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Related Concept Videos

Multimachine Stability01:25

Multimachine Stability

226
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
226
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

275
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:
275
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

332
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the...
332
Load-frequency control01:28

Load-frequency control

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Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
251
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

153
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
153
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

175
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.
175

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A Methodology for Evaluating Operator Usage of Machine Learning Recommendations for Power Grid Contingency Analysis.

John Wenskovitch1, Brett Jefferson1, Alexander Anderson2

  • 1Pacific Northwest National Laboratory, National Security Directorate, Richland, WA, United States.

Frontiers in Big Data
|July 1, 2022
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Summary

This study introduces a framework to assess human factors like trust and workload when using machine learning assistants in power grid simulations. It aims to speed up the development and evaluation of artificial intelligence tools for critical infrastructure.

Keywords:
cognitive loadcontingency analysishuman-machine teamingpower gridtrust evaluation

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Area of Science:

  • Power Systems Engineering
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Real-time power grid simulation requires efficient contingency analysis.
  • Integrating machine learning assistants presents challenges in human factors and usability.
  • Accelerating the development and evaluation loop for AI in critical infrastructure is crucial.

Purpose of the Study:

  • To apply a methodology for measuring domain expert trust and workload.
  • To elicit feedback on technological usability and impact of AI assistants.
  • To understand human factors in the deployment of machine learning in power grid simulations.

Main Methods:

  • Developed a framework to collect and analyze human factors data.
  • Measured domain expert trust and workload.
  • Elicited qualitative feedback on usability and impact.
  • Assessed an early-stage artificial neural network recommender.

Main Results:

  • The methodology successfully gathered human factors data.
  • Pilot participant provided insights into the usability of an early-stage artificial neural network recommender.
  • Identified areas for improvement in the human-AI interaction for power grid analysis.

Conclusions:

  • The proposed framework can accelerate the evaluation of AI tools in power systems.
  • Understanding human factors is key to successful deployment of machine learning assistants.
  • Early-stage technology readiness level (TRL) AI tools require focused usability assessments.