22 research outputs found
Assessment of Capacitor Voltage Transformer Affect On Distance Relay
A capacitor voltage transformer (CVT) comprises a capacitor voltage divider
connected to an iron-core voltage transformer. The capacitor voltage transformer is
used in the transmission and distribution circuit for stepping down voltages for
metering or protection.
The performance of the capacitor voltage transformer described in this
dissertation is assessed by computer simulation. The simulation tools MATLAB
and PSCAD/EMTDC were used. Various types of fault have been made to
investigate the transient response of the CVT. It can be seen that as the location of
the fault becomes more remote the period of oscillation becomes longer. The
results obtained indicate that the CVT transient behaviour is controlled by the sum
of the stack capacitances, the shape and parameters of the ferroresonance
suppression circuits and the point on wave at which the fault occurs.
Experiment results are presented in this dissertation. The objective of the
experiment is to investigate the effect on the performance of a distance relay of the
CVT transient response. From the results obtained we can see that the CVT
transient will slow down the relay for in zone faults. The type of fault applied in
this experiment is single phase to ground.
x
Power System State Estimation In Large-Scale Networks
Power system state estimation constitutes one of the critical functions that are
executed at the control centers. Its optimal performance is required in order to operate
the power system in a safe, secure and economic manner. State estimators (SE)
process the available measurements by taking into account the information about the
network model and parameters. The quality of estimated results will depend on the
measurements, the assumed network model and its parameters. Hence. SE requires to
use various techniques to ensure validity of the results and to detect and identify
sources of errors. The Weighted Least Squares (WLS) method is the most popular
technique of SE. This thesis provides solutions to enhance the WLS algorithm in
order to increase the performance of SE. The gain and the Jacobian matrices
associated with the basic algorithm require large storage and have to be evaluated at
every iteration, resulting in more computation time. The elements of the SE Jacobian
matrix are processed one-by-one based on the available measurements, and the
Jacobian matrix, H is updated suitably, avoiding all the power flow equations. thus
simplifying the development of the Jacobian. The results obtained proved that the
suggested method takes lesser computational time compared with the available NRSE
method, particularly when the size of the network becomes larger. The uncertainty in
analog measurements could occur in a real time system. Thus, the higher weighting
factor or wrongly assigned weighting factor to the measurement could lead to flag the
measurements as bad. This thesis describes a pre-screening process to identify the bad
measurements and the measurement weights before performing the WLS estimation
technique employed in SE. The autoregressive (AR) techniques. Burg and Modified
Covariance (MC), are used to predict the data and at the same time filtering the
logical weighting factors that have been assigned to the identified bad measurements.
The results show that AR methods managed to accurately predict the data and filter
the weigthage factors for the bad measurements. Also the WLS algorithm is modified
to include Unified Power Flow Controller (UPFC) parameters. The developed
methods are successfully tested on IEEE standard systems and the Sabah Electricy
Sdn. Bhd. (SESB) system without and with UPFC. The developed program is suitable
either to estimate the UPFC controller parameters or to estimate these parameter
values in order to achieve the given control specifications in addition to the power
system state variables
A High Step-Up Switched Z-Source Converter (HS-SZC) with Minimal Components Count for Enhancing Voltage Gain
Some applications such as fuel cells or photovoltaic panels offer low output voltage, and it is essential to boost this voltage before connecting to the grid through an inverter. The Z-network converter can be used for the DC-DC conversion to enhance the output voltage of renewable energy sources. However, boosting capabilities of traditional Z-network boost converters are limited, and the utilization of higher parts count makes it bulky and expensive. In this paper, an efficient, high step-up, switched Z-source DC-DC boost converter (HS-SZC) is presented, which offers a higher boost factor at a smaller duty ratio and avoids the instability due to the saturation of inductors. In the proposed converter, the higher voltage gain is achieved by using one inductor and switch at the back end of the conventional Z-source DC-DC converter (ZSC). The idea is to utilize the output capacitor for filtering and charging and discharging loops. Moreover, the proposed converter offers a wider range of load capacity, thus minimizing the power losses and enhancing efficiency. This study simplifies the structure of conventional Z-source converters through the deployment of fewer components, and hence making it more cost-effective and highly efficient, compared to other DC-DC boost converters. Furthermore, a comparison based on the boosting capability and number of components is provided, and the performance of the proposed design is analyzed with non-ideal elements. Finally, simulation and experimental studies are carried out to evaluate and validate the performance of the proposed converter
Noninvasive Methods for Condition Monitoring and Electrical Fault Diagnosis of Induction Motors
This chapter provides a comprehensive analysis of noninvasive methods to diagnose stator winding insulation faults of an induction motor. Further, a novel noninvasive method is proposed to diagnose the root cause of winding failure due to unbalanced voltage to avoid catastrophic failure. Therefore, a winding function approach is utilized to derive an analytical expression for stator winding distribution and magnetomotive force (MMF). This tactic qualifies the conductor segment that generates MMF, and it also helps to analyze a healthy current spectrum. One can easily observe higher order harmonics in current spectrum; therefore, a new series of rotor harmonics is introduced to diagnose unbalanced supply. The locus of these harmonics is dependent on the poles, rotor bars, and slip. Due to the rapid complexity in industrial plants, it is inconceivable to continue human inspection to diagnose the faults. Thus, to avoid human inspection, in addition to new series of rotor harmonic, a fully automatic method based on neural network is proposed. This method not only diagnoses unbalanced voltage but it also recognize the percentage of unbalanced voltage by use of feed-forward multilayer perceptron (MLP) trained by back propagation. Finally, the experimental results shows the validation of this research work proposed method
An Intelligent Automated Method to Diagnose and Segregate Induction Motor Faults
In the last few decades, various methods and alternative techniques have been proposed and implemented to diagnose induction motor faults. In an induction motor, bearing faults account the largest percentage of motor failure. Moreover, the existing techniques related to current and instantaneous power analysis are incompatible to diagnose the distributed bearing faults (race roughness), due to the fact that there does not exist any fault characteristics frequency model for these type of faults. In such a condition to diagnose and segregate the severity of fault is a challenging task. Thus, to overcome existing problem an alternative solution based on artificial neural network (ANN) is proposed. The proposed technique is harmonious because it does not oblige any mathematical models and the distributed faults are diagnosed and classified at incipient stage based on the extracted features from Park vector analysis (PVA). Moreover, the experimental results obtained through features of PVA and statistical evaluation of automated method shows the capability of proposed method that it is not only capable enough to diagnose fault but also can segregate bearing distributed defects
Design and Analysis of Tubular Slotted Linear Generators for Direct Drive Wave Energy Conversion Systems
Linear generator utilization in a wave energy converter (WEC) is an attractive alternative to a rotary generator. This paper presents the design of a permanent magnet linear machine (PMLM) for WEC applications in low wave power areas. In this paper, the wave height and vertical speed of Malaysian water is used for the simulation and design. Two design variants are introduced which are tubular PMLM with no spacer (TPMLM-NS) and tubular PMLM with spacer (TPMLM-S). Finite element analysis (FEA) has been conducted to investigate the performance and to refine the main dimensions of the design in terms of split ratio, pitch ratio and tooth width. The FEA results are then validated using an analytical method which is established according to the design’s magnetic field distribution. Based on main dimension refinement, it can be deduced that both the split ratio and the pitch ratio have a significant influence on the airgap flux density and back EMF of the design. The obtained FEA results also reveal that the TPMLM-NS variant is capable of producing 240 V back EMF, 1 kW output power with satisfactory efficiency. Consequently, this indicates the capability of the design to convert wave energy with good performance. Additionally, good agreement between the analytical predictions and FEA results was obtained with a low percentage of error, thus providing concrete assurance of the accuracy of the design