LabVIEW as Power Disturbances Classification Tools

*Ahmad Farid Abidin and Mohd Abdul Talib Mat Yusoh*

### **Abstract**

Power disturbances monitoring is one of the important aspects on dealing power quality issue in electrical system. The aims of conducting monitoring process are to identify the real culprit which contribute to the Power Quality (PQ) problem. One of the vital steps during monitoring process is the classifying various type of power disturbance. This classification process is very important to give a right direction towards proposing the correct mitigation technique. In order to produce reliable classification technique, the devices which has a flexibility on accommodating the software and hardware part need to be deployed. The software is need for algorithm development such as signal processing, Artificial Intelligent (AI) as well as statistical analysis. On the hardware part, the device's ability to acquire the electrical parameter within the electrical system operation is very important. The data acquisition based on the voltage and current is essential to be feed in the classification algorithm in software side. On the other hand, the interfacing devices and data acquisition module need to be developed at the hardware side, LabVIEW manage to accommodate both software and hardware need and further development of the LabVIEW for this purpose will be elaborated in this chapter.

**Keywords:** LabVIEW, power quality (PQ)

## **1. Introduction**

Power quality is a term that commonly used to represent the electrical supply to the consumer without any disruption or blackout. Technically, power quality is defined as the availability of supply voltage to be in the sinusoidal form within the permissible magnitude and frequency without misoperating the electrical equipment [1, 2]. The power quality issue becoming more important since 1980's due to the dominant use of electronic equipment among the electrical consumers. The electronic equipment is sensitive to any deviation in term of wave shape, magnitude, and frequency of supply voltage as their performance could be affected i.e. misoperation, burn, or shorten lifespan. Nowadays, most of the industries and commercial electrical consumer used the electronic equipment in their daily operation. Hence, the issues of power quality are very prevalent in business environment as the industry and commercial electrical consumer complained that they experienced power quality problem regularly.

Once the electric supply has a low power quality, all the undesirable situation would be experienced by electrical user. In order to have a good power quality, the electrical supply need to have a voltage with the attribute of pure sinusoidal(5% deviation), the permissible magnitude (nominal voltage 10%) and 50 Hz

frequency with permissible range(1%) [3, 4]. Based those three attributes, any deviation from the given permissible limit is known as power disturbances. The power disturbances consist of different type of disturbances namely, voltages sags, swell, interruptions, transient over voltages, harmonics, and voltage imbalance. The definitions of each type power disturbances can be described as follow [5]:

LabVIEW module by means of MATLAB algorithm to perform as power distur-

This research is conducted in LabVIEW software with the aid of National Instrument (NI) data acquisition module, where the entire data acquisition module, analysis module, features extraction process and data representation are developed and executed using this tools [23]. Along with enormous flexibility in interfacing and working with various type of real time input signal from different type of measurement tools, LabVIEW is offering a superiority analysis with a variety choice of programming tools that is appropriate to be implemented in real time power quality monitoring system. The flexibility of LabVIEW software also grants an excellent output result of the projects with lesser computational time. This develops system from this research basically is able to extract features and classify the PQ

Based on **Figure 1**, the developed system starts with the accumulating of PQ disturbances by using the PQ recorder i.e. Fluke 1750. Then those PQ disturbances are transferred to Chroma 61701 for storing and regenerating purpose. Then a National instruments Compaq Data Acquisition Chassis (NI cDAQ-9184) with NI 9225 card is used for high-speed acquisition of PQ disturbances. Further, the types of PQ disturbances are analyzed in LabVIEW interfacing since the voltage signals

LabVIEW is fast and widely used hardware integration which is a convenient instrument and PC constructed with data acquisition that has the ability to various interfacing type of real-time input signal [22]. Thus, it offers valuable analysis with variation choice of programming tools which is suitable to be implemented in real-

bance classifier.

**2. LabVIEW role**

**Figure 1.**

**145**

*System architecture of PQ analysis using LabVIEW software.*

time power quality monitoring system.

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

disturbance as illustrated in **Figure 1**.

Referring to **Table 1**, the type of disturbances has its own unique characteristics. Therefore, there are a paramount important to classify the type disturbance at initial stage of the PQ solution step. The classification of power disturbance is occasionally incorporating with signal processing technique which functioning as feature extraction tools. The popular signal processing technique that often used in extracting the feature are Short time Fourier Transform (STFT), Wavelet Transform (WT), S-Transform (ST), Hilbert Transform (HT), Hilbert Huang Transform (HHT), and Ensemble Empirical mode Decomposition (EEMD) [6–11]. STFT had been widely used as a tool in recognizing disturbances. Nevertheless, STFT has a limitation where it's only has a fixed windowing technique which is not fit for nonstationary signal [12]. The improved WT has been introduced to enable the nonstationary signal processing approach in continuous windows size by using multiresolution analysis (MRA) [13]. However, this technique suffers from high losses of information during MRA process [14]. On top of that, the WT has disadvantage where it fails to extract time-frequency information.

S-transform (ST) is proposed to carry out the solution which experienced by STFT and WT [15, 16]. ST is superior signal processing tool, where its technique is improved and outcome the limitation that exist in STFT and WT. ST manage to provide the distinct features which facilitate the decision making tools i.e., Artificial Intelligent (AI) to produce the accurate classification result accuracy. From the literature studies, there are several types of AI that frequently used as a decision making mechanism for classifying power disturbances, such as the Support Vector Machine (SVM), Levenberg Marquardt Neural Network (LMNN), Probability Neural Network (PNN), General Regression Neural Network (GRNN), and Radial Basic Function Neural Network (RBFNN) [17–21]. However, due to the less computational burden and resilience performance, the SVM has been chosen as a decision-making tool to be used in this work. Both ST and SVM are embedded to


#### **Table 1.**

*The definition of each type power disturbance.*

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

LabVIEW module by means of MATLAB algorithm to perform as power disturbance classifier.

LabVIEW is fast and widely used hardware integration which is a convenient instrument and PC constructed with data acquisition that has the ability to various interfacing type of real-time input signal [22]. Thus, it offers valuable analysis with variation choice of programming tools which is suitable to be implemented in realtime power quality monitoring system.
