Problem statement: In the operation of an Electric Power System (EPS), it has been usual to provide reactive power injection to avoid an under-voltage bus condition. In some situations an adequate voltage profile will not be a guarantee against Voltage Collapses (VCs) that may cause blackouts as seen in many occurrences around the world. The repeatedly injection of reactive power can turn a bus into a characteristic too much capacitive. Under this condition and in the presence of a considerable percentage of the constant power load type, there will be a high risk of a VC. Any of the indices proposed in the literature as VC Proximity Indicators (VCPIs) may alert the operator about the risk. Approach: In order to elucidate the problem stated, simulations were performed using MatLab/SimPowerSystems. It was used a basic example system composed by an infinite-bus feeding, through a large impedance line, a bus load whose power could be increased in ramp manner. It is also included a shunt capacitive compensation at the load bus every time the voltage value reaches 0.9 pu. Therefore, the VC risk increase could be shown by means of graphic results and the indications of some VCPIs sensitivity indices (including the new proposed index). Results: The graphics obtained in this study is a contribution to illustrate the voltage collapse risk problem when dealing with adjustments of voltage profile to meet the system requirements. Also, a VCPI sensitivity indicator using apparent load power was tested. The results have shown that all VCPI responses are very similar for a given case and electric system. Conclusion/Recommendations: Any VCPI information can help in the decision stage between either more reactive power injection or load shedding. A routine can also be developed for a supervisory program in order to alert the operator about VC risks

Problem statement: Under-Voltage Load Shedding (UVLS) protection of Electric Power Systems (EPS) is frequently used against Voltage Collapse (VC), however when there is automatic bus voltage regulation with excessive capacitive compensation, the UVLS scheme may not trip. In this case, load shedding must be based in a Voltage Collapse Proximity Indicator (VCPI). Many UVLS procedures may not be appropriate today. Approach: In order to elucidate the problem stated, several studies were carried out using MatLab/SimPowerSystems. In the first case, it was simulated a reduced electric system consisting of an infinite-bus feeding a load through a large impedance line. Two other cases were simulated now including a fixed capacitive impedance (representing a saturated SVC or similar) with 25 and 60 MVAr, both with a generator regulating the load bus voltage. Graphic curves representing the load bus voltage versus time were obtained with the application of a ramp power load. Results: In all cases the curves showed if there was sufficient time to command the UVLS scheme. The usual UVLS criteria failed for the third case. As the capacitive reactive power of the saturated compensation devices was increased, their equivalent capacitance, corresponding to the sum of maximum MVAr capacities, grows. The load demand increase, after MVAr saturation, can cause a voltage decrement which is too fast for UVLS adequate operation. Conclusion/Recommendations: Based in past experiences, any operator could be confident on existing UVLS protection of some area, but a VC can occur with the current situation without UVLS trip, as stated. It was suggested to check the current UVLS operation conditions, especially in areas where there was a growth of both load demand and reactive power resources. When UVLS method is found ineffective, then a suggestion is to replace it by a technique based upon some VCPI.

This work describes the development of a computational mathematical model that uses Annotated Paraconsistent Logic - APL and a concept derived from it, the effect of contradiction, to identify patterns in numerical data for pattern classification purposes. The APL admits paraconsistent and paracomplete logical principles, which allow the manipulation of inconsistent and contradictory data, and its use allowed the identification and quantization of the attribute related to the contradiction. To validate the model, series of Raman spectroscopies obtained after exposure of proteins, lipids and nucleic acids, collected from cutaneous tissue cell samples previously examined for the detection of cancerous lesions, identified as basal carcinoma, melanoma and normal, were used. Initially, the attributes related to contradiction, derivative and median obtained from spectroscopies were identified and quantified. A machine learning process with approximately 31.6% of each type of samples detects a sequence of spectroscopies capable of characterizing and classifying the type of lesion through the chosen attributes. Approximately 68.4% of the samples are used for classification tests. The proposed model identified a segment of spectroscopies where the classification of test samples had a hit rate of 76.92%. As a differential and innovation of this work, the use of APL principles in a complete process of training, learning and classification of patterns for numerical data sets stands out, with flexibility to choose the attributes used for the characterization of patterns, and a quantity of samples of about one third of the total required for characterization.

Problem statement: In the operation of an Electric Power System (EPS), it has been usual to provide reactive power injection to avoid an under-voltage bus condition. In some situations an adequate voltage profile will not be a guarantee against Voltage Collapses (VCs) that may cause blackouts as seen in many occurrences around the world. The repeatedly injection of reactive power can turn a bus into a characteristic too much capacitive. Under this condition and in the presence of a considerable percentage of the constant power load type, there will be a high risk of a VC. Any of the indices proposed in the literature as VC Proximity Indicators (VCPIs) may alert the operator about the risk. Approach: In order to elucidate the problem stated, simulations were performed using MatLab/SimPowerSystems. It was used a basic example system composed by an infinite-bus feeding, through a large impedance line, a bus load whose power could be increased in ramp manner. It is also included a shunt capacitive compensation at the load bus every time the voltage value reaches 0.9 pu. Therefore, the VC risk increase could be shown by means of graphic results and the indications of some VCPIs sensitivity indices (including the new proposed index). Results: The graphics obtained in this study is a contribution to illustrate the voltage collapse risk problem when dealing with adjustments of voltage profile to meet the system requirements. Also, a VCPI sensitivity indicator using apparent load power was tested. The results have shown that all VCPI responses are very similar for a given case and electric system. Conclusion/Recommendations: Any VCPI information can help in the decision stage between either more reactive power injection or load shedding. A routine can also be developed for a supervisory program in order to alert the operator about VC risks.

Different approaches for the use of Artificial Neural Networks - ANNs, in the recognition of image patterns, have been used with variations ranging from the processing of the image data to the ANN architecture itself. This paper describes the development of a system that aims to recognize patterns of images with ANNs of three inputs that receive images decomposed into their RGB components. The ANNs have an architecture with two hidden layers of six neurons each, and use the algorithm Backpropagation. The built model normalizes RGB components with values ​​between zero and one. The Backpropagation algorithm is used for the purpose of functional approximation of these components, and after training, the numerical arrangements obtained in the three outputs corresponding to the inputs are denormalized to form the resulting training image. Six image pattern had training in different ANNs, forming a system to recognized each pattern. The feasibility of using the model was verified with the tests for its generalization capacity. Images used to position a mechanical device, which did not participate in the training, were inserted into the system and from them the positioning of the device was performed, with a high degree of accuracy.