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RISS 인기검색어
- 검색키워드
- 저자 : Maheswarapu Sydulu (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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T. Adhinarayanan,Maheswarapu Sydulu 대한전기학회 2009 Journal of Electrical Engineering & Technology Vol.4 No.3
A simple and efficient Lambda logic (λ-logic) based algorithm is proposed for the solution of the generator constrained economic dispatch problem when the generating units having prohibited zones. The proposed method solves the economic dispatch (ED) problem that takes into account ramprate limits, transmission losses and prohibited operating zones in the power system operation. The proposed method uses a simple heuristic rule based on average power of prohibited operating zones which produces the feasibility of solution for the restricted operating units. The effectiveness of the algorithm is tested on five different test systems and the performance compared with other relevant methods reported in the literature. In all the cases, the proposed algorithm performs better than the previous existing algorithms with a less computational burden.
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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.
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S. Surender Reddy,Matam Sailaja Kumari,Maheswarapu Sydulu 대한전기학회 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.
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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.
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