Preventive Control Of Transient Stability Research Articles

A distributed deep reinforcement learning-based approach for fast preventive control considering transient stability constraints

Preventive transient stability control is an effective measure for the power system to withstand high-probability severe contingencies. It is mathematically an optimal power flow problem with transient stability constraints. Due to the constraints involved the differential algebraic equations of transient stability, it is difficult and time-consuming to solve this problem. To address these issues, this paper presents a novel deep reinforcement learning (DRL) framework for preventive transient stability control of power systems. Distributed deep deterministic policy gradient is utilized to train a DRL agent that can learn its control policy through massive interactions with a grid simulator. Once properly trained, the DRL agent can instantaneously provide effective strategies to adjust the system to a safe operating point with a near-optimal operation cost. The effectiveness of the proposed method is verified through numerical experiments conducted on New England 39-bus system and NPCC 140-bus system.

A Preventive Transient Stability Control Strategy Through Individual Machine Equal Area Criterion Framework

Power system transient stability assessment (TSA) and control is a complex problem. TSA from the perspective of individual machine Kimbark curves forms a distinctive approach. On the other hand, transient stability constrained optimal power flow (TSC-OPF) has been adopted widely for determining preventive control actions against transient instabilities under a given contingency. This paper presents a strategy for transient stability control, which mainly integrates the concepts of individual machine methods based TSA and global TSC-OPF. The proposed workflow is mainly based on the most severely disturbed machines (MDM) and their critical trajectories. These are utilized in deciding the number of TSC-OPF transient stability constraints, the minimum upper time limit for TSC-OPF solution interval, and security-based transient stability constraint of individual machines. Further, the application of the proposed method is explained with respect to stabilization of three different transient instability scenarios viz., transient instability originates from first swing instability, transient instability originates from multi swing instability but leads to first swing instability as fault duration increases, and transient instability originates from multi swing instability, but never leads to first swing instability as fault duration increases. IEEE 39 bus and 118 bus test system are considered for testing the proposed control strategy. The proposed approach, which expresses the transient stability constraints in global TSC-OPF in terms of some selective machine (i.e MDMs) critical trajectories not only forms a distinctive approach but also provide a smooth reconciliation between economy of operation and security level while ensuring transient stability under a given contingency.

Trajectory sensitivity based preventive transient stability control of power systems against wind power variation

The penetration of high-level wind power has significantly made the power system’s operation complicated. The random power variation of the wind farms may deteriorate the power system’s transient stability degree since it alters the operating state of the power system. In this paper, based on trajectory sensitivity and Extended Equal Area Criterion (EEAC) method, a new preventive control approach is proposed to optimally balance the wind power variation by rescheduling synchronous generators while maintaining the transient stability for credible contingencies. The whole problem is formulated to a programming problem with linear stabilization constraints. The proposed method is validated on the New-England 10-machine 39-bus system by using commercial simulation software Transient Security Assessment Tool (TSAT), and both single- and multiple-contingency cases are tested. Simulation results verify its satisfactory performance on control effectiveness, computational efficiency, transparency, and accuracy.

A preventive transient stability control method based on support vector machine

This paper presents a support vector machine (SVM)-based method for preventive transient stability control (TSC) of power systems. The features of the SVM model used in TSC are the same as in transient stability assessment (TSA). The features include non-control variables so as to improve the accuracy of TSA and sensitivities. A hybrid method combining SVM and time-domain simulation is also proposed to increase the TSA accuracy. First, this paper defines a transient stability assessment index based on SVM model. Second, the sensitivities of the SVM-based transient stability assessment index with respect to control variables (generator active power) are calculated and ranked to select control generators from them. Then, the power shifting amount of all the control generators and balance generators is calculated and the corresponding control scheme is verified by the hybrid TSA method. If generation redispatching measures are invalid, then load shedding measures are used at the same time. Finally, comprehensive studies are conducted on the WECC 9-bus system and a certain provincial 2036-bus system in China. The results reveal that the proposed hybrid TSA method increases the classification accuracy rates from 99.0%, 99.3% to 100% for the certain provincial 2036-bus system, and effective control measures are obtained with the proposed SVM-based TSC method for both systems.

Directional Derivative-Based Transient Stability-Constrained Optimal Power Flow

This paper proposes a novel active power redispatch sequential approach to solve the transient stability-constrained optimal power flow (TSC-OPF) problem for the preventive control of transient stability. Based on an independent transient stability assessment of the power system for a given contingency scenario, two proposed power redispatch constraints are formulated and embedded into a conventional OPF formulation. Since these two new constraints only depend on steady-state variables, the dimension of the resulting TSC-OPF model is similar to that of a conventional OPF model and can be solved by standard optimization methods to perform a nonheuristic active power redispatch. The stabilization process is performed by sequentially solving the optimization and the transient stability problems until a suitable generation dispatch that provides a transiently stable equilibrium point is assessed. The validity and the effectiveness of the proposed method are numerically demonstrated in the WSCC three-machine, nine-bus system and an equivalent model of the Mexican power system.

Preventive Control for Transient Security with Generation Rescheduling Based on Rotor Trajectory Index

The increasing need to improve transient security assessment of existing or forecasted operating conditions of networks by power system operators is major concern of the power system security monitoring problem at the Energy Management Systems. This paper proposes a preventive control of transient stability with generation rescheduling based on rotor trajectory index obtained using time domain simulations. This index may help power engineers in making operational decision and to obtain a generation configuration with better transient security dispatch. The effectiveness of the proposed methodology is demonstrated on IEEE 39-bus New England system for a three phase fault at different loading conditions with single and multiple line outage cases.

Trajectory sensitivity analysis on the equivalent one‐machine‐infinite‐bus of multi‐machine systems for preventive transient stability control

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.

Selective transient stability-constrained optimal power flow using a SIME and trajectory sensitivity unified analysis

A novel approach based on both single machine equivalent (SIME) and trajectory sensitivity methods is proposed to formulate a transient stability-constrained optimal power flow (TSC-OPF) in the Euclidian space, where only one single stability constraint is necessary in the optimization problem to represent all dynamic and transient stability constraints of the multi-machine system. This formulation remarkably reduces the dimension of the optimization problem to one similar to a conventional OPF, resulting in a tractable approach to the preventive control of transient stability in realistic power systems. A unified framework of time domain analysis is proposed, where the transient stability, trajectory sensitivity and SIME analyses are all combined to assess the system's stability and to compute the sensitivity coefficients of the proposed transient stability constraint. Based on these sensitivity coefficients, three non-heuristic selection criteria are proposed to perform the preventive control by rescheduling only a selected number of generators, which is the commonly accepted practice followed by the system's operators. The validity and the effectiveness of the proposed method are numerically demonstrated in the 10-machine 39-bus New England system and Mexican 46-machine 190-bus system.

A coherency based generator rescheduling for preventive control of transient stability in power systems

This paper proposes a preventive control of transient stability with generation rescheduling based on coherency obtained using time domain simulations. The transient security dispatch finds a generation configuration with better transient stability behavior. One of the most practiced and an obvious technique of preventive control is rescheduling the power outputs of generators in the system. However, all generators in the system need not take part in rescheduling process. Selection of participating generators has been introduced using generator coherency for given contingency. The effectiveness of the proposed methodology is demonstrated on IEEE 145-bus 50-generator system for a three phase fault at different loading conditions with single and multiple line outage cases.

Coordinated preventive control of transient stability with multi-contingency in power systems using trajectory sensitivities

In this paper, the challenging multi-contingency transient stability constrained optimal power flow problem is partitioned into two sub-problems, namely optimal power flow (OPF) and transient stability control, solved in turn with conventional well trusted power system analysis tools instead of tackling it directly using a complicated integrated approach. Preventive multi-contingency transient stability control is carried out with generation rescheduling based on trajectory sensitivities using results obtained from a conventional transient stability simulation. A new iterative approach is proposed to optimally redistribute the generation from the critical machines to noncritical machines with the help of conventional OPF. Results on the New England 10-machine 39-bus systems demonstrate that the proposed method is capable of handling multiple contingencies and complex power system models effectively with solution quantity and time comparable with conventional integrated approaches.