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Ireri TG, Murage DK, Abungu NO. "Short Term Load Forecasting Using Artificial Neural Networks.". In: Mechanical Engineering Annual Conference. Juja; 2013. Abstractjkuat_conf_paper.pdf

Load forecasting refers to the prediction of future load conditions based on present or historical data. This is important especially for transmission planning and economic dispatch. In this paper, an Artificial Neural Network (ANN) is trained using historical data for a sub-station at Ruiru, Kenya and the corresponding loading conditions for the sub-station are used to test its accuracy in forecasting the electrical load when given other parameters.

Lucy OP, Odero AN. "Solution to Economic Load Dispatch Problem using Particle Swarm Optimization.". In: KSEEE. Mombasa, Kenya; 2014. Abstractpso18sept2014.pdf

This paper proposes to determine the feasible optimal solution of the economic load dispatch power systems problem using Particle Swarm Optimization (PSO) considering various generator constraints. The objective of the proposed method is to determine the steady-state operating point which minimizes the fuel cost, while maintaining an acceptable system performance in terms of limits on generator power, line flow,
prohibited operating zone and non linear cost function. Three diff erent inertia weights; a constant inertia weight CIW, a timevarying inertia weight TVIW, and global-local best inertia weight GLbestIW, are considered with the (PSO) algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated for each of the method individually. It is observed that the PSO algorithm with
the proposed inertia weight (GLbestIW) yields better results, both in terms of optimal solution and faster convergence.

Peter Musau Moses, Abungu DNO. "Solving The Active Distribution Network Reconfiguration (ADNR) Problem Taking Into Consideration A Stochastic Wind Scenario and Load Uncertainity." International Journal of Emerging Technology and Advanced Engineering. 2013;3(7).p._musau_and_dr._abungu.pdf
Moses MP, Abungu NO, Mbuthia PMJ. "Solving The Active Distribution Network Reconfiguration (ADNR) Problem Taking Into Consideration A Stochastic Wind Scenario and Load Uncertainity By Using HBFDE Method." International Journal of Emerging Technology and Advanced Engineering. 2013;3(7):26-36. Abstractijetae_0713_05.pdfClick here to read more...

Past literature has attempted to solve the problem of network reconfiguration with Distributed Generators(DGs) without taking into consideration the intermittent renewable at a close proximity. Distribution Network Reconfiguration (ADNR) must account for uncertain behavior of loads and wind when the commercial wind based DG, Doubly Fed Induction Generators (DFIG) supports a significant part of network. In this paper, a new Hybrid Bacterial Foraging and Differential Evolution (HBFDE) algorithm is considered for the ADNR problem with minimum loss and an improved voltage profile. In the HBFDE algorithm the Differential Evolution (DE) algorithm is combined with the Bacterial Foraging (BF) algorithm to overcome slow and premature convergence of BF. Indeed, the proposed algorithm is based on the evolutionary natures of BF and DE, to take their advantage of the compensatory property, and avoid their corresponding drawbacks. In addition, to cope with the uncertainty behavior of loads and wind, a stochastic model is presented to solve the ADNR problem when the uncertainty related to wind and load forecast is modeled in a stochastic framework on scenario approach basis. The proposed algorithm is tested on the IEEE 33 - Bus Radial Distribution Test Systems. The results of the simulation show the effectiveness of proposed algorithm real time and real world optimization problems facing the smart grid.

Odero AN. A Study of the Electrical Insulation Characteristics of Woods Locally locally available in Kenya. Nelson I, ed. Nairobi: University of Nairobi; 1993. Abstract

For my thesis I did a problem formulation and then wrote a computer program to help speedily analyze various insulator profiles for use at high voltages. The program when fed the profile would output the potential and electric field patterns around the high voltage insulator, in addition to predicting it's flashover voltage. Validation of the model was obtained through practical measurement in a high voltage laboratory. Profiles that would insulate very high voltages were arrived at this way in a relatively short time.

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