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Related Concept Videos

Energy and Power Signals01:17

Energy and Power Signals

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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:
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Conservation of AC Power01:15

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The principle of power preservation is applicable to both ac and dc circuits. This principle, when applied to AC power, asserts that the complex, real, and reactive powers produced by the source are equal to the total complex, real, and reactive powers absorbed by the loads. When two load impedances are connected in parallel to an ac source V, the complex power provided by the source can be calculated using the relation
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Power System Distribution01:25

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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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Electrical Energy01:10

Electrical Energy

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Using electric appliances for a longer period of time consumes more electrical energy and results in a higher electric bill. The energy produced by the transfer of electrons from one point to another is known as electrical energy. If power is delivered at a constant rate, the electrical energy can be defined as the product of power used by the device for a period of time. The energy unit on electric bills is the kilowatt-hour, where one kilowatt-hour is equivalent to 3.6 × 106 joules.
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Electric power is the product of current and voltage, represented in units of joules per second, or watts. For example, cars often have one or more auxiliary power outlets with which you can charge a cell phone or other electronic devices. These outlets may be rated at 20 amps and 12 volts, so that the circuit can deliver a maximum power of 240 watts. Consider a 25 Watt bulb and a 60 Watt bulb. The conversion of electrical energy produces heat and light, while the kinetic energy lost by the...
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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Secure Edge-Based Energy Management Protocol in Smart Grid Environments with Correlation Analysis.

Amjad Rehman1, Khalid Haseeb1,2, Gwanggil Jeon1,3

  • 1Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia.

Sensors (Basel, Switzerland)
|December 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a secure, edge-based energy management protocol for smart grids, enhancing IoT device efficiency and data forwarding through correlation analysis and lightweight authentication for improved reliability.

Keywords:
cloud storagecommunication privacyconservationmachine learningsmart grid

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Security

Background:

  • Wireless sensor networks (WSNs) and the Internet of Things (IoT) are crucial for industrial applications and smart grids, enabling data collection and multi-hop transmission.
  • Significant research gaps exist in energy management for IoT devices and smart sensors within smart grids, particularly concerning efficient routing, energy holes, and intelligent data forwarding.
  • Managing network traffic and balancing communication overhead in smart grid routing paths present ongoing challenges.

Purpose of the Study:

  • To propose a novel secure edge-based energy management protocol for smart grid environments.
  • To address the challenges of energy efficiency, data forwarding, and network traffic management in IoT-enabled smart grids.
  • To enhance the reliability and security of smart grid communication systems.

Main Methods:

  • Development of a secure edge-based energy management protocol incorporating multi-route management.
  • Utilization of correlation analysis to predict data forwarding processes and improve IoT device management.
  • Implementation of lightweight authentication with sink coordination to achieve security goals and increase system reliability.

Main Results:

  • The proposed protocol demonstrates improved prediction of data forwarding processes.
  • Enhanced management of IoT devices through correlation analysis.
  • Increased system reliability and achievement of security objectives via lightweight authentication and sink coordination.

Conclusions:

  • The developed secure edge-based energy management protocol effectively addresses key challenges in smart grid IoT networks.
  • The protocol offers superior performance in terms of energy management, data forwarding, and security compared to existing solutions.
  • This research contributes to more reliable and secure smart grid operations through advanced IoT device management.