Poorly-damped inter-area oscillations is one of the main problems facing power systems, especially those operating in steady state. Power grids experiencing these types of oscillations are prone to frequent power outages due to loss of steady-state equilibrium, restricted interconnection expansion, and limited power transfer capability. Power system stabilizers are commonly used to provide additional damping control signals to synchronous generators. However, both stand-alone and advanced stabilizers fail to efficiently damp multiple inter-area oscillations due to a lack of global observability and limited bandwidth.
Wide-area damping controller (WADC) provides an effective and efficient alternative means of overcoming the inadequacies of local control methods. Generally, WADCs use control signals and remote measurements and require relatively smaller gains to realize the same damping effect as the local controllers. Numerous strategies for designing WADC for inter-area modes have been proposed. The main challenge of these methods is that they require a thorough understanding of grid topologies which could be unavailable due to bad data, uncertainties and communication delays. This could also lead to undetected changes in topology and operating conditions that may compromise the WADC effectiveness.
Robust WADC model-based methods and “black-box” data-driven methods have been employed to overcome the above challenges, yet they may either still require key information from the physical model or are sensitive to the applied data sets. In addition, the incorporation of artificial intelligence has benefited the research efforts to improve the performance of WADCs. Nevertheless, artificial intelligence-based methods require serious offline training and their performance depends on the adopted optimization algorithm. Thus, the realization of robust data-driven WADC techniques requires effective selection of input-output measurements, pre-tuning, offline training and clear physical interpretation of the power grid dynamics.
Herein, Dr. Ilias Zenelis and Professor Xiaozhe Wang from McGill University developed a novel and fully online model-free sparse WADC method for efficiently damping inter-area oscillations. The new method utilized phasor measurement unit data and data-driven identification approach to estimate the true dynamic system state matrix and inter-area modes around steady state. In addition, a modal linear quadratic regulator (MLQR)-based sparsity-promoting optimal state feedback controller was developed to optimize the damping of all critical inter-area modes simultaneously. The work is currently published in the International Journal of Electrical Power and Energy Systems.
The research team showed that the proposed WADC strategy was highly effective, physically interpretable and required no offline training. Compared to the existing methods, this method could simultaneously damp multiple inter-area modes with minimal control efforts. The simulations of the dynamic characteristics of the IEEE 68-bus system validated the effectiveness and robustness of this method. Although it required no power system model information and low-cost wide area communication, its performance was comparable to those of centralized data-driven or model-based WADC, while overcoming their limitations.
The MLQR-based model directly shaped the closed-loop damping behavior of the inter-area modes while optimizing the communication network and performance control. As a result, this strategy could be used to directly target specific inter-area modes, reducing the complexity of the parameter tuning and signal selection. Other advantages of this strategy were that it could be applied to PMU dataset and could be mapped for real-world power grid systems. It was possible to implement WADC algorithm online to update the control signals to the actuators adaptively and enable sufficient damping of inter-area modes even in varying system topology and operating conditions.
In summary, this is the first study to propose a model-free WADC method capable of damping all the critical inter-area modes with minimal control effort while considering the physical communication constraints of the system. This was achieved regardless of the measurement noise, PMU losses and restrictions of the communication network. Interestingly, the proposed approach could be adapted to various operating conditions attributed to its data-driven nature. In a statement to Advances in Engineering, Professor Xiaozhe Wang, the lead author explained that the proposed strategy provides a promising approach for improving the efficiency and operations of modern power systems.
Zenelis, I., & Wang, X. (2022). A model-free sparse wide-area damping controller for inter-area oscillations. International Journal of Electrical Power &Amp; Energy Systems, 136, 107609.