Development of a Wide Area Measurement System for ... - CiteSeerX

Preprints of the 18th IFAC World Congress Milano (Italy) August 28 - September 2, 2011

Development of a Wide Area Measurement System for Smart Grid Applications M. M. Amin, Student Member, IEEE, H. B. Moussa, Student Member, IEEE and O. A. Mohammed, Fellow, IEEE Energy Systems Research Laboratory, Electrical & Computer Engineering Department, Florida international university, Miami, FL 33174, USA (Tel: +1 305-348-3040; e-mail: mohammed@ fiu.edu).

Abstract: In this paper, the modeling for a complete scenario of a proposed wide area measurement system (WAMS) based on synchronized phasor measurement units (PMUs) technology with the access of a broadband communication capability is presented. The purpose is to increase the overall system efficiency and reliability for all power stages via significant dependence on WAMS as distributed intelligence agents with improved monitoring, protection, and control capabilities of power networks. The developed system is simulated using the Matlab/Simulink program. The power system layer consists of a 50 kW generation station, 20 kW wind turbine, three transformers, four circuit breakers, four buses, two short transmission lines, and two 30 kW loads. The communication layer consists of three PMUs, located at generation and load buses, and one phasor data concentrator (PDC), that will collect the data received from remote PMUs and send it to the control center for analysis and control actions. The proposed system is tested under two possible cases; normal operation and fault state. It was found that power system status can be easily monitored and controlled in real time by using the measured bus values online which improves the overall system reliability and avoids cascaded blackout during fault occurrence. The simulation results confirm the validity of the proposed WAMS technology for smart grid applications.

1. INTRODUCTION WAMS became one of the most recent technologies that are popular for upgrading the traditional electric grid. This upgrade has become a necessity to modernize the electricity delivery system following the occurrence of major blackouts in power systems around the world. Although many algorithms have been developed in the past for online monitoring of transmission systems, distribution systems and estimation of operating frequency, with a few examples included as references (A. A. Girgis 1982, M. S. Sachdev 1985 and S. Paul et al. 1987), but still the required level of details for online assessment is yet to be achieved. In early 1980s, synchronized phasor measurement units (PMUs) were first introduced and since then have become the ultimate data acquisition technology, which will be used in wide area measurement systems with many applications that are currently under development around the world (H. Hr. 2004).

synchrophasors. This validation occurs through use of interarea communication or simultaneous data collection of conditions at a single point in time (Z. Zhong 2005). In addition, Real-Time System Monitoring (RTSM) for stability assessment and state measurement is another application where phasor measurements at nodes help system operators to gain a dynamic view of the power system and initiate the necessary measures in proper time, with the latest IEEE standard (C37.118-2005) developed to standardize data transmission format and sampling rates of PMUs. This can significantly be supported by the stability assessment algorithms, which are designed to take advantage of the phasor measurement information (M. Venk. 2009).

The precise and accurate data that can be acquired from PMUs in a WAMS built on the power system confirms the need for a robust, reliable communication network with secure and high speed capabilities for online data access.

In the past, post-event analysis was an application of synchrophasors (PMUs) without wide-area communication where data was archived locally. However, it was not a useful tool for online (dynamic) control. Recently, Real-Time Control (RTC) of WAMS became a powerful control and analysis tool that provides a new view of power systems (J. De La Ree 2010). This is achieved by improving communication network capabilities while maintain PMUs as a main component in the network. The use of PMUs for RTC will increase the control accuracy since data are measured online. Also, it will enhance the power system stability and delivery automation capabilities after challenges of new data communication requirements across the system are firstly resolved (A. Bose 2010, Y. Zhang 2008 and A.G.Pha. 2007).

As smart grid applications, utility power grid analysts can get benefited from WAMS in validation of system models and components which has been one of the first uses of

Depth of observability is another advantage for PMUs. It means the ability of measuring bus voltage phasor directly or calculating it using the PMU voltage and line current of the

Synchronized phasor measurements, or synchrophasors, provide a method for comparing phase and sequence values from anywhere on a power system which can be integrated with phasor data concentrators (PDCs) at substations in a hierarchical structure (A. R. Metke 2010 and M. Pipat. 2009).

Copyright by the International Federation of Automatic Control (IFAC)

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nearest connected bus. This is a cost effective part since it reduces the number of data acquisition tools needed across the network as measuring line currents can extend the voltage measurements to buses where no PMU is installed. In Fig. 1, a simple generalization of PMU block diagram is shown, which serves as the basis of simulating such unit (R. F.2005). This paper discusses a method to utilize this type of data collection to check the health state of power system networks. This is achieved through building WAMS infrastructure communication network. The performance of the overall proposed system is investigated through a Matlab simulation of PMUs in a small scenario of a WAMS on a 4bus utility network with the associated communication network. This paper is organized as follows. In section 1, an introduction to the subject is presented. In Sections 2, 3 description and mathematical modeling of the system are introduced. In Section 4, test results are presented and discussed under different system conditions. Finally, Section 5 presents some conclusions.

Antenna

GPS Receiver Remote Communication

PLL Oscillator Micro Processor

S/H A/D

Analog Filter Analog Input

Local Communication

Fig. 1. Block Diagram of PMU Control Station

Rx

Wind Turbine

PDC R

B1

B2

B4

Gen. Station

CB1

T.L1

CB2

2 . SYSTEM DESCRIPTION The principle of a WAMS network based on synchrophasors data with the aid of a broadband communication network is described in this section. The system consists mainly of two layers as shown in Fig. 2. First, the electrical power system layer which consists of line-line 208V generation station with 50 kW output rated power, 208V wind turbine as a renewable source of 20 kW rated power, 3-power transformers (T1, T2, and T3) linking different parts of the electrical system, 2short transmission lines (T.L1, and T.L2), 4-buses (B1, B2, B3, and B4), 4- circuit breakers (CB1, CB2, CB3, and CB4) and 2-loads each of 30 kW (O. A. Mohammed 2005). Second, the WAMS layer which consists of 3-PMUs, and 1-PDC that will collect the data received from remote PMUs and send it to the control center for analysis and control actions (Bei Xu 2005). 3. SYSTEM MODELING A small size WAMS platform built on a 208V, 60 Hz testbed network was modeled as shown in Fig. 3. This proposed communication network can be implemented in the lab by locating one PMU at each generation or load bus where all PMUs will send its measured voltage and current measurements to the PDC in order to monitor the system status and taking the proper control action if required. Furthermore, depth of observability can be utilized here in order to significantly reduce system costs through reducing the number of PMUs since one can read voltage and current measurements at its bus and other buses measurements locating at same area can be calculated. However, this algorithm has less accuracy than installing one PMU at each bus. A simulation of the PMU units was done with using sampling clock pulses to achieve synchronization between synchrophasors which are phase locked to the signal provided by the Global positioning system (GPS) receiver built inside or outside the PMU. The GPS module is simulated as a clock enabling pulses sent to all PMUs at the same time so that all of them will have the same time tags and in accordance the same reference wave can be used at all different PMU locations through the WAMS.

T1

T2 CB3

PMU1

PMU2

T.L2 30 kW Load

B3 30 kW Load

CB4 T3

PMU3

Fig. 2. Schematic diagram of the proposed WAMS according to the installed test-bed power system in the lab. 3.1 PMU network analysis The PMU has to separate the fundamental frequency component from other harmonics and find its phasor representation. Discrete Fourier transform (DFT) is then applied on the sampled input signal to compute its phasor. Also, it has to compensate for the phase delay introduced to the signal by the antialiasing filters present in the input to the PMU. For x k = 0,1, … , N where N is the number of samples taken over one period, then the phasor representation will be given by;

=

√2

(1)

Since the components for real input signals at a frequency appears in DFT and are complex conjugates of each other so they can be combined giving the factor of 2 in front of the summation in (1) and then the rms of the fundamental frequency peak value can be obtained through dividing by √2. Matlab Simulink model was built to evaluate the system performance. Different cases were studied during normal

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Enable

Va Ia

1 To control center

Va-ph Ia-ph

a

B

b

C

c

Main AC Grid c C 50 KW B1 208 V

Transformer 1

Aa A

a

B

b

C

c

CB1

A B C

Bb Cc

A B C

A

a

B

b

C

c

CB2

TL1

a

A

aA

b

B

bB

c

C

cC

Transformer 2

B4 208 V

[A] From1

A B C

TL2 A B C

C

B

A

PMU1

Enable A B C A1 B1 C1

[A]

a b c

A B C

CB3

From

B2 208 V

+ -

A

PMU2 Vab2

Load 30 kW

1 Watt

Discrete, Ts = 5e-005 s. powergui

A

a

B

b

C

c

aA A

a

B

b

C

c

bB cC

CB4

AC Load : 30 kW

v

Transformer 3

+ -

aA bB

Enable C1 B1 A1 A B C

+ v -

A B C

Wind Turbine Induction Generator 20 KW

PDC

v

Rx

Vab1

A B C

clk pulses [A]

PMU3

Enable C1 B1 A1 A B C

[A] From2

B3 208 V

A B C

Vab3

Fault Breaker

Fig. 3. Simulink model for a scenario of the proposed PMUs communication network layer on a power system Smart Grid Test-bed in our Laboratory. mode of operation and fault occurrence mode. The simulated system parameters are shown in Table 1. In equation (4), the frequency !IA represents the frequency In Steady State, all generators have the same frequency of the system at this location and equals to the frequency ( Hz). In accordance, the voltage at all points of the power measured by the PMU at that bus by assuming that >!IA "#$ = system will have the same frequency which is measured >!?@A and having access to the sampled data of by PMU according to the following equation; >!?@A so !IA can be easily evaluated (R. Malpani 2010). ! "#$

3.2 Communication channel analysis

= %! &'( "2) # + +! $

(2)

In case of frequency disturbance, the power system different generators will run with different frequencies and each generator may be considered as a voltage source with different values of %! , ,-. +! as slow time varying functions. It can be assumed for a small interval of time "∆t = n cycles$ that E8 , f:: and δ8 constants. As a result, the power system can be represented as a circuit with several voltage sources of different frequencies. The actual voltage at any bus i using superposition theorem becomes as in (3) (Ning Jiaxin 2009);

C >!?@A = >!, "#$ + ⋯ + >!,C "#$ = ∑ >!, "#$ = C ∑ E!, &'(F2)C # + G!,C H

(3)

Where E!, represents the voltage at bus i due to generator j. which indicates that this bus have a multi frequency voltage that are all close to 60 HZ. In dynamic power system studies this can be estimated as in (4); >!IA "#$ = E!IA cos "2)!IA # + G!IA $

IEEE PC37.118 16 protocol format is usually used in PMUs communication. This format standard includes frequency and rate of change of frequency in each message. Once the frequency and size of the messages are known, the following equation can be used to determine the bit-per-second (bps) rate at which the data can be sent (V.K. Sood 2009); KL( = 1.2"-- . N . $

(5)

where: nn = message size (bytes) L = length of frame (1 start bit, 8 data bits, 2 stop bits, 1 parity = 12) f = messages frequency 1.2 = factor to account for system delays (based on typical experience) The synchrophasor data can be sent at various rates, depending on application requirements. The communications link connecting the substations could be a fiber-optic multiplexer. Relays communicate with the multiplexer via EIA-232 asynchronous interface.

(4)

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4. SIMULATION RESULTS A Matlab Simulink model has been constructed in order to investigate the performance of the proposed WAMS for smart grid applications. The model was carried out according to the operation that has been described in section 2. The simulation parameters are shown in Table 1. In order to estimate the PMUs characteristics, two types of tests are carried out. The first is normal operation test without any fault or unbalance in the network. The second is fault operation test which used as an extreme condition to show the behavior of the network under this condition.

Table 1. System simulation parameters

R Pg Pg L

PMU1

Value 208 Vrms

Vab Ref. & Act.

400

60 Hz 60 msg/sec

200 0 -200 -400 0 400

50 kW

Vab-Sampled

fg, ft

Parameter Generator and wind turbine output voltage Generator and wind turbine frequency PMU message report rate Generator power rating Wind turbine power rating Load rating

20 kW

0.05

0.1

0.15

0.2

0.25

0.3

0.05

0.1

0.15

0.2

0.25

0.3

0.25

0.3

300 200 100 0 0

60 kW

4 Phase Angle

Symbol vg, vt

shown in Figs. 8, 9. On the other hand, PMU 3 has extremely phase difference change (50 degrees) associated with a large drop in the voltage amplitude as a result to the fault that occurs in this area as shown in Fig. 10. Consequently, the control center has to send control signal to the relay to release the circuit breaker at that bus upon receiving these data in real time from PMUs to protect the other generation stations which are the most valuable part in the power network from damage, preventing cascaded turnoff stations which may result in major blackouts and maintaining a healthy power system (S. H. Horowitz 2003). Furthermore, it helps analysts to determine the type of fault that has been occurred using the data transmitted from PMUs.

4.1 Normal operation test The electrical system under normal operation conditions is observed. The 30 kW load on bus 2 is supplied from the wind generator sharing with generation station and the other 30 kW load on bus 3 supplied by the generation station, so all PMUs shows stable readings within the references.

Phase Diff. =2.65 3 2 1 0 0

0.1

0.15 time sec

0.2

Fig. 4. PMU1 readings under normal operation condition.

From Figs. 4-6, the three PMUs read accurate information about line voltage >?O ; a sampled data of about 296 V average voltage amplitude starts from 0 sec for bus 1 and 2. At bus 3, zero voltage amplitude for the first 0.1 sec at no load then tracking the right average voltage amplitude level as other buses with a phase difference of 2.65 degrees at stable state for all readings. The exported data by the simulated PMUs to the control center shows that the developed WAMS succeeded to accurately reflect the system status in real-time (online). However, for complete verification of its performance another test with applying a fault at bus 3 and observing their responses.

PMU2 Vab Ref. & Act.

400 200 0 -200

Vab-Sampled

-400 0 400

0.05

0.1

0.15

0.2

0.25

0.3

0.1

0.15

0.2

0.25

0.3

0.25

0.3

300 200 100

4.2 Fault operation test

0 0

0.05

4 Phase Angle

In this case, a three line to ground short circuit (3-LG SC) fault is applied at bus 3. Figs. 7-10 show the readings for all PMUs at the 3-buses. According to Fig. 7, the whole system shows normal operation for 0.2 sec while bus 3 is loaded after 0.1 sec. the fault is occurred after 0.2 and it is cleared after 0.05 sec. PMUs 1&2 reads larger phase differences (7.54 degrees) than in normal mode (2.65 degrees) while the voltage amplitude dropped with small value (10 V) which means that the fault is not located on their buses area as

0.05

Phase Diff. =2.65

3 2 1 0 0

0.05

0.1

0.15 time sec

0.2


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PMU3

PMU1 Vab Ref. & Act.

200 0 -200 -400 0 400

0.05

0.1

0.15

0.2

0.25

200 0 -200 -400 0.18

0.3

0.2

0.22

0.24

0.26

0.28

0.2

0.22

0.24

0.26

0.28

0.26

0.28

300 Vab-Amplitude

Vab-Sampled

400

300 200 100

Phase Angle

0 0 4

0.05

0.1

0.15

0.2

0.25

100 0 0.18 4

0.3

Phase Diff. =2.65

Phase Diff.=7.54 degrees

3 2 1 0 0

200

Phase Angle

Vab Ref. & Act.

400

0.05

0.1

0.15 time sec

0.2

0.25

3 2 1 0 0.18

0.3

0.2

0.22 0.24 time sec

Fig. 8. PMU1 readings during fault occurrence


PMU2 400

100

Vab Ref. & Act.

VLine-B1

300

-100 -300 -500 0

0.05

0.1

0.15

0.2

0.25

0.3

-200 -400 0.18

100 -100 -300 -500

0.22

0.24

0.26

0.28

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.2

0.22

0.24

0.26

0.28

0.26

0.28

200 100 0 0.18 4

300

Phase Diff.=7.54 degrees

100

Phase Angle

VLine-B3

0.2

300 Vab-Amplitude

VLine-B2

0

0.35

300

0

200

-100 -300 0

0.05

Normal operation with no load at B3

0.1

0.15 0.2 time sec

0.25

0.3

0.35

Return to Normal Fault normal operation operation operation with Occurrence with loading connecting at B3 at B3 load at B3

3 2 1 0 0.18

0.2

0.22 0.24 time sec

Fig. 9. PMU2 readings during fault occurrence

Fig. 7. Line voltages of Buses 1, 2 and 3 1676


PMU3

Vab Ref. & Act.

400 200 0 -200 -400 0.18

0.2

0.22

0.24

0.26

0.28

0.2

0.22

0.24

0.26

0.28

0.26

0.28

Vab-Amplitude

300 200 100 0 0.18 4 Phase Angle

Phase Diff.=50 degrees 3 2 1 0 0.18

0.2

0.22 0.24 time sec

Fig. 10. PMU3 readings during fault occurrence 5. CONCLUSIONS A performance analysis for a PMU based WAMS network was presented. The developed system was tested under two different possible conditions. The simulated PMUs shows coincident data with the real values of a maximum phase difference equal to 2.65 degrees and normal average amplitude reading which shows the system stability. In this case, no action has to be taken from the control center during the dynamic system monitoring. On the other hand, during the fault state the PMUs data shows that the system has unstable part with about 50 degrees phase difference added to a large drop in voltage amplitude in the area where the fault was occurred which should be disconnected or cleared via dynamic control signals before spreading to other parts resulting in catastrophic failure in some parts of the power system or blackouts. The developed simulated WAMS network verified its effectiveness for smart grid applications. REFERENCES A. A. Girgis and F. M. Ham (1982). A NEW FFT-based digital frequency relay for load shedding. IEEE Trans. Power App. Syst., vol. PAS-101, pp.433–439. A. Bose (2010). SMART transmission grid applications and their supporting infrastructure. IEEE Transactions on Smart Grid, vol. 1, Issue 1, pp. 11-19. A. G. Phadke, D. Novosel, and S. H. Horowitz (2007). WIDE area measurement applications in functionally integrated power systems. In: the CIGRE B-5 Colloq., Spain. A. R. Metke and R. L. Ekl (2010). SECURITY technology for smart grid networks. IEEE Transactions on Smart Grid, vol. 1, no. 1, pp. 99-107.

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