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Cyrille and Faith
Anomaly Detection within a
Hydroelectric Power Plant
Electricity everywhere and for everyone!
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About me
Cyrille Feudjio
Data Scientist & Industrial Engineer
● MSc degree in Industrial Mathematics
AIMS, Cameroon.
MSc degree in Industrial Engineering
NHPSD Doula, Cameroon.
I like learning new things and helping others

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Context
We need
Electricity

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Context
Business Losses
Loss of
communication
Lack of security
Unable to perform
daily operations

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Problem Overview
Better schedule of
maintenance
Detect, predict and
explain failures

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ETL 2
ETL1
Data Preparation
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LANDING ZONE RAW ZONE TRUSTED ZONE
Great
Expectation

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Solution 1: Model
Isolation Forest
Compute all the paths to reach
the node with one point and
choose the shortest ones based
on the contamination.

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Solution 1: Results
anomalies
normal

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Solution 2: Model
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Signature
matrices
Generation
Encoding spatial
information.
Encoding
temporal
information.
Decoding
previous
information
Reconstructed
signature
matrices
Residual
signature
matrices
-
Multi-Scale Convolutional Recurrent Encoder-Decoder Model (MSCRED)
WHY MSCRED?
● Detect anomaly events at
certain time steps.
● Identify abnormal time
series that are most likely
to be the causes of each
anomaly.

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Solution 2: Anomalies
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All the time step with
anomaly score above the
red line are anomalies

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Solution 2: Root causes
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55
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50
19
53
Root causes
sensors

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Solution 3: Model
LSTM (16,16) LSTM (16,8)
LSTM (8,16) LSTM (16,16)

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Solution 3: Data augmentation
 Slicing: The general concept behind slicing is
that the data is augmented by slicing time steps
off the ends of the pattern
Research paper: “An empirical survey of data augmentation for time series
classification with neural networks” by Brian Kenji Iwana ,Seiichi Uchida published
on july 15, 2021
 Window warping is a popular method of time
warping. It takes a random window of the time
series and stretches it by 2 or contracts it by 1/2.

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Solution 3: Results ( in progress)

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Cyrille Feudjio
cyrille@ishango.ai