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A Generalized Neuron Based Pss In A Multi-Machine Power System, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
A Generalized Neuron Based Pss In A Multi-Machine Power System, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
D. K. Chaturvedi Dr.
An artificial neural network can work as an intelligent controller for nonlinear dynamic systems through learning, as it can easily accommodate the nonlinearities and time dependencies. In dealing with complex problems, most common neural networks have some drawbacks of large training time, large number of neurons and hidden layers. These drawbacks can be overcome by a nonlinear controller based on a generalized neuron (GN) which retains the quick response of neural net. Results of studies with a GN-based power system stabilizer on a five-machine power system show that it can provide good damping over a wide operating range and significantly …
Applications Of Generalised Neural Network For Aircraft Landing Control System, D. K. Chaturvedi, R. Chauhan, P. K. Kalra
Applications Of Generalised Neural Network For Aircraft Landing Control System, D. K. Chaturvedi, R. Chauhan, P. K. Kalra
D. K. Chaturvedi Dr.
It is observed that landing performance is the most typical phase of an aircraft perfromance. During landing operation the stability and controllability are the major considerations. To achieve safe landing, an aircraft has to be controlled in such a way that its wheels touch the ground comfortably and gently within the paved surface of the runway. The conventional control theory found very successful in solving well defined problems, which are described precisely with definite and clearly mentioned boundaries. In real life systems the boundaries can't be defined clearly and conventional controller does not give satisfactory results. Whenever, an aircraft deviates …
Experimental Studies Of Generalized Neuron Based Power System Stabilizer, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
Experimental Studies Of Generalized Neuron Based Power System Stabilizer, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
D. K. Chaturvedi Dr.
Artificial neural networks (ANNs) can be used as intelligent controllers to control nonlinear, dynamic systems through learning, which can easily accommodate the nonlinearities and time dependencies. However, they require large training time and large number of neurons to deal with complex problems. To overcome these drawbacks, a generalized neuron (GN) has been developed that requires much smaller training data and shorter training time. Taking benefit of these characteristics of the GN, a new power system stabilizer (PSS) is proposed. Results show that the proposed GN-based PSS can provide a consistently good dynamic performance of the system over a wide range …
Improved Generalized Neuron Model For Short Term Load Forecasting, D. K. Chaturvedi, Ravindra Kumar, P. K. Kalra
Improved Generalized Neuron Model For Short Term Load Forecasting, D. K. Chaturvedi, Ravindra Kumar, P. K. Kalra
D. K. Chaturvedi Dr.
The conventional neural networks consisting of simple neuron models have various drawbacks like large training time for complex problems, huge data requirement to train non linear complex problems, unknown ANN structure, the relatively larger number of hidden nodes required, problem of local minima etc. To make the Artificial Neural Network more efficient and to overcome the above-mentioned problems the new improved generalized neuron model is proposed in this work. The proposed neuron models have both summation and product as aggregation function. The generalized neuron models have flexibility at both the aggregation and activation function level to cope with the non-linearity …
A Generalized Neuron Based Adaptive Power System Stabilizer, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
A Generalized Neuron Based Adaptive Power System Stabilizer, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
D. K. Chaturvedi Dr.
Artificial neural networks (ANNs) can be used as intelligent controllers to control nonlinear, dynamic systems through learning, which can easily accommodate the nonlinearities and time dependencies. However, they require long training time and large numbers of neurons to deal with complexproblems. To overcome these drawbacks, a generalised neuron (GN) has been developed that requires much smaller training data and shorter training time. Taking benefit of these characteristics of the GN, a new generalised neuron-based adaptive power system stabiliser (GNPSS) is proposed. The GNPSS consists of a GN as an identifier, which tracks the dynamics of the plant, and a GN …