M. M. Othman

Ant Colony Optimization for Maximum Loadability Search in Voltage Control Study

Several blackout occurrences in many part of the world had indicated the importance of voltage stability studies. These events could be caused by line or generator outages, stressed condition, change of loads and load shedding. The occurrence of voltage collapse is very much dependent upon the maximum permissible load that can be supported at a particular load bus. Any attempt to increase the load beyond this point could force the entire system into instability, leading to voltage collapse. This would indicate that the power system physically could not support the amount of the connected load. This paper presents the application of ant colony optimization (ACO) technique for searching the optimal point of maximum loadability point at a load bus. The optimal point identified using this technique in the off-line mode can assist the power system operators to perform pilot study prior to intended load increment in their transmission system. Comparative studies performed with respect to evolutionary programming (EP) and automatic voltage stability analysis (AVSA) algorithm had indicated the merit of the proposed technique. The capability of the developed ACO engine in solving the non-graphical optimization problems has been identified as the strength of the proposed technique.

Optimum tuning of Unified Power Flow Controller via Ant Colony Optimization technique

Unified Power Flow Controller (UPFC) is a Flexible Alternating Current Transmission System (FACTS) device that can control the power flow in transmission lines by injecting active and reactive voltage components in series with the lines using power converter modules, based on an externally dc-link voltage. As the power flow has been controlled, this device is capable of minimizing the overall system losses and simultaneously improves the voltage stability. To be able effectively control the power flow so that losses and voltage stability are at optimum level, UPFC parameters need to be tuned by using optimization algorithm. This paper describes how Ant Colony Optimization (ACO) technique can be used to tune the parameters of FACTS device in order to improve power system performance. ACO is a new cooperative agents approach, which is inspired by the observation of the behaviours of real ant colonies on the topics of ant trial formation and foraging method. The algorithm is implemented in MATLAB applied to the IEEE-30 Bus Reliability Test System (RTS). The results obtained from the study revealed that the proposed technique gave promising results.

Application of evolutionary programming in the assessment of dynamic stability

This paper presents Evolutionary Programming (EP) based optimization technique for estimating synchronizing torque coefficients, Ks and damping torque coefficients, Kd of a synchronous machine. These coefficients are used to identify the angle stability of a system. Initially, a Simulink model was utilized to generate the time domain response of rotor angle under various loading conditions. EP was then implemented to optimize the values of Ks and Kd within the same loading conditions. Validation with respect to eigenvalues determination confirmed that the proposed technique is feasible to solve the angle stability problems.

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.

Effect of population size for DG installation using EMEFA

This paper presents a new Embedded Meta Evolutionary-Firefly Algorithm (EMEFA) for DG installation which considers the effect of population size on loss and cost minimization while improving the performance of the system. The proposed EMEFA technique is to alleviate the setback experienced in the Meta-EP and firefly in terms slow convergence and less accurate. Implementation of the proposed technique in minimizing both the distribution losses and fuel cost separately has indicated promising results, while maintaining the voltage at acceptable levels. Assessment on its performance with respect to other optimization techniques revealed that the proposed technique is superior in terms fast convergence and achieving more accurate solution, validated on a chosen IEEE Reliability Test System.

Fast evaluation of available transfer capability considering the cubic-spline interpolation technique

This paper presents a new computationally fast and accurate method for evaluating available transfer capability (ATC) based on a curve fitting technique so-called as the cubic-spline interpolation technique. The advantage of the technique in the computation of ATC is that it has the ability to reduce the time consuming AC power flow computations. The cubic-spline interpolation technique traces the curves of voltage magnitude and power flow variations with respect to the increase of real power transfer. ATC is then determined at the point where the voltage or power flow limits intersect the curves. The effectiveness of the proposed method is verified by illustrating the ATC evaluation on a practical test system. ATC results obtained from the proposed cubic-spline interpolation technique prove that the method is satisfactorily accurate and it is faster than the ATC method using the recursive AC power flow computations.

Risk based Uncertainty (RibUt) Assessment of a Power System Using Bootstrap Technique

There are various approach used to prevent from the occurrence of power system blackout in order to ensure the sustainability and efficiency of energy supply. Generally, power system blackout can be mitigated by referring to the risk assessment of a power system. This paper presents the risk assessment of a power system performed by using the proposed bootstrap technique and the fuzzy set technique. The risk of the system is measured by using the expected energy not supplied (EENS) index as well as the probability of load curtailment (PLC) index. The application of bootstrap technique is important to assess the risk indices at every level of system uncertainty. The 24-bus IEEE Reliability Test System (RTS) and 2737-bus Polish system are used as a case study in the analysis of risk based EENS and PLC. Comparative studies have been made on the risk assessment determined by using the proposed bootstrap technique and the fuzzy set technique.

Particle swarm optimization (PSO) technique in economic power dispatch problems

Economic power dispatch problem plays an important role in the operation of the power systems. It is a method of determine the most efficient, low cost and reliable operation of a power system by dispatching the available electricity generation resources to supply the load on the system. The primary objective of economic dispatch is to minimize the total cost of generation while maintaining the operational constraints of the available generation resources. In this paper, a particle swarm optimization algorithms (PSO) with one of the accelerating coefficients being constant are proposed to solve the economic power dispatch problem. Particle swarm optimization (PSO) is algorithms modeled on swarm intelligence that finds a solution to an optimization problem in a search space, or model and predict social behavior in the presence of objectives. In this study, the proposed technique was tested using the standards IEEE 26-BUS RTS and the results revealed that the proposed technique has the merit in achieving optimal solution for addressing the problems.

Computational intelligence technique for solving power scheduling optimization problem

Computational Intelligence technique has become a prominent technique in solving engineering optimization problem. One of the problems which can be addressed in this issue is problems related to power system optimization. This paper presents computational intelligence technique for solving power scheduling optimization problem. Evolutionary Programming technique has been applied to minimise total transmission loss; considering the scheduling of active power and controlling the reactive power as the main control variables. In this study, the control process of reactive power and scheduling of active power at all generators will help control the power flow in the system. This has given impact to the current flow which influences the total transmission loss. Validation through a reliability test system revealed its superiority. Verification performed through comparative studies with other optimization technique revealed that the proposed computational intelligence technique produced promising results.

MOPSO approach for FACTS device installation in power system

This paper presents an approach to sigma multiobjective optimization particle swarm (σ-MOPSO) technique for optimal allocation of Flexible AC Transmission System (FACTS) devices. For this study, Static Var Compensator (SVC) is selected as a compensation device. Proposal σ-MOPSO technique has been implemented to minimize the transmission losses and the cost of investment in the system. Simulations performed on standard IEEE RTS 30-bus and IEEE 118-bus RTS. Results are compared with those obtained from the programming of multiobjective evolutionary technique (MOEP) in order to highlight its advantages.