Noor Izzri Abdul Wahab

Particle swarm optimisation applications in FACTS optimisation problem

FACTS optimisation is one of the most important and difficult problems in power systems. For solving this problem, so many different approaches have been proposed in the literature. Among them, particle swarm optimisation (PSO) has exposed so promising behavior. In this paper, applications of PSO in FACTS optimisation problem are explained and analysed from the viewpoint of the objectives, used basic PSO variant, PSO parameter selection, multi-objective handling, constraint handling and discrete variable handling. Eventually, some hints for future research is provided.

Fast transient stability assessment of large power system using probabilistic neural network with feature reduction techniques

This paper presents transient stability assessment of a large 87-bus system using a new method called the probabilistic neural network (PNN) with incorporation of feature selection and extraction methods. The investigated power system is divided into smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the amount of data sets collected for the respective areas. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulations carried out by considering three phase faults at different loading conditions. The data collected from the time domain simulations are then used as inputs to the PNN. Feature reduction techniques are then incorporated to reduce the number of features to the PNN which is used as a classifier to determine whether the power system is stable or unstable. It can be concluded that the PNN with the incorporation of feature reduction techniques reduces the time taken to train the PNN without affecting the accuracy of the classification results.

Study of impulse voltage distribution in transformer windings

In this paper, analysis impulse voltage distribution in transformer winding, and winding of the voltage impulse response with a standard full wave and clipped wave especially at the ends of the entanglement presented. Compared with experimental data EMTPDC simulation, the result of t <1.0 μs voltage difference occurs. For t > 2.0 μs was obtained near the same voltage large, between experimental and simulation results. So we can say that circuit simulation can mimic the real situation EMTDC/PSCAD. Construction of turns and the thickness of insulation (Cs and Cg) transformer, poorer have considerable effect on the distribution center of experiencing transient voltage transformer(transitional-symptoms). Winding section closest to the high voltage high voltage gradient than most parts of another entanglement, tests were performed in a standard form according to the current surge of IEC-71 8/20μs. Important information from this study is that the simulation gives a more pessimistic outcome (EMTDC/PSCAD calculations show a larger stress values compared with experimental). Although it is difficult to imitate the behavior of entanglement in the initial transient, but the real situation is not worse. Thus, this simulation can be used as EMTPDC experimental substitution want to know the response to the current surge of turns.

Comparing least squares support vector machine and probabilistic neural network in transient stability assessment of a large-scale power system

This paper presents transient stability assessment of a large practical power system using two artificial neural network techniques which are the probabilistic neural network (PNN) and the least squares support vector machine (LS-SVM). The large power system is divided into five smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the number of data sets collected for the respective areas. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the test system considering three phase faults at different loading conditions. The data collected from the time domain simulations are then used as inputs to the PNN and LS-SVM. Both networks are used as classifiers to determine whether the power system is stable or unstable. Classification results show that the PNN gives faster and more accurate transient stability assessment compared to the LS-SVM.

Evaluation and Forecasting of Long Term Electricity Consumption Demand for Malaysia by Statistical Analysis

An essential element of electric utility resource planning is forecasting the annual electricity consumption for the long term. This study includes an approach to understand the factors affecting annual electricity demand and forecast future electricity consumption, by using multivariate regression analysis in its calculations, for Malaysia. The process of study is to develop a statistical model and testing it by using past year data to understand its accuracy. After obtaining the most reliable model, forecasting the electricity consumption for the long term future electricity demand is performed. The scenario is developed by means of economical conditions and how the variables are going to be changed in the following years. The model yields very satisfactory results and the range of electricity consumption is obtained.

A Simplified Synchronous Reference Frame for Indirect Current Controlled Three-level Inverter-based Shunt Active Power Filters

This paper presents a new simplified harmonics extraction algorithm based on the synchronous reference frame (SRF) for an indirect current controlled (ICC) three-level neutral point diode clamped (NPC) inverter-based shunt active power filter (SAPF). The shunt APF is widely accepted as one of the most effective current harmonics mitigation tools due to its superior adaptability in dynamic state conditions. In its controller, the SRF algorithm which is derived based on the direct-quadrature (DQ) theory has played a significant role as a harmonics extraction algorithm due to its simple implementation features. However, it suffers from significant delays due to its dependency on a numerical filter and unnecessary computation workloads. Moreover, the algorithm is mostly implemented for the direct current controlled (DCC) based SAPF which operates based on a non-sinusoidal reference current. This degrades the mitigation performances since the DCC based operation does not possess exact information on the actual source current which suffers from switching ripples problems. Therefore, three major improvements are introduced which include the development of a mathematical based fundamental component identifier to replace the numerical filter, the removal of redundant features, and the generation of a sinusoidal reference current. The proposed algorithm is developed and evaluated in MATLAB / Simulink. A laboratory prototype utilizing a TMS320F28335 digital signal processor (DSP) is also implemented to validate effectiveness of the proposed algorithm. Both simulation and experimental results are presented. They show significant improvements in terms of total harmonic distortion (THD) and dynamic response when compared to a conventional SRF algorithm.

Area-Based COI-Referred Rotor Angle Index for Transient Stability Assessment and Control of Power Systems

This paper describes an index for judging the severity of transient events of power systems in simulation. The proposed transient stability index, known as the area-based COI-referred rotor angle index, is developed by considering the fact that a large-sized power system is divided into several areas according to the coherency of generators in a particular area. It can be assumed that an equivalent single large machine can represent all the generators in that area. Thus, the assessment of rotor angles for all generators can be simplified by only assessing the index of areas in a power system. The effectiveness of the proposed index in assessing the stability of power systems and its ability in pinpointing the weakest area in the power system is analyzed. Furthermore, this paper developed an emergency control scheme known as the combined UFLS and generator tripping in order to stabilize the system when unstable faults occurred in a power system. The proposed index is used to identify the generator to be tripped when the developed emergency control scheme operates. The performance of the proposed index and the combined UFLS and generator tripping scheme are evaluated on the IEEE 39-bus test system.

Response of D-STATCOM under unbalanced voltage condition caused by SLG fault

This work presents a study on a distribution static compensator (D-STATCOM) operating in electrical systems where the voltages are unbalanced due to SLG fault. During the SLG fault, negative sequence components exist in the system. In balanced systems the STATCOM has a very good performance, allowing compensation of capacitive or inductive reactive power with a fast transient response. When there are negative sequence components due to voltage imbalance, the D-STATCOM has reacted in reducing the unbalanced in the system. An analytical analysis, and simulations of a 12-pulse D-STATCOM using the electromagnetic transient program (PSCAD/EMTDC) show how D-STATCOM reacts during the unbalanced in the system affected by negative sequence components and how its performance increase the power systems stability. The D-STATCOM performances under unbalanced voltage are illustrated in the paper along with harmonics analysis study.

Development of a New Cascade Voltage-Doubler for Voltage Multiplication

For more than eight decades, cascade voltage-doubler circuits are used as a method to produce DC output voltage higher than the input voltage. In this paper, the topological developments of cascade voltage-doublers are reviewed. A new circuit configuration for cascade voltage-doubler is presented. This circuit can produce a higher value of the DC output voltage and better output quality compared to the conventional cascade voltage-doubler circuits, with the same number of stages.

Transient stability assessment of a large actual power system using probabilistic neural network with enhanced feature selection and extraction

This paper presents transient stability assessment of a large actual power system using the probabilistic neural network (PNN) with enhanced feature selection and extraction method. The investigated large power system is divided into five smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the amount of data sets collected for the respective areas. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulations carried out by considering three phase faults at different loading conditions. The data collected from the time domain simulations are then used as inputs to the PNN. An enhanced feature selection and extraction methods are then incorporated to reduce the input features to the PNN which is used as a classifier to determine whether the power system is stable or unstable. It can be concluded that the PNN with enhanced feature selection and extraction methods reduces the time taken to train the PNN without affecting the accuracy of the classification results.