Testing Isotropy and a related Random Walk problem

One comes across directions as the observations in a number of situations. The first inferential question that one should answer when dealing with such data is, “Are they isotropic or uniformly distributed?” The answer to this question goes back in history which we shall retrace a bit and provide an exact and approximate solution to this so-called “Pearson’s Random Walk” problem.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Analysis of Some Stochastic Inventory Models with Pooling Retrial of Customers

The thesis deals with analysis of some Stochastic Inventory Models with Pooling/Retrial of Customers.. In the first model we analyze an (s,S) production Inventory system with retrial of customers. Arrival of customers from outside the system form a Poisson process. The inter production times are exponentially distributed with parameter µ. When inventory level reaches zero further arriving demands are sent to the orbit which has capacity M(<∞). Customers, who find the orbit full and inventory level at zero are lost to the system. Demands arising from the orbital customers are exponentially distributed with parameter γ. In the model-II we extend these results to perishable inventory system assuming that the life-time of each item follows exponential with parameter θ. The study deals with an (s,S) production inventory with service times and retrial of unsatisfied customers. Primary demands occur according to a Markovian Arrival Process(MAP). Consider an (s,S)-retrial inventory with service time in which primary demands occur according to a Batch Markovian Arrival Process (BMAP). The inventory is controlled by the (s,S) policy and (s,S) inventory system with service time. Primary demands occur according to Poissson process with parameter λ. The study concentrates two models. In the first model we analyze an (s,S) Inventory system with postponed demands where arrivals of demands form a Poisson process. In the second model, we extend our results to perishable inventory system assuming that the life-time of each item follows exponential distribution with parameter θ. Also it is assumed that when inventory level is zero the arriving demands choose to enter the pool with probability β and with complementary probability (1- β) it is lost for ever. Finally it analyze an (s,S) production inventory system with switching time. A lot of work is reported under the assumption that the switching time is negligible but this is not the case for several real life situation.

Automation of Chemical Spray Pyrolysis Unit and Fabrication of Sprayed CuInS2/In2S3 Solar Cell

Aim of the present work was to automate CSP process, to deposit and characterize CuInS2/In2S3 layers using this system and to fabricate devices using these films.An automated spray system for the deposition of compound semiconductor thin films was designed and developed so as to eliminate the manual labour involved in spraying and facilitate standardization of the method. The system was designed such that parameters like spray rate, movement of spray head, duration of spray, temperature of substrate, pressure of carrier gas and height of the spray head from the substrate could be varied. Using this system, binary, ternary as well as quaternary films could be successfully deposited.The second part of the work deal with deposition and characterization of CuInS2 and In2S3 layers respectively.In the case of CuInS2 absorbers, the effects of different preparation conditions and post deposition treatments on the optoelectronic, morphological and structural properties were investigated. It was observed that preparation conditions and post deposition treatments played crucial role in controlling the properties of the films. The studies in this direction were useful in understanding how the variation in spray parameters tailored the properties of the absorber layer. These results were subsequently made use of in device fabrication process.Effects of copper incorporation in In2S3 films were investigated to find how the diffusion of Cu from CuInS2 to In2S3 will affect the properties at the junction. It was noticed that there was a regular variation in the opto-electronic properties with increase in copper concentration.Devices were fabricated on ITO coated glass using CuInS2 as absorber and In2S3 as buffer layer with silver as the top electrode. Stable devices could be deposited over an area of 0.25 cm2, even though the efficiency obtained was not high. Using manual spray system, we could achieve devices of area 0.01 cm2 only. Thus automation helped in obtaining repeatable results over larger areas than those obtained while using the manual unit. Silver diffusion on the cells before coating the electrodes resulted in better collection of carriers.From this work it was seen CuInS2/In2S3 junction deposited through automated spray process has potential to achieve high efficiencies.

Studies on the effect of randoniness on the synchronization of coupled systems and on the dynamics of intermittently driven systems

It has been shown recently that systems driven with random pulses show the signature of chaos ,even without non linear dynamics.This shows that the relation between randomness and chaos is much closer than it was understood earlier .The effect of random perturbations on synchronization can be also different. In some cases identical random perturbations acting on two different chaotic systems induce synchronizations. However most commonly ,the effect of random fluctuations on the synchronizations of chaotic system is to destroy synchronization. This thesis deals with the effect of random fluctuations with its associated characteristic timescales on chaos and synchronization. The author tries to unearth yet another manifestation of randomness on chaos and sychroniztion. This thesis is organized into six chapters.

Comparative effectiveness of solar disinfection using small-scale batch reactors with reflective, absorptive and transmissive rear surfaces

This study investigated the enhancement of solar disinfection using custom-made batch reactors with reflective (foil-backed) or absorptive (black-backed) rear surfaces, under a range of weather conditions in India. Plate counts of Escherichia coli ATCC11775 were made under aerobic conditions and under conditions where reactive oxygen species (ROS) were neutralised, i.e. in growth medium supplemented with 0.05% w/v sodium pyruvate plus incubation under anaerobic conditions. While the addition of either an absorptive or a reflective backing enhanced reactor performance under strong sunlight, the reflective reactor was the only system to show consistent enhancement under low sunlight, where the process was slowest. Counts performed under ROS-neutralised conditions were slightly higher than those in air, indicating that a fraction of the cells become sub-lethally injured during exposure to sunlight to the extent that they were unable to grow aerobically. However, the influence of this phenomenon on the dynamics of inactivation was relatively small