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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 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 comprehensive analysis of sub-synchronous interactions in a wind integrated power system to understand and mitigate them. The proposed procedure has two steps. In the first step, a frequency scan is performed to determine the presence of resonant frequencies in the sub-synchronous range. In the second step, a detailed small signal analysis is performed. Participation factors are used to identify the state variables that are involved in the interaction, and the controllability indices are used to determine the mitigation method. It is shown that the sub-synchronous interaction present in Type 3 wind turbine-generators connected to the grid through series compensated lines is an electrical resonance between the generator and the series compensated line which is highly sensitive to the rotor side converter current controller gains. [ABSTRACT FROM PUBLISHER]

Small signal stability analysis in a power system is typically concerned with electromechanical oscillations. For this purpose, it is adequate to model the transmission system using a constant admittance matrix. For torsional oscillations and HVDC interactions, the frequency of interest is much higher and the constant admittance representation is not sufficient. This paper proposes a hybrid model, which allows the parts of the transmission network in the vicinity of HVDC converters or any other dynamic devices to be modeled with their dynamics and the remaining parts to be modeled as constant admittances. The proposed hybrid methodology for small signal stability assessment is the main contribution of this paper. The proposed methodology is validated against an electromagnetic transient simulation program (PSCADIEMTDC) using time responses. The proposed model and two other small signal models are compared against each other in the frequency domain using modal analysis. [ABSTRACT FROM AUTHOR]