Turn-to-turn short circuits occur due to insulation damage in transformer windings and these may potentially result in catastrophic faults affecting electric networks. In fact, transformer diagnosis plays an important role for preventing large energy outages. A new sensitive method based on wavelet transform for identifying interturn faults during energization of single-distribution transformers is presented. The diagnosis is performed utilizing the magnetizing currents obtained during transformer energization under healthy and faulty conditions. Magnetization currents are processed with the wavelet transform and using correlation matrices, which in turn are used to obtain a time-frequency power spectral density (PSD); these are used to quantify the damage level when transformer windings are experiencing faults during their energization. The proposed method was analyzed during simulations using ATP-EMPT software and experimental tests were also carried out in laboratory to validate the results.

In this work a state-feedback controller for pole placement is presented. In addition, a model predictive control (MPC) to minimize the magnitude of the controller input is also presented. The MPC use an observer to estimate the states of the plant and optimize a cost function to maintain the input within a pre-defined limit. The proposed approach is validate trough nonlinear simulations using the equivalent Nordic power system and where a Static Var Compensator is used as actuator. Wide-area signals from Phasor Measurement Units (PMUs) are used to improve the damping of the system.

An application of the Chebyshev orthogonal polynomials and of the Legendre function approximation to efficiently synthesize electrical signals with harmonic and inter-harmonic content is presented in this paper. The synthesis is efficient in terms of the accurate results achieved in a low computational cost. After exploring the generalized Fourier series, the Chebyshev and Legendre bases were selected because they present important vector properties, namely preserving orthogonality when synthesizing electrical signals with unknown harmonic distortion and easy to compute. The correlation coefficient of the polynomials and function approximations is used to compare the accuracy of the signal synthesis with respect to the Fourier theory. The synthesis of real measured and generated test signals with harmonic and inter-harmonic content validates the numerical performance and behavior of the here-proposed application.

This paper discusses the application of a spatiotemporal analysis method based on the dynamic mode decomposition (DMD) framework to analyze harmonic distortion in a microgrid test system with several distributed generation units. The proposed technique extracts spatiotemporal coherent patterns from which the frequency components, modal energy, mode-shape and participation factors can be determined simultaneously. Due to its multiscale nature, the method is well suited for the analysis and monitoring of harmonic distortion in complex microgrid systems. The explicit representation of switching processes in the power electronic converters and non-linear loads is fully considered to assess the robustness of the proposed framework.

Modern power systems with high penetration of renewable energies, such as wind and photovoltaic (PV), commonly experiment harmonic issues and in some cases this power quality problem leads to instability. To understand and study this phenomenon, small-signal models have been proposed; however, most of these models neglect the harmonic cross-coupling and the harmonics themselves. Consequently, the harmonic interaction among controllers, network elements, loads, and power electronic converters is overlooked. This paper presents two approaches to obtain the small-signal model of a grid-connected AC microgrid constituted by multiple parallel three-phase inverters; one approach is analytical and the another is numerical. The extended harmonic domain (EHD) is used as frame of reference for the proposed small-signal models since it retains the harmonic cross coupling among all the harmonic components, and transforms a linear periodic time-varying system into a linear time invariant (LTI) system allowing the straightforward use of LTI tools.

In this paper, a semi-distributed tool for monitoring and analysis of power systems disturbances using wide-area sensors is proposed. The proposed framework can be used to integrate and process large amounts of multimodal, multi-type observational data from various monitoring technologies or data concentrators to assess the power system health in near-real time.First, a framework for fusing data from multiple sensors based on multivariate statistical tools is introduced and a model of the collected data is developed. Drawing on this model, a novel a data-driven strategy based on both, local and global energy metrics for the analysis of major system disturbances is proposed. The approach is tested using time domain simulations on the IEEE 118 bus system under various scenarios and different levels of observability of the system. The approach is complemented with a visualization technique to provide further means to analyze the system after a disturbance. (C) 2016 Elsevier B.V. All rights reserved.

This paper proposes a novel methodology for characterization of nonlinear low frequency oscillations. Selecting a window of the speed machine signals, a polynomial in the z-domain of the samples is obtained, and then Pade approximation is used to obtain a rational equivalent of the polynomial. This equivalent has a direct relationship with a discrete transfer function which captures the dominant parameters embedded in the speed machine signals. The method is tested with corrupted noisy signals showing that it may be applied to real signals. The developed methodology is applicable to determine the oscillation parameters of speed signals of the 16-Machine 68-bus NPCC power system.

A harmonic and inter-harmonic signal analysis technique is presented in this paper. The technique consists in the use of The Fourier Theory for synthesizing harmonic signals and the use of two generalized-Fourier approximations based in different orthogonal basis for inter-harmonic signals. A numerical example yields that polynomial Chebyshev and Legendre function approximations can be very efficient to analyze inter-harmonic signals in a very short running time.

Due to the recent deregulated electrical market and the increasing consumption of electrical energy, new possibilities have been opening for the electricity suppliers to formulate tariff packages and to improve the quality services to satisfy the daily demand. A key aspect to visualize and interpret a huge volume of data is to cluster customers according to their individual electrical load profile. For this purpose, it is proposed to apply dimensionality reduction techniques, namely, Principal Component Analysis (PCA), Isometric Feature Mapping (Isomap), Sammon Mapping, Locally Linear Embedding (LLE) and Stochastic Neighbor Embedding (SNE) to such data. The IEEE 30-bus system is used in this paper for evaluating these techniques. SNE performed the best when clustering the load profiles into the expected eight clusters.

In this paper a security-cost relationship analysis from the perspective of Transient Stability Constraints Optimal Power Flow (TSCOPF) is presented. The main objective of this paper is to analyze the security-cost relationship under N-1 contingencies in the Electric Power System (EPS). The Center of Inertia (COI) concept of rotor angle generators is used to obtain a time-varying trajectory equivalent of each contingency. A severity index is proposed to evaluate the power system security. At the end of this paper, the generation cost related of each contingency is computed to identify the costly contingencies. The feasibility of the proposed analysis is demonstrated on the IEEE 30-bus test system.

Wide-area monitoring substantially improves modal identification and characterization under noisy conditions. This paper discusses the use of blind source separation (BSS) techniques to extract and identify modal responses and mode shapes from simulated data. The method has advantages over other global analysis methods in that it allows for the analysis of both, transient and ambient data and is thus well suited for global system monitoring of power system oscillatory behavior using wide-area measurement systems (WAMS) data.Methods for analysis of complex datasets using BSS techniques are developed, and a physical explanation is offered. The developed procedures are tested on simulated data, and the impact of various parameters such as noise and time lags on the quality of signal separation are examined. (C) 2014 Elsevier B.V. All rights reserved.

A global multiscale method based on a dynamic mode decomposition (DMD) algorithm to characterize the global behavior of transient processes recorded using wide-area sensors is proposed. The method interprets global dynamic behavior in terms of both, spatial patterns or shapes and temporal patterns associated with dynamic modes containing essentially single-frequency components, from which the mode shapes, frequencies and growth and decay rates of the modes can be extracted simultaneously. These modes are then used to detect the coherent and dominant structures within the data. The technique is well suited for fast wide-area monitoring and assessment of global instability in the context of modern data fusion-based estimation techniques. Results of the application of the proposed method to large, high-dimensional data sets are encouraging.

The aim of this study is to present a phase compensation scheme that improves the accuracy of a power hardware-in-the-loop simulation. The ideal transformer model interface method along with feedback current filtering is used to connect a synchronous generator simulation to a test system. The generator is modeled using a voltage-behind-reactance representation. Comparison of experimental results against simulations confirms the validity of the proposal.

The aim of this study is to describe a synchronous generator emulator prototype based on a three-phase power electronics converter. The physical structure of the emulator and its relation with the generator mathematical model is described. A voltage-behind-reactance model is used to reproduce the behavior of the generator. Comparison of experimental results against simulations confirms the validity of the proposal.

This paper presents a Kalman-Wavelet transform technique for tracking harmonics in the dynamic state. The presented method combines the discrete Wavelet transform and Kalman filter to track a current or voltage waveform, producing time varying states. The results obtained are compared to other Kalman methods to prove its estimation quality, numerical accuracy and short simulation time.

The identification of critical nodes is relevant to determine the structural vulnerability of an Electric Power System. In this article, centrality indices from the area of complex networks analysis are adapted to identify nodes and lines that result critical for the transmission of energy. The centrality indices are calculated in each sub-graph, to rapidly assess the structural vulnerability of the power grid. To validate the proposal different network topologies are analyzed.

In this paper, a masking-based Hilbert Vibration Decomposition (HVD) method is proposed to analyze nonlinear and non-stationary power system data. An adaptive masking signal method derived from the data itself is used to improve the decomposition ability of the HVD method. Techniques to compute the masking signal are described and precise criteria to increase the modal resolution of the HVD method are derived. Simulation results using both synthetic and simulated data show that the technique can be used to characterize complex oscillatory processes in power systems.

The development of advanced signal processing algorithms to extract modal information from ambient system oscillations has acquired a great deal of interest in recent years. Much of this effort has been directed towards the problem of feature extraction, which involves estimating, identifying and extracting oscillatory phenomena embedded in highly noisy random processes. In this paper, a statistical framework for analyzing ambient signals from power systems is proposed. The method combines singular spectral analysis with a random decrement technique to identify and estimate power system oscillatory phenomena from ambient system oscillations. The paper also describes the experience with the application of this technique to quantify modal information on both synthetic and ambient data.

This paper develops a novel multivariate scheme to analyze power system oscillations from real system data. The approach is based on multivariate techniques especially suited to consider the hierarchical and decentralized nature of Wide Area Monitoring Systems (WAMS), where various levels of measurement and control units are scattered along the network. It employs multiblock principal component analysis to build a statistical model based on modal signals obtained from measurements. Once the global model is constructed using decentralized local information from each part of the system, principal oscillation modes are identified along with their contribution to the different blocks.

This paper proposes a contour image-based graphic method useful to estimate the relative error between approximate ground-impedance models and the algorithmic solution of the Carson's integral over a wide ranges of frequencies and soil conductivities for any overhead transmission-line configuration.