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Related Concept Videos

Fault Types01:18

Fault Types

332
When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
332
Bus Impedance Matrix01:24

Bus Impedance Matrix

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Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

295
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
295
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

422
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...
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Operational Amplifiers01:17

Operational Amplifiers

1.7K
The operational amplifier, often referred to as an op-amp, is a multifaceted building block of a circuit. This electronic component functions like a voltage-controlled voltage source and can also be used to create a voltage- or current-controlled current source. The design of an operational amplifier enables it to execute mathematical operations when external components like resistors and capacitors are linked to its terminals. An op-amp has the capacity to sum signals, amplify a signal,...
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Radial System Protection01:23

Radial System Protection

357
Radial systems employ time-delay overcurrent relays to reduce load interruptions. When a fault occurs, the nearest breaker opens first, while upstream breakers remain closed due to longer delay settings. This approach ensures minimal disruption to the rest of the system.
In a radial system with a fault downstream of the third breaker, ideally, only the third breaker will open, isolating the fault and interrupting the load connected beyond it. The second breaker has a longer delay setting,...
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Related Experiment Video

Updated: Dec 5, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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A Sensor Fault-Tolerant Accident Diagnosis System.

Jeonghun Choi1, Seung Jun Lee1

  • 1Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulju-gun, Ulsan 44919, Korea.

Sensors (Basel, Switzerland)
|October 20, 2020
PubMed
Summary

This study develops a fault-tolerant nuclear power plant accident diagnosis system using recurrent neural networks (RNNs). It compares Missforest and GRUD imputation methods to improve robustness against sensor errors and maintain diagnostic accuracy.

Keywords:
recurrent neural networkssensor fault mitigationsensor fault-tolerant accident diagnosissignal reconstruction

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Area of Science:

  • Nuclear Engineering
  • Artificial Intelligence
  • Data Science

Background:

  • Nuclear power plant emergencies require rapid, accurate accident diagnosis for operator safety and mitigation.
  • Recurrent Neural Networks (RNNs) show promise for accident identification but can be sensitive to sensor input errors.
  • Sensor faults can significantly degrade the performance of existing RNN-based diagnostic systems.

Purpose of the Study:

  • To develop a sensor fault-tolerant accident diagnosis system for nuclear power plants.
  • To evaluate and compare the effectiveness of different data imputation methods in mitigating sensor errors.
  • To enhance the robustness and reliability of RNN models in critical nuclear safety applications.

Main Methods:

  • An RNN-based accident diagnosis framework was adapted for fault tolerance.
  • The Missforest imputation algorithm was employed to handle missing or erroneous sensor data.
  • The Gated Recurrent Unit with Decay (GRUD) method, an RNN-based imputer, was utilized for multivariate time series imputation.
  • The performance of Missforest and GRUD in recovering diagnostic accuracy under simulated sensor faults was quantitatively assessed.

Main Results:

  • Both Missforest and GRUD demonstrated an ability to recover diagnostic accuracy in the presence of sensor errors.
  • Comparative analysis indicated the relative effectiveness of each imputation strategy in mitigating specific types of sensor faults.
  • The study identified optimal imputation parameters for enhancing system resilience.

Conclusions:

  • The developed RNN-based system shows improved tolerance to sensor faults, crucial for nuclear power plant safety.
  • Data imputation techniques like Missforest and GRUD are effective strategies for enhancing the robustness of AI-driven diagnostic systems.
  • Further research can optimize these methods for real-world deployment in critical infrastructure monitoring.