Detection of Unregistered Electric Vehicles Using a Recurrent Neural Network By: Ben Groene

Strategies for Intelligent Low-Voltage Network Monitoring

1. Introduction

2. Experimental Method

3. Model

4. Results

5. Conclusion & Future Development Needs

Agenda

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1. Introduction

• Germany • Goal: Reach 1 million EVs on the road by 2020

• Reality: 1 million EVs projected to be reached but not until 2021 or 2022

Projection of Accumulated New EV Registrations through 2025 in Germany

1.1 Electromobility in Germany

4 Source: Nationale Plattform Elektromobilitaet

• EV penetration expected to rise → Distribution system not designed for high EV penetration

• Most distribution systems lack monitoring devices → Distribution systems should be monitored for existing and future problems caused by EVs

• Proposed solution → Detection of EV charging events using a neural

network!

1.2 Problem Statement

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1. Program initial machine learning model using Keras machine learning framework in Python

2. Prepare background data

3. Add Artificial EV charging events to background data

4. Train model what EV charging looks like • Supervised Learning – Tell model at which timesteps there is EV charging and

at which timesteps there is not.

5. Optimize model

6. Given a sequence of previously unseen measurement data, predict at

which timesteps an EV was connected

1.3 Summary of Task

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0 = No EV charging, 1 = EV charging

1.4 Prediction of Electric Vehicles - Intuition

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2. Experimental Method

• Data obtained from PMU • 500-millisecond current data in Germany

• Measured at low-voltage substation

• Feeder characteristics (as of 2016): • 400 kVA transformer supplied four feeders with a total of:

189 households

21 PV systems (160 kW installed capacity)

• PMU installed on feeder with highest PV penetration

2.1 Background Data

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• In most cases, EVs charge to full power within 10 seconds, which can be seen in the 2 EV charging examples below

2.1 Background Data

10 Source: Fachhochschule Bingen, 2013

• Data converted to 10-second resolution • Simplify structure of charging events (current spike)

• Reduce time, computational and memory requirements

• End of charging often tapered or stepped • Difficult to detect

• Data transformed into delta values • Takes away impact of actual current level

2.1 Background Data

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2.1 Background Data

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• A month of data after converting to 10s delta values

• Many delta values in EV charging range

• Artificial EV charging events in range 15-33 A inserted into background data • 2 charging events per day → two random times, two random amplitudes in

above range

2.2 Artificial EV Injection

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• Separate data into three

different sets 1. Training set (60%)

2. Cross-validation (CV) set (20%)

3. Test set (20%)

2.3 The Three Data Sets

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3. Model

• Bidirectional Recurrent Neural Network (BRNN)? • State-of-the-art technique for dealing with sequential data such as time-series data

• Temporal dependencies within the time-series can be learned

• A single training example is an entire sequence (1 day in this case)

• Standard NNs cannot deal with sequential data effectively

3.1 Sequence Models and BRNNs

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• RNNs have trouble remembering information over long sequences • Known as vanishing gradient problem

Solution: Long Short-Term Memory (LSTM) cells

• LSTM cells have a special architecture which allows them to remember information over long sequences • State-of-the-art for time-series data

• Available for use via Keras machine learning framework

3.1 Sequence Models and BRNNs

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3.1 Sequence Models and BRNNs

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Source: Medium

• Example of BRNN • Input = Sequence (8,640 timesteps,

delta values)

• Output = Sequence (8,640 timesteps, 0…1)

• Each timestep is sent through a separate neural network with a unique output

• Each neural network shares information with previous timestep and next timestep

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4. Results

4.1 Metrics

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4.2 Final Model & Performance

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Final Model Precision Recall Test Set 0.9428 0.8545

85.5% of charging events detected with a false positive rate of 5.7%!

4.2 Final Model & Performance

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• Lower charging events more difficult to detect • Still able to detect over 70% of 15 A charging events

• Measurement data was available for one other feeder which was used to test transferability of the model

• A switching event took place on the feeder after the measurement data was taken for this project → New data was used as another transferability test as well as to test robustness of model • Test Case #1: EV charging events injected into new data exactly as before

• Test Case #2: EV charging events injected with charging events in the range of 12-16 A instead of 15-33A

4.3 Transferability

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Test Case #2 (12-16 A) Precision Recall Test Set 0.9778 0.7458

Alternative Feeder Precision Recall Test Set 1.0000 0.9857

4.3 Transferability

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Test Case #1 Precision Recall Test Set 1.0000 0.9828

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5. Conclusion & Future Development Needs

• The model was able to detect approx. 85.5% of EV charging events in the test set with a false positive rate (1-precision) of only 5.7%

• Initial transferability tests show signs that the model will also perform well on other feeders

• Model could detect events of lower charging amplitudes with only a slightly lower detection rate • Even when the model is trained only on events in the range 15-33 A

5.1 Results Summary

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• Further transferability testing

• More data should be obtained to test transferability

• Uncertainty how model will perform on actual EV charging events • Pilot projects

• Changes in EV charging characteristics • Controlled charging methods

5.2 Future Development Needs

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Thank you for your attention!

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ANNEX

Annex 1: Smart Meter Rollout

30 Source: Deloitte, 2016

Annex 2: Testing Data Preprocessing Methodology with Real Charging Event

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Annex 3: Bias vs. Variance

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• The more complex the model is → the more likely the model is to overfit the training data & vice versa

Source: cv-tricks.com

Annex 4: Standard Neural Network Example

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Annex 5: Long Short-Term Memory (LSTM)

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Source: adapted from, Colah.github.io, 2015

Annex 6: Transferability - Test Case #3

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Annex 7: Final Model & Performance

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Annex 8: Final Model & Performance

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Annex 9: Final Model & Performance

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Final Model Precision Recall Training Set 0.9672 0.9213 Test Set 0.9428 0.8545

85.5% of charging events detected with a false positive rate of 5.7%!