Leandro Rodrigues Manso Silva

Time-varying harmonic analyzer prototype

This paper presents a time-varying harmonic analyzer based on the Sliding Window DFT implemented on a DSP platform. The harmonic decomposition is carried out using the TMS320F28027 digital signal processor, and the samples of the decomposed signal are sent to a PC based computer using serial interface. Several real cases are analyzed, revealing the time-varying behavior of the harmonic component. Different from commercial harmonic analyzer, the prototype presented in this paper uses a different way to visualize the harmonic components, based on time decomposition approach. This new visualization of time-varying harmonics might help engineers and researchers to develop new insights in the transient mechanisms of waveform distortions and possible new applications for power quality monitoring, control and protection.

A gapless waveform recorder for monitoring Smart Grids

In the present paper a new concept of continuous waveform recording, or gapless waveform recording, applied to Electric Power System (EPS) signals is presented. It is shown that it is not necessary to store all the EPS signal samples, and still, keeping all the relevant information for a complete reconstruction of the signal for later oscillographic analysis. This allows store long periods of waveform measurements, spending little memory space. To make it possible, it is used the Novelty Detection Concept, that is performed by segmenting the signal into frames and comparing these frames in order to save only the ones in which some “novelty” is present. This approach contributes to the new context of Smart Grids (SGs), even in a future scenario where events that are not completely known might occur, since all the necessary waveform information is saved. Thus, the control system of the SG can use the stored signals to perform different analysis and take the suitable decision to improve the EPS performance.

Smart signal processing for an evolving electric grid

Electric grids are interconnected complex systems consisting of generation, transmission, distribution, and active loads, recently called prosumers as they produce and consume electric energy. Additionally, these encompass a vast array of equipment such as machines, power transformers, capacitor banks, power electronic devices, motors, etc. that are continuously evolving in their demand characteristics. Given these conditions, signal processing is becoming an essential assessment tool to enable the engineer and researcher to understand, plan, design, and operate the complex and smart electronic grid of the future. This paper focuses on recent developments associated with signal processing applied to power system analysis in terms of characterization and diagnostics. The following techniques are reviewed and their characteristics and applications discussed: active power system monitoring, sparse representation of power system signal, real-time resampling, and time-frequency (i.e., wavelets) applied to power fluctuations.

Gapless Power-Quality Disturbance Recorder

This paper presents and discusses the concepts of a gapless-power-quality disturbance recorder (G-PQDR) and a novelty detector. In the G-PQDR, power signals are divided into continuous frames and the energy difference between an actual frame and a reference frame is used for comparison. If the current frame is different from the reference frame, it will be recorded in the equipment and it becomes the new reference frame; otherwise, the frame is discarded and the frequency of the fundamental component is stored. The G-PQDR is able to detect and record the waveforms of the previously known PQ disturbances as well as those of the PQ disturbances not yet observed in power systems, thanks to the novelty detection concept. This characteristic is important in the context of smart grids where hidden disturbances can be detected by this methodology. For efficient data storage by minimizing the volume of the data stored, the G-PQDR uses lossy and lossless compressors. This paper describes the entire process of detection and compression, and compares the performance of the proposed method with that of a commercial-power-quality data recorder. In addition, some of the prototype implementation details are presented.

An electrical signal disturbance detector and compressor based on FPGA platform

This paper presents a prototype system used to detect and compress disturbances commonly found in electrical signal, on both voltage and current. The algorithm for detecting and compressing the disturbances are synthesized in FPGA platform. The compression algorithm operates in three different stages: the innovation stage, the wavelet stage and the bit compression stage. These three stages provide efficient compression rate. Beside the algorithm description, the paper presents details of the hardware implementation and results obtained from synthetized and real signals.

An improved DFT based method for phasor estimation in fault scenarios

This paper presents a new methodology for phasor estimation in the presence of exponentially DC decay (DC offset) and odd harmonics in the signal. The estimations are performed on a half cycle plus four samples of the fundamental component of the system. The method uses digital signal processing techniques such as windowing, DFT (Discrete Fourier Transform) modulation and look up tables (LUT). The performance of the proposed method is compared with others methods presented in the literature. The results from simulation showed that the proposed method has a better performance that the others. In addition to the floating-point implementation, the proposed method was implemented in a reconfigurable hardware platform, FPGA (Field Programmable Gate Array) using fixed-point arithmetic.

Characterization of electronic converters by time-varying harmonic phasors and waveforms

As an enabling technology, power electronics has the ability to provide effective and intelligent solutions for the grid of the future; however, the complexity and interaction with the rest of the system is non-trivial and requires careful studies. Among the issues to be addressed is the behavior of electronic converters under transient conditions. This paper investigates the behavior of electronic converters via time-frequency decomposition methods. The nonlinear electronic load is characterized by the time-varying harmonics during transient conditions such as voltage sags and variable voltage distortions. In the past, analytical expressions have been used to determine the harmonic current locus (in steady state condition) for different operating points. With the development of time-frequency decomposition tuned to the harmonic frequencies the transient performance of the harmonics can be observed with higher time resolution. This new approach enables researchers and engineers, for the first time to look into the transient behavior of harmonics and, consequently better understand the associated phenomena from a time-varying harmonic perspective. This method may become useful for determining the impact of the aggregation of multiple power electronic front-end devices. The approach may also help in setting up protection schemes based on the excursions of prominent harmonics during transient conditions.

Power Quality waveform recognition using Google Image Search Engine (iPQ-Google)

Power quality diagnostics is an important tool for keeping the performance of electric smart grids within the criteria of proper operation. Signal processing is widely used for identifying the deviations and generating event flags. The availability of the Google Image Search Engine tool led the authors to use it for identifying power quality events by recognizing the waveforms when comparing with a data bank of previously recorded and posted on the internet and properly identified signals. Some initial attempts showed great potential and the authors started by creating a specific folder where the Google Image Search Engine could investigate and recognize a particular waveform. This paper presents the concept and the initial steps for using this powerful tool, here named iPQ-Google, and which has the possibility to help utilities, customers and researchers to investigate and easily find, compare and diagnose possible PQ waveform deviations saved on the internet.

Recent developments on signal processing for smart grids

With the growing complexity of the electric grid, signal processing has a fundamental importance in characterization, diagnoses and protection. In this paper a brief review of digital signal processing techniques will be done. These techniques are focused in power systems applications, such as: signal compression, harmonic estimation and characterization of load and generation profiles.

A Virtual instrument for Time Varying Harmonic Analysis

This paper presents the implementation of a Virtual Instrument for Time Varying Harmonic Analysis based on the real time processing platform dSPACE. The time varying harmonic extraction from the power system signal was implemented using a Recursive Hanning Window DFT (RHWDFT). The prototype combines RHWDFT with dynamic and configurable trigger system, thus it is able to detect and estimate time varying harmonics in time domain. The Virtual Instrument has an analog front end. a real time platform (dSPACE) and a Human Machine Interface (HMI) implemented on a computer.