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Full-Text Articles in Engineering
Virtual Power Lab - D.E.I. Dayalbagh, D. K. Chaturvedi Dr.
Virtual Power Lab - D.E.I. Dayalbagh, D. K. Chaturvedi Dr.
D. K. Chaturvedi Dr.
Electrical Machines and Power systems are the back bone of electrical engineering and play a vital role in industry. Hence, it is essential for electrical engineering students to learn the concepts of power systems and machines. Unfortunately, the students are loosing interest in lab work due to various reasons like unavialability of experimental setups, lack of qualified and motivated lab staff, lab timing, etc. To overcome these problems the virtual Power labs are very important to impart quality experiments.
Generalized Neuron Based Pss And Adaptive Pss, D. K. Chaturvedi, O. P. Malik
Generalized Neuron Based Pss And Adaptive Pss, D. K. Chaturvedi, O. P. Malik
D. K. Chaturvedi Dr.
Artificial neural networks can be used as intelligent controllers to control non-linear, dynamic systems through learning, which can easily accommodate the non-linearities and time dependencies. However, they require large training time and large number of neurons to deal with complex problems. Taking benefit of the characteristics of a Generalized Neuron that requires much smaller training data and shorter training time, a Generalized Neuron-Based Power System Stabilizer (GNPSS) and an adaptive version of the same have been developed. The objective of this paper is to compare the performance of the GNPSS with that of an adaptive version, the weights of which …
A Generalized Neuron Based Adaptive Power System Stabilizer For Multimachine Environment, D. K. Chaturvedi, O. P. Malik
A Generalized Neuron Based Adaptive Power System Stabilizer For Multimachine Environment, D. K. Chaturvedi, O. P. Malik
D. K. Chaturvedi Dr.
Artificial neural networks can be used as intelligent controllers to control nonlinear, dynamic systems through learning, which can easily accommodate the nonlinearities and time dependencies. Taking advantage of the characteristics of a generalized neuron (GN), that requires much smaller training data and shorter training time, a GN-based adaptive power system stabilizer (GNAPSS) is proposed. It consists of a GN as an identifier, which predicts the plant dynamics one step ahead, and a GN as a controller to damp low frequency oscillations. Results of studies with a GN-based PSS on a five-machine power system show that it can provide good damping …
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 …
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 …
Neuro-Fuzzy Approach For Development Of New Neuron Model, Manmohan, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
Neuro-Fuzzy Approach For Development Of New Neuron Model, Manmohan, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
D. K. Chaturvedi Dr.
The training time of ANN depends on size of ANN (i.e. number of hidden layers and number of neurons in each layer), size of training data, their normalization range and type of mapping of training patterns (like X–Y, X–DY, DX–Y and DX–DY), error functions and learning algorithms. The efforts have been done in past to reduce training time of ANN by selection of an optimal network and modification in learning algorithms. In this paper, an attempt has been made to develop a new neuron model using neuro-fuzzy approach to overcome the problems of ANN incorporating the features of fuzzy systems …
Fuzzified Neural Network Approach For Load Forecasting Problems, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
Fuzzified Neural Network Approach For Load Forecasting Problems, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
D. K. Chaturvedi Dr.
In load forecasting, the operator or the concerned person uses his or her experience and intuitions to obtain a good guess of the load demand. This guess is normally supported by sophisticated mathematical prediction techniques. The short term load not only varies from hour to hour, but is also influenced by the nature of events, load demand, the type of the load considered, seasonal variations, weekend day or holidays, and also by sudden demand and loss of load. Accordingly, it is quite clear that the electrical load-forecasting problem is quite difficult to model with mathematical difference or differential equations. In …
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 …
A Fuzzy Simulation Model Of Basic Commutating Electrical Machines, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
A Fuzzy Simulation Model Of Basic Commutating Electrical Machines, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
D. K. Chaturvedi Dr.
Fuzzy Logic as applied to a great extent in controlling the process, plants and various complex systems, due to its inherent advantages like simplicity, ease in design, robustness and adaptivity. Aslo it is established that this approach works very well especially when the systems are not transparent. In this paper the approach is used for the modelling and simulation of an electrical machine to predict the behaviour of the machine under running conditionsas well as unde starting conditions. The starting characteristics of electrical machines are non-linear in nature, and it is very difficult to model them accurately. Also the developed …
Possible Applications Of Neural Nets To Power System Operation And Control, P. K. Kalra, Alok Srivastava, D. K. Chaturvedi
Possible Applications Of Neural Nets To Power System Operation And Control, P. K. Kalra, Alok Srivastava, D. K. Chaturvedi
D. K. Chaturvedi Dr.
Problems related to power system operation and control are complex and time consuming because of the non-linearities involved in their formulation and solution. Fast solutions to these problems can be obtained only through parallel processing. Neural nets provide massive parallel processing facilities and may also be used efficiently to model systems with non-linearities. The capabilities of neural nets can, therefore, be well utilized in modelling and processing problems related to power systems. In order to reduce the burden on computers, algorithms involving optimization and complex equations can be converted to heuristics. These heuristics can then be represented in terms of …