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Articles by N.S. Marimuthu
Total Records ( 9 ) for N.S. Marimuthu
  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.
  M. Subadra , N.S. Marimuthu , R. Ravi and G.S. Vijayakumar
  A recent development in odour sensing technology and artificial intelligence, as E-nose system, has rediscovered the use of smell in clinical diagnosis. Nowadays, this technology is becoming an interesting alternative for medical point-of-care devices. This study analyses the possibility of E-nose for rapid and accurate detection of micro-organisms in normal sterile body fluids, to ensure correct chemotherapy. In this study, 75 samples of different kinds of bacteria which are the major cause for Sepsis and Urinary Tract Infection were inoculated in 20 mL growth medium and incubated for 2 h for volatile generation. These samples were analysed with an electronic nose. The instrument, equipped with 12 Metal Oxide Semiconductor (MOS) sensors, was used to generate a pattern of the volatile compounds present in the pathological samples. The sensor responses were evaluated by Principal Component Analysis (PCA) and Artificial Neural Network (ANN). Good results were obtained in the classification of bacterial samples by using a neural network model based on a multilayer perceptron that learned using a back propagation algorithm. The methodology is simple, rapid and the results suggest that the electronic nose could be a used as a tool for detection.
  S. Joseph Jawhar , N.S. Marimuthu and N. Albert Singh
  This study describes the design and development of a novel controller for a non-linear power electronic converter. Neuro-Fuzzy controller is proposed to improve the performance of the buck converter. The duty cycle of the buck converter is controlled by Neuro-Fuzzy controller. The conventional PI controllers for such converters, designed under the worst case condition of maximum load and minimum line condition, present a lower loop band width and the system response is sluggish. The common bottleneck in fuzzy logic is the derivation of fuzzy rules and the parameter tuning for the controller. The Neural Networks have powerful learning abilities, optimization abilities and adaptation. The Fuzzy logic and Neural Networks can be integrated to form a connectionist adaptive network based Fuzzy logic controller. This integrated adaptive system modifies the characteristics of rules and the structure of the control system. This paper aims to establish the superior performance of Neuro-Fuzzy controller over the conventional PI controllers and Fuzzy controllers at various operating points of the buck converter.
  M. Marsaline Beno , N.S. Marimuthu and N. Albert Singh
  Switched Reluctance Motors (SRM) is almost always operated within the saturation region for very large operation region. This yields very strong non linearity, which makes it very difficult to derive a comprehensive mathematical model for the behavior of the machine. This study develops and compares fuzzy logic, neuro- fuzzy logic and neural network techniques for the modelling of a Switched Reluctance Motor (SRM) in view of its nonlinear magnetisation characteristics. All the models are simulated and applied for nonlinear modelling, especially for finding the rotor angle positions at different currents, from a suitable measured data set for an associated SRM. The data comprised flux linkage, current and rotor position. The model evaluation results are compared with the measured values and the error analyses are given to determine the performance of the developed model. The error analyses have shown great accuracy and successful modelling of SRMs using soft computing techniques.
  B.Hariram and N.S. Marimuthu
  This study proposes a fully digitized hardware design scheme of a vector-controlled induction motor drive. This technique uses a Very High Speed Integrated Circuit (VHSIC) Hardware Description Language (VHDL) as unique EDA environment for all phases of the design process facilitating easy Field Programmable Gate Array (FPGA) prototyping and the modular design allows the reuse of VHDL code for a range of vector control strategies. Simulation results are presented, validating the proposed vector control scheme.
  A. Arunya Revathi , N.S. Marimuthu , P.S.Kannan and V. Suresh Kumar
  This study presents an Efficient Genetic Algorithm (EGA) to solve the problem of optimal power flow with controllable FACTS devices. Two types of FACTS devices, Thyristor Controlled Series Compensator (TCSC) and Thyristor Controlled Phase Shifter (TCPS) are considered. The specified power flow control constraints due to the use of FACTS devices are included in the OPF problem in addition to the normal conventional constraints. The sensitivity analysis is carried out for the location of FACTS devices. In the solution process EGA is integrated with conventional OPF to select the best control parameters to minimize the total generation fuel cost and keep the power flows within the security limits. This method provides an enhanced economic solution with the use of controllable FACTS devices. Advanced and problem-Specific operators are introduced in order to enhance the algorithmís efficiency and accuracy. The probabilities of crossover and mutation are varied by adaptive algorithm. The effectiveness of the proposed method is demonstrated on IEEE 30-bus system.
  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.
  P. Melba Mary and N.S. Marimuthu
  This study proposes a novel approach based on genetic algorithm optimized fuzzy logic controller, for the design of a temperature control process, capable of providing optimal performance over the entire operating range of the process. Since an optimum response of the Fuzzy Controller can be expected only for a limited range of inputs, here tuning the input , output gains and the scaling factor are done for other ranges of inputs. The proposed control system combines the advantages of Genetic Algorithm and Fuzzy Logic Control schemes. In order to evaluate the performance of the proposed control system methods, results from simulation of the process are presented.
  S. Joseph Jawhar and N.S. Marimuthu
  This study describes the design and development of a novel controller for a non-linear power electronic converter. The Neuro-Fuzzy controller is proposed to improve the performance of the boost converter. The duty cycle of the boost converter is controlled by Neuro-Fuzzy controller. The conventional PI controllers for such converters designed under the worst case condition of maximum load and minimum line condition present a lower loop band width and the system response also sluggish. The common bottleneck in fuzzy logic is the derivation of fuzzy rules and the parameter tuning for the controller. The Neural Networks have powerful learning abilities, optimization abilities and adaptation. The Fuzzy logic and Neural Networks can be integrated to form a connectionist adaptive network based Fuzzy logic controller. This integrated adaptive system modifies the characteristics of rules and the structure of the control system. This study aims to establish the superior performance of Neuro-Fuzzy controller over the conventional P I controllers and Fuzzy controllers at various operating points of the boost converter.
 
 
 
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