ARTIFICIAL NEURAL NETWORK BASED FAULT LOCATION FOR TRANSMISSION LINES

This thesis focuses on detecting, classifying and locating faults on electric power transmission lines. Fault detection, fault classification and fault location have been achieved by using artificial neural networks. Feedforward networks have been employed along with backpropagation algorithm for each of the three phases in the Fault location process. Analysis on neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. Simulation results have been provided to demonstrate that artificial neural network based methods are efficient in locating faults on transmission lines and achieve satisfactory performances

FAULT IDENTIFICATION ON ELECTRICAL TRANSMISSION LINES USING ARTIFICIAL NEURAL NETWORKS

Transmission lines are designed to transport large amounts of electrical power from the point of generation to the point of consumption. Since transmission lines are built to span over long distances, they are frequently exposed to many different situations that can cause abnormal conditions known as electrical faults. Electrical faults, when isolated, can cripple the transmission system as power flows are directed around these faults therefore leading to other numerous potential issues such as thermal and voltage violations, customer interruptions, or cascading events. When faults occur, protection systems installed near the faulted transmission lines will isolate these faults from the transmission system as quickly as possible. Accurate fault location is essential in reducing outage times and enhancing system reliability. Repairing these faulted elements and restoring the transmission lines to service quickly is highly important since outages can create congestion in other parts of the transmission grid, therefore making them more vulnerable to additional outages. Therefore, identifying the classification and location of these faults as quickly and accurately as possible is crucial. Diverse fault location methods exist and have different strengths and weaknesses. This research aims to investigate the use of an intelligent technique based on artificial neural networks. The neural networks will attempt to determine the fault classification and precise fault location. Different fault cases are analyzed on multiple transmission line configurations using various phasor measurement arrangements from the two substations connecting the transmission line. These phasor measurements will be used as inputs into the artificial neural network. The transmission system configurations studied in this research are the two-terminal single and parallel transmission lines. Power flows studied in this work are left static, but multiple sets of fault resistances will be tested at many points along the transmission line. Since any fault that occurs on the transmission system may never experience the same fault resistance or fault location, fault data was collected that relates to different scenarios of fault resistances and fault locations. In order to analyze how many different fault resistance and fault location scenarios need to be collected to allow accurate neural network predictions, multiple sets of fault data were collected. The multiple sets of fault data contain phasor measurements with different sets of fault resistance and fault location combinations. Having the multiple sets of fault data help determine how well the neural networks can predict the fault identification based on more training data. There has been a lack of guidelines on designing the architecture for artificial neural network structures including the number of hidden layers and the number of neurons in each hidden layer. This research will fill this gap by providing insights on choosing effective neural network structures for fault classification and location applications

DETERMINING POWER SYSTEM FAULT LOCATION USING NEURAL NETWORK APPROACH

Fault location remains an extremely pivotal feature of the electric power grid as it ensures efficient operation of the grid and prevents large downtimes during fault occurrences. This will ultimately enhance and increase the reliability of the system. Since the invention of the electric grid, many approaches to fault location have been studied and documented. These approaches are still effective and are implemented in present times, and as the power grid becomes even more broadened with new forms of energy generation, transmission, and distribution technologies, continued study on these methods is necessary. This thesis will focus on adopting the artificial neural network method for fault location for a high-impedance grounded system, where fault currents are small for single phase to ground faults. This approach will be performed on a single 2-terminal distribution network. This thesis will also give a comprehensive explanation on the process of developing artificial neural networks (ANN) using MATLAB’s neural network app designers. The main objective of the experimental approach is to investigate the effects of different variations in ANN structures (such as number of neurons, number of hidden layers, input features, and data preprocessing) on predicting fault locations. Study results from the simulations have been presented to show performance of each ANN structure for fault location on the sample distribution system

The Use of Artificial Neural Networks in the Theoretical Investigation of Faults in Transmission Lines

This paper describes the development of a fast, efficient, artificial neural network (ANN) based fault diagnostic system (FDS) for the location of fault on transmission lines. The principal functions of this diagnostic system are: detection of fault occurrence, identification of faulted sections and classification of faults into types. This has been achieved through a cascaded, multilayer ANN structure using the back-propagation (BP) learning algorithm. This paper shows that the FDS accurately identifies High Impedance Faults, which are relatively difficult to identify with other methods. Test results are simulated and generated in MATLAB using Apo 132KV transmission line in Apo transmission substation, Abuja. These results amply demonstrate the capability of the FDS in terms of accuracy and speed with respect to detection, localization, and classification of faults in transmission lines.http://dx.doi.org/10.4314/njt.v34i4.2

Artificial neural networks and their applications to intelligent fault diagnosis of power transmission lines

Over the past thirty years, the idea of computing based on models inspired by human brains and biological neural networks emerged. Artificial neural networks play an important role in the field of machine learning and hold the key to the success of performing many intelligent tasks by machines. They are used in various applications such as pattern recognition, data classification, stock market prediction, aerospace, weather forecasting, control systems, intelligent automation, robotics, and healthcare. Their architectures generally consist of an input layer, multiple hidden layers, and one output layer. They can be implemented on software or hardware. Nowadays, various structures with various names exist for artificial neural networks, each of which has its own particular applications. Those used types in this study include feedforward neural networks, convolutional neural networks, and general regression neural networks. Increasing the number of layers in artificial neural networks as needed for large datasets, implies increased computational expenses. Therefore, besides these basic structures in deep learning, some advanced techniques are proposed to overcome the drawbacks of original structures in deep learning such as transfer learning, federated learning, and reinforcement learning. Furthermore, implementing artificial neural networks in hardware gives scientists and engineers the chance to perform high-dimensional and big data-related tasks because it removes the constraints of memory access time defined as the von Neuman bottleneck. Accordingly, analog and digital circuits are used for artificial neural network implementations without using general-purpose CPUs. In this study, the problem of fault detection, identification, and location estimation of transmission lines is studied and various deep learning approaches are implemented and designed as solutions. This research work focuses on the transmission lines’ datasets, their faults, and the importance of identification, detection, and location estimation of them. It also includes a comprehensive review of the previous studies to perform these three tasks. The application of various artificial neural networks such as feedforward neural networks, convolutional neural networks, and general regression neural networks for identification, detection, and location estimation of transmission line datasets are also discussed in this study. Some advanced methods based on artificial neural networks are taken into account in this thesis such as the transfer learning technique. These methodologies are designed and applied on transmission line datasets to enable the scientist and engineers with using fewer data points for the training purpose and wasting less time on the training step. This work also proposes a transfer learning-based technique for distinguishing faulty and non-faulty insulators in transmission line images. Besides, an effective design for an activation function of the artificial neural networks is proposed in this thesis. Using hyperbolic tangent as an activation function in artificial neural networks has several benefits including inclusiveness and high accuracy

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Update of an early warning fault detection method using artificial intelligence techniques

This presentation describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. In an earlier paper [11], a computer simulated medium length transmission line has been tested by the detector and the results clearly demonstrate the capability of the detector. Today’s presentation considers a case study illustrating the suitability of this AI Technique when applied to a distribution transformer. Furthermore, an evolutionary optimisation strategy to train ANNs is also briefly discussed in this presentation, together with a ‘crystal ball’ view of future developments in the operation and monitoring of transmission systems in the next millennium

Power system fault prediction using artificial neural networks

The medium term goal of the research reported in this paper was the development of a major in-house suite of strategic computer aided network simulation and decision support tools to improve the management of power systems. This paper describes a preliminary research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. To achieve this goal, an AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system . Simulation will normally take place using equivalent circuit representation. Artificial Neural Networks (ANNs) are used to construct a hierarchical feed-forward structure which is the most important component in the fault detector. Simulation of a transmission line (2-port circuit ) has already been carried out and preliminary results using this system are promising. This approach provided satisfactory results with accuracy of 95% or higher

Estimation of fault distance location using artificial neural network

Electricity demand in Malaysia is significantly increasing. Expanding the grid system to cater the new demand leads to several additional problems on the fault detection and protection coordination system. Single line to ground fault is commonly happen in grid system with possibility of 65% to 70% occurrence in distribution system. Fault detection is currently identified using costly special software. Typically, detection on single line to ground fault is analyzed by the pattern, placement, with and without bus monitoring approaches. Hence, this study is focused to improve the current fault detection approach on a single line to ground fault. The objective of this study is to develop a system that could estimate the faults location using artificial neural network (ANN) by Levenberg Marquardt Backpropagation (LMB) training approach. This ANN approach will be adapted in Matlab Software. A-10 bus will be developed, and faults will be simulated using Power World Software. During the implementation of the ANN, several buses will be added to enhance the capability of the neural network to detect fault distance in the system. To verify the effectiveness of the proposed ANN on the estimation fault distance determination, 21-bus distribution system has been compared for the validation purpose, with consideration of different location of the generation sources. Capability of the proposed approach has been assessed using a curving fitting tool in Matlab in terms of means square error (MSE) and regression plot (R). From the findings, it shows that LMB method can be implemented for location-based fault detection estimation once it was trained with 150 ANN hidden layer. Under the best condition, the deviation between regression of transmission line for 10-Bus single line and 21-Bus quad generation system has been achieved at 0.057%

Intelligent Fault Analysis in Electrical Power Grids

Power grids are one of the most important components of infrastructure in today's world. Every nation is dependent on the security and stability of its own power grid to provide electricity to the households and industries. A malfunction of even a small part of a power grid can cause loss of productivity, revenue and in some cases even life. Thus, it is imperative to design a system which can detect the health of the power grid and take protective measures accordingly even before a serious anomaly takes place. To achieve this objective, we have set out to create an artificially intelligent system which can analyze the grid information at any given time and determine the health of the grid through the usage of sophisticated formal models and novel machine learning techniques like recurrent neural networks. Our system simulates grid conditions including stimuli like faults, generator output fluctuations, load fluctuations using Siemens PSS/E software and this data is trained using various classifiers like SVM, LSTM and subsequently tested. The results are excellent with our methods giving very high accuracy for the data. This model can easily be scaled to handle larger and more complex grid architectures.Comment: In proceedings of the 29th IEEE International Conference on Tools with Artificial Intelligence (ICTAI) 2017 (full paper); 6 pages; 13 figure