Transients in Power Systems

Power system engineering largely focuses on steady state analysis. The main areas of power system engineering are power flow studies and fault studies - both steady state technologies. But the world is largely transient, and power systems are always subject to time varying and short lived signals. This technical report concerns several important topics in transient analyses of power systems. The leading chapter deals with a new analytical tool-wavelets-for power system transients. Flicker and electric are furnace transients are discussed in Chapters I1 and IV. Chapter 111 deals with transients from shunt capacitor switching. The concluding chapters deal with transformer inrush current and non simultaneous pole closures of circuit breakers. This report was prepared by the students in EE532 at Purdue University. When I first came to Purdue in 1965, Professor El-Abiad was asking for student term projects which were turned into technical reports. I have \u27borrowed\u27 this idea and for many years we have produced technical reports from the power systems courses. The students get practice in writing reports, and the reader is able to get an idea of the coverage of our courses. I think that the students have done a good job on the subject of transients in power systems

Application of Park's power components to the differential protection of three-phase transformers

This paper presents a new scheme for power transformers differential protection, in which the concept of the Park's instantaneous differential powers is introduced. The proposed method is able to detect winding insulation failures and to distinguish them from magnetizing inrush current transients. Experimental and simulation results are presented and discussed

Fault detection in a three-phase inverter fed circuit: Enhancing the Tripping capability of a UPS circuit breaker using wave shape recognition algorithm

Uninterruptible power supplies (UPS) are electrical devices that protect sensitive loads from power line disturbances such as source side overcurrents caused by overvoltage and power surges. The critical load in a double conversion UPS system is supplied from an invert-er. When overcurrents occur on the load side of double conversion UPS systems, both the UPS system’s inverter and the critical load connected to it stand a high risk of damage. Load side overcurrents due to short circuits, ground faults and motor/transformer start-up are very damaging to power electronic components, electrical equipment and cable connections. There exists circuit breakers on the load side designed to trip when a huge overcurrent occurs, thereby clearing the fault. A circuit breaker is normally sized and installed based on the maxi-mum capacity of the host system and trips when a predetermined overcurrent is recorded within a specific period of time. The UPS system’s inverter has a pre-set current limit value to protect insulated-gate bipolar transistors (IGBTs) from damage. During an overcurrent, invert-ers can supply a fault current whose peak value is limited to the IGBT current limit value. This inverter supplied fault current is not high enough to trip the circuit breaker. After an extended period of overcurrent, UPS internal tripping will be activated and all loads lose power. Opera-tion of the UPS in bypass mode supplies the required fault current but exposes the sensitive load to power line distortions. Therefore, it is desired to always supply the critical load via the inverter. This study targets to design a detection algorithm for short circuits and ground faults with a detection time faster than the UPS system’s internal tripping in order to isolate the faulted ar-ea, when the inverter is supplying the critical load. To achieve this, first, a MATLAB model was designed to aid in preliminary studies of fault detection through analysing the system behaviour. Secondly, literature review was conducted and a fault detection method selected with the help of the MATLAB model. Next, laboratory tests on a real UPS system were carried out and compared to the MATLAB results. Lastly, the detection algorithm was designed, im-plemented and tested on a real double conversion UPS system. The test results indicate that the implemented detection algorithm successfully detects short circuits and ground faults well within the desired time. It also successfully distinguishes short circuits and ground faults from other sources of overcurrents such as overloading and transformer inrush current. Future development of this study includes additional features such as a fault classification method proposed for implementation to improve the UPS debugging process during maintenance. Moreover, the detection algorithm will also be refined and devel-oped further to activate a circuit that discharges a current pulse to increase the fault current fed to the circuit breaker

An Effective Detection of Inrush and Internal Faults in Power Transformers Using Bacterial Foraging Optimization Technique

Abstract Power transformers in transmission network are utilized for increasing or decreasing the voltage level. Power Transformers fail to connect directly to the consumers that result in the less load fluctuations. Power transformer operation under any abnormal condition decreases the lifetime of the transformer. Power Transformer protection from inrush and internal fault is critical issue in power system because the obstacle lies in the precise and swift distinction between them. Due to the limitation of heterogeneous resources, occurrence of fault poses severe problem. Providing an efficient mechanism to differentiate between faults (i.e. inrush and internal) is the key for efficient information flow. In this paper, the task of detecting inrush and internal fault in power transformers is formulated as an optimization problem which is solved by using Hyperbolic S-Transform Bacterial Foraging Optimization (HS-TBFO) technique. The Gaussian Frequencybased Hyperbolic S-Transform detects the faults at much earlier stage and therefore minimizes the computation cost by applying Cosine Hyperbolic S-Transform. Next, the Bacterial Foraging Optimization (BFO) technique has been proposed and has demonstrated the capability of identifying the maximum number of faults covered with minimum test cases and therefore improving the fault detection efficiency in a wise manner. The HS-TBFO technique is evaluated and validated in various simulation test cases to detect inrush and internal fault in a significant manner. This HS-TBFO technique is investigated based on three phase power transformer embedded in a power system fed from both ends. Results have confirmed that the HS-TBFO technique is capable of categorizing the inrush and internal faults by identifying maximum number of faults with minimum computation cost as compared to the state-of-the-art works

Protection Scheme of Power Transformer Based on Time–Frequency Analysis and KSIR-SSVM

The aim of this paper is to extend a hybrid protection plan for Power Transformer (PT) based on MRA-KSIR-SSVM. This paper offers a new scheme for protection of power transformers to distinguish internal faults from inrush currents. Some significant characteristics of differential currents in the real PT operating circumstances are extracted. In this paper, Multi Resolution Analysis (MRA) is used as Time–Frequency Analysis (TFA) for decomposition of Contingency Transient Signals (CTSs), and feature reduction is done by Kernel Sliced Inverse Regression (KSIR). Smooth Supported Vector Machine (SSVM) is utilized for classification. Integration KSIR and SSVM is tackled as most effective and fast technique for accurate differentiation of the faulted and unfaulted conditions. The Particle Swarm Optimization (PSO) is used to obtain optimal parameters of the classifier. The proposed structure for Power Transformer Protection (PTP) provides a high operating accuracy for internal faults and inrush currents even in noisy conditions. The efficacy of the proposed scheme is tested by means of numerous inrush and internal fault currents. The achieved results are utilized to verify the suitability and the ability of the proposed scheme to make a distinction inrush current from internal fault. The assessment results illustrate that proposed scheme presents an enhancement of distinguish inrush current from internal fault over the method to be compared without Dimension Reduction (DR)

A New Approach to Power System Protection using Time-frequency Analysis and Pattern Recognition

The fault diagnosis of Electric Power System is a process of discriminating the faulted system elements by protective relays and subsequent tripping by circuit breakers. Specially, as soon as some serious faults occur on a power system, a lot of alarm information is transmitted to the control center. Under such situation, the operators are required to judge the cause, location, and the system elements with faults rapidly and accurately. Thus, good fault diagnosis methods can provide accurate and effective diagnostic information to dispatch operators and help them take necessary measures in fault situation so as to guarantee the secure and stable operation of the Electric power system. This thesis reports various techniques used for detection, classification and localization of faults on the high voltage transmission line. The distance protection scheme for transmission line is employed for various power networks such as single-circuit line, double-circuit line, and lines having FACTS ..

Advances of Mathematical Morphology and Its Applications in Signal Processing

This thesis describes some advances of Mathematical Morphology (MM), in order to improve the performance of MM filters in I-D signal processing, . especially in the application to power system protection. MM methodologies are founded on set-theoretic concepts and nonlinear superpositions of signals and images. The morphological operations possess outstanding geometrical properties which make it undoubted that they are efficient image processing methods. However in I-D signal processing, MM filters are not widely employed. To explore the applications of MM for I-D signal processing, our contributions in this area can be summarized in the following two aspects. Firstly, the fram.ework of the traditional signal processing methods is based on the frequency domain representation of the signal and the analysis of the operators' transfer function in the frequency domain. But to the morphological operations, their representations in the frequency domain are uncertain. In order to tackle this problem, this thesis presents our attempt to describe the weighted morphological dilation in the frequency domain. Under certain restrictions to the signal and the structuring element, weighted dilation is transformed to a mathematical expression in the frequency domain. Secondly, although the frequency domain analysis plays an important role in signal processing, the geometrical properties of a signal such as the shape of the signal cannot be ignored. MM is an effective method in dealing with such problems. In this thesis, based on the theory of Morphological Wavelet (MW), three multi-resolution signal decomposition schemes are presented. They are Multiresolution Morphological Top-Hat scheme (MMTH), Multi-resolution Morphov logical Gradient scheme (MMG) and Multi-resolution Noise Tolerant Morphological Gradient scheme (MNTMG). The MMTH scheme shows its significant effect in distinguishing symmetrical features from asymmetrical features on the waveform, which owes to its signal analysis operator: morphological Top-Hat transformation, an effective morphological technique. In this thesis, the MMTH scheme is employed in the identification of transformer magnetizing inrush curr~nt from internal fault. Decomposing the signal by MMTH, the asymmetrical features of the inrush waveform are exposed, and the other irrelevant components are attenuated. The MMG scheme adopts morphological gradient, a commonly used operator for edge detection in image and signal processing, as its signal analysis / operator. The MMG scheme bears significant property in characterizing and recognizing the sudden changes with sharp peaks and valleys on the waveform. Furthermore, to the MMG scheme, by decomposing the signal into different levels, the higher the level is processed, the more details of the sudden changes are revealed. In this thesis, the MMG scheme is applied for the design of fault locator of power transmission lines, by extracting the transient features directly from fault-generated transient signals. The MNTMG decomposition scheme can effectively reduce the noise and extract transient features at the same time. In this thesis, the MNTMG scheme is applied to extract the fault generated transient wavefronts from noise imposed signals in the application of fault location of power transmission lines. The proposed contributions focus on the effect of weighted dilation in the frequency domain, constructions of morphological multi-resolution decomposition schemes and their applications in power systems

Application Of Neural Network For Transformer Protection

The demand for a reliable supply of electrical energy for the exigency of modern world in each and every field has increased considerably requiring nearly a no-fault operation of power systems. The crucial objective is to mitigate the frequency and duration of unwanted outages related to power transformer puts a high pointed demand on power transformer protective relays to operate immaculately and capriciously. The high pointed demand includes the requirements of dependability associated with no false tripping, and operating speed with short fault detection and clearing time. The second harmonic restrain principle is widely used in industrial application for many years, which uses discrete Fourier transform (DFT) often encounters some problems such as long restrain time and inability to discriminate internal fault from magnetizing inrush condition. Hence, artificial neural network (ANN), a powerful tool for artificial intelligence (AI), which has the ability to mimic and automate the knowledge, has been proposed for detection and classification of faults from normal and inrush condition. The wavelet transform(WT) which has the ability to extract information from transient signals in both time and frequency domain simultaneously is used for the analysis of power transformer transient phenomena in various conditions. All the above mentioned conditions of power transformer to be analysed in a power system are modelled in MATLAB/SIMULINK environment. Secondly the WT is applied to decompose the different current signals of the power transformer into a series of detailed wavelet components. The statistical features of the wavelet components are calculated and are used to train a multilayer feed forward neural network designed using back propagation algorithm to discriminate various conditions. The best suitable architecture of ANN is selected having least mean square error during training. The ANN model is implemented in LabVIEW environment