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Thesis

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2011 - 20142
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Subject: Computer Science and Engineering × End date: 2014 × Start date: 2011 × Type: Thesis × Thesis Type: M.Sc ×
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    ThesisItemOpen Access
    OPTIMIZATION OF COST OF ELECTRICITY GENERATED BY HYDRO POWER PLANT USING ARTIFICIAL NEURAL NETWORKS
    (Punjab Agricultural University, Ludhiana, 2014) Kaur, Amninder; Sawhney, B.K.
    Economic operation and control of interconnected power systems involves the solution of difficult optimization problems that require good computational tools. Evolutionary Computation is an area of Computer Science that uses idea from biological evolution to solve problems. Evolutionary computation is one such tool that has shown its ability in solving complex problems. It can be implemented in various forms such as genetic algorithms. The current work presents a prominence for the optimization of the cost of generating units of hydro power plant by genetic algorithms. To optimize the cost of generated electricity, it is important to ensure constant generation of electricity during a time period. A good quality of the electric power system requires both the frequency and voltage to remain at standard values during operation. The foremost task is to keep the frequency constant against the randomly varying active power loads, which are also referred to as unknown external disturbance. The objectives are to minimize the transient deviations in frequency and to ensure their steady state errors to be zeros, so that constant power can be generated. Once optimized power will be generated it will automatically optimize the cost of generated power because generated power and cost of generated power are directly proportional to each other. Genetic algorithm (GA) is used for optimization of integral gains and bias factors, which are applied to automatic generation control (AGC). Tie-line bias KI and frequency bias parameter B are optimized by using real coded GA. It is used to search for the optimal set of parameters (KI and B), which in turn optimize the frequency factor. Using GA results are obtained for given set of hydro units and optimized cost corresponding to each set of hydro generating unit.
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    ThesisItemOpen Access
    DESIGN AND DEVELOPMENT OF AN INDIGENOUS YIELD MONITOR FOR GRAIN COMBINE HARVESTER
    (Punjab Agricultural University, Ludhiana, 2011) Sharma, Karun
    The rice-wheat rotation has led to an agrarian crisis in Punjab in terms of depleted aquifers, soil toxicity and salinity and over exploitation of natural resources. To preserve the natural resources and soil fertility, Precision farming is the need of hour and yield monitor is a logical first and important development in precision farming & agricultural machinery that allows farmers to assess the yield variability within the field during harvesting of crop. Although in advanced countries, high HP combines for large farms are available with yield monitors fitted as standard equipment or installed separately and the sensors & systems used for yield monitor are usually designed for those high HP combines and are very costly. However, these systems and sensors are difficult to install on small indigenous combines directly due to design constraint. So, to spread the use of yield monitoring combines in India, it was necessary to develop an original yield monitoring technique for indigenous combines. For development of indigenous yield monitor, components such as Auxiliary tank, Single point parallel type load cell and Inductive proximity sensor were identified; Micro-controller 8051 with Display unit was selected to process the yield data. Indigenous yield monitor was developed by assembling the designed and selected components on combine harvester. The present yield monitor was evaluated for rice crop at two levels of monitoring i.e. discrete & continuous type and three levels of forward speed i.e. 2.0, 3.0 and 4.5 km/h. The mean value of yield 4609.5 kg/ha with S.D 2116.7 kg/ha having C.V 45.3% with discrete type of monitoring and mean value of yield 5513.7 kg/ha with S.D 3337.7 kg/ha having C.V 60.6% with continuous type of monitoring was calculated at three forward speeds. The lag time was observed 12.6, 13.1 and 12.4 seconds at forward speed of 2.0, 3.0 and 4.5 km/h respectively. An average error 6.03% was observed in the measurement of yield by using indigenous yield monitor. Yield monitor, combine harvester, Global Positioning System (GPS), Geographical Information System (GIS) and yield variability