Linbin Huang

Linbin Huang, Huanhai Xin, Wei Dong et al.

Small-signal instability of grid-connected power converters may arise when the converters use a phase-locked loop (PLL) to synchronize with a weak grid. Commonly, this stability problem (referred as PLL-synchronization stability in this paper) was studied by employing a single-converter system connected to an infinite bus, which however, omits the impacts of power grid structure and the interactions among multiple converters. Motivated by this, we investigate how the grid structure affects PLL-synchronization stability of multi-converter systems. By using Kron reduction to eliminate the interior nodes, an equivalent reduced network is obtained which contains only the converter nodes. We explicitly show how the Kron-reduced multi-converter system can be decoupled into its modes. This modal representation allows us to demonstrate that the smallest eigenvalue of the grounded Laplacian matrix of the Kron-reduced network dominates the stability margin. We also carry out a sensitivity analysis of this smallest eigenvalue to explore how a perturbation in the original network affects the stability margin. On this basis, we provide guidelines on how to improve the PLL-synchronization stability of multi-converter systems by PLL-retuning, proper placement of converters or enhancing some weak connection in the network. Finally, we validate our findings with simulation results based on a 39-bus test system.

Kehao Zhuang, Huanhai Xin, Verena Häberle et al.

Grid-forming (GFM) technology is widely regarded as a promising solution for future power systems dominated by power electronics. However, a universally accepted definition of GFM behavior and precise method for its quantification remain elusive. Moreover, the impact of GFM converter on system stability is not precisely quantified, creating a significant disconnect between device and system levels. To address these gaps from a small-signal perspective, at the device level, the paper introduces a novel metric, the Forming Index (FI) to quantify a converter's response to grid voltage fluctuations. Rather than enumerating various control architectures, the FI provides a metric for the converter's GFM ability by quantifying its sensitivity to grid variations. At the system level, a new quantitative measure of system strength that captures the multi-bus voltage stiffness is proposed, which quantifies the voltage and phase angle responses of multiple buses to current or power disturbances. The paper further extends and defines this concept to grid strength and bus strength to identify weak areas within the system. Finally, the device and system levels are bridged by formally proving that GFM converters enhance system strength. The proposed framework provides a unified benchmark for GFM converter design, optimal placement, and system stability assessment.

Kehao Zhuang, Huanhai Xin, Verena Häberle et al.

Grid-forming (GFM) technology is widely regarded as a promising solution for future power systems dominated by power electronics. However, a precise method for quantifying GFM converter behavior and a universally accepted GFM definition remain elusive. Moreover, the impact of GFM on system stability is not precisely quantified, creating a significant disconnect between device and system levels. To address these gaps from a small-signal perspective, at the device level, we introduce a novel metric, the Forming Index (FI) to quantify a converter's response to grid voltage fluctuations. Rather than enumerating various control architectures, the FI provides a metric for the converter's GFM ability by quantifying its sensitivity to grid variations. At the system level, we propose a new quantitative measure of system strength that captures the multi-bus voltage stiffness, which quantifies the voltage and phase angle responses of multiple buses to current or power disturbances. We further extend and define this concept to grid strength and bus strength to identify weak areas within the system. Finally, we bridge the device and system levels by formally proving that GFM converters enhance system strength. Our proposed framework provides a unified benchmark for GFM converter design, optimal placement, and system stability assessment.

Ruohan Leng, Linbin Huang, Huanhai Xin et al.

Grid-connected power converters are ubiquitous in modern power systems, acting as grid interfaces of renewable energy sources, energy storage systems, electric vehicles, high-voltage DC systems, etc. Conventionally, power converters use multiple PID regulators to achieve different control objectives such as grid synchronization and voltage/power regulation, where the PID parameters are usually tuned based on a presumed (and often overly-simplified) power grid model. However, this may lead to inferior performance or even instabilities in practice, as the real power grid is highly complex, variable, and generally unknown. To tackle this problem, we employ a data-enabled predictive control (DeePC) to perform data-driven, optimal, robust, and adaptive control for power converters. We call the converters that are operated in this way DeePConverters. A DeePConverter can implicitly perceive the characteristics of the power grid from measured data and adjust its control strategy to achieve optimal, robust, and adaptive performance. We present the modular configurations, generalized structure, control behavior specification, inherent robustness, detailed implementation, computational aspects, and online adaptation of DeePConverters. High-fidelity simulations and hardware-in-the-loop (HIL) tests are provided to validate the effectiveness of DeePConverters.

Wenqi Cui, Linbin Huang, Weiwei Yang et al.

A key barrier to using reinforcement learning (RL) in many real-world applications is the requirement of a large number of system interactions to learn a good control policy. Off-policy and Offline RL methods have been proposed to reduce the number of interactions with the physical environment by learning control policies from historical data. However, their performances suffer from the lack of exploration and the distributional shifts in trajectories once controllers are updated. Moreover, most RL methods require that all states are directly observed, which is difficult to be attained in many settings. To overcome these challenges, we propose a trajectory generation algorithm, which adaptively generates new trajectories as if the system is being operated and explored under the updated control policies. Motivated by the fundamental lemma for linear systems, assuming sufficient excitation, we generate trajectories from linear combinations of historical trajectories. For linear feedback control, we prove that the algorithm generates trajectories with the exact distribution as if they are sampled from the real system using the updated control policy. In particular, the algorithm extends to systems where the states are not directly observed. Experiments show that the proposed method significantly reduces the number of sampled data needed for RL algorithms.