wide-area monitoring and control using the real time digital simulator and a synchrophasor vector...

Wide-area monitoring and control using the real time digitalsimulator and a synchrophasor vector processor

Anurag Srivastava1, Krishnanjan Gubba Ravikumar2*,y and Greg Zweigle2

1Mississippi State University, Starkville, MS, U. S. A.2Schweitzer Engineering Laboratories, Inc., Pullman, WA, U. S. A.

SUMMARY

Several major blackouts in the past have shown the need for advanced wide-area monitoring and control(WAMC) techniques. Advanced synchrophasor visualization and monitoring can be realized with time-referenced power system data that are collected by phasor measurement units (PMUs) using synchronizedclocks. Presently, PMUs are being deployed around the world at a rapid rate for various power systemapplications. When a necessary number of PMUs are installed at optimal locations throughout the network,the complete state of the system can be observed. PMUs take real-time measurements to determine the stateof the power system and can be used to enhance state estimation efficiency. With PMUs in place, there aremany control and protection schemes that can be implemented successfully to take preventive and correctiveactions. These remedial action schemes (RASs) are classified by the IEEE Power System RelayingCommittee (PSRC) as system integrity protection schemes (SIPSs). This work involved the developmentof a real-time hardware test bed in order to analyze the transient stability of a simulated power system byusing synchrophasors to visualize system stress across a transmission line with and without load-sheddingschemes. The real time digital simulator (RTDS1) was used to model the power system in real time. PMUs, asatellite-synchronized clock, and a synchrophasor vector processor (SVP) were used to test the synchro-phasor application. Copyright # 2010 John Wiley & Sons, Ltd.

key words: synchrophasors; real time digital simulator; transient stability; synchrophasor vectorprocessor; wide-area control; system integrity protection scheme

1. INTRODUCTION

Data obtained from phasor measurement units (PMUs) have facilitated the recent development of

efficient power system protection and stability methods [1]. Some of the major PMU data applications

include wide-area grid monitoring and visualization; state estimation; interarea oscillation monitoring,

analysis, and control; automated real-time control of assets; wide-area adaptive protection; system

integrity protection schemes (SIPSs); planned power system separation; forensic event analysis; and

other market operations [1].

The core idea of wide-area monitoring and control (WAMC) systems is the centralized processing of

data collected from various locations in a power system to enable evaluation of the actual power system

operating conditions with respect to its stability limits [2]. Wide-area control provides automatic

healing capabilities to some extent by proposing decisive smart topology changes and control actions

with the goal of maintaining grid integrity under adverse conditions [3]. The requirements for

optimally placing PMUs to assess system dynamic performance forWAMC are discussed in Reference

[4]. The authors in Reference [5] present the application of Hilbert analysis to determine the nature and

propagation of disturbances based on PMU data. A nonlinear robust controller that uses PMU data and

accounts for the delay introduced by remote signals as well as the incomplete information that results

EUROPEAN TRANSACTIONS ON ELECTRICAL POWEREuro. Trans. Electr. Power 2011;21:1521–1530Published online 30 December 2010 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/etep.548

*Correspondence to: Krishnanjan Gubba Ravikumar, Schweitzer Engineering Laboratories, Inc., Pullman, WA, U. S. A.yE-mail: [email protected]

Copyright # 2010 John Wiley & Sons, Ltd.

when not all generators are equipped with PMUs is described in Reference [6]. A framework based on a

sensor web and the Common Information Model (CIM) is discussed in Reference [7]; it incorporates

PMU data and considers interoperability and wide-area monitoring. A smart Discrete Fourier

Transform-based algorithm to eliminate system noise and errors for fault detection and location

with PMU data is presented in Reference [8]. An application of the extended Discrete Fourier

Transform based on PMU data for arc fault detection and location is discussed in Reference [9]. The

authors in References [10,11] discuss the optimal placement of PMUs for state estimation to allow bad

data detection and reduce measurement loss, given a branch outage. The concept of distributed

state estimation using PMU data and grid computing is presented in Reference [12]. The accuracy

of PMU data and its application in determining system stability based on an energy function

approach are discussed in Reference [13]. The author in Reference [14] developed a methodology to

compute the power system delay margin related to PMU data using time domain simulation for

determining small signal stability. Accuracy and calibration of PMU measurements are very

important to take correct control action. Testing, calibration, and characterization of PMUs are

discussed in References [15,16]. Accuracy enhancements in a multivendor environment are presented

in Reference [17].

Applications of PMU data for transient stability analysis have been discussed by several researchers

[18–21]. Transient stability refers to the ability of the system to maintain synchronism after being

subjected to a major disturbance, such as the loss of generation, a line-switching operation, a fault, or a

sudden load change. The main motivation for studying transient stability is to determine whether, for a

given disturbance, the system is either stable or unstable. One of the big challenges in the operation of a

large interconnected power system is to ensure that the generators will remain in synchronism

following a large system disturbance, such as the loss of a large generator or a transmission line

[22,23]. Most of the work presented in the past was theoretical and used digital simulation to validate

the proposed PMU-based algorithms for transient stability and control. In this work, transient stability

enhancement of a power systemwas demonstrated by creating and responding to a fault scenario in real

time using the capabilities of the real time digital simulator (RTDS1). Additional information on

RTDS is available in References [24–26]. This work was conducted in the newly developed wide-area

monitoring and control test bed at the Power & Energy Research Lab (PERL) at Mississippi State

University. The RTDS was used to simulate the power system in real time. Protection system relays

providing phasor data and connected to satellite-synchronized clocks were used as PMUs, and a

synchrophasor vector processor (SVP) functioned as a wide-area control device.

The main contribution of this work is the development of a real-time hardware test bed to model and

simulateWAMC as well as to validate phasor measurement-based transient stability algorithms. In this

work, synchrophasors are used to preserve system stability following a transient disturbance. Section 2

presents the modeling and simulation tools used. The test system model and the control algorithm

are discussed in Section 3. Section 4 presents simulation results obtained using the real-time hardware

test bed.

2. MODELING AND SIMULATION TOOLS

The primary tools used in this research consist of an RTDS, PMUs, a satellite-synchronized clock,

and an SVP. The PMUs obtain wide-area measurements from the RTDS, and the SVP runs the

wide-area control algorithm for implementing the remedial action schemes (RASs) for transient

stability.

2.1. Phasor measurement unit

The PMUs utilized in this work provide IEEE C37.118-compliant synchrophasor measurements and

also perform transmission line protection and control. The PMUs are connected to satellite-

synchronized clocks to obtain the IRIG-B timing signal, which enables them to time-stamp the power

system data. The major elements of modern PMUs include an anti-aliasing filter, a Global Positioning

System (GPS) receiver, a phase-locked oscillator, an A/D converter, and a processor to calculate

Copyright # 2010 John Wiley & Sons, Ltd. Euro. Trans. Electr. Power 2011;21:1521–1530DOI: 10.1002/etep

1522 A. SRIVASTAVA, K. G. RAVIKUMAR AND G. ZWEIGLE

phasors [27]. In this work, the PMUs were mainly used to generate synchrophasor measurements and

transfer control commands from the SVP to the simulated power system for load shedding.

2.2. Synchrophasor vector processor

An SVP is a real-time synchrophasor programmable logic controller. It can time-align the incoming

messages, process them with internal logic, and send control commands to external devices to perform

user-defined functions. The SVP measures system quantities such as phase angles, voltages, currents,

and real/reactive power. It also implements control actions such as minimizing loop flow; balancing

loads; and controlling static VAR compensators (SVCs), generators, and circuit breakers to improve

system performance. This hardware was developed on the IEC 61131 platform and uses the Controller

Development System (CoDeSys) programming language to design the wide-area control schemes.

2.3. Real time digital simulator

The RTDS is a real-time power system simulator that performs digital electromagnetic transient

simulation of electric power circuits using a time step as small as 2microseconds [24]. The real-time

operation of the RTDS makes it suitable for development and testing of protection and control

techniques for power systems. The RTDS uses a graphical user interface (GUI) known as RSCAD to

design the power system circuits. All communication between the RTDS and the workstation is carried

out by a Workstation InterFace (WIF) card. An Inter-Rack Communication (IRC) card takes care of

communication between the racks. The RTDS at Mississippi State University consists of a cubicle with

two processor racks, containing eight Triple Processor Cards (TPCs) and two Giga Processor Cards

(GPCs).

3. TEST SYSTEM MODELING AND CONTROL ALGORITHM

Figure 1 illustrates the power system test case used in this work. The test case was modeled in RTDS

and takes into account machine and system control dynamics. Along with the generator, a power

system stabilizer and an IEEE Type ST1 excitation system were also modeled. The 230 kV

transmission lines (Transmission Line 1 and Transmission Line 2) are 200 km long each. Transmission

Line 1 and Transmission Line 2 are six-conductor and three-conductor Bergeron transmission line

models, respectively. These Bergeron line models operate based on the traveling wave method under a

single-frequency condition. They are mainly characterized by their distributed nature of LC parameters

with a lumped resistance. Bergeron models are considered to be helpful in the transient simulation

Figure 1. Test case used for wide-area protection scheme.


WIDE-AREA MONITORING AND CONTROL 1523

environment, especially when the transmission lines are sufficiently long. Other power system ratings

and specifications are provided in Tables I–III.

This test case used three generators and two dynamic loads. Each load was modeled as a constant P

and Q load. Two PMUs were placed at Bus 1 and Bus 2. PMU locations were based on generator and

load location and not on complete system observability because the focus of this paper is control action

demonstration. The angle difference between these buses was measured during stable conditions and

also during the fault condition. The RSCAD model of the test case is shown in Figure 2.

The hardware test bed was constructed by connecting PMUs to the RTDS and synchronizing them

with a GPS clock. Figure 3 shows the data flow schematic for the developed test bed. The RTDS and

PMUs function in a closed-loop fashion. The phasor measurements from the PMUs are sent to the SVP

as input data for thewide-area control algorithms. An angle difference scheme (Figure 4), implemented

in the SVP, receives positive-sequence bus voltages and calculates the phase angle difference to depict

the real-time grid stress. The present hardware test bed has been further developed to include more than

one phasor data concentrator (PDC), historians, and other controllers in order to implement further the

possibility of testing and implementing PMU-based algorithms, phasor-based device testing, and event

analysis. The developed test bed can also be used for power system education to demonstrate PMU

operation and control. Work presented in this paper only relates to PMU- and SVP-based control

actions.

PMUs connect to the RTDS through the low-level interface to obtain the three-phase voltage

and current signals. The synchrophasor data from PMUs are sent directly to the SVP through

serial connections. Interfacing all of these devices was a challenge because an attempt was made to

interface industrial devices to a digital simulator in a lab environment. Because the SVP is a PDC, it

time-aligns and processes the incoming phasor data with a deterministic latency for real-time

operation. The throughput latencies observed in processing synchrophasor data by the SVP

were less than 3milliseconds. After time alignment, the SVP computes the difference between

positive-sequence voltage angles at Bus 1 and Bus 2. The schematic is equipped with alarms and

thresholds. The SVP asserts the corresponding alarm based on thresholds set at 1508 (0-millisecond

pickup timer) for Level 1 and 3008 (5-millisecond pickup timer) for Level 2. If the angle difference

Table I. Generator data.

Generator Rated MVA Real power output (MW)

1 1000 5002 2000 7003 1500 600

Table II. Load data.

RL dynamic load Real power (MW) Reactive power (MVAR)

1 800 102 1000 10

Table III. Transmission line data.

Transmission line Positive-sequence line impedance (V) Zero-sequence line impedance (V)

1 106.56/n86.87 280.87/n70.142 37.71/n87.18 127.99/n73.58



Figure 2. Test case model in RSCAD.



exceeds the threshold, the alarm is asserted and activates the fast operate (FO) function block in

the logic. When the FO function block is activated, it sends the corresponding FO command to the

external control device (in this case, PMU 1) with the assigned IDCODE when the enable (EN) input is

true.

PADM is the phase angle difference monitoring block; its inputs include Angle 1 and Angle 2 in

radians, two levels of thresholds and pickup timers, and second of century (SOC) and fraction

of second (FOS) of the incoming data. The outputs of the PADM block are angle difference,

two alarms, SOC and FOS of the output, and an indicator (OK) for valid PADM output. The

rising edge trigger (R_TRIG) is used to enable the FO block corresponding to the alarm.

The falling edge trigger (F_TRIG) is used to clear the control command after the FO block sends it to

the PMU.

4. SIMULATION RESULTS

The power system test case in Figure 1 was simulated with the RTDS. Initially, the phasor voltages,

currents, and angles were sent to the SVP through PMUs during the steady-state condition of the

simulated power system. The angle difference between Bus 1 and Bus 2 was validated by calculating it

in the SVP, using the PMU measurements, and in the RTDS. Figure 4 shows an angle difference of

27.5448 calculated by the SVP, which was the same value determined by the RTDS.

Figure 3. Data flow schematic for the test bed.

Figure 4. Angle difference schematic from the SVP.



When a line-to-ground fault of 28 cycles (0.467 seconds) was applied on all of the three phases at

Bus 5, undamped oscillations were observed throughout the system. Using several repeated

simulations with different fault-clearing times, it was determined that the system critical clearing time

was 0.45 seconds, after which the generators became unstable. This happens because the rotor angle

increases beyond its limits and the generators lose synchronism. The angle difference between Bus 1

and Bus 2, the transmission line real powers, and the bus terminal voltages are shown in Figure 5. These

plots depict the system behavior under a condition of transmission line removal due to the relay action

in response to a fault on Bus 5.

The SVP action is shown in Figure 6. The SVP calculates the angle difference and initiates an alarm

that triggers the activation of a control signal to shed Load 2 for system protection. The control signal is

transferred from the SVP to the PMU, and the PMU opens a breaker to shed the load, thus preserving

system stability.

For validation purposes, the pickup timer on the angle difference scheme was set so that the alarm

waits 1.4 seconds after the angle difference first increases beyond 1508 before initiating the load-

shedding scheme. Figure 7 shows that the angle difference crossed 1508 three times within 1.4 seconds,

after which it settled down because of the load-shedding action. Such analyses help to identify the

differences between normal and abnormal power system disturbances.

Figure 5. Angle difference between Buses 1 and 2, real power between Buses 1 and 2 at Transmission Line1, and three-phase node voltages at Buses 1 and 2 during fault condition.



5. CONCLUSIONS

This paper presented the development of a real-time hardware test bed to validate PMU-based

algorithms. It also discussed a control algorithm for shedding load based on angle difference. The

Figure 7. Angle difference between Buses 1 and 2 based on terminal node voltages in RTDS showingdelayed SVP action after fault on Bus 5.

Figure 6. Angle difference between Buses 1 and 2, real power between Buses 1 and 2 at Transmission Line1, and three-phase node voltages at Buses 1 and 2 showing effects of control (trip) signal sent by SVP to

initiate load-shedding action.



simulation results for the test system during normal and fault conditions demonstrate an improvement

in transient stability related to the SVP action. The transient stability of the power system was

improved by accurate measurements of the phase angle difference and the timely initiation of load

shedding by the SVP. Shedding the appropriate load at the verge of system instability damped the

oscillations in the system and restored the system to a stable operating point.

ACKNOWLEDGEMENTS

The authors would like to recognize the Office of Naval Research for partially funding this work through the U.S.Department of Defense (DoD) Multidisciplinary University Research Initiative (MURI) Fund #N00014-04-1-0404 and DoD Defense University Research Instrumentation Program (DURIP) Fund #N00014-06-1-0752. Theauthors would also like to thank RTDS Technologies Inc., Dr S. C. Srivastava, and Dr N. N. Schulz for providingtechnical expertise and the help needed to complete this work.

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