Izham Zainal Abidin

Contingency based congestion management and cost minimization using bee colony optimization technique

Unexpected contingency occurrence in a power system network can lead to high current flow in the system. This has made the system to be in a stressed condition which causes congestion to the system, while instability can be the next unpredictable incident. High current flow may also impose high fuel cost of the generators. Thus, congestion needs to be managed properly in order to reduce the undesired current flow in the transmission line for maintaining system security. This paper presents contingency based congestion management and cost minimization using bee colony optimization technique. In this study, bee colony optimization technique is applied to optimize the current flow in the system such that system security is preserved; considering transmission lines as the control variables. N-1 contingency is considered as the forecasted event, implemented offline so that the performance of the system can be evaluated. Cost minimization is also conducted by controlling the transmission constraints in the system. Validation through the IEEE 30-reliability test system and 6-Bus test system are conducted to simulate the scenarios.

A Study on static voltage collapse proximity indicators

In the time of rapid growth, there is an increase of demand for a reliable and stable power supply. Due to this, utility companies are forced to operate their power system nearer to its maximum capabilities since system expansion may be a costly option. As a result, the power system will be at risk to voltage collapse. Voltage collapse phenomenon is known to be complex and localised in nature but with a widespread effect. The ultimate effect of voltage collapse would be total system collapse which would incur high losses to utility companies. Thus, on- line monitoring of power system stability has become a vital factor for electric utility companies. This paper looks into combining a power flow program in MATLAB environment with two line stability indices, which are fast voltage stability index (FVSI) and line stability index, LQP for automatic contingency ranking. The IEEE 14 Bus Test System is used as a standard test system. This approach investigates each line of the system through calculating an indicator that varies from zero (no load condition) to unity (maximum permissible loading condition). The basic concept of maximum power transfer through a line is utilized. Correlation study on the results obtained from contingency ranking and voltage stability analysis were conducted and it is found that line outages at the weak lines would cause voltage instability condition to a system. Subsequently, using the result from the contingency ranking, weak areas in the system can be identified. Verification of this technique with other existing technique shows a strong agreement between them.

Congestion management based optimization technique using bee colony

Congestion management problem is a popular issue in power system which can be due to line, voltage and thermal constraints. This phenomenon can possibly lead to voltage instability occurrence, loss increment and voltage drop in power system. Therefore, a proper management of congestion should be carried appropriately in order to maintain system operability considering all the available constraints. This paper presents congestion management problem using bee colony optimization approach. The aim of the study is to optimize the cost of generation in power system network within the given available constraints. The study involved the development of bee colony algorithm in addressing congestion management, considering cost optimization as the objective function. Line constraint is also taken into consideration in this study which depends on the electrical power provider to allow the power delivered to the customers. Tests conducted on the IEEE 30-Bus Reliability Test System for performance assessment revealed that the proposed bee algorithm technique is better than evolutionary programming technique in addressing this problem.

Short Term Load Forecasting Using a Hybrid Neural Network

Short term load forecasting (STLF) is very important from the power systems grid operation point of view. STLF involves forecasting load demand in a short term time frame. The short term time frame may consist of half hourly prediction up to weekly prediction. Accurate forecasting would benefit the utility in terms of reliability and stability of the grid ensuring adequate supply is present to meet with the load demand. Apart from that it would also affect the financial performance of the utility company. An accurate forecast would result in better savings while maintaining the security of the grid. This paper outlines the STLF using a novel hybrid online learning neural network, known as the Gaussian regression (GR). This new hybrid neural network is a combination of two existing online learning neural networks which are the Gaussian adaptive resonance theory (GA) and the generalized regression neural network (GRNN). Both GA and GRNN implemented online learning, but each of them suffers from limitation. Originally GA is used for unsupervised clustering by compressing the training samples into several categories. A supervised version of GA is available, namely Gaussian ARTMAP (GAM). However, the GAM is still not capable on solving regression problem. On the other hand, GRNN is designed for solving real value estimation (regression) problem, but the learning process would involve of memorizing all training samples, hence high computational cost. The hybrid GR is considered an enhanced version of GRNN with compression ability while still maintains online learning properties. Simulation results show that GR has comparable prediction accuracy and has less prototype as compared to the original GRNN as well as the support vector regression.

Shunt active power filter for harmonic compensation of nonlinear loads

This paper presents the design and simulation of a single-phase shunt active power filter for harmonic and power factor compensation of multiple nonlinear loads. The system is modeled in Matlab Simulink to consist of an uncontrolled rectifier and an AC controller as the nonlinear loads, with an active filter to compensate for the harmonic current injected by the load. The active filter is based on a full-bridge single-phase inverter. The design of the active filter controller is based on time-domain method that consists of three main tasks; to identify the harmonic content and form a synchronized reference, to provide closed-loop control to force the current of the active filter to follow the reference and to regulate the capacitor DC voltage. The spectral analysis of the supply current shows the harmonics produced by the load has been successfully compensated by the active filter. The effect of varying the switching frequency on the performance of the active filter is also presented.

Thermal rating monitoring of the TNB overhead transmission line using line ground clearance measurement and weather monitoring techniques

Power transfer through a short transmission line is mainly limited by thermal rating. Accurate determination of thermal rating can maximize power transfer which subsequently saves utility from building new lines. Thermal rating normally is set to a certain conductor temperature which during operation the transmission line will not has problem with ground clearance and the conductor material properties will remain in its original state. The conductor temperature varies with weather parameters and loading and it has close relationship with line sag or line ground clearance. The conductor temperature can be determined by monitoring the line ground clearance and weather parameters and the actual thermal rating can be calculated. Monitoring of the line ground clearance can be done in real time using laser distance measurement sensor with data logger and real time monitoring and calculation software.

Under frequency load shedding (UFLS): Principles and implementation

Under frequency load shedding is implemented to restore power system frequency stability if system frequency drops below the operational set point during major disturbance such as lost of generation. Different countries/utility companies have their own philosophies in implementing the under frequency load shedding scheme. Generally, it is based on country/utility requirements, e.g. the overall power system network and the countrys demographic. This paper presents the principles and implementation of the under frequency load shedding (UFLS) and presented using simulations of 56 test bus-system. The performance of the developed schemes under various conditions of disturbance were compared and analyzed. All the simulation works were performed using Siemens PTI software PSS®E.

Improvement of ANN-BP by data pre-segregation using SOM

Artificial intelligence is used to predict the onset of diabetes based on data measured from Pima Indians. This research is comparing the results gained from using same artificial neural networks- back propagation (ANN-BP) engine for 2 differently prepared data. The first data set consists of the entire data set which is cross validated, while the second dataset is segregated into 2 groups using Kohonen Self Organizing Maps (SOM) which are then cross validated. Splitting the files prior to implementing the cross validation improves the general accuracy of the ANN-BP whereby the positively predicted diabetes cases percentage increased from 72% to 99%. Meanwhile the prediction of the negative diabetic cases percentage increased from 80% to 97%.

Moving holidays' effects on the Malaysian peak daily load

Malaysias yearly steady growth in electricity consumption as a result of fast development in various sectors of the Malaysian economy have increased the need to have a more robust, reliable and accurate load forecasting for short —, medium-, or long-term. A reliable method for short term load forecasting is crucial to any decision maker in a power utility company. Many studies have been made to improve the forecasting accuracy using various methods. The forecasting errors for the holiday seasons are known to be higher than those for weekends. This paper aims to determine which model would be a better model to estimate the holiday effects and therefore give a better forecasting accuracy for the peak daily load in Malaysia. Some of the holiday effects in Malaysia are from Eid ul-Fitr, Christmas, Independence Day and Chinese New Year. The seasonal ARIMA (SARIMA) and Dynamic Regression (DR) or Transfer function modelling are considered. Furthermore, the final selection of the models depends on the Mean Absolute Percentage Error (MAPE) and others such as the sample autocorrelation function (ACF), the sample partial autocorrelation function (PACF) and a bias-corrected version of the Akaikes information criterion (AICC) statistic. The Dynamic Regression (DR) model recorded 2.22% as the lowest MAPE value for the 2004 New Years Eve and 2.39% for the seven days ahead forecasting. And therefore, DR model is the most appropriate model to be considered for forecasting any public holidays in Malaysia.

Continual improvement and assessment plan for Mechanical Engineering Programme in UNITEN

This paper describes the continuous quality improvement (CQI) process plan that was developed and implemented by the Department of Mechanical Engineering (DME), Universiti Tenaga Nasional (UNITEN), Malaysia for its Bachelor of Mechanical Engineering Programme. The plan is part of the Outcome-Based Education (OBE) system that is required by the Engineering Accreditation Council (EAC) of Malaysia. DME has implemented OBE approach in the programme for the past 3 years. Throughout the implementation process, the Department has developed ongoing assessment and CQI plan to measure the outcomes and improve the teaching and learning process. This plan, which was first implemented at the end of 2006, has generally shown a positive trend in the teaching and learning process in the programme. This paper also discusses the assessment tools that are used in the continuous quality improvement (CQI) plan. Assessment is one of the important parts in the CQI plan. Direct and indirect measurement methods are used to gather the data which are to be analysed to measure the outcome attainment. The attainment results are used to identify the areas that need to be improved. The assessment plan has been continuously improved to be more effective in measuring the achievement of the OBE implementation in the department.