Frede Blaabjerg

Meng Chen, Yufei Xi, Frede Blaabjerg et al.

A common assumption in both grid-following (GFL) and grid-forming (GFM) control systems is that they are open-loop (OL) stable in the vicinity of high-frequency resonances. Hence classical loop-shaping approaches are often used for establishing stability margins and designing active damping (AD) strategies. This paper shows that single-loop GFM (SL-GFM) control schemes incorporating a widely used class of reactive power (RAP) control, referred to as droop-I control, can lead to OL unstable poles. This finding reveals a novel instability mechanism resulting in a reduced stability margin and robustness at high frequencies. The sensitivity of this phenomenon to both RAP and electrical parameters is analyzed in detail. An AD design that explicitly accounts for the newly identified instability mechanism is proposed. We also provide a comparison between such SL-GFM and well-studied GFL control schemes, highlighting quite different resonance features between them. Validation is performed through experiments.

Chao Wu, Jinhao Wang, Yong Wang et al.

Grid-forming (GFM) converters are generally expected to exhibit low impedance near the fundamental frequency due to their voltage-source behavior. However, an impedance peak and a negative-resistance region are consistently observed in this range, which contradicts this expectation and lacks a clear physical explanation. This paper reveals that these phenomena originate from the inherent dynamics of the active power control loop, where the mapping from power disturbance to the synchronous angle inherently involves an integrative action, intrinsically preventing a positive-resistance characteristic near the fundamental frequency. This finding explains why existing grid codes in China, the United States, and Europe exclude a narrow band around the fundamental frequency in impedance-based evaluations. It is further shown that the width of the excluded frequency band (e.g., +/- 3~5 Hz) is governed by the power-to-frequency dynamics. Based on this insight, a quantitative index is proposed to determine the exclusion bandwidth from the corner frequencies of the impedance magnitude curve. The proposed index provides a concise and theoretically grounded criterion for voltage-source assessment and impedance standardization of GFM converters.

Subham Sahoo, Huai Wang, Frede Blaabjerg

Artificial intelligence (AI)-driven fault diagnosis in motor drives often requires significant computational efforts and time for re-training, in addition to the limited knowledge behind the model and suitability of training and learning mechanisms. This work bridges this gap by proposing a structured mechanism of transforming untapped labeled fault data into AI parameters to leverage probabilistic data-driven learning. This novel AI reservoir modeling framework for power electronics not only eliminates exogenous efforts behind learning data patterns and its optimization, but also provides intuitive guidelines for power electronics engineers behind sizing of AI models. This alignment between data and system physics makes the proposed model transparent and interpretable, bridging practical understanding with data-driven learning. Its computational efficiency is demonstrated using experimental data that structured, physics-aware reservoirs achieve higher diagnostic accuracy and clearer explanations than conventional black-box AI methods.

Subham Sahoo, Huai Wang, Frede Blaabjerg

Deploying large artificial intelligence (AI) models in power electronics often demands high computational resources. Driven by the quantization paradigm, this digest proposes a quantization-aware training (QAT) principle to substantially minimize the number of bits required and simultaneously maximize the accuracy of computations in pre-trained AI models. Considering a pre-trained probabilistic Bayesian Neural Network (BNN) for gear fault diagnosis in motor drives as an example, we quantize its weights and activation functions from floating-point FP32 to low-precision INT8 values, which enhances the computational efficiency by a significant margin of 30-45% (for different model versions) without any compromise in the accuracy and uncertainty estimates. This substantiates a sustainable mechanism of deploying most quantized light-weight AI models into low-cost edge processors for power electronic applications.

Subham Sahoo, Huai Wang, Frede Blaabjerg

This paper introduces a novel approach to quantify the uncertainties in fault diagnosis of motor drives using Bayesian neural networks (BNN). Conventional data-driven approaches used for fault diagnosis often rely on point-estimate neural networks, which merely provide deterministic outputs and fail to capture the uncertainty associated with the inference process. In contrast, BNNs offer a principled framework to model uncertainty by treating network weights as probability distributions rather than fixed values. It offers several advantages: (a) improved robustness to noisy data, (b) enhanced interpretability of model predictions, and (c) the ability to quantify uncertainty in the decision-making processes. To test the robustness of the proposed BNN, it has been tested under a conservative dataset of gear fault data from an experimental prototype of three fault types at first, and is then incrementally trained on new fault classes and datasets to explore its uncertainty quantification features and model interpretability under noisy data and unseen fault scenarios.

Hafiz Majid Hussain, Arun Narayanan, Subham Sahoo et al.

Internet of Things (IoT) and advanced communication technologies have demonstrated great potential to manage residential energy resources by enabling demand-side management (DSM). Home energy management systems (HEMSs) can automatically control electricity production and usage inside homes using DSM techniques. These HEMSs will wirelessly collect information from hardware installed in the power system and in homes with the objective to intelligently and efficiently optimize electricity usage and minimize costs. However, HEMSs can be vulnerable to cyberattacks that target the electricity pricing model. The cyberattacker manipulates the pricing information collected by a customer's HEMS to misguide its algorithms toward non-optimal solutions. The customer's electricity bill increases, and additional peaks are created without being detected by the system operator. This article introduces demand-response (DR)-based DSM in HEMSs and discusses DR optimization using heuristic algorithms. Moreover, it discusses the possibilities and impacts of cyberattacks, their effectiveness, and the degree of resilience of heuristic algorithms against cyberattacks. This article also opens research questions and shows prospective directions.

Di Cao, Junbo Zhao, Weihao Hu et al.

Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage controls, but this is difficult to obtain in practice. This paper develops a model-free approach based on the surrogate model and deep reinforcement learning (DRL). We have also extended it to deal with unbalanced three-phase scenarios. The key idea is to learn a surrogate model to capture the relationship between the power injections and voltage fluctuation of each node from historical data instead of using the original inaccurate model affected by errors and uncertainties. This allows us to integrate the DRL with the learned surrogate model. In particular, DRL is applied to learn the optimal control strategy from the experiences obtained by continuous interactions with the surrogate model. The integrated framework contains training three networks, i.e., surrogate model, actor, and critic networks, which fully leverage the strong nonlinear fitting ability of deep learning and DRL for online decision making. Several single-phase approaches have also been extended to deal with three-phase unbalance scenarios and the simulation results on the IEEE 123-bus system show that our proposed method can achieve similar performance as those that use accurate physical models.