| 1 |
Artificial Neural Network (ANN) |
A computational model mimicking biological neurons, used in power systems for load forecasting and fault classification by learning patterns from electrical data. |
| 2 |
Deep Neural Network (DNN) |
Multi-layered ANN for complex tasks like state estimation in grids, enabling deeper analysis of electrical signals for predictive maintenance. |
| 3 |
Convolutional Neural Network (CNN) |
A DNN specialized for processing grid-like data, applied in image-based fault detection on power lines or substations using drone visuals. |
| 4 |
Recurrent Neural Network (RNN) |
Neural network handling sequential data, evolved for time-series forecasting in energy demand and renewable integration in electrical networks. |
| 5 |
Long Short-Term Memory (LSTM) |
An RNN variant that remembers long-term dependencies, used for accurate wind/solar power prediction in dynamic electrical systems. |
| 6 |
Graph Neural Network (GNN) |
Processes graph-structured data like power grids, optimizing flow analysis and topology estimation in transmission networks. |
| 7 |
Generative Adversarial Network (GAN) |
Dual-network system generating synthetic data, applied to simulate electrical scenarios for training models in scarce-data power environments. |
| 8 |
Reinforcement Learning (RL) |
Learning through trial-and-error rewards, used for adaptive control in grid optimization and emergency load shedding. |
| 9 |
Deep Reinforcement Learning (DRL) |
RL combined with DNNs, enabling autonomous decision-making in real-time power system stability and demand response. |
| 10 |
Smart Grid |
AI-enhanced electrical distribution network that uses real-time data for self-healing, load balancing, and renewable integration. |
| 11 |
Digital Twin |
Virtual AI-simulated replica of electrical infrastructure, allowing scenario testing for predictive fault avoidance in power plants. |
| 12 |
Edge AI |
Decentralized AI processing at network edges, enabling low-latency control in IoT-enabled electrical devices and microgrids. |
| 13 |
Neuromorphic Computing |
Brain-inspired hardware chips for efficient AI, reducing power consumption in electrotechnical applications like sensor networks. |
| 14 |
Tensor Processing Unit (TPU) |
Specialized ASIC for AI workloads, accelerating matrix operations in electrical system simulations and optimization. |
| 15 |
Predictive Maintenance |
AI-driven monitoring of electrical assets (e.g., transformers) to forecast failures using sensor data and ML algorithms. |
| 16 |
Optimal Power Flow (OPF) |
AI-optimized calculation of efficient power distribution, minimizing losses in transmission lines via ML approximations. |
| 17 |
Microgrid |
Localized AI-managed grid, enabling autonomous operation with renewables, using RL for energy balancing. |
| 18 |
Phasor Measurement Unit (PMU) |
High-speed sensor providing synchronized data for AI-based state estimation and oscillation detection in power systems. |
| 19 |
Supervisory Control and Data Acquisition (SCADA) |
Traditional system evolved with AI for enhanced monitoring, anomaly detection, and automated control in electrical utilities. |
| 20 |
High-Impedance Fault (HIF) Detection |
AI techniques like SVM or CNN to identify subtle faults in distribution lines, improving safety and reliability. |
| 21 |
Load Forecasting |
ML models predicting electricity demand, incorporating weather and usage patterns for grid planning. |
| 22 |
Demand Response |
AI-optimized strategy adjusting consumer loads in real-time, using RL to balance supply in volatile renewable-heavy systems. |
| 23 |
Energy Management System (EMS) |
AI-integrated platform for overseeing generation, transmission, and distribution, enhancing efficiency with predictive analytics. |
| 24 |
Power Electronic Converter (PEC) |
Devices like inverters controlled by AI for fault-tolerant operation in renewables and EVs. |
| 25 |
Composite Load Model (CLM) |
AI-tuned aggregated model of electrical loads, using ML for dynamic simulation in stability studies. |
