Waveform Disturbances Research Articles

Online automated system for incipient fault and failure detection of distribution apparatus using waveform disturbances

Online automated system for incipient fault and failure detection of distribution apparatus using waveform disturbances

Cooley-Tukey FFT Algorithm based on GDFT for Phasor Estimation by PMU under Power Quality Disturbances

Due to the high penetration of renewable energy sources, such as photovoltaic panels and wind turbines, in addition to the use of different electric power supplies in the power grid, there are major disturbances in the forms of electric waves. These variations and disturbances must be monitored and controlled for the efficient management of transmission and distribution of electrical energy, safety, and electrical protection systems. Nowadays, Phasor Measurement Unit (PMU) technology is an essential tool to develop the supervision, protection, and control of the electrical power grid. PMUs measure the amplitude and angle of current and voltage waveforms on a Coordinated Universal Time (UTC) time scale and speedily measure the fundamental frequencies and their rates of change using fast and accurate estimation algorithms. This paper presents a phasor estimation using a Modified Cooley-Tukey Fast Fourier Transform algorithm based on the Generalized Discrete Fourier Transform (DFT) used in PMUs, using simulations in MATLAB. This algorithm was utilized to accelerate and simplify the computation of DFTs. To validate the performance under waveform disturbances, several tests with different waveforms and disturbances were simulated and interpreted according to the standard and compared with DFT.

Pulsating modes of a Taylor cone under an unsteady electric field

Micro-thrusters based on the mechanism of electro-atomization are widely used in space engines and play an important role in spacecraft power supply and attitude adjustment. However, current research on the dynamic characteristics of electro-atomization is focused on a steady electric field, and the effect of an alternating electric field is helpful in order to realize the attitude adjustment of the micro-thruster pulse working mode and the self-neutralization of a plume by alternating polarity. For this reason, this work focuses on experimental investigation; the oscillation behavior of a Taylor cone under an alternating electric field excitation has also been systematically examined. Using a high-speed camera, the pulsating modes of a Taylor cone under the disturbance of a continuous sinusoidal waveform and a single pulse waveform were recorded, and the responses of different oscillation modes and geometric characteristics to different disturbance parameters analyzed and discussed. This research has shown that the oscillation of a Taylor cone under a continuous sinusoidal waveform disturbance can be divided into six different modes; the dynamic characteristics of each mode are discussed, and each mode has been assigned its own unique matching relationship for different disturbance parameters. The three-dimensional distribution phase diagrams of the six modes were drawn after the relevant parameters were made dimensionless. Under the disturbance of a single pulse waveform, the geometric oscillation of the Taylor cone displayed a specific law: the oscillation period of the diameter and cone angle was consistent and showed a strong correlation with the charge relaxation time of the liquid.

Characterization of meso-structure of glutenite reservoirs by ultrasonic characteristics and the velocity heterogeneity

Different from fine-grained sedimentary reservoirs such as sandstone and shale, glutenite reservoir is rich in gravel. Its physical and mechanical properties are affected by gravel content and gravel size and are related to the cementation and support pattern between the matrix and gravel. Characterizing the meso-structure of glutenite reservoirs and its influence on the physical and mechanical properties is of great significance for the evaluation, exploration and development of glutenite reservoirs. Thus, an experimental study on ultrasonic characteristics was conducted on glutenite of different meso-structures. Measurements were performed along different travel paths in each sample to study the effect of gravel. Combined with the meso-structure of samples, the scattering effect on ultrasonic waves by gravel was analyzed. The results show that (1) Gravel content, gravel size and the support pattern between the matrix and gravel have an important influence on the ultrasonic waveform, frequency spectrum and velocity. The higher gravel content or larger gravel size is, the more severe waveform disturbance is. And the wave velocity decreases with the increase of gravel content or gravel size. (2) In glutenite, the scattering of waves by gravel is responsible for the ultrasonic characteristics. The higher the gravel content or larger the gravel size is, the stronger the scattering effect is. (3) Because the gravel size and wavelength in glutenite are approximately the same, the effective medium theory does not work on ultrasonic propagation in glutenite. (4) Thus, we established the velocity heterogeneity model to evaluate the meso-structure of glutenite. The rise of the velocity heterogeneity reflects the increase of gravel content, gravel size and meso-heterogeneity, and the transition of support pattern from matrix-support to gravel-support. This approach is more concise and effective and may potentially realize downhole meso-structure characterization by logging.

A novel substrate for arrhythmias in Chagas disease.

BackgroundChagas disease (CD) is a neglected disease that induces heart failure and arrhythmias in approximately 30% of patients during the chronic phase of the disease. Despite major efforts to understand the cellular pathophysiology of CD there are still relevant open questions to be addressed. In the present investigation we aimed to evaluate the contribution of the Na+/Ca2+ exchanger (NCX) in the electrical remodeling of isolated cardiomyocytes from an experimental murine model of chronic CD.Methodology/Principal findingsMale C57BL/6 mice were infected with Colombian strain of Trypanosoma cruzi. Experiments were conducted in isolated left ventricular cardiomyocytes from mice 180–200 days post-infection and with age-matched controls. Whole-cell patch-clamp technique was used to measure cellular excitability and Real-time PCR for parasite detection. In current-clamp experiments, we found that action potential (AP) repolarization was prolonged in cardiomyocytes from chagasic mice paced at 0.2 and 1 Hz. After-depolarizations, both subthreshold and with spontaneous APs events, were more evident in the chronic phase of experimental CD. In voltage-clamp experiments, pause-induced spontaneous activity with the presence of diastolic transient inward current was enhanced in chagasic cardiomyocytes. AP waveform disturbances and diastolic transient inward current were largely attenuated in chagasic cardiomyocytes exposed to Ni2+ or SEA0400.Conclusions/SignificanceThe present study is the first to describe NCX as a cellular arrhythmogenic substrate in chagasic cardiomyocytes. Our data suggest that NCX could be relevant to further understanding of arrhythmogenesis in the chronic phase of experimental CD and blocking NCX may be a new therapeutic strategy to treat arrhythmias in this condition.

Electromagnetic Immunity Performance of Intelligent Electronic Equipment in Smart Substation’s Electromagnetic Environment

With the gradual increasing of the transmission voltage level, the situation of interference on secondary side equipment in smart substations is becoming more serious. The authors tested the interference waveforms caused by the circuit breaker’s operation at the smart substation, which showed the severity of the electromagnetic (EM) environment. This paper takes the electronic current transformer acquisition card as an example. The influence of EM interference sources on the reliability of the acquisition card is analyzed. Then, standardized electromagnetic compatibility (EMC) immunity tests of the international electrotechnical commission (IEC) for different interferences are implemented. Typical output characteristics of acquisition cards for different tests are analyzed. Innovatively, this paper proposes a quantitative evaluation method of output waveform disturbance to evaluate the acquisition card’s performance. This disturbance distortion is of great significance to the EM environment estimation of the acquisition card.

Mitigation Technique of Voltage Flicker in Arc Furnace Using D-STATCOM

Voltage flicker is the disturbance in voltage waveform due to the variation of load in the power system. Voltage flicker is the most important factor that effects the power quality of an electric arc furnace. voltage flicker causes the fluctuation in the light source that varies from time to time. Therefore, this paper deals with the study of Flexible AC Transmission Line (FACTS) based distribution static synchronous condenser (D-STATCOM) device that alleviates the voltage flicker from the arc furnace. The arc furnace is acting as current source and simulated in the MATLAB for obtaining the result.

An Extended Kalman Filter Approach for Accurate Instantaneous Dynamic Phasor Estimation

This paper proposes the application of a non-linear Extended Kalman Filter (EKF) for accurate instantaneous dynamic phasor estimation. An EKF-based algorithm is proposed to better adapt to the dynamic measurement requirements and to provide real-time tracking of the fundamental harmonic components and power system frequencies. This method is evaluated using dynamic compliance tests defined in the IEEE C37.118.1-2011 synchrophasor measurement standard, providing promising results in phasor and frequency estimation, compliant with the accuracy required in the case of off-nominal frequency, amplitude and phase angle modulations, frequency ramps, and step changes in magnitude and phase angle. An important additional feature of the method is its capability for real-time detection of transient disturbances in voltage or current waveforms using the residual of the filter, which enables flagging of the estimation for suitable processing.

Power Quality Feature Modelling of Waveform Disturbances for Distribution System Diagnosis

This paper presents a Power Quality (PQ) modelling and filtering processes for the distribution system disturbance recognition learning. typical PQ waveforms with mathematical applications and gathered field data are applied to our models. The process has modules to obtain the waveforms of incipient failures or abnormal operations, trouble condition waveforms in the distribution system through feature extraction and trigger condition techniques. In this regard, we found that proposed extraction criteria of determination rules can interact suitably with our parameter update method to detect and containing the waveforms. Therefore, the recurring analysis can classify the event conditions and ensures disturbance and fault recognitions which containing the waveform information.

Numerical simulation of dynamic thermal characteristics of cryogenic target

Fusion power offers the prospect of a safe and clean sustainable energy source, and is of increasing importance for meeting the world energy demand and curbing CO2 emissions. For an indirect-driven inertial confinement cryogenic target, the D-T ice layer inside the capsule should have a uniformity more than 99% and an inner surface roughness less than a root mean square value of 1 m to avoid Rayleigh-Taylor instabilities. And this highly smooth ice layer required for ignition is considered to be affected by the thermal environment around the fuel capsule. In the present study, a numerical investigation is conducted to examine the static and dynamic characteristics of the thermal environment outside the fuel capsule. Numerical model is proposed and verified by a simplified cryogenic target, and the calculated temperature distribution around the capsule shows to be in good agreement with the experimental data. Based on the established model, the propagation of periodic disturbance of cooling wall temperature in the hohlraum is investigated, and the relations between the temperature disturbance on the cooling wall and the temperature distribution around the capsule surface are obtained. The effects of disturbance amplitude, the disturbance period, and the hohlraum gas composition on the propagation process are investigated separately. The results indicate that for stable cooling temperature, the thermal environment around the capsule shows certain dependence on the gas filled in the hohlraum. The temperature uniformity of the capsule outer surface deteriorates with the increase of fill gas pressure but can be improved by increasing the He content of the filling gas mixture. At an oscillating cooling temperature, the attenuation of amplitude is significant when the periodic disturbance propagates from the cooling rings to the hohlraum and to the capsule surface. For the sine wave form disturbance investigated in the present study, shorter disturbance period results in larger attenuation of the disturbance amplitude. Higher gas pressure leads to smaller amplitude of average temperature on the capsule outer surface. The propagation process of cooling temperature disturbance also demonstrates dependence on the filling gas composition. The higher fraction of H2 in the He-H2 mixture helps to attenuate the disturbance amplitude and suppress the propagation of the temperature disturbance. However, the temperature uniformity around the capsule exhibits different characteristics from cooling temperature disturbance. Under the oscillating cooling conditions, moderate period, lower amplitude, lower pressure and higher fraction of He in the He-H2 mixture help to improve the temperature uniformity around the capsule. The results are of guiding significance for determining the controlling scheme in experiment and further design option for the cryogenic target.

A Wavelet Based Tool to Assist the Automated Analysis of Waveform Disturbances Records in Power Generators

This paper presents an automated disturbance analysis scheme for electrical power generating units. It proposes a method based on wavelet transform applied to transient (waveform) records of disturbances. The objectives are to detect faults in generators, perform a segmentation of the records and extract information that characterize the faults. The output of the method is a set of representative information of the monitored signals, which can be used in a classification system for automated fault diagnosis. The large volume of data produced by the digital recorders of power system disturbances justifies the search for automated analysis methods that can be employed to assist the utilities engineers. Tests were conducted to determine the best mother wavelet in the segmentation process. The proposed method was applied to five case studies with real disturbance data and the obtained results confirmed its efficiency.

A hybrid method based on time frequency analysis and artificial intelligence for classification of power quality events

Recognition of power quality events by analyzing voltage waveform disturbances is a very important task for power system monitoring. This paper presents a hybrid intelligent scheme for the classification of power quality disturbances. The proposed algorithm is realized through three main steps: feature extraction, feature selection and feature classification. The feature vectors are extracted using S-transform ST and Wavelet transform WT which are very powerful time-frequency analysis tools. In order to avoid large dimension of feature vector, three different approaches are applied for feature selection step, namely Sequential Forward Selection SFS, Sequential Backward Selection SBS and Genetic Algorithm GA. In the next step, the most meaningful features are applied to Probabilistic Neural Network PNN as classifier core. Various transient events, such as voltage sag, swell, interruption, harmonics, transient, sag with harmonics, swell with harmonics, and flicker, are tested. Sensitivity of the proposed algorithm under different noisy conditions is investigated in this article. Results show that the classifier can detect and classify different power quality signals, even under noisy conditions, with high accuracy.

Harmonic Reduction in Grid Connected Wind Energy System with Dynamic Voltage Restorer Using Fuzzy Logic Technique

Grid connected Wind energy conversion system uses power Electronic converters hardly in fixed speed systems except for reactive power compensation. This Power Electronic converter causes major electrical losses and affects power quality of the system. The quality of the voltage and current is also affected by sensitive loads which are used widely in most of Automated Industries and Research laboratories. Disturbances in waveforms occurs which results in harmonics. To improve such power quality problem of the system DVR which is based on fuzzy logic technique is introduced. The system is tested for conventional technique and also with fuzzy logic. The performance is investigated through MATLAB/SIMULINK software. The simulation results confirm the superior performance of the proposed DVR based fuzzy logic technique than conventional technique. The proposed system reduces harmonics as well as mitigates voltage sag/swell.

Random Generation of Arbitrary Waveforms for Emulating Three-Phase Systems

This paper describes an apparatus for generating a signal representative of steady-state and transient disturbances in three-phase waveforms of an ac electrical system as described in IEEE Std 1159-09. It can be configured as a synthesizer of randomly distorted signals for different applications: for testing the effects of disturbed grid on equipment and to generate patterns of electrical disturbances for the training of artificial neural networks, which are used for measuring power quality tasks. For the first purpose, voltage and current amplifiers are added in the output stage, which allows the generation of disturbed signals at grid level.

Wavelet transform and multi-class relevance vector machines based recognition and classification of power quality disturbances

Recognition of power quality events by analyzing the voltage waveform disturbances is a very important task for the power system monitoring. This paper presents an integrated model for recognizing power quality disturbances (PQDs) using a new wavelet multi-class relevance vector machines (WMRVMs). The so-called relevance vector machines (RVMs) is an effective classification tool. The proposed method employs the wavelet transform techniques to extract the most important and significant feature from details and approximation waves. The obtained feature vectors are used for training the RVMs to classify the PQDs. Various transient events are tested, the result shows that the classifier can detect and classify different power quality signal correctly. Finally, sensitivity of the proposed algorithm under noise condition is investigated in this paper. Copyright © 2010 John Wiley & Sons, Ltd.

Detection and Classification of Power Quality Disturbances Using Wavelet Transform and Support Vector Machines

Recognition of power quality events by analyzing voltage waveform disturbances is a very important task for power system monitoring. This article presents a novel approach for the recognition and classification of power quality disturbances using wavelet transform and wavelet-support vector machines. The proposed method employs wavelet transform techniques to extract the most important and significant feature from details and approximation waves. The obtained severable feature vectors are used for training the support vector machines to classify the power quality disturbances. Various transient events, such as voltage sag, swell, interruption, harmonic, transient, sag with harmonic, swell with harmonic, and flicker, are tested. Sensitivity of the proposed algorithm under different noise conditions is investigated in this article. The results show that the classifier can detect and classify different power quality signals, even under noisy conditions, correctly.

Recognition of power quality events by using multiwavelet-based neural networks

Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a novel approach for the recognition of power quality disturbances using multiwavelet transform and neural networks. The proposed method employs the multiwavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, swell, interruption, notching, impulsive transient, and harmonic distortion show that the classifier can detect and classify different power quality signal types efficiency.

Hybrid Wavelet–Fourier Method for Harmonics and Harmonic Subgroups Measurement—Case Study

This paper deals with a problem of fast and reliable waveform disturbances recognition. It means real-time detection and evaluation of different kinds of waveform distortions such as harmonics, interharmonics, as well as transients and notching. In this paper, the problems of sampling frequency, a resulting number of samples and a complementary application of Fourier transform and a wavelet transform have been described. The main focus of this paper has been placed on harmonics and harmonic subgroups analysis. However, there is an assumption that harmonics are to be measured concurrently with transient and notching by a single dedicated device. All analyses have been carried out for the real voltages waveforms registered in ship and land electric power systems

Wavelet-based neural network for power quality disturbance recognition and classification

Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a new approach for the recognition of power quality disturbances using wavelet transform and neural networks. The proposed method employs the wavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, voltage swell, interruption, notching, impulsive transient, and harmonic distortion. The results show that the classifier can efficiently detect and classify different types of power quality disturbance.

Classification of Power Quality Events Using Optimal Time-Frequency Representations—Part 2: Application

Classification of power-quality (PQ)-related voltage and current waveform disturbances is a key task for power system monitoring. A new method based on the optimized time-frequency representation (TFR) has been proposed in the first paper of this two-paper series. This paper (the second paper) presents a case study of PQ event classification with the proposed method. The classification algorithm has been successfully tested with 860 real world PQ events that cover five classes, achieving a recognition rate of 98%. The algorithm is implemented on a digital signal processor (DSP) based hardware system and optimized according to the DSP architecture to meet the hard real-time constraints. The DSP-based system is capable of processing a five-cycle (83.3 ms) PQ waveform within 11.2 ms. The real-time computing capability of the algorithm has been verified with this result. The scalability of this method is also discussed.