Design With Operational Amplifiers And Analog Integrated Circuits

Model output statistics downscaling using support vector machine for the projection of spatial and temporal changes in rainfall of Bangladesh

Projection of spatial and temporal changes of rainfall in Sarawak of Borneo Island using statistical downscaling of CMIP5 models

The possible future changes in temperature over Nigeria were projected in this study. Using Climate Research Unit (CRU) temperature as the reference data, gain ratio (GR), entropy gain (EG), and symmetrical uncertainty (SU) feature selection methods and a multi-criteria decision-making (MCDM) approach were used in selecting the most suitable GCMs for Nigeria from 20 Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models (GCMs). The biases in selected GCMs were corrected using power transformation (PT) method. Multi-model ensembles (MMEs) were generated for the selected GCMs for the different temperature classes’ maximum, average, and minimum for all RCPs. The MMEs were used for the projection of temperatures over the country during 2010–2039, 2040–2069, and 2070–2099. The GCMs HadGEM2-ES, CESM1-CAM5, CSIRO-Mk3.6.0, and MRI-CGCM3 were the best performing in replicating temperature characteristics of the observed temperature in Nigeria. The MME mean projections of bias-corrected (BC) GCMs using PT revealed that there will be an increase in temperature of 4.0 °C at the semi-arid and 5.0 °C at the arid regions during dry and wet seasons respectively under RCP 4.5. In the same regions, the maximum temperature is expected to increase up to 5.5 °C under RCP 8.5 during 2070–2099 in the dry season. In the wet season, temperatures are expected to be higher under RCP 8.5, with an increase of 0.0–4.0 °C in the southern region and 3.0–6.9 °C in the northern region.

GCM selection and temperature projection of Nigeria under different RCPs of the CMIP5 GCMS

Rolling element bearings are one of the most critical components of rotating machinery, with bearing faults amounting up to 50% of the faults in electrical machines. Therefore, the bearing fault diagnosis has attracted attention of many researchers. Typically, the bearing fault diagnosis is performed using vibration signals from the machine. In addition, by using deep learning algorithms on the vibration signals, the fault detection accuracy close to 100% can be achieved. However, measurement of vibration signals requires an additional sensor, which is not present in majority of the machines. Nevertheless, with an alternative approach, the stator current can be used for diagnosis. Therefore, the paper emphasizes on the diagnosis of bearing faults using the stator current. The diagnosis requires signal processing for the fault signature extraction that is buried underneath the noise in the current signal. The paper uses the Paderborn University damaged bearing dataset, which contains stator current data from healthy, real damaged inner raceway and real damaged outer raceway bearings with different fault severity. For fault signature extraction from the current signals, the redundant frequencies in the signals are filtered, then from the filtered signals eight features are extracted from the time and time-frequency domain by using the wavelet packet decomposition (WPD). Then, using these features and the well known deep learning algorithm Long Short-Term Memory (LSTM), bearing fault classification is made. The deep learning LSTM algorithm is mostly used in speech recognition due to its time coherence, but in this paper, the ability of LSTM is also demonstrated with the fault classification accuracy of 96%, which outperforms most of the present algorithms that perform bearing fault diagnosis using stator current. The method developed is independent of the speed and the loading conditions.

LSTM Based Bearing Fault Diagnosis of Electrical Machines using Motor Current Signal

In recent years due to globalization and increase in competition to capture market share, the customer is having many options of cars at much affordable prices. This has led to tremendous amount of cars on the road. The big cities in India are normally crowded and the city's existing infrastructure falls short to satisfy the growing needs. This problem is also combined with the inefficient use of parking space and uneven distribution of traffic which leads to congestion in vehicular traffic. This paper introduces a novel technique based on Internet of Things platform to improve efficiency of the existing parking systems. It guides driver to find nearest parking slots. The prototype uses cloud based architecture of Internet of things. There is minimum delay in finding an optimal parking slot which helps to ease the traffic congestion. The proposed system also helps users automatically find a free parking space at the smallest distance based on Haversine formula if nearest parking region is full.

Parking guidance system using Internet of Things

Traditional electromechanical Energy Meters for electricity are now replaced by digital meters in domestic as well as commercial applications. The trend of automation can be incorporated in presently working digital energy meter to make it smarter. Idea is to design and implement a smart prepaid energy meter prototype having wireless communication facility. The proposed system presents a total electronic three phase four wire energy meter. All power measurements are taken in the digital domain. These readings are transmitted to the mobile of user via wireless GSM technology. User can have the updates of electric energy consumption data on his mobile. Controller is used for controlling all functions of meter. Proposed algorithm for power calculation reduces hardware requirements as power factor is calculated in algorithm which eliminates the need of the zero crossing detector circuit.

Design and implementation of smart three phase energy meter

The prediction of Indian summer monsoon (ISM) variability on extended range (3–4 pentads in advance, where pentad refers to 5-day average) is crucial due to its usefulness in agricultural and hydrological planning. Monsoon rainfall occurs in the form of active/break spells which causes large-scale flood/drought condition. Prediction before 3–4 pentad may not stop this condition but can help us to prepare for the same. Hence Climate Forecast System model version 2 (CFSv2) has been adopted to develop a dynamical prediction system for monsoon rainfall on different time scales. The Extended Range Prediction (ERP) group at Indian Institute of Tropical Meteorology has indigenously developed an Ensemble Prediction System based on CFSv2 for the real-time prediction of active/break spells of ISM and has been providing the experimental forecasts up to 4 pentad lead. A multi-model ensemble (MME) framework employing different variants of CFSv2 has shown better skill in the ERP of ISM, compared to the individual variants. Still CFSv2 model shows statistical biases which cause lack of skill in MME and causes false prediction. In this study, it is proposed to employ the artificial neural network based technique to reduce the inherent biases by post-processing the raw model output. The performance is evaluated with mean square error, correlation coordinate and Brier skill score. Result shows that the probabilistic skill of the model is improved with the ANN-based technique.

Improved performance of multi-model ensemble through the bias correction based on ANN technique

This paper presents the results of laboratory work carried out for identifying the outer race bearing fault occurred in an induction motor. The knowledge about fault behavior of an induction motor is extremely important for overall operational life of the machine. The paper refers to spectral analysis of the motor stator current, which includes routine stator current, noise and specific fault current signature. Using separate healthy and faulty bearing on the machine, testing is carried out for obtaining set of healthy and faulty currents for different load conditions. The specific fault signature can be separated using feature extraction in time domain Wavelet and after getting spectral information using classification technique ANN fault is identified.

Outer race bearing fault identification of induction motor based on stator current signature by wavelet transform

Power Systems suffer from various types of faults such as Phase and Earth Faults These faults lead to overcurrent in the circuits Numerical Relays embedded with Digital Signal Processor (DSP) can be used to protect the system components as they can perform complex processing faster with higher accuracy and also avoid problem of false tripping DSP based processing can also be optimized for real time In this paper, TMS320F28335 based Implementation of Numerical Relay is discussed TMS320F28335 is a floating point Digital Signal Controller (DSC) Short Time Fourier Transform (STFT) is used to detect the spurious signals and faults more accurately The proposed overcurrent protection system is implemented on real time basis with the help of AntiAliasing filter and Signal Conditioning Circuit to scale the inputs from Current Transformer Anti-Aliasing filter is designed based on the fault current simulation data generated using Power System Simulation Software Fault current patterns generated are analyzed by calculating Fourier Transform coefficients that decide the filter specifications Code Composer Studio (CCS) IDE is used for the development of algorithm The paper discusses development of overcurrent protection algorithm based on RMS calculation for single phase circuits using TMS320F28335 The overcurrent relay Inverse Definite Minimum Time (IDMT) characteristics curves based on the simulated fault current data are used as basis and are implemented for Single Phase Circuits

G. N. Mulay

Papers

Parking guidance system using Internet of Things

Design and implementation of smart three phase energy meter

Improved performance of multi-model ensemble through the bias correction based on ANN technique

Outer race bearing fault identification of induction motor based on stator current signature by wavelet transform

TMS320F28335 based single phase overcurrent protection Implementation using Numerical Relay