*3.5.4. Paraconsistent Algorithm Extractor of Contradiction Effects – ParaExtrctr*

The Paraconsistent Algorithm Extractor of Contradiction effects (*ParaExtr ctr*) is composed by connections among PANs. This configuration forms a Paraconsistent Analyze Network ca‐ pable to extract the effects of the contradiction in gradual way of the signals of information that come from Uncertain Knowledge Database.

The hypothesis of extraction of the effects of the contradiction has as principle that; if the first treated signals are the most contradictory and then the result of the paraconsistent anal‐ ysis will converge for a consensual value.

In his typical operation the *ParaExtr ctr* receives a group of signals of information represented by Degrees of Evidence (µE) the regarding certain proposition *P* and, independently of other external information, it makes paraconsistent analysis in their values where, gradually, it is going extracting the effects from the contradiction to remain as output a single resulting Re‐ al Evidence Degree µER. The µER is the representative value of the group of input signals af‐ ter the process of extraction of the effects of the contradiction.

The figure 7 shows the representation of the algorithm Extractor of Contradiction effects that uses a network of three PANs.

**Figure 7.** Paraconsistent Algorithm Extractor of Contradiction effects (ParaExtrctr).

The description of the *ParaExtr ctr* Algorithm is shown to proceed.

*1. Present n values of Evidence Degrees that it composes the group in study.*

Gµ= (µA, µB, µC,..., µn ) \*/Evidence Degrees 0 ≤ µ ≤ 1\*/

*2. Select the largest value among the Evidence Degrees of the group in study.*

µmaxA= max (µA, µB, µC,..., µn )

*3. Consider the largest value among the Evidence Degrees of the group in study in favorable Evidence Degree.*

µmaxA= µsel

If *d≥*1 Then do S1*=* 0.5: Indefinite logical state and go to the steep 10

ER

*3.5.4. Paraconsistent Algorithm Extractor of Contradiction Effects – ParaExtrctr*

that come from Uncertain Knowledge Database.

ter the process of extraction of the effects of the contradiction.

ysis will converge for a consensual value.

that uses a network of three PANs.

1 2

The Systems with the Paraconsistent Analysis Nodes (PAN) deal with the received signals through algorithms, and present the signals with a real evidence Degree value in the output

The Paraconsistent Algorithm Extractor of Contradiction effects (*ParaExtr ctr*) is composed by connections among PANs. This configuration forms a Paraconsistent Analyze Network ca‐ pable to extract the effects of the contradiction in gradual way of the signals of information

The hypothesis of extraction of the effects of the contradiction has as principle that; if the first treated signals are the most contradictory and then the result of the paraconsistent anal‐

In his typical operation the *ParaExtr ctr* receives a group of signals of information represented by Degrees of Evidence (µE) the regarding certain proposition *P* and, independently of other external information, it makes paraconsistent analysis in their values where, gradually, it is going extracting the effects from the contradiction to remain as output a single resulting Re‐ al Evidence Degree µER. The µER is the representative value of the group of input signals af‐

The figure 7 shows the representation of the algorithm Extractor of Contradiction effects

*DCR* <sup>+</sup> <sup>=</sup> (17)

Or else go to the next step

42 Advances in Expert Systems

*If* DC> 0 DCR*=* (1 – *d* )

*If* DC< 0 DCR*=* (*d* – 1)

*7. Present the output.*

*9. Present the output.*

*10. End.*

[3].

Do S1 = µER and S2= Dct

Do S1 = DCR

*6. Calculate the real Certainty Degree.*

*8. Calculate the real Evidence Degree.*

*4. Consider the smallest value among the Evidence Degrees of the group in study in favorable Evi‐ dence Degree.*

µminA= min (µA, µB, µC,..., µn )

*5. Transform the smallest value among the Evidence Degrees of the group in study in unfavorable Evidence Degree.*

1 – µminA= λsel

*6. Make the Paraconsistent analysis among the selected values:*

µR1 = µsel ◊ λsel \*/ where ◊ is a paraconsistent action of the PAN \*/

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

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‐

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

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

45

Figure 8 shows the paraconsistent logical model composed by risk evidence degrees config‐

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‐

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‐

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

tions are related to states of outage risks by overloading.

ured by the real electric power system.

loading degree risk before the occurrence.

These analysis procedures can be seen on Figure 9.

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

tem [13].

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

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

Go to item 2 until Gµ = (µER)
