have theoretical advantages, they are typically more complex and require detailed system models, which limits their practical applications.

In recent years, intelligent computing technologies have become powerful tools for PSS design, particularly in nonlinear and multi-objective optimization. Fuzzy logic controllers utilize heuristic rules to capture expert knowledge and handle system nonlinearities without detailed system models. Artificial neural networks (ANNs) enable data-driven learning and adaptive control. Metaheuristic optimization algorithms, such as genetic algorithms (GA), particle swarm optimization (PSO), differential evolution (DE), and bacterial foraging optimization (BFO), are widely used to optimize PSS parameters across various operating conditions [6-10].

Considering the controller interactions, coordinating PSS in large power systems is a significant challenge [11]. Inappropriate coordination can reduce the damping and even introduce new oscillation modes. Although traditional sequential tuning methods are simple and straightforward, they are inherently suboptimal. Synchronous tuning methods, particularly those based on metaheuristic optimization, demonstrate better performance by optimizing multiple PSS parameters to maximize the minimum damping ratio. Furthermore, coordinated control with Flexible AC Transmission Systems (FACTS) devices offers significant advantages [11].

The energy transition presents new challenges and opportunities for PSS design. The increasing penetration of renewable energy sources changed the power system dynamics. Power electronic interfaced generations could provide synthetic inertia and damping [12], but must be effectively coordinated with traditional PSS. Wide-area damping control (WADC) based on phasor measurement units (PMUs) improves inter-area oscillation damping using remote signals. However, issues such as communication delays, reliability, and cybersecurity must be carefully addressed [13-14]. Furthermore, data-driven methods, including reinforcement learning and deep reinforcement learning, have garnered significant attention due to their ability to implement adaptive, model-free control strategies. Hardware-in-the-loop (HIL) testing is increasingly recognized as important for verifying whether PSS designs meet actual operating conditions before field implementations [15-16].

In summary, while traditional PSS designs remain effective in damping power system oscillations and are widely used in practice, they have limitations in terms of robustness and multimodal performance. Modern control theories offer more systematic solutions, but they still face challenges in practical applications. Intelligent computing technologies possess significant advantages in handling nonlinearity, uncertainty, and coordination issues, making them an ideal choice for future applications. In modern power systems, PSS designs must integrate robustness, adaptability, and coordination while addressing challenges in renewable energy integration, wide-area control, and cybersecurity. Particularly, HIL is important for validating PSS designs in large power systems.

References

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