power quality disturbance eviction using som neural network · classification of power quality...

Journal of Recent Advances in Electronics and Communication Engineering 2018; 1(1): 1-15

1

Research Article Open Access

Power Quality Disturbance Eviction using SOM Neural Network

Swapnil B. Mohod1, Vikramsingh R. Parihar2*, Ketki R. Ingole3

1Department of Electrical Engineering, PRMCEAM, Amravati, India.

2Department of Electrical Engineering, PRMCEAM, Amravati, India.

3Department of Computer science and Engineering, Sipna College of Engineering and Technology, Amravati,

India.

Abstract

Power quality disturbances disintegrate the expected power waveforms and recurring disturbances leads to

acute ramifications such as massive economic losses. Current researchers are indulged in addressing the issue

of power quality disturbance problem as it is inherently a consumer-driven issue. In this paper, we have

designed and optimized Artificial Neural Network (ANN) system to analyze the power quality disturbances and

classify them with a higher degree of accuracy. The frequently observed power quality disturbances were

predicted and classified using well-organized tools from ANN. Training of the Self Organizing Map (SOM)

network was done using the different data partitioning methods and its performance on seen and unseen data

was tested in terms of classification accuracy, Mean Square Error and correlation coefficient. The performance

of proposed ANN system was verified with six types of power quality disturbances. By performing sensitivity

analysis, numbers of inputs were reduced from 80 to 34(42.5%). Furthermore, observing that time elapsed per

epoch per exemplary highly reduced from 2.150ms to 0.49701µ-sec, and a number of connection weights also

decrease from 2442 to 1892 (22.52%), which is reasonably good. Dimension reduction is also achieved by using

the principle of sensitivity analysis which classifies the six types of power quality disturbances with a

classification accuracy of 95.849%.

Key words: Kohonen self-organizing map, Power quality disturbance, Wavelet transform.

1. Introduction

Power quality disturbance is the most common phenomenon that affects the industrial as well as commercial

JOURNAL OF RECENT ADVANCES IN ELECTRONICS AND COMMUNICATION ENGINEERING

Journal Home Page: https://uniquepubinternational.com/upi-journals/journal-recent-advances-electronics-

communication-engineering-jraece/

Copyright: © 2018 Unique Pub International (UPI). This

is an open access article under the CC-BY-NC-ND License

(https://creativecommons.org/licenses/by-nc-nd/4.0/).

Correspondence to: Parihar VR. Department of Electrical Engineering, PRMCEAM, Amravati, India. Email: [email protected]

Funding Source(s): NA How to Cite: Mohod SB, Parihar VR, Ingole KR. Power Quality Disturbance Eviction using SOM Neural Network. Journal of Recent Advances in Electronics and Communication Engineering 2018; 1(1): 1-15.

Editorial History: Received : 13-07-2018, Accepted: 16-10-2018, Published: 17-10-2018


2

electricity end user. Systematic working of electrical equipment depends on the higher quality of power and,

also, the expectations of consumers are the disruption free operation of equipment. Hence, detection and

classification of power quality disturbance events within stipulated time is needed. Previous studies have

analyzed power quality using spectral content obtained as a function of time using fractional Fourier transform

and Short Time Fast Fourier transform (STFT) [1-3]. Some other studies have used several signal-processing

and statistical analysis tools for detection of power quality events [4-8]. Some of the past studies have extracted

relevant features of experimental data using S-Transform (ST) algorithm and fuzzy decision for classification of

events [9-13].

During the recent times, because of discriminative training ability and easy implementation, the ANN find

extensive use in classification of the type of power quality disturbances. It turns out that the selection of a

number of nodes for an ANN is an important criterion in the analysis of power quality. However, a large network

means more computational expenses, resulting in more hardware and time related cost. Some of the

researchers have, therefore, tried to compact and optimize the design of neural network towards real time

detection of power quality disturbance analysis [14-20].

Among the various existing approaches to remove the power quality disturbances, the techniques incorporating

MLP-neural network, RBF Fault Classifier, Wavelet, ANN are found to have better results [21-25]. Through the

vast literature analysis, it is found that the power transformer faults and transmission line faults are a major

factor in contributing to the power quality disturbances as they inherently result in voltage surges, voltage sags,

short circuits, power surges and undesired transients. Many researchers are proposing their methods to improve

the power quality by protecting the power transformer and transmission lines. Also, fault detection, classification

and analysis play a pivotal role for enhancing power quality. Among the various existing methods, the ones

incorporating discreet tools from Fuzzy Logic and Neural Networks have found to yield better results [26-45]. In

this presented work, we have proposed a method to reduce the power quality disturbances using SOM Neural

network.

2. Experimental

After an exhaustive study of the literature, we have summarised our experimentation by giving more emphasis

on the power quality events such as Voltage Sag, Voltage Swell, and Arcing load influence.

2.1. Data Collection and Experimental Setup

Mains fed 1 HP, single phase, 50 Hz squirrel cage induction motor was used for analysis of sag as well swell in

the system by switching ON/OFF operation. 230V, single phase, 50Hz, welding machine was used to generate

actual arcing load influence in the system, welding electrodes keep short to experience a short circuit

phenomenon in the lab. The Tektronix Digital Storage Oscilloscope (DSO), TPS 2014 B, with 100 MHz bandwidth

and an adjustable sampling rate of 1GHz was used to capture the current signals. The Tektronix voltage probes

of rating 1000 V and bandwidth of 200 MHz approximately, 100 sets of signals were captured with a sampling

frequency of 10 kHz, at different mains supply conditions. The experimental setup as shown in Figure 1 uses an

Advantech data acquisition system having the specification as PCLD-8710 - 100 KS/s, 12-bit, 16-ch PCI

Multifunction Card. Overall to create a weak system inside the laboratory 2 ½ core, 200-meter-long cable was


3

used so that influence of sag, swell and arc load will be observable.

Figure 1. Experimental setup.

2.2. Design and Optimization of SOM-NN Classifier

The Kohonen self-organizing map (SOM) network performs a mapping from a continuous input space to a

discrete output space, preserving the topological properties of the input. This means that points close to each

other in the input space are mapped to the same or neighbouring Processing Elements (PEs) in the output

space. The basis of the Kohonen SOM network is soft competition among the PEs in the output space. The

Kohonen SOM is a fully connected, single-layer linear network. The output generally is organized in a one or

two-dimensional arrangement of PEs, which are called neighbourhoods. All the units in the neighbourhood that

receive positive feedback from the winning unit participate in the learning process. Even if a neighbouring unit’s

weight is orthogonal to the input vector, its weight vector will still change in response to the input vector. This

simple addition to the competitive process is sufficient to account for the order mapping. The general learning

algorithm used is as follows,

Step 1: Set the topological neighbourhood parameters. Set learning rate and initialize weights.

Step 2: While stopping condition is false, do steps 3-9

Step 3: For each input vector X do steps 4-6

Step 4: For each j compute squared Euclidean distance.

(1)

i = 1 to n and j = 1 to m

Step 5: Find index J when D(j) is minimum.

Step 6: For all units J with the specified neighbourhood of J and for all I update the weights.

2( ) ij iD j w x


4

(2)

Step 7: Update the learning rate

Step 8: Reduce the radius of the topological neighbourhood at specified times.

Step 9: Test the stopping condition.

2.3. Selection of Error Criterion

Supervised learning requires a metric to measure how the network is doing. Monitoring the output of a network

and compare it with some desired response and report any error to the appropriate learning procedure is done

by members of the Error Criteria family. In gradient descent learning, the metric was determined by calculating

the sensitivity that a cost function with respect to the network's output. The cost function, J, should decay

towards zero as the network approaches the desired response even if it is normally positive. The literature has

presented several cost functions, in which p is to define such as p=1, 2, 3, 4… ∞ criterion is L1, L2, L3, L4 …L∞

Components in the Error Criteria family were defined by a cost function of the form:

(3)

and error functions:

(4)

Where d(t) and y(t) were the desired response and network's output, respectively. To select the correct error

criterion, various error criterions has been

3. Results and Discussion

To classify the six conditions, we have considered six PEs were in the output layer. Remaining parameters of the

network, such as Neighbourhood shape, number of rows and columns of neighbourhood shape, number of

processing elements in the hidden layer, step size and momentum of hidden and output layer were selected and

optimized by experimentations and the results were as shown in Figure 3-9.

Figure 3.Variation of average classification accuracy and average minimum MSE with Neighbourhood shape.

( ) ( ) ( ) ij new ij old i ij oldw w x w

1

1( ) ( ( ) ( ))

2

p

i i

i

J t d t y t

( ) ( ( ) ( ))i i ie t d t y t


5

Figure 4. Variation of average minimum MSE and classification accuracy with number of rows and columns of

Neighbourhood.

Y: Processing Elements X: No. of Epochs Figure 5. Variation of average MSE with no. of processing elements in the hidden layer.

Figure 6. Variation in average Classification Accuracy with testing on Testing and Training dataset and percent

data tagged for training.


6

Figure 7. Variation in average classification accuracy and average minimum MSE with Epoch.

Y: Momentum rate X: No. of Epochs

Figure 8. Variation of average minimum MSE with momentum rate of hidden layer and output layer.

Y: No of Steps X: No. of Epochs

Figure 9. Variation of Average minimum MSE with step size of hidden layer and output layer.


7

From the results, it was observed that Square Kohonen Full neighbourhood shape with 18 rows and 18 columns

and starting radius 3 and final radius 0 given the optimum results. For supervised learning, the network was

retrained for five times with different number of hidden layers and number of PEs in hidden layers. It was

observed that single hidden layer with 28 PEs in hidden layer giventhe better results. Similar experimentations

were performed to decide the step size and momentum rate of hidden layer and output layer considering the

average minimum MSE as the performance index.

Finally, with above experimentations, SOM-NN was designed with following specifications,

Number of Inputs : 80

Neighborhood Shape : Square Kohonen Full,

Number of Rows in Neighborhood Shape : 18

Number of columns in Neighborhood Shape : 18

Starting Radius of Neighborhood Shape : 3

Final Radius of Neighborhood Shape : 0

Number of unsupervised epochs = 1000

Number of supervised epochs = 5000

Exemplars for training = 70%

Exemplars for cross validation = 15%

Exemplars for Testing = 15%

Number of connection weights : 2442

Number of PEs in Hidden Layer : 28

Time elapsed per epoch per exemplar : 2.150 ms

Hidden layer

Transfer Function : Tanh

Learning Rule : Momentum

Step size : 0.9

Momentum : 0.4

Output layer

Transfer Function : Tanh

Learning Rule : Momentum

Step size : 0.1

Momentum : 0.7

Different datasets were formed using variable split ratios. Proposed NN was trained on various datasets and

later validated carefully so as to ensure that its performance does not depend on the specific data partitioning

scheme. The performance of the NN should be consistently optimal over all the datasets with respect to MSE

and classification accuracy. Finally designed SOM-NN was trained five times with different random weight


8

initialization and tested on Testing dataset, CV dataset and Training dataset. For training and testing, the data

tagging by percent and data tagging by various groups were used. The algorithm trains the network multiple

times, each time omitting a different subset of the data and using that subset for testing. The outputs from each

tested subset were combined into one testing report and the model was trained one additional time using all of

the data. The set of weights saved from the final training run can then be used for additional testing. To check

the learning ability and classification accuracy, the total data was divided in four groups. First two groups (50%

data) were tagged as Training data and third and fourth group (each 25%) was tagged for Cross-Validation and

Testing (1234:1, 2-TR, 3-CV, 4-Test). Similarly, 24 combinations were prepared and the network was trained

and tested for each group [Figure 10].

Figure 10. Variation of average Minimum MSE with Training and CV for all 24 group of Dataset.

3.1. Feature Selection (Sensitivity)

One problem appears after the feature extraction that there were too many input features that would require

significant computational efforts to calculate and this may result in low accuracy. From the analysis, most

sensitive parameters were selected as input parameters. These input parameters versus average minimum MSE

on training and cross-validation is as shown in Figure 11. To reduce the number of inputs and dimensions of the

network model, the sensitivity analysis about mean and tanh axon- momentum was performed and total thirty-

four numbers of inputs were selected as indicated in Figure 12 and Figure13 respectively.

3.2. Selection of Error Criterion

To evaluate working of network supervised learning requires a metric. Any error in the appropriate learning

procedure was reported by comparing the members of the Error criteria with some desired response. From

equations (3) and (4), the desired response and network’s output are d (t) and y (t) respectively. The various

error criterions have been tested to select the correct error criterion and finally, it was found that the L-2

criterion showed optimal results (Figure 14-16).

Adopting the same procedure, the NN model was optimized by several experiments and finally, the required

network was designed as shown in Figure 17 and Figure 18 with following specifications:

Finally, by considering the most sensitive elements only, we have developed the new SOM Neural Network

based Dimensionally Reduced Sensitive classifier (SOM-DR-S classifier). This classifier is same as SOM-NN, the


9

only difference being that only the most sensitive elements are considered. The classifier was then trained and

tested for following conditions:

Number of Inputs : 34

Number of Hidden Layers : 01

Number of PEs in Hidden Layer : 46

Hidden Layer

Transfer function : tanh

Learning Rule : momentum

Step size : 0.9

Momentum : 0.9

Output Layer

Transfer function : tanh

Learning Rule : momentum

Step size : 0.1

Momentum : 0.7

No. of Epoch : 6000

Error Criterion : L2

Number of connection weights : 1892

Time required per epoch per exemplar : 0.49701 µ-secs

Percent reduction in weight : 22.52%

Figure 11. Sensitivity Analysis about Mean.


10

Figure 12. Variation of average classification accuracy and average minimum MSE with neighbourhood shape.

Figure 13. Variation of average minimum MSE on training and CV dataset with number of inputs.

Y: Error Criterion X: No. of Epochs

Figure14. Variation in average minimum MSE with Error Criterion.


11

Figure 15. Variation of average minimum MSE with test on Testing and Training dataset and percent data tagged for training.

Figure 16. Variation of average MSE on training and CV with Average classification accuracy for number of Epoch.

Figure 17. Variation of average minimum MSE with Training and CV for all 24 group of dataset.


12

Figure 18. Variation of Average minimum MSE and classification Accuracy with number of rows and columns of

neighborhood.

4. Conclusion

In this paper, we have examined the comparative results of SOM-NN based and SOM-DR-S classifier. In

proposed SOM-NN classifier, all samples including cross-validation and testing samples were correctly classified

with good classification accuracy to the corresponding classes (classification accuracy is 95.849%). By

performing Sensitivity analysis, numbers of inputs are reduced from 80 to 34 (42.5%). Furthermore, observing

that time elapsed per epoch per exemplary highly reduced from 2.150 ms to 0.49701 µsec and a number of

connection weights also decreased from 2442 to 1892 (22.52%), which is reasonably good. These results show

that the proposed SOM-NN classifier can reliably recognize classes of power quality disturbances. The

comparison results proved that the proposed SOM-NN have higher recognition accuracy and provided a better

classification and generalization capability.

5. Conflicts of Interest

The author(s) report(s) no conflict(s) of interest(s). The author along are responsible for content and writing of

the paper.

6. Acknowledgments

NA

7. References

1. Hamid EY, Kawasaki ZI. Wavelet-based data compression of power system disturbances using the

minimum description length criterion. IEEE Transactions on Power Delivery 2002; 17 (2): 460-466.

2. Dash PK, Panigrahi BK, Sahoo DK, Panda G. Power quality disturbance data compression, detection, and

classification using integrated spline wavelet and S- transform. IEEE Transactions on Power Delivery 2003;

18(2): 595-600.

3. Swain SD, Ray PK, Mohanty KB. Improvement of Power Quality Using a Robust Hybrid Series Active Power

Filter. IEEE Transactions on Power Electronics. Institute of Electrical and Electronics Engineers (IEEE);

2017; 32(5): 3490-8


13

4. Pillay P, Ribeiro P, and Pan Q. Power quality modeling using wavelets. IEEE Proceedings of the 7th

International Conference on Harmonics and Quality of Power (ICHQP), Las Vegas, NV, USA, 1996.

5. Santoso S, Powers EJ, Grady WM, and Hofmann P. Power quality assessment via wavelet transform

analysis. IEEE Transaction on Power Delivery 1996; 11(2): 924-930.

6. Faisal M, Mohamed A, Shareef H, Hussain A. Power Quality diagnosis using time frequency analysis and

rule based techniques. Expert System with Applications 2011; 38(10): 12592-12598.

7. Zang H, Zhao Y. Intelligent identification system of power quality disturbance. 2009 WRI Global Congress

on Intelligent Systems, IEEE, China, 2009.

8. Elango MK, Kumar AN, Duraiswamy K. Identification of power quality disturbances using Artificial Neural

Networks. 2011 International Conference on Power and Energy Systems, IEEE, India, 2011.

9. Huang NT, Zhang YJ, Xu DG, Liu XS, Qi JJ. Power quality disturbances classification by ensemble and

hybrid Neural networks. 2010 International Conference on Power System Technology, IEEE, China, 2010.

10. Hua L, Yuguo W, Wei Z. Power Quality Disturbances Detection and Classification Using Complex Wavelet

Transformation and Artificial Neural Network. 2007 Chinese Control Conference, IEEE, China, 2006.

11. Hu G, Zhu F, Zhang Y. Power quality faint disturbance identification using wavelet packet energy entropy

and weighted support vector machines. Third International Conference on Natural Computation (ICNC

2007), IEEE, China, 2007.

12. Yinghui Kong, Jinsha Yuan, Linlin Che, Tiefeng Zhang. Online power quality disturbances identification

using incremental wavelet decomposition and support vector machine. 2008 Third International Conference

on Electric Utility Deregulation and Restructuring and Power Technologies, IEEE, China, 2008.

13. Wei Liao, Hua Wang, Pu Han. Neural network-based detection and recognition method for power quality

disturbances signal. 2010 Chinese Control and Decision Conference, IEEE, China, 2010.

14. Ipinnimo O, Chowdhury S, Chowdhury SP. ANN-based voltage dip mitigation in power networks with

distributed generation. 2011 IEEE/PES Power Systems Conference and Exposition, IEEE, USA, 2011.

15. Manke PR, Tembhurne SB. Artificial neural network classification of power quality disturbances using time-

frequency plane in industries. 2008 First International Conference on Emerging Trends in Engineering and

Technology, IEEE, India, 2008.

16. Morsi WG, El-Hawary ME. A new fuzzy-wavelet based representative quality power factor for stationary and

non-stationary power quality disturbances. 2009 IEEE Power & Energy Society General Meeting, IEEE,

Canada, 2009.

17. Ming Zhang, Kai-Cheng Li, Wei-Bing Hu. Automated classification of power quality disturbances using the

S-transform. 2008 International Conference on Wavelet Analysis and Pattern Recognition, IEEE, China,

2008.

18. Reaz MBI, Choong F, Sulaiman MS, Mohd-Yasin F, Kamada M. Expert system for power quality disturbance

classifier. IEEE Transactions on Power Delivery. Institute of Electrical and Electronics Engineers 2007;

22(3): 1979-1988.


14

19. Levitin G, Kalyuzhny A, Shenkman A, Chertkov M. Optimal capacitor allocation in distribution systems using

a genetic algorithm and a fast energy loss computation technique. IEEE Transactions on Power Delivery.

Institute of Electrical and Electronics Engineers 2000; 15(2): 623-628.

20. Yuan S, Tong W, Tong C, Li Z. A novel method for power quality comprehensive evaluation based on ANN

and subordinate degree. 2008 Fourth International Conference on Natural Computation, IEEE, China, 2008.

21. Mohod SB, Ghate VN. Techniques for detection of power quality Disturbance waveform-A review.

International Journal of Electrical, Electronics and Computer Systems 2012; 8(2): 563-567.

22. Mohod SB, Ghate VN. MLP-neural network based detection and classification of Power Quality

Disturbances. International Conference on Energy Systems and Applications, IEEE, India, 2015.

23. Mohod SB, Ghate VN. Automated classification and detection of power quality disturbances using RBF fault

classifier. International Journal of Recent Technology and Engineering 2015; 4(4): 17-22.

24. Mohod SB, Ghate VN. Automatic recognition system for power quality disturbances based on wavelet and

ANN. International Journal on Technical and Physical Problems of Engineering 2015; 7(24): 1-7.

25. Mohod SB, Ghate VN. Comparative analysis of MLP-RBF based networks for detection and classification of

power quality disturbances. International Journal of Engineering Sciences & Research Technology 2015;

4(9): 623-641.

26. Parihar VR, Nimkar SD, Warudkar S, Deshmukh R, Thakare M. Power transformer protection using fuzzy

logic based controller. International Journal of Engineering Research 2017; 6(7): 366-370.

27. Parihar VR, Thakare M, Nimkar SD, Nage RS, Dhote AP. Neural network and fuzzy logic based controller for

transformer protection. International Journal of Current Engineering and Scientific Research 2017; 4(9):

33-38.

28. Parihar VR, Dhote AP. A novel approach to power transformer fault protection using artificial neural

network. International Journal of Current Engineering and Scientific Research 2017; 4(9): 33-38.

29. Parihar VR, Thakare M. Power transformer fault protection using artificial neural network. Journal of

Electrical and Power System Engineering 2017; 3(3): 1-5.

30. Parihar VR, Nimkar SD, Dhote AP. Fuzzy logic based controller for power transformer protection. Journal of

Electrical and Power System Engineering 2017; 3(2-3): 1-5.

31. Sanganwar A, Chalkhure K, Jijankar S, Dhore A, Parihar VR. Transmission line multiple fault detection: A

review and an approach. International Journal of Current Engineering and Scientific Research 2017; 4(10):

1-7.

32. Parihar VR. Distance protection problem in series-compensated transmission lines. International Journal of

Advanced Trends in Technology, Management and Applied Science 2017; 3(10): 44-48.

33. Parihar VR. Series-compensated transmission line problem in distance protection. International Journal of

Electrical, Electronics and Communication Engineering 2017; 3(10): 1-9.

34. Parihar VR, Thakare M, Dahake C. Series compensated line protection using artificial neural network.

International Advanced Research Journal in Science, Engineering and Technology 2017; 4(10): 102-111.


15

35. Parihar VR. Protection scheme of fault detection in high voltage transmission line. International Journal of

Advanced Trends in Technology, Management and Applied Science 2017; 3(11): 1-4.

36. Parihar VR. IOT Based Communication Technology for High Voltage Transmission System, Journal of

Electrical and Power System Engineering 2017; 3(3): 1-6.

37. Parihar VR. Transmission line protection analysis using STATCOM. International Journal of Advanced

Trends in Technology, Management and Applied Science 2017; 3(11): 23-26.

38. Parihar VR. A Review on transmission line fault detection techniques. International Journal of Advanced

Trends in Technology, Management and Applied Science 2017; 3(11): 27-32.

39. Parihar VR. Transmission line protection using distance relays. International Journal of Electrical,

Electronics and Communication Engineering 2017; 3(1): 1-15.

40. Parihar VR. Protection of power transformers using artificial neural network and fuzzy logic. International

Journal of Advanced Trends in Technology, Management and Applied Science 2017; 3(11): 72-79.

41. Parihar VR, Jijankar S, Dhore A, Sanganwar A, Chalkhure K.Automatic Fault detection in transmission lines

using GSM technology. International Journal of Innovative Research in Electrical, Electronics,

Instrumentation and Control Engineering 2018; 6(4): 90-95.

42. Parihar VR. UPFC based distance relays for protection of transmission systems employing FACTS,

International Journal of Advanced Engineering and Technology 2018; 2(2): 4-7.

43. Parihar VR, Warudkar S. Power substation protection from lightening over voltages and power surges.

International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control

Engineering 2017; 6(6): 26-31.

44. Nimkar SD, Parihar VR, Dhote AP, Dhake KG. Line trap and artificial intelligence based double circuit

transmission line fault classification. International Conference on Energy, Communication, Data Analytics

and Soft Computing (ICECDS 2017), IEEE, India, 2017.

45. Mohod SB, Parihar VR, Nimkar SD. Hybrid power system with integration of wind, battery and solar PV

system. IEEE International Conference on Power, Control, System and Instrumentation Engineering

(ICPCSI), India, 2017.

power quality disturbance eviction using som neural network · classification of power quality...

Documents