Narayanan Kumarappan

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There is a general consensus that the movement of electricity price is crucial for electricity market. The binary electricity price classification method is as an alternative to numerical electricity price forecasting due to high forecasting errors in various approaches. This paper proposes a binary classification of day-ahead electricity prices that could(More)
This paper presents new hybrid genetic algorithm (GA) to solve the combined economic and emission dispatch (CEED) problem. Focus is on the reduction of single pollutant nitrogen oxide (NO/sub x/). The equality constraints of power balance; the inequality generator capacity constraints and the prohibited zone constraints are considered. Here real coded GA is(More)
An appropriate method for fault location on Extra High Voltage (EHV) transmission line using Support Vector Machine (SVM) is proposed in this paper. It relies on the application of SVM and frequency characteristics of the measured single end positive sequence voltage and current measurement of transient signals of the system. This paper is proposing a new(More)
This paper presents a methodology for maintenance scheduling (MS) of generators using binary particle swarm optimization (BPSO) based probabilistic approach. The objective of this paper is to reduce the loss of load probability (LOLP) for a power system. The capacity outage probability table (COPT) is the initial step in creating maintenance schedule using(More)
A number of factors determined the outcome of electricity prices and exhibits a very complicated and irregular fluctuation. The accurate forecasting of various approaches is high in forecasting errors. In this work an application of probabilistic neural networks (PNN) mode is applied to national electricity market of Singapore (NEMS), i.e. Asia's first(More)