Generalized Grid Security Platform for Real-Time Power Distribution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional power grid management systems, such as SCADA/EMS, lack the necessary intelligence and adaptability to handle the increased complexities and uncertainties in power transmission and distribution due to growing energy demands, intermittent renewable resources, and decentralized power generation, leading to inefficiencies and reliability issues.
Innovation Solution
A Generalized Grid Security Platform (GGSP) that integrates data from various sources, including Phasor Measurement Units and Intelligent Equipment Devices, to analyze and correlate power system data in real-time, enabling predictive analytics, root cause analysis, and dynamic control of power transmission and distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional SCADA/EMS systems are used for power grid management, then the system structure is simple and easy to operate, but the system lacks intelligence and adaptability to handle increased complexities and uncertainties
Solution Approach 1:
The system is divided into multiple intelligent agents distributed across different grid components (generators, transformers, transmission lines, etc.). Each agent independently monitors and controls its local component, enabling decentralized intelligence without requiring a monolithic complex system architecture.
Solution Approach 2:
The system dynamically adapts to changing grid conditions by having agents continuously exchange information and adjust control strategies in real-time. This dynamic behavior allows the system to handle uncertainties and complexities without requiring a statically complex architecture.
2Reliability
If data is collected at multiple second scan rates, then the system is easier to manage, but the response time is too slow to address rapid changes and maintain reliability
Solution Approach 1:
The system transitions from static periodic scanning to dynamic event-driven monitoring. Agents continuously monitor their local components and immediately communicate changes to the central server, enabling the system to respond to rapid changes in real-time while maintaining manageable data flow through selective reporting.
Solution Approach 2:
The system implements continuous feedback loops where agents monitor local conditions, report to the central server, receive control commands, and execute adjustments in real-time. This rapid feedback mechanism enables the system to maintain reliability by quickly detecting and responding to grid anomalies without requiring continuous high-rate data collection from all components.
3Loss of information
If conventional network monitoring is used, then the monitoring scope is limited and easier to implement, but it does not include monitoring of equipment health which reduces situation awareness
Solution Approach 1:
The intelligent agents perform multiple functions simultaneously: they monitor operational parameters, assess equipment health, predict failures, and execute control actions. This multi-functionality comprehensively captures all relevant information about grid status without requiring separate specialized systems for each monitoring aspect.
Solution Approach 2:
Each agent autonomously monitors its local component, assesses its own health status, and identifies potential issues without requiring external intervention. This self-service capability enables comprehensive equipment health monitoring across the entire grid while keeping individual agent complexity low.
4Adaptability or versatility
If centralized hierarchical control is used, then the control structure is simple to implement, but it lacks the adaptability to handle decentralized power generation and intermittent resources
Solution Approach 1:
The centralized hierarchical control is segmented into distributed intelligent agents that operate autonomously at local levels while maintaining communication with the central server. This segmentation enables the system to handle decentralized power generation and intermittent resources by allowing local agents to make real-time decisions without requiring complex reconfiguration of the entire control architecture.
Solution Approach 2:
The system adds a new dimension of intelligence by introducing autonomous agents at the component level, transforming the traditional two-level hierarchical structure into a multi-layered architecture with distributed intelligence. This dimensional change enables adaptability to decentralized resources while maintaining the simplicity of centralized coordination through the server-agent communication framework.
Data Source
AI summary
The subject specification comprises a generalized grid security platform (GGSP) that can control power distribution and operations in a power transmission and distribution grid (PTDG) in real or near real time. The GGSP can receive data from one or more data sources, including a PMU(s) or an IED(s), which can obtain power system related data and provide at least a portion of such data to the GGSP at a subsecond rate. The GGSP can correlate data from the data sources based at least in part on a temporal, geographical, or topological axis. The GGSP can analyze the data, including performing predictive analysis, e.g., via simulation, root cause analysis, post mortem analysis, or complex event processing, when desired, to facilitate identifying a current or predicted future state of the PTDG, a cause or source of an abnormal condition, or a remedial action execution plan, new operation or maintenance guidance, etc.