Machine Learning Model to Forecast Power System Breakdowns

D.Sharanya

, G. Swetha

, Raiz Israni

and T. Mrudula

Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Telangana, India

Department of EEE, RKU, Tamil Nadu, India

Department of Electrical and Electronics Engineering, MLRIT, Telangana, India

Keywords: Fault Classification, Fault Detection, Power System, Machine Learning

Abstract: For the modern power system to be protected from transmission line failures, real-time monitoring and quick

control are necessary. For power systems to operate with reliability, it is essential to identify and classify

fault conditions. The conventional methods for fault diagnosis rely on several scholars have suggested the

manually extracted feature of experienced engineers for defect identification and classification. Any analogue

circuit’s reliability depends heavily on the capacity to detect problems. Early detection of circuit failures can

considerably aid in system maintenance by preventing potentially damaging from the issue. In particular for

fault orientations and severity levels, intelligent fault detection still has a significant challenge in accurately

finding the emerging micro-fault in the power system. Intelligent fault detection methods based on machine

learning are the topic of a research boom in fault diagnosis

1 INTRODUCTION

The power transmission network, which carries

substantial volumes of high-voltage power from

generators to substations, is the most crucial

component in the nation’s energy sys tem. Being a

complex network, the modern electricity system

demands a quick, accurate, and reliable protection

mechanism. It is unavoidable for the power system

to develop faults, and these faults frequently include

important components connected to higher overhead

transmission lines. Being able to predict faults (type

and location) with a high degree of accuracy,

therefore, increases the operational reliability and

stability of the power system and aids in preventing

catas- trophic power outages(K. Eldeeb 2021).Power

system faults can happen for a number of reasons,

despite the fact that numerous essential protection

devices are used in their detection These errors must

be detected and identified as soon as in possible,

and they occasionally cause full system failures with

an effect on clients Yet, in order to avoid the

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aforementioned issues, it is vital to anticipate the

defects in advance. These defects must be predicted

and identified as soon as possible, though, as they

occasionally cause total system outages that have an

impact on the client Even though, it is fundamental to

identify the defects in advance in order to avoid the

problems [Y.Zhang 2021].

As a consequence of the arrival of digital

technology and the development of a smart grid, it is

now feasible to install sensors along power lines that

can record data on large defects. These sensors give

crucial data that will be used to identify problems in

transmission lines.

2 THE GENERATION,

TRANSMISSION AND

DISTRIBUTION OF ELECTRICITY

The generation, transmission and distribution is

shown in figure.1

Sharanya, D., Swetha, G., Israni, R. and Mrudula, T.

Machine Learning Model to Forecast Power System Breakdowns.

DOI: 10.5220/0012540800003808

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Intelligent and Sustainable Power and Energy Systems (ISPES 2023), pages 161-166

ISBN: 978-989-758-689-7

161

Fig. 1. Generation, Transmission and Distribution

2.1 Knowledge of transmission

Electricity is moved through the energy

transmission process from power plants and other

electricity-generating facilities to substations that are

closer to customers. Transmission lines transmit a lot

of high voltage energy over vast distance

Transmission lines carry energy at a voltage that is

too hi to be directly transmitted to customers. As a

result, before rsupplying electricity to households and

businesses, energy distributors typically reduce the

voltage levels of transmitted electricity [B. Guo

2020].A transmission line can be recognized by in

structure and height. These long, lofty lines, which

are nor mally 30 feet above the ground, contain

numerous wires, as they cross great distances

2.2 Knowledge of distribution

The final step in the delivery process from energy

generation to user is the distribution system. The

distribution system transports electricity at a voltage

suitable for use in homes and businesses(Y.Zhang

2020). Consumers can quickly spot distribution lines

since they follow residential streets. Appliances and

other essentials are powered by the electricity

delivered through distribution ..

3 FAULTS

Generally speaking, the appliances or equipment

in the power system are made to execute a

continuously needed function, with the exception of

times when preventive maintenance is required or

there are no external sources available. As this

defect can develop under any conditions at any

given time in the power system, it is an

unpredictable element that might enter the system

to stop it from performing its essential function.

Transformer failure is an example of an electrical

fault, which is an unusual condition caused by

moving equipment, operator error, and climate

changes(Y.Zhang 2020). Electric flow

interruptions, equipment damage, and even human,

bird, and animal deaths are all results of these

faults.

4 TYPES OF FAULTS

Electrical faults are specified as voltage and

current di- vergence from reference value or states.

Usually working under typical circumstances,

power system components or lines operate with

typical voltages and currents, making the system

safer to use. The equipment was harmed as a

result of the very huge currents that flow when a

fault occurs. The electrical infrastructure is prone to

two different forms of problems. There are faults

which can be both symmetrical and asymmetrical.

4.1 Knowledge of distribution

The proportion of symmetric system errors is

only 2–5 percent. If these problems occur, the

system remains balanced, but the machinery of the

transmission system for power is seriously affected.

Three-phase symmetrical faults come in two

varieties: (L-L-L-G) and (L-L-L).These faults are

easy to analyze, and the process is frequently

carried through phase by phase basis.

Fig. 2. Example of Symmetrical faults.

Line to Ground (L-G), Line to Line (L-L), and

Double line to ground (LL-G) faults are three basic types

of Un- symmetrical faults. Line to Ground faults (L-G),

which make

ISPES 2023 - International Conference on Intelligent and Sustainable Power and Energy Systems

162

Fig. 3. Example of Unsymmetrical faults

up from 65 to 70 percent among all

problems, are the most common form of fault.

Double line to Ground faults happen for 15 to

20 percent of all faults and put both

conductors into the path of the ground (O. A.

S. Youssef ,2004).Line to Line faults are

however known as unbalanced faults because

they cause the system to become unbalanced

when they occur. An unbalanced system

flows unbalanced current because each phase

has a different impedance level (F. B. Costa

et al.,2012, A.M Gauoda et al.,2002)

5 MACHINE LEARNING

Machine learning makes predictions using

statistical models. Simplest and basic technical

terms, machine learning refers to the

employment of algorithms that take empirical or

previous data as input, analyze it and then

output results based on the that analysis. In

certain techniques, the algorithms first run on

so-called” training data” drawing conclusions,

and working out strategies to enhance their

accuracy over time [9].

Table1.Fault parameters

5.1 Types of Machine learning

The three basic techniques employed in machine

learning are supervised, unsupervised and

reinforcement learning. One of the proposed

methods that may be adapted to the problem a

scientist seeks to answer is semi-supervised

learning. Each tactic has certain advantages as well

as disadvantages, and some are greater than others

at solving particular sorts of problems.

Supervised Learning: In supervised learning,

inputs and outputs from a variety of data sets are

utilized to train a computer in order to teach it a

universal principle that maps inputs to outputs. The

two fundamental subcategories are classification,

which entails estimating a target class, and

regression, which entails predicting a numerical

value.

Unsupervised Learning: With unsupervised

learning, the learning algorithm is not given this

form of guidance; instead, it makes an independent

attempt to recognise the pattern in the data.

Clustering, which involves finding groups in the

dataset that share characteristics which examine

the statistical distribution of the data set.

Reinforcement Learning: In reinforcement

learning, a challenge is presented to the machine

and algorithms in a dynamic environment, and as

they try to achieve a goal, they are provided

feedback , which encourages their learning and

goal-seeking efforts [10]

6 TECHNIQUES FOR FAULT

CLASSIFICATION BY

MACHINE LEARNING

•

Support Vector Machine.

•

Bayesian Learner (Na¨ıve Bayes).

•

Sequential Minimal Optimization.

•

Logistic Regression.

•

Decision Tree.

•

K Nearest Neighbour.

1.Support Vector Machine: Regression as well

as classifica- tion problems can be solved using

Support Vector Machine, or SVM, one of the most

used supervised learning techniques. However, it’s

mainly used in classification by machine learning

problems. [11]. Support Vector Machine can be of

two types:

Linear SVM: Linearly separable data are those that

can be divided into two subgroups using just a single

straight line. Linear SVM is used to classify such

data

•

Non-linear SVM: When a dataset cannot

be identified us- ing a straight line, the

classification method is considered to as

a non-linear SVM classifier.

Bayesian Learner (Na¨ıve Bayes):

Bayesian learning is a probabilistic technique

to machine learning which involves finding a

probability over the model parameters. It is

based on the Bayes theorem, which states that

Machine Learning Model to Forecast Power System Breakdowns

163

the likelihood of the data given the hypothesis

and the prior value of the hypothesis are

combined in order to determine the possibility

of a hypothesis given the data.

Sequential Minimal Optimization

(SMO): SMO breaks down this substantial

quadratic programming problem into a

number of more accessible ones. Solving such

simple quadratic programming issues

theoretically eliminates the requirement for a

time-consuming inner loop quadratic

programming optimization. When matrix

calculation for a variety of test problems

focused on linear and quadratic equations is

omitted, SMO scales the size of training set.

learning.

Logistic Regression: The likelihood of

a binary answer variable is modelled using the

statistical technique of logistic regression.

Making predictions and categorizing

observations based on a collection of

independent factors is a typical task in data

analysis.In logistic regression, the dependent

variable Y has interesting outcome values

between 1 and 0.

Logit

(

= 1) =

[

(

= 1)

(

= 0)] =

Βx

(1)

Input values (x) combined continuously

with weights or coef- ficient values to

anticipate an outcome value (y). In contrast,

a binary value (0 or 1) compared to a numeric

number is the output value being represented.

In which b0 is the bias or intercept term, b1 is

the coefficient for the single input value, and

y is the predicted output (x). The data from

your training data set are all included in

each part of your training set. The following

expression may be employed to represent the

probability that an input (X) corresponds to

the default class (Y=1):

P (X) = P (Y = 1|X) (2)

Decision Tree (DT): A really well machine

learning ap- proach called decision trees is

utilised both for classification and regression

problems. This simple yet efficient model

gen- erates a tree-like pattern of decisions that

leads in a projected output by recursively

dividing the information depending on the

input’s value variables. The representation of

decision trees is one of its key benefits since

the resultant tree is simple to see and

understand for people. Along with processing

category and numerical data, decision trees are

also capable of capturing complex linkages in

between variables being input and output [12].

K nearest neighbour (KNN): The K-NN method

places each new instance in the group that most

closely resembles the existing categories, assuming

that the new case and the existing instances are

equivalent. After storing all previous data, a new

data point is classified using the K-NN algorithm

based on similarity. This shows that applying the K-

NN technique, new data may be quickly sorted into

a group that best fits it

Although classification problems typically are

handled using the K-NN approach, predictions may

also be made to use this method.

7 CLASSIFICATION OF APP

Fig. 4. Classification Learner Workflow

In Classification Learner, training a model

entails two steps:

•

Train a model using a validation strategy, or

validated model. By default, the programme

uses cross-validation to guard against

overfitting. You can also select holdout

validation as an alternative.

•

Complete Model: Without validation,

train a model using all the data. The software

also trains this model in addition to the

approved model. The whole data model,

however, is not accessible in the app. The

whole model is exported when you select a

classifier to send towards the workspace in

Learner.

The confusion matrix’s error rate (ERR) and

accuracy (ACC) measurements are the most widely

used.

1) Error rate: The error rate is determined by

dividing the total number of wrong predictions by

its total number of observations included in the data

ISPES 2023 - International Conference on Intelligent and Sustainable Power and Energy Systems

164

set. The ideal error rate is0.0, the worst rate of error

is 1.0.

Fig 5. Confusion Matrix

𝐸𝑅𝑅 =

𝐹𝑃 + 𝐹𝑁

𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁

𝐹𝑃 + 𝐹𝑁

𝑃 + 𝑁

2) Accuracy: Accuracy is calculated as the

total number of correct predictions divided by

the entire size of the dataset (ACC). From 0.0

to 1.0, having 1.0 being the best. This may

also be determined by dividing by the ERR.

𝐴𝐶𝐶 =

𝑇𝑃 + 𝑇𝑁

𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁

𝑇𝑃 + 𝑇𝑁

𝑃 + 𝑁

Fig 6. Scatter

Pot

The function scatter (x, y) emits a plot with

spherical mark- ers in which the vectors x and

y specify the locations. Set both directions as

similar vectors to plot a single set of

coordinates. You must provide at least one x

or y as a matrix of values to plot several sets

of values on one set of axes. The graphs that

indicate the relationship between the

variables in a data set are termed as scatter

plots.

8 CONCLUSION

Transmission lines need to be safeguarded

if reliable and efficient power flow is to be

achieved. The crucial safety tasks of

transmission lines include fault detection and

classification. In light of machine learning

technologies, it is now easier to manage the

power system’s complicated challenges.

Existing techniques are not computationally

practical solutions due to their inability to

handle huge quantities of information (includ-

ing bits from diverse data sets) from units of

measurement like measures of phasor analysis.

In order to protect the electricity system,

defects must be found and fixed before they

cause harm to utility infrastructure or

consumer property. Given the growing

number of measurements in distribution

systems, there is a chance to improve defect

detection methods. The power transmission

system protection procedures may be im-

proved by using potent machine learning

algorithms to predict faults. Also, it will save

the amount of time needed to repair defects,

particularly on long transmission lines,

improving the overall dependability and

effectiveness. The use of algorithms has been

found to not only simplify the problem but

also to ensure more precise and predictable

execution. Current experiments may

recommend validating the current process via

data. from an ongoing simulation or a real

network. The efficiency, simplicity, and

preparation time of various machine learning

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