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Energy and Power Signals

Energy and Power Signals

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:

Power System Three-Phase Short Circuits

Power System Three-Phase Short Circuits

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...

Fast Decoupled and DC Powerflow

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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:

Energy Losses in Transformers

Energy Losses in Transformers

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.
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Power System Distribution

Power System Distribution

Power system distribution involves delivering electrical energy from power plants to consumers through a network of transmission and distribution systems. The process begins at power plants, where energy from coal, gas, nuclear, water, and wind is converted into electrical energy. These plants use three-phase generators, typically rated between 50 to 1300 MVA, with terminal voltages ranging from a few kV to 20 kV, depending on the size and age of the units.
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Zones of Protection

Zones of Protection

In power systems, the entire setup is divided into protective zones to isolate faults and protect the rest of the network. These zones include generators, transformers, buses, transmission lines, distribution lines, and motors. Each zone can be visualized as a separate room in a house, with each room protected by its own circuit breaker.
Protective zones are defined by closed dashed lines, containing one or more components. A key characteristic of these zones is the strategic placement of...

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Enhancing cybersecurity in virtual power plants by detecting network based cyber attacks using an unsupervised

Kumari Nutan Singh¹, Arup Kumar Goswami¹, Nalin Behari Dev Chudhury¹

¹Electrical Engineering Department, National Institute of Technology Silchar, Assam, 78801, India.

Scientific Reports

|September 5, 2025

Summary

This study introduces an Autoencoder (AE) deep learning method to detect False Data Injection Attacks (FDIA) in Virtual Power Plants (VPPs). The AE model effectively identifies malicious data, enhancing cybersecurity for IoT-enabled energy systems.

Keywords:

Auto-encoder (AE)Cyber security Energy market (EM)False data injection attack (FDIA)Renewable energy sources (RES)Virtual power plant (VPP)

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Area of Science:

Cybersecurity in Energy Systems
Machine Learning Applications
Internet of Things (IoT) Security

Background:

The integration of the Internet of Things (IoT) in energy systems, particularly Virtual Power Plants (VPPs), increases cybersecurity vulnerabilities.
VPPs are susceptible to cyber-attacks like False Data Injection Attacks (FDIA) that manipulate critical operational data.
FDIA pose significant risks to system reliability, market stability, and financial performance in VPP operations.

Purpose of the Study:

To propose and validate an unsupervised Autoencoder (AE) deep learning approach for detecting FDIA in VPP systems.
To enhance the cybersecurity posture of IoT-based energy infrastructures.
To ensure the reliability and stability of energy markets and VPP operations.

Main Methods:

An unsupervised Autoencoder (AE) deep learning model was developed for anomaly detection.
The methodology was tested on 9-bus and IEEE-39 bus systems using MATLAB Simulink.
Time-series data spanning 1,000 days, including renewable energy sources, energy storage, and variable loads, was utilized for model training and validation.

Main Results:

The AE model demonstrated high accuracy in detecting anomalies by analyzing reconstruction errors.
The approach successfully identified instances of false data injection within the VPP systems.
Validation on standard test systems confirmed the model's effectiveness in detecting FDIA.

Conclusions:

The proposed AE deep learning approach is effective in detecting FDIA in VPP systems.
Implementing this method ensures system reliability, mitigates financial losses, and maintains energy market stability.
Advanced machine learning techniques are crucial for securing IoT-based energy systems and VPP operations.