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Summary Wide‐area early warning systems are dependent on synchrophasor data‐based applications for providing timely information to operators so that preventive actions can be taken. This article proposes the use of voltage sensitivities computed from synchrophasor data for voltage stability monitoring, and a visualization approach that can be implemented in wide‐area early warning systems. In order to provide reliable information, this article addresses the issue of data filtering and correction and proposes a filtering methodology for robust voltage sensitivity computation. The methodology is developed considering both positive‐sequence simulations for methodology development purposes, and real phasor measurement data from a real‐time (RT) hardware‐in‐the‐loop (HIL) laboratory for testing the robustness of the developed approach under more realistic conditions. The limitations of the positive‐sequence simulation approach for developing phasor measurement unit (PMU)‐data applications are highlighted, and the challenges of working with the RT‐HIL lab are recognized. The proposed sensitivity computation approach has also been applied to a real PMU‐data obtained from the Nordic transmission system where results are sustained. Copyright © 2014 John Wiley & Sons, Ltd.

Voltage instability is a major challenge facing power system (PS) that has affected some organizations in achieving their desired goals. Therefore, voltage instability is the incapability of the PS to maintain the voltage standard under no disturbance and after subjecting to disruption. This paper describes the voltage instability phenomena; voltage stability indices include Line Stability Index ( $${L_P}$$ L P ), Line voltage stability index, Fast Voltage Stability Index (FVSI), line stability factor (LQP), Bus voltage collapse prediction index (BVCPI), L index, voltage stability index (VCP-1), and so on. This review focuses on some stability indices that could identify the weak bus in the electrical PS network. The application of particle swarm optimization (PSO) to minimize losses that cause voltage instability is discussed. It started a detailed understanding of the power blackouts and the detrimental effects on the global economy. This was followed by a thorough understanding of the voltage instability/stability phenomenon, classification in power systems, and the corresponding formulations. The study presents an overview of voltage assessment techniques prior to applying PSO in discrete and multi-objective optimization and the corresponding advantages over others. These are followed by the progress and advances in voltage stability using PSO involving single and hybrid optimization methods. Lastly, to bridge the research gaps, the present study highlighted challenges and future prospects to foster further advancement in the field.

The penetration of solar energy into centralized electric grids has increased significantly during the last decade. Although the electricity from photovoltaics (PVs) can deliver clean and cost-effective energy, the intermittent nature of the sunlight can lead to challenges with electric grid stability. Smart inverter-based resources (IBRs) can be used to mitigate the impact of such high penetration of renewable energy, as well as to support grid reliability by improving the voltage and frequency stability with embedded control functions such as Volt-VAR, Volt–Watt, and Frequency–Watt. In this work, the results of an extensive experimental study of possible interactions between the unstable grid and two residential-scale inverters from different brands under different active and reactive power controls are presented. Two impedance circuits were installed between Power Hardware-in-the-loop (P-HIL) equipment to represent the impedance in an electric distribution line. Grid voltage and frequency were varied between extreme values outside of the normal range to test the response of the two inverters operating under different controls. The key findings highlighted that different inverters that have met the same requirements of IEEE 1547-2018 responded to grid instabilities differently. Therefore, commissioning tests to ensure inverter performance are crucial. In addition to the grid control, the residential PV installed capacity and physical distances between PV homes and the substation, which impacted the distribution wiring impedance which we characterized by the ratio of the reactive to real impedance (X/R), should be considered when assigning the grid-supporting control setpoints to smart inverters. A higher X/R of 3.5 allowed for more effective control to alleviate both voltage and frequency stability. The elimination of deadband in an aggressive Volt-VAR control also enhanced the ability to control voltage during extreme fluctuation. The analysis of sudden spikes in the grid responses to a large frequency drop showed that a shallow slope of 1.5 kW/Hz in the droop control resulted in a >65% lower sudden reactive power overshoot amplitude than a steeper slope of 2.8 kW/Hz.

An intelligent power system is either a system that is smartly designed from zero to 100, or a system that was not smartly designed but currently uses all its facilities to be smartly operated in different sectors. This paper presents a novel data‐driven model for real time voltage instability diagnosis and instability mitigating. The method combines deep recurrent neural techniques to forecast future voltage stability and mathematical morphology (MM) tools to pinpoint the specific on‐load tap changers (OLTCs) contributing to instability and issuing blocking commands to prevent their operation and consequently instability. The approach for voltage stability assessment is centralized, using real‐time data, while the method for voltage instability mitigation is localized, focusing on real‐time voltage magnitude related to the secondary side of the load transformer. The network was trained and tested on the Nordic32 test system. Results show that the method accurately predicted the stability status just one second after a disturbance, and successfully mitigated all voltage instability events related to load restoration by blocking only the OLTCs that were effective in causing instability. This selective approach provides a significant selectivity index and improves the system resiliency index.

In this study, a novel approach is proposed for fast prediction of dynamic voltage instability occurrence (as a short term phenomenon and/or a long term one) and voltage stability stiffness of the system, against load disturbances. The main contribution of this paper is in introducing a procedure for generating novel features to be applied to a pattern classifier, by which dynamic voltage stability status of a power system can be predicted. The proposed feature generation procedure only needs measured pre-disturbance variables and disturbance severity provided by phasor measurement units as inputs whereas a set of output variables are derived from an unconstrained power flow program. Since the proposed method does not need any measured post disturbance data, the prediction task can be performed just after the disturbance. Thus, corrective actions can be executed in a short time after the disturbance to inhibit voltage instability. Moreover as no measured post-disturbance data are needed, the proposed method can also be employed in preventive procedures for voltage stability enhancement and/or decreasing possibility of voltage instability occurrence. Training a decision tree based classifier with the proposed features and testing the method on a modified version of Nordic32 test system, the simulation results have demonstrated that the proposed method effectively predicts the status of dynamic voltage stability in the test system.

This paper presents a reliability study of a conventional 650 V SiC planar MOSFET subjected to pulsed HTRB (High-Temperature Reverse Bias) stress and negative HTGB (High-Temperature Gate Bias) stress defined by a TCAD static simulation showing the electric field distribution across the SiC/SiO2 interface. The instability of several electrical parameters was monitored and their drift analyses were investigated. Moreover, the shift of the onset of the Fowler–Nordheim gate injection current under stress conditions provided a reliable method to quantify the trapped charge inside the gate oxide bulk, and it allowed us to determine the real stress conditions. Moreover, it has been demonstrated from the cross-correlation, the TCAD simulation, and the experimental ΔVth and ΔVFN variation that HTGB stress is more severe compared to HTRB. In fact, HTGB showed a 15% variation in both ΔVth and ΔVFN, while HTRB showed only a 4% variation in both ΔVth and ΔVFN. The physical explanation was attributed to the accelerated degradation of the gate insulator in proximity to the source region under HTGB configuration.

Intelligent voltage stability monitoring remains an essential feature of modern research into secure operations of power system networks. This research developed an adaptive neuro-fuzzy expert system (ANFIS)-based predictive model to validate the viability of two contemporary voltage stability indices (VSIs) for intelligent voltage stability monitoring, especially at intricate loading and operation points close to voltage collapse. The Novel Line Stability Index (NLSI) and Critical Boundary Index are VSIs deployed extensively for steady-state voltage stability analysis, and thus, they are selected for the predictive model implementation. Six essential power system operational parameters with data values calculated at varying real and reactive loading levels are input features for ANFIS model implementation. The model’s performance is evaluated using reliable statistical error performance analysis in percentages (MAPE and RRMSEp) and regression analysis based on Pearson’s correlation coefficient (R). The IEEE 14-bus and IEEE 118-bus test systems were used to evaluate the prediction model over various network sizes and complexities and at varying clustering radii. The percentage error analysis reveals that the ANFIS predictive model performed well with both VSIs, with CBI performing comparatively better based on the comparative values of MAPE, RRMSEp, and R at multiple simulation runs and clustering radii. Remarkably, CBI showed credible potential as a reliable voltage stability indicator that can be adopted for real-time monitoring, particularly at loading levels near the point of voltage instability.

This paper presents a new method to quantify near interface oxide traps (NIOTs) that are responsible for threshold voltage instability of 4H-SiC MOSFETs. The method utilizes the shift observed in capacitance–voltage (C–V) curves of an N-type MOS capacitor. The results show that both shallow NIOTs with energy levels below the bottom of conduction band and NIOTs with energy levels above the bottom of the conduction band of SiC are responsible for the C–V shifts, and consequently, for the threshold voltage instabilities in MOSFETs. A higher density of NIOTs is measured at higher temperatures.

This paper investigates the threshold voltage shift (ΔVTH) induced by positive bias temperature instability (PBTI) in silicon carbide (SiC) power MOSFETs. By analyzing ΔVTH under various gate stress voltages (VGstress) at 150 °C, distinct mechanisms are revealed: (i) trapping in the interface and/or border pre-existing defects and (ii) the creation of oxide defects and/or trapping in spatially deeper oxide states with an activation energy of ~80 meV. Notably, the adoption of different characterization methods highlights the distinct roles of these mechanisms. Moreover, the study demonstrates consistent behavior in permanent ΔVTH degradation across VGstress levels using a power law model. Overall, these findings deepen the understanding of PBTI in SiC MOSFETs, providing insights for reliability optimization.