Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reclosers and Fuses01:26

Reclosers and Fuses

105
Automatic circuit reclosers enhance the protection of distribution circuits by interrupting and auto-reclosing an AC circuit according to a preset sequence. They effectively manage temporary faults on overhead distribution lines, often caused by tree limbs or wildlife, by briefly disrupting service to improve overall reliability. However, contact with reclosers or energized broken conductors on the ground can pose serious hazards.
A comprehensive protection scheme for radial distribution...
105
Series R—L Circuit Transients01:22

Series R—L Circuit Transients

100
In a series resistor-inductor (R-L) circuit, closing the switch at the start of the time period simulates a three-phase short circuit, a fault condition where all three phases of an unloaded synchronous machine are short-circuited. When there is no fault impedance and no initial current, the initial voltage is determined by the phase angle of the source voltage.
Using Kirchhoff's Voltage Law (KVL) to analyze this circuit helps determine the total asymmetrical fault current, which consists...
100
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

84
Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
84
Energy Losses in Transformers01:21

Energy Losses in Transformers

872
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
872
RL Circuit without Source01:14

RL Circuit without Source

905
When a DC source is suddenly disconnected from an RL (Resistor-Inductor) circuit, the circuit becomes source-free. Assuming the inductor has an initial current denoted as I0, the initial energy stored in the inductor can be determined.
Applying Kirchhoff's voltage law around the loop of the circuit and substituting the voltages across the inductor and resistor yields a first-order differential equation. A logarithmic equation is obtained by rearranging the terms in this equation,...
905
Energy and Power Signals01:17

Energy and Power Signals

289
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
289

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Telomerase as a therapeutic Bullseye: advances in cancer treatment and clinical trials : A comprehensive review of small molecule inhibitors, antisense oligonucleotides, and immunotherapeutic strategies targeting the telomerase catalytic subunit (hTERT).

Biological research·2026
Same author

Gut dysbiosis associated with neonatal respiratory distress syndrome and biological plausibility of disease-specific probiotic intervention: a translational study.

Journal of translational medicine·2026
Same author

Loss-of-Function Variants in CCDC189 Cause Human Oligoasthenoteratozoospermia by Disrupting Sperm Flagellar and Acrosomal Architecture.

Andrology·2026
Same author

Insights into the antibacterial, antifungal, and antiparasitic activities of functionalized ZnO quantum dots.

Scientific reports·2026
Same author

Expanding the Genetic Landscape of Congenital Stationary Night Blindness Through the Analysis of Consanguineous Pakistani Families.

Genes·2026
Same author

Analysis of the Relationship Between Microbial Community Succession and Volatile Flavor Compounds During Fermentation of Yunnan Traditional Rose Jam.

Foods (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jul 1, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

533

RNN-BiLSTM-CRF based amalgamated deep learning model for electricity theft detection to secure smart grids.

Aqsa Khalid1, Ghulam Mustafa2, Muhammad Rizwan Rashid Rana2

  • 1Department of Computer Science, COMSATS University, Islamabad, Pakistan.

Peerj. Computer Science
|March 4, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced deep learning model to combat electricity theft, achieving 93.05% accuracy in detecting non-technical losses (NTLs) in smart grids. The novel approach enhances detection by analyzing both 1-D and 2-D electricity data.

Keywords:
BiLSTMCRFElectricity-theftRNNSmart gird

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

Related Experiment Videos

Last Updated: Jul 1, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

533
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Electricity theft causes significant non-technical losses (NTLs) in power grids, impacting grid stability and electricity supply quality.
  • Traditional electricity theft detection methods analyzing only one-dimensional (1-D) data lack sufficient accuracy for complex smart grids.
  • Smart grids enable advanced solutions for detecting and mitigating issues like electricity theft due to bidirectional data flow.

Purpose of the Study:

  • To develop a robust, deep learning-based model for accurate electricity theft detection in smart grids.
  • To overcome the limitations of existing 1-D data analysis methods for identifying non-technical losses.
  • To enhance the security and reliability of power supply through improved theft detection mechanisms.

Main Methods:

  • Proposed an ensemble deep learning model, the Recurrent Neural Network-Bidirectional Long Short-Term Memory-Conditional Random Field (RNN-BiLSTM-CRF).
  • Integrated both one-dimensional (1-D) and two-dimensional (2-D) electricity consumption data for enhanced analysis.
  • Leveraged the strengths of RNN and BiLSTM architectures for sophisticated pattern recognition in consumption data.

Main Results:

  • The proposed RNN-BiLSTM-CRF model achieved a high accuracy rate of 93.05% in detecting electricity theft.
  • The model demonstrated superior performance compared to existing methods that primarily rely on 1-D data analysis.
  • The amalgamation of 1-D and 2-D data significantly improved the effectiveness of the theft detection process.

Conclusions:

  • The deep learning-based RNN-BiLSTM-CRF model offers a highly effective solution for detecting electricity theft in smart grids.
  • Utilizing multi-dimensional electricity consumption data is crucial for improving the accuracy of non-technical loss detection.
  • The developed model contributes to securing smart grids and ensuring a more reliable power supply.