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Fault diagnosis is of critical importance to the safety of power electronic devices in dc microgrids. To detect and isolate different component faults in dc microgrids, this paper introduces a comprehensive protection scheme using reduced-order unknown input observers (ROUIOs). As opposed to conventional protection strategies, the proposed method provides a centralized fault detection and isolation (FDI) solution for dc microgrids that covers multiple faults occurring in different components in a unified process. Moreover, it reduces the complexity of observer model and relaxes the requirements of measurement signals compared with existing observer-based FDI methods for dc microgrids. To this end, the state-space model of a multi-terminal dc microgrid with different faults is first established. On this basis, a bank of ROUIOs are designed with the aim of classifying different component faults in the system. At last, the performance of the proposed FDI method is verified through numerical simulations with MATLAB/Simulink and hardware tests. Test results show that the proposed method can accurately detect and isolate different component faults in dc microgrids in a short response time of 1 ms.

Considerable efforts have been made to analyze the small-signal stability of doubly fed induction generator (DFIG) systems. However, commercial confidentiality and frequency coupling make the DFIG system a grey-box multiple-input-multiple-output (MIMO) system with highly challenging stability analysis. This article proposes an Argument-principle based stability assessment method to analyze the stability of the grey-box DFIG system. The frequency sweeping technique is first used to acquire the MIMO model of the black-box device, as well as the determinant of the system's return difference matrix. Then a stability criterion based on the determinant trajectory is presented. This criterion applies to the stability analysis of grey-box MIMO systems without detailed system models. Further, a critical-pole estimation method with trajectory information is developed to assess the dominant mode of the target system. The simulation and hardware-in-loop experiment results demonstrate the effectiveness of the proposed method. Finally, some concerns about this method, such as model selection, estimation errors and application potential, are thoroughly analyzed and clarified.

Short-circuit faults in high-voltage DC (HVDC) grids must be timely isolated. Conventional fault isolation schemes for HVDC grids typically rely on numerical simulations or analytical calculations for threshold setting, which complicate engineering implementation. To solve this issue, this article first introduces the convex point of backward traveling wave (BTW) voltage as the reference for the faulty line isolation in HVDC grids, which is interpreted as the singular feature in the second derivative (SD) of BTW voltage. The singularity in the SD of BTW voltage is independent of system or fault parameters, having good generality in HVDC grids. Through recognizing the unique pattern in the wavelet transform modulus maxima of the singular feature, the proposed protection method can identify faulty lines in HVDC grids in the initial phase of faults. Numerical studies carried out with PSCAD/EMTDC and MATLAB/Simulink demonstrate the effectiveness, robustness and applicability of the proposed method in detecting and isolating various single- and double-pole faults in HVDC grids.

When a slightly faulty transformer closes without load, the current waveform presents the coexistence of inrush and fault current. At this time, the inrush blocking module will block the relay, which may delay the removal of the slight fault and lead to more serious faults. To address this problem, this paper proposes a data-aided power transformer differential protection without inrush blocking module. The key to eliminating the negative influence of inrush current is to extract the fundamental component from the non-inrush part of the current waveform, which corresponds to the unsaturation period of the transformer core. Firstly, a data-aided module, namely an Attention module embedded Fully Convolutional Network (A-FCN), is built to distinguish the inrush and non-inrush parts of the current waveform. Then, a physical model of the current waveform is built for the non-inrush part, and the fundamental component is extracted by the nonlinear least square (NLS) algorithm. The proposed method can avoid the block of differential protections when inrush current occurs, which improves the sensitivity and rapidity of the relay, especially in the case of a weak internal fault hidden in inrush current. Finally, simulation and experimental data verify the effectiveness and generalization of the proposed method.

The safety of DC microgrids is threatened by multiple types of faults occurring in different components, entailing multi-target fault detection and isolation solutions. To address this issue, this paper introduces a comprehensive fault detection and isolation framework based on the mathematical models of DC microgrids. In this work, the DC microgrids with non-linear characteristics are modeled via polytopic linear parameter varying modeling, which has the remarkable property to adapt to changing operating points. On this basis, a bank of unknown input observers are built. At last, settingless fault classification is achieved through reconstructing the patterns in the outputs of the observers. Compared with existing fault detection and isolation methods for DC microgrids, the proposed system-level solution can simultaneously diagnose multiple faults in DC microgrids in non-stationary operating states. Moreover, the proposed signature recognition scheme solves the difficulty in threshold setting and greatly improves the tolerance of the proposed method to modeling and measurement uncertainties. The accuracy, adaptivity and robustness of this method are verified through extensive numerical tests with MATLAB/Simulink.