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Articles by T. Sree Renga Raja
Total Records ( 4 ) for T. Sree Renga Raja
  T. Sree Renga Raja , N.S. Marimuthu and R. Shankara Narayanan
  An artificial neural network based cascade correlation algorithms is investigated for the short term generation scheduling of thermal units considering the real power limit of generators, real power demand, spinning reserve, minimum up and down times of the units. In order to expedite the execution, an Artificial neural network is used to generate a possible unit commitment schedule and a heuristic procedure is employed to modify the unit commitment to achieve a feasible and near optimal solution. The cascade correlation algorithm employs several novel modifications, including the ability to add units when necessary. The results of this method are promising when compared to other existing methods.
  T. Sree Renga Raja , N.S. Marimuth and N. Albert Sing
  Reliable power production is critical to the profitability of electricity utilities. Power generators need to be scheduled efficiently to meet electricity demand. Economic load dispatch and economic emission dispatch have been applied to obtain optimal fuel cost and optimal emission of generating units, respectively. Combined economic emission dispatch problem is obtained by considering both the economy and emission objectives. The use of orthogonal polynomials will give a very convenient means to obtain the equivalent cost function of the generating units. This method is sufficiently accurate and easy to implement for real time operation and control of power system. A general formulation and the development of Cascade Correlation algorithm to solve the environmentally constrained dispatch problem are presented. The objective is the minimization of the cost of operation, subject to all the usual and emissions constraints. It is shown that the proposed solution technique is capable of yielding good optimal solution with proper selection of control parameters.
  T. Sree Renga Raja , N.S. Marimuthu and T. Sree Sharmila
  Reliable power production is critical to the profitability of electricity utilities. Power generators need to be scheduled efficiently to meet electricity demand. This dissertation develops a solution method to schedule units for producing electricity while determining the estimated amount of surplus power each unit should produce taking into consideration the stochasticity of the load and its correlation structure. This scheduling problem is known as the dispatch problem in the power industry. A general formulation and the development of cascade correlation algorithm to solve the environmentally constrained dispatch problem are presented. The objective is the minimization of the cost of operation, subject to all the usual and emissions constraints. The algorithm handles multiple pollutants and for each pollutant the constraints include the maximum hourly emission on every unit, the maximum hourly emission on every set of on-line units and the maximum daily emission for the system constraints. Three closed-form dispatch strategies and two feasibility conditions are established to eliminate unfeasible unit combinations thus rendering a very efficient commitment algorithm. Test results are provided to show the efficiency of the proposed method.
  J. Mahil and T. Sree Renga Raja
  Electromagnetic interference produced by the incubator medical equipments may interrupt or degrade the premature infant Electrocardiography (ECG) signal. The premature infant ECG is always contaminated by an interference caused by the incubator devices. This study describes the interference noise cancelling techniques for filtering of the corrupted infant ECG signal using the biological inspired Particle Swarm Optimization (PSO) algorithm. The Active Noise Control System is designed using an adaptive learning ability of artificial neural network back propagation algorithm. The neural weights are adapted based in PSO in an adaptive manner. In this study, the hybrid Particle Swarm Optimization-Artificial Neural Network (PSO-ANN) feed forward algorithm is used for the Active Noise Control (ANC) of the fundamental electromagnetic interference in the incubators. The performance of the proposed noise cancellation approach is compared with gradient based algorithms and this proposed approach is successfully removing the noise.
 
 
 
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