Anomaly Detection and Incentive Optimization for Power Grids
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Solution Overview
Problem
Current systems lack the ability to effectively monitor power grids and their ecosystems for operational performance and carbon footprint degradation, leading to inefficient and ineffective sustainability services due to the inability to determine significant degradations in power networks.
Innovation Solution
A dual system combining a knowledge graph and reinforcement learning with Graph Neural Networks (GNNs) to detect network anomalies and generate optimized incentives for reducing energy demand, using sensor data to establish a knowledge base and predict performance parameters, and then using iterative optimization processes to adjust incentives based on actual performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring systems are used to track power grid performance, then system simplicity is maintained, but the ability to detect network anomalies and measure carbon footprint degradation is insufficient
Solution Approach 1:
The system segments the power grid monitoring into multiple specialized modules: a knowledge graph module for entity relationships, a GNN module for predictive analytics, and a reinforcement learning module for incentive optimization. Each module handles specific aspects of anomaly detection, allowing high measurement precision through specialized processing while managing overall system complexity through modular design.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary layer between raw sensor data and anomaly detection algorithms. The knowledge graph structures relationships among power grid entities (substations, transformers, consumers) and serves as a mediator that enriches data context before feeding into the GNN predictive model, thereby enhancing detection precision without directly increasing algorithmic complexity.
2Productivity
If manual analysis of power grid performance is used, then implementation simplicity is maintained, but productivity and responsiveness to anomalies are reduced
Solution Approach 1:
The system implements self-service through automated anomaly detection and incentive generation. The reinforcement learning module autonomously analyzes performance deviations, identifies anomalies without human intervention, and automatically generates optimized incentive programs for demand reduction. This automation dramatically increases productivity by processing grid data in real-time and responding to anomalies immediately without manual analysis delays.
Solution Approach 2:
The patent establishes continuous feedback loops where the system monitors actual power consumption, compares it against predicted values from the GNN model, detects anomalies, and adjusts incentive programs accordingly. The reinforcement learning module learns from the outcomes of previous incentive actions and refines future decisions, creating a self-improving automated system that enhances productivity through iterative optimization.
3Reliability
If generic incentive programs are used for demand reduction, then implementation complexity is minimized, but effectiveness in reducing energy demand is insufficient
Solution Approach 1:
The system applies local quality by generating customized incentive programs tailored to specific entities or regions within the power grid. The reinforcement learning module analyzes local consumption patterns, anomaly characteristics, and entity-specific factors to create targeted incentive strategies. This localized approach ensures high reliability and effectiveness of incentives for each specific context rather than applying generic one-size-fits-all solutions.
Solution Approach 2:
The patent dynamically adjusts incentive parameters based on real-time grid conditions and learned patterns. The reinforcement learning module modifies incentive magnitude, timing, and targeting parameters according to the specific anomaly detected and historical effectiveness data. This parameter optimization ensures maximum incentive effectiveness for demand reduction while adapting to changing grid conditions without requiring complete system redesign.
Data Source
AI summary
An embodiment establishes a knowledge base based at least in part on sensor data received from a network. The embodiment generates a predicted performance parameter for a designated entity of the network using a first machine learning algorithm. The embodiment compares the predicted performance parameter to an actual performance parameter and determines whether the actual performance parameter exceeds a threshold difference from the predicted performance parameter. The embodiment generates, responsive to determining that the threshold difference is exceeded, incentive data using a second machine learning algorithm, where the incentive data is representative of an action selected by the second machine learning algorithm using an iterative optimization process, and where the iterative optimization process comprises performing the action and determining that the actual performance parameter approaches the threshold value in response to the action.