Power System Contingency Analysis via HPC and Visual Situational Awareness
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Solution Overview
Problem
Current power system contingency analysis tools face challenges such as high computational requirements, difficulty in data aggregation and interpretation, lack of available parameters for protection elements, and prediction of small-signal instabilities, which hinder effective analysis of cascading outages in power systems.
Innovation Solution
Deploying a contingency analysis tool in a high-performance computing environment and incorporating visual situational awareness approaches, along with calculations and coordination of protection element settings using small-signal nomograms, to enhance the ability of power system engineers to evaluate and analyze cascading events efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If steady-state and transient simulations are performed using conventional power system analysis tools, then simulation results can be obtained, but the computing power required is extensive and the computation speed is inherently slow
Solution Approach 1:
The simulation process is divided into multiple independent tasks that can be executed in parallel across a distributed computing cluster. Each computing node handles specific simulation tasks, allowing the overall computation to be performed concurrently rather than sequentially, thus improving computation speed while maintaining simulation accuracy.
Solution Approach 2:
A task management system acts as an intermediary between the user interface and the distributed computing cluster. This intermediary coordinates task distribution, manages computing resources, and aggregates results, enabling efficient utilization of multiple computing nodes without requiring users to directly manage the complexity of parallel computing.
2Loss of information
If comprehensive data from power system simulations is collected, then complete analysis information is available, but the data aggregation and interpretation become difficult
Solution Approach 1:
Multiple data sources from different simulation tasks and computing nodes are merged into a unified data structure. The system combines results from various contingency analyses, protection element data, and system state information into a single coherent view that can be easily interpreted while maintaining complete information.
Solution Approach 2:
The system creates simplified representations or copies of complex simulation data in multiple formats suitable for different analysis purposes. This allows the same underlying data to be accessed and interpreted in various ways without requiring users to manually process the raw comprehensive data sets.
3Measurement precision
If protection element parameters such as relay configurations are included in the analysis, then more accurate simulation results are achieved, but the required parameters are not available to power-planning engineers
Solution Approach 1:
Protection element parameters such as relay configurations are pre-configured and stored in a database before the contingency analysis is performed. This preliminary preparation ensures that accurate protection data is readily available when needed for simulation, eliminating the need for engineers to manually gather these specialized parameters at the time of analysis.
Solution Approach 2:
The system automatically retrieves and utilizes protection element parameters from its own database without requiring external input from engineers. The contingency analysis tool self-serves by accessing pre-stored relay configurations and protection settings, making accurate protection data accessible without requiring engineers to have specialized knowledge or external resources.
4Reliability
If small-signal instabilities are analyzed in detail, then cascading outage prediction is improved, but these instabilities are difficult to predict and simulate
Solution Approach 1:
The system replaces complex mechanical simulation of small-signal instabilities with computational algorithms that calculate stability margins using mathematical models. Instead of performing full dynamic simulations of minor disturbances, the system uses computational methods to assess stability, reducing simulation complexity while improving the ability to predict cascading events.
Data Source
AI summary
The present disclosure describes systems and techniques that enhance effectiveness and efficiency of a contingency analysis tool that is used for studying the magnitude and likelihood of extreme contingencies and potential cascading events across a power system. The described systems and techniques include deploying the contingency analysis tool in a high-performance computing (HPC) environment and incorporating visual situational awareness approaches to allow power system engineers to quickly and efficiently evaluate multiple power system simulation models. Furthermore, the described systems and techniques include the power system contingency-analysis tool calculating and coordinating protection element settings, as well as assessing controls of the power system using small-signal nomograms, allowing power system engineers to more effectively comprehend, evaluate, and analyze causes and effects of cascading events against a topology of a power system.