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This paper presents a novel approach to dynamic phasor-based transient stability simulation. The proposed method is based on the modified nodal analysis (MNA) approach to circuit simulation, which is used to construct continuous differential-algebraic equations (DAEs). The proposed method makes use of the stamp technique, which makes it possible to construct a general purpose MNA-based simulator. Stamp-based models for common power system components are derived in this work. A new MNA-based synchronous machine model is presented, which represents machines as nonlinear inductances instead of subtransient equivalents. The resultant continuous DAEs are numerically solved using the general purpose variable step and variable order library IDA. Simulation results from the IEEE 68 bus test system, a real 400 bus power system, and the IEEE 39 bus test system with an embedded HVdc transmission system demonstrate that the proposed method is suitable for large ac networks with power electronic devices. The results demonstrate good agreement between the proposed method and electromagnetic transient (EMT) simulation. The results also demonstrate that the proposed method is fast and scalable with CPU times that are up to 200 times faster than EMT simulation. [ABSTRACT FROM AUTHOR]

The absence of complete model data of a power system hinders complete eigenvalue analysis of that system. System identification methods have been used to identify operating-point-dependent equivalent linear model of a power system. Such identified models do not allow a complete eigenvalue analysis. To resolve this, the identification problem can be formulated in such a way to utilize any a-priori knowledge of the system. Model data of a synchronous machine consists of machine data and controller parameters. Methods to determine synchronous machine parameters are well established. In this paper, knowledge of the machine data and knowledge of eigenvalues of the system at multiple distinct operating points, extracted by a system identification method, are used to determine auxiliary controller parameters of the synchronous machine. Model data that are determined by the proposed method is independent of the operating point. Therefore, it can be easily integrated into a complete small signal stability study at any operating point of the synchronous machine. [ABSTRACT FROM AUTHOR]

This paper applies modern statistical nonparametric methodology to the problem of prediction of the transient stability boundary of large-scale power engineering systems. The stability issue is characterized by the critical clearing time (CCT) that is employed to determine whether a precontingency steady-state condition is stable for a given fault in the power system. The multidimensional mapping between the precontingency steady-state conditions and the corresponding CCT is modeled as an additive structure of one-dimensional functions. Nonparametric kernel estimation methods are applied to the assumed additive model yielding the boundary prediction algorithm that is easily interpretable and avoids the curse of dimensionality. The precision of our additive nonlinear modeling is demonstrated in the context of fault prediction of the 470-bus power network. For the specified fault type, we demonstrate a stronger prediction accuracy compared to other large-scale machine learning methods that have been used for the transient stability boundary problem so far. [ABSTRACT FROM PUBLISHER]

This paper presents a design of a power system damping controller using partial right eigenstructure assignment. The eigenstructure assignment technique selects a set of closed-loop eigenvalues along with their right or left eigenvectors. The selection of eigenvectors offers extra flexibility which is exploited in this paper by designing a robust damping controller which provides the required damping under multiple operating conditions. A multi-input controller is used to increase the degrees of freedom available for the design. Remote measurements available from synchronised phasor measurements are also considered. The problem is formulated as a multi-objective nonlinear optimization problem and solved using the nonlinear simplex function in MATLAB. The proposed technique is used to design a robust power system stabilizer for the interconnected New England New York simplified power system model. Four contingencies are selected as additional operating conditions. The designed controller is validated using a nonlinear simulation. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to enable frequent computational cycles in online dynamic security assessment by using the terms of the transient energy function (TEF) as input features to a machine learning algorithm. The aim is to train a single classifier that is capable of classifying stable and unstable operating points independent of the contingency. The network is trained based on the current system topology and the loading conditions. The potential of the proposed approach is demonstrated with the New England 39-bus test power system model using the support vector machine as the machine learning technique. It is shown that the classifier can be trained using a small set of data when the terms of the TEF are used as input features. The prediction accuracy of the proposed scheme was tested under the balanced and unbalanced faults with the presence of voltage sensitive and dynamic loads for different operating points. [ABSTRACT FROM PUBLISHER]

This paper presents a brief review of techniques available for reducing large systems to smaller equivalents. This paper is divided into high frequency equivalents, low frequency equivalents, and wideband equivalents. Numerical examples are presented to demonstrate selected methods of high frequency equivalencing. [ABSTRACT FROM PUBLISHER]