4 resultados para Active power-factor correction

em Cochin University of Science


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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.

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The rapid developments in fields such as fibre optic communication engineering and integrated optical electronics have expanded the interest and have increased the expectations about guided wave optics, in which optical waveguides and optical fibres play a central role. The technology of guided wave photonics now plays a role in generating information (guided-wave sensors) and processing information (spectral analysis, analog-to-digital conversion and other optical communication schemes) in addition to its original application of transmitting information (fibre optic communication). Passive and active polymer devices have generated much research interest recently because of the versatility of the fabrication techniques and the potential applications in two important areas – short distant communication network and special functionality optical devices such as amplifiers, switches and sensors. Polymer optical waveguides and fibres are often designed to have large cores with 10-1000 micrometer diameter to facilitate easy connection and splicing. Large diameter polymer optical fibres being less fragile and vastly easier to work with than glass fibres, are attractive in sensing applications. Sensors using commercial plastic optical fibres are based on ideas already used in silica glass sensors, but exploiting the flexible and cost effective nature of the plastic optical fibre for harsh environments and throw-away sensors. In the field of Photonics, considerable attention is centering on the use of polymer waveguides and fibres, as they have a great potential to create all-optical devices. By attaching organic dyes to the polymer system we can incorporate a variety of optical functions. Organic dye doped polymer waveguides and fibres are potential candidates for solid state gain media. High power and high gain optical amplification in organic dye-doped polymer waveguide amplifier is possible due to extremely large emission cross sections of dyes. Also, an extensive choice of organic dye dopants is possible resulting in amplification covering a wide range in the visible region.

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The performance of circular, rectangular and cross irises for the coupling of microwave power to rectangular waveguide cavity resonators is discussed. For the measurement of complex permittivity of materials using cavity perturbation techniques, rectangular cavities with high Q-factors are required. Compared to the conventional rectangular and circular irises, the cross Iris coupling structure provides very high loaded quality factor for all the resonant frequencies. The proposes cross iris coupling structure enhances the accuracy of complex permittivity measurements.

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Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year