Apparatus, System and Method for Detecting Anomalies in a Grid

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The invention provides an apparatus, system, and method for automatically detecting anomalies in electrical power grids using advanced signal processing and machine learning techniques. It transforms real-time grid data (such as voltage, current, and frequency) using Fast Fourier Transformation (FFT) or spectrograms, fits this data with models, reduces data complexity with autoencoders, and detects anomalies by identifying outliers in comparison to established 'normal' operational fingerprint data. This system works without requiring additional expensive measurement hardware or technical experts, making it suitable for integration directly with existing grid-connected equipment like frequency converters in industrial environments.

Use CasesContent extracted from patent full text and abstract with AI.

• Monitoring industrial facility electrical grids for real-time anomaly detection to prevent equipment failures.
• Condition monitoring of power grids to improve reliability and reduce unplanned downtime in manufacturing plants.
• Integration into industrial drive systems and motor controllers to ensure grid stability and early detection of voltage or frequency abnormalities.
• Remote or cloud-based supervision of multiple grid systems at different sites from a centralized control center.
• Predictive maintenance of electrical equipment based on early anomaly detection.
• Grid health monitoring for utility companies, industrial plants, and large-scale power consumers.

BenefitsContent extracted from patent full text and abstract with AI.

• Reduces the need for expensive measurement hardware or dedicated technical experts to analyze grid health.
• Allows continuous, real-time monitoring and detection of subtle or creeping anomalies in the grid, improving reliability.
• Highly customizable ('plug-and-play') for specific sites or equipment, enhancing accuracy compared to fixed-threshold systems.
• Integrates seamlessly with existing industrial systems (e.g., frequency converters, drive systems) for ease of deployment.
• Utilizes advanced signal processing and machine learning for robust detection even in noisy or variable environments.
• Scalable across multiple sites or machines using edge and cloud computing architectures.
• Enables proactive maintenance and fast response, reducing the risk of costly equipment failures or production losses.

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Patent Abstract

Apparatus, System and Method for detecting anomalies in a grid are disclosed. The method comprising transforming data acquired from the grid (110, 110A-110C) based on at least one of a Fast Fourier Transformation (FFT) and a spectrogram of the data, wherein the data acquired comprises data associated with at least one of grid voltage, grid current, grid frequency, phase; fitting the data using a fitting function initialized using at least the transformed data, wherein the fitting function includes at least one of a sinusoidal function; or generating a lower representation of at least one of the data acquired and the transformed data; and detecting the anomaly in the grid (110, 110A-110C) based on at least one outlier detected in the fitted data or the lower representation of data using at least one of a parameter deviation and the similarity index.