Hasimah Abd Rahman

Design and simulation of electronic ballast performance for high pressure sodium street lighting

A novel method of reducing the energy consumption of street lighting using electronic ballasts was simulated on 277 units of 250u2009W high pressure sodium (HPS) street lights for a month. The design of the electronic ballast is detailed. A control system was utilised to dim the lights at hours of reduced traffic. The energy consumption was compared to that of the conventional system. The electronic ballast operates at a higher frequency and corrects any power distortion in the supply. Together with the light dimming capability, the electronic ballast increased the efficiency of the lighting system resulting in a monthly saving of 10.7u2009MWh, giving a monthly equivalent carbon dioxide reduction of 6.1 tonnes. In Malaysia, this would translate to a monthly cost saving of 37.7%.

Efficiency optimization of an induction machine using optimal flux control

This paper presents the efficiency optimization of a variable speed induction machine (VSIM) using an Optimal Flux Control (OFC). To optimize the efficiency, an approach called as Maximum Torque per Ampere (MTA) algorithm was used to provide the constant optimal flux (COF) during the speed control, where the minimisation of the winding losses is considered along the operation. In this paper, the total power and total power loss during machine motoring is presented. The performance of the proposed OFC design is compared with the conventional open-loop scalar-control (OLSC) method. From the finding, it was found that the COF approach provides better performance than the OLSC in terms of settling time, steady-state error and efficiency.

Hourly Photovoltaics Power Output Prediction for Malaysia Using Support Vector Regression

Reliable solar energy forecasting enables grid operators to manage the grid better as PV penetration level increases. This research explores the use of support vector regression to forecast hourly power output from a grid-connected PV system in Malaysia. Data is obtained from a grid-connected PV system that is equipped with a weather monitoring station. Three parameters are used as input to the forecast model; global irradiance, tilted irradiance and ambient temperature. Results were compared against a persistence model. The SVR model manages to forecast hourly power production with satisfactory accuracy.

Hourly irradiance forecasting in Malaysia using support vector machine

This paper investigates the use of support vector machine (SVM) to forecast hourly solar irradiance for a tropical country. The hourly irradiance data was obtained from Sepang Malaysia, recorded throughout 2011. The data is converted into corresponding clearness index values to facilitate model convergence. The forecast is tested against the standard multilayer perceptron (MLP) technique and persistence forecast. The evaluation metrics used to validate each models performance are mean bias error, root mean square error, mean absolute error/average, and Kolmogorov-Smirnov integral test. Results show that the SVM performs significantly better than the conventional MLP technique.