Power Quality Disturbance Eviction using SOM Neural Network · classification of power quality...
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Journal of Recent Advances in Electronics and Communication Engineering 2018; 1(1): 1-15
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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
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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
Journal of Recent Advances in Electronics and Communication Engineering 2018; 1(1): 1-15
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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
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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
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(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
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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.
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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.
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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
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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
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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.
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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.
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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.
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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
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