1. Application of Artificial Neural Network and
Soft Computing Techniques to Engineering World Problems By
Dr. K.S. Madhavan,
Sr. DGM, Programmable Control Systems,
Corporate Research & Development Division,
Bharat Heavy Electricals Limited,
Hyderabad , India.
Corporate Research & Development, BHEL 1

2. Impact of R&D
2010 – 2020 is a decade of innovation primarily to be steered by Research & Development. In India the play has already begun.
Act I : Develop products based on global R&D collaboration
Act II : Focus on Basic Research
Act III : Build a R&D Power-house base within India
Developed economies are facing a problem of declining competitiveness on a global scale. We as developing nations have to capitalize on this aspect to strengthen our R&D base.
R&D is defined as a creative work undertaken on a systematic basis in order to increase the stock of knowledge including knowledge of man, culture, society and the use of this stock of knowledge to devise new applications.
BHEL has invested 2.5% of turnover on R&D. This will shortly be increased to 4%

3. INTRODUCTION
The term ‘Artificial Intelligence’ was coined by John McCarthy
in the Dartmouth Conference in 1956.
AI was developed at the initial stages in Laboratories at Princeton,
MIT, CMU and Stanford Universities.
Artificial Neural Network is an offshoot of AI called the ‘Weak AI’.
It is related to the cognitive and intuitive aspects of the Human
Brain. It is to do with the way Humans Act.
In fact ANN evolved even earlier with the formulation of the first
Artificial Neuron by Warren McCulloch and Walter Pitts in 1943.

4. Neural => neuron A carbon based chip from the brain- the biological computer4 4

5. Brain: A carbon-based computer
Neuron is the Basic Building
block of our Central Nervous
System (CNS)
Human Brain has 1010 neurons.
Human Nervous System has over 1011 neurons about the same number as the stars in our galaxy.
Each neuron, on an average,
process about 104 inputs 5 5

6. Basic Biological Structure of a Neuron
Neural Inputs
Synapse
Axon (Neural output)
Soma
Dendrites 6 6

7. What is Human Intelligence?
The Center for Brains Minds and Machines (CBMM) of MIT has been started for the above purpose: to decipher human intelligence.
Do studies from functional Magnetic Resonance Imaging (fMRI) give predictions about human intelligence?
Or is there a stability-plasticity dilemna associated with this study?
That is to say human brain continues to learn new things while retaining long term memory or retaining learning which was done in the long past.
Human Intelligence still remains an Enigma!

8. Data Requirements for Various Applications of ANN

9. Neural Network Application to Character Recognition using Backpropagation
Imaging system
Noisy Character Recognized Character
A is represented As
M is represented As
Corporate Research & Development, BHEL

10. MATLAB Program clf; figure(gcf) echo on [alphabet, targets] = prprob;
[R, Q] = size(alphabet);
[S2, Q] = size(targets);
S1 =13; S2 = 26;
net = newff(minmax(alphabet),[S1 S2],{'logsig' 'logsig'}, 'traingdx');
net.LW{2,1} = net.LW{2,1}*0.01;
net.b{2} = net.b{2}*0.01;
noisyL = alphabet(:,1) + randn(35,1)*0.2;
plotchar(noisyL);
pause % strike any key to continue...
net.performFcn = 'sse'; % Sum-Squared Error performance function
net.trainParam.goal = 0.1; % Sum-Squared error goal
net.trainParam.show = 20; % Frequency of progress displays (in epochs)
net.trainParam.epochs = 300; % Maximum number of epochs to train
net.trainParam.mc = 0.95; % Momentum constant
% Training begins...please wait..
P = alphabet;
T = targets;
[net,tr] = train(net,P,T);
A2 = sim(net,noisyL);
A2 = compet(A2);
answer = find(compet(A2) == 1);
plotchar(alphabet(:,answer));
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11. Corporate Research & Development, BHEL

12. Elman networks are used to predict: Drum Level Feed water flow
Furnace Pressure
Steam flow
Corporate Research & Development, BHEL

13. Elman Network Program in MATLAB for PHMS
Net = newelm([0 1],[10 1],{‘tansig’,’logsig’});
Net.trainparam.epochs = 300/1000/2000*;
P = […………………………………….];
T = […………………………………….];
Pseq = con2seq (P);
Tseq = con2seq (T);
Net = train (net, Pseq, Tseq);
Y = sim(net, Pseq);
Z = seq2con(Y);
Z{1,1};
Diff1 = T – Z{1,1};
Corporate Research & Development, BHEL

14. Time Series Prediction of Boiler Drum Level
A time series is a sequence of vectors, x (t), t = 0,1,…where t represents elapsed time.
In practice, for any given physical system, x will be sampled to give a series of discrete data points, equally spaced in time.
Formally this can be stated as: find a function ƒ: RN → R such as to obtain an estimate of x at time t + d, from the N time steps back from time t, so that:
x ( t+ d) = f(x(t),x(t-1)… x(t-N+1))
Normally d will be one, so that f will be forecasting the next value of x.
Corporate Research & Development, BHEL

15. Recurrent Neural Networks
a – activation level
P’ – input neurons
LW – Weights
b – bias
D - Delay b1
LW21 b2
a1(k) P’
a2(k) D
LW11
a1(k-1)
The actual/neural network predicted variation of the drum level parameter and the error.
Corporate Research & Development, BHEL

16. Neural Network Input/Output patterns for Drum Level and error obtained after training
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17. Corporate Research & Development BHEL

18. Residual Life Assessment Studies of Transformers using
Artificial Neural Networks
Corporate Research & Development, BHEL

19. Dissolved Gas Analysis using Artificial Neural Networks
Rogers Fault Diagnosis Table i j k l
Diagnosis
code
Normal deterioration n 5
Partial discharges o
1 or 2
Slight overheating(< 1500 C) p 1
Overheating(1500C–2000 C) q
Overheating(2000C–3000 C) r
General conductor overheating s
Winding circulating currents t 2
Core & tank circulating currents, overheated joints u
Flash-over without power follow through v
Arc with power follow through w
Continuous sparking to floating potential x
Partial discharge with tracking y

20. A common method for identifying incipient faults in power transformers is the Dissolved Gas Analysis (DGA).
Analysis of ratios of specific dissolved gas concentrations, their generation rates, and the measure of total combustible gases are used as the attributes for classification of the faults.
Thresholds are designed to partition the attributes into intervals. Specific combinations of these intervals are then used to identify the type of fault. However, the conventional fault diagnosis methods, i.e. the ratio methods and the key gas method, have limitations such as the “no-decision” problem. Various Artificial Intelligence (AI) techniques may help to solve the problems and present a better solution.
Table-II : Rogers Ratio Codes
Ratio
Range
Code i
 0.1
 0.1,  1.0
 1.0,  3.0
 3.0 5 1 2 j
 1.0
 1.0 k l
 0.5
 0.5,  3.0
Table-I : Gas Ratio Codes
Description
Code
CH4/H2 i
C2H6/CH4 j
C2H4/C2H6 k
C2H2/C2H4 l
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21. Table-III : Rogers Fault Diagnosis Table Table-IV : IEC Ratio Codesj k l
Diagnosis
code
Normal deterioration n 5
Partial discharges o
1 or 2
Slight overheating(< 1500 C) p 1
Overheating(1500C–2000 C) q
Overheating(2000C–3000 C) r
General conductor overheating s
Winding circulating currents t 2
Core & tank circulating currents, overheated joints u
Flash-over without power follow through v
Arc with power follow through w
Continuous sparking to floating potential x
Partial discharge with tracking y
Range of ratios l i k
< 0.1 1
0.1 – 1
1 – 3 2
> 3
Table-V : IEC Fault Diagnosis Table l i k
Characteristic faults
Code
No fault a 1
Partial discharges of low energy density b
Partial discharges of high energy density c
1 or 2
Discharges of low energy d 2
Discharges of high energy e
Hot spots (T < 1500 C) f
Hot spots
(1500 C < T < 3000 C) g
(3000 C < T < 7000 C) h
Hot spots (T > 7000 C) m
Corporate Research & Development, BHEL

22. LIMITATION OF THE RATIO METHODS : THE “NO-DECISION” PROBLEM
In real-life situations, Ratio Methods lead to codes that are way off from the norms set by the methods. In such a case inference of fault directly from the code pattern becomes difficult and vague.
Interpolations on patterns have to be made which are way beyond normal methods. Sometimes the gas content is undetectable or present as a trace. In such cases assumptions have to be made as to the negligible content of gas present. Ratio Methods fail to give logical diagnosis as to the nature of the fault. No decision can be taken on the diagnostic pattern generated by the Ratio Methods.
Artificial Neural Network (ANN) methods have to be used in order to arrive at a decision regarding the faults.
Corporate Research & Development, BHEL

23. SIGNIFICANCE OF THE ANN APPROACH
ANNs look for patterns in training sets of data, learn these patterns, and develop the ability to correctly classify new patterns or to make forecasts and predictions.
Artificial Neural Networks have the remarkable ability to extract meaningful information from incomplete or imprecise data.
An Artificial Neural Network does not require intimate knowledge of the system.
The network is massively parallel, extremely fast and intrinsically fault-tolerant.
Through exposure to many such examples of a situation, the neural network generalises to form its “own rule” to solve a problem.
Corporate Research & Development, BHEL

24. TRAINING WITH ANN
The neural network used in the present instance is a 3-layer Probabilistic Neural Network (PNN) for analysis. There are 4 neurons in the input layer, 12 in the hidden layer, and 12 in the output layer. A smoothing factor of was decided upon through NET-PERFECT for all the links.
The distance metric used is Vanilla Euclidean. PNN networks work by clustering patterns based upon their distance from each other. The Vanilla Euclidean distance metric is recommended for most networks because it works the best. In Vanilla, the output of the network is the square of the distance between the patterns and the weight vector for the neuron; therefore, the winner is the neuron with minimum activation.
Corporate Research & Development, BHEL

25. TRAINING WITH ANN (Contd.)
The training of ANN made is based on data shown in Tables I to V. When an input is presented, the first layer computes distances from the input vector to the training input vectors, and produces a vector whose elements indicate how close the input is to a training input.
The second layer sums these contributions for each class of inputs to produce as its net output a vector of probabilities.
Finally, a transfer function on the output of the second layer picks the maximum of these probabilities, and produces a 1 for that class and 0 for the other classes.
The Rogers Ratio Table and IEC Ratio Table with known inputs and outputs have been independently stored and trained in the reference ANN model.
Corporate Research & Development, BHEL

26. Fault Diagnosis of Generator Transformer of Power Station-I using ANN Approach
Date
CH4/H2
C2H6/CH4
C2H4/C2H6
C2H2/C2H4
Fault Predicted 2 1
Winding circulating currents
-- do --
Core & tank circulating currents; overheated joints 5
--do--
Normal deterioration
Overheating (1500C – 2000C)
Partial discharges
Corporate Research & Development, BHEL

27. Flowchart of ANNRBS (Artificial Neural Network Rule Based System)
Suggestion : DGA samples from CTs of EHV transformers can also be tested through ANNRBS
Data Input
Neural Network Based Abnormal Detector
Rule Based Abnormal Detector Y
Both indicate “Normal”? N
Rule Based fault detector
Neural Network based fault detector
Combined fault diagnosis
Maintenance Action Recommendation
Outputs
Corporate Research & Development, BHEL

28. Limits of Gases Beyond Which Fault Diagnosis Becomes Necessary Source
TDCG
Values Used (parts per million : ppm)
100
120 1 50 65
200
2500
536
IEEE(C57.104) 35
350
720
Doble 5 60
250 --
610
GE(Lyke 77) 25
2000
*IEEE(G)
240
160 11
190
115
580
1296
*IEEE(T) 30
700
Manufacturer 15
150
300
500
1000
1076
1985
**Electra CIGRE
28.6
42.2
74.6
85.6
289
3771
520
Dornenburg
Note : *Before (C57.104) ** Corrected values IEEE(G) : Generation; IEEE(T) : Transmission
Values in italics are of transformers 6-7 years old
Unmarked sources are all cited from [Griffin86, Griffin88]
TDCG : Total Dissolved Combustible Gases
Corporate Research & Development, BHEL

29. Architecture of the General Regression Neural Network used
Estimation of degree of polymerization and residual age of transformers from Furan concentration dissolved in oil
Architecture of the General Regression Neural Network used n P1
Σ YI exp (-DI2/22)
I=1 P2 Y1 A X1 P3 B
Input Units Y2
(I = 1)
Output Units
Summation Units
(O = 2) Pn n
 exp (-DI2/22)
I=1
Pattern Units
(n=31)
Corporate Research & Development, BHEL

30. Furfurals are major degradation products of cellulose insulation paper and are found in insulation oils of operating transformers. Furfural analysis is an indirect method to estimate the integrity of cellulose insulation compared to the direct measurement of Degree of Polymerisation of insulating paper.
The tensile strength of the paper decreases corresponding to an increase in the concentrations of the Furfural in the oil. 5-Hydroxymethyl-2-Furfuraldehyde and 2-Furfuraldehyde are present in the oil at significantly greater concentrations than any other Furfural components.
Furfural levels range from 0.1 ppm to 10 ppm depending on the age and condition of the transformer insulation. The residual life of the transformer can be predicted by estimating Furfural content in the oil or by the Degree of Polymerisation of cellulose paper taken from lead insulation.
Corporate Research & Development, BHEL

31. The life assessment can be made faster by estimating furfural from oil which can be collected from the transformer in running condition. The collection of cellulose paper involves cumbersome procedure of shutdown of the transformer and removal of paper from lead insulation after opening the transformer. Hence life assessment by furfural estimation is more popular and rapid method as compared to DP estimation of paper.

32. Choice of Artificial Neural Networks
The Degree of Polymerisation and Residual Age of transformers are continuous functions of Furfural levels of transformers. Therefore continuous function approximation of multiple outputs through Generalized Regression Neural Networks is one solution for predicting output patterns.
GRNN based on radial units, giving estimates of continuous variables rather than discrete decisions, overcoming the disadvantage of slow training inherent in backpropagation thereby lending itself well to real-time application, is the appropriate choice for our present analysis. Least square method has been used to minimize the error in prediction.
Corporate Research & Development, BHEL

33. Partial Discharge Classification of 145 kV GIS using
Artificial Neural Networks
Corporate Research & Development, BHEL

34. 3-layer BP 79.41% 4-layer BP 88.24% 5-layer BP 91.18% PNN 100%
Data collection process
Model
Accuracy achieved
3-layer BP
79.41%
4-layer BP
88.24%
5-layer BP
91.18%
PNN
100%

35. INTRODUCTION
Gas-Insulated Substation (GIS) equipment is being used worldwide in transmission and distribution of electrical energy. However its superior performance severely deteriorates with the presence of foreign particles in the system which causes Partial Discharge, leading to degradation of the insulation.
Reduction in the insulation strength to as low as 80% of the value under clean condition, has been reported. Partial Discharge is one of the effective indications of the defect and degradation of GIS.
The signals obtained from discharges occurring within GIS equipment due to microprotrusion on the enclosure and spacer, voids in solid insulation or due to floating particles are very stochastic in nature.
Corporate Research & Development, BHEL

36. Partial Discharge due to “Protrusion” (P)
In the present study, the following three types of faults have been investigated :
Partial Discharge due to “Protrusion” (P)
Partial Discharge due to “Floating Particles” (FP)
Partial Discharge due to “Particle Sticking on Insulator” (PSI)
The data for these Partial Discharges has been collected from experimentation based on cycle, phase and amplitude in pico-Coulomb (pC).
Corporate Research & Development, BHEL

37. Detection of Partial Discharges
In order to determine the extent of partial discharge, a specified voltage was applied from the transformer to the busbar. The discharge signal was picked up from the ungrounded outer enclosure .
The signal is fed to the partial discharge detector and transmitted to the Computerised Discharge Analyser (CDA) for recording and processing the data.
The voltage is slowly increased, from zero, across the test object until corona is noticed in the PD detector. This is the discharge inception voltage. The voltage is continuously increased up to 145/3 ( 83.72) kV until all the three different kinds of partial discharges in the present study are detected.
The voltage is then slowly reduced until the smallest discharge disappears, the discharge extinction voltage. The circuit uses a Straight Detection Method with a potential divider arrangement across the test object.
Corporate Research & Development, BHEL

38. PD Data Collection
For a given case, the data has been recorded for phase angle varying from 00 to After the data has been captured by the CDA, the data has been examined to determine the largest discharge magnitude noticed within each of the 360 equal time windows, and this maximum has been recorded in graphical form.
Windowing is done to increase phase resolution and reduce phase suppression noise. For the purpose of simulation, the total phase angle of 3600 has been divided into 18 parts (200 each).
In each part, the total discharge magnitude is summed up and an average is determined. The same procedure was followed in each cycle for all the three types of defects included in the present study.
Individual PD records give the following detailed information relative to every partial discharge pulse recorded during the test: cycle number; phase position in degrees; pulse polarity; pulse magnitude in pC; and pulse energy in J.
Corporate Research & Development, BHEL

39. Floating particles are seen with an applied voltage of 145/3 (83
Floating particles are seen with an applied voltage of 145/3 (83.72) kV, period 5 seconds, noise 15 dB, pressure 2 bar. PDmax (pC) : , PDavg (pC) : Similar recordings are obtained for protrusion and particles sticking on the surface of insulator.
The digital data was recorded by CDA in terms of magnitude (pC) vs phase angle, and number of counts vs phase angle. In order to establish the artificial neural network (ANN) technique, only magnitude vs phase has been considered for simulation. This is considered for all three types of defects simulated for this study.

42. Prediction of fault probabilities of rotating machinery using GRNN techniques
Corporate Research & Development, BHEL

43. Probability of occurrence
Fault
Symptoms of sensors
Probability Function
Probability of occurrence F1
f(S1,S13,S17)
P(U,S1Y,S13H,~S1,~S13)
1.0,0.7,0.3,0.2,0.4 F2
f(S2,S14,S17)
P(U,S2L,S14Y, ~S2, ~S14)
1.0,0.7,0.3,0.1,0.3 F3
f(S3,S15,S17)
P(U,S3H,S15H,~S3,~S15)
1.0,0.4,0.6,0.6,0.4 F4
f(S5,S6,S17)
P(U,S5H,S6L,~S5,~S6)
1.0,0.3,0.25,0.05,0.1 F5
f(S7,S8,S17)
P(U,S7L,S8HvS8L,~S7,~S8) -- F6
f(S3,S9,S11,S17)
P(U,S3H,S9H,S11H,~S3,~S9,~S11)
1.0,0.45,0.6,0.4,0.3,
0.2,0.15 F7
f(S10,S17)
P(U,~S10)
1.0,0.5 F8
f(S2,S17)
P(U,~S2) F9
f(S12,S17)
P(U,~S12)
F10
f(S18,S19,S20,S23,S17)
P(U,S18B,S19Y,S20B,S20J,S23Y)
1.0,.6,.3,.5,.4..2,.3,.35,.3,.05,.25,.1,.2,.02,.15,.2
F11
P(U,S18B,S19Y,S23Y,(S20A,S20B,S20C,S20I,S20H))
1.0,.7,.5,.7,.4,.4,.6,.3,.25,.05,.5,.1,.5,.6,.05,.6
F12
P(U,S18B,S19Y,S23Y,(S20A,S20B))
1,.75,.6,.8,.4,.5,.55,.3,.1,.05,.35,.05,.3,.55,.05,.6
F13
1,.7,.65,.7,.15,.5,.45,.3,.05,.02,.4,.1,.5,.55,.1,.6
Y/N – Yes/NO
H/N/L – High/Normal/Low
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44. Table – II Distribution of bit patterns as inputs to GRNN1 2 3 4 5 6 7 8 9 10 11 12 13 14
Fault No Related Symptoms
(High/Normal/Low)
(Yes/No)
(Faulty/Non faulty) P1 X1 P2
(Trend in Vibration) A X2 P3 Y X3
Output
Units B Xd
Summation
Units
Input Units Pm
Pattern
Units
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45. Actual
Network
Error
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46. Evaluation of Gas Turbine Control Constants
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47. PREDICTION OF GAS TURBINE CONTROL CONSTANTS
Artificial Neural Networks based solution for prediction of only important Site specific tunable control constants has been attempted considering the limitation on the availability of number of data patterns. Data for 8 sites have been taken with 7 site-specific inputs and 21 output control constants.

48. Gas Turbines are mechanical devices operating on a thermodynamic cycle (Brayton cycle) with air as the working fluid. The air is compressed in a compressor, mixed with fuel and burnt in a combustor with the gas expanded in a turbine to generate power used in driving the compressor and external loads (thrust or shaft power) depending on requirements.
The thermodynamic cycle that represents the common turbomachine is the "open" Brayton cycle.
Corporate Research

49. This h-s diagram represents the ideal enthalpy and entropy relationship for the Brayton cycle.
                                                                                        
Cycle Processes: 1-3 Isentropic Compression (q = 0) 3-4 Isobaric Heat Addition (w = 0) 4-9 Isentropic Expansion (q = 0) 9-1 Isobaric Heat Rejection (w = 0)
Corporate Research & Development, BHEL

50. Gas Turbine power-plant performance under ISO conditions (burning a reference fuel, such as natural gas, at 150 C, atmospheric pressure and 60% relative humidity) is information provided by machine manufacturers.
With the increasing utilization of gas turbines in industrial and cogeneration applications, they are taking on a greater role in base load service. Because of their inherent responsiveness, they also offer operating characteristics that can enhance their contribution to utility systems as a generator prime mover.
Corporate Research & Development, BHEL

51. Detailed discussion were held with RCPuram and were informed that there are about 560 control constants required for this type of machine and about 70 machines have been already supplied by BHEL. Also, information had been obtained that these machines have been supplied to 10 sites in all, since multiple machines have been supplied to the same site.
GT Engg. RCPuram has identified 183 Tunable Control Constants after analysing the individual Big Blocks of Control Sequence Programs (CSPs) and Control Constants. However, from the overall data for 10 sites provided, only 8 sites are found to be useful since the data patterns provided for 2 sites are matching and 1 data set was for a machine with DLN combustor.
Corporate Research & Development, BHEL

52. A final list of 21 control constants were prepared based on:
In this project, Artificial Neural Network (ANN) techniques are applied to analyze, predict the gas turbine control constants from generic blocks and application specific big blocks of the gas turbine control system. Constants from gas flow calculation, comparator, command state, Fuel Stroke Reference (FSR), Inlet Guide Vane (IGV) fault detection, acceleration control of FSR, Temperature control reference were short-listed to be predicted by ANN.
A final list of 21 control constants were prepared based on:
1. The data patterns available are enough for training ANN with reasonable accuracy in prediction.
2. Data spread is uniform, without any discontinuities.
Corporate Research & Development, BHEL

53. 7 site-specific variants were identified as input to ANN: Machine type
Site Elevation (m)
Site Design Temperature (0C)
Site Relative Humidity (%)
Type of Cycle
Output (MW)
Lower Heating Value of Fuel (Kcal/kg)
21 output variables were identified as output to ANN.
Corporate Research & Development, BHEL

54. 1. Model / Type of ANN (Training algorithm) 2. No. of Epochs
In order to obtain a best possible solution, ANNs have been trained by changing the following variants.
1. Model / Type of ANN (Training algorithm)
2. No. of Epochs
3. Data formats
ANNs have been trained for 2 types of Models and 2 types of data formats by varying the no. of Epochs (Low, Medium & High) for arriving at an optimum solution. The Input/Output data format has been used in engg units as well as normalized for training. The data has been normalised in such a way that the spread is in the range of -1 to +1.The normalised values for the data patterns are computed using the formula (Actual-Mean) / (Maximum-Minimum)
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55. The number of neurons in hidden layer are computed as, (No
The number of neurons in hidden layer are computed as, (No. of input neurons + No. of output neurons)/2 + Sqrt. (No. of patterns). The number of these neurons is either 17 or 19, depending upon the type of input data format.
The initial weights of these neurons are assumed to be a low value and are subsequently modified during the training. Selection of very low values for initial weights will result in a longer duration for training and very large values in saturation of the network. Hence, an optimum value of 0.3 has been assumed in this case. The learning rate and momentum are taken to be 0.1 and 0.3 respectively.
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56. The validation of ANN model is the most essential part of identification process. An engineer would never deliver a product without a mention about its accuracy. The approach to the validation of a trained ANN model is to establish the accuracy to which the model is able to predict. A naïve approach to this problem is to validate the trained ANN models with the data that the model was trained with. This is called Naïve Validation. Under Naïve Validation, an ANN model is always bound to predict closely to the trained data, since it was trained on that data. This does not mean that the ANN model is capable of representing the system, but only that the model is able to adjust to the trained data.
Corporate Research & Development, BHEL

57. 3 layer back propagation
Overall absolute deviations (max and min) for different sets of ANN Parameters under Naive validation
Set.
No.
ANN Parameters
Abs errors(%)
Model
No. of Epochs
Data Format
Min
Max 1
3 layer back propagation
2585
Engineer-
ing units
0.0
+8.8 2
- do -
3081 3
3524 4
Feed Forward BP using combination of linear, Gaussian, tanh, Gaussian complement and logistic
1153 5
1587 6
2180 7
2122
Normalised units
+20.8 8
2583 9
3200 10
661 11
778 12
1053
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58. Graphs indicating actual/predicted output values of RPCL Kalugurani
Graph indicating the error curve
Three Layer Backpropagation was used as the neural network model and training was done for 2500 epochs with seven input variables and twenty one output variables
Corporate Research & Development, BHEL

59. It is thus necessary, in order to check the ability of the ANN model to “generalise,” to validate the trained models on an independent set of data, called the Validation data. This final and decisive test for any trained model is a Cross Validation, which involves training with new data sets and making predictions and comparison with the actual data. The predicted data should be identical or within the prescribed uncertainty bounds.
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60. Sets of parameters used for training ANN for cross validation Set 1:
Data Type : Engineering units
ANN Model : 3-layer Back Propagation (BP)
No. of Epochs : 2,500
Set 2:
Data Type : Normalised units
Set 3:
ANN Model : Feed Forward BP using combination of linear, Gaussian, tanh, Gaussian complement and logistic
No. of Epochs : 1,115
Set 4:
ANN Model : Feed Forward BP using combination of linear, Gaussian, tanh, Gaussian complement and logistic
No. of Epochs : 1,087
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61. Image and signal processing; Control systems; Medical diagnosis;
Other Applications
Neural networks are nothing but a mathematical processing unit. It can be used for various applications, for example in:
Image and signal processing;
Control systems;
Medical diagnosis;
Incipient failures detection, diagnosis & prognosis;
Power systems reliability;
Function Approximations;
Speech Processing
Corporate Research & Development, BHEL

62. Dynamic Neural Controllerp a x e
: Input signal[Step or square Inputs]
: Neural unit
: Control signal for the plant
: Desired output
: Output of the plant T ] [ 2 1
are the neural inputs
: Error between the desired output
and the output of the plant 62
Corporate Research & Development, BHEL

63. Neural Edge Detector

64. Evolutionary Computation
Evolutionary Algorithms
- Genetic Algorithms
- Evolutionary Programming
- Evolutionary Strategy
- Genetic Programming
- Learning Classifier System
Swarm Intelligence
- Ant Colony Optimization (based on pheromone search)
- Particle Swarm Optimization (based on simulated annealing)
- Stochastic Diffusion Search
Self Organization
- Self Organizing Maps
- Growing Neural Gas
- Competitive Learning
Corporate Research & Development, BHEL

65. Differential Evolution (based on Genetic Annealing)
- a population based combinatorial algorithm
Artificial Life
- Strong Alife (based on Cellular Automata inspired by Von Neumann)
- Weak Alife (based on Neural Networks)
Harmony Search Algorithms
- Metaheuristic algorithm mimicking the improvisation of musicians
Artificial Immune Systems
Learnable Evolution Model
Cultural Algorithms
- Bridging the gap between Belief Space and Population Space
Corporate Research & Development, BHEL

66. Studies done on Supercritical Test Rig Facility Data through
Genetic Programming
Corporate Research & Development, BHEL

67. SUPERCRITICAL
TEST FACILITY

69. Does it satisfy the Dittus-Boelter equation? Nud = 0.023Red0.8Pr0.4
To find the correlation between Nusselts number (output) and Reynolds number & Prandtl number (inputs).
Does it satisfy the Dittus-Boelter equation?
Nud = 0.023Red0.8Pr0.4
(for sub-critical conditions satisfying heat transfer in fully developed turbulent flow in smooth tubes)
If not find the function linking Nusselts number to Reynolds number & Prandtl number using Genetic Programming.
Corporate Research & Development, BHEL

70. Corporate Research & Development, BHEL

72. #include <math.h> #include <float.h>
#define TRUNC(x)(((x)>=0) ? floor(x) : ceil(x))
#define C_FPREM (_finite(f[0]/f[1]) ? f[0]-(TRUNC(f[0]/f[1])*f[1]) : f[0]/f[1])
#define C_F2XM1 (((fabs(f[0])<=1) && (!_isnan(f[0]))) ? (pow(2,f[0])-1) : ((!_finite(f[0]) && !_isnan(f[0]) && (f[0]<0)) ? -1 : f[0]))
float DiscipulusCFunction(float v[]) {
long double f[8];
long double tmp = 0;
int cflag = 0;
f[0]=f[1]=f[2]=f[3]=f[4]=f[5]=f[6]=f[7]=0;
double Re=v[0] ;
double Prandtl=v[1] ;
L0: f[0]+= f;
L1: f[0]+=Prandtl;
L2: f[2]+=f[0];
L3: f[0]*=f[0];
Corporate Research & Development, BHEL

73. L8: f[0]*=pow(2,TRUNC(f[1])); L9: f[0]*=-1.642645597457886f;
L5: f[0]*=Prandtl;
L6: f[1]+=f[0];
L7: f[0]*=f[2];
L8: f[0]*=pow(2,TRUNC(f[1]));
L9: f[0]*= f;
L10: f[0]/=Re;
L11: f[0]=fabs(f[0]);
L12: f[0]/= f;
L13: f[1]+=f[0];
L14: f[0]*=pow(2,TRUNC(f[1]));
L15: f[0]*=f[1];
L16: f[0]+=f[1];
L17: f[0]=-f[0];
L18: f[0]+=f[0];
L19: f[0]+=Re;
L20: f[0]=sqrt(f[0]);
L21: f[0]-=f[2];
L22: f[0]*= f;
L23: if (!_finite(f[0])) f[0]=0; return f[0]; }
Corporate Research & Development, BHEL

74. float DiscipulusCRegressionFunction(float v []){
float ret = DiscipulusCFunction(v) ;
return ret; }

75. Boiler Performance Analysis Using Soft Computing Techniques
Corporate Research & Development, BHEL

76. 500 MW BOILER
(Sub critical once-through)
Corporate

77. PROBLEM FORMULATION
Presently Boiler performance analysis is being made through standard procedure. But there are deviations in the performance analysed using this procedure.
Presently Boiler Performance is estimated based on indirect method of calculating the cumulative losses from different sources and subtracting it from the theoretical design output.
But this method does not take into account the variations in the coal quality actually fed into the hopper nor does it take into account the variations in design of the boiler based on site requirements. 77

78. PROBLEM FORMULATION (Contd.)
Collaborator’s specifications are based on US grade coal. The deviations arising out of usage of Indian grade coal needs to be studied thoroughly.
For this purpose, the black box technique of neural network is being used to study the input-output relationship between various Boiler performance features and relevant inferences are drawn. 78

79. To study the performance aspects of various boiler data
APPROACH
To study the performance aspects of various boiler data
supplied by BHEL TRICHY R&D
Establish relationships between critical input-output data
(Feature Selection)
Preprocess the data to conform to ANN software
Identify suitable ANN model & train ANN
Carry out analysis and generate DLL that can be deployed
on remote computer
Development of ANN Prediction Tool 78

80. FEATURE SELECTION
Feature selection encompasses a wide variety of methods for selecting a restricted number of input variables or “features”, which are “relevant” to a problem at hand.
Feature selection is a problem pervasive in all domains of application of machine learning and data mining: engineering applications, robotics and pattern recognition (speech, handwriting, and face recognition), Internet applications (text categorization), econometrics and marketing applications and medical applications (diagnosis, prognosis, drug discovery).
Restricting the input space to a (small) subset of available input variables has obvious economical benefits in terms of data storage, computational requirements, and cost of future data collection. It often also provides better data or model understanding and even better prediction performance. 79

81. Reasons for performing feature selection include:
Improving performance prediction.
Reducing computational requirements.
Reducing data storage requirements.
Reducing the cost of future measurements.
Improving data or model understanding. 81

82. Where x is the Tilt in %. Tilt varies from -30 deg. to +30 deg.
BURNER TILT
Burner Tilt was averaged on all the corner points. Tilt expressed in % was converted to degrees using the formula:
((60/100)*x – 30)
Where x is the Tilt in %. Tilt varies from -30 deg. to +30 deg.
MILL COMBINATION
Mill Combination was taken to be the distance from Platen Super heater to the topmost mill fired. This is calculated as follows:
B8 = *1575 = mm Where distance
M8 = *1575 = mm from topmost mill is 18525mm &
T8 = mm there are 10 mills in total 81

83. The average of Left (L) and Right (R) values are taken LOAD
EXCESS AIR
Excess Air Percentage
(Calculated indirectly from dry O2 percentage in Economizer Outlet by the formula
100 * O2 / (21 – O2))
The average of Left (L) and Right (R) values are taken
LOAD
Load is expressed in MW
REHEATER SPRAY AND SUPERHEATER SPRAY
RH Spray & SH Spray are expressed in tonnes per hour and the sum of L & R values are taken. 82

84. DEVELOPMENT OF ANN PREDICTION TOOL83

85. Visual Basic Front End GUI
Block Diagram indicating dynamic program flow
ANN
DEF
File
ANN
DLL
Visual Basic Front End GUI
Predicted Sprays 84

86. Benefits of the ANN Prediction Tool
The tool will serve as a ready reckoner for operators to predict the quantity of Re Heater Spray and Super Heater Spray, after data entry of other input features like Burner Tilt, Mill Combination, Excess Air and Load.
A good understanding of the Boiler performance and the relationship between different input / output parameters will in due course of time lead to quality improvement in Boiler design and performance and acquiring analytical / design / modelling capabilities for better product design.
The performance analysis techniques used in this project can be extended to different types of Boilers. 85

87. Cross Validation (3rd Data Pattern):
Results Achieved
Backpropagation was tried to start with for 70 sets of power plant data classified as Training set & Test set. Data is sorted in terms of Burner Tilt, Excess Air, Mill combination and Load individually. Then it is trained with different learning rates, momentum, epochs & events since minimum average error > 40,000. With 90% Training Data,10% Test Data, epochs varying from 2019 to correlation coefficient of RH Spray is found to vary from to , correlation coefficient of SH Spray is found to vary from to
GRNN proved better with correlation coefficient of RH Spray varying from to , correlation coefficient of SH Spray varying from to with an optimum smoothing factor of approx which was generated through genetic algorithm. 80% Training data and 20% Test data was used.
Cross Validation (3rd Data Pattern):
Tilt MC Excess Air Load RH Spray SH Spray
(Actual)
(Cross Validation)
7.96% 3.07% (Error)

88. Soft Computing Components Methodology used
Boiler Performance Inputs
Training using Back-propagation
Training using GRNN
Generation of smoothing factor using Genetic
Algorithm
Boiler Performance Inputs
Generation of smoothing factor using Genetic
Algorithm
Training using Back-propagation
Training using GRNN 87

89. Function Approximation (General Regression Neural Network)
One of the most significant characteristics of the neural networks is their ability to approximate any arbitrary nonlinear function to desired degree of accuracy.
Neural networks potentially offer a general framework for modelling and control of nonlinear systems. 89 89

90. Function Approximation90 90

91. Mathematical Model of GRNNy (
X,y) dy -
E[y
|X] = ( X
,y) dy -
Multiplying the measured value of the output with the
appropriate probability density function of the Euclidean
distance (
Di) of any input variable X from other input
variables occurring in the attribute space and averaging
yields the estimated value of the predicted output n
 Yi exp(-D2/22)
I=1 n
 exp(-D2/22)
Y (X) =

92. Smoothing Factor
Many spheres of influence will be formed for various points.
The appropriate sphere of influence is defined as the one that
produces the smallest mean square error between the actual
and predicted output values. Determination of this appropriate
sphere of influence i.e.  (smoothing factor) is where learning
takes place in GRNN.
In order to find out the optimum smoothing factor, Genetic Algorithms are used. A genetic breeding pool size of 20 is used here.

93. What are Genetic Algorithms?
Search/Optimization technique by choosing survival of the fittest
through chromosome crossover or mutation. Unit of GA is allele. 92

94. How Genetic Algorithm Works? (Contd.)
After dozens or even hundreds of generations, a population eventually emerges wherein the individuals will solve the problem very well. In fact the most fit (elite) individual will be an optimum or close to optimum solution.
The processes of selection, mutation and crossover are called genetic operators. Genetic Algorithm includes additional genetic operators. One is called diversity operator.
Corporate Research & Development, BHEL

95. Genetic Algorithm for Boiler Performance Analysis
In the project “Boiler Performance Analysis using Soft Computing Techniques”, the twin objectives of minimizing ReHeater Spray as well as SuperHeater Spray become essential.
SuperHeater Spray and ReHeater Spray are functions of burner tilt in our curve fits generated. The other parameters like Mill Combination, Excess Air, Load can become part of the Chromosome (continuous type) input into the Genetic Algorithm software.
Therefore we now have fitness functions as well as chromosomes to achieve multiple objectives of minimizing ReHeater Spray as well as SuperHeater Spray.
The Curves are shown in the following slides:
Corporate Research & Development, BHEL

96. Power Station - I
Corporate Research & Development, BHEL

97. Power Station II
Corporate Research & Development, BHEL

98. HYBRID OPTIMIZATION USING GENETIC ALGORITHMS
The coefficients of the trend line equation, centroid of the excess air values after grouping, centroid of the Mass Flow values after grouping, different values of tilt after grouping were all taken as continuous chromosomes to be further treated by Genetic Algorithm (GA).
The sum of the squares of the error function between calculated values from the trend line equation and the actual RH, SH Spray were taken as two fitness functions.
The multiple objectives were to minimize both the fitness functions for RH as well as SH Spray.
The steam temperature corresponding to the data sets were taken as constraints and these were not to exceed 545 deg. C with a tolerance of 5 deg. C. The ranges of all the input chromosomes were defined. The GA was run for 650 generations before it converged on an optimum solution set.
Corporate Research & Development, BHEL

100. Development of Artificial Neural Networks (ANN) based Prediction Model for NOx Emissions from Utility Boilers
Corporate Research & Development, BHEL

101. With increasing environmental protection requirements in the. Global
With increasing environmental protection requirements in the Global Scenario reducing NOx emissions from utility boilers becomes mandatory.
The buck does not stop here. In fact there is a research group in Finland working on reducing NOx emissions from Fluidized Bed Combustion boilers.
According to an EPA report from North Carolina, NOx emissions could be broadly categorized into Thermal NOx, Prompt NOx and Fuel NOx.
101
Corporate Research & Development, BHEL

102. NOx can be controlled in two phases: During Combustion Process
Thermal NOx and Fuel NOx may be controlled but Prompt NOx remains uncontrolled and is quantitatively less compared to the former two.
NOx can be controlled in two phases:
During Combustion Process
Post Combustion
Combustion Controls:
1. Flue Gas Recirculation
2. Over Fire Air
3. Low NOx burners
4. Reburn
Post Combustion Controls:
Selective Non-Catalytic Reduction (SNCR)
Selective Catalytic Reduction (SCR)
Corporate Research & Development, BHEL

103. Thermal, Prompt and Fuel NOx are controlled by modifying:
Thermal NOx can be controlled by bringing Temperature down. Also bringing concentration of O2 or N2 down. Thermal NOx rises slowly.
Fuel NOx can be controlled by delayed mixing of fuel and air to form N2 rather than NOx. Fuel NOx rises rapidly.
Thermal, Prompt and Fuel NOx are controlled by modifying:
Combustion Gas Temperature
Residence Time
Turbulence
(The Three T’s)
Alternative Techniques for reducing NOx
Burner Out Of Service (BOOS): Stopping fuel flow from individual Burners. Air flow is maintained through idle burners.
Corporate Research & Development, BHEL

104. Close Coupled Over Fire Air (CCOFA): In the Main Wind Box
Biased Firing (BF): Injecting more fuel to some burners and reducing the amount of fuel to other burners.
Close Coupled Over Fire Air (CCOFA): In the Main Wind Box
Separated Over Fire Air (SOFA): Installed above main Wind Box using separate ducting.
SNCR: By injecting Ammonia in the flue gas. Ammonia is a pollutant and can react with SOx in the flue gas to produce Ammonium salts which can deposit in downstream equipment such as air heaters.
SCR: By injecting Ammonia in the presence of catalysts like Platinum or Palladium / Vanadium or Titanium / Zeolites (crystalline ammonio-silicate compounds)
Reduce Excess Air
Corporate Research & Development, BHEL

105. The Inputs and Output to the Neural Network
Features (with units)
MAX
MIN
MEAN
INPUT C%
50.07
33.02
41.84 H%
3.42
1.92
2.69 N%
1.15
0.81
0.96 O%
12.96
3.98
9.64
Total Air Flow (T/Hr.)
393.50
744.10
Excess Air (%)
48.20
18.71
24.93
Mill Combination
15.00
285.41
Burner Tilt (deg.)
30.00
-27.02
0.28
Load (MW)
514.10
51.09
328.16
Coal GCV (Kcal/kg)
Total Coal Flow (T/Hr.)
361.48
106.86
211.41
Capacity Load (MW)
500.00
210.00
428.98
Moisture (%)
21.10
5.71
12.75
Ash (%)
42.28
11.24
31.00
Volatile Matter (%)
31.24
18.84
24.98
OUTPUT
NOx (ppm)
489.33
140.78
289.00
Corporate Research & Development, BHEL

106. NOx Prediction Tool (15 features)
Corporate Research & Development, BHEL

107. Feature Selection was tried out by
Weights Method
First Order Derivative Method
Principal Component Analysis
Independent Component Analysis
F-distribution Method
1 & 2 results were not consistent and lacked repeatability.
Both PCA and ICA were tried out. They turned out to be academic exercises. PCA returned two principal components. On forcing, it returned two more principal components. These were in the form of eigenvalues (by minimizing co-variance and maximizing variance of the input matrix) to which no physical significance could be attributed to.
ICA turned out to be slightly better and converged on 3 eigenvalues. These were combined by Linear Algebra for the entire input matrix and could not be unmixed.
F-statistics approach based on ANOVA yielded best result.

108. Ten dominant features are extracted from 15 features
Feature Selection based on F-distribution and its corresponding p value
Ten dominant features are extracted from 15 features
F Value
p Value
Load
Total Coal Flow
Total Air Flow
Mill Combination N%
Moisture C%
Coal GCV
Excess Air
Volatile Matter

109. Application of Artificial Neural Networks for NOx Prediction
(General Regression Neural Network Model)
Network type : GRNN, genetic adaptive
Patterns processed : 49
Smoothing factor :
Output : NOx (in ppm)
R squared :
r squared :
Mean squared error :
Mean absolute error : 6.557
Min. absolute error : 0
Max. absolute error :
Correlation coefficient r :
Percent within 5% :
Percent within % :
Percent within % : 8.163
Percent within % : 0
Percent over 30% : 0
NOx

110. NOx Prediction Tool (10 features)
Corporate Research & Development, BHEL

111. Some Suggested Projects !!!
An Assessment of Back Propagation Neural Networks for Weather Forecasting
This research project looks at using local weather conditions and a Back Propagation neural network to predict the following day's barometric pressure.
An Assessment of Brain State in a Box Neural Networks for Database Assessment
This research project looks at using Brain State in a Box Neural Networks to assess the contents of a database and find data according to partial patterns provided.
Corporate Research & Development, BHEL

112. SURVEY OF RURAL STUDENT PROSPECTIVE ANALYSIS
1. Course of engineering is most usable in day-to-day life.
2. Engineering course takes background of 12th standard / pre-university course.
3. It is risky to get admission for engineering during recession.
4. Jobs / opportunities are more in engineering stream.
5. Innovation / discovery opportunity is more in engineering.
6. Relatives taking admission in engineering is an influencer.
7. 12th standard / pre-university teachers inspire engineering study.
8. Foreign opportunity is more after BE , B-Tech.
9. Family members suggest to go for engineering study.
10. Admission into any stream of engineering is OK.
11. Government providing loan for engineering is an enabler for engineering education.
12. Engineering is better than other courses in science, math, arts, fine arts.
13. Entrance exam for engineering is a hectic task.
14. Criteria for admission affects student behaviour.
We can introduce student behaviour system towards Engineering and Research with the help of artificial neural network model in Microsoft Excel. Excel is a popular and low cost technical education modelling technique with decision analysis.

113. Some More Applications
Automobile
Agriculture
Aerospace
Forestry
Construction
113
Milling / Drilling
Robotics
113

114. ! THANK YOU !
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