In this study, a novel algorithm for detecting and classifying faults in high‐voltage transmission lines is proposed. The algorithm is based on the discrete wavelet transform (DWT) and singular value decomposition (SVD). The DWT is used for extracting the currents’ high‐frequency components under fault conditions. Signals under each fault condition are scaled in frequency, in order to build a wavelet matrix. By means of the SVD, the maximum singular value is calculated and employed in this proposal. The attained results exhibit that the maximum singular value represents a good indicator for the issue. This novel approach for detecting and classifying faults in power systems is called maximum wavelet singular value. Phase‐to‐ground, two‐phase to ground, and three‐phase faults’ simulations under different fault impedances are carried out by DIgSILENT Power Factory. The analysed fault conditions are evaluated demonstrating that the proposal reduces the computational burden and the time detection.

This study deals with the faults’ detection and classification in AC transmission lines based on power spectral density (PSD), introducing PSD in time and frequency for analysing transient information under faulted conditions. The discrete wavelet transform is used for scaling current signals in time–frequency at different decomposition levels by approximation and detail coefficients. Then, a wavelet‐covariance matrix is shaped with the aim to obtain its PSD, being this the key to detect and classifying faults. Results show that the proposed method attains the detection in a short time and the classification is accomplished via the Hellinger distance, whose straightforward implementation is carried out in this study. The classification process is compared adopting different classifiers to cope with a set of signals in a time frame. Finally, the proposal is extensively assessed using real and simulated signals stemming from multiple fault cases of radial and interconnected power grids.

In this study, a new algorithm for discrimination between internal faults and inrush current conditions in power transformers is presented. First, the proposed algorithm removes the pre-fault conditions of the fault currents. Then, those currents are processed using the discrete wavelet transform to obtain the wavelet matrix. The correlation matrix is computed from wavelet matrix to know the eigenvalues associated with each frequency scale and to reduce the analysed information. This algorithm, which the authors are going to call wavelet correlation modes, is used to identify inrush and internal fault currents in power transformer. To validate the proposed algorithm, simulations are carried out in power systems computer-aided design software, and the obtained signals are analysed in order to evaluate its performance and sensitivity.

Phase-locked loop (PLL) systems play a crucial role in grid synchronization. Since variations on the grid voltage parameters are in general uncertain, the dynamics of PLLs are by nature nonlinear. This fact imposes a relevant challenge to guarantee their stability. Unfortunately, as argued in this paper, there is a remarkable lack of global stability tests even for the most common approaches, such as the synchronous reference frame (SRF). In this case, there is only available a small-signal—linearized—model-based approach to stability. The main issue of resorting to local conclusions is that synchronization is forced to operate under conservative conditions, where large-signal variations are forbidden. Needless to say, this has a detrimental impact on applications, since performance is limited by small disturbance assumptions. Motivated by this problem, in the present work, we provide global asymptotic stability tests for two fundamental PLL algorithms. Namely, the conventional SRF-PLL and the enhanced SRF-EPLL. To do so, we use a Lyapunov approach acting directly on nonlinear trajectories. Consequently, the provided stability proofs do not rely on any linearization technique, frequency-domain conversion, or small-signal model representation. The main contribution in this paper is a set of stability conditions that are valid in a global sense. Moreover, it is shown that these results have direct practical implications in the form of new robust PLL gain tuning guidelines.

We introduce a model-free scheme that is capable of identifying topology changes in distribution networks using data from distribution-level phasor-measurement units (DPMUs). To do so, we use algebraic tools of behavioral system theory to develop easy-to-implement algorithms. The intrinsic difficulties associated with D-PMU measurements and their proposed solution are discussed as an additional challenge for the implementation of the proposed algorithms. Experimental results using real D-PMUs and laboratory real-time hardware are provided as a proof of concept using a standard IEEE-test node feeder.