Mohammad Nizam Ibrahim

Impact of CT saturation on phasor measurement algorithms: Uncertainty and sensitivity study

An important element of any Intelligent Electronic Device (IED) is the phasor measurement algorithm. The role of such algorithm is to extract fundamental frequency phasors from signals measured on secondary winding of a current transformer (CT). As a result of faults and switching in primary network we can expect to have current signals with high amplitude of exponentially decaying DC. In these conditions, the measured current signal can also be influenced by high remanent flux in the CT core. These factors may lead to different degrees of CT saturation. During saturation, the CT output is no longer accurately representing the primary current signal. Consequently, the implemented measurement and protection functions in IEDs are affected. This paper investigates the sensitivity of the measurement algorithms implemented in IEDs, in particular Discrete Fourier Transform (DFT) and Two-Sample algorithms, to factors which cause CT to saturate. Impact analysis of the following uncertain factors will be presented: amplitude and time constant of the asymmetrical fault current, and remanent flux in CT core. The study has been performed at three different CT burden values. Uncertainty of the measurement algorithms during CT saturation was assessed using mean, standard deviation and bounds of measurement error. Uncertainty results show that higher measurement bias is encountered at higher burden. Sensitivity results show that performance of the full-cycle DFT measurement algorithm is the most sensitive to the amplitude of asymmetrical fault current at all burden values. Two-Sample measurement algorithm shows similar sensitivity result only at lower burden values. However, at higher burden the algorithm is also sensitive to remanent flux.

Hierarchical QoS admission control mechanism for non-transparent relay in WiMAX networks

This paper presents the admission control mechanism which can minimise the overwhelming effect of limited radio resources as the demand for network service increases. The simulation study was conducted using NCTUns module with hierarchical Quality of Service (QoS) priority method. The result shows the proposed admission control mechanism is able to increase the number of subscriber being served and reduces the radio resources wastage by about 35%.

Factor-space dimension reduction for sensitivity analysis of intelligent electronic devices

Implementation of uncertainty and sensitivity analysis for testing measurement accuracy of a commercial Intelligent Electronic Device (IED) requires unaffordable number of an IED tests. The main reason is a large number of factors (i.e. high dimension of factor space) that potentially affects the accuracy. The factor values are sampled within practical domains by using Monte Carlo simulation to produce test cases. Therefore for high-dimensional factor space, a number of test cases is prohibitively large, making the systematic uncertainty and sensitivity analysis impossible in practice. To deal with this limitation, we propose in this paper an application of Morris factor screening algorithm for the factor space dimension reduction. The screening method identifies the input factors that have a negligible impact on the measurement accuracy. The measurement accuracy is tested for transient condition by injecting signals having step-change. The accuracy is quantified using two performance indices: overshoot and steady state error. Factors selected for this study model deviation from the perfect step-change that are possible during faults on transmission lines. The proposed methodology is demonstrated by testing Cosine filter, which is commonly implemented in IEDs. In addition, we have tested a commercial IED using injection test set. It should be emphasized that although measurement algorithm for a commercial IED is not fully documented, we are able, through a number of systematically designed tests, to describe performance of the measurement algorithm for a complete range of practical cases.

Comparison of MLP network training algorithms for fault classification in transmission lines

Fast and accurate fault classification in transmission lines is important for any protection devices. The present of extra transient signals cause to phase misclassification and malfunction of protection devices. Thus, it is crucial to provide a proper protection scheme that offer a good classification performance when the signals are influenced by noise. This paper presents a comparative study of protection scheme using combination of wavelet transform (WT) with multilayered perceptron (MLP) network classifier using various types of training algorithms for fault classification in extra high voltage (EHV) transmission lines. The performance of the suitable training algorithm in MLP network resulted the highest accuracy for fault classification. The wavelet transform is used as a tool to decompose the input three-phase current signals and extracting the significant features. After extracting all these important features, the MLP network is trained by using eight types of different training algorithms. Classification performance of the MLP network is evaluated using two types of datasets; ideal dataset (without noise) and dataset with Signal-to-noise ratio (SNR) of 30. Simulation results show that the MLP network trained using the Conjugate Gradient backpropogation with Powell Beale Restars (traincgb) algorithm indicated the highest accuracy for both case with or without noise.

Weighted QoS admission control for IEEE 802.16j WiMAX networks

Scheduler is one of the crucial mechanisms that influence the delivery of quality of services in WiMAX (Worldwide Interoperability for Microwave Access) networks. With the rapid advances in telecommunication technologies, Mobile Multi-hop Relay (MMR) WiMAX is introduced to satisfy the ever increasing demand for higher speed and throughput. This paper discusses the scheduling technique specifically for the admission control of real-time Polling Service (rtPS). The simulation studies were conducted by adopting the weighted QoS method from the NCTUns module. The result shows the proposed admission control is capable to increase the number of subscriber being served and minimize the radio resources wastage. The capability of the admission control was also evaluated for various number of mobile stations deployed.

Performance evaluation for multihop WDM optical networks

A comprehensive performance analysis for multihop WDM networks is presented. Focus is place on the characteristics, properties and a few network performance parameters. In addition, the effect of channel sharing on the network performance is studied. From the simulation, the optimal degree of channel sharing that maximizes the throughput is determined. From the comparison of the network performance, the most effective multihop network structure is proposed.

Fault Classification in Transmission Line Using Wavelet Features and Fuzzy-KNN

Fault occurred in transmission line can cause many problems such as failure of equipment, instability in power flow, and economical losses. Many of the techniques proposed for fault classification in transmission line have applied steady state component as it is easily affected by the surroundings. Then, protection scheme based on fault generated transient that can offer an accurate result for fault classification in power system should be proposed. This paper presents the fault classification scheme using Fuzzy-KNN (Fuzzy k-Nearest Neighbor) classifier and wavelet features. Two wavelet features were calculated in this study which are Wavelet Mean (μ) and Wavelet Standard Deviation (σ). Then, the Fuzzy-KNN classifier was tested with three datasets categories: Ideal, 30 dB noise, and 20 dB noise datasets. The overall results in accuracy performance show that the Fuzzy-KNN classifier performed better than the KNN (k-Nearest Neighbor) classifier.

Single-ended fault location for transmission lines using traveling wave and multilayer perceptron network

Transmission lines are subjected to many kind of fault. Therefore fault location scheme is needed to determine the exact location of fault. This paper proposed a method for estimating transmission line fault location system using traveling wave method and artificial neural network (ANN). The method starts by decomposing the current signal from the faulted phase using DWT technique. Then, time fault measurement is extracted from the decomposed signal. Finally, a type of ANN called multilayer perceptron network (MLP) is used to locate the fault location. The proposed method is benchmark against the existing method and evaluated by using 5 measurement indexes; Coefficient of Determination (R2) as well as four error measures-Percentage Prediction Error (PPE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results shows that integration of traveling wave and ANN absolutely could improve the performance especially for fault occurs in short distance.

Selection of suitable features for fault classification in transmission line

Power system protective relaying is an important feature for efficient and reliable power flow operation. The traditional fault classification scheme using the steady state component is easily affected by environment factors. Then, it is important to provide power protection scheme that offers a better classification performance based on fault-generated transient signals where it is immune to the surrounding factors. This paper search for important features for fault classification in transmission lines using Wavelet Transform (WT) and multilayered perceptron (MLP) network. Six (6) features namely wavelet energy, mean, standard deviation, entropy, kurtosis and skewness are obtained from the WT. For analyzing these features, a MLP network trained by Levenberg-Marquardt (LM) algorithm is used as classifier to identify the fault types. The classification accuracy is evaluated using three types of dataset conditions; ideal dataset (no noise involvement), dataset with Signal-to-noise ratio (SNR) of 30 (30 dB noise) and dataset with SNR of 20 (20 dB noise). Simulation results show that combination of the wavelet mean and standard deviation shows the highest performance accuracy for all conditions tested.

Screening the sensitivity of Least Square Algorithm implemented in digital protective relays

Fault in the transmission line network initiates a number of unpredictable parameters. These parameters influence the Least Square Algorithm (LSA) implemented in digital protective relays for estimating fundamental frequency phasor. The sensitivity analysis method is applied for analyzing the degree of influential parameters since the investigated parameters are unpredictable. The Morris sensitivity analysis method is selected to screen input unpredictable parameters to identify the high influential parameters, also known as important parameters, on the LSA output. The result of this Morris method identifies three important parameters namely: the amplitude of the decaying DC offset and its time constant, as well as the fundamental frequency variation.