Norwegian University of Science and Technology

Adaptive frequency estimation method

for ROCOF islanding detection relay

Maciej Grebla

14.05.19 Vaasa

Norwegian University of Science and Technology 2

Introduction

SMART GRID

MICROGRID

LV or MV network of load clusters with

distributed energy resources, both

generator and storage systems, able

to operate in grid-connected or

islanded mode

An electricity supply network that uses

digital communications technology to

detect and react to local changes in

usage

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Introduction

Frequency estimation

ROCOF calculation

>Relay Setting

β

LPF #1

LPF #3

LPF #2

Trip

Voltage

ROCOF relay model [1]

Methods

Local passive

O/U frequency O/U voltage VVS ROCOF

‒ big NDZ‒ nuissance trippings

Local active

Sandia frequency shift Sandia voltage shift

Remote

DTT Synchrophasors PLC

[1] Motter, D., Vieira, J.C.M. and Coury, D.V., „Development of frequency-based anti-islanding protection models for synchronous distributed generators suitable for real-time simulations”, IET Generation, Transmission and Distribution, vol. 9, no. 8, pp. 708 – 718, 2015.

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State-of-the-art methods

• Zero Crossing• Fourier

𝑋 𝑛 =2

𝑁

𝑘=0

𝑁−1

𝑥 𝑛 − 𝑘 𝑒−𝑗𝜋𝑓𝑏𝑇𝑘

𝑓 =𝛼𝑛 − 𝛼𝑛−1

𝑇∙𝑁

2𝜋∙ 𝑓𝑏

0

/2

3 /2

d/dt

𝑓 =1

2 𝑡𝑧𝑐 − 𝑡𝑧𝑐𝑙𝑎𝑠𝑡

𝑡𝑧𝑐 =𝑡𝑛−1 ∙ 𝑉𝑛 − 𝑡𝑛 ∙ 𝑉𝑛−1

𝑉𝑛 − 𝑉𝑛−1

Norwegian University of Science and Technology 5

Kalman

𝑥𝑘+1 = Φ𝑘𝑥𝑘 + 𝑤𝑘

𝑧𝑘 = 𝐻𝑘𝑥𝑘 + 𝑣𝑘

Process equation

Measurement equation

ො𝑥𝑘 = ො𝑥𝑘− + 𝐾𝑘 𝑧𝑘 − 𝐻𝑘 ො𝑥𝑘

−

Estimate update equation Simplification in phasor estimation

𝐸 𝑤𝑘𝑤𝑖𝑇 = ቊ

𝑄𝑘 , 𝑘 = 𝑖0, 𝑘 ≠ 𝑖

𝐸 𝑣𝑘𝑣𝑖𝑇 = ቊ

𝑅𝑘 , 𝑘 = 𝑖0, 𝑘 ≠ 𝑖

𝑅𝑘 and 𝑄𝑘 determine sensitivity of the algorithm

[2] Brown, R. G., „Introduction to random signal analysis and kalman filtering”, John Wiley & Son, 1985, New York

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Kalman with power system signals𝑥 𝑡 = 𝐴 𝑡 𝑐𝑜𝑠 𝜔𝑡 + 𝜃 = 𝐴 𝑡 𝑐𝑜𝑠𝜃𝑐𝑜𝑠 𝜔𝑡 − 𝐴 𝑡 𝑠𝑖𝑛𝜃𝑠𝑖𝑛 𝜔𝑡

𝑥𝑘+1 = Φ𝑘𝑥𝑘 + 𝑤𝑘 𝑧𝑘 = 𝐻𝑘𝑥𝑘 + 𝑣𝑘

Process equation Measurement equation

ො𝑥𝑘 = ො𝑥𝑘− + 𝐾𝑘 𝑧𝑘 − 𝐻𝑘 ො𝑥𝑘

−

Estimate update equation

𝑥1𝑥2 𝑘+1

=1 00 1

𝑥1𝑥2 𝑘

+𝑤1𝑤2 𝑘

𝑧𝑘 = cos 𝜔𝑡𝑘 −𝑠𝑖𝑛 𝜔𝑡𝑘𝑥1𝑥2 𝑘

+ 𝑣𝑘

Stationary phasor

𝑥2𝑥1

[3] Girgis, A. A., Bin Chang, W. and Makram, E.B., „A digital recursive measurement scheme for on-line tracking of power system harmonics”, IEEE Transactions on Power Delivery, vol. 6, no. 3, pp. 1153 – 1160, 1991.

𝐾𝑘 = 𝑃𝑘−𝐻𝑘

𝑇 𝐻𝑘𝑃𝑘−𝐻𝑘

𝑇 − 𝑅𝑘−1

Blending factor

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0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

Rk = 0.001

Rk = 1

Phasor – real part

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

t1

t1t1

Measurement

Estimate

Measurement

Estimate

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

t1

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

t1

Blending factor Blending factor

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

t1

Kalman with power system signals

ො𝑥𝑘 = ො𝑥𝑘− + 𝐾𝑘 𝑧𝑘 − 𝐻𝑘 ො𝑥𝑘

−

Estimate update equation

Rk = 0.001Rk = 1

𝐻𝑘 ො𝑥𝑘−

𝑧𝑘

𝐾𝑘 = 𝑃𝑘−𝐻𝑘

𝑇 𝐻𝑘𝑃𝑘−𝐻𝑘

𝑇 − 𝑅𝑘−1

Blending factor

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Kalman based adaptive algorithm

iph(t)

rLS filter Kalman filter

vph-g(t)

Frequency

ROCOF

If ROCOF > β TRIPY

1

2

3

4

5

6

if I < I

elseBlending factor Kk

Blending factor αKk

phDC phDC_thresh

phDC, IphI

• Fault detection criterion – DC offset in phase currents due to fault

• Fast DC offset estimation

• Use different blending factor to change frequency esitmation sensitivity

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Lab setup

MATLAB/SIMULINKMV network model

IEC 61850 Sampled Values 4kHz

uabc(t), iabc(t)

IEC 61850 GOOSE Tripping signal

OPAL RT 5600 STM32F746G-DISCO STM32F746NG Cortex-M7

Discovery

Access to microprocessor memory by PC through

USB

• μC w/ protection logic and IEC 61850 (GOOSE and SVs)

• OP5600 w/ CIGRE distribution network

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MV network

1

2

3

4

5

8

79

10

11

6

2.8

km

4.4

km

0.6

km

0.6

km

0.5

km

0.3 km

0.8

km

0.3

km

0.2

km

1.7

km

1.5 km

1.3

km

S2

S3

12

13

14

CB1b

CB1a CB2a

4.9

km

3.0

km

2.0 kmS1

CB2b

HV/MVHV/MV

110kV

20kV 20kV

SGCBDG

FEEDER 1 FEEDER 2

110kV Grid

F1

F2

F3

Vph

Iph

Isl. relay

CIGRE benchmark distribution network:

• Representative European MV network

• 2 feeders‒ OHL dominated (feeder 2)‒ cable dominated (feeder 1)

• Synch. Generator at bus 7

[4] Strunz, K., Abassi E., Fletcher, R., Hatziargyriou, N. D., Iravani, R., and Joos, G., „Benchmark systems for network integration of renewable and distributed energy resources”, CIGRE WG C6.04, 2014.

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Demonstration

1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7-1.5

-1

-0.5

0

0.5

[Hz/s

]

Rate-of-change-of-frequency

1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.749

49.5

50

[Hz]

Frequency

1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7

Time [s]

0

100

200

300

[A]

DC offset in phase current

1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7-2

0

2

[Hz/s

]

Rate-of-change-of-frequency

1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7

49.8

50

50.2

[Hz]

Frequency

1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.70

100

200

300

[A]

DC offset in phase current

ROCOFth = 1 Hz/s

ROCOFth = -1 Hz/s

DCoffth = 50 A

ZC

DFT

Kalman

ROCOFth = -1 Hz/s

DCoffth = 50 A

Time [s]

Two phase fault at the adjacent feeder Islanding

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Performance measures

• Generator output power – 0..1 pu• HV system short circuit power – 1000..5000 MVA• Fault location – three different locations • Fault type – 3ph, 2ph, 2ph-g

𝑆 =𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑜𝑟𝑟𝑒𝑐𝑡 𝑟𝑒𝑠𝑡𝑟𝑎𝑖𝑛𝑡𝑠

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑒𝑥𝑝𝑜𝑠𝑢𝑟𝑒𝑠∙ 100%

Security as [5]

Security

[5] Udren, E., Zipp, J., Michel, G., et al., „ Proposed statistical performance measures for microprocessor-based transmission-line protective relays;part i - explanation of the statistics, preceding companion paper”, IEEE Transactions on Power Delivery, vol. 12, no. 1, pp. 134 – 143, 1997.

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Performance measures

Kalman

DFT

ZC

0.75 Hz/s1 Hz/s 0.5 Hz/s 0.25 Hz/sNon-detection zone

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Performance measuresComputational resources consumption

• Number of operations measured

• Computationally efficient due to recursive nature and precalculated blending factor

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Performance measures

2ph 3ph

Faults

Use three phase

other criterion

or

Fault detection criterion

1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7

Time [s]

-1000

-800

-600

-400

-200

0

200

400

600

800

[A]

DC offset

Ph A

Ph B

Ph C

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Conclusion

• Application of Kalman method with variable blending factor performs better in case of security

• Increased security allows for setting the relay to be more sensitive and decrease non-detection zone