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Secure Grid Management
Resilient and Interpretable Grid Management
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Intractābilis delivers advanced cognitive solutions that address the evolving complexity of grid infrastructure by embedding intelligence across every layer of grid management. Designed to be cyber-resilient, interpretable, and scalable, our systems enable grid stakeholders to make more reliable, efficient, and informed operational decisions, even in the face of decentralisation, digitisation, and increasing volatility.
Physics-Informed Neural Networks for Energy Supply and Demand Modelling
Modelling the Energy Supply and Demand (ESD) system is a complex problem that requires capturing the dynamic interactions between supply and demand over time, as well as the distribution of energy across different regions. This task demands an understanding of how these variables evolve in relation to the development profiles and socio-economic indicators of each area.
Intractābilis has developed a Physics-Informed Neural Network (PINN) solution designed to address this complexity. The approach draws on the inherent adaptability of PINNs, enabling the integration of physical constraints derived from systems of differential equations into the modelling framework. This ensures that the simulated behaviour of the ESD system remains closely aligned with the governing physical laws.
By embedding these constraints within the learning process, the model is capable of generating more realistic predictions for supply and demand variables. This methodology represents a significant advance in energy systems analysis, offering a robust and innovative path for tackling the multifaceted challenges of energy supply, demand, and regional distribution.
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Power Grid Event Diagnosis
Diagnosing event signatures in power grid such as faults, anomalies, and equipment failures, remains a core challenge for grid management systems. Data from diverse sensors are often high-dimensional, complex, and interlinked, making it difficult to detect patterns and determine root causes through conventional analysis.
Intractābilis has developed an advanced visual analytics framework that combines time-series foundational models with Variational Autoencoders to generate meaningful latent representations from complex temporal data. These representations are transformed into two-dimensional graphs through topological data analysis and clustering. The resulting visual maps, enriched with interactive exploration tools, enable domain experts to intuitively examine and interpret multivariate temporal behaviours in the grid.
Our solution approach makes it possible to separate and identify multiple event types, such as faults and anomalies with distinct physical causes, and to infer missing event labels by analysing spatial patterns in the latent space. By converting complex high-dimensional time-series data into accessible two-dimensional visualisations, the system provides grid operators with a clear view of intricate relationships and patterns. The human-in-the-loop process embedded in the solution facilitates expert validation, builds trust in the models, supports iterative refinement, and promotes actionable insights. The framework’s computational efficiency and resilience to varied data forms ensure scalability for both real-time and large-scale grid monitoring.
Our grid management capabilities empower utilities, energy service providers, and equipment manufacturers to manage increasing operational complexity with clarity, confidence, and resilience. From autonomous microgrid control and interpretable diagnostics to high-fidelity system modelling and intelligent transmission switching, we deliver scalable infrastructure that learns, adapts, and explains.