A Novel Approach for Power System Protection in High Voltage Power System at 132kv
Chapter One
Aims and objectives of the research
The aim of the research is to provide a new approach for protection of 132kV lines. The specific Objectives of this research include:
- To detect fault cases on transmission line within shortest possible time by using Wavelet Transformation technique to analyze the fault
- To classify the fault based on number of phases that constitute the fault. For instance, single line-to- ground fault, line-to-line fault, double line-to-ground fault
- To improve on the reliability of the power system by prompt handling of faults on the transmission network
CHAPTER TWO:
LITERATURE REVIEW
Faults and Causes of Faults in Power Systems
In an electrical power system comprising generators, switch gears, transformers, power receivers, transmission and distribution circuits, it is inevitable that sooner or later some failures will occur somewhere in the system. A fault in an electrical equipment or apparatus is a defect in the electrical circuit due to which current is diverted from the intended path [4]. Fault is an abnormal flow of current which may be open-circuit or short-circuit. Open circuit or series faults can occur if for example a circuit breaker controls the lines and does not open all the three phases. In this case, one or two phases of the line may be opened while the other/s are closed.
A fault is also the unintentional and undesirable creation of a conducting path or a blockage of current [5]. In a three phase system, a fault may involve one or more phases and ground, or may occur only between phases. Most of the faults in power system lead to short circuit condition. Regarding the transmission lines, fault occurs when energized conductor or conductors come in contact with the ground (earth fault) or when two or more conductors come in contact (line-to- line fault). The probability of the fault occurrence in power lines is about one-half of the total faults occurring in a power system [4]. This is because the power lines are widely branched with greater length being operated under variable weather conditions and are subject to the actions of atmospheric disturbances. The distribution of faults in the various sections of a power system are shown in Table 2.1
To design a very good protection for power systems, knowledge of the root causes of failure in power system is pertinent. There are many causes of faults in power system including natural phenomena, human impact and animal contributions.
Lightning: Lightning is a significant cause of failure in power system. Lightning strike occurs when the voltage generated between a cloud and the ground exceeds the dielectric strength of the air. This results in massive current being injected into the power line; thereby leading to system overvoltage. Lightning, especially Cloud-to-Ground (C-G) lightning could damage power transmission lines, distribution lines, substations and power plants. Furthermore, such hazard may lead to loss of the system stability and uncontrolled separation of power network even threatens the whole electric power grid [6].
Trees and animal contacts: Growing trees can fall onto conductors, can drop branches onto conductors and can push conductors together. Nesting birds commonly build their homes on transmission towers and in substations. Snakes, squirrels and other animals can also bridge the conductors resulting in short circuit faults. Utilities as part of their maintenance carry out regular tree trimming to minimize tree contacts.
Human factors: Sometimes vehicular accident on electric pole can cause conductors to make contact with the ground. Vandalism and theft also contribute to power system failure.
Also equipment ageing and system overloads are potential cause of failure in power systems.
The approximate percentage of the contribution of various causes of fault in power systems is given in Table 2.2 below
CHAPTER THREE:
METHODOLOGY
MATLAB SOFTWARE
MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth generation programming language developed by MathWorks. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, and has the capability of interfacing with programs written in other languages. An additional package, SIMULINK, adds graphical multi-domain simulation and model-based design for dynamic and embedded system. MATLAB users come from various backgrounds of engineering, science and economics. MATLAB is widely used in academic and research institutions as well as industrial enterprises. Also MATLAB gives an attractive environment with hundreds of reliable and accurate built-in functions. MATLAB family works together with SIMULINK software to model electrical, mechanical and control systems. MATLAB has both command line interface and graphical user interface GUI which makes it easy to use.
The network under study is modeled and simulated with the help of SIMULINK software and the resulting signals routed to WAVELET toolbox for decomposition and analysis.
Wavelet Toolbox
The Wavelet Toolbox is one of the numerous toolboxes embedded in MATLAB. It is a collection of functions built on the MATLAB Technical Computing Environment. Wavelet Toolbox provides functions and apps for analyzing and synthesizing signals, images, and data that exhibit regular behavior punctuated with abrupt changes. The toolbox includes algorithms for continuous wavelet transform (CWT), scalogram, and wavelet coherence. It also provides algorithms and visualizations for discrete wavelet analysis, including decimated, nondecimated,
dual-tree, and wavelet packet transforms. In addition, the toolbox algorithms can be extended with custom wavelets. The toolbox makes it easy to analyze how the frequency content of signals changes over time and reveals time-varying patterns common in multiple signals. With wavelet toolbox one can perform Multi-resolution analysis (MRA) to extract fine-scale or large-scale features, identify discontinuities, and detect change points or events that are not visible in the raw data. Wavelet Toolbox can also be used to efficiently compress data while maintaining perceptual quality and to denoise signals and images while retaining features that are often smoothed out by other techniques. Essential feature of wavelet toolbox is its ability to analyze data and signals imported from other applications or software environment.
CHAPTER FOUR :
SIMULATION AND RESULTS
The results we presented here are obtained in two stages viz: (1) simulation of fault scenarios to generate fault signals and (2) signal analysis with wavelet toolbox.
CHAPTER FIVE:
CONCLUSION AND RECOMMENDATION
Conclusion
Power system disturbances are in nature non-stationary, very short in duration and impulsive. Ability of wavelet transform to extract information from transient signals simultaneously in time and frequency domain has been skillfully employed in this thesis to detect and classify transmission line faults. A case study of 132kV, 160km transmission line has been used to test the novel approach developed in this thesis. With the approach presented in this work, ten classes of fault (A-G, B-G, C-G, A-B, B-C, A-C, AB-G, BC-G, AC-G & ABC) could be correctly identified and classified within 0.085 seconds of fault duration. The current signals have been analyzed using Daubechie-4 (d4) mother wavelet at 7th level decomposition with the help of Wavelet Toolbox embedded in MATLAB.
All types of fault have been identified and classified by investigating the coefficient energies and coefficient energy ratios of the current signals. It is concluded that a fault occurs whenever the sum of the coefficient energies of the phase currents is greater than the sum of coefficient energies calculated during pre-fault condition of the line.
From the results, the coefficient energy and the total ratios were found to be increasing as the severity of the fault increases, except for L-L-L fault whose total ratio would be less than that calculated under normal condition. Therefore, the use of both coefficient energy and coefficient energy ratio precisely and reliably detects and classifies transmission line faults.
Recommendations
The following recommendations among others are made:
- The case study system reveals that the proposed approach can yield approximately 99% faultdetection and classification accuracy in a relatively small We however recommend that the efficiency of the approach be verified on larger systems, which may demand additional intelligent classification algorithms to achieve high degree of accuracy.
- As an extension to this work, advanced signal processing technique like S-Transform may be used to improve on the
- In order to implement this approach in Nigerian power network, we recommend that analogue system be changed to automatic and modern computer compliant
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