Hazlie Mokhlis

Voltage sags pattern recognition technique for fault section identification in distribution networks

This paper presents a method to identify a faulted section in a distribution network using voltage sags pattern characteristics. The method starts with fault analysis to establish analytical voltage sags database. When a fault occurs, the voltage sag at the monitored node is compared with the established voltage sags in the database to find all the possible faulted sections. Finally, the method applied rank reasoning analysis to prioritize all the possible faulted sections. The method has been tested on an urban distribution network feeder. The results show that the most fault sections in the tested distributed network feeder can be located by the first attempt. All remaining faulted sections can be found by the second attempt.

Reusability techniques in load-flow analysis computer program

This paper describes the implementation of reusability aspects in producing load-flow analysis software application. The reusability in the algorithm and codes are obtained by applying matrix partitioning approach for Newton method and sequential approach for Fast Decoupled method. The software is built by using component-based development and object oriented programming methodologies. These methodologies made the developed load-flow analysis software flexible for future enhancement. Adding and changing codes and algorithm of the software can be done without affecting much the existing codes and algorithms. By maximizing the reusability aspects, the cost and time of software maintenance for updating can be reduced.

Comparative Study on Distributed Generator Sizing Using Three Types of Particle Swarm Optimization

Total power losses in a distribution network can be minimized by installing Distributed Generator (DG) with correct size. In line with this objective, most of the researchers have used multiple types of optimization technique to regulate the DGs output to compute its optimal size. In this paper, a comparative studies of a new proposed Rank Evolutionary Particle Swarm Optimization (REPSO) method with Evolutionary Particle Swarm Optimization (EPSO) and Traditional Particle Swarm Optimization (PSO) is conducted. Both REPSO and EPSO are using the concept of Evolutionary Programming (EP) in Particle Swarm Optimization (PSO) process. The implementation of EP in PSO allows the entire particles to move toward the optimal value faster. A test on determining optimum size of DGs in 69 bus radial distribution system reveals the superiority of REPSO over PSO and EPSO.

Global Particle Swarm Optimization for High Dimension Numerical Functions Analysis

The Particle Swarm Optimization (PSO) Algorithm is a popular optimization method that is widely used in various applications, due to its simplicity and capability in obtaining optimal results. However, ordinary PSOs may be trapped in the local optimal point, especially in high dimensional problems. To overcome this problem, an efficient Global Particle Swarm Optimization (GPSO) algorithm is proposed in this paper, based on a new updated strategy of the particle position. This is done through sharing information of particle position between the dimensions (variables) at any iteration. The strategy can enhance the exploration capability of the GPSO algorithm to determine the optimum global solution and avoid traps at the local optimum. The proposed GPSO algorithm is validated on a 12-benchmark mathematical function and compared with three different types of PSO techniques. The performance of this algorithm is measured based on the solutions’ quality, convergence characteristics, and their robustness after 50 trials. The simulation results showed that the new updated strategy in GPSO assists in realizing a better optimum solution with the smallest standard deviation value compared to other techniques. It can be concluded that the proposed GPSO method is a superior technique for solving high dimensional numerical function optimization problems.

Optimum Simultaneous DG and Capacitor Placement on the Basis of Minimization of Power Losses

and the results will also be discussed in detail.

A New Under-Frequency Load Shedding Technique Based on Combination of Fixed and Random Priority of Loads for Smart Grid Applications

This paper presents a new under-frequency load shedding technique based on the combination of random and fixed priority of loads. It has been observed that placing all of the loads in the distribution system with fixed priority results in un-optimum load shedding. On the other hand, designing the load priority with a combination of random and fixed priority provides the technique with some sort of flexibility in achieving the optimal load shedding. The validation of the proposed scheme on different scenarios proves that the proposed technique is capable of achieving the optimal load shedding and recovering frequency to nominal value without any overshoot.

Transmission loss minimization using SVC based on Particle Swarm Optimization

This paper describes optimal sizing of static var compensator (SVC) based on Particle Swarm Optimization for minimization of transmission losses considering cost function. Particle Swarm Optimization (PSO) is one of the artificial intelligent search approaches which has the potential to solve such a problem. For this study, static var compensator (SVC) is chosen as the compensation device. Validation through the implementation on the IEEE 26-bus system shows that the PSO is found feasible to achieve the task. The simulations results are compared with those obtained from the Bee Algorithm (BA) technique in the attempt to highlight its merit.

Optimal Location and Sizing of SVC Using Particle Swarm Optimization Technique

This paper describes optimal location and sizing of static var compensator (SVC) based on Particle Swarm Optimization for minimization of transmission losses considering cost function. Particle Swarm Optimization (PSO) is population-based stochastic search algorithms approaches as the potential techniques to solving such a problem. For this study, static var compensator (SVC) is chosen as the compensation device. Validation through the implementation on the IEEE 30-bus system indicated that PSO is feasible to achieve the task. The simulation results are compared with those obtained from Evolutionary Programming (EP) technique in the attempt to highlight its merit.

PSO based technique for loss minimization considering voltage profile and cost function

This paper describes optimal sizing of FACTS devices based on Particle Swarm Optimization for minimization of transmission loss considering voltage profile and cost function. Particle Swarm Optimization (PSO) is one of the artificial intelligent search approaches which have the potential in solving such a problem. In this study one of FACTS devices is used as a scheme for transmission loss. For this study, static var compensator (SVC) is chosen as the compensation device. The effect of population size during the optimization process towards achieving the solution is also investigated. Validation through the implementation on the IEEE 30-bus RTS indicated that PSO is feasible to achieve the task.

Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques

It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.