advances in adaptive learning methods for disruption prediction … · 2019. 6. 23. · advances in...

Advances in adaptive learning methods for

disruption prediction in JET

J. Vega1, A. Murari2, S. Dormido-Canto3, D. Gadariya1, G. A. Rattá1 and JET Contributors*

EUROfusion Consortium, JET, Culham Science Centre, Abingdon, OX14 3DB, UK1Laboratorio Nacional de Fusión, CIEMAT, Madrid, Spain2Consorzio RFX (CNR, ENEA, INFN, Universitá di Padova, Acciaierie Venete SpA), Corso Stati Uniti4, 35127 Padova, Italy3Dpto. Informática y Automática - UNED, Madrid, Spain

* See the author list of “E. Joffrin et al 2019 Nucl. Fusion to appear″

This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme2014-2018 under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission

This work was partially funded by the Spanish Ministry of Economy and Competitiveness under the Projects No. ENE2015-64914-C3-1-R and ENE2015-64914-C3-2-R

Objectives

• Centroid method (CM) for disruption prediction

• Conceptually simple

• Reliable for mitigation purposes with a single signal: ML/Ip

• JET and JT-60U databases

• At present, it is being installed in the JET RT-network for next campaigns

• Evaluate the centroid method as adaptive disruption predictor from scratch

• Prediction from scratch: predictors are created in an adaptive way with a minimum of information from past discharges

• JET data

• Single signal: ML/Ip

• Compare the results of the CM adaptive approach with a CM classical approach (large training datasets)

J. Vega | 3rd IAEA TM on Fusion Data Processing | May 29th, 2019 | 2

Outline

• Introduction

• Nearest centroid method

• Adaptive disruption prediction from scratch

• Comparison with classical prediction

• Summary


Disruption prediction

• General machine learning methods (SVM, ANN, fuzzy logic, …)

have been used to determine a separation frontier between

disruptive/non-disruptive behaviours in multi-dimensional spaces

• ML, plasma current, poloidal beta, poloidal beta time derivative, safety

factor, safety factor time derivative, total input power, plasma internal

inductance, plasma internal inductance time derivative, plasma vertical

centroid position, plasma density, stored diamagnetic energy time

derivative and net power

• Particular dimensions can be amplitudes in the time domain or in the

frequency domain

• APODIS predictor

• Separation frontiers are represented by complex equations that do

not allow physics interpretations

• However, they work!

General machine learning methods have shown to be useful for machine control


Adaptive disruption prediction

• In machine learning, the more training examples the better• The APODIS disruption predictor was trained with about 9000 JET

discharges

• ITER or DEMO cannot wait for hundreds of disruptions to have a reliable predictor

• Prediction from scratch: predictors are created in an adaptive way with a minimum of information from past discharges• Predictors add new knowledge as it is required

• Previous works on prediction from scratch• APODIS based: S. Dormido-Canto et al. Nucl. Fus. 53 (2013) 113001 (8pp)

• 7 signals

• Probabilistic predictor based on Venn prediction: J. Vega et al. Nucl. Fus. 54 (2014) 123001 (17pp)

• 3 signals

• Probabilistic predictor based on SVM: A. Murari et al. Nucl. Fus. 58 (2018) 056002 (16 pp)

• 2 signals


Nearest centroid method

• Disruptive/non-disruptive behaviours are summarised in

two single points in the parameter space

• The training process carries out the computation of

centroids


1 2, ,..., mP x x x

1 2, ,...,D mC d d d

1 2, ,...,N mC c c c

, DP Cd

, NP Cd

2 2

1 1

, ,disruptive behaviour :

m m

i i i ii i

D NP C P C

x d x c

d d

1

,

, , ,

m

i ii

i i i i i i

A x K

A A c d K K c d

Linear!!

Selections for specific implementation


• Signals

• Parameter space

• Concentration of the non-disruptive behaviour in a single

point

• Concentration of the disruptive behaviour in a single point

Signals

• The mode lock (ML) signal is a practical approach to predict incoming disruptions

• Conceptually, the CM is very simple and the use of a single signal helps in the achievement of simplicity to predict from scratch

• In particular, ML/Ip has been chosen• ML or ML/Ip thresholds are tipically used to trigger alarms for mitigation purposes

• Signal increases when

– The rotation of an MHD mode slows down and can be locked

– The MHD mode amplitude grows

• Signal decreases when

– The MHD mode amplitude drops

– The MHD mode unlocks and the rotation speeds up

• Almost 100% of disruptions in JET show a mode lock (however, when a mode lock is detected, it does not necessarily leads to a disruption 100% of the time)

• A simple threshold in the mode lock signal is not enough to recognise a fraction of disruptions close to 100% (A. Murari et al. Nucl. Fus. 58 (2018) 056002 (16 pp))

• The prediction cannot be based on a simple threshold• How to define a multi-dimensional parameter space with a single signal?


For a description of saddle loops in JET see:https://users.euro-fusion.org/pages/mags/equilibrium/eq-coil-loop/saddle-loop/saddle-loop.htm

https://users.euro-fusion.org/pages/mags/equilibrium/eq-coil-loop/saddle-loop/saddle-loop.htm

Parameter space selection

• The parameter space can be based on characterising in

some way how the ML/Ip signal evolves

• This is directly related to the possible changes in the rotation

(braking-locking-unlocking-accelerating) and amplitude (growth-

declining) of MHD modes

• In other words, it is necessary to quantify the ML/Ip signal

increments


x2

x1

Sampling period: 2 ms



P1(1.26e-4, 1.74e-4)

P2(3.20e-4, 3.27e-4)

P3(3.15e-4, 3.14e-4)

P4(2.98e-4, 2.52e-4)

P5(2.34e-4, 2.72e-4)

(11.363, 1.26e-4)

(11.365, 1.74e-4)

(11.367, 3.20e-4)

(11.369, 3.27e-4)

(11.371, 3.15e-4)

(11.373, 3.14e-4)

(11.375, 2.98e-4)

(11.377, 2.52e-4)

(11.379, 2.34e-4)

(11.381, 2.72e-4)

Feature space: two-dimensional space whose points are the ML/Ipamplitudes of consecutive samples (constant sampling period)



P1(1.26e-4, 1.74e-4)

P2(3.20e-4, 3.27e-4)

P3(3.15e-4, 3.14e-4)

P4(2.98e-4, 2.52e-4)

P5(2.34e-4, 2.72e-4)

(11.363, 1.26e-4)

(11.365, 1.74e-4)

(11.367, 3.20e-4)

(11.369, 3.27e-4)

(11.371, 3.15e-4)

(11.373, 3.14e-4)

(11.375, 2.98e-4)

(11.377, 2.52e-4)

(11.379, 2.34e-4)

(11.381, 2.72e-4)

Feature space: two-dimensional space whose points are the ML/Ipamplitudes of consecutive samples (constant sampling period)

Non-disruptive Disruptive

Scatterplots of ML signals in the parameter space of two consecutive samples corresponding to different discharges

X(t)

X(t-t)

Selection criterion of the non-disruptive centroid


1

1

1

1

1

1

1

1

1

,

,

,

.....

,

where is the first time 750 and is the last time 750

, , 0,1, 2,...

, 1,...,

5

2

4

22

3

1

n

SHOT

N j N

n n

t Ip kA t

x t

x t

x t

x t

x t

C

x

Ip kA

C me

t

x t

x t

x t

x t Kan me Kan K

C mean j N

t

t

t

t

t

t

t t

Poin

ts in

a s

ho

t

• A dataset of NN non-disruptive discharges is chosen

• One centroid per discharge (CSHOT) is determined in

the space (x(t-t), x(t))

• The global centroid (CN) is the mean value of the

individual centroids

Selection criterion of a disruptive centroid


Statistically, most disruptions occur with a monotonous increasing of the ML amplitude

(x1, x2)(x1, x2)

• A dataset of ND disruptive discharges is chosen

• One centroid per discharge (CSHOT) is determined in the space (x(t-T), x(t))

• The last pair (x(t-T), x(t)) before the disruption

• There is one restriction: x(t-T) < x(t)

• The global centroid (CD) is the mean value of the individual centroids

Dataset (ILW)

• Adaptive approach from scratch

• Discharges are processed in chronological order

• First predictor: 1 disruptive shot and 5 non-

disruptive shots

• Centroids are recomputed after either a missed

alarm or a tardy detection

• All previous individual centroids are used to

compute the new global centroids


Type Number of shots Range

Disruptive 113 80176-82550

SEP 2011-MAR 2012Non-disruptive 1397

,P x t x tt

1 2,DC d d

1 2,NC c c

, DP Cd

, NP Cd

2 2 2 2

1 1 1 2 1 2

2 2 2 2

( )2

d c d d c cx t x t

d c d ct

Linear frontier

Results


Type Number of shots

Disruptive 113

Non-disruptive 1397

Initial centroids

1 disr., 5 non-disr.

Tardy alarm after 20 disruptions

Global success rate: 95.0%

No false alarms (421 non-d. shots)

1st re-training


Missed alarm after 3 disruptions


No false alarms (12 non-d. shots)

2nd re-training




Global false alarm rate: 0.8%

3rd re-training





4th re-training





5th re-training


All remaining disruptions recognised



Results

• Global fraction of detected disruptions: 99.11%

• Predictions with positive warning time: 95.54%

• Tardy detections (negative warning time): 3.57%

• Missed alarms: 0.89%

• False alarm rate: 1.58%

• 5 re-trainings were necessary

• 1 missed alarm and 4 tardy detections



Disruptive 113 80176-82550


( ) 0.9539 exp

0.261

tf t

0.9539±0.00310.261±0.002R-square: 0.9987


Comparison between adaptive and classical (the

more training data the better) approachesType Number of shots Range

Disruptive 113 80176-82550


Predictor obtained after adaptive training from scratch• 5 re-trainings• X(t) > -0.672·X(t - t) + 8.02e-10


Disruptive test set 277 82552-92504

MAR 2012-NOV 2016Non-disruptive test set 3027

Only 1 training with all available discharges• X(t) > -0.669·X(t – t) + 7.79e-10

( ) 0.9497 exp

0.281

tf t

0.9497±0.00200.281±0.002R-square: 0.9962

( ) 0.9543 exp

0.298

tf t

0.9543±0.00190.281±0.002R-square: 0.9966

( ) 0.9543 exp

0.298

tf t

0.9543±0.00190.281±0.002R-square: 0.9966



• Tardy detections: 1.44%





• Tardy detections: 1.44%



Missed alarms


Centroids

Non-disruptive behaviours

Separation frontier

0.669 7.79 10X t X t et

Disruptive behaviours successfully recognised


Centroids Disruptive behaviours

First recognition of disruptive behaviourNon-disruptive behaviours

Separation frontier

0.669 7.79 10X t X t et

Summary

• The use of the centroid method for adaptive disruption prediction from scratch shows success rates above 95% with positive warning times, about 1% of false alarms and about 250 ms of warning time• About 92% with warning times greater than 10 ms (enough for mitigation

actions with the JET Disruption Mitigation Valve)

• Disruption prediction is carried out with a single signal (ML/Ip) and a simple linear equation in two variables• X(t) = A·X(t – t) + B

• A and B depend on the centroid coordinates

• Only five retrainings were necessary with a JET database of 113 disruptive and 1397 non-disruptive shots

• The comparison between the adaptive predictor and a classical one (the more training examples the better) shows that both predictors are equivalent• Adaptive predictors from scratch are candidates for ITER or DEMO



Thank you very much for your attention!

advances in adaptive learning methods for disruption prediction … · 2019. 6. 23. · advances in...

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