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Induction Motor
CHAPTER - 3
Induction Motor External Faults and its
Simulation
3.1 Induction Motor
Induction motors are complex electromechanical devices widely used for conversion of power from electrical to mechanical form in various industrial applications because they are robust, controlled and most suitable for many applications like pumps, fans, compressors, machine tools etc. The focus of this study is mainly related with LV/MV small and medium size squirrel cage induction motors faults identification.
3.2 Induction Motor External Faults
3.2.1 Overload
As the mechanical load on induction motor increases the motor begins to draw high current and speed decrease. Below the normal rated current heat dissipation is more than the heat produced and vice versa above normal rated current. After certain amount of load heat generation rate is higher than heat dissipation rate than the insulation is threatened.
Overload protection is always applied to motors to protect them against overheating. The National Electric Code requires that an overload protective device be used in each phase of induction motor unless protected by other means as because single phasing in the primary of a delta-wye transformer that supplies motor will produce three phase motor currents in a 2:1:1 relationship. If the two units of current appeared in the phase with no overlaod device the motor would be unprotected [1].
The overload protection can also divide in two stages, alarming and tripping. In case of pre-warning alarm (for example 90 % of full load) operator get some time to find out
Ch. 3 Induction Motor External Faults and its Simulation
possible source of overload and to resolve the cause. If the overload becomes higher (for example greater than 10-15 %) than tripping is required [2].
The limitation of this scheme that ambient temperature and cooling effect will not be considered on current base fault identification so soft computing based overload protection can be used for prewarning.
3.2.2 Overvoltage
Induction motor is designed to withstand overvoltage upto +10% as general voltage design motor manufacture specification. When voltage increases beyond it motor overheat because of increase in core losses. Current draw is only controlled by the load and at rated current and 10% overvoltage the motor will be overloaded by approximately 10%. The core loss is 20 to 30% greater than normal and could cause the machine to overheat.
3.2.3. Undervoltage
As the voltage across motor reduces slip increases, motor speed drops and current increases. This is because the power to be delivered remains constant and voltage is reduced from normal rated voltage. The increase of current can harm the insulation of the motor windings.
When the voltage is reduced of normal the developed torque moves to lower and in order to develop the torque to drive the load motor slow down (slip increase) and draws more current from supply. The current changes drastically as voltage reduces below 75 to 80% of rated voltage. In some cases, a large drop in voltage may cause the motor to stall also [3].
3.2.4 Single Phasing
Single phasing is the worst case of voltage unbalance and can be happened because of open winding in motor or any open circuit in any phase anywhere between the secondary of transformer and the motor or one pole of circuit breaker open or opening of fuse [4] [5]. The single phasing causes unbalanced currents to flow and the negative sequence component of these unbalanced current causes the rotor to overheat. The negative sequence current increases the rotor copper losses also. It is the worst case of voltage unbalance. Negative sequence currents generated will be approximately six times the negative
Ch. 3 Induction Motor External Faults and its Simulation
unbalance and one phase, two phase angle displacement are considered in the simulation data sets.
TABLE 3. Relative Insulation Life for Different % Voltage Unbalances for Induction Motor (for 100% Motor Loading and 1 Service Factor) [11]
Percentage line unbalance considered based on NEMA definition
% Line Unbalance Voltage Ratio = (Maximum Voltage from average line voltage magnitude /Average Voltage) x 100% ……………………………………………… (3.1)
The magnitude of the NEMA unbalanced voltage in percentage and negative sequence voltage in percentage is almost equivalent for all practical purpose [8].
3.3 Induction Motor External Faults Simulation
A three phase induction motor external faults simulation is prepared in Matlab/Simulink environment [12] with varying operating voltages and load. OL, OV, UV, SP (for each phase), VUB and normal conditions are simulated to obtain three phase RMS line voltages and RMS line current values. The fault simulation is prepared using 3 phase, 50Hz, 4 kW/5.4 HP, 400 V, 1430 rpm, star connected induction motor. Induction motor block in Matlab/Simulink is based on arbitrary reference frame theory and contains highly nonlinear modelling equations. Induction motor is used in stationary reference frame. Data sets are obtained using ode 23tb stiff solver in simulation. The three phase steady state RMS voltages and currents values are obtained as data sets (patterns) and used as input feature vectors for training, for example in MLPNN based fault identification algorithm for MLPNN training. We have prepared 174 data sets for training and 46 data sets for
Voltage Unbalance (%)
Derating Required 0 1 1 0. 2 0. 3 0. 4 0.
Induction Motor External Faults Simulation
independent testing. The training data sets and testing data sets are shown in Appendix A and Appendix B respectively. The number of train and test data sets patterns used for six output conditions are shown in Table 3.2. Subsection 3.3.1 to 3.3.6 discusses details of how different output conditions data sets are obtained and also shows example of how the independent test data sets (patterns) are obtained for output conditions. Three phase RMS voltages and current values obtained using simulation at 1.2s and used as test vector. Table 3.3 shows some example of independent test patterns. TABLE 3. Number of Patterns for simulation Train and Independent Test Data Sets
Some of the test (unseen) patterns used for MLPNN testing (in chapter 4) obtained for different external fault conditions alongwith normal conditions are shown in Table 3.3.
TABLE 3. Examples of Test Inputs for Simulation Data Sets
Sr. No.
Output Inputs VRY (In1)
VYB
(In2)
VBR
(In 3)
IR
(In 4)
IY
(In 5)
IB
(In 6) 1 N 405.1 405.4 405.6 7.74 7.72 7. 2 N( 92.5% UV within normal limit)
3 N (VUB 0.52%) 397.7 394.4 396.2 8.08 7.68 7.
4 OL 399.7 400 400 10.33 10.33 10.
5 OV 443.2 443.4 443.5 7.44 7.45 7.
6 UV 347.6 347.8 348 8.55 8.54 8.
7 SP(R phase) 300.2 412.7 374.6 0 16.26 16. 8 SP(y phase) 370.6 265.7 400.4 20.1 0 20. 9 SP(B phase) 384.3 352.4 263.6 17.6 17.6 0 10 VUB (2 phase UV ) 365.7 390.0 355.8 6.819 11.62 8. 11 VUB (3 phase OV) 427.1 438.4 432.5 6.51 7.68 8.
Sr. No. Condition Train Data Independent Test Data 1 Normal Output (N) 31 6 2 Overload (OL) 19 6 3 Overvoltage(OV) 30 6 4 Undervoltage(UV) 20 5 5 Single Phasing (SP) 25 15 6 Voltage Unbalance (VUB) 49 8
Induction Motor External Faults Simulation
Fig. 3.2 (a) shows the three phase voltage and currents and Fig. 3.2 (b) shows three phase RMS voltages and currents for normal condition (with 92.5% rated normal voltages ) Sr. No. 2 of Table 3.3.
(a)
(b) FIGURE 3. Normal Condition (Sr. No. 2 of Table 3.3) (a) Three Phase Voltages and Currents for (b) Three Phase RMS Voltages and Currents
Ch. 3 Induction Motor External Faults and its Simulation
Fig. 3 (a) shows the three phase voltages and currents and Fig. 3(b) shows three phase RMS voltages and currents for normal condition with 0.52% VUB in supply voltages. 0.52% VUB initiated at 1.14s.
(a)
(b) FIGURE 3. Normal Condition (Sr. No. 3 of Table 3.3) (a) Three Phase Voltages and Currents for (b) Three Phase RMS Voltages and Currents
Ch. 3 Induction Motor External Faults and its Simulation
3.3.3 Overvoltage Condition
The operating voltages more than 10% rated operating voltages are considered as overvoltage condition in simulation. Fig. 3.5 (a) shows the three phase voltages and currents and Fig. 3.5 (b) shows three phase RMS voltages and currents for OV condition. 110.8% OV of rated voltage initiated at 1.12s.
(a)
(b) FIGURE 3. OV Condition (Sr. No.5 of Table 3.3) (a) Three Phase Voltages and Currents for (b) Three Phase RMS Voltages and Currents
Induction Motor External Faults Simulation
3.3.4 Undervoltage Condition
The operating voltages less than 10% rated operating voltages are considered undervoltage condition in simulation.
Fig. 3.6 (a) shows the three phase voltages and currents and Fig. 3.6 (b) shows three phase RMS voltages and currents for UV condition. 87% UV of rated voltage initiated at 1.14s.
(a)
(b) FIGURE 3. UV Condition (Sr. No. 6 of Table 3.3) (a) Three Phase Voltages and Currents (b) Three Phase RMS Voltages and Currents
Induction Motor External Faults Simulation
3.3.6 Voltage Unbalance Condition
Standard motor are capable of operating under condition of supply voltage unbalance of 1% for long period. Derating is requiring for voltage unbalance between 1 to 5% for safe operation which is generally not taken care in field. We have considered voltage unbalance more than 1% as fault which. All types of voltage unbalance like single phase and two phase undervoltage and overvoltage unbalance, three phase undervoltage and overvoltage unbalance and one phase, two phase angle displacement considered in the case. Fig. 3.8 (a) shows the three phase voltages and currents and Fig. 3.8 (b) shows three phase RMS voltages and currents for VUB condition. Two phase undervoltage VUB initiated at 1.13s.
(a)
(b) FIGURE 3. VUB Condition (Sr. No. 10 of Table 3.3) (a) Three Phase Voltages and Currents for (b) Three Phase RMS voltages and Currents
Ch. 3 Induction Motor External Faults and its Simulation
3.3.7 Scatter Plot Visualization of Simulation Data Sets
Fig. 3.9 shows the scatter plot visualization for fault data sets patterns obtained using simulation. Plot displays input variable relations with respect to different fault class and found linearly nonseparable and overly complex.
FIGURE 3.
Scatter Plot of Simulation Data Sets
3.3.8 External Faults Identification With Linear Discriminant Analysis (LDA) and Discussions
3.3.8.1 LDA Besides visualization of data sets complexity using scatter plot we have also tested with conventional and widely used LDA for classification results. Discriminant analysis approaches are well known statistical approaches and widely used in pattern recognition tasks. It can be easily extended to multiclass cases Via multiple discriminant analysis [13] [14]. LDA analysis can be used to study the difference between groups of objects (two or more) with respect to several variables simultaneously; for determining wheather meaningful differences exist between the groups [13].
Ch. 3 Induction Motor External Faults and its Simulation
Multi-Class LDA:
If the numbers of classes are more than two, then a natural extension of Fisher linear discriminant possible using multiple discriminant analysis. As in two-class case, the projection is from high dimensional space to a low dimensional space and the transformation suggested still maximizes the ratio of intra-class scatter to the inter-class scatter. The maximization should be done among several competing classes unlike the two- class case. Suppose that now there are p classes. The intra-class matrix is calculated similar to (3.3):
̂ w S 1 +…+Sp ∑ pi 1 ∑ (^) x ci (X-X̅ (^) i)(X-X̅ (^) i) …………………………………………….. (3.6)
Inter-class scatter matrix slightly differs in computation and is given by
̂ b ∑ pi 1 mi(X̅ i- X̅ )(X̅ (^) i- X̅ ) …………………………………………………………… (3.7)
Where in (3.7), is the number of training samples for each class, X̅ i is the mean for
each class and X̅ is the total mean vector given by X̅ (^) m^1 ∑ pi 1 miX̅ (^) i , Transformation
can be obtained by solving generalized eigenvalue problem
̂ b ̂ w ……………………………………………………………………….…. (3.8) is known as eigenvalue. Once the transformation is given, the classification is then performed in the transformed space based on some distance metric such as Euclidean distance
d(X,Y) √∑ (i Xi-Yi )^2 and cosine measure d(X,Y) 1 - (^) √∑ ( ∑)^ i X√∑ (iYi )
. Then upon arrival of the new instance Z, it is classified to arg mink d(Z ,X̅ k ), where X̅ k is the centroid of k-th class [13].
3.3.8.2 Classification Results Obtained Using LDA and Discussions
We have used MATLAB classify function for linear discriminant analysis based fault diagnosis with own written codes.
Induction Motor External Faults Simulation
The classification accuracy results obtained for total train (174) and (46) independent test data sets are obtained as follows. Total classification Accuracy is defined as the total number of correct decisions to total number of cases.
Total train classification accuracy (with 174 total train data sets) = 70.11%
Independent test classification error=0.261 (with independent 46 test data sets) =26.1%
Independent test classification accuracy (1- classification error) = 73.9%.
The programme is also tested with 10-fold cross validation by splitting total train data sets in 10-fold train and test data sets.
CVMCR (misclassification test error with 10-fold cross validation) = 0.3448 = 34.48%,
It is observed the classification error obtained with widely LDA is high for this complex and multi-class fault identification problem.
We have used ANN in next chapter and shown results obtained for ANN. The three phase steady state RMS values of voltages and currents are obtained for normal and external faults condition which used as input patterns for MLPNN training. We have tested MLPNN with separate test patterns and also discussed results obtained in next chapter. The test pattern variables are mostly within ±7.5% of train pattern variables values for simulation and practical data sets. It is observed MLPNN can identify any unseen external faults with high classification accuracy.
As single phasing is more severe fault among all these faults and requires early tripping we have taken RMS values after 2 cycles for simulation train data sets. Steadystate RMS values can also be taken for SP case. Present numerical protection for single phasing provides delay about 5 sec. External faults not demand instantaneous tripping and can protected with proper time delayed protection. The same phenomena can be possible with the use of ANN and Fuzzy with suitable and less time delay than present numerical protection.
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