M. N. A. Rahim

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.

Computational intelligence approach for SVC-PID controller in angle stability improvement

This paper presents a new optimization approach for tuning conventional PID controller parameters of a static var compensator (SVC) which controls a synchronous machine. SVC is designed and implemented to improve the damping of a synchronous generator. Particle Swarm Optimization (PSO) method based PID controller using SVC is implemented in this study. The study involves the optimization of proportional gain, KP, interval gain, KI and derivative gain, KD of PID controller. Validation with respect to eigenvalues determination and damping ratio confirmed that the proposed technique is effective to improve the angle stability problem.

Mathematical model of sea wave energy in electricity generation

This paper presents the conceptual model for pressure amplification in harnessing sea wave energy to generate electricity. It is named as AH Presto 1 which is the short form for Air-Hydraulic Pressure Storage Version 1. The idea of the concept is converting the air pressure generated from undulating sea water free surface inside the oscillating wave column (OWC) chamber adopting the concept done from previous study by few scientists and to amplify the hydraulic pressure which can be stored in hydraulic accumulator for consistent pressure distribution source for electricity generation. The OWC is commonly and widely used in the existing wave energy converter (WEC) devices to extract the energy from sea waves. Mathematical model is used in proving the energy conversion and pressure amplification. Initially, basic conceptual model is developed to represent the whole process from the sea wave signal until the final output. Several sub-modules have been developed to integrate the complete system. Amongst the sub-modules are; sea wave energy extraction and air pressure development using OWC, air pressure to hydraulic pressure amplification, hydraulic pressure storage; hydraulic pressure distribution, gearing system and electricity generation. Mathematical equations have also been derived in this study to represent the processes involved in each stage.

A novel technique for generation tracing via Evolutionary Programming

On account of the inability of transaction based charging method to provide fair and non-discriminatory transmission service pricing, tracing the flow of electricity becomes an essential part in deregulated power system and has attracted many researchers regarding on how to improve and streamline the method of charge allocation. The existing power tracing techniques still necessitate for an assumption and also demands for matrix inversion operation, which has limited their usage especially when the matrix to be inversed is not singular. This paper presents a novel technique for allocating the losses and load powers to all generators by means of Evolutionary Programming (EP) algorithm. The developed algorithm has been experimented on IEEE 57-bus RTS and the results revealed that EP can also be a sophisticated tool to be applied in power tracing field with tolerable computation time.

Bees algorithm technique for loss minimization in power transmission network using Static Var Compensator

This paper presents the application of Bees Algorithm (BA) technique for loss minimization in power transmission network. The loss minimization technique is implemented through Static Var Compensator (SVC) installation and the optimization technique is the Bees Algorithm. Bees Algorithm was developed to optimally determine the SVC values to be installed in the power transmission network. In this study, SVC was modeled as an effective capacitive reactance equivalent which aims to alter the transmission lines properties in the system. In order to perform validation process, a 26-bus IEEE Reliability Test System (RTS) will be utilized and tested in minimizing total transmission loss in the system.

An evolutionary algorithm approach to design SVC-PI controller for angle stability improvement

This paper presents Evolutionary Programming (EP) based optimization technique for estimating PI controller parameters of a static var compensator (SVC) which controls a synchronous machine. SVC is one type of Flexible AC Transmission Systems (FACTS) device, designed and implemented to improve the damping of a synchronous generator, as well as controlling the system voltage. This paper introduced Evolutionary Programming (EP) optimization method based SVC-PI controller. The study includes the placement of SVC with PI controller, while computational intelligence technique is used in optimizing the parameter values of PI controller: proportional gain, KP and interval gain, KI. Validation with respect to eigenvalues determination and synchronizing and damping torque coefficients (KS and KD) value confirmed that the proposed technique is effective to improve the angle stability problem.

Fuzzy bee colony approach to mitigate congestion problems through TCSC installation for voltage corrective strategy

Application of the viable technologies in the new millennium has discovered successfulness in reducing undesirable problems related to power system operation. The advent of computational intelligence methods are mostly assisting to achieve high efficiency for controlling the changing of sophisticated process flow in industrials. Congestion problem in power system is a crucial issue as it leads to the smoothness of power delivery to the consumer. One of the techniques to alleviate this problem is the installation of Flexible Alternating Current Thyristor into the existing power system network. This paper presents fuzzy bee colony approach to mitigate congestion problems through TCSC installation for voltage control strategy. The bee colony optimization technique (BCO) is used for searching the optimal sizing for TCSC. Validation was conducted using the IEEE 26 Bus Reliable Test System (RTS), particle swarm optimization is used for verification purposes.

Stability index tracing for determining FACTS devices placement locations

Locating the suitable locations in power system for the purpose of Flexible Alternating Current Transmission System (FACTS) devices installation can be realized via wide variety of techniques, and commonly applied by many researchers are sensitivity analysis and optimization approach. On the other hand, there are also various power tracing techniques have been invented by researchers, but their application is only for transmission service pricing. To try a new method for locating the most suitable locations for FACTS devices placement, this paper has invented a new method for ranking the priority of buses, namely Fast Voltage Stability Index Load Tracing (FVSI-LT) via a new hybrid algorithm, Blended Crossover Continuous Ant Colony Optimization (BX-CACO). The method reflects reliable ranking results after comparison with other methods.

Comparative studies between various power tracing techniques for reactive power allocation

The inability of traditional methods in providing fair and non-discriminatory transmission service pricing has attracted many researchers to streamline the existing methods, or invent some methods that can improve the weakness. Transaction based allocation like MW mile, contract path, and postage stamp allocation are the examples of traditional technique applied by many transmission service providers (TRANSCO) for allocating transmission usage cost among consumers; the generation (GENCO) and distribution (DISCO) companies. Unfortunately, such methods ignore the physical power system constraints such as flow, sink and source constraints that should be taken into account when performing the cost allocation. Thus, a method that can accurately determine the cost of utilizing transmission service while satisfying those constraints has been developed, known as power tracing. This paper tries to conduct an investigation on various power tracing techniques that are based on Artificial Intelligence (AI) based optimization, proportional sharing principle (PSP), and circuit theory. Validation has been performed on IEEE 14 bus reliability test system (RTS) and it is revealed that different power tracing techniques provide different reactive power allocation on consumers.

Hybrid ant colony technique for reactive power tracing in deregulated power system

Providing high security and reliability for a power system is compulsory for a transmission service provider (TRANSCO) as this will affect the performance in terms of stability and economy. This can only be achieved by means of ancillary services like reactive power provision. However, to cover the cost of supplying such services, determination of appropriate methods for allocating the cost to consumers (which are generation company (GENCO) and distribution company (DISCO)) without any discrimination is necessary. After struggle development performed by researchers, the new technique for allocating such cost considering fairness and free-discrimination has been proposed; that is power tracing. The recent power tracing techniques are majorly based on proportional sharing principle (PSP) and circuit theory. In addition, they are very dependent on matrix singularity and thus increasing the probability for matrix inversion failure if the matrix to be inversed is singular. To counter this weakness, this paper proposes optimization assisted reactive power tracing via hybrid ant colony algorithm; Blended Crossover Continuous Ant Colony Optimization (BX-CACO). After experiment on IEEE 14-bus system, it is discovered that the proposed technique can also be a potential tool for providing non-discriminatory tracing results as compared to other conventional methods.