On the Application of heuristic Method and Saddle Node Bifurcation for Optimal Placement of FACTS Devices in Power System

This study focuses on an optimal placement of five major types of FACTS devices, namely, Static Var Compensator (SVC), Thyristor Controlled Series Compensator (TCSC), Static Synchronous Compensator (STATCOM), Static Synchronous Series Compensator (SSSC) and Unified Power Flow Controller (UPFC) in power system network using a well-known and applicable heuristic method known as genetic algorithm to seek the optimum location and setting of these controllers in the system. The locations of controllers are determined based on Saddle- Node Bifurcation theory on voltage collapse. In this paper, all the possible control parameters of each device including its location are optimized simultaneously to increase the distance to collapse point of the system. The IEEE 118-bus test system is utilized to verify the recommended method. The achieved results clearly proved that the proposed method is an effective approach for the placement of FACTS in power system

On basic definition of optimal allocation of FACTS devices in power system

This paper presents the importance of FACTS elements allocation to describe the effect of FACTS devices and placement of these devices in the electric power system. Optimal allocation and control of these devices will be very important for ISO or other power market regulators. The best location, appropriate size and setting of FACTS devices are important in the deregulated electricity markets. Two types of FACTS devices are considered in this study, which are Static var compensator (SVC) and thyristor controlled series compensator (TCSC). Modeling and simulation is performed on IEEE 14 bus test system and results will be presented. The proposed research is effective and helpful in the study of voltage stability with the consideration of the FACTS elements

A Prediction Model for Natural Frequencies on Kevlar/Glass Hybrid Laminated Composite using Artificial Neural Networks (ANN)

This paper aims to develop a prediction model for the natural frequencies on Kevlar/Glass hybrid laminated composite plates using Artificial Neural Networks (ANN). Finite element simulations were performed to generate data for the natural frequencies under various lamination schemes and fibre angles. Rectangular symmetric and anti-symmetric hybrid laminated composite plates were modeled using commercial software, ANSYS, and meshed using shell elements. The Matlab-ANN tool was used to generate the prediction model, where the generated data (natural frequencies) from the finite element simulations were used for training and testing of the prediction model. The network adapted a two-layer feed-forward algorithm. The adequacy of using ANN in predicting natural frequencies was verified, where the coefficient of determination, R2, was found to be over 0.995. The overall results proved that ANN could be a useful tool, where the prediction model produced an error of less than 5%, when compared to the simulated values of natural frequency of various hybrid laminated composites using finite element analysis. These findings concluded that the current study had contributed significant knowledge in understanding the prediction of natural frequency on hybrid laminated composite using the ANN model

A Prediction Model for Natural Frequencies on Kevlar/Glass Hybrid Laminated Composite using Artificial Neural Networks (ANN)

This paper aims to develop a prediction model for the natural frequencies on Kevlar/Glass hybrid laminated composite plates using Artificial Neural Networks (ANN). Finite element simulations were performed to generate data for the natural frequencies under various lamination schemes and fibre angles. Rectangular symmetric and anti-symmetric hybrid laminated composite plates were modeled using commercial software, ANSYS, and meshed using shell elements. The Matlab-ANN tool was used to generate the prediction model, where the generated data (natural frequencies) from the finite element simulations were used for training and testing of the prediction model. The network adapted a two-layer feed-forward algorithm. The adequacy of using ANN in predicting natural frequencies was verified, where the coefficient of determination, R2, was found to be over 0.995. The overall results proved that ANN could be a useful tool, where the prediction model produced an error of less than 5%, when compared to the simulated values of natural frequency of various hybrid laminated composites using finite element analysis. These findings concluded that the current study had contributed significant knowledge in understanding the prediction of natural frequency on hybrid laminated composite using the ANN model

A novel implementation for generator rotor angle stability prediction using an adaptive artificial neural network application for dynamic security assessment

This paper addresses a new approach for predicting the generator rotor angle using an adaptive artificial neural network (AANN) for power system stability. The aim of this work is to predict the stability status for each generator when the system is under a contingency. This is based on the initial condition of an operating point, which is represented by the generator rotor angle at a certain load level. An automatic data generation algorithm is developed for the training and testing process. The proposed method has been successfully tested on the IEEE 9-bus test system and the 87-bus system for Peninsular Malaysia

Simulation of an adaptive artificial neural network for power system security enhancement including control action

This paper presents a new method for enhancing power system security, including a remedial action, using an artificial neural network (ANN) technique. The deregulation of electricity markets is still an essential requirement of modern power systems, which require the operation of an independent system driven by economic considerations. Power flow and contingency analyses usually take a few seconds to suggest a control action. Such delay could result in issues that affect system security. This study aims to find a significant control action that alleviates the bus voltage violation of a power system and to develop an automatic data knowledge generation method for the adaptive ANN. The developed method is proved to be a steady-state security assessment tool for supplying possible control actions to mitigate an insecure situation resulting from credible contingency. The proposed algorithm is successfully tested on the IEEE 9-bus and 39-bus test systems. A comparison of the results of the proposed algorithm with those of other conventional methods reveals that an ANN can accurately and instantaneously provide the required amounts of generation re-dispatch and load shedding in megawatts

Determination of proximity to static voltage collapse using CPF-GMRES method

Voltage collapse is an event that causes major concern to the power system utility nowadays. The effect can be catastrophic to the power system where it can cause total collapse to the operation of the system. The study of the voltage collapse phenomenon can provide a way to prevent this event from happening. There have been many methods developed to study the criteria of voltage collapse phenomenon but static analysis probably provide the best way to study this phenomenon. Conventional Newton Raphson method has the singularity problem on its Jacobian matrix and thus could not give the solution. To overcome this problem, one of the solutions is the continuation power flow (CPF) method. CPF method is a very powerful method that can give the solution without having the singularity problem. The key to the CPF method is through the predictor and corrector technique used. This paper focuses on improvement of the time taken by the CPF method by enforcing the general minimal residual (GMRES) method at the initial point at the start up. The robustness of the standard CPF method is also improved using the new CPF-GMRES method. The convergence properties of this new method will be analysed and compared with the standard CPF method

ANALYSIS OF ROTOR ASYMMETRY FAULT IN THREE-PHASE LINE START PERMANENT MAGNET SYNCHRONOUS MOTOR

This article proposed a detection scheme for three-phase Line start permanent magnet synchronous motor (LSPMSM) under different levels of static eccentricity fault. Finite element method is used to simulate the healthy and faulty LSPMSM with different percentages of static eccentricity. An accurate laboratory test experiment is performed to evaluate the proposed index. Effects of loading condition on LSPMSM are also investigated. The fault related signatures in the stator current are identified and an effective index for LSPMSM is proposed. The simulation and experimental results indicate that the low frequency components are an effective index for detection of the static eccentricity in LSPMSM

State of the art for voltage collapse point approximation using continuation power flow

In this study we investigate the relative ability of comprehensive income and net income to summarize firm performance as reflected in stock returns. We also examine which comprehensive income adjustments improve the ability of income to summarize firm performance. We also investigate this claim that income measured on a comprehensive basis is a better measure of firm performance than other summary income measures. The results do not show that comprehensive income is superior to net income for evaluating firm performance on the basis of stock return and price. Except for investment industrial group, In Tehran Stock Exchange, we found no evidence that comprehensive income for firm performance evaluation on the basis of cash flows prediction is superior to net income. While, we found the better results for the state companies (only in other companies group), i.e., firm performance evaluation on the basis of cash flows prediction using comprehensive income is superior to net income. Collectively, our results provide some weak evidence that show comprehensive income adjustments improve ability of income for reflecting firm performance. Continuation power flow is one method to determine the proximity to voltage collapse point and can be described as a power flow solution, which is used to analyze the stability of power system under normal and disturbance conditions. The main purpose of Continuation Power Flow is to find a continuity of power flow solution for a given load change. Conventional power flow algorithms are subjected to the convergence problems at operating condition near the stability limit. Therefore researchers proposed to use the Continuation Power Flow to solve this problem by reformulating the power flow equations and ensuring the system remains in well-conditioned at all possible loading condition. This Continuation Power Flow uses an iterative process involving predictor and corrector step. However the continuation step, parameter variation and the reliability of the system are still in question. This paper discusses several issues including the needs, demands and expectations of continuation power flow. Several solutions have been proposed by the previous researchers is been discussed