A Testing to BS171 for Oil-Immersed Power Transformers Routine Tests

Appendix: A Testing to BS171 for Oil-immersed Power Transformers

Routine tests

All large oil-immersed power transformers are subjected to the following tests: 1. Voltage ratio and polarity. 2. Winding resistance. 3. Impedance voltage, short-circuit impedance and load loss. 4. Dielectric tests. a. Separate source AC voltage. b. Induced over-voltage. c. Lightning impulse tests. 5. No-load losses and current. 6. On-load tap changers, where appropriate.

Type tests

Type tests are tests made on a transformer which is representative of other transformers to demonstrate that they comply with speciﬁed requirements not covered by routine tests. 1. Temperature rise test. 2. Noise level test.

195 196 Appendix A: Testing to BS171 for Oil-immersed Power Transformers Special tests

Special tests are tests, other than routine or type tests, agreed between a manufacturer and a purchaser, for example: 1. Test with lightning impulse chopped on the tail. 2. Zero-sequence impedance on three-phase transformers. 3. Short-circuit test. 4. Harmonics on the no-load current. 5. Power taken by fan and oil-pump motors. Index

B D Bayesian network, 51 Dissolved gas analysis, 4 Bayes net, 50 Bootstrap, 107 Bayes’ theorem, 50 Dörnenberg ratio method, 99 Chain rule of probability theory, 51 Gas evolution, 96 Conditional probability Gassing rate, 99 distributions, 51 Halstead’s thermal equilibrium, 139 Conditional probability table, 52 Key gas method, 98 Directed arc graph, 155 Key gases, 95 Likelihood, 50 Relative partial pressures, 139 Posterior probability, 50 Rogers ratio method, 99 Prior or marginal Total of all combustible gases, 95 probability, 50 Prior probability, 50 E Evidential reasoning, 38 C Analytic hierarchy process, 132 Computational intelligence, 9 Attribute level, 39 Artiﬁcial neural network, 108 Basic evaluation analysis model, 186 Bayesian network, 50 Basic probability assignment, 40 Cybernetic techniques, 108 Basic probablity assignment matrix, 42 Evidential reasoning, 38 Basic probability mass, 46 Evolutionary algorithms, 15, 17 Beneﬁt attributes, 44 Expert system, 37 Combined degrees of belief, 46 Fuzzy logic, 48 Combined probability mass, 46 Genetic algorithm, 18 Decision tree model, 127 Genetic programming, 24 Cost attributes, 45 K nearest neighbour, 109 Degree of belief, 46 Logical approach, 37 Dempster-Shafer theory, 40 Particle swarm optimisation, 29 Evaluation analysis model, 39 Support vector machine, 109 Evaluation grade level, 39 Condition monitoring Extended decision matrix, 39 and assessment, 2 Factor level, 39 Dissolved gas analysis, 4 Frame of discernment, 40 Frequency response analysis, 5 Hypothesis, 40 Partial discharge analysis, 6 Local probability assignment, 43 Thermal model, 4 Multiple-attribute decision-making, 38

197 198 Index

E. (Cont.) Deterministic algorithms, 17 Ordinary decision matrix, 45 Enumerative schemes, 16 Original evidential reasoning algorithm, 38 Objective function, 16 Overall probability assignment, 44 Random search algorithms, 17 Partial combination rules, 42 Qualitative attributes, 39 Quantitative attributes, 39 P Remaining probability mass, 46 Particle swarm optimisation, 29 Revised evidential reasoning algorithm, 45 Active aggregation, 31 Unassigned degree of belief, 47 Best previous position, 30 Uncertainty, 37 Global best position, 30 Particle, 30 Particle swarm optimiser with F passive congregation, 31 Frequency response analysis, 5 Passive aggregation, 31 Construction-based comparison, 170 Passive congregation, 31 Ladder network model, 178 Position, 30 Low voltage impulse method, 166 Social congregation, 31 Sweep frequency response analysis, 167 Standard particle swarm optimiser, 29 Time-based comparison, 170 Swarm, 30 Transfer function, 165 Velocity, 30 Type-based comparison, 170 Power transformer, 30 Fuzzy logic, 48 Copper loss, 1 Crisp set, 48 Core loss, 66 Fuzzy set, 48 Heat run test, 79 Membership function, 48 Stray loss, 66 Transformer fault, 2 Transformer thermal dynamics, 62 G Winding temperature indicator, 67 Genetic algorithm, 19 Crossover function, 21 Fitness function, 22 T Initial population, 22 Thermal model, 4 Mutation function, 21 Comprehensive thermoelectric Roulette wheel selection, 21 analogy thermal model, 61 Selection function, 20 Equivalent heat circuit, 63 Simple genetic algorithm, 20 Fourier theory, 61 Termination criteria, 22 Ohm’s law, 61 Genetic programming, 24 Oil natural air natural and oil force Crossover, 27 air force cooling, 63 Fisher’s discriminant ratio, 28 Simpliﬁed thermoelectric analogy Fitness function, 28 thermal model, 68 Functions, 25 Thermal capacitance, 66 Mutation, 27 Thermal conductance, 67 Population initialisation, 25 Thermoelectric analogy theory, 60 Reproduction, 27 Thermoelectric analogy Selection pressure, 28 thermal model, 63 Terminals, 25 Transformer fault, 2 Arcing, 98 Corona, 98 M Overheating of cellulose, 98 Mathematical optimisation, 16 Overheating of oil, 98 Constraints, 16 Partial discharge, 6 Decision variables, 16 Winding deformation fault, 164 Index 199

Transformer thermal dynamics, 62 W Bottom-oil temperature, 56 Winding deformation fault, 164 Forced oil cooling, 59 Axial displacement, 173 Natural oil cooling, 59 Clamping failure, 172 Oil directed cooling, 59 Hoop buckling, 173 Steady-state temperature, 56 Normal winding, 171 Thermal time constant, 57 Poor cable grounding, 173 Top-oil temperature, 56 Residual magnetisation, 173 Transient-state temperature, 56 Short circuited turns, 172