DIRECT TORQUE CONTROL OF BLDC MOTOR USING FUZZY LOGIC IN LABVIEW

DIRECT TORQUE CONTROL OF BLDC MOTOR USING FUZZY LOGIC IN LABVIEW

ABSTRACT

Brushless dc motors are widely used in many industrial applications due to their high efficiency, high power density and ease of control. In this paper, sensorless direct torque control (DTC) of bldc motor is implemented using fuzzy logic. In actual DTC both torque and stator flux linkage is controlled. In the proposed system, the control of stator flux linkage is avoided because every commutation will cause the stator flux linkage decreasing dramatically and sharp dip appears on the locus of the stator flux linkage every 60 electrical degrees. The best way to control the stator flux linkage amplitude is to know the exact shape of it, but it is considered too cumbersome in the constant torque region. Therefore the amplitude of stator flux linkage can be considered as a constant. The proper voltage vector selection is done using fuzzy logic controller which improves the dynamic performance. The sensorless operation is achieved by using a state observer. All the simulations were done in LabVIEW software of virtual instrumentation.

Keywords—Brushless DC motor(BLDC), Direct torque control (DTC), fuzzy Logic controller, stator flux linkage, voltage vector selection, state observer, LabVIEW, virtual instrumentation.

INTRODUCTION

Permanent magnet Brushless DC (BLDC) motors are nowadays widely used in industries such as HVAC industry, medical, electric traction, road vehicles, aircrafts, military equipment, hard disk drive, etc. due to their high efficiency, high power density, reliability and ease of control. Today there are basically two types of instantaneous electromagnetic torque controlled ac drives used for high-performance applications: vector and direct torque control (DTC) drives. The vector control, the most popular method uses a decoupling control which transforms the motor equations into a coordinate system that rotates in synchronism with the rotor flux vector. The second method, Direct Torque Control (DTC) is a form of hysteresis or bang-bang control to control torque (and thus speed) of electric motors. The basic concept behind the DTC of ac drive, as its name implies, is to control the electromagnetic torque and flux linkage directly and independently by the use of six or eight voltage space vectors found in lookup tables. The advantages associated with DTC are simplicity and high dynamic performance, no vector transformation and faster torque response. The torque ripple is the major problem associated with DTC (1-8).Various papers proposed new methods for eliminating the problems associated with classical DTC. Some papers proposed multilevel inverter in which there are more voltage space vectors available to control the flux and torque and hence smoother torque can be obtained

(2). Here more power switches are required which increases system cost and complexity. In (6) and (11) two PI regulators are required to control the flux and torque and they need to be tuned properly. In DTC,\ the stator flux is estimated by integrating the back- EMF which should be reset regularly to reduce the effect of the dc offset error. In papers (12), (13) dc offset is eliminated by introducing low pass filters for estimating the stator flux linkage. In this paper fuzzy logic is used for proper voltage vector selection. Fuzzy logic can be considered as a mathematical theory combining multi-valued logic, probability theory, and artificial intelligence to simulate the human approach in the solution of various problems by using an approximate reasoning to relate different data sets and to make decisions. It has been reported that fuzzy controllers are more robust to plant parameter changes than classical PI or controllers and have better noise rejection capabilities. The introduction of fuzzy logic quickens the torque response and provides smooth torque. Hence overall static and dynamic performance can be improved (7- 8).The torque and rotor speed are obtained from the back-EMFs, which are estimated using a state observer. In this paper, all simulations had done in the LabVIEW (Laboratory Virtual Instrument Engineering Workbench) software. In comparison with the other software tools, the simulation with LabVIEW provides easy debugging features and user friendly environment. The conventional six step control of BLDC motor is also simulated. The simulation results shows that the proposed DTC scheme using fuzzy logic has good control performance, compared with conventional method.

BLDC MOTOR DRIVE MODEL

The assumptions made for modeling BLDC motor are

1) The motor’s stator is a star wound type.

2) The motor’s three phases are symmetric, including their resistance, inductance and mutual inductances.

3) There is no change in rotor reluctance with angle due to non-salient rotor.

The BLDC motor is modeled in the stationary reference frame using phase currents, speed, and rotor position as state variables. The BLDC motor is modeled as

(1)

Where Va,Vb,Vc, Ia,Ib,Ic, and Ea,Eb,Ec are the stator voltages, stator currents and back-EMFs of all the three phases respectively, R and L are the resistance and inductance of stator phase winding respectively and p is the differential operator(d/dt).

The generated electromagnetic torque is given by

(2)

where ω is the speed.

The induced back-EMFsis of trapezoidal shapes and can be written as

Ea = fa(θ)λω (3)

Eb = fb(θ)λω (4)

Ec = fc(θ)λω (5)

wherefa(θ), fb(θ), fc(θ) are functions having trapezoidal shapes as back-EMFs and λ is the back-EMF constant.

BLDC – DTC SYSTEM

A conventional six-switch 3-phase inverter fed BLDC motor in two-phase conduction mode, as show in Fig. 1.

Fig. 1. An inverter driving BLDC motor

The primary voltages Van, Vbn, and Vcn are determined by the status of the six switches, S1, S2, S3, S4, S5 and S6. There are six nonzero voltage vectors:- V1(100001), V2(001001), V3(011000), V4(010010),V5(000110), V6(100100) and one zero voltage vector V0(000000). The six nonzero voltage vectors are 60 degrees apart from each other as in Fig. 2.

Fig. 2. Stator flux linkage space vector representation

The basic principle of direct torque control (DTC) is to choose the appropriate stator voltage vector out of eight possible voltage vectors according to the difference between the reference and actual torque and flux linkage so that the stator flux linkage vector rotates along the stator reference frame trajectory and produces the desired torque. The stator flux is controlled by properly selecting voltage vectors and hence the torques by stator fluxes rotation. The faster torque response is achieved by increasing the stator vector rotation speed. In this proposed paper (14), the DTC of a BLDC motor drive operating in two-phase conduction mode is simplified by controlling only torque and by intentionally keeping the stator flux linkage amplitude constant by eliminating the flux control in the constant torque region. Since the flux control is removed, fewer algorithms are required for the proposed control scheme. There is no need to control the stator flux linkage amplitude of a BLDC motor in the constant torque region due to the sharp changes which occur every 60 electrical degrees and hence the flux control is quite difficult. Therefore the stator flux linkage amplitude is kept almost constant on purpose and only torque is controlled. Also the zero voltage vector suggested in to decrease the electromagnetic torque could have some disadvantages, such as generating more frequent and larger spikes on the phase voltages that deteriorate the trajectory of the stator flux-linkage locus, increase the switching losses, and contributes to the large common-mode voltages that can potentially damage the motor bearings. To overcome these problems, a new simple voltage space vector look-up table is developed. The rotating speed of the stator flux linkage can be controlled easily by selecting proper voltage vector. For instance, in the region I of Fig. 2, for counterclockwise operation, if the actual torque is bigger than the reference, voltage vector V5 is selected to keep flux linkage rotating in the reverse direction. The torque angle decrease as fast as it can, and the actual torque decrease as well. Once the actual torque is smaller than the reference, voltage vector V2 is selected to increase torque angle and the actual torque. Hence the region of the stator flux linkage is known, selecting proper voltage vector can reach fast torque

control. The new simplified switching table is as follows:

TABLE I

THE SWITCHING TABLE FOR INVERTER

Here the ET represents the error in torque and is determined by the difference between reference torque and estimated torque. The value ‘0’ or ‘1’ stands for that the estimated value is smaller or bigger than the reference value respectively. I, II, III…, VI denotes the stator flux linkage region.

BLDC-DTC SYSTEM USING FUZZY LOGIC

In the past decade, fuzzy logic control techniques have gained much interest in many applications. Fuzzy Logic is a form of many-valued logic or probabilistic logic. It deals with reasoning that is approximate rather than fixed and exact. In traditional logic theory, have two valued logic-true or false (0 or 1). It has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false. They have a real time basis as a human type operator, which makes decision on its own basis. In the proposed BLDC-DTC system the fuzzy logic controller is used for selecting the proper voltage vector. It involves basically three steps:-

A. Fuzzification

The fuzzification is the process of a mapping from input to the corresponding fuzzy set in the input universe of discourse. There are two inputs to the fuzzy logic controller: - Torque error (ET) and angle information. Output of the fuzzy logic controller is proper voltage vector (V1 to V6).Torque error (ET) is divided into four fuzzy subsets with the linguistic value {PB, PS, NS, NB} and its universe of discourse is [-0.1 0.1]. Flux linkage angle (Angle) is divided into six fuzzy subsets {A1, A2, A3, A4, A5, A6} and its universe of discourse is [-π π]. The output Space voltage vector (V) is divided into six singleton fuzzy subsets {V1, V2, V3, V4, V5, V6}. Membership functions of twofuzzy input variables (ET and Angle) and one fuzzy output variables (Vi) are triangle type as shown in Fig.3.

(a) Membership function of torque error

(b) Membership function of flux linkage angle

(c) Membership function of space voltage vector

Fig. 3. Membership functions of the fuzzy controller

B. Rules and fuzzy reasoning

Fuzzy control rules are expressed in the IF-THEN format as IF ET is Ai and Angle is Bi, THEN V is Vi Where Ai, Bi, Vi denote fuzzy sets. The entire fuzzy rules expressed as a table as shown in the table II

TABLE II

FUZZY REASONING RULES FOR BLDC- DTC

Mamdani’s Min-Max method is employed in the fuzzy reasoning.

C. Defuzzification

The defuzzification is not required in the controller because output of the fuzzy controller is just six singleton fuzzy subsets which are the actual PWM voltage vector sequence composed of only seven different states, and these states could be directly used as the successor of the fuzzy rules.

SENSORLESS OPERATION USING BACK-EMF OBSERVER

In BLDC motor, the electromagnetic torque can be estimated directly from the back-EMF and the speed.

Here in this proposed method, an observer is used to estimate the back-EMF waveform. By choosing α- axis and β-axis stator currents and back-EMFs as the state-variables, the following state equations can be obtained

ẋ = Ax + Bu(6)

y = Cx(7)

Where x = [ia, ib, ea, eb]T is the state vector, u = [ua, ub]T is the input vector, y = [ia, ib]T is the output vector, and

The back-emf is estimated as

e^= K (y -y^)(8)

Where e^ = [ea, eb]T is the back-EMF vector, K= diag(k1, k2) is the gain matrix, k1 and k2 are positive constants. The relation between the rotor speed and amplitude of the back-EMF is given by

E = PKeω(9)

Where P is the number of pole pairs, Ke is the back- EMF constant of the motor, E is the amplitude of every phase back- EMF. The estimated speed is given by

The estimated rotor position is obtained by θ􀷠

 

LabVIEW IN VIRTUAL INSTRUMENTATION

LabVIEW (Laboratory Virtual Instrumentation Engineering Workbench) is a graphical programming language that uses icons instead of lines of text to create applications.LabVIEW programs are called virtual instruments, or Vis, because their appearance and operation imitate physical instruments, such as oscilloscopes and millimeters. Every VI uses functions that manipulate input from the user interface or other sources and display that information or move it to other files or other computers. LabVIEW object oriented programming uses concepts from other object oriented programming languages such as C++ and Java, including class, structure, encapsulation, and inheritance. We can use these concepts to create code that is easier to maintain and modify without affecting other sections of code within the application. We can use object oriented programming in LabVIEW to create user-defined data types. That is different types of physically existing systems can be simulated using this software. Here in this project we are using this software to design the control schemes of hybrid electric vehicle. The advantage of the LabVIEW software over other simulating software is that a wide range of hardware components are available which are helpful in testing as well as implementation of various applications that we develop in this software. In this proposed paper all the simulations had done in LabVIEW software.

BLOCK DIAGRAM OF SENSORLESS FUZZY BLDC-DTC SYSTEM

The block diagram of a sensorless fuzzy based BLDC DTC system is shown in Fig. 4. In the proposed system, there is an inner torque loop and outer speed loop. The reference torque is obtained from the speed controller and is limited at a certain value. Both voltages and currents are measured and then transformed into the stationary reference frame alphabeta components in the system.A back-EMF observer provides the estimated back-EMF. A fuzzy logic controller generates the switching signal which drives the inverter.

Fig. 4. Block diagram of proposed fuzzy BLDC-DTC system

SIMULATION RESULTS

All the simulations had done in LabVIEW software.The parameters of the BLDC used in the system are listed in the table 3.

TABLE III

BLDC MOTOR SIMULATION PARAMETERS

Fig. 6 shows the performance comparison between the conventional PWM method using the sensor and the proposed method. The torque ripple and the current ripple are much less, compared with the PWM scheme. A load torque of 2 Nm is applied. The figure shows the response performance of the proposed sensorless drive at 1000 rpm. Although the small deviation occurred after injecting the load, the performance is generally good.

(a) Theta waveform

(b) Current waveform

(c) Actual speed response for a reference speed of 1000 rpm

(d) Electromagnetic torque for a load torque of 2 Nm

Fig. 5. Simulation results of the proposed fuzzy based DTC scheme

(e) Actual speed response for a reference speed of 1000 rpm

(f) Electromagnetic torque for a load torque of 2 Nm

Fig. 6. Simulation results of conventional PWM method

CONCLUSION

The proposed two-phase conduction mode for DTC of BLDC motors is introduced as opposed to the conventional PWM control in the constant torque region. Much faster torque response is achieved compared to conventional PWM current and especially voltage control techniques. It is also shown that in the constant torque region under the two-phase conduction DTC scheme, the amplitude of the stator flux linkage cannot easily be controlled due to the sharp changes and hence it is kept constant. The proper voltage vector selection is done using fuzzy logic controller which improves the dynamic performance. The sensor less operation is achieved by using a state observer which also improves the performance. Fuzzy controller is virtually created in LabVIEW and utilized to implement the algorithm.

Speed control of BLDC motor is achieved using virtual instrumentation. This proposed method controls the speed for various ranges. The simulation results show that the proposed scheme has good estimation performance in low and high speed range and good control performance, compared with the conventional PWM method.

REFERENCES

[1] Y. Liu, Z. Q. Zhu, D. Howe, ”Direct torque control of brushless DCdrives with reduced torque ripple,” IEEE Trans. Ind. Appl., vol. 41, no.2, pp. 599-608, 2005.

[2] L. Zhong, M. F. Rahman, W. Y. Hu, K. W. Lim, ”Analysis of DirectTorque Control in Permanent Magnet Synchronous Motor Drivers,” IEEETrans. on Power Electronics, vol. 12, no. 3, pp. 528-535, 1997.

[3] Won Chang-hee, Song Joong-Ho,lck Choy, ”Commutation torque ripplereduction in brushless DC motor drives using a single DC current sensor,” Power Electronics, IEEE Transactions on, vol. 19, no. 2, pp, 312-319, 2004.

[4] S. J. Kang, S. K. Sul, ”Direct torque control of brushless DC motorwithnon-ideal trapezoidal back-EMF,” IEEE Trans. Power Electron., vol. 10, no. 6, pp. 796-802, 1995.

[5] S. K. Chung, H. S. Kim, C. G. Kim, and M. J. Youn, “A new instantaneoustorque control of PM synchronous motor for high-performance direct-drive applications,” IEEE Trans. Power Electron., vol. 13, no. 3, pp. 388–400, May 1998.

[6] M. Ehsani, R. C. Becerra, ”High-speed torque control of brushlesspermanent magnet motors,” IEEE Trans. Ind. Electron.. vol. 35, no. 3, pp. 402-406, 1988.

[7] Do Wan Kim, Ho Jae Lee, and Masayoshi Tomizuka, “FuzzyStabilization of Nonlinear Systems under Sampled- Data Feedback: An Exact Discrete-Time Model Approach,” IEEE Transactions on Fuzzy Systems, Vol. 18, No. 2, Apr.

2010, pp: 251 – 260.

[8] Zdenko Kovaccic and Stjepan Bogdan, “Fuzzy Controller designTheory and Applications”, © 2006 by Taylor & Francis Group. international, 2002.

[9] D. Grenier, L. A. Dessaint, O. Akhrif, J. P. Louis, “A parklike transformationfor the study and the control of a nonsinusoidal brushless dc motor,” in Proc. IEEE-IECON Annu. Meeting, Orlando, FL, Nov. 6-10, 1995, vol. 2, pp.

836–843.

[10] K. Y. Cho, J. D. Bae, S. K. Chung, and M. J. Youn, “Torque harmonicsminimization in permanent magnet synchronous motor with back-EMF estimation,” in Proc IEE Elec. Power Appl., vol. 141, no. 6, pp. 323–330, 1994.

[11] C. Lascu, I. Boldea, and F. Blaabjerg, “A modified direct torque control forinduction motor sensorless drive,” IEEE Trans. Ind. Appl., vol. 36, pp. 122–130, Jan./Feb. 2000.

[12] B. K. B. And and N. R. Patel, “A programmable cascaded low-pass filter-basedflux synthesis for a stator flux-oriented vector-controlled induction motor drive,” IEEE Trans. Ind. Electron., vol. 44, pp. 140–143, Feb. 1997.

[13] M. F. Rahman, Md. E. Haque, L. Tang, and L. Zhong, “Problems associated withthe direct torque control of an interior permanent-magnet synchronous motor drive and their remedies,” IEEE Trans. Ind. Electron., vol. 51, pp. 799–809, Aug. 2004.

[14] Y. Liu, Z. Q. Zhu, and D. Howe, “Direct torque control of brushless dc driveswith reduced torque ripple,” IEEE Trans. Ind. Appl., vol. 41, no. 2, pp. 599–608, Mar./Apr. 2005.

[15] W. S. H. Wong, D. Holliday, “Constant inverter switching frequency directtorque control,” in Proc. IEE-PEMD Annu. Meeting, Bath, UK, Jun. 4-7, 2002, pp. 104–109.

POWER QUALITY MONITORING AND POWER MEASUREMENTS BY USING VIRTUAL INSTRUMENTATION

POWER QUALITY MONITORING AND POWER MEASUREMENTS BY USING VIRTUAL INSTRUMENTATION

ABSTRACT

The presented paper describes a virtual instrument used for monitoring and analysis of the relevant power quality parameters and power measurements. The metrological support block is realized in LabVIEW environment which uses advanced methods for measurement and recording of the power quality parameters in accordance with the European quality standards. In that way, a suitable hardware solution for signal conditioning and load control is proposed. The most important parameters (voltage, current, power) are recorded into text files which are further used for measurement data analyses. The measurement results are obtained by using waveform simulator METREL.

key words - Power Quality, Data Acquisition, Virtual

INTRODUCTION

The electric power is essential for running industrial production processes, for commercial use, for transport and other purposes. In the last years this dependency has increased and all these processes relay on the quality of electricity supply, namely power quality. The detection of the disturbances affecting the line voltages is one of the most qualifying points in the estimation of the “voltage quality” or “supply quality”. The correct assessment of the quality of the supplied voltage has become one of the key issues in the deregulated electricity market. Ensuring a “high quality” of the supply voltage is the main requirement for ensuring a high “power quality”. Great attention is therefore paid to the definition of suitable indexes of voltage quality and the definition of suitable measurement procedure to evaluate these indexes. A large number of power quality disturbances have been reported in the literature-some of them being transient in nature and others being related to periodic, steady – state operation.

Some of the more common disturbances are: voltage and current harmonics, voltage dips, electric noise, impulses, notches and flicker.

Because of these disturbances measurement of the electric quantities, such as voltage, current and power by using equipment commonly used for measurement of sinusoidal signals can result in errors. In this way, inclusion of the digital signal processing techniques can be much more adequate. Anyway, a suitable digital signal processing approach must be provided. In the recent years adoption of personal computers (PCs) in the field of the measurement technique offers great progress and flexibility. Step ahead for development of modern measurement systems is achieved by adopting the concept of Virtual Instrumentation. It is a methodology for realization of measurement instruments by using standard PC’s, hardware data acquisition components for signal conversions and specialized program platforms for processing and recording of the measurement results. In this paper a Virtual Instrument for power quality monitoring is proposed.

POWER QUALITY PARAMETERS

The ideal supply voltage is pure sinusoidal voltage with nominal frequency and nominal amplitude. Any variation from this is considered as a power quality event or a disturbance. One important aspect in the field of power quality is monitoring and control of the qualitative parameters of the electrical energy according to today’s standards. In that way, a big attention is paid to define the disturbances and determination of procedures for their measurement. A large number of power quality disturbances have been reported in the literature. In general, the parameters could be divided in two groups - voltage amplitude variations and wave-form distortion. A short classification of power quality parameters is given in Table I.

TABLE I

POWER QUALITY PARAMETERS

In the following section some theoretical analyses relied on the signal processing are reported.

SIGNAL PROCESSING ANALYZES

Analyzing a periodic signal u(t) with angular frequency w and having in mind the Nyquist criteria, the signal limited with it’s Nth harmonic can be represented by 2N+1 samples over the period T.

The active power value of the voltage u(t) and current i(t) is represented with the equation:

 ............................(1)

Analyzing (1), measurement of the active power demands estimation of two time dependent components.

The p(t) spectrum is given by:

.....................(2)

According to relation (2) the spectrum of p(t) is wider than that of u(t) and i(t) and is limited to its 2Nth harmonic. From this analysis it can be clearly seen that if the moment value and the spectrum of the power is required, u(t) and i(t) must be sampled with frequency twice than the sampling theorem criteria. Theoretically, it is possible to acquire only 2N+1 samples for the voltage and current, but in practice the sampling frequency must be significantly increased.

The same considerations can be applied for evaluation of the RMS value of u(t) which is expressed by the relation:

 ...............................(3)

The appropriate evaluation of (1) and (3) also demands for proper definition of the observation interval. The observation interval needs to be an integer multiply of the signal period T in order to minimize the leakage errors in the frequency domain. Otherwise under non-synchronous sampling conditions an interpolation algorithm must be employed.

HARDWARE SOLUTION

The hardware is realized by using National Instruments multifunctional data acquisition (DAQ) card containing 32 analog input channels with resolution of 16 bits, programmable input range (±10V) and sampling rate up to 250kS/s. Two hardware boards for voltage and current signal conditioning are realized using six analog input channels, and three digital channels for load switching. The current measurement signals are obtained by using three electronic transducers incorporating current transformers and the load switching is realized by three relay switches controlled by the DAQ card. The voltage measurement signals from the power lines are obtained with precise resistive dividers. Block diagram of the hardware solution is shown in Fig.1

fig. 1. Hardware block diagram

The signal conditioning circuits should provide few functions like: galvanic isolation from supply network, attenuation or amplification of the measured signals, protection of DAQ card and noise suppression. The main role of the signal conditioning circuit is to adjust the sensor’s output signal span to match the analog-to-digital converter (ADC) input range. The block diagram of the signal conditioning circuits is shown on Fig.2

fig. 2. Signal conditioning circuit block diagram

In the absence of proper signal conditioning the signal can exceed the ADC input range and cause saturation of its output. The signal is first attenuated or amplified and DC level shifted with the input attenuator/amplifier. The next block is a unity gain buffer with very high input impedance which is used for adaptation of the impedances of the attenuator and the filter. Sixth order active anti-aliasing filter has been designed with cut-off frequency of 6 kHz and near flat amplitude frequency and phase-frequency characteristics. The filter is used before a signal sampler to restrict the signal’s bandwidth and to satisfy the sampling theorem. Fast circuits for limiting the input voltage to the ADC input range have been designed. These circuits allow signals below a specified input level to pass unaffected while attenuating the peaks of stronger signals that exceed this level. The used data acquisition card is with galvanic isolated inputs. The galvanic separation eliminates all forms of operating disturbances such as ground loop and potential separation.

LABVIEW BASED VIRTUAL INSTRUMENT

LabView is a National Instrument development software that allows rapidly and cost-effectively interface with measurement and control hardware, data analyzes, share results, and distribute systems. It is based on graphical programming techniques that allow programming with visual expressions, spatial arrangements of text and graphic symbols. The software is based on a block diagram (intended for graphical program development) and front panel (graphic interface formed by switches and panels intended for user interaction).The Virtual Instrument described in this paper consists of two parts:

1) Power line voltage analyzes

2) Current and power analyzes

Fig.3 represents the voltage analyzes block diagram. This block is identical for all three power lines.

Fig. 3. Signal conditioning circuit block diagram

Samples from three analog channels are successively taken with sampling frequency of 2kHz per channel for sampling interval of 100ms and are fed to a signal selection block. Every sample is multiplied by a constant factor which indeed is the attenuation coefficient of the signal conditioning circuits. The obtained signal is further processed and used for measurement of the RMS, total harmonic distortion (THD), frequency and phase difference of the input signal.

The virtual instrument contains two sub-virtual blocks for filtration of the spectral components and data recording. The filtration block contains sixth order Chebyshev band pass IIR filters with central frequency at the odd spectral components and the data recording sub-virtual block stores the results for RMS, frequency and THD of the input signal in interval of 100ms.

The programming points are implemented as follows:

• Sample gathering;

• Voltage RMS calculation, equation (3);

• Frequency measurement;

• Phase difference calculation;

• Analyzes of the Total Harmonic Distortion, relation (4);

100,n 2,3..N

....................(4)

• Analyze amplitude spectrum by using Amplitude

spectrum VI, equation (5);

......................(5)

• Analyze signal power spectrum using Auto Power spectrum VI, equation (6);

...............(6)

where * is a complex conjugate.

• Display the signal waveform, amplitude and power spectrum on a waveform graph;

• Filtration and measurement of RMS for 5 odd spectral components;

For this purpose a sixth order Chebyshev band pass IIR filters are used with central frequency at the odd spectral components. The pass band of the filters is 20Hz.

• Write the amplitude RMS, signal frequency and THD into text files;

All measurement data with time and date of recording are recorded into separate text files. These data can be further used for data storage and analysis by using some graphical presentation software such as DIAdem or MS Excel.

Fig.4 represents the front panel of the virtual instrument

Fig. 4. Front panel of the virtual instrument

CURRENT AND POWER ANALYZES

Three current channels are sampled with frequency of 2kHz per channel and a sampling interval of 100ms. Every sample is multiplied by constant factor corresponding to the transducer attenuation. The obtained signal is further processed and used for measurement of the RMS, total harmonic distortion (THD), and the active and reactive power of the input signal.

Fig.5 shows the LabView programming block diagram for current and power measurements for one measurement channel.

Fig. 5. Current and power measurements block diagram

The programming points are implemented as follows:

•Sample gathering;

•Current RMS calculation;

•Analyzes of the Total Harmonic Distortion;

•Analyze amplitude spectrum;

•Current phase measurement and current-voltage phase difference calculation;

• Active (7) and reactive (8) power calculation;

• Display the signal waveform and amplitude spectrum on a waveform graph;

• Write the current RMS, active and reactive power into a text file;

The front panel corresponding to the LabView programming sequence is shown in (in) Fig.6

Fig. 6. Front panel of the virtual instrument for current and power measurements

MEASUREMENT RESULTS

Measurement of the power quality is usually defined as a measurement of low frequency conducted disturbance with the addition of transient phenomena. The ideal single phase supply voltage is a pure sine wave with nominal frequency and voltage amplitude. Any variation of this is considered as a power quality disturbance.

The following parameters of supply voltage are influenced by disturbances:

• Frequency

• Voltage level

• Wave shape

• Symmetry of three phase system

In the experimental tests one phase power simulator Metrel MI 2191 is used. The instrument is able to simulate typical voltage and current shapes, such as voltage and current harmonics, flickers, transients, voltage interruptions etc. Three examples for measurement of transients, flickers and harmonics are shown in the results.

• Transient is a term for short, highly damped momentary voltage or current disturbance Fig.7 and Fig.8;

• Flicker is a visual sense caused by unsteadiness of a light. The level of the sense depends on the frequency and magnitude of a light change and the observer itself Fig.9;

• Harmonics are any periodic deviation of a pure sinusoidal voltage Fig.10;

Fig.11.a, Fig.11.b and Fig.11.c represent the recorded values for the RMS voltage, frequency and THD during 10 hour interval by using the text files from the virtual instrument. In the second experiment measurement of current and power of a 100W light is presented (Fig.6).

Switching of the light is controlled by the DAQ card.

Fig. 7. Transients caused by SRC switching

Fig. 8. High transient pulse caused by lightning

Fig. 9. Flicker with square distribution

Fig. 10. Highly distorted signal of a simple chopper voltage converter

Fig. 11.a Current and power measurements block diagram

Fig. 11.b. Current and power measurements block diagram

Fig. 11.c. Current and power measurements block diagram

In Fig.11.a, Fig.11.b and Fig.11.c recorded values for the RMS voltage, frequency and THD during 10 hour interval are presented. The graphs are obtained by using the data records from text files presented in MS Excel. The virtual instrument detected appearance of short voltage interruption, as it can be seen from the results.

CONCLUSIONS

This paper has summarizes theoretical and practical facts concerning the monitoring and analysis of power quality parameters. One possible hardware solution for signal conditioning in combination with DAQ card is implemented. This system is used for measurement and analyzes of different power quality disturbances. The signal conditioning module is developed in a way so it can be used for measurement of all power quality parameters. The voltage, current and power analyses are completely developed using virtual instrumentation techniques implemented in LabView software. Measurement data are recorded in text files for further analysis by using some graphical presentation software such as DIAdem or MS Excel. The performance of the proposed equipment is good enough for an effective application to test the power quality parameters.

The implemented system worked correctly in real time and detected and stored different types of disturbances.

REFERENCES

[1] M. H. J. Bollen, “What is power quality?”, Elect. Power Syst. Res., vol.66, pp. 5-14, 2003

[2] E. Acha, M. Madrigal. (2002, January) Power systems harmonics, Wiley

[3] R. G. Ellis, “Harmonic analysis of industrial power systems”, IEEE Trans. Ind. Apl., vol.32, no.2, pp.209-214, May 2001

[4] EN50160 Power quality standard, Power quality access meters and EN50160, Simens, May 2003

[5] G. Proakis, Dimitris G. Manolakis. (2007) Digital Signal Processing, Pearson Prentice Hall

[6] National Instruments, LabView Measurements Manual

[7] L. Cristaldi, A. Ferrero, R. Ottoboni: “Measuring equipment for the Electric Quantities at the Terminals of an Inverter-Fed Induction Motor”, IEEE Tech. Update Series, Instr. and Meas. Technology and Applications, ed. E. Petriu, 1998

[8] Power Simulator MI 2191 Instruction Manual

[9] D. Kottick. (2008, August) Power Quality monitoring system – voltage dips, short interruptions and flicker, Elect. Power Qual. & Utilisagion Magazine Vol.3. (Issue.2), [Online]. Available: http://www.scribd.com/doc/3306358/Power-Quality-Monitoring-System-Voltage-Dips-Short-Interruptions-and-Flicker

[10] A. Greenwood. (1991, April 4). Electrical transients in power systems (2nd ed.) [Online] . Available: http://www.amazon.ca/gp/reader/0471620580/ref=sib_dp_pt/187-5038544-5391220#reader-link