The document describes a proposed approach for electricity theft detection in smart grids using deep neural networks. It involves developing a robust DNN model and compiling a diverse dataset. The DNN would be trained on the dataset and integrated with the smart grid for real-time anomaly detection. It aims to enhance accuracy, adapt to changing patterns, provide real-time monitoring and feedback to improve prevention of electricity theft over existing methods.

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

7. PROBLEM IDENTIFICATION

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

12. SYSTEM ARCHITECTURE AND
DESIGN Contd…

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.

14. ADVANTAGES
 High Detection Accuracy
 Real-time Monitoring
 Adaptability to Changing Patterns
 Proactive Prevention
 Continuous Improvement
 Comprehensive Dataset

15. CONCLUSION