1210 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 3, MAY 2014

Continuous Monitoring of Smart Grid DevicesThrough Multi Protocol Label Switching

A. K. M. Baki, Member, IEEE

Abstract—Data refreshment rate of a present power system isrelatively slow and in some cases this slow refreshment rate maynot be capable of providing information about dynamic state ofpower system devices. The use of multi protocol label switchingbased technique is one option to overcome this problem. A cost ef-fective and simpler detection device is needed for condition moni-toring and integrating the sensor signals into the smart grid mon-itoring system. Remote and on-line monitoring of ultra wide bandpartial discharge signal in a switch-yard is a new concept in smartgrid technology that will help avoid damage of switchyard devices.It is possible to convert the radio frequency partial discharge signalinto an equivalent milli-volt or milli-ampere output for remote orcentralizedmonitoring of switchyard devices.Multi Protocol LabelSwitching based data transfer rate is faster than the conventionaldata transfer system of a power grid. In this paper a new and tech-nically better method of on-line radio frequency partial dischargemonitoring from a remote smart grid control room through multiprotocol label switching is discussed.

Index Terms—Antenna, dielectric break down, insulators, mon-itoring, partial discharges, signal detection, UHF detectors.

I. INTRODUCTION

T HE ’smart grid’ concept would facilitate the intelligentmonitoring and control of power equipment from distant

location in order to increase the existing high levels of systemreliability, efficiency, security and quality of services (QoS). Ex-isting infrastructure of a power system does not allow a central-ized and continuous condition monitoring of power apparatusat different power stations and substations. In order to obtain ahighly reliable and non-disruptive power system, a smart condi-tion monitoring system of different power apparatus is essential.Continuous on-line diagnostic, preventive maintenance, fore-casting of failure risk and minimization of outage can save ahuge amount of cost associated with power system failure.Continuous condition monitoring and diagnostic of different

power equipment (e.g., power transformer, circuit breaker,current transformers, potential transformers etc.) from a cen-tralized location (e.g., SCADA control room) is essential inorder to obtain a highly reliable and nondisruptive smart gridsystem. Present SCADA techniques refresh the data typically

Manuscript received March 04, 2013; revised November 29, 2013; acceptedJanuary 15, 2014. Date of publication February 03, 2014; date of current versionApril 17, 2014. Paper no. TSG-00217-2013.The author is with the Department of Electrical and Electronic Engineering,

Ahsanullah University of Science and Technology (AUST), Dhaka 1208,Bangladesh (e-mail: akalam@daad-alumni.de).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TSG.2014.2301723

once every two seconds to feed command, control, and mon-itoring systems. Several abnormal behaviors and outages ofpower system have shown that present data measurement andrefreshment rate of SCADA system is not sufficient enoughto provide adequate information of situational awareness ofthe grid network. A simple, cost effective and smart detectiondevice for condition monitoring of power equipment is essen-tial for integrating all the detected signals into the smart gridmonitoring system. According to the IEEE definition the “smartgrid” is a next-generation electrical power system that uses thecommunications and information technology in the generation,delivery and consumption of electrical energy.For a smart grid power network it is essential to closely mon-

itor and control the grid operations and behavior of the devices.If the small disturbances in grid devices are not detected earlyenough, it may lead to widespread cascading failures in the gridsystem. One such disturbance is the partial discharge (PD) phe-nomena in power devices. The formation of PD is an alarmingsign of a dielectric breakdown. The consequences of some par-tial discharges are the stresses on the insulator, severe defects,and in the long run failure to the power equipment as well aspower outage.PDs from different sources have different characteristics

and measurable phenomena; such as acoustic emission, chem-ical decomposition, current impulse, electromagnetic (EM)radiation, light flash and voltage drop. PD causes stressesand sometimes severe defects on the insulator. In the longrun severe PD may cause failure of the power system if it isnot detected at an early stage. It would be possible to avoidcatastrophic disaster due to the failure of power system if PDcan be monitored and diagnosed in a timely manner. Thereare several different ways of monitoring the PD activities. PDmonitoring system through a combined IEC 60270 and RadioFrequency (RF) method is described in [1]. Other methods ofPD monitoring systems within Ultra High Frequency (UHF)range by using different couplers and sensors, are described in[2]–[7]. The detection sensors are placed inside the equipmentin most of the research activities and this kind of monitoringsystem may not be feasible in on-line real time situation.Some partial discharges are ultra-wide band in nature and the

frequency of the PD may extend up to 5 GHz [8]. This kindof radio frequency PD (RFPD) can be detected through an an-tenna from outside of the equipment. The detected RFPD canbe converted to an equivalent milli-volt or milli-ampere outputwhich then can be fed to a communication channel of the smartgrid network. A conceptual block diagram of such kind of smartgrid device monitoring system is shown in Fig. 1. Since thedata scanning rate through Remote Terminal Unit (RTU) and

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BAKI: CONTINUOUS MONITORING OF SMART GRID DEVICES 1211

Fig. 1. Conceptual block diagram of smart grid condition monitoring system.

Power Line Carrier (PLC) of the SCADA system is not fastenough, a smart grid may fail to monitor some dynamic statesof power system devices, such as PD activities. Multi ProtocolLabel Switching based data communication channel is one pro-posed option to monitor the smart grid devices. MPLS can senddata with faster rate than that of present RTU and PLC basedsystem. MPLS based dynamic monitoring system can be im-plemented in smart grid to facilitate the on-line diagnostic ofdifferent smart grid devices, their preventive maintenance, fore-casting of failure risk and to enhance the zero outage rates.

II. NATURE OF PARTIAL DISCHARGE

It was mentioned in Section I that the frequency spectra ofPDs may extend up to 5 GHz [8] and they may come from dif-ferent sources such as oil corona, gas discharge in oil, floating

discharge. Time domain signals and frequency domain naturesof PD signals in oil, SF6 gas and in air are described in [9]. It ispossible to differentiate between air discharges in oil and SF6by analyzing the frequency spectrum [9]. Some PD pulse shapesare Gaussian in nature [10]. The time-domain and frequency do-main characteristics of a laboratory based UHF PD signals forapplied AC (0–50 kV) and DC (0–100 kV) voltages are com-pared in [11]. For AC condition, the frequency contents of UHFsignals due to particle movement in transformer oil lies in therange 0.5–1.2 GHz [11]. Though PD is ultra wideband in nature,it was found that most of the energy for a particular type of PD isconcentrated on or around a particular frequency or narrow fre-quency spectrum. For the continuous monitoring and early stagediagnostic of power equipment it is necessary to deploy verysimple detection device within the power station or switchyard.

1212 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 3, MAY 2014

For example, simple dipole antennas with particular frequencybands (e.g., 1000 MHz) can be used as a Radio Frequency PD(RFPD) sensor. The detected signal then can be converted toan equivalent voltage or current output (within 4~20 milli-am-pere range). Current output within 4~20 milli-ampere range isused in industrial instrumentation and measurement purposes.The milli-ampere output from the RFPD detector then can befed to a terminal device for real-time monitoring of the powerequipment from a remote smart grid control room. Since the PDis UWB in nature, the bandwidth and sampling rate requirementfor the time domain PD signal processing can be tremendouslyreduced by converting the PD into its equivalent milli-volt ormilli-ampere signal. The sampling rate requirement for the ADCconversion of a PD signal of centre frequency 1 GHz is at least2 GHz which is very high. It is also not cost effective to imple-ment such high sampling rate ADC.Signals from more than one sensor can be multiplexed be-

fore feeding it to the communication link. The integrated andprocessed signals can be monitored from the SCADA controlroom by using the existing communication links [e.g., PowerLine Carrier (PLC), optical fiber, UHF link, WiMAX, or in-ternet]. The status of a certain power transformer or circuit-breaker could be used to formulate a future strategy of responsesif the collected data from these apparatus fall within ’warringranges’. Time stamping of data capturing would allow capturingthe sequence of condition monitoring which helps in the diag-nosis of the exact cause of complex situations like blackouts.Now a day’s GPS timing is utilized in power system by havingsynchronized measurement units based on GPS signals [12].On-line PD measurements synchronized with the phasor mea-surements would offer a unique opportunity to bring in the re-mote diagnostics of power equipment to the smart grid controlroom.Presently there are some unmanned terminal stations in

power grid systems. Real time monitoring and control of theequipment of these unmanned stations are done from a remotecontrol room. Therefore real time PD information can be trans-ferred to the remote monitoring and control room for propermonitoring and diagnosis of the devices.

III. CONVERSION OF PARTIAL DISCHARGE INTO ANEQUIVALENT MILLI-AMPERE SIGNAL

A single PD pulse can be modeled by using the followingGaussian distribution:

(1)

A Gaussian modulated impulse radio signal can be repre-sented by:

(2)

In (1) and (2),

amplitude;

number of samples;

Fig. 2. Radio Frequency Partial Discharge (RFPD) detection circuit.

Fig. 3. Radio Frequency PD pulses of center frequency 1 GHz.

delay;

center frequency (Hz);

sampling interval;

= ;

attenuation;

normalized bandwidth;

The RMS value of a continuous time signal can be ex-press by (3):

(3)

Here, and are the integration time limits.The value of the PD signal then can be converted

to an equivalent milli-ampere signal. A block diagram of theRF signal to milli-ampere conversion circuit is shown in Fig. 2.The milli-ampere output of the circuit can be used for remoteor centralized monitoring and diagnostic of the device. Fig. 3shows a RFPD signal that was simulated by using (2). The centrefrequency of the source signal is 1 GHz. This kind of PD can bedetected by using an antenna.Fig. 4 shows the corresponding convertedmilli-ampere signal

of the PD. The milli-ampere signal shown in Fig. 4 is the scaledversion of the simulated milli-ampere signal. The peak valueof the signal is about 10 milli-amperes. In real practice it willbe possible to calibrate and amplify the converted milli-amperesignal. The duration of the PD pulse, shown in Fig. 3, for onehalf of the power cycle (50 Hz) is about 3 milli-seconds. Theduration of the converted milli-ampere signal, that crosses thevalue of 4 milli-amperes, is about 2 milli-seconds. The back-ground noises and interferences of a switchyard will corrupt the

BAKI: CONTINUOUS MONITORING OF SMART GRID DEVICES 1213

Fig. 4. Equivalent milli-ampere output of the RFPD shown in Fig. 3.

Fig. 5. First milli-ampere PD pulse (of Fig. 4) on hold (ideal case).

detection and measurement system. Therefore a clipping cir-cuit should be used after the mV conversion. The milli-ampereoutput of the circuit, that crosses the 4 milli-ampere value, canbe fed to the RTU or any other terminal equipment of the smartgrid system.Present data scanning or refreshment rate of a power system

(through RTU and PLC) is relatively slow and in some cases thisslow refreshment rate may not be capable of providing informa-tion about dynamic state of a power system. For example, if thesignal shown in Fig. 4 is not held for 2 seconds it won’t be pos-sible to monitor this signal from a remote data centre. Thereforea peak-hold circuit is necessary that can hold the milli-amperesignal for about 2 seconds. Presently there are some square lawdetectors available in the market those can convert the RF signalto mV up to 4 GHz frequency range. An RC circuit can be usedfor holding the peak of the converted PD. It would be possibleto control the time for holding the peak by choosing the appro-priate values of resistors ( ) and capacitors ( ). If necessary,an RF amplifier circuit can be used before the square law de-tector for low amplitude signal. Though the original shape ofthe PD information is lost as shown in Fig. 4, but this kind ofreal time on-line signal detection will be an indication of PD inthe switch-yard.Fig. 5 shows the held-PD (milli-ampere) of the first PD pulse

(Figs. 3 and 4) in ideal case. It is mentioned before that the de-tected signal can be held through an RC circuit. This kind ofsignal on hold can be read from a remote or centralized moni-toring centre through RTU if the milli-ampere signal is held for2 seconds. On the other hand if the pulse is held for 2 seconds thesystem will fail to monitor about 100 power cycles (for 50 Hzsystem) or about 125 power cycles (for 60 Hz system). A falsealarm might be initiated in that situation. Therefore it is neces-sary to use an alternate communication channel which is faster.

Fig. 6. Block diagram of smart grid device monitoring through MPLS tech-nology.

The use of Multi Protocol Label Switching (MPLS) based tech-nique is one option to overcome this problem which is describedin the next section.

IV. MULTI PROTOCOL LABEL SWITCHING

A better and less costly strategy is required for an integratedcommunications network that supports all applications, withproper QoS, reliability, security and unified network manage-ment tools those ensure delivery of critical smart grid appli-cation traffic [13]. One of the proposed models for smart gridWide Area Network (WAN) connectivity is multi-service corewith Multi Protocol Label Switching (MPLS) and through thisservice the new application requirement can be easily supported[14]. One such feature is known as Routed Pseudo wire. RoutedPseudo wire is a combination of Layer 3 Virtual Routing andForwarding (VRF) instances and its extension through Layer 2Pseudo wire across anMPLS cloud to a branch or substation site[14]. The concept of the proposed smart grid device monitoringtechnique throughMPLS system is shown in Fig. 6. It was men-tioned in Section I that severe PD may cause failure of powersystem. It was also mentioned in Section II that there are someunmanned terminal stations in power grid system where a realtime remote monitoring and control of the switch-yard devicesis necessary. Teleprotection (TPR) devices of smart grid shouldmonitor the conditions of power equipment in a coordinatedway for quick tripping of faulty power device (e.g., device withsevere PD) and transmission line in order to prevent damagesto expensive power equipment and instability of power system.The captured signal must be transferred with minimal latency inorder to ensure the teleprotection of power system. This strin-gent latency requirement for the smart grid can be achievedby implementing an MPLS technology for the condition mon-itoring of smart grid devices. MPLS system can minimize theneed of parallel maintenance of TDM teleprotection network.MPLS supports several service classes including real-time datatraffic by prioritizing the QoS. Traffic is marked with speciallabels in MPLS network in order to designate and prioritize dif-ferent classes of service. A special Label Edge Router (LER)provides these labeling functionalities. According to RFC 3031[15] a label is defined as ’a short fixed length physically con-tiguous identifier which is used to identify a Forward Equiva-lence Class (FEC)’. The traffic classification process in MPLSis called the FEC. A label is located at a Shim header, as shown

1214 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 3, MAY 2014

Fig. 7. Insertion of MPLS shim header between level 2 and level 3 of OSIcommunication model.

in Fig. 7, which is 32 bit long and resides between layer 2 andlayer 3 of an Open Systems Interconnection (OSI) model.LER sorts the incoming data packets and place the highest

priority traffic on to the most expensive circuits, while allowingthe routine traffic to take other less expensive paths.When a datapacket enters the MPLS network, the packet is mapped into anFEC. The mapping can be done on a wide variety of parameters:data type, address prefix (or host), source/destination addresspair, or ingress interface.For smart grid teleprotection system the latency requirements

is less than 10 milli-seconds and that for synchrophasors appli-cation is about 20 milli-seconds; latency requirements for mostsmart grid supervisory control and data acquisition (SCADA)system is 100~200 milli-seconds [13]. The refreshment rate andlatency requirement for real-time condition monitoring of smartgrid apparatus should be within 20 milli-seconds. Through theQoS of MPLS, the transmission delay can be effectively min-imized and high degree of reliability and quality also can beachieved [16]. It is possible to transmit data within 9 milli-sec-onds through MPLS system.

V. CONCLUSION

Present data scanning and refreshment rate of the supervisorycontrol and data acquisition (SCADA) system of a power net-work is not fast enough to provide information about dynamicstate of a power system. The data scanning rate and latencyfor on-line and remote condition monitoring of smart grid de-vices should be within 20 milli-seconds. The data monitoringand refreshment rate through Remote Terminal Unit (RTU) andPower Line Carrier (PLC) system is about 2 seconds. With thisslower data refreshment rate a smart grid may fail to monitorsome dynamic states of power devices. Therefore a faster datatransfer channel is essential for the smart grid device monitoringsystem. Smart, simple and cost effective device monitoring sen-sors and faster data transfer channel will facilitate the smart griddevice monitoring system. One such simple detection system isthe radio frequency partial discharge (RFPD) detection and con-version technique into an equivalent milli-ampere. PD pulsesare ultra-wide band in nature and frequency spectrum of somePD pulses may extend up to 5 GHz. The duration of the PDpulses for half of the power cycle is less than 10 milli-seconds

for a 50 Hz power cycle. The duration of the PD pulse and con-verted milli-ampere signal, described in this paper, is less than3 milli-seconds. Therefore it is necessary to hold this milli-am-pere about 2 seconds for monitoring the signal through presentRTU-PLC based SCADA communication channel. An alternatesolution of this slow refreshment rate is the use ofMulti ProtocolLabel Switching (MPLS) based data communication channel.MPLS based technique can send the data within 9milli-seconds.By implementing an MPLS based smart-grid device monitoringsystem, the necessity of dynamic-data-holding for a longer timecan be avoided. MPLS based dynamic monitoring of smart griddevices from a remote and centralized location would facili-tate online diagnostic, preventive maintenance, forecasting offailure risk and minimization of outage rate.

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A. K. M. Baki (M’06) received the B.Sc. degreein electrical and electronic engineering degreefrom Bangladesh University of Engineering andTechnology (BUET), Bangladesh, in 1992 and theM.Sc. degree from the University of Bolton (formerBolton Institute of Higher Education), U.K., in 2003having followed a postgraduate program inElectronicSystem and Engineering Management at the South-Westphalia University of Applied Science, Germany.He received the Ph.D. degree in electrical engineeringfrom Kyoto University, Kyoto, Japan in 2007.

From 2005 to 2006 he was a research assistant in the Research Institute forSustainable Humanosphere (RISH), Kyoto University by recognizing the RadioScience Center for Space and Atmosphere (RASC). From 2004 to 2005 he was

a research assistant in the Radio Science Center for Space and Atmosphere(RASC), Kyoto University. From 2003 to 2004 he was a research student inRadio Science Center for Space and Atmosphere (RASC), Kyoto University. Hehas been working as an Associate Professor in the department of Electrical andElectronic Engineering of Ahsanullah University of Science and Technology,Bangladesh since 2014. From 2007 to 2013 he was an Assistant Professor in In-dependent University, Bangladesh. From 2009 to 2011 heworked as a Post-Doc-toral Research Fellow in Monash University, Australia. From 2003 to 2007he was a Sub-Divisional Engineer in Bangladesh Power Development Board(BPDB). From 1994 to 2003 he was an Assistant Engineer in BPDB.Dr. Baki is the former recipient of scholarship from German Academic Ex-

change Service (DAAD – Deutscher Akademischer Austauschdienst) for thestudy of the M.Sc. degree. He received Japan Government (Monbukagakusho:MEXT) scholarship for the study of the Ph.D. degree.