Impedance analysis of a power line distribution network using short-time Fourier transform

Abstract: The impedance of a low voltage distribution network is analyzed and presented. Data collected from field measurement which was done over one week is used in this analysis. Basically, a chirp is injected into the electric grid, and the voltage and current signals (corrupted by various noises, including the 50 Hz mains signal) are time-sampled and stored for processing. The voltage and current are processed to obtain the impedance of the electric grid. Simulations are performed to establish the efficacy of the method of analysis used to obtain the impedance. The sliding window method of the Discrete Fourier Transform (DFT) is used in analyzing these impedance values. An eventual channel model describing the network is also presented

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Frequencies Dominations for Different Rating of Distribution Transformer under Transients

Power transients faults on high voltage lines are prominently due to high frequency transients. These transients affect the predicted life and efficiency of equipment. The Fast Fourier Transform (FFT) is helpful in analysing the effect of high frequencies and Frequency Response Analysis (FRA) provide support in diagnosis and detection of deformation in a transformers. The major aim of this study is to analyse the incorporation of frequencies based on resonating core of a particular transformer. Using transfer function method an impedance change in transformer has been observed when equipment is subjected to high voltage transients. The effect of change in impedance is that it degrade the life of a core with respect to time. In this paper, research that has been done already on Transformers of different ratings i.e. 100, 50 and 30 kVA are studied and then an experiment is performed on 50-kVA transformer. It was concluded that the core of a transformer having rating equal or less than 50 kVA practically shows single resonance behavior while above 50 kVA for instance 100-kVA transformer core resonates twice. In actual, result defines the core deviating frequency with respect to the rating of a transformer

Arcing High Impedance Fault Detection Using Real Coded Genetic Algorithm

Safety and reliability are two of the most important aspects of electric power supply systems. Sensitivity and robustness to detect and isolate faults can influence the safety and reliability of such systems. Overcurrent relays are generally used to protect the high voltage feeders in distribution systems. Downed conductors, tree branches touching conductors, and failing insulators often cause high-impedance faults in overhead distribution systems. The levels of currents of these faults are often much smaller than detection thresholds of traditional ground fault detection devices, thus reliable detection of these high impedance faults is a real challenge. With modern signal processing techniques, special hardware and software can be used to significantly improve the reliability of detection of certain types of faults. This paper presents a new method for detecting High Impedance Faults (HIF) in distribution systems using real coded genetic algorithm (RCGA) to analyse the harmonics and phase angles of the fault current signals. The method is used to discriminate HIFs by identifying specific events that happen when a HIF occurs

SARAS 2: A Spectral Radiometer for probing Cosmic Dawn and the Epoch of Reionization through detection of the global 21 cm signal

The global 21 cm signal from Cosmic Dawn (CD) and the Epoch of Reionization (EoR), at redshifts $z \sim 6-30$, probes the nature of first sources of radiation as well as physics of the Inter-Galactic Medium (IGM). Given that the signal is predicted to be extremely weak, of wide fractional bandwidth, and lies in a frequency range that is dominated by Galactic and Extragalactic foregrounds as well as Radio Frequency Interference, detection of the signal is a daunting task. Critical to the experiment is the manner in which the sky signal is represented through the instrument. It is of utmost importance to design a system whose spectral bandpass and additive spurious can be well calibrated and any calibration residual does not mimic the signal. SARAS is an ongoing experiment that aims to detect the global 21 cm signal. Here we present the design philosophy of the SARAS 2 system and discuss its performance and limitations based on laboratory and field measurements. Laboratory tests with the antenna replaced with a variety of terminations, including a network model for the antenna impedance, show that the gain calibration and modeling of internal additives leave no residuals with Fourier amplitudes exceeding 2~mK, or residual Gaussians of 25 MHz width with amplitudes exceeding 2~mK. Thus, even accounting for reflection and radiation efficiency losses in the antenna, the SARAS~2 system is capable of detection of complex 21-cm profiles at the level predicted by currently favoured models for thermal baryon evolution.Comment: 44 pages, 17 figures; comments and suggestions are welcom

Distinguishing cancerous from non-cancerous cells through analysis of electrical noise

Since 1984, electric cell-substrate impedance sensing (ECIS) has been used to monitor cell behavior in tissue culture and has proven sensitive to cell morphological changes and cell motility. We have taken ECIS measurements on several cultures of non-cancerous (HOSE) and cancerous (SKOV) human ovarian surface epithelial cells. By analyzing the noise in real and imaginary electrical impedance, we demonstrate that it is possible to distinguish the two cell types purely from signatures of their electrical noise. Our measures include power-spectral exponents, Hurst and detrended fluctuation analysis, and estimates of correlation time; principal-component analysis combines all the measures. The noise from both cancerous and non-cancerous cultures shows correlations on many time scales, but these correlations are stronger for the non-cancerous cells.Comment: 8 pages, 4 figures; submitted to PR

Cause Identification of Electromagnetic Transient Events using Spatiotemporal Feature Learning

This paper presents a spatiotemporal unsupervised feature learning method for cause identification of electromagnetic transient events (EMTE) in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurement, and using the convolutional neural network (CNN) as the spatiotemporal feature representation along with softmax function. Despite the existing threshold-based, or energy-based events analysis methods, such as support vector machine (SVM), autoencoder, and tapered multi-layer perception (t-MLP) neural network, the proposed feature learning is carried out with respect to both time and space. The effectiveness of the proposed feature learning and the subsequent cause identification is validated through the EMTP simulation of different events such as line energization, capacitor bank energization, lightning, fault, and high-impedance fault in the IEEE 30-bus, and the real-time digital simulation (RTDS) of the WSCC 9-bus system.Comment: 9 pages, 7 figure

Performance Analysis of Discrete Wavelet Multitone Transceiver for Narrowband PLC in Smart Grid

Smart Grid is an abstract idea, which involves the utilization of powerlines for sensing, measurement, control and communication for efficient utilization and distribution of energy, as well as automation of meter reading, load management and capillary control of Green Energy resources connected to the grid. Powerline Communication (PLC) has assumed a new role in the Smart Grid scenario, adopting the narrowband PLC (NB-PLC) for a low cost and low data rate communication for applications such as, automatic meter reading, dynamic management of load, etc. In this paper, we have proposed and simulated a discrete wavelet multitone (DWMT) transceiver in the presence of impulse noise for the NB-PLC channel applications in Smart Grid. The simulation results show that a DWMT transceiver outperforms a DFT-DMT with reference to the bit error rate (BER) performance