**4. The Paraconsistent Logical Model of The Expert System (PESPAL2v)**

An expert system is designed and developed to attend a certain and limited application of human knowledge. Moreover, equipped with an information base, it must be capable of providing a decision based on justified knowledge. Doing so, the algorithms which compose the computational programs of the expert system need to represent knowledge from the do‐ main they have to analyze and assist the user in solving problems.

The precision of the results depends on the capability of knowledge acquisition and trans‐ ference methods of this information through a computational language which can be ac‐ cordingly treated and on returning a consistent response.

Following this model, the application of the paraconsistent logics PAL2v in the analysis of electric power systems is done with the reception of data corresponding to the values of voltage and current captured by the SCADA system where they are normalized in order to be adjusted to the concepts of the PAL2v. These signals receive adequate treatments by the PAN algorithms in their normal configuration or interconnected, composing networks of blocks which extract the contradiction effects building a paraconsistent logical model related to the risk state of overload on the system.

**Figure 8.** Paraconsistent logical model composed by risk evidence degrees obtained with values of current and volt‐ age captures in the real electric power system.

According to the paraconsistent expert system (PESPAL2v) the real electric power system in operation owns its paraconsistent logical model based on evidence degrees whose proposi‐ tions are related to states of outage risks by overloading.

Figure 8 shows the paraconsistent logical model composed by risk evidence degrees config‐ ured by the real electric power system.

#### **4.1. Contingency Analysis for Electric Power Systems Using Paraconsistent Logics**

The operation of the PESPAL2v starts when there is the occurrence of a contingency or failures with electric power outage. This is when the algorithms of the Paraconsistent Expert System receive data for analysis of pre-failure states which were stored in the SCADA system data‐ base. This allows the PESPAL2v to check the risk degrees of overloading with measures of voltage and current before the occurrence. The verification of the resultant evidence degrees detects with a certain evidence degree which branch of the power network had a high over‐ loading degree risk before the occurrence.

This pre-failure analysis offers conditions such that at the time of contingency we can com‐ pare the obtained evidence degree of overloading risk with the risk state that the system had in the condition previous to the event. So, it is possible, through the results from the compa‐ rative analysis between the two moments and the condition of the topology of the electric net‐ work in its area affected by the contingency that the PESLPA2v can do the most convenient adaptation of maneuvers to be applied to the optimized restoring of the electric power system.

According to the results of the comparisons among the evidence degrees of overloading risk, the analysis of the paraconsistent expert system PESPAL2v will suggest control actions to the restoring of the electric power system based in three states of the sub-transmission sys‐ tem [13].

These analysis procedures can be seen on Figure 9.

7. Increase the obtained value µR1 in the group in study, excluding of this the two values µmax

8. Return to the item 2 until that the Group in study has only 1 element resulting from the

**4. The Paraconsistent Logical Model of The Expert System (PESPAL2v)**

main they have to analyze and assist the user in solving problems.

cordingly treated and on returning a consistent response.

to the risk state of overload on the system.

age captures in the real electric power system.

An expert system is designed and developed to attend a certain and limited application of human knowledge. Moreover, equipped with an information base, it must be capable of providing a decision based on justified knowledge. Doing so, the algorithms which compose the computational programs of the expert system need to represent knowledge from the do‐

The precision of the results depends on the capability of knowledge acquisition and trans‐ ference methods of this information through a computational language which can be ac‐

Following this model, the application of the paraconsistent logics PAL2v in the analysis of electric power systems is done with the reception of data corresponding to the values of voltage and current captured by the SCADA system where they are normalized in order to be adjusted to the concepts of the PAL2v. These signals receive adequate treatments by the PAN algorithms in their normal configuration or interconnected, composing networks of blocks which extract the contradiction effects building a paraconsistent logical model related

**Figure 8.** Paraconsistent logical model composed by risk evidence degrees obtained with values of current and volt‐

and µmin, selected previously.

44 Advances in Expert Systems

Go to item 2 until Gµ = (µER)

analyses.

Gµ= (µA, µB, µC,..., µn, µR1) – (µmaxA, µminA)

**Figure 9.** Flowchart of the analysis states in the process.


#### *4.1.1. Propositions Used in the Paraconsistent analysis*

The paraconsistent analysis in the PESPAL2v is based on the configurations of the PANs where the paraconsistent logical signals are extracted from measured values of voltage and electric current. The PAL2v analysis is performed with applied paraconsistent logical signals with annotations composed of evidence degrees related to 5 partial propositions.

The two first analyze the tension outage and overcurrent at the measurement points and generate evidence degrees related to the existence of overloading in the sub-transmission network. They are:

**Figure 10.** Representation of the pre-failure analysis with its partial prepositions which generate evidence degrees for

Electric Power System Operation Decision Support by Expert System Built with Paraconsistent Annotated Logic

http://dx.doi.org/10.5772/51379

47

The project of PESPAL2v starts with the definition of the methods of acquisition of data through the evidence degree extracting algorithms with the goal of generating the paracon‐ sistent logical signals for the analysis network composed of algorithms based on the PAL2v.

The first task to be performed by the paraconsistent expert system PESPAL2v is the acquisition of values of measurements performed in the system so that the overload risk levels can be detected. For this purpose, we use the data available in the SCADA (Supervisory Control and Data Acquisition) system which, in this phase, has to receive several types of measure‐

The SCADA system is responsible for the interface between the measurements of electric

The measurements required by SCADA and stored into database are performed by the re‐ mote stations RTU (Remote Terminal Units) and / or by signal capturing devices IED (Intel‐ ligent Electronic Devices). In the practice due to the unbalancing of loads, errors in measurements performed by SCADA and other factors which influence the electric system, it is verified that the amplitude values of quantities of interest (voltage and current) are dif‐

This condition shows that the measured values bring contradiction levels among them right from the origin. So, in order to obtain reliable values in the signal treatment of the Paracon‐

quantities and the communication network interconnected to the analysis systems.

**5. Project of the Paraconsistent Expert System - PESPAL2v**

the post-failure analysis.

**5.1. Acquisition of Measurements**

*5.1.2. Block Diagrams of Primary Signals*

ferent among the three phases of the transmission line.

ments from the field.

Pp1: There is overcurrent in the electric power network

Pp2: There is sub-voltage in the electric power network

Next, through the PANs algorithms, the paraconsistent analysis with the degrees of subvoltage and overcurrent generated by this initial analysis which result evidence degrees, now related to the annotation of the object proposition:

Po: There is the risk of drop by overload in the electric power network.

For the decision-making about the optimized restoring of the sub-transmission system after a contingency, PESPAL2v still analyzes other two propositions related to the restrictions and the topology of the power network:

Po1: There are restrictions of loads in the electric power network.

Po2: The network topology is ideal for the current situation.

That being so, the sequence of maneuvers which are offered to the operation will be condi‐ tioned directly to the configuration of topologies, technical norms and restrictions which in‐ volve the area of the sub-transmission system of the power network affected by the contingency.

The classification performed by the paraconsistent analysis network (PANet) generates a re‐ sulting evidence signal whose value will define the type of operation and sequences of re‐ storing closest to the ideal, given the conditions of the sub-transmission system.

Figure 10 shows the pre-failure analysis with its partial propositions which generate the evi‐ dence degrees for its object proposition and whose result will be used for the post-failure analysis.

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**Figure 10.** Representation of the pre-failure analysis with its partial prepositions which generate evidence degrees for the post-failure analysis.
