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New Neuron Model For Simulating Rotating Electrical Machines And Load Forecasting Problems, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
New Neuron Model For Simulating Rotating Electrical Machines And Load Forecasting Problems, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
D. K. Chaturvedi Dr.
The existing neuron structure has an aggregation function (usually summation) and its transformation through non-linear filter or squashing or thresholding functions. Such structure of neural networks has a number of disadvantages like large number of neurons, hidden layers and huge training data required for complex function approximations. The present paper proposes new neuron models to overcome the above problems in the existing neural networks. The model has been developed and tested for modelling of electrical machines like DC motor, induction motor and synchronous generator and load forecasting problems using different new neuron models in the neural network like S neuron …
Load Frequency Control: A Generalized Neural Network Approach, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
Load Frequency Control: A Generalized Neural Network Approach, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
D. K. Chaturvedi Dr.
Variation in load frequency is an index for normal operation of power systems. When load perturbation takes place anywhere in any area of the system, it will affect the frequency at other areas also. To control load frequency of power systems various controllers are used in different areas. but due to non-linearities in the system components and alternators, these controllers cannot control the frequency quickly and efficiently. Simple neural networks which are in common use at present have various drawbacks like large training time, requirement of large number of neurons, etc. The present work deals with the developmetn of a …