Amit Jain

Power system topological observability analysis using artificial neural networks

This paper presents a new method for the power system topological observability analysis using the artificial neural networks. The power system observability problem, related to the power system configuration or network topology, called as the topological observability, is studied utilizing the artificial neural network model, based on multilayer perceptrons using the back-propagation algorithm as the training algorithm. Another training algorithm, quickprop is also applied for training the similar artificial neural network to further check the suitability of other training algorithm also. The proposed artificial forward neural network model has been tested on sample power systems and results are presented.

Ab Initio Study of Hydrogen Hydrate Clathrates for Hydrogen Storage within the ITBL Environment

Recently, for the first time a hydrate clathrate was discovered with hydrogen. Aside from the great technological promise that is inherent in storing hydrogen at high density at modest pressures, there is great scientific interest as this would constitute the first hydrate clathrate with multiple guest molecules per cage. The multiple cage occupancy is controversial, and reproducibility of the experiments has been questioned. Therefore, in this study we try to illucidate the remarkable stability of the hydrogen hydrate clathrate, and determine the thermodynamically most favored cage occupancy using highly accurate ab initio computer simulations in a parameter survey. To carry out these extraordinary demanding computations a distributed ab initio code has been developed using the SuperSINET with the Information Technology Based Laboratory (ITBL) software as the top-layer.

Stochastic load flow analysis using artificial neural networks

Stochastic load flow is a method for calculation of the effects of inaccuracies in input data on all output quantities through the load flow calculations. This gives a range of values (confidence limit) for each output quantity, which represent the operative condition of the system, to a high degree of probability or confidence. This paper presents a new method for stochastic load flow analysis using artificial neural networks. It is desirable to know the state of the power system in a range with certain confidence, with consideration of input data uncertainties and inaccuracies, on instant-to-instant basis in the fastest possible way. Present method using artificial neural networks to stochastic load flow problem is an effort in that direction and will be a very useful technique in effectively dealing with demand side uncertainties for power system planning and operation. The proposed artificial neural network model has been tested on a sample power system using two different training algorithms and simulation results are presented

Power network observability: a fast solution technique using graph theory

This paper presents a fast solution technique for power network observability problem. This method is based on graph theory and provides an efficient way to find the topological observability of power system network for a given set of measurement. A power network is topologically observable if it is possible to find an observable spanning tree of measurement graph. The observability problem is split in P-/spl delta/ observability and Q-V observability by P-/spl delta//Q-V decouple characteristic of power systems. The existence of an observable spanning tree is searched through the methodology suggested in this paper and a conclusion about the observability or unobservability is reached. This solution technique has been successfully tested on the standard IEEE test systems and results obtained are presented for illustration.

Network observability: a solution technique using neural networks

A new method for the network observability solution of the power networks using the neural networks with quickprop as training algorithm is presented in this paper. The network observability problem related to the power network configuration or network topology, called as the topological observability, is taken for the solution. The topological network observability is determined using a neural network model, based on the quickprop algorithm, which uses the second order derivatives of the error function to speed up the learning. This neural network based method has been applied on sample power networks and results are presented.

Topological observability analysis using heuristic rule based expert system

This paper presents a novel approach for topological observability analysis using heuristic rule based expert system. The observability problem is split in P-delta observability and Q-V observability by P-delta/Q-V decouple characteristic of power systems. A heuristic rule based expert system is developed for finding the existence of an observable spanning tree for P-delta measurement graph and Q-V measurement graph. This expert system finds the existence of an observable spanning tree in measurement graph on the basis of heuristic rules, directly without making a spanning tree. Inference in this approach is done by the process of chaining through rules until a conclusion about the observability or unobservability is reached. The proposed heuristic rule based expert system has been tested on the standard IEEE 5 bus and 14 bus test systems and an 87 bus real power system, which is a part of Northern grid network of India. Results obtained are presented for illustration

Neural network based stochastic load flow analysis

Neural network based method for the analysis of stochastic load flow is presented in this paper. Stochastic load flow is a method for calculation of the effects of inaccuracies in input data on all output quantities through the load flow calculations. This gives a range of values for each output quantity, which represent the operative condition of the system, to a high degree of probability. It is desirable to know the state of the power system, with consideration of input data accuracies, on instant-to-instant basis and present method of neural network application to stochastic load flow problem is an effort in that direction. The proposed neural network model has been tested on a sample power system and simulation results are presented.

Realization of a computer simulation environment based on ITBL and a large scale GW calculation performed on this platform

An extraordinarily large GRID environment has been established over Japan by using SuperSINET based on ITBL connecting 4 supercomputer facilities. This new supercomputing environment has been used for a large scale numerical simulations using original ab initio code TOMBO and several remarkable results have already been obtained to proof that this newly built computer environment is actually useful to accelerate the speed of designing and developing advanced functional materials expected to be used in nanotechnology.