Mamdani Fuzzy Expert System Based Directional Relaying Approach for Six-Phase Transmission Line

Traditional directional relaying methods for 6-phase transmission lines have complex effort, and so there is still a need for novel direction relaying estimation scheme. This study presents a Mamdani-fuzzy expert system (MFES) approach for discriminating faulty section/zone, classifying faults and locating faults in 6-phase transmission lines. The 6-phase fundamental component of currents, voltages and phase angles are captured at single bus and are used in the protection scheme. Simulation results substantiate that the protection scheme is very successful against many parameters such as different fault types, fault resistances, transmission line fault locations and inception angles. A large number of fault case studies have been carried out to evaluate reach setting and % error of proposed method. It provides primary protection to transmission line length and also offers backup protection for a reverse section of transmission line. The experimental results show that the scheme performs better than the other schemes

Fault Classification and Location Identification on Electrical Transmission Network Based on Machine Learning Methods

Power transmission network is the most important link in the country’s energy system as they carry large amounts of power at high voltages from generators to substations. Modern power system is a complex network and requires high-speed, precise, and reliable protective system. Faults in power system are unavoidable and overhead transmission line faults are generally higher compare to other major components. They not only affect the reliability of the system but also cause widespread impact on the end users. Additionally, the complexity of protecting transmission line configurations increases with as the configurations get more complex. Therefore, prediction of faults (type and location) with high accuracy increases the operational stability and reliability of the power system and helps to avoid huge power failure. Furthermore, proper operation of the protective relays requires the correct determination of the fault type as quickly as possible (e.g., reclosing relays). With advent of smart grid, digital technology is implemented allowing deployment of sensors along the transmission lines which can collect live fault data as they contain useful information which can be used for analyzing disturbances that occur in transmission lines. In this thesis, application of machine learning algorithms for fault classification and location identification on the transmission line has been explored. They have ability to “learn” from the data without explicitly programmed and can independently adapt when exposed to new data. The work presented makes following contributions: 1) Two different architectures are proposed which adapts to any N-terminal in the transmission line. 2) The models proposed do not require large dataset or high sampling frequency. Additionally, they can be trained quickly and generalize well to the problem. 3) The first architecture is based off decision trees for its simplicity, easy visualization which have not been used earlier. Fault location method uses traveling wave-based approach for location of faults. The method is tested with performance better than expected accuracy and fault location error is less than ±1%. 4) The second architecture uses single support vector machine to classify ten types of shunt faults and Regression model for fault location which eliminates manual work. The architecture was tested on real data and has proven to be better than first architecture. The regression model has fault location error less than ±1% for both three and two terminals. 5) Both the architectures are tested on real fault data which gives a substantial evidence of its application

Detection and Classification of Fault Types in Distribution Lines by Applying Contrastive Learning to GAN Encoded Time-Series of Pulse Reflectometry Signals

T This study proposes a new method for detecting and classifying faults in distribution lines. The physical principle of classification is based on time-domain pulse reflectometry (TDR). These high-frequency pulses are injected into the line, propagate through all of its bifurcations, and are reflected back to the injection point. According to the impedances encountered along the way, these signals carry information regarding the state of the line. In the present work, an initial signal database was obtained using the TDR technique, simulating a real distribution line using (PSCADTM). By transforming these signals into images and reducing their dimensionality, these signals are processed using convolutional neural networks (CNN). In particular, in this study, contrastive learning in Siamese networks was used for the classification of different types of faults (ToF). In addition, to avoid the problem of overfitting owing to the scarcity of examples, generative adversarial neural networks (GAN) have been used to synthesise new examples, enlarging the initial database. The combination of Siamese neural networks and GAN allows the classification of this type of signal using only synthesised examples to train and validate and only the original examples to test the network. This solves the problem of the lack of original examples in this type of signal of natural phenomena which are difficult to obtain and simulate

Modelling of a protective scheme for AC 330 kV transmission line in Nigeria

Transmission lines play a vital role in the reliable and efficient delivery of electrical power over long distances, and these lines are affected by faults that occur due to lightning strikes, equipment failures, human, animal or vegetation interference, environmental factors, ageing equipment, voltage sag or grid faults adverse effects on the line. Therefore, protecting these transmission lines becomes crucial with the increasing demand for electricity and the need to ensure grid stability. The modelling process involves the development of a comprehensive protection scheme utilising modern technologies and advanced algorithms. The protection scheme encompasses various elements, including fault detection, fault classification, fault location, and fault clearance. It incorporates intelligent devices, such as protective relays and communication systems, to enable rapid and accurate fault identification and isolation. First, a 330 kV, 500 km three-phase Delta transmission line is modelled using MATLAB/SIMULINK. A section of the Delta network in Delta State Nigeria was used since the entire Nigeria 330 kV network is large. Faulty current and voltage data were generated for training using the CatBoost, 93340 data sizes comprising fault data from three-phase current and voltage extracted from the Delta transmission line model in Nigeria were designed, and twelve fault conditions were used. The CatBoost classifier was employed to classify the faults after different machine language algorithm was used to train the same data with other parameters. The trainer achieved the best accuracy of 99.54%, with an error of 0.46%, at 748 iterations out of 1000 compared to GBoost, XBoost and other classification techniques. Second, the Artificial Neural Network technique was used to train this data, and an accuracy of 100% was attained for fault detection and about 99.5% for fault localisation at different distances with 0.0017 microseconds of detection and an average error of 0% to 0.5%. This model performs better than Support Vector Machine and Principal Component Analysis with a higher fault detection time. The effect of noise signal on the ANN model was studied, and the discrete wavelet technique was used to de-noise the signal for better performance and to enhance the model’s accuracy during transient. Third, the wavelet transforms as a data extraction model to detect the threshold value of current and voltage and the coordination time for the backup relay to trip if the primary relay does not operate or clear the fault on time. The difference between the proposed model and the model without the threshold value was analysed. The simulated result shows that the trip time of the two relays demonstrates a fast and precise trip time of 60% to 99.87% compared to other techniques used without the threshold values. The proposed model can eliminate the trial-and-error in programming the instantaneous overcurrent relay setting for optimal performance. Fourth, the PSO-PID controller algorithm was used to moderate the load frequency of the transmission network. Due to the instability between the generation and distribution, there is always a switch in the stability of the transmission or load frequency; therefore, the PSO-PID algorithm was used to stabilise the Delta power station as a pilot survey from the Nigerian transmission network. Also, a hybrid system with five types of generation and two load centres was used in this model. It has been shown that the proposed control algorithm is effective and improves system performance significantly. As a result, the suggested PSO-PID controller is recommended for producing high-quality, dependable electricity. Moreover, the PSO-PID algorithm produces 0.00 seconds settling time and 0.0005757 ITAE. It’s essential to carefully consider potential drawbacks like complexity and computational overhead, sensitivity to algorithm parameters, potential parameter convergence and limited interpretability and assess their impact on the specific LFC application before implementing a PSO-PID controller in a power system. When implemented with the model in this research, the Delta transmission line network will reduce the excessive fault that occurs in the transmission line and improve the energy efficiency of the entire network when replicated with the Nigerian network. Generally, for the effective design and implementation of the protection scheme of the 330 kV transmission line, the fault must be detected and classified, and the exact location of the fault must be ascertained before the relay protection and load frequency control will be applied for effective fault management and control system

Measurements, Models, Systems and Design

531 s.

Large Grid-Connected Wind Turbines

This book covers the technological progress and developments of a large-scale wind energy conversion system along with its future trends, with each chapter constituting a contribution by a different leader in the wind energy arena. Recent developments in wind energy conversion systems, system optimization, stability augmentation, power smoothing, and many other fascinating topics are included in this book. Chapters are supported through modeling, control, and simulation analysis. This book contains both technical and review articles

Fault diagnosis for transmission lines using chromatic processing

Diagnosing the type of fault and its location in a transmission lines is performed by a variety of techniques and mainly relies on monitoring currents and voltages in the transmission line. Accurate fault diagnosis plays an important role in improving the overall system reliability, has a significant effect on the quality of service provided, improves the protection system efficiency, reduces power outage time and limits the risks and the economic losses. Transmission lines extend over wide areas and are exposed to vulnerable situation, to the harsh and uncontrolled environment random events (e.g.~lightning), this can lead to loss of lines due to various faults. This fact has an attraction for researchers to focus on utilising possible methods to improve protection system and supporting fault diagnosis solutions to overcome many of the transient fault conditions. This thesis explores an alternative method of fault diagnosis and location. The approach uses chromatic methodology to extract information from current and voltage waveforms from a simulated transmission line with different fault conditions. These waveforms are processed chromatically. The process involves two steps, filtering which is performed on a cycle by cycle basis of the three symmetrical components for each waveform, and then using the chromatic transformations to represent the outputs in an information space. Various chromatic models are available but the hue, lightness and saturation (HLS) model is employed in this study and the relation between changes in the waveforms and changes in the chromatic parameters forms the foundation for building the proposed diagnosis algorithms. A fault type classifier algorithm for the asymmetrical faults has been proposed for both, double and single line transmission systems. It employs the chromatic H parameter variation with the fault type for the negative sequence component. The processed waveforms are either the voltage or the current at a single terminal of the transmission line. L chromatic parameter values of the zero sequence component are incorporated in the algorithm to add the ground fault distinguishing element and the L parameter values of the rectified negative sequence component were used to support the classification decision even with high fault resistance. Another algorithm for fault location estimation for all types of faults has been used for the double transmission line system. It employs the L chromatic parameter values of the rectified positive sequence component. The processed waveforms are the current collected from both terminals of the transmission line. Finally, the proposed algorithms have been tested by variation of possible conditions of the faults, such as changing the fault location, the fault resistance, the line configurations and parameters, etc. In addition to robustness testing with different fault scenarios. Experimental results taken from a lumped parameter laboratory system have been also used to verify the outputs of the chromatic processing. The performance of the chromatic approach and other reported methods have been compared. The error of the chromatic method compares favourably with others. As such overall performance can be described as being good, this is encouraging and future work through proposing diagnostic tools for other power system components is needed

Methodology for designing the fuzzy resolver for a radial distribution system fault locator

The Power System Automation Lab at Texas A&M University developed a fault location scheme that can be used for radial distribution systems. When a fault occurs, the scheme executes three stages. In the first stage, all data measurements and system information is gathered and processed into suitable formats. In the second stage, three fault location methods are used to assign possibility values to each line section of a feeder. In the last stage, a fuzzy resolver is used to aggregate the outputs of the three fault location methods and assign a final possibility value to each line section of a feeder. By aggregating the outputs of the three fault location methods, the fuzzy resolver aims to obtain a smaller subset of line sections as potential faulted sections than the individual fault location methods. Fuzzy aggregation operators are used to implement fuzzy resolvers. This dissertation reports on a methodology that was developed utilizing fuzzy aggregation operators in the fuzzy resolver. Three fuzzy aggregation operators, the min, OWA, and uninorm, and two objective functions were used to design the fuzzy resolver. The methodologies to design fuzzy resolvers with respect to a single objective function and with respect to two objective functions were presented. A detailed illustration of the design process was presented. Performance studies of designed fuzzy resolvers were also performed. In order to design and validate the fuzzy resolver methodology, data were needed. Due to the lack of real field data, simulating a distribution feeder was a feasible alternative to generate data. The IEEE 34 node test feeder was modeled. Time current characteristics (TCC) based protective devices were added to this feeder. Faults were simulated on this feeder to generate data. Based on the performance studies of designed fuzzy resolvers, the fuzzy resolver designed using the uninorm operator without weights is the first choice. For this fuzzy resolver, no optimal weights are needed. In addition, fuzzy resolvers using the min operator and OWA operator can be used to design fuzzy resolvers. For these two operators, the methodology for designing fuzzy resolvers with respect to two objective functions was the appropriate choice

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis