Quality Power

***

Journal of Applied Sciences Research, *(*): *18-625, 2011

ISSN 1995-0748

This is a refereed journal and all articles are professionally screened and reviewed

ORIGINAL ARTICLES

Power Quality Problem: A Statistical Classification on Industrial Perception in

Malaysia

1

M.A. Hannan, 1Azah Mohamed, 1Aini Hussain, 2R.A. Begum

1

Department of Electrical, Electronics and Systems Engineering

2

Institute of Environment and Development Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia

ABSTRACT

The power quality (PQ) is one of the main issues in the Malaysian industries. The issue is not just a

power quality, but also loss of profit as well. Thus, the industrial PQ problem is an important area of research

in term of classification and awareness development. This study developed a framework of survey design, data

collection and an analytical model for the statistical classification of the PQ problem and its severity level in

Malaysia based on the practical perception of industrial respondents. A statistical classification is done by

affected equipments and the matching correlation value between PQ severity level and the normalization

duration. The identified PQ severity factors (PQSF) are considered for different types of equipment for

industrial PQ problem. The findings will assist in the formulation of appropriate policies that address the

industrial PQ problem in Malaysia as well as indirectly improving the industrial PQ in the country.

Key words: Power quality, severity, classification, assessment, perception, PQ severity factors

Introduction

Power quality is the most prevalent problem in the industry in worldwide and Malaysia is not an

exception. The PQ problem may originate in the power system, but most frequently they are generated by the

equipment or load connected to it. For example inverter, arc furnaces, welders, alternators, motors, electronic

devices, process controllers, frequency converters and so many industrial equipments (Oliver et al.,2002;

Hannan and Kevin, 2004; Hannan et al., 2005; Hannan et al. 2009; Brooks et al.,2009). Prolonged exposure

of power quality problem can gained lethality or shorten the expected life of electronic equipment and

machines (Romely, 2010).

Based on damage, defect and short-life of the industrial equipment as well as quality of the final product,

the voltage sag is the most faced problem in Malaysia as shown in Table 1 (Vannoy et al., 2007; Mago et

al., 2008). The number of voltage sag incidents and percentage of PQ problems are 15 and 50.0 %,

respectively in 30 industries. Similarly, in case of harmonic, the numbers of incidents are 13 and percentages

of PQ problems are 43.3 %, respectively. Thus, it is found that the voltage sag, harmonics and flicker are the

most faced problem in the industries.

There have been several studies on the cause of PQ problem, sources, evaluation techniques, index and

severity and limit that can serve as guidelines to verify whether the amount of PQ is a problem (Wang et al.,

2004; He et al., 2006; Hannan et al. 2009; Salarvand et al., 2010). Many techniques were proposed in the

literature for the classification and assessment of the PQ problems such as optimal time-frequency

representations and wavelet transform (Mishra et al., 2008), s-transform and probabilistic neural network

(Samantaray, 2010), rule-based decision tree (Shukla et al., 2009), empirical-mode decomposition with Hilbert

transform (Andreotti et al., 2009), adaptive prony method (Baran et al., 2004), site-level PQ assessment

(Mazadi et al., 2007), generalized logarithmic mean and classical means (Chu et al., 2010), etc.

Dr. M.A. Hannan, Department of Electrical, Electronics and Systems Engineering

Corresponding Author:

E-mail: [******@***.***.**]

J. Appl. Sci. Res., 7(5): 618-625, 2011 619

Table 1: Industrial power quality problem summaries

Power quality events Number of incidents and % of PQ problems

incidents Percent

Flicker 11 36.7%

Voltage sag 15 50.0%

Voltage swell 8 26.7%

Harmonic 13 43.3%

Transient 4 13.3%

Interruption 1 3.3%

All of these existing methodologies are not well-developed in terms of statistical classification, PQ

assessment, its severity analysis and revealing inconsistent performance (Wang et al., 2004; IEEE Std., 1995).

Thus, industrial PQ is an important area of research that requires assessments, awareness and decisions for the

Malaysian high-tech industries, utilities and all power consumers.

IEEE is defined some standards to classify the PQ events, provide limit and recommendations for better

understanding on PQ monitoring, assessment and its severity level (Targosz et al., 2007). Generally, PQ

severity is expressed based on estimation, observation and regular operating conditions [IEEE Std., 1995;

Targosz et al., 2007; Grzegorz, 2008). PQ estimation is used to generate the best estimate of the most

significant severe effect by the PQ problem. For example, weighted least square method and measurement

matrix can be used to determine performance criteria, linear and nonlinear map between the measured signals,

the desired estimated states and the unknown variable, respectively (Stevic, 2010). However, infeasibility or

uncertainty can be problems due to singular measurements or a high number of required measurements. Also,

PQ measurements rarely use state estimations due to the deterioration of the Jacobian condition number

(Arrillaga et al., 2000). These drawbacks are effectively eliminated by formulating a time domain model and

a measurement matrix for PQ such as flicker estimation (IEEE Std., 1995; Bohner et al., 2007). Again, in

industry, the qualitative mapping of factors such as product quality, reliability, and direct cost effects can help

to develop PQ assessment techniques and an awareness of its effects (Berenguer et al., 2009). To deal with

these issues, this study developed a new kind of decision based industrial PQ severity assessment that creates

awareness and enables decision-making on power quality improvement.

This paper deals with the data obtained from a survey regarding industrial perceptions on PQ for an

assessment of classification, its severity and awareness. The PQ severity level classification system used in this

paper is based neither on experimental nor theoretical values, but rather on the practical observations of

industry personnel. Three parameters are used to determine the significant PQ severity: the weighted average

severity score (ASS), severity index value (SIV) and rank of severity index (RSIV). In ASS, four levels of PQ

severity classification are used to represent the parameter of equipment damage. At level 1 the PQ is not a

problem at all; level 2 indicates light effects resulting from PQ problem; level 3 is for moderate PQ effects;

and level 4 indicates severe damage caused by PQ problem. The aim is to increase the awareness level of

industry personnel and provide a decision-making tool for industry and utilities consumers. This paper describes

a new way to convert a practical and qualitative perception of industrial PQ into quantitative and qualitative

assessments, awareness and decisions.

Materials and methods

In industrial PQ severity classification includes methodological framework, data collection and analytical

models. Details of the assessment methods are given below.

Methodological Framework

The methodological framework is defined as the detailed statement of the problem, survey framework, data

collection and processing, data analysis, severity class, and awareness as shown in Figure 1. The problems of

the existing publications have been reviewed to develop a preliminary classification (Poon et al., 2001; Shen

et al., 2002; Begum et al., 2007). A survey framework was developed by creating a questionnaire and a

sampling procedure. Some initial questionnaire is pretested for final questionnaire development. The most

important part of this framework is data collection and processing, including data recording, entry, coding and

computations in order to obtain a industrial PQ severity analysis. Several PQ parameter and indices, such as

ASS, SIV and RSIV were developed for PQ classification.

J. Appl. Sci. Res., 7(5): 618-625, 2011 620

Data Collection

Data were collected through interviews with technical personnel registered with the high-tech industry

between July 2009 and March 2010 in the Klang Valley, Malaysia. In total, 30 industries participated in the

data collection, including semiconductor industries, process industries, manufacturing industries, heavy industries

and light industries. The semiconductor industries includes the companies that producing the semiconductor

raw material, components and packaging. Process industries are mainly composed of electronics, air-

conditioning, chemical, and pharmaceutical industries. Manufacturing industries includes rubber and furniture

industries. Heavy industries on the other hand include glass making company, steel mill, oil and gas companies.

The remains are the light industries involved in making the clothes and shoes.

Fig. 1: Block diagram of methodological framework used for industrial PQ severity classification.

In this study, a stratified random sampling method is applied to the four major groups of industries. In

the first stage of the data collection, the samples of the types of industries in high-tech activities were selected.

Then, the samples were stratified into three sub-groups in order to perform data collection, data entry and

coding and data computation. The final survey was based on 30 samples of high-tech industries. The interviews

were based on a set of questionnaires that were pre-tested and modified before use in the survey.

Analytical Model

Upon data collection, the data were analyzed by converting qualitative industrial data into a quantitative

and statistical value using the SPSS (Statistical Package for the Social Sciences) software. Three models of

the PQ severity and indices are as follows.

Average severity score (ASS):

The study employed the weighted average model to assess the relative significant level of the PQ severity

factor (PQSF) for different types of equipment in industry based on how the equipment is affected and

damaged. The weighted average model is written as:

J. Appl. Sci. Res., 7(5): 618-625, 2011 621

4

X Nij

j

j 1

ASSi ( 1)

N

where ASSi is the average significant score to the severity factor i, and Xj is the PQ severity level, which is

assumed to be in between level 1 to level 4 where 1 indicates not a problem at all, level 2 indicates a light

problem, level 3 is a moderate problem and level 4 is a severe problem, respectively. Also, Nij is the number

of respondents who give the factor i for the level Xj and N is the total number of respondents.

Rank of severity index value (RSIV):

After calculating the SIV, we ranked the PQ severity factor (PQSF) of the severity index value according

to the RSIV significance level.

Results and discussion

There are many ways to assess industrial data that has been collected in qualitative and quantitative forms.

However, we have limited our focus to only the PQ severity assessment and awareness in this paper.

Thirty different types of industries were surveyed for this study. The participating industries were

categorized as semiconductor industries, process industries, manufacturing industries, heavy industries and light

industries. The percentages of industries participating in this study are shown in Figure 2. The figure shows

that 40.00% of the survey respondents were from process industries such as air-conditioning, chemical and

pharmaceutical industries; 26.67% were from the semiconductor industries; heavy industries comprised 11.54%

and the remaining respondents were from light industries.

Fig. 2: Types of participating industries in the survey.

Methodological Framework:

The PQ severity was assessed from the perception of the industrial personnel. As mentioned earlier, the

level of PQ severity was classified into 4 levels based on equipment damage parameters. Figure 3 shows that

26.67% of industries faced severe PQ problems (L4), and 30.00% were faced with moderate problems (L3).

The percentages of industrial PQ problem at level L2 and L1 were 40.00% and 3.33%, respectively. Thus, it

can be concluded that the PQ severity level in Malaysia is a significant problem in a number of industries.

The industrial PQ problem classification as made based on analytical model as mentioned. The

classifications were given some information about which PQ problems are more severe in Malaysia. In this

paper, six PQ problems was classified and ranked such as flicker, voltage sag, voltage swell, harmonic,

transient and interruptions as shown in Table 2. In order to assessment the ranking significance of the industrial

PQ problems, the combination of the weighted average and coefficient of variation were used. Table 4.1 shows

the result of the PQ severity index value and the ranks of the industrial PQ problems. The result shows that

the highest PQ severity index value was 1.48 for the voltage sag. Thus, voltage sag becomes the most PQ

problem that occurred in Malaysian industries. While the interruption was the lowest ranking of the PQ

problem as it had the lowest PQ severity index value 0.19. Similarly, the other parameters such as percentage

of problems, mean and standard deviation were higher in case of voltage sag, the most severe PQ problems

in the industries.

J. Appl. Sci. Res., 7(5): 618-625, 2011 622

Fig. 3: Industrial power quality severity in percentage.

Table 2: Industrial power quality classification

PQ problems Problems Mean Std. dev., SIV RSIV

No Yes%

Flicker 11 36.7 0.37 0.490 1.13 3

Voltage sag 15 50.0 0.50 0.509 1.48 1

Voltage swell 8 26.7 0.27 0.450 0.87 4

Harmonic 1 43.3 0.43 0.504 1.28 2

Transient 4 13.3 0.13 0.346 0.51 5

Interruption 1 3.3 0.03 0.183 0.19 6

The sensitive industrial equipments that being affected by the PQ problems were also classified using

analytical model as shown in Table 3. The analysis was done to ensure that which equipments are most

vulnerable upon PQ problems through severity index value, SIV and its ranking, RSIV. The result shows that

the highest PQ severity index value was 1.701 for inverter. Thus, the inverter becomes the most vulnerable

equipment in Malaysian industries. While the compressor was the lowest ranking of the vulnerability as it has

the lowest PQ severity index value 0.19. Table 3 summarized the equipments classification based on PQ effects

using mean, standard deviation, SIV and RSIV. Thus, the analysis concluded that which equipments would

suitability of this vector in representing the PQ severity class. The performance of these equipments were

tripped or stopped when the PQ problems happened. Then the others process that related to these equipments

will also get trouble. Sometimes, these equipments damage and need to be changed with the new one. Even

though the power quality problem only occurred less than 1 minute, the process to recover from this problem

may take almost 12 hours.

Table 3: Classification of affected equipments due to the PQ problems

Equipments Mean Std. dev., SIV RSIV

Induction motor 0.53 0.507 1.575 2

Synchronous motor 0.17 0.379 0.619 7

DC motor 0.23 0.430 0.765 5

Microprocessor controller device 0.47 0.507 1.397 3

Inverter 0.57 0.504 1.701 1

Generator 0.07 0.254 0.346 8

Arc furnace 0.03 0.183 0.194 9

Static rectifier 0.23 0.430 0.765 5

Lighting 0.33 0,479 1.019 4

Compressor 0.03 0.183 0.194 9

This study presents 10 PQ severity factors (PQSFs) in terms of equipments for an industrial PQ

assessment. The identified severity factors are considered as having different effects on the different types of

equipment in Malaysian industries. The estimated results of the weighted value of the ASS, standard deviation, severity index value (SIV) and the rank of severity index value (RSIV) are summarized in Table 4. The

relative significance levels from the 30 respondents for each severity factor shows that the highest ASS is 4.00

for the synchronous motor (PQSF-2), arc furnace (PQSF-4), compressor (PQSF-9) and generator (PQSF-7).

This indicates that PQSF-2, PQSF-4, PQSF-7 and PQSF-9 cause the least severe effects or less significant

damage. Similarly, the ASS values for the severity factors are between 4.00 and 3.00 such as inverter (PQSF-

6), induction motor (PQSF-8), microprocessor controller device (PQSF-3) and lighting (PQSF-10) are the most

J. Appl. Sci. Res., 7(5): 618-625, 2011 623

severe effects or damage due PQ problems. PQSF-1 and PQSF-5 indicates DC motor and static rectifier,

respectively.

This study used the combined value of the weighted average and the coefficient of variation to rank the

significance of the PQ severity factors. It should be mentioned that the ASS is a weighted average and can

be used to rank all of the PQSFs. However, a commonly recognized weakness of using the weighted average

is that it does not consider the degree of variation between individual responses. In fact, a smaller variation

between individual responses can give a better weighted average value. Therefore, when two factors have the

same or very close average values, the factor carrying the smaller variation should be given a higher rank. One

common technique is to mitigate the weakness of ranking attributes using weighted average value and apply

a measure called the coefficient of variation, which is obtained by dividing the weighted average by the

standard deviation. Thus, an effective classification of ranking attributes should consider both the weighted

average and the coefficient of variation. The coefficients of variation are measured by the SIV model.

Table 4 also shows the results of the SIV and the ranks of the severity factors (RSIV). The results show

that the highest SIV was 11.33 for the inverter (PQSF-6) and the lowest SIV was 0 for the synchronous motor

(PQSF-2), arc furnace (PQSF-4), compressor (PQSF-9) and generator (PQSF-7). In fact, the result shows that

the ranks of the PQ severity factors did not change much for the two criteria of ASS and SIV. It was

reasonable to assume that the ranks established by either ASS or SIV effectively provide a PQ severity

assessment for the industrial devices. Thus, PQ severity is perceived through the average score of severity,

standard deviation, severity index value and provided the rank of severity index value for implementing the

industrial PQ classification.

Table 4: Weighted value of ASS and severity index value for different PQSF

PQSF ASS SIV RSIV

PQSF-1 3.25 0.71 7.85 6

PQSF-2 4.00 0.00 0.00 7

PQSF-3 3.33 0.62 8.73 3

PQSF-4 4.00 0.00 0.00 7

PQSF-5 3.67 0.82 8.16 5

PQSF-6 3.71 0.49 11.33 1

PQSF-7 4.00 0.00 0.00 7

PQSF-8 3.50 0.58 9.56 2

PQSF-9 4.00 0.00 0.00 7

PQSF-10 3.30 0.67 8.19 4

The result therefore can be greatly used as a guide to consider the steps to be taken in order to counter

the damaging effect due to PQ problems in the industries. It also provide guidelines to start being alert and

aware from PQ problems. Thus, the industry should take precautions in order to avoid loss and damage due

to PQ problems.

Conclusion

This study classifies various types of PQ problem by analyzing statistical method. The statistical

classification shows that 26.67% of the industries face severe problems, and 30.00% of industries have a PQ

severity level of 3, which is considered lethal and unhealthy for the industries. The findings were indicated

that among the PQ events, the voltage sag was the most faced (50.00%) problem, followed by harmonics

(43.30%), observed by 30 participating industries. Again, PQ severity level was classified its rank based on

affected equipment through median, standard deviation and SIV and median duration. It was found that

prolonged median duration and higher SIV could be lethal to the industries. The normalized correlation

transform between parameters vector values are indicating the class of PQ severity index level. Transformation

value more than 0.5 was suggested as a matching criterion. For example, the normalized duration of PQ for

the health hazard was 0.55, which was more than 0.50, indicates severe PQ problem. Similarly, matching PQ

severity level 2 i.e. light problems with PQ problem yields 0.288, which was less than 0.5. This means the

two vectors are not related. The classification quantifies the PQSF as mentioned earlier. According to the

respondents, inverter was the most significant PQSF that contributes PQ problem, while the synchronous motor,

arc furnace compressor and generator were the least significant in Malaysian industries. Thus, the average PQ

severity score, severity index value and severity index value ranking would provide valuable information for

classification industrial equipment and also create enough sense on PQ problem. A guideline can be

recommended through evaluating the various types of classification to make an appropriate policy on industrial

PQ problem in Malaysia. Thus, local authorities in industry or the government should provide guidelines for

industry personnel specifying PQ factors for the equipment via government industrial ordinance.

J. Appl. Sci. Res., 7(5): 618-625, 2011 624

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