Congestion Management in Deregulated Power System by Optimal Choice and Allocation of FACTS Controllers Using Multi-Objective Genetic Algorithm

Reddy, S. Surender,Kumari, M. Sailaja,Sydulu, M. The Korean Institute of Electrical Engineers 2009 Journal of Electrical Engineering & Technology Vol.4 No.4

Congestion management is one of the technical challenges in power system deregulation. This paper presents single objective and multi-objective optimization approaches for optimal choice, location and size of Static Var Compensators (SVC) and Thyristor Controlled Series Capacitors (TCSC) in deregulated power system to improve branch loading (minimize congestion), improve voltage stability and reduce line losses. Though FACTS controllers offer many advantages, their installation cost is very high. Hence Independent System Operator (ISO) has to locate them optimally to satisfy a desired objective. This paper presents optimal location of FACTS controllers considering branch loading (BL), voltage stability (VS) and loss minimization (LM) as objectives at once using GA. It is observed that the locations that are most favorable with respect to one objective are not suitable locations with respect to other two objectives. Later these competing objectives are optimized simultaneously considering two and three objectives at a time using multi-objective Strength Pareto Evolutionary Algorithms (SPEA). The developed algorithms are tested on IEEE 30 bus system. Various cases like i) uniform line loading ii) line outage iii) bilateral and multilateral transactions between source and sink nodes have been considered to create congestion in the system. The developed algorithms show effective locations for all the cases considered for both single and multiobjective optimization studies.

Adaptive Wavelet Neural Network Based Wind Speed Forecasting Studies

D. Rakesh Chandra,M. Sailaja Kumari,Maheswarapu Sydulu,F. Grimaccia,M. Mussetta 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.6

Wind has been a rapidly growing renewable power source for the last twenty years. Since wind behavior is chaotic in nature, its forecasting is not easy. At the same time, developing an accurate forecasting method is essential when wind farms are integrated into the power grid. In fact, wind speed forecasting tools can solve issues related to grid stability and reserve allocation. In this paper 30 hours ahead wind speed profile forecast is proposed using Adaptive Wavelet Neural Network (AWNN). The implemented AWNN uses a Mexican hat mother Wavelet, and Morlet Mother Wavelet for seven, eight and nine levels decompositions. For wind speed forecasting, the time series data on wind speed has been gathered from the National Renewable Energy Laboratory (NREL) website. In this work, hourly averaged 10-min wind speed data sets for the year 2004 in the Midwest ISO region (site number 7263) is taken for analysis. Data sets are normalized in the range of [-1, 1] to improve the training performance of forecasting models. Total 8760 samples were taken for this forecasting analysis. After the forecasting phase, statistical parameters are calculated to evaluate system accuracy, comparing different configurations.

Adaptive Wavelet Neural Network Based Wind Speed Forecasting Studies

Chandra, D. Rakesh,Kumari, Matam Sailaja,Sydulu, Maheswarapu,Grimaccia, F.,Mussetta, M. The Korean Institute of Electrical Engineers 2014 Journal of Electrical Engineering & Technology Vol.9 No.6

Wind has been a rapidly growing renewable power source for the last twenty years. Since wind behavior is chaotic in nature, its forecasting is not easy. At the same time, developing an accurate forecasting method is essential when wind farms are integrated into the power grid. In fact, wind speed forecasting tools can solve issues related to grid stability and reserve allocation. In this paper 30 hours ahead wind speed profile forecast is proposed using Adaptive Wavelet Neural Network (AWNN). The implemented AWNN uses a Mexican hat mother Wavelet, and Morlet Mother Wavelet for seven, eight and nine levels decompositions. For wind speed forecasting, the time series data on wind speed has been gathered from the National Renewable Energy Laboratory (NREL) website. In this work, hourly averaged 10-min wind speed data sets for the year 2004 in the Midwest ISO region (site number 7263) is taken for analysis. Data sets are normalized in the range of [-1, 1] to improve the training performance of forecasting models. Total 8760 samples were taken for this forecasting analysis. After the forecasting phase, statistical parameters are calculated to evaluate system accuracy, comparing different configurations.

Utilization of Low Glucosinalate and Conventional Mustard Oilseed Cakes in Commercial Broiler Chicken Diets

Rao, S.V.Rama,Raju, M.V.L.N.,Panda, A.K.,Shashibindu, M. Sailaja Asian Australasian Association of Animal Productio 2005 Animal Bioscience Vol.18 No.8

An experiment was conducted to study the effect of replacing soyabean meal (SBM) at 50 and 100% with conventional (CMC) and low glucosinalate mustard cakes (LGMC) in iso-caloric and iso-nitrogenous diets in broiler chickens. All these diets contained 0.1% choline chloride with a purity of 50% (w/w). Another diet was prepared by replacing SBM in toto with CMC with no supplemental choline to find out the possible role of supplemental choline in mustard cake (MC) based diets. Two hundred and seventy day-old broiler chicks were distributed randomly in 54 stainless steel battery brooder pens of five chicks in each pen. Each experimental diet was allotted at random to nine battery brooders and offered ad-libitum from day 2 through 42 days of age. Body weight gain was significantly depressed by total replacement of SBM with either LGMC or CMC at 21 days of age. Non-supplementation of choline significantly depressed the growth compared to those fed CMC 100% with supplemental choline. However, at 42 days of age, such an effect was seen only with CMC. Replacement of SBM with CMC 100% with or without choline supplementation depressed the body weight gain. The concentrations of cholestorol and tryglicerides in serum and the relative weights of ready to cook yield, giblet and gizzard decreased by incorporation of mustard cakes in broiler diets. The trend in fat and protein contents in breast and thigh muscles and liver was not clearly attributable to the treatment effect. Based on the results, it is concluded that SBM can be replaced in toto with LGMC (535.0 and 466.5 g/kg starter and finisher diets, respectively) or up to 50% (215.0 and 186.7 g/kg starter and finisher diets, respectively) with CMC in commercial broiler chicken diets. Choline supplementation at 0.1% level in broiler diets containing CMC was found to be beneficial during starter phase.

Analysis of High-Temperature Effects on InAs∕In0.3Al0.7As∕InSb∕In0.3Al0.7As pHEMTs on Accessing RF/Analog performance: A Machine Learning Predictive Modeling

G. Lakshmi Vara Prasad,Venkatagurunatham Naidu Kollu,M. Sailaja,S. Radhakrishnan,K. Jagan Mohan,A. Kishore Reddy,G. Rajesh Chandra 한국전기전자재료학회 2024 Transactions on Electrical and Electronic Material Vol.25 No.1

In this paper, we delve into the intriguing realm of Pseudo-morphic High Electron Mobility Transistors (pHEMTs) composed of InAs∕In0.3Al0.7As∕InSb∕In0.3Al0.7As layers, utilizing Silvaco-TCAD for simulation. Our focus centers on the assessment of RF and analog electrical characteristics, with a keen eye on the high-temperature eff ects. The influence of temperature on device performance is meticulously evaluated in comparison to a reference device operating at room temperature. Traditionally, the critical parameters such as threshold voltage ( Vth ), transconductance ( gm ), and Ion∕Ioff ratio have been calculated within the temperature range spanning from 300 K to 700 K. The primary pHEMT device in our study exhibits impressive attributes, featuring a drain current of 950 mA, a threshold voltage of -1.75 V, a high transconductance ( gm ) value of 650 mS/mm, an Ion∕Ioff ratio of 1 × 106 , a transition frequency ( ft ) soaring to 790 GHz, and a maximum frequency ( fmax ) reaching a staggering 1.4 THz. However, as we traverse the temperature spectrum, our findings unveil a compelling narrative. The impact of rising temperature is unequivocal, triggering a cascade of transformations within the device. Notably, as the temperature escalates, we observe a noticeable decrease in current, a reduction in transconductance ( gm ), and a diminishing Ion∕Ioff ratio. To unravel the intricacies of these temperature-induced effects, we introduce the infusion of Machine Learning (ML) into our analysis.