Download or read book Dynamic Neural Networks for Time Series Modeling with Application to Power System Fault Detection written by Monica Paula Carley and published by . This book was released on 1997 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Investigates the use of dynamic neural network technology as a diagnostic tool for detecting faults in three-phase voltages in electric power systems. Following a comparison of the ability of a feedforward dynamic neural network and a recurrent modified Elman network to predict power distribution bus voltages, a new cost function is developed to enhance the dynamic network's modeling capabilities.
Download or read book TIME SERIES FORECASTING USING NEURAL NETWORKS EXAMPLES WITH MATLAB written by Cesar Perez Lopez and published by CESAR PEREZ. This book was released on with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB has the tool Deep Leraning Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, timeseries forecasting, and dynamic system modeling and control. Dynamic neural networks are good at timeseries prediction. You can use the Neural Net Time Series app to solve different kinds of time series problems It is generally best to start with the GUI, and then to use the GUI to automatically generate command line scripts. Before using either method, the first step is to define the problem by selecting a data set. Each GUI has access to many sample data sets that you can use to experiment with the toolbox. If you have a specific problem that you want to solve, you can load your own data into the workspace. With MATLAB is possibe to solve three different kinds of time series problems. In the first type of time series problem, you would like to predict future values of a time series y(t) from past values of that time series and past values of a second time series x(t). This form of prediction is called nonlinear autoregressive network with exogenous (external) input, or NARX. In the second type of time series problem, there is only one series involved. The future values of a time series y(t) are predicted only from past values of that series. This form of prediction is called nonlinear autoregressive, or NAR. The third time series problem is similar to the first type, in that two series are involved, an input series (predictors) x(t) and an output series (responses) y(t). Here you want to predict values of y(t) from previous values of x(t), but without knowledge of previous values of y(t). This book develops methods for time series forecasting using neural networks across MATLAB
Download or read book Application of Signal Processing Tools and Artificial Neural Network in Diagnosis of Power System Faults written by Nabamita Banerjee Roy and published by CRC Press. This book was released on 2021-07-21 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explores methods of fault identification through programming and simulation in MATLAB Examines signal processing tools and their applications with examples Provides knowledge of artificial neural networks and their applications with illustrations Uses PNN and BPNN to identify the different types of faults and obtain their corresponding locations Discusses the programming of signal processing using Wavelet Transform and S-Transform
Download or read book Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes written by Krzysztof Patan and published by Springer Science & Business Media. This book was released on 2008-06-24 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: An unappealing characteristic of all real-world systems is the fact that they are vulnerable to faults, malfunctions and, more generally, unexpected modes of - haviour. This explains why there is a continuous need for reliable and universal monitoring systems based on suitable and e?ective fault diagnosis strategies. This is especially true for engineering systems,whose complexity is permanently growing due to the inevitable development of modern industry as well as the information and communication technology revolution. Indeed, the design and operation of engineering systems require an increased attention with respect to availability, reliability, safety and fault tolerance. Thus, it is natural that fault diagnosis plays a fundamental role in modern control theory and practice. This is re?ected in plenty of papers on fault diagnosis in many control-oriented c- ferencesand journals.Indeed, a largeamount of knowledgeon model basedfault diagnosis has been accumulated through scienti?c literature since the beginning of the 1970s. As a result, a wide spectrum of fault diagnosis techniques have been developed. A major category of fault diagnosis techniques is the model based one, where an analytical model of the plant to be monitored is assumed to be available.
Download or read book International Journal of Applied Mathematics and Computer Science written by and published by . This book was released on 1999 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Neural Networks written by M. Ananda Rao and published by Alpha Science Int'l Ltd.. This book was released on 2003 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Intelligent Systems and Signal Processing in Power Engineering written by Abhisek Ukil and published by Springer Science & Business Media. This book was released on 2007-09-23 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: This highly experienced author sets out to build a bridge between two inter-disciplinary power engineering practices. The book looks into two major fields used in modern power systems: intelligent systems and the signal processing. The intelligent systems section comprises fuzzy logic, neural network and support vector machine. The author looks at relevant theories on the topics without assuming much particular background. Following the theoretical basics, he studies their applications in various problems in power engineering, like, load forecasting, phase balancing, or disturbance analysis.
Download or read book Deep Recurrent Neural Networks for Fault Detection and Classification written by Jorge Ivan Mireles Gonzalez and published by . This book was released on 2018 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning is one of the fastest growing research topics in process systems engineering due to the ability of deep learning models to represent and predict non-linear behavior in many applications. However, the application of these models in chemical engineering is still in its infancy. Thus, a key goal of this work is assessing the capabilities of deep-learning based models in a chemical engineering applications. The specific focus in the current work is detection and classification of faults in a large industrial plant involving several chemical unit operations. Towards this goal we compare the efficacy of a deep learning based algorithm to other state-of-the-art multivariate statistical based techniques for fault detection and classification. The comparison is conducted using simulated data from a chemical benchmark case study that has been often used to test fault detection algorithms, the Tennessee Eastman Process (TEP). A real time online scheme is proposed in the current work that enhances the detection and classifications of all the faults occurring in the simulation. This is accomplished by formulating a fault-detection model capable of describing the dynamic nonlinear relationships among the output variables and manipulated variables that can be measured in the Tennessee Eastman Process during the occurrence of faults or in the absence of them. In particular, we are focusing on specific faults that cannot be correctly detected and classified by traditional statistical methods nor by simpler Artificial Neural Networks (ANN). To increase the detectability of these faults, a deep Recurrent Neural Network (RNN) is programmed that uses dynamic information of the process along a pre-specified time horizon. In this research we first studied the effect of the number of samples feed into the RNN in order to capture more dynamical information of the faults and showed that accuracy increases with this number e.g. average classification rates were 79.8%, 80.3%, 81% and 84% for the RNN with 5, 15, 25 and 100 number of samples respectively. As well, to increase the classification accuracy of difficult to observe faults we developed a hierarchical structure where faults are grouped into subsets and classified with separate models for each subset. Also, to improve the classification for faults that resulted in responses with low signal to noise ratio excitation was added to the process through an implementation of a pseudo random signal(PRS). By applying the hierarchical structure there is an increment on the signal-to-noise ratio of faults 3 and 9, which translates in an improvement in the classification accuracy in both of these faults by 43.0% and 17.2% respectively for the case of 100 number of samples and by 8.7% and 23.4% for 25 number samples. On the other hand, applying a PRS to excite the system has showed a dramatic increase in the classification rates of the normal state to 88.7% and fault 15 up to 76.4%. Therefore, the proposed method is able to improve considerably both the detection and classification accuracy of several observable faults, as well as faults considered to be unobservable when using other detection algorithms. Overall, the comparison of the deep learning algorithms with Dynamic PCA (Principal Component Analysis) techniques showed a clear superiority of the deep learning techniques in classifying faults in nonlinear dynamic processes. Finally, we develop these same techniques to different operational modes of the TEP simulation, achieving comparable improvements to the classification accuracies.
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1991 with total page 1102 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Application of Machine Learning and Deep Learning Methods to Power System Problems written by Morteza Nazari-Heris and published by Springer Nature. This book was released on 2021-11-21 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses.
Download or read book Robust and Fault Tolerant Control written by Krzysztof Patan and published by Springer. This book was released on 2019-03-16 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust and Fault-Tolerant Control proposes novel automatic control strategies for nonlinear systems developed by means of artificial neural networks and pays special attention to robust and fault-tolerant approaches. The book discusses robustness and fault tolerance in the context of model predictive control, fault accommodation and reconfiguration, and iterative learning control strategies. Expanding on its theoretical deliberations the monograph includes many case studies demonstrating how the proposed approaches work in practice. The most important features of the book include: a comprehensive review of neural network architectures with possible applications in system modelling and control; a concise introduction to robust and fault-tolerant control; step-by-step presentation of the control approaches proposed; an abundance of case studies illustrating the important steps in designing robust and fault-tolerant control; and a large number of figures and tables facilitating the performance analysis of the control approaches described. The material presented in this book will be useful for researchers and engineers who wish to avoid spending excessive time in searching neural-network-based control solutions. It is written for electrical, computer science and automatic control engineers interested in control theory and their applications. This monograph will also interest postgraduate students engaged in self-study of nonlinear robust and fault-tolerant control.
Download or read book 17th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2022 written by Pablo García Bringas and published by Springer Nature. This book was released on 2022-10-11 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains accepted papers presented at SOCO 2022 conference held in the beautiful and historic city of Salamanca (Spain), in September 2022. Soft computing represents a collection or set of computational techniques in machine learning, computer science, and some engineering disciplines, which investigate, simulate, and analyze very complex issues and phenomena. After a thorough peer-review process, the 17th SOCO 2022 International Program Committee selected 64 papers which are published in these conference proceedings and represent an acceptance rate of 60%. In this relevant edition, a particular emphasis was put on the organization of special sessions. Seven special sessions were organized related to relevant topics such as machine learning and computer vision in Industry 4.0; time series forecasting in industrial and environmental applications; optimization, modeling, and control by soft computing techniques; soft computing applied to renewable energy systems; preprocessing big data in machine learning; tackling real-world problems with artificial intelligence. The selection of papers was extremely rigorous to maintain the high quality of the conference. We want to thank the members of the program committees for their hard work during the reviewing process. This is a crucial process for creating a high-standard conference; the SOCO conference would not exist without their help.
Download or read book Advances in Neural Networks isnn 2006 written by and published by Springer Science & Business Media. This book was released on 2006 with total page 1507 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Industrial Control Systems written by Adedeji B. Badiru and published by CRC Press. This book was released on 2016-04-19 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Issues such as logistics, the coordination of different teams, and automatic control of machinery become more difficult when dealing with large, complex projects. Yet all these activities have common elements and can be represented by mathematics. Linking theory to practice, Industrial Control Systems: Mathematical and Statistical Models and Techni
Download or read book Nuclear Power Plant Equipment Prognostics and Health Management Based on Data driven methods written by Jun Wang and published by Frontiers Media SA. This book was released on 2021-09-13 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Predictive Maintenance in Dynamic Systems written by Edwin Lughofer and published by Springer. This book was released on 2019-02-28 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power.
Download or read book Measurement Science written by Komyo Kariya and published by IOS Press. This book was released on 2000 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt:
