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Corporate Research & Development, BHEL

Corporate Research & Development, BHEL

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Corporate Research & Development, BHEL

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  1. Application of Artificial Neural Networkand 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 - 500093, India. Corporate Research & Development, BHEL

  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 => neuronA carbon based chip from the brain-the biological computer 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 1011neurons about the same number as the stars in our galaxy. Each neuron, on an average, process about 104inputs 5

  6. NeuralInputs Synapse Axon (Neural output) Soma Dendrites Basic Biological Structure of a Neuron 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. Corporate Research & Development, BHEL Neural Network Application to Character Recognition using Backpropagation Imaging system Noisy Character Recognized Character A is represented As 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 1 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 0 0 0 1 M is represented As 1 0 0 0 1 1 1 0 1 1 1 0 1 0 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 1

  10. Corporate Research & Development, BHEL MATLABProgram 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));

  11. Corporate Research & Development, BHEL

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

  13. Corporate Research & Development, BHEL 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};

  14. Corporate Research & Development, BHEL 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.

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

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

  17. Corporate Research & Development BHEL

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

  19. Dissolved Gas Analysis using Artificial Neural Networks Rogers Fault Diagnosis Table

  20. Corporate Research & Development, BHEL • 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 Table-I : Gas Ratio Codes

  21. Corporate Research & Development, BHEL Table-III : Rogers Fault Diagnosis Table Table-IV : IEC Ratio Codes Table-V : IEC Fault Diagnosis Table

  22. Corporate Research & Development, BHEL 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.

  23. Corporate Research & Development, BHEL • 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.

  24. Corporate Research & Development, BHEL 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 1.5990609 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.

  25. Corporate Research & Development, BHEL 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.

  26. Corporate Research & Development, BHEL Fault Diagnosis of Generator Transformer of Power Station-I using ANN Approach

  27. Corporate Research & Development, BHEL 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 RuleBased Abnormal Detector Y Both indicate “Normal”? N RuleBased fault detector Neural Network based fault detector Combined fault diagnosis Maintenance Action Recommendation Outputs

  28. Corporate Research & Development, BHEL Limits of Gases Beyond Which Fault Diagnosis Becomes Necessary Note : *Before (C57.104) ** Corrected values 1978 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

  29. Corporate Research & Development, BHEL 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 ΣYI exp (-DI2/22) I=1 P2 Y1 A P3 X1 B Y2 Input Units (I = 1) Output Units Summation Units (O = 2) Pn n  exp (-DI2/22) I=1 Pattern Units (n=31)

  30. Corporate Research & Development, BHEL 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.

  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. Corporate Research & Development, BHEL 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.

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

  34. Data collection process

  35. Corporate Research & Development, BHEL 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.

  36. 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. Corporate Research & Development, BHEL 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.

  38. Corporate Research & Development, BHEL • PD Data Collection • For a given case, the data has been recorded for phase angle varying from 00 to 3600. 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.

  39. 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) : 172.51, PDavg (pC) : 93.5. 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.

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

  41. Corporate Research & Development, BHEL Y/N – Yes/NO H/N/L – High/Normal/Low

  42. Corporate Research & Development, BHEL Table – II Distribution of bit patterns as inputs to GRNN 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

  43. Corporate Research & Development, BHEL Actual Network Error

  44. Corporate Research & Development, BHEL Evaluation of Gas Turbine Control Constants

  45. 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.

  46. Corporate Research 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.

  47. Corporate Research & Development, BHEL 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)

  48. Corporate Research & Development, BHEL 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.