Hybrid AC/DC Grid Early Warning Using Fast TTC and HVDC Control
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Solution Overview
Problem
Existing early warning methods for large scale hybrid AC/DC power grids fail to include HVDC set-point control, cannot meet online application speed requirements, and do not adequately reflect operating states or provide comprehensive decision information for preventive control.
Innovation Solution
A rolling early warning system using deep learning to estimate available transfer capability (ATC) with dynamic security constraints, incorporating HVDC set-point control, and implementing a layered and hierarchical warning system based on operating states and preventive control actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If time-domain simulation is used for dynamic security assessment, then assessment accuracy is improved, but computing time increases significantly
Solution Approach 1:
The patent pre-calculates and stores security constraint conditions and their corresponding control actions before online operation. When a security risk is detected, the system directly retrieves pre-prepared control strategies from the database, avoiding real-time complex calculations and achieving fast response while maintaining assessment accuracy.
Solution Approach 2:
The patent replaces traditional time-domain simulation methods with a database-based lookup approach. Instead of performing computationally intensive simulations in real-time, the system uses pre-computed results stored in databases, substituting mechanical calculation processes with information retrieval operations that are much faster.
2Reliability
If the number of operating conditions to be assessed is increased to cover more future scenarios, then early warning completeness is improved, but computational complexity increases
Solution Approach 1:
The patent segments the assessment process into offline preparation and online execution phases. In the offline phase, multiple future operating conditions are pre-assessed and their results stored in databases. During online operation, only simple lookup and comparison operations are needed, dramatically reducing computational complexity while maintaining comprehensive coverage of future scenarios.
3Ease of manufacture
If existing early warning methods are used, then implementation simplicity is maintained, but HVDC set-point control is not included and decision information is insufficient
Solution Approach 1:
The patent merges security assessment with control strategy generation by integrating the two functions into a unified system. The database stores not only security constraint violations but also corresponding control actions (including HVDC set-point control). This combination provides comprehensive decision information while maintaining implementation simplicity through a single integrated database lookup process.
Data Source
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AI summary
The patent discloses a rolling early warning method and system of dynamic security risk situation for large scale hybrid alternating current/direct current (AC/DC) grids, which includes: Construct the original feature set of the fast total transfer capability (TTC) estimation model. Generate the training sample set of the fast TTC estimation model based on the network topology and the forecast information of a period of time in the future. Construct the fast TTC estimation model based on stacking denoising autoencoders (SDAE) and extreme learning machine (ELM). Generate the future operating conditions (OCs), and determine the type of preventive control actions needed to ensure the system security based on the fast TTC estimation model and the heuristic search algorithm. Conduct the layered and hierarchical early warning for OCs according to the type of operating state and the type of preventive control actions that is needed. The rolling early warning of dynamic security risk situation can be fast performed based on the deep learning technology. The final layered and hierarchical early warning results can reflect the security of the system more comprehensively, and provide valuable decision information for the subsequent preventive control decision-making.