Asset management and maintenance: a smart grid perspective

This paper presents the importance, issues and challenges related to Smart Grid. It also evaluates various approaches for Smart Grid planning and operation. It discusses tools for asset management and their applicability to the next generation grid. Aging assets, uncertainty in load demand profile and renewable energy resources, and demand management create a challenge for the optimal operation and maintenance of electrical grid. This paper addresses the challenges and opportunities to improve transmission and distribution systems asset maintenance. This paper also presents the asset replacement alternatives. This paper also presents the cost-benefit analysis of asset management using the information/real time data from the utility company. This paper will serve a guide for doing the asset management to the electrification  process, investment and  recovery to sustain reliable and efficient power delivery

Intestinal carriage of Staphylococcus aureus: How does its frequency compare with that of nasal carriage and what is its clinical impact?

The bacterial species Staphylococcus aureus, including its methicillin-resistant variant (MRSA), finds its primary ecological niche in the human nose, but is also able to colonize the intestines and the perineal region. Intestinal carriage has not been widely investigated despite its potential clinical impact. This review summarizes literature on the topic and sketches the current state of affairs from a microbiological and infectious diseases' perspective. Major findings are that the average reported detection rate of intestinal carriage in healthy individuals and patients is 20% for S. aureus and 9% for MRSA, which is approximately half of that for nasal carriage. Nasal carriage seems to predispose to intestinal carriage, but sole intestinal carriage occurs relatively frequently and is observed in 1 out of 3 intestinal carriers, which provides a rationale to include intestinal screening for surveillance or in outbreak settings. Colonization of the intestinal tract with S. aureus at a young age occurs at a high frequency and may affect the host's immune system. The frequency of intestinal carriage is generally underestimated and may significantly contribute to bacterial dissemination and subsequent risk of infections. Whether intestinal rather than nasal S. aureus carriage is a primary predictor for infections is still ill-defined

Power system low frequency oscillation mode estimation using wide area measurement systems

Oscillations in power systems are triggered by a wide variety of events. The system damps most of the oscillations, but a few undamped oscillations may remain which may lead to system collapse. Therefore low frequency oscillations inspection is necessary in the context of recent power system operation and control. Ringdown portion of the signal provides rich information of the low frequency oscillatory modes which has been taken into analysis. This paper provides a practical case study in which seven signal processing based techniques i.e. Prony Analysis (PA), Fast Fourier Transform (FFT), S-Transform (ST), Wigner-Ville Distribution (WVD), Estimation of Signal Parameters by Rotational Invariance Technique (ESPRIT), Hilbert-Huang Transform (HHT) and Matrix Pencil Method (MPM) were presented for estimating the low frequency modes in a given ringdown signal. Preprocessing of the signal is done by detrending. The application of the signal processing techniques is illustrated using actual wide area measurement systems (WAMS) data collected from four different Phasor Measurement Unit (PMU) i.e. Dadri, Vindyachal, Kanpur and Moga which are located near the recent disturbance event at the Northern Grid of India. Simulation results show that the seven signal processing technique (FFT, PA, ST, WVD, ESPRIT, HHT and MPM) estimates two common oscillatory frequency modes (0.2, 0.5) from the raw signal. Thus, these seven techniques provide satisfactory performance in determining small frequency modes of the signal without losing its valuable property. Also a comparative study of the seven signal processing techniques has been carried out in order to find the best one. It was found that FFT and ESPRIT gives exact frequency modes as compared to other techniques, so they are recommended for estimation of low frequency modes. Further investigations were also carried out to estimate low frequency oscillatory mode with another case study of Eastern Interconnect Phasor Project (EIPP) data with the seven signal processing techniques (FFT, PA, ST, WVD, ESPRIT, HHT and MPM). It was found from the simulation results of EIPP data that two low frequency oscillatory mode (0.51 Hz, 0.2 Hz) exist

Development of an Irradiance Meter using Arduino Technology

This paper proposes the use of microcontroller development boards like the Arduino UNO for measuring solar irradiance. Solar irradiance is the amount of solar power received per unit area. Measuring solar irradiance is of prime importance as it is essential to determine the feasibility of a site for establishing a solar power plant. However, the measurement poses significant challenges, such as site-specific measurements and climatic dependency. Commercial meters that are used for measurement are quite expensive. In the proposed methodology, various types of equipment such as Arduino UNO, LCD, Solar panel, current sensor, Datalogger Shield, and additional sensors for humidity and temperature are used for the measurement. This proposed device can act as an inexpensive alternative to commercial ones. The Solar cell sets forth a small amount of current that is detected by the current sensor and interfaced with the Arduino UNO. This data is logged into the Secure Digital (SD) memory card using the Datalogger shield module. Similarly, the data from the DHT11 (Humidity and temperature sensor) is also recorded on the SD card. The recorded data can be organized into a Comma Separated Value (CVS) file listed according to the date and time of measurement

Support vector machine based fault classification and location of a long transmission line

This paper investigates support vector machine based fault type and distance estimation scheme in a long transmission line. The planned technique uses post fault single cycle current waveform and pre-processing of the samples is done by wavelet packet transform. Energy and entropy are obtained from the decomposed coefficients and feature matrix is prepared. Then the redundant features from the matrix are taken out by the forward feature selection method and normalized. Test and train data are developed by taking into consideration variables of a simulation situation like fault type, resistance path, inception angle, and distance. In this paper 10 different types of short circuit fault are analyzed. The test data are examined by support vector machine whose parameters are optimized by particle swarm optimization method. The anticipated method is checked on a 400 kV, 300 km long transmission line with voltage source at both the ends. Two cases were examined with the proposed method. The first one is fault very near to both the source end (front and rear) and the second one is support vector machine with and without optimized parameter. Simulation result indicates that the anticipated method for fault classification gives high accuracy (99.21%) and least fault distance estimation error (0.29%

Modified Complete Ensemble Empirical Mode Decomposition based HIF detection approach for microgrid system

Detection of high impedance fault (HIF) in an active distribution system is a challenging task. It is learned from the fault characteristics that detection and discrimination of HIF during different critical conditions is impossible using the fault current magnitude. Under dependable situations such as energization of a transformer, nonlinear load and capacitor bank detection and discrimination of HIF is challenging. In a distribution system with an inverter based distributed generator (IBDG), the current contribution during islanding mode is very low and also for HIF condition. To mitigate these issues, an intelligent approach applying Modified Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (MCEEMDAN) on residual current signal is developed. The second intrinsic mode function (IMF2) is extracted using MCEEMDAN and its Teager Kaiser Energy Operator (TKEO) is computed to detect and discriminate the HIF against other physical events. The novelty of MCEEMDAN approach lies with its noise free output and faster response as compared to other time–frequency approaches. The method is tested for several fault and non-fault cases including, presence of noise, harmonics, and unbalance loadings. The comparison with recently reported techniques for very HIF and ungrounded system proofs the efficacy of the method.publishedVersio