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With a large number of inverter‐interfaced renewable power generation, fault‐induced delayed voltage recovery (FIDVR) events have become a serious threat to power system stability assessment. This study proposes a novel data‐driven method based on probabilistic prediction, ensemble learning, and multi‐objective optimisation programming (MOP) to rapidly predict the FIDVR severity index for real‐time FIDVR assessment. Distinguished from the existing single machine learning (ML) algorithm data‐driven method, the proposed method combines different randomised learning algorithms to acquire a more diversified ML outcome. The probabilistic prediction models the uncertainties existing in the prediction process, which quantifies the prediction confidence over a progressive observation window. Besides, the FIDVR can be evaluated through the time‐adaptive framework to achieve the best FIDVR speed and accuracy with the MOP framework. The simulation results on the New England 10‐machine 39‐bus system display its preponderance over the single ML, and also demonstrate its better speed and accuracy performance in FIDVR assessment. [ABSTRACT FROM AUTHOR]

High‐level wind power penetration has significantly changed the power system's static and dynamic characteristics, which tends to decrease the overall system transient instability. This study proposes a multi‐objective preventive‐emergency coordinated control method against transient instability in the presence of uncertain wind power generation. This method optimally coordinates preventive control (PC) and emergency control (EC) to simultaneously minimise the total control cost and the expected stability margin. PC includes synchronous generator dispatch and wind farm power curtailment to reduce the overall percentage of renewable power to a safe level. EC adopts an emergency demand response scheme, in which the demand is immediately reduced for stabilising post‐contingency system and avoiding wide‐spread blackout. Wind power uncertainty is modelled through Taguchi's testing scenarios. The proposed method was verified on both New England 39‐bus system and Nordic‐32 system using industry‐grade dynamic models and simulation software. Simulation results verified that the proposed method can optimally balancing system control cost and system expected stability margin. [ABSTRACT FROM AUTHOR]

Static compensators (STATCOMs) are able to provide rapid and dynamic reactive power support within a power system for voltage stability enhancement. While most of previous research focuses on only an either static or dynamic (short‐term) voltage stability criterion, this study proposes a multi‐objective programming (MOP) model to simultaneously minimise (i) investment cost, (ii) unacceptable transient voltage performance, and (iii) proximity to steady‐state voltage collapse. The model aims to find Pareto optimal solutions for flexible and multi‐objective decision‐making. To account for multiple contingencies and their probabilities, corresponding risk‐based metrics are proposed based on respective voltage stability measures. Given the two different voltage stability criteria, a strategy based on Pareto frontier is designed to identify critical contingencies and candidate buses for STATCOM connection. Finally, to solve the MOP model, an improved decomposition‐based multi‐objective evolutionary algorithm is developed. The proposed model and algorithm are demonstrated on the New England 39‐bus test system, and compared with state‐of‐the‐art solution algorithms. [ABSTRACT FROM AUTHOR]

A new approach for power system transient stability preventive control is proposed by performing trajectory sensitivity analysis on the one‐machine‐infinite‐bus (OMIB) equivalence of multi‐machine systems. Exact instability time/angle are determined from the equivalent OMIB power–angle curve; the trajectory sensitivity is calculated at the instability time and the transient stability of the multi‐machine system is controlled by constraining the OMIB's angle excursion at the instability time to that of the critical OMIB which corresponds to the marginally stable condition of the system. The required preventive control action (generation rescheduling) can be efficiently solved via a linear programming model with the OMIB trajectory sensitivities‐based constraints. Simulation results on the New England 10‐machine 39‐bus system and a 285‐machine and 1648‐bus system validate its effectiveness and superiority over previous trajectory sensitivity applications to this problem. [ABSTRACT FROM AUTHOR]

Intelligent system (IS) using synchronous phasor measurements for transient stability assessment (TSA) has received continuous interests recently. For post‐disturbance TSA, one pivotal concern is the response time, which was reported in the literature as a fixed value ranging from 4 cycles to 3 s after fault clearance. Since transient instability can develop very fast, there is a pressing need for faster response speed. This paper develops a novel IS to balance the response speed and accuracy requirements. A set of classifiers are sequentially organised, each is an ensemble of extreme learning machines (ELMs), whose inputs are post‐disturbance generator voltage trajectories and outputs are the classification on the stable/unstable status of the post‐disturbance system and an evaluation of the credibility of the classification. A self‐adaptive TSA decision‐making mechanism is designed to progressively adjust the response time, such that the IS can do the classification faster, thereby allowing more time for emergency controls. The ELM ensemble classifiers can also be updated by on‐line pre‐disturbance TSA results due to its very fast learning speed. Case studies on the New England system and IEEE 50‐machine system have validated the high efficiency and accuracy of the IS. [ABSTRACT FROM AUTHOR]