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A MATLAB BASED POWER QUALITY ANALYZER (PQA) FOR ENHANCING POWER

QUALITY IN THE SYSTEM

B. KUMARA SWAMY, P. PAVAN KUMAR & CH V K R M V PRASAD

Associate Professor, EEE Department, Raghu Engineering College, Visakhapatnam, India

ABSTRACT

Non - linear loads injects harmonics into the supply system. The injected harmonics, reactive power burden,

unbalance and excessive neutral currents cause low system efficiency and power factor. Modern power electronic loads are

creating a growing concern for harmonic distortion levels in and use facilities and on the overall power system. Harmonic

pollution is phenomenon, which affects all electrical networks in industrial, commercial and home appliances. Therefore,

increased concern for power quality has resulted in significant advances in power quality analyzing equipment. In this

paper a MATLAB based Power Quality Analyzer (PQA) is developed and simulated, which monitors the power quality of

supply at a given time and location on a power system. Assuming voltage and current signals are obtained and sampled atthat instinct of time over five cycles and transferred to personal computer (PC), using voltage and current sensor circuits,

sampling circuits and serial port respectively. A MATLABTM

based program was developed to analyze sampled data to

determine up to the 50th harmonic of the power frequency and can compute important power quality indices (PQI) like total

harmonic distortion (THD), power factor, real power, reactive power etc. The proposed PQA is flexible and can be altered

to perform practical real time analysis and various other functions.

KEYWORDS:Power Quality Analyzer, Power Quality, Power Quality Indices, Power Quality Variations

INTRODUCTION

Now a days electric power quality is an issue that is becoming increasingly important to electricity consumers at

all levels of usage. Sensitive equipment and non-linear loads such as computer and communication equipment, electronic

devices, semiconductor applications etc. for the growth of productivity and life quality are now more commonplace in both

the industrial/commercial sectors and the domestic environment. Also, this equipment is more interconnected in networks

and industrial processes so that the impacts of a problem with any piece of equipment are much more severe. Power quality

refers to a wide variety of electromagnetic phenomena, that characterizes the voltage and current at a given time and

location on a power system [1]. Very broadly, power quality is concerned with maintaining the near sinusoidal. Power

quality, like quality of other goods and services is difficult to quantify. There can be completely different definitions for

power quality, depending on ones frame of reference. Perhaps the best definition of power quality is the provision of

voltages and system design so that the user of electric power can utilize electric energy from the distribution system

successfully, without interference or interruption.

Power quality, the concept of powering and grounding sensitive equipment in a manner that is suitable to the

operation of that equipment. Power quality problem is defined appropriately as follows; Any power problem manifested

in voltage, current, or frequency deviations that results in failure or disoperation of customer equipment. Power quality is

the set of limits of electrical properties that allows electrical systems to function in their intended manner without

significant loss of performance or life [2]. The term is used to describe electric power that drives an electrical load and the

load's ability to function properly with that electric power. Without the proper power, an electrical device (or load) may

International Journal of Electrical and Electronics

Engineering Research (IJEEER)ISSN 2250-155X

Vol. 3, Issue 1, Mar 2013, 73-86

TJPRC Pvt. Ltd.


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74 B. Kumara Swamy, P. Pavan Kumar & CH V K R M V Prasad

malfunction, fail prematurely or not operate at all. There are many ways in which electric power can be of poor quality and

many more causes of such poor quality power.

The electric power industry comprises electricity generation (AC power), electric power transmission and

ultimately electricity distribution to an electricity meter located at the premises of the end user of the electric power [3].

The electricity then moves through the wiring system of the end user until it reaches the load. The complexity of the

system to move electric energy from the point of production to the point of consumption combined with variations in

weather, generation, demand and other factors provide many opportunities for the quality of supply to be compromised.

While "power quality" is a convenient term for many, it is the quality of the voltage - rather than power or electric current -

that is actually described by the term. Power is simply the flow of energy and the current demanded by a load is largely

uncontrollable.

As the use of power electronics and other sensitive devices in power supply network are increasing now a day,

which put a strain on the network, hence electricity suppliers and customers need a detailed data on the quality of their

network.

There are several important reasons to monitor power quality. The primary reason behind all others is economic,

particularly if critical process loads are being adversely affected by electromagnetic phenomena. Effects on equipment and

process operations can include disoperation, damage, process disruption, and other such disturbances. Such disruptions are

costly since a profit-based operation is interrupted unexpectedly and must be restored to continue production [4].

In this paper a prototype PQA based on PC for single phase using MATLABTM

is developed and simulated for

lower end applications, which will enable any user to conduct diagnostic testing on low voltage (230V) power supply.

Present study does not deal with development and implementation of hardware required to process the analog voltage and

current signals.

It deals only with software development to analyze voltage and current samples, to present harmonic spectrum ofvoltage and current signals along with PQI of study state variations like THD, power factor, real power, apparent power

etc., and interacts with user using Graphical User Interface (GUI) in MATLABTM

. System Modelling

POWER QUALITY ISSUES

To develop a PQA, which monitors power quality problems of power system, it is important to first understand

the kinds of power quality variations that can cause power quality problems. Categories for these variations must be

developed with a consistent set of definitions so that measurement equipment can be designed in a consistent manner and

so that information can be shared between different groups performing measurements and evaluations [3].

The first step in the implementation of a power quality-measuring instrument is then the identification and

definition of PQIs. Hence it is important to identify and develop PQIs, which are to be monitored to identify required

power quality variations. Categories and typical characteristics of these power quality variations according to IEEE

Standard 1159- I995 are tabulated in table 1.

Transients

Transient over voltages are brief, high-frequency increases in voltage on AC mains. Broadly speaking, there are

two different types of transient over voltages: low frequency transients with frequency components in the few-hundred-

hertz region typically caused by capacitor switching, and high-frequency transients with frequency components in the few-

hundred-kilohertz region typically caused by lighting and inductive loads [3]. Transients are very short duration (sub-


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A Matlab Based Power Quality Analyzer (PQA) for Enhancing Power Quality in the System 75

cycle) events of varying amplitude. Often referred to as "surges", transients are probably most frequently visualized as the

tens of thousands of volts from a lighting strike that destroys any electrical device in its path.

TransientsImpulsive

These are commonly known as switching surges or voltage spikes. They can be caused by circuit breakers out of

adjustment, capacitor switching, lightning, or system faults. They are characterized by a sudden, non-power frequency

change, high amplitude, fast rise and decay times, and high energy content.

Table.1 Categories and Typical characteristics of Power Quality Variations

Categories Typical Spectral Content Typical Duration Typical Voltage Magnitude

1.0 transients

1.1impulsive 5ns rise 1ms

1.1.3milli second

1.2 oscillatory

1.2.1 low frequency 1min 1.1-1.2pu

4.0voltage imbalance Steady state 0.5-2%

5.0waveform distortion

5.1DC offset Steady state 0-0.1%

5.2 harmonics Steady state 0-20%

5.3 interharmonics Steady state 0-2%

5.4 notching Steady state

5.5 noise Steady state 0-1%

6.0 voltage fluctuations intermittent 0.1-7%

7.0 power frequency

variations


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TransientsOscillatory

This is a sudden, bidirectional, non-power frequency change: a ringing. For high-frequency ringing over 500 kHz

of 1-s duration and for 5-500 kHz ringing with tens ofs duration, it is likely the result of either the system response or

the load response to an impulsive transient. With a frequency of less than 5 kHz and 0.3-50 ms duration, it could have one

of a number of causes.

Voltage Sag

The American "sag" and the British "dip" are both names for a decrease in voltage to between 10 and 90% of

nominal voltage for one-half cycle to one minute. Sags account for the vast majority of power problems experienced by

end users. They can be generated both internally and externally from end users facility. External causes of sags primarily

come from the utility transmission and distribution network.

Sags coming from the utility have a variety of cause including lightning, animal and human activity, and normal

and abnormal utility equipment operation. Sags generated on the transmission or distribution system can travel hundreds of

miles thereby affecting thousands of customers during a single event.

Voltage Swell

A swell is the opposite of sag - an increase in voltage above 110% of nominal for one-half cycle to one minute.

Although swells occur infrequently when compared to sags, they can cause equipment malfunction and premature wear.

Swells can be caused by shutting off loads or switching capacitor banks on [4].A swell can result from a single line-to-

ground fault that raises the voltage on the other two phases. It can also result from dropping a large load or energizing a

capacitor bank.

Interruption

When the voltage drops below 10% of its nominal value it is called an interruption or a blackout. Interruptionshave three classifications: momentary (lasting 30 cycles to 3 seconds), temporary (lasting 3 seconds to 1 minute) and

sustained (lasting more than 1 minute).

Voltage Flicker

Flicker comes from the aggravating, rapid on-off sensation of incandescent and fluorescent lamps as perceived by

the human eye. It results from the rapid variation in voltage within the normal allowable voltage range tolerance of 90-

110%. Flicker can result from electric arc furnaces, welders, rapidly cycling loads, or it can result from a large Adjustable

Speed Drive (ASD) with inadequate dc-link filtering on a weak distribution system [5]. With inadequate dc-link filtering,

the inverter harmonics, which are a function of a non-50-Hz fundamental, flow into the power system, causing a pulsating

of the 50-Hz fundamental.

Voltage Regulation

The term "voltage regulation" is used to discuss long-term variations in voltage. It does not include short term

variations, which are generally called sags, dips, or swells.

Low voltage during peak load periods can result from overloaded lines, improperly set transformer taps, or

maladjusted automatic voltage regulators [4]. The voltage is less than the normal 90% lower limit. Symptoms are dim light

bulbs, light bulbs burning out too often, and electric motors failing to start.


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Frequency Fluctuations

Normally, the variation in frequency is not significant enough to cause any problems. Frequency tends to lag a

little during the day, as central plant generators are well loaded, but at night, with light load, the frequency leads a little, so

that, at the end of a 24-hour period, all clocks are correct.

Deviations in frequency can occur in weak electric systems, such as, an island system with no main supporting

ties to the mainland or at an industrial plant with its own generating system.

A weak system could develop during an area-wide system disturbance that separates one part of the system from

another.

Over Voltage

This is a long-term increase in voltage for more than one minute duration. The magnitude of the increase is more

than 10% and less than 80%. Over voltages can be the result of load switching (e.g., switching off a large load), or

variations in the reactive compensation on the system (e.g., switching on a capacitor bank) [5]. Poor system voltage

regulation capabilities or controls result in over voltages. Incorrect tap settings on transformers can also result in system

over voltages.

Under Voltage

Under voltage is a decrease in voltage below 90% of its nominal value for more than one minute. Under voltage is

sometimes called a "brownout" although this term is not officially defined. Brownout is often used when the utility

intentionally reduces system voltage to accommodate high demand or other problems [5]. The symptoms of under voltage

can range from none to daily equipment malfunction or premature equipment failure. Under voltage may go unnoticed

until new equipment is installed or the electrical system is otherwise changed and the new combined load depresses (see

Sags) the voltage to a point where symptoms become apparent.

Voltage Imbalance

Voltage imbalance (or unbalance) is defined as the ratio of the negative or zero sequence components to the

positive sequence component. The negative or zero sequence voltages in a power system generally result from unbalanced

loads causing negative or zero sequence currents to flow. The primary source of voltage imbalance less than 2% is

unbalanced single-phase loads on a three-phase circuit [5]. Voltage imbalance can also be the result of capacitor bank

anomalies, such as a blown fuse on one phase of a three-phase bank. Severe voltage imbalance (greater than 5%) can result

from single-phasing conditions.

Voltage Distortion

Voltage distortion is the degree to which the voltage wave shape deviates from a sine wave. Distortion can result

from the following.

HarmonicsInter-harmonicsVoltage notchingNoise


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DC offset.CATEGORIES OF POWER QUALITY VARIATIONS

In this study, attention is limited to study state variations and waveform distortions, as these power quality

variations occur frequently within customer premises and are therefore of particular interest to power quality engineers.

Disturbances

These are almost always caused by fault conditions, the energization of large loads that require high starting

currents, or intermittent loose connections in power wiring. Depending on the fault location and the system conditions, the

fault can cause either temporary voltage rises (swells) or voltage drops (sags), or a complete loss of voltage (interruptions).

Each type of variation can be designated as instantaneous, momentary or temporary depending on its duration.

Steady State Variations

These include normal RMS voltage variations and waveform distortion. RMS voltage variations are deviations

outside the normal tolerance in the ac voltage for a period exceeding 1 minute. Over voltage and under voltage are thestandard terms employed for a long-duration voltage increase and decrease, respectively [3]. Long-duration variations

result from addition or removal of system load or reactive compensation, and can be controlled by improving the voltage

regulation characteristics of the system.

An interruption involves complete loss of voltage for 30 cycles or longer. Interruptions are caused by faults.

Waveform distortion is defined as a steady state deviation from an ideal sine wave of power frequency, principally

characterized by the spectral content of the deviation.

POWER QUALITY INDICES

Power quality indices, like any other engineering indices are used as a tool to represent, quantify, and compare

complex phenomena. Several indices are in common use for the quantification of electric power quality. These indices are

convenient for condensing complex time and frequency domain waveform phenomena into one number.

Present study is limited to only single phase power supply and system can sample at a particular instant of time

only, so we can determine the state of the supply at that instant using these indices [4]. The hardware and software

developed for the present system to obtain required indices are discussed later.

Various Power Quality Indices and their main applications as shown in the table

Table 2: Power Quality Indices

Index Definition Main Applications

Total harmonic

distortion(THD)

General purpose standards

Power factor(PF) Potentially in revenue metering

Telephone

influence factor

Audio circuit interference


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Table 2:

C message index Communications interference

IT product Audio circuit interference, shunt

capacitor stress

VT product Voltage distortion index

K factor Transformer derating

Crest factor Dielectric stress

Unbalance factor Three phase circuit balance

Flicker factor Incandescent lamp operation, bus

voltage regulation, sufficiency of

short circuit capacity

Power Quality Analyzer

Assuming voltage and current signals are first obtained via sensor circuits and they are converted into digital data

and transmitted to PC using Microcontroller, DSP or DAQ. As soon as a block of 500 samples from each channel i.e.,

voltage and current is obtained, PQA begins to process the data using Fast Fourier Transform (FFT) to calculate PQI

discussed above. After performing calculations PQA waits for the next block of sampled data.

Figure 1: Block Diagram of Power Quality Analyzer

Present study involves development and testing of software for implementation of PQA. Software development is

discussed along with algorithms and flowcharts in present chapter. Developed program is tested using pure sine and square

waves sampled data generated using simulink in MATLABTM.

Development of Software

Sampled data obtained by PQA are taken into voltage (vk) and current (ik) arrays. Arrays are multiplied by scaling

factor to get the actual magnitudes of voltage and currents. Analysis of the sampled data is carried out as follows using the

voltage (vk) and current (ik) arrays. Peak magnitude spectrum of voltage and current are obtained by applying FFT on

voltage (vk) and current (ik) arrays. Peak magnitudes voltages (Vi) and currents (Ii) corresponding to harmonics (up to 50th

)

are plotted. The following formulae are used for calculating PQIs.


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Figure 2: Flow chart for Calculating Frequency

Calculation of RMS Values of Voltage and Current

RMS Voltage,N

v

V

N

k

k

rms

=

=1

2

(5.1)

RMS Current,N

i

I

N

k

k

rms

=

=1

2

(5.2)

Calculating Apparent Power, Average Power and Power Factor

Apparent power, rmsrms IVS = (5.3)

Average power,N

iv

P

N

k

kk=

=1 (5.4)

Power Factor,

S

Ppf = (5.5)

Where k represents kth

sampled data and N represents the total no of sample data per cycle.

Measurement of Frequency

In present study, measurement of frequency is carried out based on zero crossings, i.e. calculating the no of

samples between two consecutive positive to negative transitions (Assuming that line frequency will never go below 40 Hz

and above 60 Hz). Here samples for 5 cycles have been considered so, N and S represent no of samples in 5 cycles and

sample rate respectively. Flowchart for measurement of line frequency is given in Fig.2.


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Calculation of Fundamental Voltage and Current

The fundamental voltage, V1 =22

2 BA +

=

=

N

k

k

N

kv

N

A

1

2cos

1

=

=

N

k

kN

kv

NB

1

2sin

1 (5.6)

The fundamental current, I1 =22

2 BA +

=

=

N

k

kN

ki

NA

1

2cos

1

=

=

N

k

kN

ki

NB

1

2sin

1 (5.7)

Where k represents kth

sampled data and N represents the total no of sample data per cycle.

Calculation of Total Harmonics Distortion (THD)

THD of Voltage, 1001

2=

=

V

V

THDi

i

v (5.8)

THD of Current, 1001

2=

=

I

I

THDi

i

i(5.9)

Where, Vi and Ii are the RMS voltage and current values of ith harmonic.

All the above PQI values are calculated using the MATLABTM

program, and presented graphically in an

interactive mode with user.

Testing of Software

Software for PQA is developed as discussed above, but this software needs to be tested for its validity and

accuracy. Standard sine and square waves at 50 Hz are used for testing, in case of sine wave voltage and current signals are

considered to be 30 degrees phase shifted and in square wave it is zero. Sine wave is used for testing PQIs and square

wave is used for testing magnitude spectra up to 50th

harmonic generated in PQA. Testing samples are generated using a

simulation developed in simulink in MATLABTM as shown in Fig.3 and Fig.4.

Figure 3: Simulation for Sine Wave Samples


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SINE WAVE

Voltage wave is considered to be 230V sine wave and current wave is considered to be 5A sine wave which is

lagging voltage wave by 30 degrees.

Here we have generated a sine wave of magnitude=1.5

Sine wave is used for testing PQIs.

Figure 4: Simulation for Square Wave Samples

SQUARE WAVE

Voltage wave is considered to be 230V square wave and current wave is considered to be 5A square wave with

zero phase shift.

Here we have generated a square of magnitude=1.5

Square wave is used for testing magnitude spectra up to 50th

harmonic generated in PQA.

One problem that faces power systems nowadays is the low frequency oscillations arising from interconnected

systems. Sometimes, these oscillations sustain for minutes and grow to cause system separation. The separation occurs ifno adequate damping is available to compensate for the insufficiency of the damping torque in the synchronous generator

unit.

RESULTS

Results of Sine Wave

Comparison of theoretical values and results obtained using PQA were given in table 3.

Table 3: Comparison of Theoretical and PQA values for Sine Wave


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Figure 5: Sine Wave Voltage and Current Output

Figure 6: Peak Magnitude Spectrum of (Sine wave) Voltage


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Figure 7: Peak Magnitude Spectrum of (Sine wave) Current

Results of Square wave

Comparison of theoretical values and results obtained using PQA were given in table 4.

Table 3: Comparison of Theoretical and PQA values for Square Wave


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Figure 8: Square Wave Voltage and Current Output

Figure 9: Peak Magnitude Spectrum of (Square Wave) Voltage

Figure 10: Peak Magnitude Spectrum of (Square Wave) Current

CONCLUSIONS

PQA was developed and implemented in present work using MATLABTM

in PC for low end applications, which

will enable any user to conduct diagnostic testing on power supply. This program was tested using standard Sine and

Square wave inputs and presented results in comparison with theoretical values. Results obtained in PQA were matching

with the theoretical values.

In present work a block of 500 samples per wave were considered for analysis which is obtained by sampling

voltage and current waves forms over 5 seconds. This block can be refreshed in every 5 seconds so that this program can

be utilized for real time purpose. This program can be enhanced for applications in three phase power system.


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However in future, it is desired to make system to measure more accurately regarding frequency and harmonic

measurement, since in present study frequency measurement is based on negative and positive transitions and fundamental

frequency is always treated as 50 Hz during FFT regardless of actual fundamental frequency.

REFERENCES

1. Akagi, H, (1996) Trends in active power line conditioner. IEEE Transactions on Power Electronic.2. Akagi, H., Watanabe, H. and Aredes, M. (2007). Instantaneous Power Theory and Applications to Power

Conditioning. New York. IEEE Press.

3. Arrillaga. J.,Bradley, D. A., and Bodger. P. S. (1985).Power System Harmonics Analysis. Jhon Wiley and sons.4. Arrillaga, J, Watson , N, R, and Chens , S. (2000).Power System Quality Assessment. Jhon Wiley and Sons.5. Schlabbach. J, Blume. D, S. (2000). Voltage Quality in Electrical Power Systems. The Institution of Engineering

and Technology.

6. Abur, F. Magnago, Y. Lu, Educational Toolboxes for Power System Analysis, IEEE Computer Applications inPower,vol.13, no.4, 2000,pp.31-35.

7. F. Milano, An Open Source Power System Analysis Toolbox, IEEE Trans. on Power Systems,vol. 20, no. 3,2005, pp. 1199 -1206.

8. R.D. Zimmerman, C.E. Murrillo-Snches, D. Gan, MatPower, A Matlab Power System SimulationPackage.Version 3.1b2.Users Manual, Power Systems Engineering Research Center, Cornell University, Ithaca, NY.

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