Speed Prediction of DC Shunt Motor by using Artificial Neural Network.

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Speed Prediction of DC Shunt Motor by using Artificial Neural

Network.

Prashant Choudhary

, Badal Bepari

, Amit Kumar

, Aritra Ghosh

1,2,3,4_{Department of Electrical Engineering, Murshidabad College of Engineering & Technology, Murshidabad-742102}

Abstract— This paper explicitly and effectively examines the application of ANN to speed forecast of dc motors. Artificial neural networks (ANN’s) is an approach to evolve an efficient model for prediction of DC motor speed, based on a set of input conditions. Neural network algorithms are developed for use as a direct modeling method, to predict DC motor speed control operations. The training of the networks is performed with experimental machining data. Levenberg-Marquardt standard back-propagation algorithm with normalized preprocessed data is used to predict and show the pattern of correlation of input in relation to the output. All machine manufactures and industrial engineers often find ways of minimizing engineering time, reducing prototyping cost and optimizing product quality by expanding the data set for future year development.

Keywords—Artificial neural network (ANN), DC shunt Motor, Error chart, Feed-forward Back-propagation, MATLAB and Speed prediction .

I. INTRODUCTION

D.C. machine is a highly versatile energy conversion device. It can meet the demand of loads requiring high starting torques, high accelerating and decelerating torques. At present, the annual production of dc machines is about 40% of the rupee volume in electrical-machine production and sales. For these purpose, almost millions of dc motors are built each year. In industrial application requiring accurate control of speed and/or torque, dc motor is unrivalled.To record the speed of dc shunt motorwe varied the load torque by keeping constant the applied voltage and the field current at a particular desired value. In this method field circuit resistance is varied to control the speed of a d.c shunt motor. So it is typically time consuming and also it will vary on the ambient conditions.

The machine manufactures and industrial engineers find the way of minimizing time and maximizing product quality to achieve a sustainable and competitive goal and this can be achieved by application of Artificial neural network in forecasting the speed of dc shunt motor. The neural network approach is more faster and accurate than the other methods compared to traditional computing methods accordingly with the comparison on the testing results.

ANN is faster than other algorithms because of their parallel structure and it does not require solution of any mathematical model. Further it is not dependent on the parameters, so the parameters variations do not affect the result. Because of this, ANNs are widely used for system modeling function optimizing and intelligent control. In this paper ANNs give an implicit relationship between the input and output by learning from a training data set that represents the behavior of speed of dc shunt motor.

II. DIFFERENT TECHNIQUES OF ANNTO PREDICT THE DATA

In the year of 1943 Warren McCulloch and Walter Pitts created a computational model for neural network based on mathematics and algorithms. They called this model threshold logic. ANNs combine artificial neurons in order to process information. By adjusting the weights of an artificial the output can be obtain for any specific input. This process of adjusting the weights is called learning or training. There are mainly two types of learning 1. “Parameter Learning” which updates the connecting weight and 2.“Structure Learning” which focuses on the change in the network structure. By using graphical user interface(GUI) in MATLAB we generate MATLAB code files with command-line implementation of the GUI operations to predict the data by ANN technique. At first Network fitting tool GUI is used for prediction by ANNs in MATLAB. In Network fitting tool a neural network is used to map between a data set of numeric inputs and a set of numeric targets.

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And the additional optional test for testing data on the basis of network design by the training data is unable to produce the error of the testing data. To overcome this type of error of Network fitting tool, Neural network/Data manager in GUI using MATLAB is used. The graphical user interface also enables to create, initialize, train, simulate and manage networks. Once the Neural network/Data Manager window is set up and running, one can create a network, view it, train it, simulate it, and export the final results to the workspace.

III. EXPERIMENTAL APPROACH

The constructional features of dc machine is stated as the armature winding is a distributed winding around the periphery of the cylindrical rotor and the field winding is a concentrated winding consists of small cross-section fine wire and is connected in parallel with armature. Therefore, the voltage across the armature terminals and the shunt field is the same and it is for this reason that a shunt field may be called a voltage-operated field. By keeping constant two variables field current and applied voltage at a particular value and varying load torque around 8 to 9 readings of speed data have been taken. The voltage have been varied from 132 to 212 and field current is varied from 0.15 to 0.45.

Fig. I

All the collecting speed data are arranged and divide into two parts „training value‟ & „testing value‟ in excel sheet, so that it will be easy to access the data. Then the work started in MATLAB at first Neural network fitting tool is used for training, validation & testing purpose by uploading the data from excel sheet to MATLAB workspace. However, due to some problem as stated above that occurs in neural network fitting tool the error can‟t be calculated.

One can‟t see the relative error between the testing value and predictive value and can only satisfy ourselves by seeing the plot regression graph from which not many error information can be carried out.

To overcome the above problem another MATLAB software command i.e. neural network/Data Manager is used. In which the neural network can be design as per the requirement and allow initiating, training and simulating the data. In this tool the data is train as the network and the parameter is set and it will give the performance graph, training state, and regression plot. Now the testing data can be simulate on the network set up by the training data value. From the result of the simulation the relative error can be calculated from the target value and testing output value.

IV. ANNPREDICTION MODEL

Feed-forward Back propagation:- The network type used for predictive nn-model is Feed-forward Back propagation. Back propagation is a systematic method of training multilayer artificial neural network. Rumelhart, Hinton and Wilham(1986); Bryson and Ho(1969); werbos(1974); lecun(1985); Parker(1985) presented a clear description of the propagation algorithm. The back-propagation algorithm is used in layered feed-forward ANNs. This means that the artificial neurons are organized in layers and send their signals “forward”, and then the errors are propagated backwards. The network receives inputs by neurons in the input layer, and the output of the network is given by the neurons on the output layer. There may be one or more intermediate hidden layers. The back-propagation algorithm use supervised learning, which means that we provide the algorithm with examples of the inputs and outputs to compute network and then the error is calculated. The idea of the back-propagation algorithm is to reduce this error, until the ANN learns the training data.

Fig. II

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Self-organizing maps learn both the distribution and topology of the input vectors they are trained on. On the basis of this self-organizing map the testing output data is applied to compute the target data. The training of back-propagation network is done in three stages the feed-forward of the training pattern, the calculation and back-propagation of the error, and updating of weights. The testing of the back-propagation network involves the computation of feed-forward phase only. Even though the training is very slow, once the network is trained it can produce its outputs very rapidly.

A. Learning Factors of Back-propagation Network

1.Initial weights:- They are initialize at small random

values. The choice of the initial weight determines how fast the network converges.

2.Learning rate α:- The learning rate(α) affects the

convergence of back-propagation. A larger value of α may speed up the convergence but might result in overshooting, while a smaller value of α has vice-versa effect.

3.Momentum:- The gradient descent is very slow if

the learning rate α is small and oscillates widely if α is too large. One very efficient and commonly used method that allows a larger learning rate without oscillations is by adding a momentum factor to the normal gradient descent method. It also helps in faster convergence.

4.Generalization:- The best network for generalization

is back-propagation. A network is said to be generalized when it sensibly interpolates with input networks that are new to the network. For improving the ability of the network to generalize from a training data set to test data set, it is desirable to make small changing in the input space of a pattern, without changing the output components.

5.Number of training data: - The training data should

be sufficient and proper. We have use 61 no. of data for the training input and 18 no. of data for the testing output. We have three inputs and one output data value.

6.Number of hidden layer nodes:- If there exists more

than one hidden layer in a back-propagation, then the calculations performed for a single layer are repeated for all the layers and are summed up at the end. For a network of a reasonable size, the size of hidden nodes has to be only a relatively small fraction of the input layer.

V. RESULT

The Feed-forward back-propagation by using Neural network/Data manager has given three result :-

i. The performance graph

ii. The training state graph

iii. The regression graph

By changing the properties of hidden neurons layers and using various procedures we obtain a lot of performance results out of which the best of the data has been driven out.

TABLE I

Pro ced ure

No. of hidden layers

Hidden layer 1

Hidden layer 2

Min. value (err) %

Max. value (err) %

Avg. value (err) %

1. 10

Log-sigmoi d

Pure-linear

-0.05 2.259 -0.087

2. 10

Tan-sigmoi d

Pure-linear

0.02 2.99 -0.26

3. 2

Tan-sigmoi d

Pure-linear

-0.38 3.15 0.965

4. 4

Tan-sigmoi d

Pure-linear

-0.055

2.27 0.47

The performance goal was met during:-TRAINLM, Epoch 0/1000(28 iteration)

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Fig. 3. The performance graph

Fig. 4. The Training state graph

Fig. 5. The Regression graph

VI. ERROR CHART

TABLE II

Actual value of speed

Predicted speed (Process 1)

Error %

1380 1508 1335 1331 1323 1237 1218 1212 1140 1360 1125 1122 1175 1153 1417 1381 1518 1476

1392.7 1524.02 1345.05 1347.9 1351.4 1228.01 1204.7 1207.9 1151.2 1353.09 1123.6 1102.04 1151.2 1127.5 1435.4 1400.2 1529.3 1476.7

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218 TABLE III Actual value of speed Predicted speed (process 2) Error % 1380 1508 1335 1331 1323 1237 1218 1212 1140 1360 1125 1122 1175 1153 1417 1381 1518 1476 1409.358 1519.1066 1350.7926 1350.601 1346.8895 1248.1667 1222.4986 1219.6797 1152.4722 1359.6814 1108.9444 1089.4099 1152.4722 1139.217 1424.6271 1385.0943 1530.3748 1483.7142 -2.08 -0.73 -1.16 -1.45 -1.77 -0.89 -0.36 -0.62 -1.08 0.02 1.44 2.99 1.954 1.209 -0.53 -0.29 -0.80 -0.51

TABLE IV

Actual value of speed Predicted speed (process 3) Error % 1380 1508 1335 1331 1323 1237 1218 1212 1140 1360 1125 1122 1175 1153 1417 1381 1518 1476 1420.02 1546.39 1297.71 1290.26 1284.45 1223.67 1194.00 1183.58 1141.80 1338.7 1129.35 1113.76 1141.80 1124.34 1401.46 1363.72 1520.87 1495.03 -2.8 -2.4 2.8 3.1 3.0 1.0 2.0 2.4 -0.1 1.5 -0.3 0.7 2.9 2.5 1.1 1.2 -0.1 -1.2

VI. DISCUSSION

From that above mention result it is clearly shown that procedure 1 gives us better result with less percentage of error so procedure 1 is better than other predictive model to predict the speed of the DC shunt motor. But some problems are there regarding data selection procedure i.e. slight perturbation is there due to the heating effect of the machine.

VII. CONCLUSION

But it has to remember that this technique has some limitations. This system will give us the predicted data only within boundary layer. It means it can‟t extrapolate the data outside the training data region. That is the main drawback of ANN. Now support vector machine (SVM) will able to handle the data outside the boundary region and overcome the limitations of ANN approach.

REFERENCES

[1] Salvatore Cavalieri and Orazio Mirabella 1999. A novel learning algorithm which improves the partial fault tolerance of multilayer neural networks. University of Catania.

[2] Igor Kuzmanovski and Marjana Novič 2008 Counter-propagation neural networks in MATLAB. National Institute of Chemistry, Ljubljana, Hajdrihova 19.

[3] Davide Ballabio and Mahdi Vasighi, 2012. A MATLAB tool box for self organizing maps and supervised neural network learning strategies. Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran.

[4] Bernard Widrow, Aaron Greenblatt, Youngsik Kim and Dookun Park. 2013 The No-Prop algorithm: A new learning algorithm for multilayer neural networks. Stanford University, CA, United States. [5] Adepoju G. A., Aborisade, D.O and Eluwole O. T. 2011. Speed