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Related Experiment Videos

Participant-level anomaly detection for generation and load data using dual-side LSTMs.

Qianya He1, Qi Liu1, Xueliang Gong1

  • 1Guangdong Power Exchange Center Co., Ltd., Guangdong, China.

Scientific Reports
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Multimachine Stability01:25

Multimachine Stability

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:
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...

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This study introduces a novel dual-side LSTM method for precise anomaly detection in electricity markets. The approach accurately identifies anomalies at the participant level, improving market settlement reliability.

Area of Science:

  • Electrical Engineering
  • Data Science
  • Machine Learning

Background:

  • Anomaly detection is crucial for reliable market settlement in power systems.
  • Existing methods struggle with diverse, sparse anomalies and dynamic participant loads, leading to inaccuracies.
  • Participant-level localization is challenging due to the time-varying nature of load data.

Purpose of the Study:

  • To develop a participant-level anomaly detection method for generation and load metering data.
  • To overcome limitations of conventional statistical and outlier detection methods.
  • To improve the accuracy and reduce false positives in anomaly detection for market settlement.

Main Methods:

  • A dual-side Long Short-Term Memory (LSTM) network is proposed.
Keywords:
Anomaly detectionFrequency-domain featuresMultimodal fusionParticipant embedding

Related Experiment Videos

  • Multimodal features are engineered from intra-day measurements, daily totals, FFT components, and calendar context.
  • A zero-padding and masking mechanism handles varying participant numbers; participant embedding and a combined error-confidence rule enable precise localization.

    • Main Results:

    • Achieved a 100.0% F1-score, recall, and precision on real-world provincial-grid data.
    • Demonstrated highly accurate participant-level anomaly positioning and abnormal time-slot identification.
    • Significantly outperformed traditional statistical thresholding and Local Outlier Factor (LOF) baselines.

    Conclusions:

    • The proposed dual-side LSTM method offers a robust solution for participant-level anomaly detection.
    • This approach enhances the reliability of market settlement by providing accurate anomaly identification.
    • The method effectively addresses nonstationarity and temporal dependencies in power grid data.