Alfonso Prieto-Guerrero

Wavelet-Based Method for Instability Analysis in Boiling Water Reactors

Abstract This paper introduces a wavelet-based method to analyze instability events in a boiling water reactor (BWR) during transient phenomena. The methodology to analyze BWR signals includes the following: (a) the short-time Fourier transform (STFT) analysis, (b) decomposition using the continuous wavelet transform (CWT), and (c) application of multiresolution analysis (MRA) using discrete wavelet transform (DWT). STFT analysis permits the study, in time, of the spectral content of analyzed signals. The CWT provides information about ruptures, discontinuities, and fractal behavior. To detect these important features in the signal, a mother wavelet has to be chosen and applied at several scales to obtain optimum results. MRA allows fast implementation of the DWT. Features like important frequencies, discontinuities, and transients can be detected with analysis at different levels of detail coefficients. The STFT was used to provide a comparison between a classic method and the wavelet-based method. The damping ratio, which is an important stability parameter, was calculated as a function of time. The transient behavior can be detected by analyzing the maximum contained in detail coefficients at different levels in the signal decomposition. This method allows analysis of both stationary signals and highly nonstationary signals in the timescale plane. This methodology has been tested with the benchmark power instability event of Laguna Verde nuclear power plant (NPP) Unit 1, which is a BWR-5 NPP.

Decay Ratio Estimation of BWR Signals Based on Wavelet Ridges

Abstract A wavelet ridge application is proposed as a simple method to determine the evolution of the linear stability parameters of a boiling water reactor nuclear power plant (NPP) using neutronic noise signals. The wavelet ridges are used to track the instantaneous frequencies contained in a signal and to estimate the decay ratio (DR). The first step of the method consists of denoising the analyzed signals by a discrete wavelet transform to reduce the interference of high-frequency noise and concentrate the analysis in the band where crucial frequencies are presented. Next is computation of the wavelet ridges by a continuous wavelet transform to obtain the modulus maxima from the normalized scalogram of the signal. In general, associations with these wavelet ridges can be used to compute the instantaneous frequency contained in the signal and the DR evolution with the measurement. To study the performance of the wavelet ridge method, by computing the evolution of the linear stability parameters, both simulated and real neutronic signals were considered. The simulated signal is used to validate methodically and to study some features of the wavelet ridge method. To demonstrate the method applicability, three real neutronic signals related to instability events in the Laguna Verde NPP and Ringhals and Forsmark stability benchmarks were analyzed. The investigations show that most of the local energies of the signal are concentrated and that DR variations of the signals were observed along the measurements.

Lost Sample Recovering of ECG Signals in e-Health Applications

This paper shows the interest of an interpolation method based on parametric modeling to retrieve missing samples in ECG signals. This problem occurs more and more with the emergence of telemedicine applications. The different links (fixed access network (PSTN), mobile access network (GSM/GPRS and future UMTS) or satellite interfacing (DVB-RCS technology)) involved in e-health applications are liable to induce errors on the transmitted data. These errors/losses can occur anytime and anywhere (according to the channel availability, memory overflows, protocols, etc) during a transmission process. Therefore the recovering of missing samples for biomedical signals is of great interest. The method used in this paper is based on a left-sided and right-sided autoregressive model, i.e., the interpolation algorithm uses the samples before and after the sequence of missing samples. An objective measure is used to assess the method performance. Results show that this interpolation method represents a really suitable technique to ECG signal reconstruction in a possible corrupted transmission.

OURSES: A telemedicine project for rural areas in france. Telemonitoring of elderly people

Several telemedicine applications are proposed within the frame of OURSES project, French acronym for Offer of Rural Use of Services by Satellite, providing services for elderly people. The main objective of this project is to show the interest of using satellites as a complement to terrestrial technologies, in areas where telecommunication infrastructure is lacking or incomplete. This paper describes one of these applications: an ECG monitoring system. This telemonitoring system allows, thanks to a wireless wearable sensor, to detect possible cardiac problems of elderly people. ECG signals are analyzed through signal processing algorithms and if some abnormal condition is detected, an alarm is raised and sent via satellite to the physicians office. The corresponding physician is able to access at any time the recorded ECG signals, whenever he is willing to, in the presence of an alarm or not. This allows a constant monitoring of the elderly people. Tests realized in a real environment have demonstrated the feasibility and the interest of this application.

Non-Linear Stability Analysis of Real Signals from Nuclear Power Plants (Boiling Water Reactors) Based on Noise Assisted Empirical Mode Decomposition Variants and the Shannon Entropy

There are currently around 78 nuclear power plants (NPPs) in the world based on Boiling Water Reactors (BWRs). The current parameter to assess BWR instability issues is the linear Decay Ratio (DR). However, it is well known that BWRs are complex non-linear dynamical systems that may even exhibit chaotic dynamics that normally preclude the use of the DR when the BWR is working at a specific operating point during instability. In this work a novel methodology based on an adaptive Shannon Entropy estimator and on Noise Assisted Empirical Mode Decomposition variants is presented. This methodology was developed for real-time implementation of a stability monitor. This methodology was applied to a set of signals stemming from several NPPs reactors (Ringhals-Sweden, Forsmark-Sweden and Laguna Verde-Mexico) under commercial operating conditions, that experienced instabilities events, each one of a different nature.

Dynamic OFDM Transmission for a Cognitive Radio Device Based on a Neural Network and Multiresolution Analysis

Cognitive radio communications depend on methods for sensing the spectrum as well as adapting transmission parameters to available resources. In this context, this work proposes a novel system that makes use of prediction to dynamically allocate subcarriers to different transmissions in an orthogonal frequency division multiplexing (OFDM) system. To this end, the proposal is comprised of a predictive component which makes use of a neural network and multiresolution analysis and a second component, which uses wavelet analysis and cognitive radio functions to carry out a dynamic allocation of subcarriers in an OFDM system. The use of wavelets allows the system to split the data stream in blocks of information to be transmitted over multiple orthogonal subcarriers. This proposed system makes use of the decision-making functions of a cognitive radio device to select the number and position of the subcarriers used for communications without interference. Although there exist other OFDM systems using wavelets, they are not used in combination with the decision-making functions implemented in cognitive radio devices. In contrast, the proposed OFDM system operates using some of these functions, thus being able to better adapt its operational parameters. The use of wavelets combined with a neural network model improves the prediction of the bandwidth utilization as shown in this work. It is concluded that the proposed system improves spectral efficiency and data rate by using the decision-making functions of cognitive radios to select the appropriate OFDM subcarriers to be used during the data transmissions.

Spectrum sensing algorithm based on the modulation spectrum

Spectrum sensing is without doubt the most important part in the implementation of cognitive radio networks. Therefore, many algorithms for signal detection of primary users have been proposed, being those based on spectral correlation the most outstanding, because of their good performance in environments with low signal-to-noise ratio. However, the computational complexity is one of their major disadvantages. In this paper a new method for detecting primary users is proposed using the modulation spectrum, which offers a good performance in environments with low signal to noise ratio and a low computational complexity. To prove that, a performance comparison is done, considering a DS-CDMA scheme, between the based-spectral correlation algorithm and the proposed in this work.

Real time synchronization for an OFDM/PLC system implemented with a DSP

PLC is a technology for data transmission over the power line and, it is a very attractive area of research since PLC is potentially suitable for sending data at high rates over the existing power system infrastructure. Synchronization is a fundamental process in any OFDM communication system and its implementation must be efficient and fast. This work presents a real time implementation featuring a synchronization method for an OFDM Power Line Communication (PLC) system using a TMS320C6416 DSP. The synchronization method presented in this paper combines techniques based on correlation, linear regression and phase rotation. We propose a specific design for the preamble sequence used in synchronization and channel estimation and present some implementation details that could be useful to implement the referred synchronization algorithm in a real PLC modem.

Wavelet-Based Improvement for Channel Estimation in a Power-Line Communication Environment Impaired by Impulsive Noise

Wavelet decompositions ability to deal with heterogeneous and intermittent behavior and its effectively noisy removing property, has become the wavelet-based estimation in a very interesting non parametric technique. This paper proposes a wavelet-based methodology to improve channel estimation, via wavelet-based denoising approach, for a power-line communication network impaired with significant impulsive noise.