Marcelo A. A. Lima

Estimation of harmonic currents injected by nonlinear loads for a distorted power supply scenario using Artificial Neural Networks

The determination of the true current harmonic distortion produced by a nonlinear load is problematic due to the fact that the voltage waveform provided by the power supply at the Point of Common Coupling (PCC) rarely is free of harmonic distortion. This paper proposes a method based on Artificial Neural Networks (ANN) in order to realize the estimation of the harmonic content presented in the nonlinear load current waveform supposing a pure sinusoidal power supply. However, this estimation is made when the nonlinear load is in fact connected to a distorted power supply. For this, a dynamic ANN with time delays is utilized for modeling the nonlinear load admittance in a pre-established condition of PCC voltage with little or none harmonic distortion. Once achieved a satisfactory training, now for a PCC voltage with any level of harmonic distortion, the ANN is utilized for predicting the nonlinear load current waveform by a power supply estimation that preserves similarities with that applied during its training. One of the advantages of the method is that only the instantaneous values of voltage and current are necessary for modeling the loads. It does not need the knowledge of any load or power system parameter.

Estimation of harmonics and interharmonics based on Single Channel Independent Component Analysis

The Discrete Fourier Transform (DFT) is one of the most used tools for spectral analysis. However, the DFT-based harmonic estimation is inaccurate when the power system frequency deviates from its nominal value. Also, an interharmonic may be not distinguishable when the DFT frequency resolution does not match the interharmonic frequency. This paper proposes the application of Single Channel Independent Component Analysis (SCICA) to blindly estimate harmonics and interharmonics of a power system signal. The SCICA operation is equivalent to an adaptive linear filter bank which can blindly separate the harmonics and interharmonics of the signal. It will be shown that the SCICA algorithm obtains more accurate results for estimation of arbitrary harmonic and interharmonic frequencies than the DFT.

An ICA-based method for adaptive decomposition of multiple PQ disturbances

This paper proposes a method for adaptive decomposition of Multiple Power Quality Disturbances based on Independent Component Analysis. This method aims to decompose power system signals (voltage or current) into their isolated disturbances when a multiple disturbance situation occurs, providing more specific information about the different disturbances which occur simultaneously. This work proposes a methodology for the Independent Component Analysis application when only a single measured signal is available (single channel). Also, it presents a procedure based on the Least Mean Square algorithm to recover the scales of the independent component estimations. Besides this, it shows that the proposed method is an adaptive filter bank that blindly separate out spectrally distinct independent disturbances.

Voltage elevation analysis of overhead distribution lines using the finite element method

This paper presents a methodological analysis to increase the power transfer capability of an overhead distribution line, by changing the rated voltage from 34.5 kV to 69 kV. The distribution line chosen for analysis was modeled on ATP software, using ATPDraw interface, and was submitted to various electromagnetic transients. From the overvoltage values observed in the simulations, new computer analysis were performed using the software FLUX®, which is based on the finite element theory. FLUX® was employed to evaluate the behavior of the electrical field in different pole types of the distribution line, considering the wood crossarms and ceramic insulators. Thus, this study evaluates the computer simulations carried out and points out which poles and crossarms of the original 34.5 kV line are already able to operate on 69 kV, without changing the overhead line spacing.

A method of Dynamic Resampling for DFT-based harmonic analysis under time-varying frequency conditions

This paper presents a resampling method based on time-domain interpolation with applications in harmonic analysis in the time-varying frequency scenario. The method dynamically determines the resampling moments of a given signal that was originally sampled at a fixed rate. This process is based on real-time frequency tracking (fundamental component) by means of a PLL (Phase-Locked Loop) structure. Then, the values of the signal in the resampling moments are approximated by piecewise polynomials interpolation functions (Splines). This process is performed in an iterative way, so that a period of the resampled signal always contains the same integer number N of samples. The performance of the method for real-time harmonics tracking in common situations of asynchronous sampling is evaluated by applying the DFT (Discrete Fourier Transform) in the resampled signal using sliding windows. The simulation results show that the use of the method can effectively reduce the energy spreading effect (leakage) caused by asynchronous sampling and improve the estimates accuracy of the analyzed signal harmonics components significantly.

A method for PQ multiple disturbance decomposition based on digital filtering and Independent Component Analysis

This paper proposes a method for Power Quality disturbance analysis based on Independent Component Analysis. This method aims to decompose power system signals (voltage or current) into their isolated disturbances when a multiple disturbance situation occurs, providing more specific information about the different disturbances which occur simultaneously. As the Independent Component Analysis is originally a technique applied to Multivariate Data Analysis, this work proposes a methodology for its application when only a single measured signal is available. The performance of the technique is evaluated using synthetic, as well as actual power system signals.

Nonlinear filtering for Power Quality disturbances analysis

In this work, a new method for PQ analysis is proposed based on the use of neural processing aiming to decouple the information from different disturbances from the power system signal. The system was implemented and tested using a simulated database showing the good performance of the proposed technique for signal separation. It was shown that the proposed method could also be used as a preprocessing technique for PQ monitoring systems.

Harmonic extraction based on Independent Component Analysis and quadrature matched filters

This paper presents a new method for harmonic extraction based on single channel independent component analysis (SCICA). There are in the literature some studies related to the extraction of harmonics and interharmonics from the power system signal based on SCICA, but usually they have a common limitation in the clustering stage required by the SCICA approach. The main innovation of the proposed method is the use of a quadrature matched filter bank to cluster the harmonics and separate them from the other signal components without the previously knowledge of the signal content. Additionally, the proposed method allows an adaptive response to the power system signal window. The proposed method was evaluated with three simulated cases, considering harmonics, interharmonics, sags and noise, showing promising results.