Lixin Wang

Synchrophasor-based sub-synchronous oscillations (SSOs) parameter identification is effective for monitoring SSOs, while its performance is significantly degraded by measurement noise, posing challenges to reliable parameter identification. This paper proposes a novel technique using multi-channel enhanced synchro-reassigning transform (MESRT) for robust and accurate SSO parameter identification. First, each measurement channel is transformed using the short-time Fourier transform (STFT) to construct a global multi-channel time-frequency (TF) representation. Then, an average modal energy-based strategy is developed to eliminate noise-induced spurious modes in the STFT spectrum. Subsequently, a three-step selection rule is applied to extract TF coefficients representing oscillation modes, from which the time-domain components are reconstructed. Finally, the Hilbert transform (HT) method is employed to identify the oscillation frequency and damping factor of each mode. The proposed MESRT method effectively improves the noise immunity and accuracy of conventional SRT by considering inter-channel correlations and leveraging modal energy to remove spurious modes. Case studies validate that the proposed method performs exceptionally well in terms of accuracy and noise robustness, demonstrating superior performance compared to existing methods.

Synchrophasor-based sub-synchronous oscillation (SSO) parameter identification is effective for monitoring SSOs, while its performance can be significantly affected by measurement noise, posing serious challenges to reliable identification. This study proposes an improved identification scheme that combines adaptive empirical Fourier decomposition (AEFD) with the Prony method to enable accurate and simultaneous estimation of sub/super-synchronous modes. First, the AEFD method is applied to oscillation signals, effectively decomposing the signals into sub-synchronous and super-synchronous modal components. Particularly, the sparsity index (SI) is introduced to determine the number of oscillation modes contained in the signal. Subsequently, the Prony method is employed on the decomposed components to extract modal parameters. The proposed method effectively suppresses modal aliasing and improves noise robustness of the empirical wavelet transform by employing an improved spectrum segmentation technique and a zero-phase filter bank, thereby enhancing the extraction accuracy of the estimation results. Through comparisons with existing methods and simulated case studies, it is verified that the proposed method performs exceptionally well in terms of accuracy, mode mixing suppression and noise robustness, demonstrating its superiority and effectiveness in the extraction of sub/super-synchronous oscillation parameters.

Synchrophasor-based identification of low-frequency oscillation is an effective means for monitoring the security and stability of power systems, while the loss of identification accuracy due to measurement noise remains a major obstacle. This article proposes an improved fast independent component analysis (improved FastICA) algorithm to identify oscillation modes (oscillation frequency and damping ratio) from noise-contaminated synchronized measurements. To improve the ability of conventional FastICA to extract modes from noise-polluted synchrophasor, nonlinear matching pursuit (NMP) is introduced to replace the negative entropy calculation of mono-frequency modes in FastICA. NMP enables FastICA to identify oscillation frequency and damping ratio even under strong noise conditions, thereby significantly improving identification accuracy. The performance of the proposed method is evaluated on synthetic signals and numerical simulation signals from the IEEE 16-machine system. The results of the identified modes in all cases, as well as comparison results from conventional FastICA, empirical mode decomposition (EMD), and variational mode decomposition (VMD), all combined with Hilbert transform (HT), confirm the accuracy, robustness, and efficiency of the proposed method for oscillation mode identification.

Modal parameters extraction from synchronized ambient data for small-signal stability evaluation (SSSE) plays a crucial part in power system oscillation events prevention. While the randomness and uncertainty of large-scale renewable energy bring risks of error to extraction results. This paper proposes a hybrid method of automated frequency domain decomposition (AFDD) with empirical cumulative distribution-based outlier detection (ECOD) for modal parameter extraction from synchronized ambient data. First, the “automated peak picking” algorithm of AFDD is proposed to guarantee the accuracy of modal parameters extraction by avoiding the clumsy manually and inefficiently peak selection process. Then, ECOD is proposed with the potential to further enhance the reliability of bi-dimensional modal parameters including both frequency and damping ratio through outlier detection and elimination. The effectiveness of the proposed method is studied based on the simulation data of IEEE 16-generator 5-area system and synchronous ambient data of real power system, where the maximum steady-state error of the parameter extraction results was only 0.035, proving the accuracy and reliability of the proposed method.

The accuracy and reliability of the Q/V sensitivity for voltage security assessment is influenced by the outliers present in the calculation results. An unsupervised machine learning approach, empirical- cumulative- distribution- based outlier detection (ECOD), is introduced in this letter to detect and eliminate outliers to address this issue. A comparison of the results with those of the proposed approaches on the standard test power system CSEE-VS demonstrate that, compared with advanced outlier detection algorithms, ECOD can eliminate outliers from the Q/V sensitivities with higher accuracy and less computation time and realize online voltage security assessment with superior accuracy and reliability.

Short-term voltage stability (STVS) prediction is a critical technology for modern power systems with high penetration of renewable energy resources. To address the limitations of the traditional maximum Lyapunov exponent (MLE) in handling short-time window data during transient dynamics, this paper proposes a novel dominant Koopman operator-enhanced MLE (DKO-MLE) method for STVS prediction. First, the dominant Koopman operator is extracted using a proposed approach that combines the Hankel matrix with the extended dynamic mode decomposition (HEDMD) method. Subsequently, the extracted dominant Koopman operator is predicted in the time domain and utilized for MLE calculation. The proposed method leverages the data dimensionality expansion capability of the Hankel matrix and the time-domain prediction ability of the dominant Koopman operator to effectively generate sufficient data for online STVS prediction. Case studies conducted on the Nordic32 system and a standard test system provided by the China Electric Power Research Institute (CEPRI) demonstrate the accuracy and rapidity of the proposed method in predicting short-term voltage stability.

Dynamic state estimation (DSE) is a fundamental task in many fields, including control systems, robotics, and signal processing. Traditional DSE methods, which rely on mathematical models to describe system dynamics, are often limited in their applicability to real-world scenarios due to inaccuracies and assumptions. In this paper, we propose a purely data-driven DSE framework based on a Koopman operator-based linear predictor. The Koopman operator is a powerful tool in dynamical systems theory that allows us to analyze and predict the behavior of nonlinear systems. By leveraging the Koopman operator extracted solely from measured input‒output data through the extended dynamic mode decomposition with control (EDMDc) method, a linear predictor that can accurately estimate the state variables of a dynamic system is developed. Introducing the extended Kalman filter (EKF) as an estimation method, the learned Koopman operator-based linear predictor is then used to estimate the current state of the system given only the past and present input‒output measurements. To evaluate the effectiveness of the proposed framework, we conduct experiments on both simulated and real-world datasets of complex power systems. The results demonstrate that the proposed data-driven approach outperforms traditional model-based approaches in terms of accuracy and robustness. Moreover, the proposed framework is capable of handling nonlinear and time-varying systems, making it applicable to a wide range of practical cases.