This guide outlines options for brain-inspired computing technologies, which is defined as computational models, methods, and systems inspired by the structure and function of biological nervous systems, particularly the human brain. These technologies include but are not limited to system-level brain-like architectures, hybrid neural networks, and neuro-symbolic computing paradigms. The guide focuses on their application in enhancing accurate observation, prediction, optimization, and decision-making for the high-dimensional, nonlinear, and complex dynamic characteristics of power systems. Specifically, brain-inspired computing in this context encompasses: (1)Architectures that emulate neural structures, such as spiking neural networks and neuromorphic hardware. (2)Computational models that mimic cognitive functions, including learning, adaptation, and decision-making processes. (3)Integration of symbolic reasoning with neural computation to address complex problem-solving tasks. This guide supports a range of operational scenarios that includes but is not limited to: 1) Detection and assessment of substation equipment defects; 2) Cross-terminal recognition of personnel behavior; 3) Intrusion detection in high-voltage intervals; 4) Health status assessment, fault diagnosis and trend forecasting of critical equipment such as converter transformers; 5) Ultra-short-term power prediction for wind and photovoltaic (PV) generation at the single-station level. These applications cover visual, multimodal, and temporal data scenarios and typically involve integration with control and monitoring systems across multiple segments of the power system.
- Standard Committee
- CIS/SC - Standards Committee
- Status
- Active PAR
- PAR Approval
- 2025-06-19
Working Group Details
- Society
- IEEE Computational Intelligence Society
- Standard Committee
- CIS/SC - Standards Committee
- Working Group
-
BCIS - Brain-Inspired Computing and Intelligent Systems
- IEEE Program Manager
- Malia Zaman
Contact Malia Zaman - Working Group Chair
- Fei Zhou
Other Activities From This Working Group
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