Lisandro Lovisolo

The Compression of Electric Signal Waveforms for Smart Grids: State of the Art and Future Trends

In this paper, we discuss the compression of waveforms obtained from measurements of power system quantities and analyze the reasons why its importance is growing with the advent of smart grid systems. While generation and transmission networks already use a considerable number of automation and measurement devices, a large number of smart monitors and meters are to be deployed in the distribution network to allow broad observability and real-time monitoring. This situation creates new requirements concerning the communication interface, computational intelligence and the ability to process data or signals and also to share information. Therefore, a considerable increase in data exchange and in storage is likely to occur. In this context, one must achieve an efficient use of channel communication bandwidth and a reduced need of storage space for power system data. Here, we review the main compression techniques devised for electric signal waveforms providing an overview of the achievements obtained in the past decades. Additionally, we envision some smart grid scenarios emphasizing open research issues regarding compression of electric signal waveforms. We expect that this paper will contribute to motivate joint research efforts between electrical power system and signal processing communities in the area of signal waveform compression.

Efficient coherent adaptive representations of monitored electric signals in power systems using damped sinusoids

This paper presents coherent representations of electric power systems signals. These representations are obtained by employing adaptive signal decompositions. They provide a tool to identify structures composing a signal and constitute an approach to represent a signal from its identified components. We use the matching pursuits algorithm, which is a greedy adaptive decomposition, that has the potential of decomposing a signal into coherent components. The dictionary employed is composed of damped sinusoids in order to obtain signal components closely related to power systems phenomena. In addition, we present an effective method to suppress the pre-echo and post-echo artifacts that often appear when using the matching pursuits. However, the use of a dictionary of damped sinusoids alone does not ensure that the decomposition will be meaningful in physical terms. To overcome this constraint, we develop a technique leading to efficient coherent damped-sinusoidal decompositions that are closely related to the physical phenomena being observed. The effectiveness of the proposed method for compression of synthetic and natural signals is tested, obtaining high compression ratios along with high signal-to-noise ratio.

Optimum Rate-Distortion Dictionary Selection for Compression of Atomic Decompositions of Electric Disturbance Signals

In this letter, we address rate-distortion-optimum compression of signals from electric power system disturbances, using atomic decompositions. Usually, such optimization is obtained assuming a single dictionary and consists of finding the best compromise between the quantization of the coefficients in the atomic decomposition and its number of terms. Here, several parameterized dictionaries are used instead. This allows the selection of the dictionary leading to the best rate-distortion (R-D) compromise. Distinct dictionaries correspond to different quantizers for the parameters of the atoms. Side information must be transmitted in order to indicate the dictionary employed. The R-D performance in this case depends on a complex interplay between the quantizers of the parameters of the atoms and the coefficient quantizers. Using a training stage, we select a reduced set of parameter and coefficient quantizers that give near-optimum R-D performance. Simulation results show that the proposed scheme indeed achieves near-optimum R-D performance with low computational complexity in the coding stage

A Fast Database Correlation Algorithm for Localization of Wireless Network Mobile Nodes using Coverage Prediction and Round Trip Delay

In this work a fast database correlation algorithm is proposed for the localization of mobile stations in wireless metropolitan area networks. The algorithm is entirely based on built-in functionalities of mobile nodes. The set of measured parameters used for localization is called radio-frequency fingerprint. To provide a position estimate, it is correlated with a database built from field measurements or propagation modeling. The latter alternative was selected, as it allows a less expensive and faster database update. A novel use of the round trip delay to reduce the correlation space and improve positioning accuracy is presented. Vehicular field tests and Monte Carlo simulations were conducted to evaluate the algorithm’s accuracy in GSM and WCDMA cellular networks, respectively.

Coherent decompositions of power systems signals using damped sinusoids with applications to denoising

In this paper we propose a method to decompose electric power systems signals using damped sinusoids. It is inspired in the matching pursuits algorithm. The matching pursuits algorithm has the potential of decomposing a signal into coherent components. However, it is not a trivial task to obtain a decomposition which is meaningful in physical terms, as it is desirable in the case of power systems signals. We describe enhancements to the basic matching pursuits algorithm that generate decompositions in damped sinusoids with high correlation to the actual physical phenomena. We test the effectiveness of the proposed method in a signal denoising application, where the physically meaningful components are extracted from signals corrupted by noise. From the extracted components, a version of the signals almost free from noise are obtained.

How far can one compress digital fault records? Analysis of a matching pursuit-based algorithm

In this paper, we verify how far electric disturbance signals can be compressed without compromising the analysis of encoded fault records. A recently proposed compression algorithm, referred to as Damped Sinusoidal Matching Pursuit (DSMP) has the remarkable feature of obtaining both compact and physically interpretable representations. However, for fault analysis applications, one is primarily interested in how accurate can be the analysis performed on compressed signals, instead of evaluating mean-squared error figures. Unlike previous works in digital fault records compression, the performance of the DSMP compression method is evaluated using a protocol based on fault analysis procedures commonly performed by expert engineers. This protocol is applied for comparing the results obtained in the analysis of both uncompressed records and their compressed versions at different compression ratios. The results show that the DSMP is a reliable compression system since it achieves high compression ratios (6.4:1) without causing fault analysis misinterpretation.

On the statistics of matching pursuit angles

Matching pursuit decompositions have been employed for signal coding. For this purpose, matching pursuit coefficients need to be quantized. However, their behavior has been shown to be chaotic in some cases; posing difficulties to their modeling and quantizer design. In this work, a different approach is presented. Instead of trying to model the statistics of matching pursuit coefficients, the statistics of the angle between the residue signal and the element selected in each iteration of the matching pursuit are studied, what allows to model matching pursuits coefficients indirectly. This approach results in a simple statistical model. This is so because one observes that the statistics of such angles do not vary substantially after the first matching pursuit iteration, and can be approximately modeled as independent and identically distributed. Moreover, it is also observed that the probability density functions of matching pursuit angles are reasonably modeled by a single probability density function. This function depends only on the dictionary employed and not on the signal source. The derived statistical model is validated by employing it to design Lloyd-Max quantizers for matching pursuit coefficients. The Lloyd-Max quantizers obtained show good ratexdistortion performance when compared to the state-of-the-art methods.

Location methods for legacy GSM handsets using coverage prediction

In this work several methods for the location of legacy GSM handsets are compared. These methods use data available in the mobile station measurement report - communication channel server identity, received power levels from the serving and neighbor cells, received signal quality - and time alignment with the server. It is discussed how these parameters can be used for mobile station positioning. A new approach for the cell identity plus time alignment method, using propagation modeling, is proposed. The efficiency of radio-frequency fingerprint correlation using coverage prediction maps of different resolutions is also analyzed. Field tests were conducted to evaluate the methods in real scenarios.

Fall detection monitoring system with position detection for elderly at indoor environments under supervision

We propose a fall detection monitoring system with the identification of the acident location for the elderly under care in indoor environments. The main objective of the system is to reduce the time until emergency personnel responds to falls, especially when the victim is unconscious or delirious. Reducing the response time to initiate the appropriate patient care may imply simultaneously in reductions of morbidity and mortality rates. The proposed system can be introduced in hospitals, asylums or nursing home environments rapidly and with low cost. The fall is detected using an embedded accelerometer and the location is identified using Zigbee transceivers (a Zigbee Network is employed for issuing the alarms). The results show a high success rate in the fall detection and provide input to design the Zigbee network to offer the required accuracy on the identification of the location of the fall in terms of the room where it had occurred.

Consistent quality control in ECG compression by means of direct metrics.

The aim of electrocardiogram (ECG) compression is to reduce the amount of data as much as possible while preserving the significant information for diagnosis. Objective metrics that are derived directly from the signal are suitable for controlling the quality of the compressed ECGs in practical applications. Many approaches have employed figures of merit based on the percentage root mean square difference (PRD) for this purpose. The benefits and drawbacks of the PRD measures, along with other metrics for quality assessment in ECG compression, are analysed in this work. We propose the use of the root mean square error (RMSE) for quality control because it provides a clearer and more stable idea about how much the retrieved ECG waveform, which is the reference signal for establishing diagnosis, separates from the original. For this reason, the RMSE is applied here as the target metric in a thresholding algorithm that relies on the retained energy. A state of the art compressor based on this approach, and its PRD-based counterpart, are implemented to test the actual capabilities of the proposed technique. Both compression schemes are employed in several experiments with the whole MIT-BIH Arrhythmia Database to assess both global and local signal distortion. The results show that, using the RMSE for quality control, the distortion of the reconstructed signal is better controlled without reducing the compression ratio.