arXiv:1711.03026v1 [] 8 Nov 2017

Intelligent Fault Analysis in Electrical Power Grids
Biswarup Bhattacharya

Abhishek Sinha

Department of Computer Science
University of Southern California
Los Angeles, CA 90089. USA.
Email: bbhattac@usc.edu

Adobe Systems Incorporated
Noida, UP 201301. India.
Email: abhishek.sinha94@gmail.com

Abstract—Power grids are one of the most important components of infrastructure in today’s world. Every nation is dependent on the security and stability of its own power grid to provide
electricity to the households and industries. A malfunction of
even a small part of a power grid can cause loss of productivity,
revenue and in some cases even life. Thus, it is imperative to
design a system which can detect the health of the power grid
and take protective measures accordingly even before a serious
anomaly takes place. To achieve this objective, we have set out
to create an artificially intelligent system which can analyze the
grid information at any given time and determine the health
of the grid through the usage of sophisticated formal models
and novel machine learning techniques like recurrent neural
networks. Our system simulates grid conditions including stimuli
like faults, generator output fluctuations, load fluctuations using
Siemens PSS/E software and this data is trained using various
classifiers like SVM, LSTM and subsequently tested. The results
are excellent with our methods giving very high accuracy for the
data. This model can easily be scaled to handle larger and more
complex grid architectures.

I. I NTRODUCTION
Power grids are now been considered to be one of the important components of infrastructure on which the modern society
depends. The primary objective of power system operation is
to supply uninterrupted power to the customers. But small
and large scale faults and disturbances in the grid often cause
power outages and thereby affect the system reliability and
customer satisfaction.
Electrical power grids are huge systems, especially at the
national level. The “synchrophasors project” in India [1] has
been deployed with the help of which system operators are
now able to monitor the magnitude and angle of each phase of
the three phase voltage and current, frequency, rate of change
of frequency and angular separation at every few millisecond
intervals (40 milliseconds) at a Load Dispatch Centre (LDC).
Thus the transient or dynamic behavior of the power system
can be observed in near real-time at the control centre which
hitherto was possible only in offline mode in the form of
Substation Disturbance Records or through offline dynamic
simulations performed on network models.
A phasor measurement unit (PMU) is a device which
measures the electrical waves on an electricity grid using a
common time source for synchronization. PMUs provide us
with a huge amount of data [2] and monitoring such huge data
manually is an infeasible task. Thus, automated algorithms

are required for monitoring the power grids, determining the
health of the grid and performing intelligent fault analysis in
real-time.
The importance of fault analysis lies in the fact that electrical faults cause maintenance issues, financial losses for
companies and general inconvenience to consumers. Typically,
electrical faults take multiple hours, if not days, to repair.
Such kind of inefficient infrastructure causes a lot of problems
to commercial establishments and households as almost all
civilizations are nowadays heavily dependent on electricity to
perform almost every task. Thus, with the help of intelligent
fault analysis, one can, in a way, predict faults before they
are just about to happen so as to ensure that the relays and
the protective infrastructure in the power grid switch on in
time and function as required. With accessibility to cutting
edge technologies like recurrent neural networks, such kind
of prediction capabilities are now possible to be performed
within milliseconds and with high accuracy. Machine learning
and deep learning models have been explored in mild detail
previously in research literature, however we demonstrate with
clarity that neural networks can indeed perform extremely
well when applied in power system domains and especially
electrical fault analysis. The approach we have used is such
that it can even be scaled up to handle all grid sizes, from
the small simulator grids to large real grids, like the Indian
electricity grid, and can be deployed in the real world.
II. R ELATED W ORK
Majority of the literature that exists on fault analysis is
usually focused on numerical optimization techniques and
statistical techniques. Relevant literature does exist for the
kind of work, though sparingly, where some have applied
artificial intelligence techniques on power systems to derive
various relations. However, the methodology adopted by us
using state-of-the-art techniques and achieving great accuracy
has not been attempted before.
Some of the existing literature on fault analysis are discussed here. In [3], a scheme was proposed which first identifies fault locations using an iterative estimation of load and
fault current at each line section and then an actual location
is identified, applying the current pattern matching rules. In
[4], expert system for the diagnosis of faults was discussed.
In [5], the paper proposes alternatives to improve the electric
power service continuity using the learning algorithm for

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multivariable data analysis (LAMDA) classification technique
to locate faults in power distribution systems. This was a
data analytic approach to find location of faults whereas we
are using a machine learning technique. With respect to AI
approaches, in [6], the authors discuss their experience of
developing a multi-agent system that is robust enough for
continual online use within the power industry. In [7], the
paper presents an artificial neural network (ANN) and support
vector machine (SVM) approach for locating faults in radial
distribution systems. Our approach is novel compared to this
paper as we realized that long short-term memories (LSTMs)
may be the better way to represent time series voltage and
angle data and the hypothesis has been confirmed by our
experiments.

Short Term Memory) network, is used to take care of long
time dependencies. Each LSTM cell gives as an output the
hidden state till and the output vector at that point of time
[11]. The LSTM cell structure is shown in Figure 2.

Fig. 2. Basic structure of a LSTM cell (image from [12])

III. BACKGROUND & P RELIMINARIES
A. Support Vector Machines (SVM)
In machine learning, support vector machines (SVMs) are
supervised learning models that analyze data and provide
output for classification and regression analysis [8]. SVM is a
non-probabilistic binary linear classifier, such that it classifies
examples to be falling into one of two classes. The SVM
margin, which is the separation between the two classes,
is trained to be as optimal as possible. New examples are
then mapped into that same training space and predicted to
belong to one of the classes depending on which side of the
hyperplane they fall in.
B. Recurrent Neural Networks (RNN)
The idea behind RNNs is to make use of sequential
information. In a traditional neural network, it is assumed
that all inputs and outputs are independent of each other.
However, that may not be a useful choice of architecture
always. For example, when trying to predict words that will
come in a sentence, it is important to consider the words
that came before, thereby incorporating a type of “temporal”
or “sequential” aspect to the learning process. RNNs are
called recurrent because they perform the same task for every
element of a sequence, with the output being depended on
the previous computations. RNNs have a “memory” which
captures information about what has been calculated so far
[9]. A typical RNN structure has been shown in Figure 1.

IV. P ROBLEM D ESCRIPTION
In the current situation of Indian electrical power grids,
when a small disturbance is seen at a dispatch center, then
the norm is to generate a report and check with other dispatch centers. If the disturbance is found to be local, then
it is ignored. Else, if it is found to be correlated (similar
disturbances observed at other dispatch centers), then further
diagnostics are conducted. Our goal is to perform this fault
analysis automatically using machine learning.
We aim to monitor the grid continuously and then in the
case of a fault determine the type of the fault as and when it
occurs. Apart from just predicting the nature of the fault we
aim to give further insight into the fault such as in which bus
line the fault had been triggered. The entire process has to be
done automatically using machine learning techniques without
using any human supervision and using just the current as well
as past states of the grid as an input.
V. DATASET
A. Software for Data Collection
The experiments required the use of power grid and power
system data. For our simulation and testing purposes, we used
simulation data from the Siemens PSS/E software [13] and
PowerWorld Simulator [14].
The Siemens PSS/E software package can do fast and robust
power flow solution for network models up to 200,000 buses.
It has an array of features useful for dynamic and transient
analysis. It also has an useful scripting system named psspy.
Using psspy, one can use the modules of PSS/E remotely
through Python. We used this scripting system for porting data,
simulating and creating the dataset.
B. Methodology

Fig. 1. A recurrent neural network and the unfolding in time (image from
[10]

Since in practice RNNs are limited in the span of time they
can look back to, a variant of it, called as a LSTM (Long

1) Inputs and Disturbances: One can modify a huge number of parameters through PSS/E. However, for our analysis,
we focused on mainly the power injection and load values
at each bus. Keeping in mind our goal, it made sense to vary
only the power values (real & reactive) as that will correspond
to a change in the bus voltages and angles. Using these bus
voltages and angles, we can perform our analysis.

Using psspy, we changed the power input and load at
every bus at a certain timestamp suddenly during the simulation. This essentially mimics the fluctuations which happens
in the real grids all the time. The value of the fluctuation was
distributed along a uniform distribution with suitable upper
and lower limits. Hence, the fluctuation could be very small
in one bus whereas very large in another bus. We also made
sure that no bus becomes totally disconnected in the grid due
to too high fluctuations, as that essentially corresponds to a
fault.
The network (electric grid) used by us consisted of 23 buses
with 6 generators and 8 loads at various buses throughout the
network. The base frequency used was 50 Hz which is the
standard for India. The base MVA for the network was 100
MVA.
2) Outputs: In the PMUs, one can measure the values of
voltage and angle in real time. Hence, given the inputs and
disturbances, we logged the voltage and angle at every bus
at every timestamp. The logging was done at every 40 ms
which mimics the actual metering done by the PMUs. For
every simulation, the data was collected for upto 4 seconds
of simulation time to capture the transient and also observe
the changes due to generator or load fluctuations and faults
suitably.
3) Simulating the faults: In an electric power system, a
fault or fault current is any abnormal electric current [15]. For
example, a short circuit is a fault in which current bypasses
the normal load. An open-circuit fault occurs if a circuit is
interrupted by some failure. In three-phase systems, a fault
may involve one or more phases and ground, or may occur
only between phases. In a “ground fault” or “earth fault”,
current flows into the earth. The prospective short circuit
current of a predictable fault can be calculated for most
situations. In power systems, protective devices can detect fault
conditions and operate circuit breakers and other devices to
limit the loss of service due to a failure.
In a polyphase system, a fault may affect all phases equally
which is a “symmetrical fault”. If only some phases are
affected, the resulting “asymmetrical fault” becomes more
complicated to analyze. The analysis of these types of faults
is often simplified by using methods such as symmetrical
components.
For our analysis, we focused on simulating the symmetrical
faults and unsymmetrical faults. The faults simulated were
namely:
•
•
•

•

3φ bus fault: This is a symmetrical fault as it affects all
the phases at a bus equally.
Branch Trip fault: This is a symmetrical fault which trips
the transmission line (all 3 phases) between two buses.
LL fault: This is an unsymmetrical fault and it short
circuits two phases (in PSS/E, these are Phase A and
Phase B).
LG fault: This is an unsymmetrical fault and it short
circuits one phase (in PSS/E, this is Phase A) with the
ground.

Fig. 3. A typical voltage varying voltage plot for a particular bus line in the
presence of a fault

The voltage profile during a fault is shown in Figure 3. The
fault was initiated and subsequently cleared at points during
the simulation pre-decided by the data generator. This was
however not known to the classifier built and the classifier
was able to predict this time of initiation and clearing of faults
without this knowledge simply by learning the data collected.
C. Data Collected
We collected data for all the 4 fault types with generator
or load fluctuations as specified. We simulated every type
of fault when the fault occurs at each individual bus. Thus,
this translates to data corresponding to 23 buses for all 4
types of faults. For each bus, the simulation was run 100
times to generate different variations so that there are enough
examples. Also, there were examples which consisted of just
the generator or load fluctuations, and no faults so as to
make the machine learn to differentiate between the allowed
generator or load fluctuations and the fault conditions.
Each training example consisted of the volt and angle
information for the particular bus at timestamps ranging from
0 seconds to 4 seconds with an interval of 0.04 seconds. The
amount of time required to generate the data on the server
provided to us was around 20 minutes per bus per fault (100
simulations per bus).
VI. F ORECASTING THE MAXIMUM VOLTAGE DEVIATION
USING PRE - FAULT DATA
A. The Model
The data corresponding to a network having 23 different
buses and subjected to transmission fault (line trip) was
obtained. A neural network model [16] was constructed to
predict the maximum voltage deviations corresponding to each
bus line just by looking at the pre-fault state of the network.
The model consists of 2 hidden layers consisting of 60 and
40 neurons respectively. The input to the model is a vector
of size equal to the number of bus lines corresponding to
the pre-fault voltage data of each bus. Since the post fault
voltage value will depend on the states of each of the buses,
thus the prediction for each bus is not done independently, but
rather the forecasting of entire network is done simultaneously.
The output is of the same size as the input and contains the

forecasted voltage value for each bus. The data was divided
into 80% training and 20% test data and the results were
obtained on test set after having trained the model on training
data. The model used by us has been shown in Figure 4.

Fig. 6. Variation of voltage value in presence of LL fault
Fig. 4. Model for prediction of maximum voltage deviation after fault
triggering (image adapted from [17])

B. Results
After 2000 steps of training the following losses were
obtained between the actual output and predicted output corresponding to the network. The mean L2 error was equal to
2.8 × 10−3 and the mean L1 error was equal to 2.3 × 10−2 .
The plot of L2 error with progress of training has been shown
in Figure 5.

Fig. 7. Variation of voltage value in presence of LG fault

B. Classification using LSTM (Recurrent neural network)

Fig. 5. Variation of L2 -error with progress of training

VII. C LASSIFICATION OF FAULTS
Classification of faults given the entire network data was
done for the case of LL (line to line) and LG (line to ground)
faults. The voltage data corresponding to 100 different time
steps and for each of the bus lines present in the network
are given as an input to the classifier. The classifier gives an
output probability corresponding to the two fault classes. The
variation of voltage values in the presence of LL and LG faults
have been shown in Figures 6 and 7 respectively.
A. Classification using SVM
Out of the 2300 data examples available per fault, 2000 were
used for training while the rest 300 were used for testing. The
input to the SVM model was the voltage & angle data of all
the buses for 100 time steps. The output of the SVM classifier
was the fault type or class.
The classification accuracy on the test set was observed to
be around 87-88% for SVM classifier.

The above SVM model had a major disadvantage in the
sense that it did not utilize the information present in the
data. It is the variation of voltage with time that tells us as
to what fault had occurred in the network. However the data
was presented just as a normal vector to the model. To utilize
this time varying information models need to be constructed
which take data in accordance with the variation of data with
the time and this is where recurrent neural networks (RNN)
come into play.
1) The Model: The model consists of 100 unfoldings in
time of LSTM cells corresponding to 100 time varying voltage
values for each of the bus. Each LSTM cell gets a vector of
size equal to the number of buses corresponding to the voltage
values of the buses at that time step. Both the hidden vector
and output vectors are of size 128. The output vector of the
last cell contains the temporal information present in the entire
data. This information extracted can then be further used to
classify the type of fault.
For the classification of faults from the information obtained
from LSTM a second model is built entirely of fully connected
layers. The model consists of 1 hidden layer consisting of 64
neurons followed by an output layer of size 2. The output
obtained determines the probability of the fault belonging to
LL or LG class. The model has been described by a block
diagram (Figure 8).

A. The Model

Fig. 8. Model using LSTM for classification of faults

C. Results
With LSTM the classification accuracy jumped to 94-95%,
an improvement of around 6% from the SVM model. The plot
of accuracy and loss with training have been shown in Figures
9 and 10.

The data input to the classifier was again the voltage data
corresponding to each of the bus lines for the 100 different
steps. The classifier was trained to output the bus number
corresponding to where the fault had been triggered or an
output of 0 if the network data corresponded to non-faulty
one whereby no fault had been triggered on any bus line.
LSTM was again used to extract meaningful time dependent
information from the data which was then used to correctly
classify the data via fully connected neural network layers. The
fully connected layer consisted of one hidden layer consisting
of 128 neurons. The output size was one more than the number
of bus lines present in the network.
The plots of the bus line in which the fault had been
triggered and the other bus line where the fault had not been
triggered have been shown in Figure 11.

Fig. 9. Variation of cross entropy loss with training

Fig. 11. Blue line: Voltage variation with time for the bus line in which fault
was triggered; Red line: Voltage variation with time for the bus line in which
no fault was triggered

B. Results

Fig. 10. Variation of training accuracy with progress of training

For the 3φ fault, the classifier gave an accuracy of 97%
corresponding to predicting the bus number. The bus number
was outputted or classified as “0” in case of non-faulty data.
For the LL fault the accuracy was also 97%. The plots of
accuracy and loss with progress of training for LL fault have
been shown in Figures 12 and 13.

VIII. L OCATION OF BUS LINE IN WHICH FAULT WAS
TRIGGERED

Once the type of fault had been determined by the classification model, different models were constructed for each of
the different fault types to determine the bus line in which the
fault had been triggered. Again the voltage variations of the
network with time can be used to find out the bus number.
This classification task was done for both 3-phase (3φ) and
LL faults. The source of the fault (if any) was not part of the
input for obvious reasons. This model is essential as often it
is necessary to find out the location of the fault given just the
PMU voltage graphs data.

Fig. 12. Variation of training accuracy with progress of training

vulnerability of the grid, issues like load shedding, power
surges etc. can be handled efficiently. With time, the system
will have the ability to be more sophisticated to handle various
types of networks and situations which will be suitable for
deployment at the national level.
ACKNOWLEDGMENT
The authors were undergraduate students of the Indian
Institute of Technology (IIT) Kharagpur, West Bengal, India
during the period of research for this project which culminated
in this paper. The authors would like to thank the institute for
their support.
Fig. 13. Variation of training loss with progress of training

IX. F URTHER W ORK & C ONCLUSION
In conclusion, we created a grid to perform intelligent fault
analysis where we forecasted the maximum voltage deviation
using pre-fault data, classified the type of faults and found out
the location of the fault, all using machine learning and deep
learning techniques. The different models are inter-related as
given the PMU data in crisis situations, running the above
models will give a great deal of insight into the severity and
extent of a possible undesirable situation. The results obtained
were very encouraging and thus, further analysis can be done
on various avenues starting from this. Some of these avenues
are discussed henceforth.
Dynamic thresholds are required mainly for taking into
account the difference in loading conditions. These can be
developed. Another major task in the monitoring of the health
of the grid is prognosis of the grid vulnerability when the
actual load-generation of the grid deviates from the predicted
schedule. The monitoring of grid states need to be done
continuously and once the current state is found to differ
significantly than the predicted state, then a close monitoring is
required. The challenge is to come up with some health metrics
which can, from the close monitoring of grid states, determine
whether the network is going to a vulnerable state or not. The
input to be considered is a time window of past network states,
the current network state and the predicted state for the current
time and the output will be either a yes or a no depending on
whether a vulnerable state is going to be reached or not. Once
the health metrics have been defined and vulnerability of the
states determined, the data can then be used as training data
for building or learning rules so that any new data which is not
on the training set can be easily classified regarding the grid
vulnerability. Issues such as congestion of the power network
and optimal generator subset selection can also be explored
[18]. Finally the whole system can be tested on the real-world
grid data obtained from national agencies.
We started with an aim of deriving accurate prognostics
information so as to make power grids artificially intelligent.
The working system will be especially useful in renewable
energy power grids where the generation periods are erratic
(e.g. solar power generators generate power only during the
day when the Sun is up). With the knowledge about the

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