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.

Changing Role of Academic Libraries in the E-learning Environment: Issues and Challenges

The development of computer and network technology is changing the education scenario and transforming the teaching and learning process from the traditional physical environment to the digital environment. It is now possible to access vast amount of information online and enable one to one communication without the confines of place or time. While E-learning and teaching is unlikely to replace face-to-face training and education it is becoming an additional delivery method, providing new learning opportunities to many users. It is also causing an impact on library services as the increased use of ICT and web based learning technologies have paved the way for providing new ICT based services and resources to the users. Online learning has a crucial role in user education, information literacy programmes and in training the library professionals. It can help students become active learners, and libraries will have to play a greater role in this process of transformation. The significance of libraries within an institution has improved due to the fact that academic libraries and information services are now responsible for e-learning within their organization.

Industrial Growth in Madhya Pradesh: Structure and Economic Backwardness

It examine the aspects of Madhya Pradesh’s industrial structure which throw light on the development, viability and the efficiency of not only the over all industrial sector but also some of the selected industries of the state. The major objectives of are to examine the nature and characteristics of economic backwardness in Madhya Pradesh in an inter-state comparative framework and to analyse the pace and pattern of industrial growth in Madhya Pradesh against the backdrop of liberalization. To explore the industrial structure of Madhya Pradesh using the major structural ratios and industry mix. This study has underlined some structural as well as region specific constraints to the accelerated growth of the manufacturing industry in Madhya Pradesh. The industrial structure of Madhya Pradesh is concentrated and lop-sided. This is evidenced by the dominancy of single industry, basic metal and alloys. A diversified industrial structure is essential for promoting interdependent growth of the manufacturing industry based on the inter-industry linkages and agglomeration. The thesis gives a broad spectrum of regional disparities in development and evidence for Madhya Pradesh’s backwardness also portrayed and reflects the changing industrial structure of the state.

Single Frame Image Super Resolution Using Learned Directionlets

In this paper, a new directionally adaptive, learning based, single image super resolution method using multiple direction wavelet transform, called Directionlets is presented. This method uses directionlets to effectively capture directional features and to extract edge information along different directions of a set of available high resolution images .This information is used as the training set for super resolving a low resolution input image and the Directionlet coefficients at finer scales of its high-resolution image are learned locally from this training set and the inverse Directionlet transform recovers the super-resolved high resolution image. The simulation results showed that the proposed approach outperforms standard interpolation techniques like Cubic spline interpolation as well as standard Wavelet-based learning, both visually and in terms of the mean squared error (mse) values. This method gives good result with aliased images also.