Fault Diagnosis for Substation with Redundant Protection Configuration Based on Time-Sequence Fuzzy Petri-Net

Due to timing inconsistency, dual protection configuration and uncertainty diagnosis result characteristics of 750kV substation, fault diagnosis method of substation with redundant protection configuration which based on time sequence fuzzy Petri nets is proposed. In this method, redundant knowledge about fault component is represented by using two sets of protected information. On that basis, component redundant diagnosis-model based on time sequence fuzzy Petri net is constructed, which can be decomposed into main and redundant subnet-model. In this model, initial-information credibility is determined using information-entropy, timing constraint is checked, and initial-information credibility is corrected using the relationship between acted protection and breaker. Compared with fuzzy Petri net diagnosis method take no account of timing constraint, this method can not only identify the malfunction information, but also obtain a certain result

Power system fault analysis based on intelligent techniques and intelligent electronic device data

This dissertation has focused on automated power system fault analysis. New contributions to fault section estimation, protection system performance evaluation and power system/protection system interactive simulation have been achieved. Intelligent techniques including expert systems, fuzzy logic and Petri-nets, as well as data from remote terminal units (RTUs) of supervisory control and data acquisition (SCADA) systems, and digital protective relays have been explored and utilized to fufill the objectives. The task of fault section estimation is difficult when multiple faults, failures of protection devices, and false data are involved. A Fuzzy Reasoning Petri-nets approach has been proposed to tackle the complexities. In this approach, the fuzzy reasoning starting from protection system status data and ending with estimation of faulted power system section is formulated by Petri-nets. The reasoning process is implemented by matrix operations. Data from RTUs of SCADA systems and digital protective relays are used as inputs. Experiential tests have shown that the proposed approach is able to perform accurate fault section estimation under complex scenarios. The evaluation of protection system performance involves issues of data acquisition, prediction of expected operations, identification of unexpected operations and diagnosis of the reasons for unexpected operations. An automated protection system performance evaluation application has been developed to accomplish all the tasks. The application automatically retrieves relay files, processes relay file data, and performs rule-based analysis. Forward chaining reasoning is used for prediction of expected protection operation while backward chaining reasoning is used for diagnosis of unexpected protection operations. Lab tests have shown that the developed application has successfully performed relay performance analysis. The challenge of power system/protection system interactive simulation lies in modeling of sophisticated protection systems and interfacing the protection system model and power system network model seamlessly. An approach which utilizes the "compiled foreign model" mechanism of ATP MODELS language is proposed to model multifunctional digital protective relays in C++ language and seamlessly interface them to the power system network model. The developed simulation environment has been successfully used for the studies of fault section estimation and protection system performance evaluation

An expert system model using predicate transition nets

Cover title.Includes bibliographical references.Support provided through the Office of Naval Research. N00014-85-K-0782Didier M. Perdu, Alexander H. Levis

A bidirectional diagnosis algorithm of fuzzy Petri net using inner-reasoning-path

Fuzzy Petri net (FPN) is a powerful tool to execute the fault diagnosis function for various industrial applications. One of the most popular approaches for fault diagnosis is to calculate the corresponding algebra forms which record flow information and three parameters of value of all places and transitions of the FPN model. However, with the rapid growth of the complexity of the real system, the scale of the corresponding FPN is also increased sharply. It indicates that the complexity of the fault diagnosis algorithm is also raised due to the increased scale of vectors and matrix. Focusing on this situation, a bidirectional adaptive fault diagnosis algorithm is presented in this article to reduce the complexity of the fault diagnosis process via removing irrelevant places and transitions of the large-scale FPN, followed by the correctness and algorithm complexity of the proposed approach that are also discussed in detail. A practical example is utilized to show the feasibility and efficacy of the proposed method. The results of the experiments illustrated that the proposed algorithm owns the ability to simplify the inference process and to reduce the algorithm complexity due to the removal of unnecessary places and transitions in the reasoning path of the appointed output place

Scheduling With Alternatives Machine Using Fuzzy Inference System And Genetic Algorithm.

As the manufacturing activities in today's industries are getting more and more complex, it is required for the manufacturing company to have a good shop floor production scheduling to plan and schedule their production orders. Industri pengeluarcim kini telah berkembang pesat dan aktiviti pengeluarannya semakin kompleks, dengan itu syarikat pengeluar memerlukan jadual lantai pengeluaran (shop floor) yang terbaik untuk merancang permintaan pengeluaran (product)

Application of Weighted Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis in Traction Power Supply Systems of High-speed Railways

This paper discusses the application of weighted fuzzy reasoning spiking neu- ral P systems (WFRSN P systems) to fault diagnosis in traction power supply systems (TPSSs) of China high-speed railways. Four types of neurons are considered in WFRSN P systems to make them suitable for expressing status information of protective relays and circuit breakers, and a weighted matrix-based reasoning algorithm (WMBRA) is intro- duced to fulfill the reasoning based on the status information to obtain fault confidence levels of faulty sections. Fault diagnosis production rules in TPSSs and their WFRSN P system models are proposed to show how to use WFRSN P systems to describe different kinds of fault information. Building processes of fault diagnosis models for sections and fault region identification of feeding sections, and parameter setting of the models are described in detail. Case studies including normal power supply and over zone feeding show the effectiveness of the presented method.Ministerio de Economía y Competitividad TIN 2012-373

Intelligent Economic Alarm Processor (IEAP)

The advent of electricity market deregulation has placed great emphasis on the availability of information, the analysis of this information, and the subsequent decision-making to optimize system operation in a competitive environment. This creates a need for better ways of correlating the market activity with the physical grid operating states in real time and sharing such information among market participants. Choices of command and control actions may result in different financial consequences for market participants and severely impact their profits. This work provides a solution, the Intelligent Economic Alarm Processor to be implemented in a control center to assist the grid operator in rapidly identifying the faulted sections and market operation management. The task of fault section estimation is difficult when multiple faults, failures of protection devices, and false data are involved. A Fuzzy Reasoning Petri-nets approach has been proposed to tackle the complexities. In this approach, the fuzzy reasoning starting from protection system status data and ending with estimation of faulted power system section is formulated by Petri-nets. The reasoning process is implemented by matrix operations. Next, in order to better feed the FRPN model with more accurate inputs, the failure rates of the protections devices are analyzed. A new approach to assess the circuit breaker’s life cycle or deterioration stages using its control circuit data is introduced. Unlike the traditional “mean time” criteria, the deterioration stages have been mathematically defined by setting up the limits of various performance indices. The model can be automatically updated as the new real-time condition-based data become available to assess the CB’s operation performance using probability distributions. The economic alarm processor module is discussed in the end. This processor firstly analyzes the fault severity based on the information retrieved from the fault section estimation module, and gives the changes in the LMPs, total generation cost, congestion revenue etc. with electricity market schedules and trends. Then some suggested restorative actions are given to optimize the overall system benefit. When market participants receive such information in advance, they make estimation about the system operator's restorative action and their competitors' reaction to it

Power system fault analysis based on intelligent techniques and intelligent electronic device data

This dissertation has focused on automated power system fault analysis. New contributions to fault section estimation, protection system performance evaluation and power system/protection system interactive simulation have been achieved. Intelligent techniques including expert systems, fuzzy logic and Petri-nets, as well as data from remote terminal units (RTUs) of supervisory control and data acquisition (SCADA) systems, and digital protective relays have been explored and utilized to fufill the objectives. The task of fault section estimation is difficult when multiple faults, failures of protection devices, and false data are involved. A Fuzzy Reasoning Petri-nets approach has been proposed to tackle the complexities. In this approach, the fuzzy reasoning starting from protection system status data and ending with estimation of faulted power system section is formulated by Petri-nets. The reasoning process is implemented by matrix operations. Data from RTUs of SCADA systems and digital protective relays are used as inputs. Experiential tests have shown that the proposed approach is able to perform accurate fault section estimation under complex scenarios. The evaluation of protection system performance involves issues of data acquisition, prediction of expected operations, identification of unexpected operations and diagnosis of the reasons for unexpected operations. An automated protection system performance evaluation application has been developed to accomplish all the tasks. The application automatically retrieves relay files, processes relay file data, and performs rule-based analysis. Forward chaining reasoning is used for prediction of expected protection operation while backward chaining reasoning is used for diagnosis of unexpected protection operations. Lab tests have shown that the developed application has successfully performed relay performance analysis. The challenge of power system/protection system interactive simulation lies in modeling of sophisticated protection systems and interfacing the protection system model and power system network model seamlessly. An approach which utilizes the "compiled foreign model" mechanism of ATP MODELS language is proposed to model multifunctional digital protective relays in C++ language and seamlessly interface them to the power system network model. The developed simulation environment has been successfully used for the studies of fault section estimation and protection system performance evaluation

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated