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Intertie protection of Synchronous Distributed Generation Using Intelligent Relays

Harmeet Cheema (*), Anthony J. Rodolakis (*), Geza Joos (*) *McGill University, Montreal, Canada.

SUMMARY The Distributed Generation (DG) Intertie Protection, also known as DG Fault interconnection protection, merits particular attention when connecting DGs in distribution networks. Its main purpose is to disconnect the DG as soon as possible upon the inception of an area-EPS fault. The relatively large fault currents that may be contributed by the synchronous DGs, even under the sole influence of its flux dynamics, constitute the objective of this investigation. These contributions may expose near-by distribution system fuses to danger and compromise host-feeder fuse-saving schemes. On the other hand, detection of faults whose signature is low-magnitude fault currents is not trivial despite their reduced shock impact on the system infrastructure. Low magnitude fault currents, typically caused by elevated arc/ground fault resistances at rather remote fault locations near feeder extremities, present a difficult to meet protection requirement and can be quite challenging to both area-EPS and DG Fault Interconnection protection. This article describes a methodology for training and setting Intelligent Relays that fulfills the fundamental DG Intertie protection requirements by detecting all types of shunt fault types occurring anywhere within the geographical span of the distribution feeder the DG is connected at. The fault types investigated in this article are Three Phase (LLL), Single Line to Ground (SLG), Line to line (LL) and Line to line to ground (LLG) shunt faults with and without arc/ground resistances, on solidly grounded distribution feeders. An equally fundamental requirement the intelligent relay must, by design, meet is that it avoids DG nuisance trips under non-faulted system conditions. The performance of the Intelligent Relays, based on indices quantified per the above-mentioned considerations is compared against the performance of several variants of over-current relays. KEYWORDS Synchronous Distributed Generation, DG Fault Interconnection Protection, Intertie Protection, Shunt Faults, Intelligent Relay, Arcing impedance, Ground Impedance, Multivariate Analysis, Data Mining, Dependability, Security, Data Mining, Decision Trees, Arc Resistance

CIGR-351 2014 CIGR Canada Conference

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1. INTRODUCTION AND SCOPE The issues that Distributed Generation forces protection engineers to address are diverse and pose several rather non-trivial for both the area-EPS and the DG itself [1-5]. In the case of an area-EPS fault, both the feeder protection and the DG Intertie protection are responsible for detecting it. The fault detection sensitivity requirements, typically dictated by remote and/or high impedance faults, can be quantified for grounded distribution systems that form the focus of this investigation, in terms of phase/zero sequence current thresholds responsible for tripping phase/ground protective devices. The alternative approach, to specify a reasonable upper limit for an effective value of the arc/ground fault impedance that will limit the service frequency symmetrical RMS fault current, has been adopted here. The same sensitivity requirements are normally imposed on both the feeder and the DG Intertie protection but it is not unusual to demand higher sensitivity from the DG Intertie protection. The issue of detecting high-resistance ground faults is not straightforward and has been approached by analyzing the non-linearity of the arcing/ground resistance and/or the harmonic content of the resulting fault current [6]. Since this article addresses the setting of Intelligent Relays using 1st cycle symmetrical RMS fault currents, the only attribute of the high-resistance fault considered here is its resistance. Last, but not least, immediate and unconditional DG disconnection upon fault occurrence [1-5] is sought in this article, with no regard for possible Voltage Ride Through requirements. This article is structured as follows: Section 2 summarizes the principle and the methodology used to obtain the intelligent relay settings. Section 3 describes the benchmark distribution feeder and illustrates two alternative approaches for setting intelligent relays to detect all 4 types of shunt faults in the presence of one synchronous DG. The method is readily extended to identify the faulted phase(s). Section 4 provides results on the performance characteristics of all considered protective devices and proposes an implementation for the intelligent relay based on the results obtained. Section 5 encapsulates the conclusions of this investigation. The performance of all the considered protective devices, including the intelligent relay, is quantified in terms of their overall reliability by virtue of a Dependability index and a Security index. The Dependability index (DI) earmarks the protective devices ability to reliably detect shunt system faults of any type and severity. The Security index (SI) reflects the protective devices ability to avoid nuisance trips for system events that may involve first-cycle conditions resembling faults. 2. METHODOLOGY FOR SETTING THE INTELLIGENT RELAY The occurrence of an area-EPS fault is a fundamental frequency transient phenomenon particularly under the influence of synchronous DG flux dynamics, rendering the signature of the incipient fault time-dependent. The time-varying variables of interest, selected for studying the particular phenomenon, are shown in Table I. Many different parameters can be considered e.g. voltage phase and sequence measurements, active and reactive power at the interconnection etc. but for the sake of keeping the intelligent relay settings as simple as possible only current variables are considered. For any given time t, for any DG (subscripted i ), and for any system event (superscripted k ), a DG state vector containing all the postulated DG variables can be stated as:

)]()...(),([)]([ 621 txtxtxtXki

ki

ki

Tki =

where )(1 txki to )(6 tx

ki are the current measurements provided in the Table I. For N system events, the

set Si(t) defined for any DG can be written as: })](,......[)]([,)]([,)]({[)( 321 TNi

Ti

Ti

Tii tXtXtXtXtS =

that includes DG state vectors from both fault and non-fault events. The intelligent relay considers only the first cycle so ignoring the time stamps, the set reduces to:

}],......[][,][,]{[ 321 TNiT

iT

iT

ii XXXXS =

3

DG VARIABLES OF POTENTIAL INTEREST TO INTERTIE PROTECTION

AIx =1 Phase-A

Sym-RMS Current

BIx =2 Phase-B

Sym-RMS Current

CIx =3 Phase-C

Sym-RMS Current

14 Ix = Positive Sequence RMS

Current

25 Ix = Negative Sequence RMS

Current

06 Ix = Zero Sequence RMS

Current

Table I: DG variables monitored for Intertie Protection Duty

Given the prior nature of system events, a classifier Ci can be constructed based on the information contained in a training set Si that categorizes the first-cycle DG states as coming from an area-EPS fault or not. The classifier itself can take the form of a Decision Tree (DT) obtained via data mining methods [7-8]. DTs consist of consecutively-layered decision nodes, each featuring a decision making procedure that relies on a numerical value of one and only one DG variable called the range, i.e. the resulting DT is univariate. This approach suits well the task at hand because the DT structure encapsulates the relay tripping logic. The relay protection handles/thresholds can be directly taken to be the respective decision node variable/ranges. The future classification ability of the resulting DT is quantified in terms of an independent testing set containing system events different from the ones included in the training set, also used to calculate the relays performance indices. In terms of constructing the DT classifiers, the pertinent machine learning techniques [7-8] rest on: a) efficient node splitting criteria and b) Tree pruning methods. The end-goal is to arrive at a reasonably sized DT without compromising its classifying ability. The node splitting criterion adopted here is the well-established Gini criterion. Its general multi-class formulation, within the CART algorithm used for this work as:

Gini(t)=1 i=1

k p[(i|t)]2

where: a) k is the number of classes envisaged to be created at node t, b) p(i|t) is the fraction of records contained within class i, at node t. Physically, the Gini index indicates the cost of misclassifying DG states, the objective being to have an index reduction for every new generation of nodes.

3. CAPTURING THE SIGNATURE OF AREA-EPS SHUNT FAULTS The benchmark distribution feeder One Line Diagram and Data used is shown in Appendix I. The feeder is balanced, exhibiting no negative sequence voltage content during steady state operation at the DG interconnection point [3-5]. A 6th order model was used for the synchronous DG to capture the sub-transient flux dynamics [1]. No excitation system reaction was modeled [1-5]. The presence or absence of speed governors is immaterial for the time of interest [1]. The DG was assumed to operate, prior to the fault inception, under a near unity constant power factor [1-5]. The intelligent relay was trained for full-feeder load with the DG supplying 30% of it. The MATLAB simulation environment has been used to generate the necessary results and to filter out the unidirectional (DC offset) component of the fault current. Two basic approaches were taken to produce DT classifiers for the considered fault types, namely the multi-class and the combined two-class approach. The former constructs a five-class DT classifier trained to recognize the solid or arcing shunt faults. A training set containing system events, consisting of standard-trim shunt faults are shown in Table II. The resulting DT is shown in Figure 1.

4

YESNO

YESNO

I0 > 3.74

LL Fault

I1 > 100.72

YESNO

I2 > 27.80

YESNO

I1 > 110.12

YESNO

I2 > 11.90

No Fault LL Fault

LLL Fault

SLG Fault LLG Fault

YESNO

I2 > 24.20

SLG Fault

YESNO

I1 > 126.43

YESNO

I2 > 21.54

No Fault LLG Fault

Figure 1 Multi-class Decision Tree for the various shunt fault types.

It is seen that the resultant DT, pretty much in compliance with current practice: a) has selected sequence currents as the intelligent relay protection handles, b) has used zero sequence currents to discriminate between ground and phase faults. The Dependability index (DI), of this DT was determined to be .93 meaning that 93% of the faults within the testing set were recognized as such. Similarly, its security index, SI, was determined to be 100%, meaning that no non-fault events were misclassified classified as fault events. The testing set consisted of 23 non-fault events and 34 fault events of all types, each with fault impedances assuming the discrete values of .001 (practically solid faults), 2, 3, 8 and 15 . The fact that the Decision Tree classified any fault event as a fault and tripped the intelligent relay was, by design, sufficient. The misclassified fault events were further scrutinized and found to be electrically remote form the DG while involving high arc/ground resistances. The classifier lack of sufficient detection sensitivity and its complex and rather non-intuitive structure, was the motivation to explore the below explained alternative approach. In this second approach, dedicated two-class DTs were constructed aiming at detecting shunt faults exclusively on a per type basis. The training events are provided in the Table II. All the events were simulated at 100% system loading. The faults were introduced at 14 different locations in the feeder, provided in the Appendix.

TRAINING EVENTS

Non-faults 31 events

- Normal steady state operation - Connection or disconnection of one or group loads - Circuit breakers inadvertent opening operation

LLL (ABC) 28 events

- LLL faults with Rarc = 0 and 3

LL (BC) 28 events

- LL faults with Rarc = 0 and 3

LLG (BCG) 28 events

- LLG faults with Rarc = 0 and with Rg = 0 - LLG faults with Rarc = 3 and with Rg = 0

SLG (AG) 28 events

- SLG faults with Rg = 0 and 30

Table II: Training data for constructing two-class decision trees.

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DTs were determined for each shunt fault type using non-fault and fault system events. The DTs were trained, again, for current sequence variables. For example, the LLL DT was constructed using LLL fault events and non-fault events, per Table II. The resultant DTs are shown in Figure 2.

YESNO

I1 > 174.83

NO FAULT LLL

YESNO

I2 > 34.34

NO FAULT LL

YESNO

I2 > 21.20

NO FAULT LLG

YESNO

I0 > 3.10

NO FAULT SLG Figure 2 Dedicated Two-class Decision trees for all considered shunt fault types

It is readily seen that these DTs are much simpler with LLL, LL, LLG and SLG faults directly identified by virtue of positive, negative and zero sequence currents exclusively. LLL faults are symmetrical and the DT uses only positive sequence to detect these faults. LL and LLG faults are unbalanced and are strongly characterized by negative sequence currents. Lastly, SLG faults DT are identified by zero sequence currents, their value depending on system neutral grounding/ground impedances. A further improvement is, however, possible by producing DTs that feature faulted phase recognition as well, for recording purposes. Figure 3 depicts these faulted-phase Augmented Decision Trees (ADTs). They are, clearly, not applicable to LLL faults. The training set used to produce these trees comprises the events shown in Table II augmented with LL, LLG, and SLG faults simulated on all different possible phase combinations at 100% system loading. The phase faults had arc resistance of 0 and 2 and ground faults had ground resistance of 0 and 20 . Any differences in thresholds with respect to the ones of Figure 2 are due to slightly different fault resistances.

YESNO

I2 > 37.14

NO FAULT

YESNO

Ia > 118.2

LL-BC

YESNO

Ib > 118.7

LL-AC LL-AB

YESNO

I2 > 25.31

NO FAULT

YESNO

Ia > 157.78

LLG-BC

YESNO

Ic > 164.6

LLG_AB LLG_AC

YESNO

I0 > 3.48

NO FAULT

YESNO

Ia > 181.13

SLG-A

YESNO

Ib > 111.3

SLG-C SLG-B

a) ADT for LL faults b) ADT for LLG faults c) ADT for SLG fault

Figure 3 Faulted Phase Detection Augmented Decision Trees for LL, LLG, SLG faults

4. PROTECTIVE DEVICE SETTINGS AND PERFORMANCE The normal phase full load current at the PCC, i.e. at the high voltage side of the DG interconnection transformer is approximately 80 A (74.74A). The phase over-current relay tripping threshold was taken to be 1.5 times full-load current, i.e. 120A, the Ground over-current relay pick up threshold was taken to be 0.75 times the full load current, i.e. 60A and the voltage restraint relay settings were calculated as = 1.5 / where Ip is the pickup current, Is is the steady state current, Va is the actual voltage and Vs is the nominal voltage.

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The intelligent relay model implementation can be seen in the Figure 4. It has four decision trees for each fault type. This model detects whether there is a fault or not and sends trip signal if one of the four decision trees conditions are met.

I1> 174.83 A

I2> 2.7 kV

I2> 34.35 A

> 21.20 A

> 3.10 AI0

TRIP

SLG

LLG

LL

LLL

Figure 4 Protective Device performance indices for high-resistance SLG faults

The intelligent relay provided in the Figure 4 and conventional protective elements were tested on an exhaustive list of testing events provided in the Table III. These events were simulated for three different loading conditions of 20%, 60%, and 100% . The faults were simulated on different phases, with different arc resistances than what the decision trees were trained for.

TESTING EVENTS

Non-faults 69 events

- Normal steady state operation - Connection or disconnection of one or group loads - Circuit breakers inadvertent opening operation

LLL (ABC) 243 events

- LLL faults with Rarc ranging from 0 to 3

LL (AB,BC,AC) 270 events

- LL faults with Rarcranging from 0 and 3

LLG (ABG,BCG,ACG) 225 events

- LLG faults with Rarc = 0 to 3 with Rg = 0

SLG (AG,BG,CG) 270 events

- SLG faults with Rg from 0 to 30

Table III: Testing data for determining performance of DTs and conventional protection

Table IV illustrates the performance indices of DTs shown in Figure 2, along with the performance of the other considered protective devices for all types of faults (whenever applicable). Given the structure of the intelligent relay, these performance indices reflect the intelligent relay performance as well. It is seen that in terms of dependability, the intelligent relay performs as well as the other devices in detecting LLL, LL, and LLG faults, but slightly better for SLG faults. In terms of security the intelligent relay is capable of correctly identifying the non-fault event of inadvertent CB1 breaker opening that increases the DG phase current to 150A (its DT trip threshold being 174A) something the other devices cannot do (interpreting it as a system fault due to the 120 A phase threshold), a fact that reduces their security indices. The same holds true LL and LLG faults.

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Shunt Fault Type

Intelligent Relay Relay Indices, %

Phase-O/Current Relay Indices, %

Voltage Restraint Relay Indices, %

Ground-Over Current Relay Indices, %

DI SI DI SI DI SI DI SI

LLL 100 100 100 97 100 97 N.A. N.A. LLG 100 100 100 97 100 97 92 100 LL 100 100 100 97 100 97 N.A. N.A. LG 100 97 86 97 92 97 88 100

Table IV. Performance indices of protective devices for DTs shown in Figure 2

Figure 5 illustrates the performance indices of all devices for wide varying ground resistance SLG faults. In terms of the results portrayed in Figure 6, it is seen that the Intelligent Relay retains a dependability advantage for high-ground resistance faults. No reduction in the tripping performance indices was observed when testing the Augmented Decision Trees of Figure 3, with the sole exception of having a single SLG fault event on phase C interpreted as a phase B fault.

Figure 5 Protective Device performance indices for high-resistance SLG faults

5. CONCLUSIONS

A methodology based on multivariate analysis and data mining methods was developed to set Intelligent Relays for DG Intertie protection. The methodology was based on using sequence and phase DG fault current contributions as protection handles. It proved capable of credibly capturing the characteristics of the shunt fault inception phenomenon using a training process that involved standard trim faults. The methodology provides a readily interpretable relay tripping logic, based on first-cycle symmetrical RMS sequence fault currents resembling the tripping logic of currently used protective devices. The capability of the intelligent relay to correctly trip for faults on other system phases has also been demonstrated. The intelligent relay can also be trained to identify the system phase involved in the various types of faults. The performance of the intelligent relay was found to be at par with the remaining considered protective devices with a definite advantage for high-ground resistance faults.

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APPENDIX I FEEDER DATA

The substation has a LLL short circuit level of 1000 MVA and a X/R ratio of 10. It feeds the 25 kV four wire multi-grounded distribution system through a 15 MVA, 114.3 kV/24.94 kV /Yg transformer. The 25 kV distribution system has total demand of 11.064 MW and 2.345 MVAr. The distribution main feeder X/R ratio ranges from 3.4 near substation down to 0.8 at the feeder end. The laterals X/R ratio ranges from 3.4 to 0.6. A 1.2 MVAr capacitor is present near the feeder end.

A 5 MVA 4.16 kV synchronous generator supplies 30% of the system load in addition to auxiliary load of 250 kW. The DG operates in power factor control mode and maintains 0.95 lagging power factor at the PCC. It is connected to the distribution system through a 12 MVA, 25 kV/4.16 kV /Yg transformer.

120

kVSC

Lev

el =

1000

MV

AX

/R r

atio

= 1

0

GY

15 MVA114.3 kV/24.94 kV

R = 0.2888 %X = 7.464%

GY

5000 kVA/4.16 kVXd =3.12 pu

Xd = 0.592 puXd = 0.354 pu

12 MVA24.94 kV/4.16 kV

R = 0.8956 %X = 8.956 %

DL-01 DL-02 DL-03 DL-04 DL-06 DL-16 DL-17DL-08 DL-09 DL-10 DL-15D

L-07

DL-13

DL-14

DL-12

L-01 L-02 L-03 L-04

L-06

DL-05

L-08

L-07 L-10 L-12

DL-11

L-11

L-14

C1

1.2 MV

AR

L-13

L-15 L-16 L-17L-05

250 kW

Transformer 2

Transformer 1

Generator 1

B12

B6

B7 B8 B9 B10 B11B5B4B3B2B1

L-09

B13

B14

CB-1 CB-2

Figure 6 One line diagram of the distribution system

BIBLIOGRAPHY [1] "IEEE Application Guide for IEEE Std 1547, IEEE Standard for Interconnecting Distributed

Resources with Electric Power Systems," in IEEE Std 1547.2-2008, ed, 2009. [2] "Interconnection of Distributed Resources and Electricity Supply Systems," in CAN/CSA C22.3

No. 9-08, ed, 2008. [3] Hydro One Networks Inc., "Distributed Generation Technical Interconnection Requirements.

Interconnections at Voltages 50kV and Below," DT-10-015 Revised, February 2009. [4] Planification du rseau de distribution dHydro Qubec pour lintegration de la production

dcentralise. E.12-02, Dcembre 2012. [5] 35 kV and below Interconnection Protection Requirements for power generators, BC Hydro,

Vancouver, BC, Canada, 2004 [6] Daqing Hou, "Detection of High Impedance Faults in Power Distribution Systems, Power

Systems Conference: Advanced Metering, protection, Control, Communications and Distributed Resources, 2007

[7] Breiman L, Friedman J, .Olshen R, Stone C, Classification And Regression Trees, Chapman and Hall, NY-London, 1993.

[8] L. Rokach and O. Maimon, Data Mining and Knowledge. Springer, 2005.