Mohd Herwan Sulaiman

Transmission loss and load flow allocations via genetic algorithm technique

Transmission loss and load flow allocations become important issues under deregulation system. Due to nonlinear nature of power flow, tracing the loss and power flow through the mesh network becomes more complicated. Since the complexity of electricity transmission system, it is not straightforward to determine the contribution of particular generator to a particular line loss and/ or load. This paper will discuss load flow and loss allocation using Genetic Algorithm (GA) technique. GA is one of the optimization techniques that apply natural phenomena, viz. genetic inheritance and Darwinian strive for survival. Transmission loss and load flow allocations problem will be treated as an optimization problem. In this paper, Ward-Hale 6-bus test system will be used to demonstrate the effectiveness of the technique and validated by IEEE 30-bus test system. Comparison with other method is also given.

Distributed generation installation using particle swarm optimization

This paper presents a particle swarm optimization approach for the placement of distributed generation (DG) in the distribution system. DG installation in the distribution system is very useful in reducing the line losses, as well as improving the voltage profiles. The proposed method combines particle swarm optimization and the Newton-Raphson load flow method to determine the location and size of the DG. The objective function to be minimized in this problem is the total power losses of the system. The proposed approach has been tested on IEEE 69-bus distribution test system and the program was simulated using MATLAB software. Test results show the effectiveness of the developed algorithm.

Firefly Algorithm technique for solving Economic Dispatch problem

This paper presents the implementation of Firefly Algorithm (FA) in solving the Economic Dispatch (ED) problem by minimizing the fuel cost and considering the generator limits and transmission losses. ED is one of the most challenging problems of power system since it is difficult to determine the optimum generation scheduling to meet the particular load demand with the minimum fuel cost and transmission loss. Until now, there are a lot of researches that have been done to seek for closest optimum result in determining the power generation of each generator especially in large scale power system. FA is a meta-heuristic algorithm which is inspired by the flashing behavior of fireflies. The primary purpose of fireflys flash is to act as a signal system to attract other fireflies. In this paper, 26-bus system is utilized to show the effectiveness of the FA in solving the ED problem. Comparison with Continuous Genetic Algorithm (CGA) and conventional method are also given.

Grey Wolf Optimizer for solving economic dispatch problems

This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) which inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 20 generating units in economic dispatch, and the results are verified by a comparative study with Biogeography-based optimization (BBO), Lambda Iteration method (LI), Hopfield model based approach (HM), Cuckoo Search (CS), Firefly, Artificial Bee Colony (ABC), Neural Networks training by Artificial Bee Colony (ABCNN), Quadratic Programming (QP) and General Algebraic Modeling System (GAMS). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics.

Optimal allocation and sizing of Distributed Generation in distribution system via Firefly Algorithm

This paper presents an application of Firefly Algorithm (FA) in determining the optimal location and size of Distributed Generation (DG) in distribution power networks. FA is a meta-heuristic algorithm which is inspired by the flashing behavior of fireflies. The primary purpose of fireflys flash is to act as a signal system to attract other fireflies. In this paper, IEEE 69-bus distribution test system is used to show the effectiveness of the FA. Comparison with other method is also given.

Implementation of DG for loss minimization and voltage profile in distribution system

This paper presents the effects of DG on the performance of an existing distribution network in terms of voltage stability, loss minimization and voltage profile. In this study, a new program was developed based on Artificial Immune System optimization technique in order to determine the optimal size of distributed generator. Various loading conditions were tested in order to evaluate the effectiveness of the proposed technique in determining the optimal size of the distributed generator. The suitable location of distributed generator is identified based on the results from voltage stability index. The proposed technique was tested on IEEE Reliability Test systems namely the IEEE 69-bus and the program was developed using the MATLAB programming software.

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.

Training LSSVM with GWO for price forecasting

This paper presents a hybrid forecasting model namely Grey Wolf Optimizer-Least Squares Support Vector Machines (GWO-LSSVM). In this study, a great deal of attention was paid in determining LSSVMs hyper parameters. For that matter, the GWO is utilized an optimization tool for optimizing the said hyper parameters. Realized in gold price forecasting, the feasibility of GWO-LSSVM is measured based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Percentage Error (RMSPE). Upon completing the simulation tasks, the comparison against two hybrid methods suggested that the GWO-LSSVM capable to produce lower forecasting error as compared to the identified forecasting techniques.

Optimal Reactive Power Dispatch Solution by Loss Minimization Using Moth-Flame Optimization Technique

Abstract In this paper, a newly surfaced nature-inspired optimization technique called moth-flame optimization (MFO) algorithm is utilized to address the optimal reactive power dispatch (ORPD) problem. MFO algorithm is inspired by the natural navigation technique of moths when they travel at night, where they use visible light sources as guidance. In this paper, MFO is realized in ORPD problem to investigate the best combination of control variables including generators voltage, transformers tap setting as well as reactive compensators sizing to achieve minimum total power loss and minimum voltage deviation. Furthermore, the effectiveness of MFO algorithm is compared with other identified optimization techniques on three case studies, namely IEEE 30-bus system, IEEE 57-bus system and IEEE 118-bus system. The statistical analysis of this research illustrated that MFO is able to produce competitive results by yielding lower power loss and lower voltage deviation than the selected techniques from literature.

Power flow allocation method with the application of hybrid genetic algorithm-least squares support vector machine

This paper proposes a new power flow allocation method in pool based power system with the application of hybrid genetic algorithm (GA) and least squares support vector machine (LS-SVM), namely GA-SVM. GA is utilized to find the optimal values of regularization parameter, γ and Kernel RBF parameter, σ2, which are embedded in LS-SVM model so that the power flow allocation problem can be solved by using machine learning adaptation approach. The supervised learning paradigm is used to train the LS-SVM model where the proportional sharing principle (PSP) method is utilized as a teacher. Based on converged load flow and followed by PSP technique for power tracing procedure, the description of inputs and outputs of the training data are created. The GA-SVM model will learn to identify which generators are supplying to which loads. In this paper, the 25-bus equivalent system of southern Malaysia is used to illustrate the proposed method. The comparison result with artificial neural network (ANN) technique is also will be presented.