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.

Studies on some nonlinear optical phenomena-optical phase conjugation and continuous wave second harmonic generation

Nonlinear optics has emerged as a new area of physics , following the development of various types of lasers. A number of advancements , both theoretical and experimental . have been made in the past two decades . by scientists al1 over the world. However , onl y few scientists have attempted to study the experimental aspects of nonlinear optical phenomena i n I ndian laboratories. This thesis is the report of an attempt made in this direction. The thesis contains the details of the several investigations which the author has carried out in the past few years, on optical phase conjugation (OPC) and continuous wave CCVD second harmonic generation CSHG). OPC is a new branch of nonlinear optics, developed only in the past decade. The author has done a few experiments on low power OPC in dye molecules held in solid matrices, by making use of a degenerate four wave mixing CDFWND scheme. These samples have been characterised by studies on their absorption-spectra. fluorescence spectra. triplet lifetimes and saturation intensities. Phase conjugation efficiencies with r espect to the various parameters have been i nvesti gated . DFWM scheme was also employed i n achievi ng phase conjugation of a br oadband laser C Nd: G1ass 3 using a dye solution as the nonlinear medium.

Development & evaluation of different acoustic models for Malayalam continuous speech recognition

Performance of any continuous speech recognition system is dependent on the accuracy of its acoustic model. Hence, preparation of a robust and accurate acoustic model lead to satisfactory recognition performance for a speech recognizer. In acoustic modeling of phonetic unit, context information is of prime importance as the phonemes are found to vary according to the place of occurrence in a word. In this paper we compare and evaluate the effect of context dependent tied (CD tied) models, context dependent (CD) and context independent (CI) models in the perspective of continuous speech recognition of Malayalam language. The database for the speech recognition system has utterance from 21 speakers including 11 female and 10 males. Our evaluation results show that CD tied models outperforms CI models over 21%.

Continuous production of L-glutaminase by an immobilized marine Pseudomonas sp BTMS-51 in a packed bed reactor

A marine Pseudomonas sp BTMS-51, immobilized by Ca-alginate gel entrapment was used for the production of extracellular Lglutaminase under repeated batch process and continuous process employing a packed bed reactor (PBR). Immobilized cells could produce an average of 25 U/ml of enzyme over 20 cycles of repeated batch operation and did not show any decline in production upon reuse. The enzyme yield correlated well with the biomass content in the beads. Continuous production of the enzyme in PBR was studied at different substrate concentrations and dilution rates. In general, the volumetric productivity increased with increased dilution rate and substrate concentrations and the substrate conversion efficiency declined. The PBR operated under conditions giving maximal substrate conversion efficiency gave an average yield of 21.07 U/ml and an average productivity of 13.49 U/ml/h. The system could be operated for 120 h without any decline in productivity

Continuous Production of Extracellular L-Glutaminase by Ca-Alginate-Immobilized Marine Beauveria bassiana BTMF S-10 in Packed-Bed Reactor

L-Glutamine amidohydrolase (L-glutaminase, EC 3.5.1.2) is a therapeutically and industrially important enzyme. Because it is a potent antileukemic agent and a flavor-enhancing agent used in the food industry, many researchers have focused their attention on L-glutaminase. In this article, we report the continuous production of extracellular L-glutaminase by the marine fungus Beauveria bassiana BTMF S-10 in a packed-bed reactor. Parameters influencing bead production and performance under batch mode were optimized in the order-support (Na-alginate) concentration, concentration of CaCl2 for bead preparation, curing time of beads, spore inoculum concentration, activation time, initial pH of enzyme production medium, temperature of incubation, and retention time. Parameters optimized under batch mode for L-glutaminase production were incorporated into the continuous production studies. Beads with 12 × 108 spores/g of beads were activated in a solution of 1% glutamine in seawater for 15 h, and the activated beads were packed into a packed-bed reactor. Enzyme production medium (pH 9.0) was pumped through the bed, and the effluent was collected from the top of the column. The effect of flow rate of the medium, substrate concentration, aeration, and bed height on continuous production of L-glutaminase was studied. Production was monitored for 5 h in each case, and the volumetric productivity was calculated. Under the optimized conditions for continuous production, the reactor gave a volumetric productivity of 4.048 U/(mL·h), which indicates that continuous production of the enzyme by Ca-alginate-immobilizedspores is well suited for B. bassiana and results in a higher yield of enzyme within a shorter time. The results indicate the scope of utilizing immobilized B. bassiana for continuous commercial production of L-glutaminase

Characterizations of some continuous distributions using partial moments

Inthis paper,we define partial moments for a univariate continuous random variable. A recurrence relationship for the Pearson curve using the partial moments is established. The interrelationship between the partial moments and other reliability measures such as failure rate, mean residual life function are proved. We also prove some characterization theorems using the partial moments in the context of length biased models and equilibrium distributions

Design and Programming of Cathodic Protection for SHIPS

In order to minimize the risk of failures or major renewals of hull structures during the ship's expected life span, it is imperative that the precaution must be taken with regard to an adequate margin of safety against any one or combination of failure modes including excessive yielding, buckling, brittle fracture, fatigue and corrosion. The most efficient system for combating underwater corrosion is 'cathodic protection'. The basic principle of this method is that the ship's structure is made cathodic, i.e. the anodic (corrosion) reactions are suppressed by the application of an opposing current and the ship is there by protected. This paper deals with state of art in cathodic protection and its programming in ship structure