A simple predictive method of critical flicker detection for human healthy precaution

Interharmonics and flickers have an interrelationship between each other. Based on International Electrotechnical Commission (IEC) flicker standard, the critical flicker frequency for a human eye is located at 8.8 Hz. Additionally, eye strains, headaches, and in the worst case seizures may happen due to the critical flicker. Therefore, this paper introduces a worthwhile research gap on the investigation of interrelationship between the amplitudes of the interharmonics and the critical flicker for 50 Hz power system. Consequently, the significant findings obtained in this paper are the amplitudes of two particular interharmonics are able to detect the critical flicker. In this paper, the aforementioned amplitudes are detected by adaptive linear neuron (ADALINE). After that, the critical flicker is detected by substituting the aforesaid amplitudes to the formulas that have been generated in this paper accordingly. Simulation and experimental works are conducted and the accuracy of the proposed algorithm which utilizes ADALINE is similar, as compared to typical Fluke power analyzer. In a nutshell, this simple predictive method for critical flicker detection has strong potential to be applied in any human crowded places (such as offices, shopping complexes, and stadiums) for human healthy precaution purpose due to its simplicity

Power quality disturbance detection and classification using signal processing and soft computing techniques

The quality of electric power and disturbances occurred in power signal has become a major issue among the electric power suppliers and customers. For improving the power quality continuous monitoring of power is needed which is being delivered at customer’s sites. Therefore, detection of PQ disturbances, and proper classification of PQD is highly desirable. The detection and classification of the PQD in distribution systems are important tasks for protection of power distributed network. Most of the disturbances are non-stationary and transitory in nature hence it requires advanced tools and techniques for the analysis of PQ disturbances. In this work a hybrid technique is used for characterizing PQ disturbances using wavelet transform and fuzzy logic. A no of PQ events are generated and decomposed using wavelet decomposition algorithm of wavelet transform for accurate detection of disturbances. It is also observed that when the PQ disturbances are contaminated with noise the detection becomes difficult and the feature vectors to be extracted will contain a high percentage of noise which may degrade the classification accuracy. Hence a Wavelet based de-noising technique is proposed in this work before feature extraction process. Two very distinct features common to all PQ disturbances like Energy and Total Harmonic Distortion (THD) are extracted using discrete wavelet transform and are fed as inputs to the fuzzy expert system for accurate detection and classification of various PQ disturbances. The fuzzy expert system not only classifies the PQ disturbances but also indicates whether the disturbance is pure or contains harmonics. A neural network based Power Quality Disturbance (PQD) detection system is also modeled implementing Multilayer Feed forward Neural Network (MFNN)

Power Quality Disturbance Detection and Classification

Power quality (PQ) monitoring is an essential service that many utilities perform for their industrial and larger commercial customers. Detecting and classifying the different electrical disturbances which can cause PQ problems is a difficult task that requires a high level of engineering knowledge. The vast majority of the disturbances are non-stationary and transitory in nature subsequently it requires advanced instruments and procedures for the examination of PQ disturbances. In this work a hybrid procedure is utilized for describing PQ disturbances utilizing wavelet transform and fuzzy logic. A no of PQ occasions are produced and decomposed utilizing wavelet decomposition algorithm of wavelet transform for exact recognition of disturbances. It is likewise watched that when the PQ disturbances are contaminated with noise the identification gets to be troublesome and the feature vectors to be separated will contain a high amount of noise which may corrupt the characterization precision. Consequently a Wavelet based denoising system is proposed in this work before feature extraction process. Two extremely distinct features basic to all PQ disturbances like Energy and Total Harmonic Distortion (THD) are separated utilizing discrete wavelet transform and is nourished as inputs to the fuzzy expert system for precise recognition and order of different PQ disturbances. The fuzzy expert system classifies the PQ disturbances as well as demonstrates whether the disturbance is unadulterated or contains harmonics. A neural network based Power Quality Disturbance (PQD) recognition framework is additionally displayed executing Multilayer Feedforward Neural Network (MFNN)

Tracking Rhythmicity in Biomedical Signals using Sequential Monte Carlo methods

Cyclical patterns are common in signals that originate from natural systems such as the human body and man-made machinery. Often these cyclical patterns are not perfectly periodic. In that case, the signals are called pseudo-periodic or quasi-periodic and can be modeled as a sum of time-varying sinusoids, whose frequencies, phases, and amplitudes change slowly over time. Each time-varying sinusoid represents an individual rhythmical component, called a partial, that can be characterized by three parameters: frequency, phase, and amplitude. Quasi-periodic signals often contain multiple partials that are harmonically related. In that case, the frequencies of other partials become exact integer multiples of that of the slowest partial. These signals are referred to as multi-harmonic signals. Examples of such signals are electrocardiogram (ECG), arterial blood pressure (ABP), and human voice. A Markov process is a mathematical model for a random system whose future and past states are independent conditional on the present state. Multi-harmonic signals can be modeled as a stochastic process with the Markov property. The Markovian representation of multi-harmonic signals enables us to use state-space tracking methods to continuously estimate the frequencies, phases, and amplitudes of the partials. Several research groups have proposed various signal analysis methods such as hidden Markov Models (HMM), short time Fourier transform (STFT), and Wigner-Ville distribution to solve this problem. Recently, a few groups of researchers have proposed Monte Carlo methods which estimate the posterior distribution of the fundamental frequency in multi-harmonic signals sequentially. However, multi-harmonic tracking is more challenging than single-frequency tracking, though the reason for this has not been well understood. The main objectives of this dissertation are to elucidate the fundamental obstacles to multi-harmonic tracking and to develop a reliable multi-harmonic tracker that can track cyclical patterns in multi-harmonic signals

Voltage flicker estimation based on pair of inter-harmonics analysis method

This paper presents a voltage flicker estimation based on a pair of inter-harmonics analysis method. The proposed algorithm is able to estimate flicker frequency and amplitude changes of a voltage waveform. The correlation of the pair of inter-harmonics, flicker frequency, and amplitude changes are presented and their formulas highlighted. Experimental results indicate the amplitude of pair of inter-harmonics can detect the voltage flicker. Furthermore, the experimental results are compared with the measurement results obtained by using the Fluke power analyzer (Pst)

An Investigation into Vibration Based Techniques for Wind Turbine Blades Condition Monitoring

The rapid expansion of wind power has been accompanied by reported reliability problems and the aim is to provide a means of increasing wind turbine reliability, prevent break downs, increase availability and reduce maintenance costs and power outages. This research work reports the development of condition monitoring (CM) for early fault detection in wind turbine blades based on vibration measurements. The research started with a background and a survey of methods used for monitoring wind turbines. Then, finite element modelling (FEM) of three bladed horizontal axis wind turbine (HAWT) was developed to understand the nature and mechanism of the induced vibration. A HAWT test rig was constructed and equipped with computerised vibration measuring system for model verification. Statistical and spectral processing parameters then were used to analyse vibration signals that collected in healthy and faulty cases. Results obtained using time and frequency based techniques are not suitable for extracting blades condition related information. Consequently, empirical mode decomposition method (EMD), principal component analysis method (PCA) and continuous wavelet transform (CWT) are applied for extraction blade condition related features from the measured vibration. The result showed that although these methods generally proved their success in other fields, they have failed to detect small faults or changes in blade structure. Therefore, new techniques were developed using the above mentioned methods combined with feature intensity level (FIL) and crest factor. Namely, those are EDFIL, RMPCA and wavelet based FIL. The new techniques are found to be reliable, robust and sensitive to the severity of faults. Those analysis techniques are suitable to be the detection tool for an integrated wind turbine condition monitoring system. Directions for future work are also given at the end of the thesis

Characterization of Power Quality Disturbances using Signal Processing and Soft Computing Techniques

The power quality of the electric power has become an important issue for the electric utilities and their customers. In order to improve the quality of power, electric utilities continuously monitor power delivered at customer sites. Thus automatic classification of distribution line disturbances is highly desirable. The detection and classification of the power quality (PQ) disturbances in power systems are important tasks in monitoring and protection of power system network. Most of the disturbances are non-stationary and transitory in nature hence it requires advanced tools and techniques for the analysis of PQ disturbances. In this work a hybrid technique is used for characterizing PQ disturbances using wavelet transform and fuzzy logic. A no of PQ events are generated and decomposed using wavelet decomposition algorithm of wavelet transform for accurate detection of disturbances. It is also observed that when the PQ disturbances are contaminated with noise the detection becomes difficult and the feature vectors to be extracted will contain a high percentage of noise which may degrade the classification accuracy. Hence a Wavelet based de-noising technique is proposed in this work before feature extraction process. Two very distinct features common to all PQ disturbances like Energy and Total Harmonic Distortion (THD) are extracted using discrete wavelet transform and is fed as inputs to the fuzzy expert system for accurate detection and classification of various PQ disturbances. The fuzzy expert system not only classifies the PQ disturbances but also indicates whether the disturbance is pure or contains harmonics. A neural network based Power Quality Disturbance (PQD) detection system is also modeled implementing Multilayer Feedforward Neural Network (MFNN)