Mohd Wazir Mustafa

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.

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.

Transient stability evaluation of electrical power system using generalized regression neural networks

Transient stability evaluation (TSE) is part of dynamic security assessment of power systems, which involves the evaluation of the systems ability to remain in equilibrium under credible contingencies. Neural networks (NN) have been applied to the security assessment of power systems and have shown great potential for predicting the security of power systems. This paper proposes a generalized regression neural networks (GRNN) based classification for transient stability evaluation in power systems. In the proposed method, learning data sets have been generated using time domain simulation (TDS). The GRNN input nodes representing the voltage magnitude for all buses, real and reactive powers on transmission lines, the output node representing the transient stability index. The proposed GRNN was implemented and tested on IEEE 9-bus and 39-bus test systems. NN results show that the stability condition of the power system can be predicted with high accuracy and less misclassification rate.

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.

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.

An improved usage allocation method for deregulated transmission systems

This paper suggests an improved method for usage allocation to individual generators in a deregulated power system. Based on solved load flow, the method converts power injections and line flows into real and imaginary current injections and flows. These currents are then represented independently as real and imaginary current networks. Since current networks are acyclic lossless networks, proportional sharing principle and graph theory is used to trace the relationship between current sources and current sinks. The contributions from each current source are finally translated into power contributions to each load, line flows and losses. IEEE 14-bus test system is used to illustrate the effectiveness of the method. Comparison of the results with previous methods is also given

New algorithm for detection and fault classification on parallel transmission line using DWT and BPNN based on Clarke's transformation

This paper presents a new algorithm for fault detection and classification using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) based on Clarkes transformation on parallel transmission. Alpha and beta (mode) currents generated by Clarkes transformation were used to convert the signal of discrete wavelet transform (DWT) to get the wavelet transform coefficients (WTC) and the wavelet energy coefficient (WEC). Daubechies4 (Db4) was used as a mother wavelet to decompose the high frequency components of the signal error. The simulation was performed using PSCAD/EMTDC for transmission system modeling. Simulation was performed at different locations along the transmission line with different types of fault and fault resistance, fault location and fault initial angle on a given power system model. Four statistic methods utilized are in the present study to determine the accuracy of detection and classification faults. The results show that the best Clarke transformation occurred on the configuration of 12-24-48-4, respectively. For instance, the errors using mean square error method, the errors of BPNN, Pattern Recognition Network and Fit Network are 0.03721, 0.13115 and 0.03728, respectively. This indicates that the BPNN results are the lowest error.

Voltage stability calculations in power transmission lines: Indications and allocations

In recent years, voltage stability problems have been increasing since power systems operate close to stability limits and large amounts of power are being transferred from long distance. Accurate determination of voltage collapse points with rapid line voltage stability analysis, indications and allocations, allows operators to take necessary action to prevent such collapse incidents. Although several methods have been used in voltage stability analysis, successful avoidance of power collapse is depends on accuracy, rapid indications and very low computation time. This paper presents an efficient method for conducting line voltage stability analysis in power systems. This newly developed method is accurate, fast, simple, and theoretically proven for finding precise voltage collapse points and for determining voltage stability at each transmission line. Voltage stability margins can be easily calculated, providing an indication of how far the transmission line is from its severe load condition and allowing separate analysis if one transmission line is highly stressed. The proposed method was demonstrated on the IEEE 14-bus system and compared with existing methods to show its effectiveness and efficiency.

A Comparison of Electric Power Tracing Methods Used in Deregulated Power Systems

Tracing the flow of electricity becomes an important issue under transmission open access. In practice, power flow follows the physical laws of electricity and therefore it is not straightforward to map out how the participants make use of the system. This paper focuses on presenting an analysis of the performance of major power tracing methods based on proportional sharing principle namely the graph, node and commons method. A number of simulations are performed based on their assumptions to critically compare their performance and validate the exactness of each method. Finally, conclusions and recommendations are stated.