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Optimal Location of Facts Device for Improved Power Transfer Capability and System Stability
Hassan Natala,
Kingsley Monday Udofia,
Chinedu Pascal Ezenkwu
Issue:
Volume 6, Issue 3, June 2017
Pages:
22-27
Received:
25 October 2016
Accepted:
4 January 2017
Published:
3 June 2017
Abstract: The work entailed in this paper is to develop a model for the optimal location of shunt Flexible Alternating Current Transmission System (FACTS) along a transmission line so as to enhance controllability and increase power transfer capability of the transmission network. Mathematical models for maximum power transfer and transmission angles for transmission line were developed. The investigation was done for both lossless and actual transmission lines. MATLAB software was used for the simulation of the models. Aloaji – Itu transmission 132 KV transmission line in South-eastern Nigeria was used as a case study. Performance analysis was conducted on the various maximum power and transmission angles data for different degree of series compensation and FACTS locations along the transmission lines to determine the optimal location of the FACTS device for both lossless and actual transmission lines. The results obtained showed that the optimal location of the shunt FACTS device is not fixed, but changes with the change in degree of series compensation. Both the power transfer capability and stability of the system can be improved much more if the shunt FACTS device is placed at the new optimal location instead of the mid-point of the line.
Abstract: The work entailed in this paper is to develop a model for the optimal location of shunt Flexible Alternating Current Transmission System (FACTS) along a transmission line so as to enhance controllability and increase power transfer capability of the transmission network. Mathematical models for maximum power transfer and transmission angles for tra...
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Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1
Imo Enoidem Ebukanson,
Chukwu Benedict Chidi,
Abode Innocent Iriaoghuan
Issue:
Volume 6, Issue 3, June 2017
Pages:
28-33
Received:
8 January 2017
Accepted:
18 January 2017
Published:
7 June 2017
Abstract: This study presents statistical analysis of electricity generation in Nigeria using two different statistical models, namely; multiple linear regression model and box-Jenkins’ autoregressive model of order 1. Two climatic variables (rainfall and temperature) were used as the explanatory variables. Data on electricity generation in Nigeria between 2002 and 2014 were obtained from the Central Bank of Nigeria Statistical Bulletin while Data on rainfall and temperature between 2002 and 2014 were extracted from the National Bureau of Statistics (NBS) abstract. Test of model fitness and forecasting accuracy were done using generic statistical approach which include coefficient of determination and root mean square error. The prediction accuracy of the two statistical models was compared and the best model was selected. Furthermore, correlation between power generation and the two climatic variables (rainfall and temperature), were carried out and the result reveals that the amount of rainfall has significant and positive relationship with power generation in Nigeria. Specifically, rainfall has correlation value of r = 0.927 with the power generation at probability, p = 0.000 and the relationship was significant at 1% (p<0.01). However, temperature although it is positively correlated, does not significantly affect power generation. Temperature has correlation value of t = 0.136 with power generation at probability, p = 0.658 (p>0.05) and the relationship was significant at 5% (p<0.05). Among the two statistical models, multiple linear regression model was selected as the best model as it gave the highest value of coefficient of determination (r2=99.77%) and the least Root Mean Square Error (60.27%).
Abstract: This study presents statistical analysis of electricity generation in Nigeria using two different statistical models, namely; multiple linear regression model and box-Jenkins’ autoregressive model of order 1. Two climatic variables (rainfall and temperature) were used as the explanatory variables. Data on electricity generation in Nigeria between 2...
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Potentials for Sustainable Power Supply in Nigeria: An Overview of Energy Resources in Western Nigeria
Issue:
Volume 6, Issue 3, June 2017
Pages:
34-39
Received:
25 March 2017
Accepted:
20 April 2017
Published:
23 June 2017
Abstract: Availability of affordable energy is essential for the improvement of living standard and economic growth in any country. This study examined available energy resources in Western Nigeria and analyzed how they can be harnessed for sustainable energy supply. The study approach used involved literature survey, observation tour of some locations across the region and analysis of the observed scenario. The development of the proposed sustainable solution is based the study of countries in similar situation and how they were able to surmount their energy supply problems as well as personal understanding of the necessary adjustments for differences in locational factors and culture. The framework recommends integration of small number of abundantly available renewable energy sources at a scale manageable by locally available hands and in collaboration with all the stakeholders. It is believed that the proposed sustainable energy technology would ameliorate the persistent energy problems in the region. The proposed energy supply system is also expected to be economically affordable, environmentally friendly and culturally compatible.
Abstract: Availability of affordable energy is essential for the improvement of living standard and economic growth in any country. This study examined available energy resources in Western Nigeria and analyzed how they can be harnessed for sustainable energy supply. The study approach used involved literature survey, observation tour of some locations acros...
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Time Series Analysis of Industrial Electricity Consumption in Nigeria Using Harvey Model and Autoregressive Model
Ogungbemi Emmanuel Oluropo,
Edet Joseph Archibong,
Nsikak John Affia
Issue:
Volume 6, Issue 3, June 2017
Pages:
40-46
Received:
3 January 2017
Accepted:
18 January 2017
Published:
27 June 2017
Abstract: In this paper time series modeling and forecasting of industrial electricity consumption in Nigeria is presented. Specifically, Harvey Model and Autoregressive Model, (AR) are used. The data used are obtained from Central Bank of Nigeria (CBN) Statistical Bulletin for industrial electricity consumption ranging from 1979 – 2014. The results shows that Harvey Model has (r2) = 80.1% and RMSE = 65.2513 whereas Autoregressive Model has (r2) = 50.1% and RMSE = 71.3985. Obviously, Harvey model has better prediction accuracy than the AR model. The Harvey model was then used to forecast industrial electricity consumption in Nigeria for the next 15 years (from 2015 to 2029). According to the forecast result by the year 2029 the industrial consumption of Nigeria will stand at 539.65 MW/h as against 468.18 MW/h in 2015.
Abstract: In this paper time series modeling and forecasting of industrial electricity consumption in Nigeria is presented. Specifically, Harvey Model and Autoregressive Model, (AR) are used. The data used are obtained from Central Bank of Nigeria (CBN) Statistical Bulletin for industrial electricity consumption ranging from 1979 – 2014. The results shows th...
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