1. CARRIED OUT BY:
CHANDANA R [1GG21CS401]
FARHEEN TAJ [1GG21CS403]
GEETHA C [1GG21CS404]
KUSUMA S [1GG21CS407]
GOVERNMENT ENGINEERING COLLEGE RAMANAGARA
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
VII SEM
PROJECT PHASE -1 SEMINAR
ON
ELECTRICITY THEFT DETECTION IN SMART GRIDS BASED ON DEEP NEURAL NETWORK
Under The Guidance of:
Mrs.Prathibha T
Assistant Professor
Department of CSE
2. AGENDA
Abstract
Introduction
System Requirements
Literature review
Problem Identification
Objectives
Methodology
System Design
Conclusion
References
3. ABSTRACT
Electricity theft poses a significant challenge in smart grids, leading to financial losses and compromised grid integrity.
This research proposes a novel approach for electricity theft detection based on deep neural networks.
The system leverages the inherent patterns and anomalies present in smart grid data to identify potential instances of
theft.The methodology involves the utilization of a DNN architecture, specifically designed for sequence modeling and
pattern recognition in time-series data.
The system's working mechanism involves real-time monitoring of grid data, capturing parameters such as power
consumption, voltage levels, and load patterns. The trained DNN analyzes this data, identifying patterns consistent with
normal grid behavior and flagging anomalies that may signify potential theft.
This adaptive learning process enhances the system's resilience against evolving theft techniques, contributing to a
sustainable solution for electricity theft detection in smart grids.In experimental evaluations, the proposed approach
demonstrated high accuracy in identifying instances of theft while minimizing false positives.
4. INTRODUCTION
In this project, we developed power theft detection and power billing system using atmega 328p and node mcu micro
controller.
Generate power bill automatically and has features to monitor power in real time using iot technology.
Also our system will be able to detect power variations such as under voltage , over voltage , over load and alert user
and also aims to detect power theft
It combines automation, real-time monitoring, safety measures, and anti-theft technology to create an intelligent and
user-centric electrical ecosystem. With this system, we usher in a brighter, safer, and more sustainable future for all
energy consumers.
5. INTRODUCTION OF PROJECT
Electricity theft is a global problem that negatively affects both utility companies and electricity users,
It destabilizes the economic development of utility companies,causes electric hazards and impacts
the high cost of energy for users.
The development of smart grids plays an important role in electricity theft detection since they
generate massive data that includes customer consumption data which through deep learing
techniques.
As a result,utility companies suffer a huge revenue lost due to electricity theft.Implementation of
smart grid comes with many opportunities to slove the electricity theft problem
Recently,researchers have worked towards detecting electricity theft by utilizing machine learning
classification techniqes using readily available smartmeters data.These theft detection methods have
proved to be of relatively lower costs.
6. SYSTEM REQUIREMENTS
Processor
RAM
GPU
Storage
HARDWARE REQUIREMENTS SOFTWARE REQUIREMENTS
Operating System
Python
Machine learning frame works
Development Tools
Libraries
6
8. EXISTANCE SYSTEM
In the current landscape of electricity theft detection in smart grids, the prevailing
systems primarily rely on conventional methodologies such as rule-based systems,
statistical analysis, and pattern matching algorithms.
Rule-based systems involve setting predefined thresholds and rules to identify anomalies
in grid data, reacting to sudden deviations in power consumption or load patterns.
Statistical analysis utilizes historical consumption patterns for anomaly detection but may
struggle to adapt to dynamic changes in grid behavior.
Pattern matching algorithms attempt to identify known theft patterns based on historical
data, but they might fall short in recognizing emerging or evolving theft techniques.
9. PROPOSED SYSTEM
The proposed system for electricity theft detection in smart grids introduces a paradigm
shift by leveraging advanced technologies, specifically deep neural networks (DNNs).
In this innovative approach, a robust DNN architecture is designed for sequence
modeling and pattern recognition in smart grid data.
The system begins by compiling a comprehensive dataset, encompassing a wide range of
legitimate energy consumption patterns and historical instances of confirmed theft.
During the training phase, the DNN learns intricate patterns inherent in the data, enabling
it to discern subtle deviations indicative of unauthorized energy usage.
The system aims not only to enhance the accuracy of theft detection but also to contribute
proactively to grid security by providing insights for preventive measures.
10. OBJECTIVES
Develop a Robust DNN Model
Compile Diverse Dataset
Real-time Monitoring Integration
Dynamic Adaptation to Grid Changes
Feedback Mechanisms for Model Refinement
Evaluation of Detection Accuracy
Mitigation and Prevention Strategies
Integration with Smart Grid Infrastructure
Ethical and Legal Implications
Contribution to Smart Grid Security
11. SYSTEM ARCHITECTURE AND
DESIGN
Data Acquisition Module
Data Preprocessing Module
Deep Neural Network (DNN) Module
Adaptive Learning Module
Feedback Loop Module
Alerting and Reporting Module
Integration with Smart Grid Infrastructure
Monitoring and Evaluation Module
13. METHODOLOGY
Gather a diverse dataset encompassing normal energy consumption patterns and historical
instances of confirmed electricity theft.
Clean and preprocess the collected data to remove noise, outliers, and irrelevant information.
Develop a deep neural network architecture suitable for sequence modeling and time-series
analysis. Tailor the model to effectively capture and learn patterns associated with both
legitimate and anomalous grid behavior.
Train the DNN using the prepared dataset, employing techniques such as supervised learning.
Implement a mechanism for real-time integration of grid data with the trained DNN model.
Deploy the trained DNN for real-time anomaly detection.
Incorporate adaptive learning mechanisms within the DNN to dynamically adjust to changes in
grid conditions and consumption patterns.
![CARRIED OUT BY:
CHANDANA R [1GG21CS401]
FARHEEN TAJ [1GG21CS403]
GEETHA C [1GG21CS404]
KUSUMA S [1GG21CS407]
GOVERNMENT ENGINEERING COLLEGE RAMANAGARA
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
VII SEM
PROJECT PHASE -1 SEMINAR
ON
ELECTRICITY THEFT DETECTION IN SMART GRIDS BASED ON DEEP NEURAL NETWORK
Under The Guidance of:
Mrs.Prathibha T
Assistant Professor
Department of CSE](https://image.slidesharecdn.com/presentation-240314150105-45500405/85/presentationggjjfdfbbjhggvnnjjgvvbbnn-pptx-1-320.jpg)
