Simulation Analysis of a Power System Protection using Artificial Neural Network

There has been significant development in the area of neural network based power system protection in the previous decade. Neural network technology has been applied for various protective relaying functions including distance protection. The aim of this Paper is to develop a software module acting as a protective relay using neural network techniques. The Artificial Neural Network (ANN) software developed module employs the back-propagation method to recognize the waveform patterns of impedance in a transmission line. The input waveforms are generated using PSCAD. The generated waveforms then are used as training and testing data for the ANN software. The ANN software is simulated using the Neural Network Toolbox. The design has been tested for different fault conditions including different fault resistances and fault inception angles. The test results show that the relay is able to detect faults in lesser time as compared to conventional relay algorithms.DOI:http://dx.doi.org/10.11591/ijece.v3i1.193

The use of artificial intelligence techniques for power analysis

This thesis reports the research carried out into the use of Artificial Intelligence techniques for Power System Analysis. A number of aspects of Power System analysis and its management are investigated and the application of Artificial Intelligence techniques is researched. The use of software tools for checking the application of power system protection systems particularly for complex circuit arrangements was investigated. It is shown that the software provides a more accurate and efficient way of carrying out these investigations. The National Grid Company's (plc, UK) use of software tools for checking the application of protection systems is described, particularly for complex circuit arrangements such as multi-terminal circuits and composite overhead line and cable circuits. Also described, is how investigations have been made into an actual system fault that resulted in a failure of protection to operate. Techniques using digital fault records to replay a fault into a static model of protection are used in the example. The need for dynamic modelling of protection is also discussed. Work done on automating the analysis of digital fault records using computational techniques is described. An explanation is given on how a rule-based system has been developed to classify fault types and analyse the response of protection during a power system fault or disturbance in order to determine correct or incorrect operation. The development of expert systems for on-line application in Energy Control Centres (ECC), is reported. The development of expert systems is a continuous process as new knowledge is gained in the field of artificial intelligence and new expert system development tools are built. Efforts are being made for on-line application of expert systems in ECC as preventive control under normal/alert conditions and as a corrective control during a disturbance. This will enable a more secure power system operation. Considerable scope exists in the development of expert systems and their application to power system operation and control. An overview of the many different types of Neural Network has been carried out explaining terminology and methodology along with a number of techniques used for their implementation. Although the mathematical concepts are not new, many of them were recorded more than fifty years ago, the introduction of fast computers has enabled many of these concepts to be used for today's complex problems. The use of Genetic Algorithm based Artificial Neural Networks is demonstrated for Electrical Load Forecasting and the use of Self Organising Maps is explored for classifying Power System digital fault records. The background of the optimisation process carried out in this thesis is given and an introduction to the method applied, in particular Evolutionary Programming and Genetic Algorithms. Possible solutions to optimisation problems were introduced to be either local or global minimum solutions with the latter being the desirable result. The evolutionary computation that has potential to produce a global solution to a problem due to the searching mechanisms that are inherent to the procedures is discussed. Various mechanisms may be introduced to the genetic algorithm routine which may eliminate the problems of premature convergence, thus enhancing the methods' chances of producing the best solution. The other, more traditional methods of optimisation described include Lagrange multipliers, Dynamic Programming, Local Search and Simulated annealing. Only the Dynamic Programming method guarantees a global optimum solution to an optimisation problem, however for complex problems, the method could take a vast amount of time to locate a solution due to the potential for combinatorial explosion since every possible solution is considered. The Lagrange multiplier method and the local search method are useful for quick location of a global minimum and are therefore useful when the topography of the optimisation problem is uni-modal. However in a complex multi-modal problem, a global solution is less likely. The simulated annealing method has been more popular for solving complex multi-modal problems since it includes techniques for the search to avoid being trapped in local minimum solutions. Artificial Neural Network and Genetic Algorithm have been used to design a neural network for short-term load forecasting. The forecasting model has been used to produce a forecast of the load in the 24 hours of the forecast day concerned, using data provided by an Italian power company. The results obtained are promising. In this particular case, the comparison between the results from the Genetic Algorithm - Artificial Neural Network and Back Propagation - Neural Network shows that the Genetic Algorithm - Artificial Neural Network does not provide a faster solution than the Back Propagation - Neural Network. The application of Evolutionary Programming to fault section estimation is investigated and a comparison made with a Genetic Algorithm approach. To enhance service reliability and to reduce power outage, rapid restoration of power system is required. As a first step of restoration, the fault section should be accurately estimated quickly. The Fault Section Estimation (FSE) identifies fault components in a power system by using information on the operation of protection relays and circuit breakers. However this task is difficult especially for cases where the relay or circuit breaker fails to operate and for multiple faults. An Evolutionary Programming (EP) approach has been developed for solving the FSE problem including malfunctions of protection relays and/or circuit breakers and multiple fault cases. A comparison is made with the Genetic Algorithm (GA) approach at the same time. Two different population sizes are tested for each case. In general, EP showed faster computational speed than GA with an average factor of 13 times more. The final results were almost the same. The convergence speed (the required number of generations to get an optimum result) is a very important factor in real time applications. Test results show that EP is better than GA. However, as both EP and GA are evolutionary algorithms, their efficiencies are largely dependent on the complexity of the problem that might differ from case to case. The use of Artificial Neural Networks to classify digital fault records is investigated showing theat Self Organising Maps could be useful for classifying records if integrated into other systems. Digital fault records are a very useful source of information to the protection engineer to assist with the investigation of a suspected unwanted operation or failure to operate of a protection scheme. After a widespread power system disturbance, due to a storm for example, a large number of fault records can be produced. A method of automatically classifying fault records would be very helpful in reducing the amount of time spent in manual analysis, thus assisting the engineer to focus on records that need in depth analysis. Fault classification using rule base methods have already been developed. The completed work is preliminary in nature and an overview of an extension to this work, involving the extraction of frequency components from the digital fault record data and using these as input to a SOM network, is described

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

An integrated under frequency load shedding protection based on hybrid intelligent system

Recent blackouts, which are associated with severe technical and economic damages, show that current protection systems are not reliable enough when power system is in an emergency condition. This research attempts to address the issue by introducing a novel, integrated and optimized frequency modelling approach and Under Frequency Load Shedding (UFLS) protection for electric power systems. This system is capable to consider various aspects of the problem simultaneously in modern power systems. Furthermore, it takes advantage of a new multi-objective decision making approach considering all required criteria and risk indicators based on the related standards of power system operation. In this approach, a new frequency response modelling system, named Extended System Frequency Response (ESFR) model and new aggregated load modelling system are proposed. This approach does not only consider all factors which contribute to frequency performance of power system simultaneously, but also is capable to consider advanced components of electric power systems. This modelling system is designed in consistent with the new generation of advanced power system simulators. In the next step, Genetic Algorithm (GA) as an Artificial Intelligent (AI) method is used for designing an optimal and integrated UFLS system. The technical implementation of this step leads to the creation of a new methodology for coupling two software or simulators together. This approach is applied to create a junction between the advanced power system simulator and the GA provider. This method does not only decrease the simulation time dramatically, but also makes the remote communications possible between two or more software. Finally, an AI system, namely Artificial Neural Network (ANN), is used in a hybrid structure to execute the GA UFLS system design as an online Wide Area Protection (WAP) system. The results of the first step show the high capability of the proposed frequency response modelling system. The new approach of under frequency protection system design shows clear advantages over the conventional methods. Finally, the performance of ANN is promising as a new generation of intelligent WAP systems

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

CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information

Machine learning has become mainstream across industries. Numerous examples proved the validity of it for security applications. In this work, we investigate how to reverse engineer a neural network by using only power side-channel information. To this end, we consider a multilayer perceptron as the machine learning architecture of choice and assume a non-invasive and eavesdropping attacker capable of measuring only passive side-channel leakages like power consumption, electromagnetic radiation, and reaction time. We conduct all experiments on real data and common neural net architectures in order to properly assess the applicability and extendability of those attacks. Practical results are shown on an ARM CORTEX-M3 microcontroller. Our experiments show that the side-channel attacker is capable of obtaining the following information: the activation functions used in the architecture, the number of layers and neurons in the layers, the number of output classes, and weights in the neural network. Thus, the attacker can effectively reverse engineer the network using side-channel information. Next, we show that once the attacker has the knowledge about the neural network architecture, he/she could also recover the inputs to the network with only a single-shot measurement. Finally, we discuss several mitigations one could use to thwart such attacks.Comment: 15 pages, 16 figure

Early warning fault detection using artificial intelligent methods

This paper 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. A simulated medium length transmission line has been tested by the detector and the results demonstrate the capability of the detector. Furthermore, comments on an evolutionary technique as the optimisation strategy for ANNs are included in this paper

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions