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Due to increasing of uncertainty in nowaday power systems, probabilistic security assessment has become one of the challenging issues in power systems. This paper, proposes a new real-time approach for calculating probabilistic critical clearing time (CCT) which is applicable to probabilistic transient stability assessment. The proposed method aims to provide a low computational burden and high-accuracy to calculate the probabilistic density function (PDF) of CCT in two stages. In the first stage, proposed method presents a novel sensitivity based approach, which calculates CCT in the first step of the simulation. Determination of CCT by taking structure preserving into account, guarantees the accuracy of the proposed method. In the second stage, by applying Quasi Monte Carlo (QMC) simulation, the PDF of CCT is obtained. Several different parameters that affect uncertainty of CCT such as load and generation and also fault location are considered in order to obtain the PDF of CCT. Several case studies have been conducted on IEEE 9 bus and 68 bus test systems to show the efficiency of the proposed method. Simulation results indicate that the proposed method has notable accuracy along with substantial speed in performing the required calculations. [ABSTRACT FROM AUTHOR]

• This paper presents the mathematical model of the heterogenous three-machine system, considering the dynamics of the pre-fault, fault-on, and post-fault systems. • Based on the developed model, the critical clearing time is derived analytically for transient stability assessment. The method can deal with the nonlinearity between SG's rotor angle and VSC's PCC voltage phase. • Decreasing the active current component of VSCs has the potential to increase the maximum active power transmission capacity between the interconnected AC systems, so that the transient stability can be enhanced. • With the help of WAMS, the proposed enhancement control strategy achieves dynamic current modulation based on the angle difference between SGs and the PCC voltage phase of VSCs. • The proposed enhancement control strategy is adaptable to multimachine systems with the concept of COI. This paper analyzes the transient angle stability mechanism of interconnected AC power systems with voltage source converter (VSC) based generations and presents a stability improvement control strategy for the VSCs. Firstly, the mathematical model of the prototype AC system with both synchronous generator (SG) and VSC is developed, considering the dynamics of the pre-fault, fault-on, and post-fault systems. For the heterogenous three-machine system, the analytical method for the critical clearing time (CCT) is proposed for transient stability assessment. Then, the evolution of the transient stability margin of the AC system is studied, considering the impact of the VSCs. Under the conditions that VSCs can keep synchronism with the grid during the fault, reducing the active current of the VSCs benefits the transient angle stability of SGs. Based on the mechanism analysis, an enhancement control strategy is developed. It achieves active and reactive current modulation of the VSC using the frequency measurements of the critical SGs (CSGs) in the AC system. The proposed method devotes to boosting the electromagnetic power of CSGs during the entire transient process. Finally, time-domain simulations in PSCAD/EMTDC demonstrate the theoretical analysis. The effectiveness and robustness of the enhancement controller are further validated by the time-domain simulations in a 2-area interconnected system with 4 SGs and a modified New England 39-bus test system. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-objective evolutionary algorithm method for Distribution feeder reconfiguration (DFR) with distributed generators (DG) in a practical system. Considering the low inertia constant of DG units in order to take the transient stability of DGs into account is one of the major issues in power systems. Especially when the penetration of DGs is low, the impacts of them on the distribution system transient stability may be neglected. However, when the penetration of DG increases, the transient stability of them must be taken into account (more DGs, more transient issues). To this end, the DFR problem has been solve by an enhanced Gravitational Search Algorithm (EGSA) to improve the transient stability index and decrease losses and operation cost in a distribution test system with multiple micro-turbines. The effectiveness of the proposed approach is studied based on a typical 33-bus test system. For getting close to the practical condition and considering the detailed dynamic models of the generators and other electric devices in power system, simulation and programming of this approach are done by the DIgSILENT® Power Factory software. [ABSTRACT FROM AUTHOR]

Transient stability constrained optimal power flow (TSCOPF) is a nonlinear optimization problem with both algebraic and differential equations. This paper utilizes the Imperialist Competitive Algorithm (ICA) as an evolutionary optimization algorithm and Artificial Neural Network (ANN) to develop a robust and efficient two stages scheme to solve TSCOPF problem. In the first stage an Artificial Neural Network is constructed to predict the rotor-angle transient stability margin, and is then incorporated in the TSC-OPF as the transient stability estimator. To solve the proposed TSC-OPF problem the ICA is used as the optimizer. The performance of the proposed method is verified over the WSCC three-machine, nine-bus system under different loading conditions and fault scenarios. [ABSTRACT FROM AUTHOR]

This paper describes an approach using an adaptive neuro-fuzzy inference system (ANFIS) for the assessment of online critical clearing time (CCT). The ANFIS can integrate neural networks and fuzzy logic principles, and has a potential to combine the advantages of both in a single framework. In this paper, the ANFIS is applied for the prediction of CCT by varying load levels and fault locations in buses and transmission lines. The IEEE 39-bus system and 9-bus western system coordinating council are tested and implemented in this study. All machines of the IEEE 39-bus system are considered as the classical model without considering any generator’s exciters. While three machines in the 9-bus western system coordinating council are considered as detailed models, forth-order differential equation is described for all machines by considering the excitation system controller. CCT values obtained by the time domain simulation method using step-by-step calculation are used as the benchmark. The power world version 17 is used for transient simulation, and the ANFIS is implemented using MATLAB version 2014B. The results obtained from the ANFIS approach are quite satisfied with high accurate solutions and much lower computation time. Finally, the graphical user interface in MATLAB is applied for the online CCT estimation of two test power systems by using appropriate ANFIS models obtained from simulations. [ABSTRACT FROM AUTHOR]

Transient stability, an important issue to avoid the loss of synchronous operation in power systems, can be achieved through proper coordination and operation of protective devices within the critical clearing time (CCT). In view of this, the development of an intelligent decision support system is useful for providing better protection relay coordination. This paper presents an intelligent distributed agent-based scheme to enhance the transient stability of smart grids in light of CCT where a multi-agent framework (MAF) is developed and the agents are represented in such a way that they are equipped with protection relays (PRs). In addition to this, an algorithm is developed which assists the agents to make autonomous decision for controlling circuit breakers (CBs) independently. The proposed agents are responsible for the coordination of protection devices which is done through the precise detection and isolation of faults within the CCT. The agents also perform the duty of reclosing CBs after the clearance of faults. The performance of the proposed approach is demonstrated on a standard IEEE 39-bus test system by considering short-circuit faults at different locations under various load conditions. To further validate the suitability of the proposed scheme a benchmark 16-machine 68-bus power system is also considered. Simulation results show that MAF exhibits full flexibility to adapt the changes in system configurations and increase the stability margin for both test systems. [ABSTRACT FROM AUTHOR]

Transient stability assessment (TSA) of large power systems by the conventional method is a time consuming task. For each disturbance many nonlinear equations should be solved that makes the problem too complex and will lead to delayed decisions in providing the necessary control signals for controlling the system. Nowadays new methods which are devise artificial intelligence techniques are frequently used for TSA problem instead of traditional methods. Unfortunately these methods are suffering from uncertainty in input measurements. Therefore, there is a necessity to develop a reliable and fast online TSA to analyze the stability status of power systems when exposed to credible disturbances. We propose a direct method based on Type-2 fuzzy neural network for TSA problem. The Type-2 fuzzy logic can properly handle the uncertainty which is exist in the measurement of power system parameters. On the other hand a multilayer perceptron (MLP) neural network (NN) has expert knowledge and learning capability. The proposed hybrid method combines both of these capabilities to achieve an accurate estimation of critical clearing time (CCT). The CCT is an index of TSA in power systems. The Type-2 fuzzy NN is trained by fast resilient back-propagation algorithm. Also, in order to the proposed approach become scalable in a large power system, a NN based sensitivity analysis method is employed to select more effective input data. Moreover, In order to verify the performance of the proposed Type-2 fuzzy NN based method, it has been compared with a MLP NN method. Both of the methods are applied to the IEEE standard New England 10-machine 39-bus test system. The simulation results show the effectiveness of the proposed method in compare to the frequently used MLP NN based method in terms of accuracy and computational cost of CCT estimation for sample fault scenarios. [ABSTRACT FROM AUTHOR]

In this paper we propose two techniques to estimate critical values of parameters of interest in a power system such as clearing time of circuit breakers, mechanical input power, etc. One is via the sensitivity of the transient energy function (TEF) and the other through computation of the norm of the trajectory sensitivities. Both these methods require some a-priori information about the range of the critical parameters. In real time operation, this information is generally available to the operator. A changing operating condition will result in new values of the critical parameters and the proposed technique can thus monitor them closely without the use of direct methods. The advantage of the first technique lies in not having to compute the unstable equilibrium point (uep) while in the second technique even the computation of stable equilibrium (sep) is not needed. However, there are additional computational costs involved in both the techniques, which can be addressed by faster algorithms for computing the sensitivities. [Copyright &y& Elsevier]

• Transient stability assessment with increasing the wind power penetration. • Using the Gershgorin theorem to tune the control parameters of the converter. • Neglecting the stator transients in closed-loop DFIG. • For power system stability studies, the 3rd order model of DFIG can be used. In the power systems where a significant part of the total generated power is supplied by wind energy, the transient stability of the grid should be analyzed properly. This paper discusses the influence of the closed-loop control system of doubly-fed induction generator (DFIG) on transient stability. In this process, the stator and rotor electrical dynamics are considered during tuning the controllers. Accordingly, the DFIG with power electronics converters in the closed-loop control mode with generic PI controllers is considered. For this study, the dynamic model of the rotor side converter with more detail of the control system is used, because it has a direct effect on the dynamics of the generator speed/torque and hence on the system stability. Also, to tune the control parameters of the power electronics converters, the Gershgorin theorem is applied. Once an appropriate set of control parameters is obtained, the DFIG model is simplified and time-domain simulation is performed. For validation of the influence of modeling adequacy on closed-loop controlled DFIG, transient stability study under the different operating conditions is investigated. According to the simulation results, it is observed that for the closed-loop DFIG, neglecting the stator transients does not remove the high-frequency mode. Thus, the high-frequency mode is due to the rotor electrical dynamics. Additionally, the power system stability studies for closed-loop DFIG are not model order dependent. Accordingly, for the closed-loop controlled DFIG in the transient stability studies where fast electrical transients are not of interest, a simplified model, whereby both stator and rotor dynamics are neglected, is adequate. [ABSTRACT FROM AUTHOR]

• Conflict between computation efficiency and accuracy exists in model-driven method. • Data-driven method is able to correct error of model-driven method. • Error tolerance difference is considered in training objection data-driven method. The critical clearing time (CCT) is one of the most important indexes for large-disturbance rotor angle stability margin evaluation. In practice, model-driven methods are usually realized based on simplified models to ease the computational burden, but the accuracy is sacrificed. To solve this problem, a data-driven method is adopted in this paper for fast error correction of a model-driven method, creating an integrated method. Both a reliable accuracy and an acceptable computation speed can be achieved with this integrated method. Meanwhile, involvement of model-driven method helps enhance robustness of the integrated method to training sample insufficiency, measurement error and power system scale. In addition, the data-driven method is further transformed on the basis of a cost-sensitive approach where the error tolerance for different actual CCT values should be differentiated during the training process instead of being treated equally in the common data-driven method. To mitigate the negative effect caused by such transformations, an ensemble learning structure is also constructed. In this paper, an integrated extended equal-area criterion (IEEAC) and an extreme learning machine (ELM) are applied as model-driven and data-driven methods, respectively. A genetic algorithm (GA) is used in the ensemble learning structure construction. Validations show that the proposed integrated method with the transformed data-driven method can improve the CCT prediction accuracy and avoid the polarization of the error distribution. [ABSTRACT FROM AUTHOR]