**2.5 Solution**

*Fault Detection, Diagnosis and Prognosis*

unstacked and a new rule will be loaded.

*2.4.4 Neural networks*

work processing units.

*2.4.5 Fuzzy logic*

true or partially false.

search for new rules, or ask the user directly.

The evaluation order on the blackboard follows a stack-like structure to achieve the most recent goal. The rule will continue to be evaluated as long as the assumption conditions are true, otherwise the rule will be dropped, the set goal will be

When a value of a parameter in a given context is not known and is not in the stack structures, one should then look for new information in the knowledge base,

An artificial neural network is made up of several processing units whose operation is quite simple. These units are usually connected by communication channels that are associated with a certain weight. Units perform operations only on their local data, which is input received by their connections. The intelligent behavior of an Artificial Neural Network comes from interactions between net-

Neural networks allow optimized selection of a particular solution alternative for a given event or change. The neural network is used in this process to evaluate the results of the expert system, that is, the final solution should be selected as the

Fuzzy logic is based on fuzzy set theory. Traditionally, a logical proposition has two extremes: either it is completely true or it is completely false. However, in Fuzzy logic, a premise varies in degree of truth from 0 to 1, which leads to being partially

Fuzzy logic is the logic that supports the modes of reasoning that are approximate rather than exact. Fuzzy systems modeling and control are techniques for rigorously handling qualitative information. Derived from the concept of fuzzy sets, fuzzy logic forms the basis for the development of process modeling and control methods and algorithms, reducing the complexity of design and implementation, making it the solution to control problems hitherto intractable classic techniques. In classical and modern control theories, the first step in implementing process control is to derive the mathematical model that describes the process. The procedure requires knowing in detail the process to be controlled, which is not always feasible if the process is too complex. Existing control theories apply to a wide

However, all of these techniques are not capable of solving real problems whose mathematical modeling is impractical. For example, in many situations a considerable amount of essential information is only known a priori qualitatively. Similarly, performance criteria are only available in linguistic terms. This picture leads to inaccuracies and inaccuracies that make it impossible to use most of the theories used so far.

Fuzzy modeling and control theory are techniques for rigorously handling qualitative information. It assesses how imprecision and uncertainty should be managed and, in so doing, become powerful enough to properly manipulate knowledge. This technology considers the relationship between inputs and outputs, aggregating various process and control parameters. This allows processes considered complex to be reconsidered so that the resulting control systems provide a more accurate result as well as stable and robust performance. The sheer simplicity of implementing fuzzy control systems can reduce the complexity of a project to a point where previously intractable problems are now solvable.

best of all presented, the neural network allows to establish this solution.

variety of systems where the process is well defined.

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The intelligent monitoring and supervision system becomes autonomous to decide, but this decision should indicate the best action that should be taken to mitigate or eliminate a particular change. This intervention must be in real time and in online mode. It should show the type of application that will be performed, the point where the intervention will be made, the components that will be reached and their intervention time. This solution can be given by the following procedure:

#### *2.5.1 Maintenance*

Depending on the situation, it can be in real time and in Online mode, meaning the maintenance team can make the necessary adjustments without shutting down the equipment and reducing its availability in the shortest possible time. The smart system should provide recommendations for making these correctives without compromising equipment operation. This is accomplished through expert system intervention. This system will decide on what type of maintenance to perform.

#### *2.5.2 Predictive and proactive maintenance*

Depending on the situation, it can be in real time and in Online mode, meaning the maintenance team can make the necessary adjustments without shutting down the equipment and reducing its availability in the shortest possible time.

The smart system should provide recommendations for corrective action without compromising equipment operation. This is accomplished through expert system intervention.

#### *2.5.3 Element or device replacement*

The intelligent system must have the ability to make this decision, supported by technical and economic criteria (losses).

#### **2.6 Innovation**

The final and ultimate solution will demonstrate the versatility, autonomy and efficiency of monitoring and supervision systems when they work and are structured as intelligent systems. Errors in procedures with these systems will be minimal to ensure safety and accuracy.

#### **2.7 Benefits**

Safe (high availability and reliability) and efficient operation of production systems, balanced and timely investments, reduced operating and energy costs.

Following is a typical output (report) of the software developed for the implementation of the methodology, especially the part related to data evaluation, fault diagnosis, root cause finding and determination, action decision making to be performed and execution of these actions [3, 4].

Following is an output of the software developed to implement the proposed intelligent system framework [1, 15]. All the tools presented in this chapter have been included:

#### **3. Conclusions**

Intelligent monitoring and control systems allow minimize the risks of failure of production systems, as is the case with generation systems (will be taken as a reference for this project), where intelligent systems are widely used, especially in large and top technology plants, and consequently increase its reliability (reduced failure rate/year) and the availability, improving the quality of energy supply by reducing the periods and interruption frequency of power supply, by improving indicators DEC, FEC, DIC, FIC and DMIC, and reliable management charge and distributed generation.

Centralization of information processing by intelligent monitoring systems will improve the efficiency of operation of electrical systems, optimize maintenance processes within the generation plants and consequently increase or maintain the estimated useful life of the generators, economically benefiting utilities power.

The transformation of the current systems for monitoring and supervision of hydroelectric plants in intelligent systems, effectively represents a technological advancement over conventional monitoring systems. What defines the quality of the response of these systems, in relation to the supervision and diagnosis, it is the experience of those responsible for analysis of failure modes.

The data management infrastructure, established by the power utilities, will be responsible for more or less extracted benefit of the system as well as for maintaining the efficient operation of the same. The choice of the best strategy for the Data and Information Management, will depend on the policies adopted by companies to their treatment. The benefits of smart grid technology, in monitoring systems will come when there is a data management policy functional within the utilities should be avoided as much as possible "excess of monitored parameters". Prioritization criteria of failures "detectable" or "observable" must be considered. For each observable failure mode, there will always be a form of detection, which keeps a relationship "sensitivity/installation cost" more, and that in principle, should be chosen.

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**Author details**

South America

Gustavo Pérez Alvarez

*Real-Time Fault Detection and Diagnosis Using Intelligent Monitoring and Supervision Systems*

An action of great interest, which should be considered in the monitoring and supervision systems is the integration of auxiliary systems, to conduct their analysis and diagnosis, together with those from the main systems, causing minimal impact on its cost of installation. The influence of failures in auxiliary systems (ancillary services) with the probability of generating, forced stops of the equipment and system is high and in some situations similar to those of the main systems.

Importantly, there is not a single application and solution of systems or smart grids. Many of these functions will not become viable if coexist with others and should be implemented according to the needs of utilities. Thus, individual functions such as monitoring and fault detection in generators or feeder circuits, may

Department of Electrical Engineering, Federal University of Sergipe, Brazil,

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: raguan120996@gmail.com

provided the original work is properly cited.

*DOI: http://dx.doi.org/10.5772/intechopen.90158*

not have their benefits evaluated separately.

#### *Real-Time Fault Detection and Diagnosis Using Intelligent Monitoring and Supervision Systems DOI: http://dx.doi.org/10.5772/intechopen.90158*

An action of great interest, which should be considered in the monitoring and supervision systems is the integration of auxiliary systems, to conduct their analysis and diagnosis, together with those from the main systems, causing minimal impact on its cost of installation. The influence of failures in auxiliary systems (ancillary services) with the probability of generating, forced stops of the equipment and system is high and in some situations similar to those of the main systems.

Importantly, there is not a single application and solution of systems or smart grids. Many of these functions will not become viable if coexist with others and should be implemented according to the needs of utilities. Thus, individual functions such as monitoring and fault detection in generators or feeder circuits, may not have their benefits evaluated separately.
