ARTIFICIAL INTELLIGENCE USED IN HEAVY CURRENT ENGINEERING 1. INTRODUCTION

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1 Prace Naukowe Instytutu Maszyn, Napędów i Pomiarów Elektrycznych Nr 49 Politechniki Wrocławskiej Nr 49 Studia i Materiały Nr Č. ONDRŮŠEK*, F. VESELKA* ARTIFICIAL INTELLIGENCE USED IN HEAVY CURRENT ENGINEERING The contribution deals with the optimisation of electromechanical and electrical systems using artificial intelligence. The practical examples of optimisation are presented. 1. INTRODUCTION The use of artificial intelligence algorithms is useful for the solution of different practical problems. The main reason for their use is the failure of classical numerical methods in case of great amount of computation and the problem with acceptable convergence to the minimum of penalty function. Artificial intelligence methods e.g. Genetic Algorithm (GA) and or Simulated Annealing (SA) overcome the above mentioned obstacles, do not require enormous mathematical knowledge of a designer and moreover they enable the optimisation in such cases in which the optimisation by classical methods was unable. 2. ALGORITHM OF ARTIFICIAL INTELLIGENCE There is a great deal of Artificial Intelligence (AI) algorithms, which are frequently used in the technology of knowledge-base, digital image processing and evolotionary optimisation methods such as (GA) and (SA) which were used for optimisation in this article OPTIMISATION OF INDUCTION MOTOR Genetic Algorithm was used in optimisation of induction motor. The parameters selected for generation process are in Tab. 1. * Fakulta elektrotechniky a informatiky, Ústav výkonové elektrotechniky a elektroniky, ÚVEE FEI VUT v Brně, CZ. Technická 8, Brno. Tel: 05/ ondrusek@uvee.fee.vutbr.cz; veselka@uvee.fee.vutbr.cz

2 The penalty function is one of the most important part of each optimisation method. Generaly it is a function evaluating the quality of computing solution. Penalty function was chosen as the sum of absolute values of the differences of real values and the best computed values multiplied by suitable weight factors. The following parameters were chosen for induction motor optimisation. 113 Table 1. The parameters for generation process Lower limit Upper limit Parameter De [mm] Outer stator diameter D [mm] Inner stator diameter li [mm] Rotor stator packet length Bδ [T] Air gap flux density k dr1 [ ] (0.8) Slot loading of stator δ [mm] Air gap length σ Stator winding current density [A/mm 2 ] σ t [A/mm 2 ] Rotor rod current density σ k [A/mm 2 ] Rotor ring current density B z [T] Tooth flux density q 1 [ ] Number of stator slots per pole & phase Motor volume V [dm 3 ] Nominal motor heating ϑ [K] Nominal cosϕ cos ϕ [ ] Nominal motor efficiency η [ ] Torque overload capacity m pn [ ] The formula of penalty function is: ε(gr i ) = abs(kv V) + abs(kϑ(0.89 ϑ d ϑ n )) + abs(kcosϕ(1 ϕ n )) + abs(kη(1 η n )) where k is a weight coefficient. Tabλε 2. Optimized induction motor parameters + abs(km p (m p + 1 m pn )) (1) After optimisation Weight coefficient V [dm 3 ] ϑ [K] cosϕ [-] η [-]

3 114 m p [-] M [ Nm ] I [ A ] cos ϕ, η I = f (s) M = f (s) cos ϕ = f (s) η = f (s) s [-] Fig. 1. Induction motor characteristics The optimisation was carried out with induction motor 5.5 kw, 380 V. The optimised parameters obtained are in Table OPTIMISATION OF CURRENT INSTRUMENT TRANSFORMER Genetic Algorithm and Simulated Annealing were used for instrument current transformer. The parameter selected for transformer optimisation are in Table 3. Table 3. Selected parameters for optimisation Lower limit Upper limit Name F1 [Az] Primary loading Correction_1 [z] 0 10 Secondary winding turns correction of measuring part of core h_1 [mm] Height of magnetic core of measuring part of core %Py [%] Percentual value of Py material in measuring part of core Correction _2 [z] 0 10 Secondary winding turns correction of protecting part of core where: The following parameters were used to form penalty function. Volume of transformer active parts V [dm 3 ] Price of transformer active parts C [Kč] Sum of current errors for 100% and 25% of nominal loading ε I1 [%] Error of the real overcurrent number ε n1 [ ] The sum of current error for nominal current and the total transformer error for nominal overcurrent ε I2 [%] Angle error ε α2 [%] The penalty function was used in the form (2): 2 2 V V + kcc + ki1 ε I1 + kn1( n0 n ) + ki 2ε I 2 + kα 2εα 2 ε = k, (2)

4 n real current number of measured part of an instrument transformer, n 0 requested overcurrent number of measured part of an instrument transformer. The influence of every parameter on the total error of optimisation is given by adjustment of appropriate value of weight coefficient k. The optimisation procedure was applied to instrument transformers bank consisting of 15 transformers with the range (10 400) A of primary current. The aim of optimisation was the minimum volume of active materials or the minimum price OPTIMISATION OF TRANSFORMER BANK WITH RESPECT TO MINIMUM PRICE OF ACTIVE MATERIALS Comparing optimised and nonoptimised transformers one can find out that the price dropped in average about 19% in each transformer. The price drop was of course accompanied by worsening of other parameters of penalty function, but nevertheless the parameters are kept in allowable boundaries given by czech standard. The results of optimisation of current transformer with nominal current I = 100 A are in Table 4. Table 4. Results of optimisation of 100 A current transformer with respect of minimum price Without optimisation With optimisation Change [%] Weight coefficient Price [Kč] % 20 Volume [dm 3 ] % 1 ε I1 [%] % 1 N 1 [ ] % 1 ε I2 [%] % 1 ε α [min] % OPTIMISATION OF THE WHOLE TRANSFORMER BANK WITH RESPECT OF MINIMUM VALUE OF ACTIVE MATTERIALS Comparing optimised and nonoptimised values was found that volume dropped about 22% per one transformer. The volume is accompanied by worsening of other parameters of penalty function as well. Nevertheless the parameters are in standard boundaries. The optimisation results are in Table 5. Table 5. Results of optimisation of 100 A current transformer with respect of minimum volume Without optimisation With optimisation Change [%] Weight coefficient Price [Kč] % 1 Volume [dm 3 ] % 20 ε I1 [%] % 1 N 1 [ ] % 1 ε I2 [%] % 1

5 116 ε α [min] % 1 3. CONCLUSION It hase been proved that GA and SA are able to be generaly used for optimisation. The only condition for application of GA and SA algorithms is the existence of functional relationship between optimised parameters and penalty function. The main advantage of the application of GA and SA for optimisation is the minimum of mathematical knowledge and the easy fulfillment of a customer special requirements. Application of artificial intelligence for the solution of problems in synchronous and DC machines are now a matter of our interest. Special attention will be given to commutation process optimisation. REFERENCES [1] ONDRŮŠEK Č., VESELKA F., BŘEZINA T., Neuronová síť jako model asynchronního motoru, International Workshop on Electrical Machines in Prague, September [2] VESELKA F., ONDRŮŠEK Č., Artifical Neuron Networks for IM Simulation, MIS 96 International X. Symposium on Micromachines and Servodrives. [3] ŠTENCL Z., Diplomová práce, ÚESP FEI VUT v Brně, Brno [4] KREJČÍ P., Diplomová práce,úmt FS VUT v Brně, Brno Pozn: Tento příspěvek byl zpracován s podporou grantových projektů GAČR 102/00/1586 a Cez: 322/98: