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A methodology has been presented to assess the transient stability of grid-tied type-3 wind turbine generator systems based on the concept of trajectory sensitivity theory. The objectives here are to estimate the critical clearing time (CCT) and critical values of parameters for doubly fed induction generator-based wind turbine generator (DFIG-WTG) system and identify the parameter that has the highest influence on the system stability. To achieve them, the recently developed reduced-order model of DFIG-WTG system amplifying the dynamics of the phase-locked loop synchronization is considered. The model is numerically solved for computing the sensitivities of the state trajectories with respect to a pre-defined set of parameters. Consequently, the norm of the vector of sensitivities is used as an indicator of the stability margin for the DFIG-WTG system. Following this, the inverse of the maximum value of sensitivity norms obtained from two different simulations is extrapolated to estimate the CCT as well as the critical values of parameters for the DFIG-WTG system. Finally, extended case studies based on trajectory sensitivity analysis have been conducted to demonstrate the application of the methodology for multi-DFIG-WTG systems.

This article addresses the challenge of mitigating line overloading/congestion caused by renewable penetration and contingencies. Traditional methods of mitigating overloading involve generation rescheduling in tertiary dispatch every 5–30 minutes. This approach is generally based on forecasted load and generation, and not effective in real-time corrective actions. The article introduces a model-free Neuro Predictive Controller (NPC) that can map line power flows to the inputs of dispatching resources such as generators and/or battery energy storage systems. The NPC receives synchrophasor measurement of line power flows and generates control inputs (i.e., set points) to generators and/or battery energy storage systems. The controller does not require frequent update of the system model. The efficacy of the controller was tested on 16-machine 68-bus test system and the Indian grid for various scenarios, such as line outage, generator outage, variable renewable generation, and load change. The proposed controller demonstrated better performance as compared to the existing controllers. Upon successful implementation, the controller can regulate line flow in less than 60 seconds, allowing the lines to operate closer to the thermal limit and resolve limit violations due to contingencies and variable renewable generation in real-time.

A decentralized method for estimating the interior states of a synchronous machine using analogue measurements from instrument transformers (that is, current transformer and potential transformer) has been proposed in this paper. The method is robust to instrumentation chain anomalies, which have not been considered in the existing dynamic state estimation literature. The method works in a two-step manner, wherein a robust adaptive detection scheme removes instrument transformer anomalies, harmonics, noise, and DC components, and estimates the phasors of the analogue measurements, and subsequently uses these estimated phasors in the decentralized dynamic state estimation algorithm. Robust and adaptive version of square-root-cubature-Kalman-filter has been employed to enhance estimation accuracy irrespective of the type of noise distribution. The superiority of the algorithm over existing methods has been established in terms of numerical accuracy, computational efficacy, and robustness. IEEE 68 bus power system has been used to test the effectiveness of the developed strategy. Opal-RT based setup has also been used to implement the case studies in real-time.