Volume-8 Issue-5
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Volume-8 Issue-5, January 2019, ISSN: 2231-2307 (Online) Published By: Blue Eyes Intelligence Engineering & Sciences Publication |
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1. |
Authors: |
Sreevidya.L, S. U. Prabha, S. Sathiya | |
Paper Title: |
Evaluation of The Reliability of Distribution System with Distributed Generation using ETAP | ||
Abstract: Distributed Generation (DG) is an electric source connected directly to the distribution network. DG has been growing rapidly in deregulated power systems due to their potential solutions to meeting localized demands at distribution level and to mitigate limited transmission capacities from centralized power stations. In this paper effort has been made to study the impact of DG on the reliability of the distribution network. IEEE 33 Bus distribution network was used for the study. Firstly, DGs were optimally sized and located in the network using Modified Particle swarm optimization and ETAP software was used to model and evaluate the reliability indices. Two scenarios were considered. Scenario one was the integration of one DG and two was integration of two DGs. The results obtained showed that as the number of DG in the system increases the reliability of the system also increases. Keywords: Distributed Generation, Reliability indices, Modified Particle Swarm Optimization, ETAP. References:
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2. |
Authors: |
Fredrick Otieno Okuta, Abednego Gwaya, Wanyona Githae | |
Paper Title: |
An investigation of the Adequacy of Monitoring and Evaluation Practice in Public Projects in Kenya | ||
Abstract: The Practice of Monitoring and evaluation (M&E) has become an increasingly important tool within the global efforts in achieving environmental, economic and social sustainability. Monitoring and evaluation (M&E) help those involved with projects to assess if progress is being achieved in line with expectations or not so that reasonable measures can be taken in good time to ensure the project success. While the knowledge on monitoring and evaluation of projects exists, the administrative components of monitoring and evaluation seem to be lacking in the management of county government funded projects. The purpose of this study was to investigate the adequacy of Monitoring and Evaluation in county government funded projects in Kenya. The study investigated the influence of policy, level of planning, resources and the process on the adequacy of monitoring and evaluation exercise in the county government funded projects in Kenya. The study adopted a survey research design and the target population were the county government projects coordination department members. Two completed projects were sampled from each of the 47 counties in Kenya. 72 out of 94 questionnaires sent to the field were returned translating to 76% response rate. The data was analyzed using SPSS version 16 which involved descriptive and inferential statistics. The study established that policies (r = 0.604, P < 0.01), planning (r = 0.596, P < 0.01), availability of resources (r = 0.815, P < 0.01) and process (r = 0.889, P < 0.01) have a significant influence on the adequacy of monitoring and evaluation of county government funded projects in Kenya Keywords: Monitoring, Evaluation, County Government Funded Projects. References:
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3. |
Authors: |
Kiambigi, Maina, Gwaya, A.O, Koteng, D.O | |
Paper Title: |
Concrete Strength Prediction using Multi-Linear Regression Model: A case study of Nairobi Metropolitan | ||
Abstract: Early prediction of strength is key in effective and efficient planning for concrete construction projects. There are several empirical correlations that have been developed to determine concrete strength estimation from early age results though each model has its own limitations when applied. A multi-staged evaluation of the existing prediction models (BS modification factors, German model, Abrams model, Bolomey’s model and ACI model) was performed for concrete strength data obtained from experimental work conducted under standard conditions in the laboratory. The data on compressive strength was obtained from concrete made from 6 different samples of fine aggregates whose physical and chemical properties had been determined. The limitations for each model was noted which then gave a basis for need for a statistical method that could predict strength more accurately. A multiple linear regression technique was used. The variables used to predict were water-cementations ratio, quantities of mix design constituents, physical and chemical properties of the fine aggregates. Multiple-linear regression models developed for this study yielded coefficients of determination (CODs) for concrete strength prediction at 7, 14, 28, 56, 112 and 180-days curing. The regression models were then validated using a different set of samples that were not included in the formulated models. The predicted values of compressive strength obtained using the regression models were found to be in agreement with the experimental results obtaining CODs of 0.7821, 0.7186, 0.8416, 0.755, 0.7695 and 0.8444 for 7, 14, 28, 56, 112 and 180 days respectively. Keywords: Concrete Mix Design, Concrete Strength, Fine Aggregates. References:
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4. |
Authors: |
Arindam Roy, SusmitaRoy, Partha P. Biswas | |
Paper Title: |
Minimizing Loss in Larger Distribution Networks by Optimal Allotment of DG and Capacitorusing an Advanced Adaptive Differential Evolution | ||
Abstract: Minimization of Power Loss expenses of the rapidly expanding larger distribution network is always an attention for Electric Power Utilities. Moreover, if power loss can be minimized at the highest extent, network voltage improvesinherently in overall, therefore enhancing the quality of power at the consumer end.This paper presents an integrated method for optimal allotment of distributed generation (DG) and shunt capacitor (SC) simultaneously in the large scale distribution system with theprimary objective to minimizethe network power loss. To perform this task of optimization, one latest algorithm named L-SHADE, linear population size reduction technique of success history based adaptive differential evolution, has been utilized. This is an advanced one of the previous Differential Evolution algorithm,namely SHADE [17] where the control parameters scaling factor (F) and the crossover rate (CR) are only adapted.In L-SHADE [18], the control parameter population size (Np)is also reducedlinearlyover successive generations. The algorithm optimizes the rating (continuous variable) and corresponding bus number (discrete variable) for both DG and SC. IEEE 69 bus, 119 bus standard distribution networksand a practical 83 bus distribution network have been studied. The simulation results have been compared with similar equivalent algorithms in the large scale distribution system and found as the best among them. Keywords: Power loss minimization, Larger Distribution networks, voltage profile, distributed generation, shunt capacitor,L-SHADE algorithm. References:
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