Imd

Operational Weather Research & Forecasting – Advanced Research WRF in short WRF (ARW) 9 km x 9 km Model (IMD) based rainfall forecast of India Meteorological Department (IMD) is utilized to compute rainfall forecast over River basins in Odisha during Flood season 2014. The performance of the WRF Model at the sub-basin level is studied in detail. It is observed that the IMD’s WRF (ARW) day1, day2, day3 correct forecast range lies in between 31-47 %, 37-43%, and 28-47% respectively during the flood season 2014.

Precipitation obtained from rain gauges is an essential input for hydrological modelling. It is often sparse in highly topographically varying terrain, exhibiting a certain amount of uncertainty in hydrological modelling. Hence, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. In this study, an attempt was made to evaluate the Tropical Rainfall Measuring Mission (TRMM) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), employing a semi-distributed hydrological model, i.e., Soil and Water Assessment Tool (SWAT), for simulating streamflow and validating them against the flows generated by the India Meteorological Department (IMD) rainfall dataset in the Gurupura river catchment of India. Distinct testing scenarios for simulating streamflow were made to check the suitability of these satellite precipitation data. The TRMM was able to better estimate rainfall than CHIRPS after performing categorical and continuous statistical results with respect to IMD rainfall data. While comparing the performance of model simulations, the IMD rainfall-driven streamflow emerged as the best followed by the TRMM, CHIRPS-0.05, and CHIRPS-0.25. The coefficient of determination (R 2), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS) were in the range 0.63 to 0.86, 0.62 to 0.86, and −14.98 to 0.87, respectively. Further, an attempt was made to examine the spatial distribution of key hydrological signature, i.e., flow duration curve (FDC) in the 30-95 percentile range of non-exceedance probability. It was observed that TRMM underestimated the flow for agricultural water availability corresponding to 30 percent, even though it showed a good performance compared to the other satellite rainfall-driven model outputs.

This paper presents a closed loop Model Reference Adaptive system (MRAS) observer with artificial intelligent Nuero fuzzy controller (NFC) as the adaptation technique to mitigate the low speed estimation issues and to improvise the performance of the Sensorless Direct Torque Controlled (DTC) Induction Motor Drives (IMD). Rotor flux MRAS and reactive power MRAS with NFC is explored and detailed analysis is carried out for low speed estimation. Comparative analysis between rotor flux MRAS and reactive power MRAS with PI as well as NFC as adaptive controller is performed and results are presented in this paper. The comparative analysis among these four speed estimation methods shows that reactive power MRAS with NFC as adaptation mechanism shows reduced speed estimation error and actual speed error at steady state operating conditions when the drive is subjected to low speed operation. Simulation carried out using MATLAB-Simulink software to validate the performance of the drive especially at low speeds with rated and variable load conditions. 1. INTRODUCTION Sensorless DTC induction motor drives plays a major role in high performance industrial applications due to its instantaneous torque and speed control resulting in fast torque and speed response [1]-[3]. In DTC drive, the optimal inverter switching voltage vectors [4] selects the selection of required torque and flux. The switching state of the inverter is determined by space vector modulation (SVM).The torque and flux ripples are reduced in SVM DTC compared to conventional DTC [5],[21]. Sensorless drives have the advantages of reduced hardware complexity, cost, robustness and reliability of the drive etc. However, the sensorless techniques depend on machine parameter, which varies with temperature, saturation levels, integrator drift and dc offset. Hence conventional speed estimators are not giving accurate speed estimation in the low speed ranges due to which the steady state and transient performance of the drive deteriorates [6].Various methods of speed estimators like open loop and closed loop observers are available in literature. The closed loop estimators have the correction term to improve the performance of estimator than in open loop estimators. MRAS is a stable, good performance, simple closed loop observer for speed estimation in Induction Motor Drive (IMD). MRAS consists of reference model, an adaptive model and an adaptation mechanism.

Low speed estimation in DTC IMD is not accurate due to the presence of transient offset, drift and domination of ohmic voltage drop in the measured stator voltages and currents used for estimating the stator flux required for accurate estimation of speed. EKF is a nonlinear, recursive adaptive algorithm capable of estimating speed ranging from very low speed to rated speed using equation of motion from noisy measured currents and voltages based on state space technique. In the previous work a new state space model of IM was developed for estimation in EKF by feeding load torque profile as an input variable instead of estimating it by considering load torque as constant, validated using MATLAB-Simulink software. In this paper real time validation of the EKF controller with load profile fed as input for speed estimation in DTC IMD is carried out using OPAL-RT simulator and real time results validates the simulation results and proves the effectives of the new EKF for low speed estimation in DTC IMD.