1. The article written by Boyarshinova Vera Scientific adviser: Eltyshev Denis THE USE OF NEURO-FUZZY MODELS FOR INTEGRATED ASSESSMENT OF THE CONDITIONS OF OIL-FILLED POWER EQUIPMENT

2. The goals and tasks of research The goals of research: Development of a simplified model for assessment of the conditions of oil- filled transformer on the basis of intellectual technologies, aimed at making objective decisions on managing the process of equipment operation on the basis of data of different volume and content. Tasks: 1.Analysis and comparison of intelligent methods of assessment of technical condition of electrical equipment 2.The choice of the optimal method for this model 3.The possibility of using neuro-fuzzy models for condition assessment of power oil-filled transformer 4.Analysis of the results 2

3. The task of integrated assessment of the state of power equipment 3 The task of integrated assessment of the state of oil-filled transformers can be represented as an elements array of the following form: S = {X, M, N, P} X = {x 1,…, x n } – the list of controlled means of monitoring and diagnostics of physical and chemical parameters, which characterize the change of technical condition under the influence of various factors; M = {m 1,…,m k } – the various methods used to control the parameters X and methods of the fault detection; N = {n 1,…,n n } – the normative values for each researched parameters X, which is clearly indicated in the normative technical documentation; P = {p 1,…,p l } – the decision rules characterizing the relation of parameters X with state of the power oil transformer;

4. Overview of intelligent methods of evaluation of technical condition of electrical equipment Neural networks Advantages: the ability to task modeling with large number of variables; minimal user intervention in the learning process; minimal requirements for the structure of the neural network; the high-speed and parallel computations; the ability to work with various information Disadvantages: the lack of formalized algorithms network settings; the lack of guarantee of successful solution of the problem; the high complexity of the internal structure of the network. Fuzzy logic Advantages: the simplicity of the model constructions (including using the experience of the human); the high quality of results; the reduced amount of calculation; the high speed of calculation; Disadvantages: the lack of analysis of the obtained models using traditional methods; the high computational complexity; the necessity to expand the rule base by increasing the number of input variables; 4

5. The possibility of using neuro-fuzzy models for condition assessment of oil-filled transformer As an example of using ANFIS, built a simplified model for assessment of the condition of power oil-filled transformers by two parameters: x 1 - the temperature of the high layers of oil of transformer x 2 - the temperature of the transformer windings Consider the problem of classification of the transformer’s condition, and as the model output variable Y choose the parameter characterizing the different levels of the transformer’s condition: “Normal”, “Within normal limits”, “Alarm”. 5

6. The model structure of the generated fuzzy inference system for assessment of the condition of transformer 6

7. The development of a knowledgebase to determine the condition of the transformer depending on the values of selected parameters 1.IF (x 1 = “Low”) AND (x 2 = “Low”) THEN (y = “Normal”); 2.IF (x 1 = “Low”) AND (x 2 = “Average”) THEN (y = “Within normal limits”); 3.IF (x 1 = “Low”) AND (x 2 = “High”) THEN (y = “Alarm”); 4.IF (x 1 = “Average”) AND (x 2 = “Low”) THEN (y = “Within normal limits”); 5.IF (x 1 = “Average”) AND (x 2 = “Average”) THEN (y = “Within normal limits”); 6.IF (x 1 = “Average”) AND (x 2 = “High”) THEN (y = “Alarm”); 7.IF (x 1 = “High”) AND (x 2 = “Low”) THEN (y = “Alarm”); 8.IF (x 1 = “High”) AND (x 2 = “Average”) THEN (y = “Alarm”); 9.IF (x 1 = “High”) AND (x 2 = “High”) THEN (y = “Alarm”); 7

8. The structure of the system neuro-fuzzy inference 8

9. The membership functions of the inputs of the model for parameters: x 1 – the temperature of the high layers of oil of transformer x 2 – the temperature of the transformer windings 9

10. The interpretation of the process of assessing the condition of transformer with the validation of work of the neuro-fuzzy system 10

11. The graphical representation of the results of ANFIS models 11

12. Conclusion In this work, based on a comparative analysis of modern intelligent methods there are: the conclusion about the possibility of using neuro-fuzzy models for condition assessment of electrical equipment. the hybrid network (ANFIS) for the assessment of the technical condition of power oil-filled transformer in two key parameters (“The temperature of the high layers of oil of transformer” and “The temperature of the transformer windings”). The test of the obtained neuro-fuzzy structures showed that it is accurate to 0.86 correctly identifies the actual state of the power oil-filled equipment. 12

13. Thank you for your attention! 13