Current Status and Future Trends of Power Quality Analysis

In this article, a systematic literature review of 153 articles on power quality analysis in PV systems published in the last 20 years is presented. This provides readers with an overview on PQ trends in several fields related to instrumental techniques that are being used in the smart grid to visualize the quality of the energy, establishing a solid literature base from which to start future research. A preliminary appreciation allows us to intuit that higher-order statistics are not implemented in measurement equipment and that traditional instrumentation is still used for the performance of measurement campaigns, not yielding the expected results since the information processed does not come from an electrical network from 20 years ago. Instead, current networks contain numerous coupled load effects; thus, new disturbances are not simple; they are usually complex events, the sum of several types of disturbances. Likewise, depending on the type of installation, the objective of the PQ analysis changes, either by detecting certain events or simply focusing on seeing the state of the network

A FPGA system for QRS complex detection based on Integer Wavelet Transform

Due to complexity of their mathematical computation, many QRS detectors are implemented in software and cannot operate in real time. The paper presents a real-time hardware based solution for this task. To filter ECG signal and to extract QRS complex it employs the Integer Wavelet Transform. The system includes several components and is incorporated in a single FPGA chip what makes it suitable for direct embedding in medical instruments or wearable health care devices. It has sufficient accuracy (about 95%), showing remarkable noise immunity and low cost. Additionally, each system component is composed of several identical blocks/cells what makes the design highly generic. The capacity of today existing FPGAs allows even dozens of detectors to be placed in a single chip. After the theoretical introduction of wavelets and the review of their application in QRS detection, it will be shown how some basic wavelets can be optimized for easy hardware implementation. For this purpose the migration to the integer arithmetic and additional simplifications in calculations has to be done. Further, the system architecture will be presented with the demonstrations in both, software simulation and real testing. At the end, the working performances and preliminary results will be outlined and discussed. The same principle can be applied with other signals where the hardware implementation of wavelet transform can be of benefit

Fault detection in wind turbine's doubly fed induction generators

El següent treball final de grau té com a objectiu proporcionar una manera d'identificar fallades en aerogeneradors basats en generadors d'inducció doblement alimentats (DFIG, per les seves sigles en anglès), un dels tipus de generadors més utilitzats actualment en el camp dels aerogeneradors de mida industrial. A causa de les severes condicions en les quals operen els aerogeneradors DFIG, la detecció de fallades és un tema de gran preocupació i importància. L'enfocament proposat simula l'aerogenerador utilitzant el programari MATLAB/Simulink, una eina molt comuna per a modelar sistemes dinàmics. L'aerogenerador, inclosos els convertidors del costat del rotor i l'estator a més del sistema de control del DFIG, es modelen amb precisió en el model de simulació. El model de generador utilitzat per a la simulació s'ha modificat per a simular amb major precisió el comportament de l'aerogenerador. L'enfocament que ha pres el treball consisteix a combinar mètodes d'aprenentatge automàtic i tècniques de processament de senyals per a trobar errors en el convertidor del costat del rotor i l'estator, a més de la xarxa. L'anàlisi wavelet s'utilitza per a examinar els senyals del DFIG amb la finalitat d'identificar les característiques indicadores de fallades. A continuació, les característiques recopilades s'utilitzen per a entrenar classificadors d'aprenentatge automàtic com Support Vector Machines (SVM), K-Nearest Neighbors (KNN) i Arbres de Decisió per a trobar errors en el DFIG. Per a avaluar la tècnica proposada, es realitzen simulacions amb diversos tipus de fallades, com a curtcircuits i baixades i de tensió. L'estudi ofereix una eina pràctica perquè els operadors d'aerogeneradors i enginyers puguin trobar defectes del DFIG i evitin llargs períodes d'inactivitat, que poden resultar costosos.El siguiente trabajo final de grado tiene como objetivo proporcionar una forma de identificar fallos en aerogeneradores basados en generadores de inducción doblemente alimentados (DFIG, por sus siglas en inglés), uno de los tipos de aerogeneradores más utilizados actualmente en el campo de los aerogeneradores de tamaño industrial. Debido a las severas condiciones en las que operan los aerogeneradores DFIG, la detección de fallos es un tema de gran preocupación e importancia. El enfoque propuesto simula el aerogenerador utilizando el software MATLAB/Simulink, una herramienta muy común para modelar sistemas dinámicos. El aerogenerador, incluidos los convertidores del lado del rotor y estator además del sistema de control del DFIG, se modelan con precisión en el modelo de simulación. El enfoque que ha tomado el trabajo consiste en combinar métodos de aprendizaje automático y técnicas de procesamiento de señales para encontrar errores en el convertidor del lado del rotor, estator y en la red. El análisis wavelet se utiliza para examinar las señales del DFIG con el fin de identificar las características indicadoras de fallos. A continuación, las características recopiladas se utilizan para entrenar clasificadores de aprendizaje automático como Support Vector Machines (SVM), K-Nearest Neighbors (KNN) y Árboles de Decisión para encontrar errores en el DFIG. Para evaluar la técnica propuesta, se realizan simulaciones con varios tipos de fallos, como cortocircuitos y subidas y bajadas de tensión. El estudio ofrece una herramienta práctica para que los operadores de aerogeneradores e ingenieros puedan encontrar defectos en el DFIG y eviten largos periodos de inactividad, que podrían resultar costosos.The following bachelor’s thesis has the objective of providing a way to identify faults in doubly fed induction generators (DFIG) based wind turbines, one of the most widely used types of wind turbines in the field of industrial-sized wind turbines currently. Due to the severe conditions that DFIG wind turbines are operating in, fault detection becomes a topic of big concern. The suggested approach simulates the wind turbine using MATLAB/Simulink software, a very common and well-liked tool for modelling dynamic systems. The wind turbine, including the rotor-side and stator-side converters and the control system for the DFIG, are all precisely modelled in the simulation model. The approach studied combines machine learning methods and signal processing techniques to find errors in the rotor-side, stator-side and grid. Wavelet analysis is used to examine the DFIG signals in order to identify fault-indicating characteristics. The collected characteristics are then used to train machine learning classifiers like Support Vector Machines (SVM), K-Nearest Neighbours (KNN) and Decision Trees to find errors in the DFIG. Simulations with several fault types, such as short circuits and voltage rise and drop are used to assess the suggested technique. The study offers a practical tool for wind turbine staff and engineers to find DFIG defects and prevent long periods of downtime, which could be expensive

A target guided subband filter for acoustic event detection in noisy environments using wavelet packets

This paper deals with acoustic event detection (AED), such as screams, gunshots, and explosions, in noisy environments. The main aim is to improve the detection performance under adverse conditions with a very low signal-to-noise ratio (SNR). A novel filtering method combined with an energy detector is presented. The wavelet packet transform (WPT) is first used for time-frequency representation of the acoustic signals. The proposed filter in the wavelet packet domain then uses a priori knowledge of the target event and an estimate of noise features to selectively suppress the background noise. It is in fact a content-aware band-pass filter which can automatically pass the frequency bands that are more significant in the target than in the noise. Theoretical analysis shows that the proposed filtering method is capable of enhancing the target content while suppressing the background noise for signals with a low SNR. A condition to increase the probability of correct detection is also obtained. Experiments have been carried out on a large dataset of acoustic events that are contaminated by different types of environmental noise and white noise with varying SNRs. Results show that the proposed method is more robust and better adapted to noise than ordinary energy detectors, and it can work even with an SNR as low as -15 dB. A practical system for real time processing and multi-target detection is also proposed in this work

Application of Spectral Kurtosis to Characterize Amplitude Variability in Power Systems’ Harmonics

The highly-changing concept of Power Quality (PQ) needs to be continuously reformulated due to the new schemas of the power grid or Smart Grid (SG). In general, the spectral content is characterized by their averaged or extreme values. However, new PQ events may consist of large variations in amplitude that occur in a short time or small variations in amplitude that take place continuously. Thus, the former second-order techniques are not suitable to monitor the dynamics of the power spectrum. In this work, a strategy based on Spectral Kurtosis (SK) is introduced to detect frequency components with a constant amplitude trend, which accounts for amplitude values’ dispersion related to the mean value of that spectral component. SK has been proven to measure frequency components that follow a constant amplitude trend. Two practical real-life cases have been considered: electric current time-series from an arc furnace and the power grid voltage supply. Both cases confirm that the more concentrated the amplitude values are around the mean value, the lower the SK values are. All this confirms SK as an effective tool for evaluating frequency components with a constant amplitude trend, being able to provide information beyond maximum variation around the mean value and giving a progressive index of value dispersion around the mean amplitude value, for each frequency component

Long-range correlation and multifractality in Bach's Inventions pitches

We show that it can be considered some of Bach pitches series as a stochastic process with scaling behavior. Using multifractal deterend fluctuation analysis (MF-DFA) method, frequency series of Bach pitches have been analyzed. In this view we find same second moment exponents (after double profiling) in ranges (1.7-1.8) in his works. Comparing MF-DFA results of original series to those for shuffled and surrogate series we can distinguish multifractality due to long-range correlations and a broad probability density function. Finally we determine the scaling exponents and singularity spectrum. We conclude fat tail has more effect in its multifractality nature than long-range correlations.Comment: 18 page, 6 figures, to appear in JSTA

Wavelet energy moment and neural networks based particle swarm optimisation for transmission line protection

In this study, a combined approach of discrete wavelet transform analysis and a feed forward neural networks algorithm to detect and classify transmission line faults. The proposed algorithm uses a multi -resolution analysis decoposition of three-phasecurrents only to calculate the wavelet energy moment of detailed coefficients. In comparison with the energy spectrum, the energy moment could reveal the energy distribution features better, which is beneficial when extracting signal features. Theapproach use particle swarm optimization algorithm to train a feed forward neural network. The goal is the enhancement of the convergence rate, learning process and fill up the gap of local minimum point.The purposed scheme consists of two FNNs, one for detecting and another for classifying all the ten types of faults using Matlab/Simulink. The proposed algorithm have been extensively tested on a system 400 kV, 3 phases, 100 km line consideringvarious fault parameter variations