**4. Classification method**

and then connect to the waveform graph. The feedback on the array block is utilized to store and display each cycle of data per windows on the waveform graph. This is a simple manner to capture the features of PQ problems according to real-time signal of measurement. It is also to gain S-matrix graph of frequency time represent time series signal. Inside this mathscript node, MATLAB coding of ST and statistical command

*LabVIEW - A Flexible Environment for Modeling and Daily Laboratory Use*

Extracted features will be in statistical data waveform. All the wanted features extraction coding is assemble inside the mathscript node which are mean, median, standard deviation, variance, and others. This statistical method suitable to be used when lot of data is picked for training process and it is profitable to produce an effectiveness in SVM method of classifying. This reason statistical method is choosing because the data collected is in a set of possible value of the measured quantity. All the extracted features of the PQ disturbance signal from ST are introduce in

the form of the frequency-amplitude graph. The extracted features in form of statistical data are perform based on mean, median, standard variation, variance,

> *<sup>μ</sup>* <sup>¼</sup> <sup>1</sup> *N* X *N*�1

*m* ¼ *L* þ

Mean is the average value of a signal. The value of mean can be gain by adding all amplitude of signal and divide by N, sample. Mathematical equation as below (12).

*i*¼1

*N* <sup>2</sup> � *F f* � �

Standard deviation (STD) is a range of how far the signal varies from the mean and average deviation. This variance value can be gain by the average of the squaring

Median is a middle number of arrangements of amplitude in numerical order

*xi* (12)

*C* (13)

are embedded.

*Block diagram of high NTGV features extraction.*

**Figure 7.**

kurtosis and skewness.

**150**

and the mathematical equation state below:

In order to complete this project, the construction of last part is handled in MATLAB software. The coding of classification PQ disturbance is created, and result is recorded. Simulation of SVM method is done to gain the accuracy of classification.

SVM classifier start with load a big number of PQ disturbance sample, i.e. 100. Which randomly consist of 9 types of statistical pattern. All data is randomize using for loop. After record the whole randomized data, 2 stages of SVM algorithm will be perform. Step first for SVM is performing training samples which based on flowchart been initialized at 50%. In order to produce good generalization performance function of Gaussian or polynomial can be chosen in training model. Then fitness function will be calculated referring on the training data Therefore, the remaining data sample are used as testing process. The training result will be model for testing process. Then the accuracy is evaluated whether reach satisfaction of this project or far from reaching desired accuracy.

Target value will be compared with testing. Should be the classification success approaching 100% to reach high accuracy level. If the classification evaluate is effective enough, thus process will be start again with randomized data.

**Figure 8** above is created at editor window. The training data percentage is select for 50%. In randomize data code loop for is used. For loop function to executing a specific statement repeatedly until some false condition is met. In this application, it's for the purpose of different type of PQ disturbance. The Data is the original statistical feature of each sample. Then all the statistical data is identified which class each. It is arranged into their type of disturbance and statistical features is assemble randomly.

**Figure 9** above is coding of training classification of PQ disturbances. X interval is the input of predictor data which each column will be observe while Y interval is target value. At line 40, there will be non-linear type of default value for the twoclass learning that separate the data with the hyperplane. It is important to choose type of kernel function in order to accomplish success training. RBF is used for one type training while gaussian or polynomial can be used for two type class or multiple. Polynomial is chosen due to disorder symmetry. The 'class names' to distinguishes the different type data which represent PQ disturbances.


ECOC error correction output code

HHT Hilbert Huang transform

NI national instruments OVO one versus one OVA one versus all

PNN probability neural network

MRA multiresolution analysis RMS root-mean-square RBF radial basis function

HT Hilbert Huang IOT Internet of Thing

PQ power quality

ST S-transform STD standard deviation STFT short time fast transform SVM support vector machine THD total harmonic distortion WT wavelet transform

**Author details**

Selangor, Malaysia

**153**

GRNN general regression neural network

*LabVIEW as Power Disturbances Classification Tools DOI: http://dx.doi.org/10.5772/intechopen.96079*

LMNN Levenberg Marquardt neural network

RBFNN radial basic function neural network

Ahmad Farid Abidin\* and Mohd Abdul Talib Mat Yusoh

\*Address all correspondence to: ahmad924@uitm.edu.my

provided the original work is properly cited.

Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam,

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

EEMD empirical embedded mode decomposition

#### **Figure 8.**

*Coding of randomized data of classification using SVM in MATLAB software.*

#### **Figure 9.**

*Training coding of SVM in MATLAB software.*
