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.

Development of Free Space Method for Material Characterisation and GPR Study & A Case Study on Biological Samples

The thesis mainly focuses on material characterization in different environments: freely available samples taken in planar fonn, biological samples available in small quantities and buried objects.Free space method, finds many applications in the fields of industry, medicine and communication. As it is a non-contact method, it can be employed for monitoring the electrical properties of materials moving through a conveyor belt in real time. Also, measurement on such systems at high temperature is possible. NID theory can be applied to the characterization of thin films. Dielectric properties of thin films deposited on any dielectric substrate can be determined. ln chemical industry, the stages of a chemical reaction can be monitored online. Online monitoring will be more efficient as it saves time and avoids risk of sample collection.Dielectric contrast is one of the main factors, which decides the detectability of a system. lt could be noted that the two dielectric objects of same dielectric constant 3.2 (s, of plastic mine) placed in a medium of dielectric constant 2.56 (er of sand) could even be detected employing the time domain analysis of the reflected signal. This type of detection finds strategic importance as it provides solution to the problem of clearance of non-metallic mines. The demining of these mines using the conventional techniques had been proved futile. The studies on the detection of voids and leakage in pipes find many applications.The determined electrical properties of tissues can be used for numerical modeling of cells, microwave imaging, SAR test etc. All these techniques need the accurate determination of dielectric constant. ln the modem world, the use of cellular and other wireless communication systems is booming up. At the same time people are concemed about the hazardous effects of microwaves on living cells. The effect is usually studied on human phantom models. The construction of the models requires the knowledge of the dielectric parameters of the various body tissues. lt is in this context that the present study gains significance. The case study on biological samples shows that the properties of normal and infected body tissues are different. Even though the change in the dielectric properties of infected samples from that of normal one may not be a clear evidence of an ailment, it is an indication of some disorder.ln medical field, the free space method may be adapted for imaging the biological samples. This method can also be used in wireless technology. Evaluation of electrical properties and attenuation of obstacles in the path of RF waves can be done using free waves. An intelligent system for controlling the power output or frequency depending on the feed back values of the attenuation may be developed.The simulation employed in GPR can be extended for the exploration of the effects due to the factors such as the different proportion of water content in the soil, the level and roughness of the soil etc on the reflected signal. This may find applications in geological explorations. ln the detection of mines, a state-of-the art technique for scanning and imaging an active mine field can be developed using GPR. The probing antenna can be attached to a robotic arm capable of three degrees of rotation and the whole detecting system can be housed in a military vehicle. In industry, a system based on the GPR principle can be developed for monitoring liquid or gas through a pipe, as pipe with and without the sample gives different reflection responses. lt may also be implemented for the online monitoring of different stages of extraction and purification of crude petroleum in a plant.Since biological samples show fluctuation in the dielectric nature with time and other physiological conditions, more investigation in this direction should be done. The infected cells at various stages of advancement and the normal cells should be analysed. The results from these comparative studies can be utilized for the detection of the onset of such diseases. Studying the properties of infected tissues at different stages, the threshold of detectability of infected cells can be determined.

Spray Pyrolysed Tin Chalcogenide Thin Films: Optimization of optoelectronic properties of SnS for possible photovoltaic application as an absorber layer

In the early 19th century, industrial revolution was fuelled mainly by the development of machine based manufacturing and the increased use of coal. Later on, the focal point shifted to oil, thanks to the mass-production technology, ease of transport/storage and also the (less) environmental issues in comparison with the coal!! By the dawn of 21st century, due to the depletion of oil reserves and pollution resulting from heavy usage of oil the demand for clean energy was on the rising edge. This ever growing demand has propelled research on photovoltaics which has emerged successful and is currently being looked up to as the only solace for meeting our present day energy requirements. The proven PV technology on commercial scale is based on silicon but the recent boom in the demand for photovoltaic modules has in turn created a shortage in supply of silicon. Also the technology is still not accessible to common man. This has onset the research and development work on moderately efficient, eco-friendly and low cost photovoltaic devices (solar cells). Thin film photovoltaic modules have made a breakthrough entry in the PV market on these grounds. Thin films have the potential to revolutionize the present cost structure of solar cells by eliminating the use of the expensive silicon wafers that alone accounts for above 50% of total module manufacturing cost.Well developed thin film photovoltaic technologies are based on amorphous silicon, CdTe and CuInSe2. However the cell fabrication process using amorphous silicon requires handling of very toxic gases (like phosphene, silane and borane) and costly technologies for cell fabrication. In the case of other materials too, there are difficulties like maintaining stoichiometry (especially in large area films), alleged environmental hazards and high cost of indium. Hence there is an urgent need for the development of materials that are easy to prepare, eco-friendly and available in abundance. The work presented in this thesis is an attempt towards the development of a cost-effective, eco-friendly material for thin film solar cells using simple economically viable technique. Sn-based window and absorber layers deposited using Chemical Spray Pyrolysis (CSP) technique have been chosen for the purpose

Sustainable Watershed Management: Illusion or Reality? A case of Kerala State in India

Soil erosion is more detrimental and affects the chemical, physical and biological properties of the soil. Degradation of soil and water resources is a worldwide problem. Over the next two decades, it is expected that the world will need 17% more water to grow food for the increasing population in developing countries and that total water use will increase by 40%. The total land area subjected to human-induced soil degradation is estimated as 20 x 106 (km)2 Hence conservation of soil and water is essential for the subsistence of life. This can be made possible through sustainable watershed management. This thesis aims at investigating the condition under which sustainable watershed management is possible in Kerala, in South India. The research has been carried out in three stages. In the first stage a conceptual framework is formulated (Chapter 3) based on the relevant literature (Chapter 2) in the field of watershed management. In the second stage this framework is applied to two existing case studies in Kerala State (Chapter 4). In the third stage, the methodology is used to test out geo textile innovation (Chapter 5) in two field experiments (Chapter 6).