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The impact of wind turbine system (WTS) on power system stability is elaborately addressed in the literature. However, low-frequency oscillations, especially inter-area oscillations (IAOs) can be adversely affected due to their intermittent nature. This article proposes a wide-area damping controller (WADC) for WTS-integrated power system to damp IAOs effectively. The feedback signals (linear combination of the system states) are directly measurable from phasor measurement units. Therefore a reduced-order estimator is proposed instead of a full-order estimator to design the WADC. Furthermore, to select appropriate feedback signals for WADC design, combined geometric measure and modal decomposition techniques are utilized. The overall procedure to design the reduced-order WADC includes feedback signal selection, state feedback formulation using modal transformation approach, and reduced-order estimator design using loop transfer recovery procedure. Moreover, the WADC parameters are selected considering operating point uncertainties and variable time delays in feedback signals. Performance of the proposed WADC is validated on WTS-integrated 16-machine, 68-bus system. The simulation results show that the proposed WADC gives robust performance over broad operating scenarios. [ABSTRACT FROM AUTHOR]

Forced Oscillation (FO) has emerged as a major concern in power system due to its unpredictable frequency range. When the frequency of FO lies in the vicinity of that of the inherent natural oscillation, it can lead to resonance, posing a serious risk to the system. Improper identification and dissociation of FO in a power system can imprint huge detrimental effects, including blackouts. To address this issue, a novel method based on pattern recognition using artificial neural networks (ANNs) is proposed in this paper to classify FO and electromechanical oscillation. Multivariate time series data are collected from phasor measurement units (PMUs) placed at different buses are used as input for a two-layer NN, which is optimized using the stochastic scaled conjugate gradient algorithm. The proposed algorithm is simulated on the standard IEEE 10 machine, 39 bus system for various cases. The accuracy of the proposed algorithm is evaluated using the F1 score, obtained from the confusion plot. The results demonstrate that the network achieves a training accuracy of 97.5% and a testing accuracy of 96.67% with an F1 score of 96.7%. Additionally, ROC curves shows the efficiency of the designed model to predict the FO. • Classification of forced and natural oscillations in the grid system. • Utilization of backpropagation neural network based approach. • Classification problem framed as a sequential, multivariate time series problem. • Training network using stochastic conjugate scaled gradient optimization algorithm. • Robustness of technique using different statistical evaluation performance measures. [ABSTRACT FROM AUTHOR]

This article introduces an innovative approach for online detection and localization of forced oscillation (FO) in power systems with significant integration of wind turbine generation (WTG). The synergistic combination of Multichannel Prony analysis with Periodogram and dissipating energy flow (DEF) approach is proposed in this paper to overcome the challenges inherent in detecting FO amidst the complexities introduced by WTG integration. An adaptive version of multichannel Prony analysis is proposed in this paper that dynamically estimates Prony's system order via the recursive process. It estimates the frequency and damping ratio of all oscillatory modes, which is used to identify low damping modes that are potentially attributed to FO. Leveraging this information, the proposed method enhances both FO detection and source localization capabilities. The proposed method is verified on measurements taken from IEEE benchmark 4-machine, 11-bus system and 10-machine, 39-bus system with high wind energy penetration. Comprehensive testing demonstrates the proposed approach's effectiveness across various FO scenarios, including governor valve malfunction, exciter malfunction, and cyclic loads. Furthermore, the methodology is evaluated under resonant conditions and scenarios involving the simultaneous identification of multiple disturbances. • Detection & source location in WTG integrated power system. • MDMMO for dynamic estimation of Prony model order. • OFDM using Periodogram technique. • OFSLM using dissipating energy flow method. [ABSTRACT FROM AUTHOR]