Enterprise AI Analysis
A Review on Simulation Application Function Development for Computer Monitoring Systems in Hydro-Wind-Solar Integrated Control Centers
This paper explores simulation application functions for the computer monitoring system of a hydro-wind-solar integrated control center, focusing on five core areas: platform management, operational training, performance optimization, exception handling, and emergency drills. Against the "dual carbon" backdrop, multi-energy complementary system simulation faces key challenges including multi-energy coupling, real-time response, and cybersecurity protection. Research shows that integrating digital twin, heterogeneous computing, and artificial intelligence technologies markedly improve simulation accuracy and intelligent decision-making. Dispatch strategies have shifted from single-energy optimization to system-level coordination, while cybersecurity frameworks now provide comprehensive safeguards covering algorithms, data, systems, user behavior, and architecture.
Key Performance Indicators (KPIs) & Strategic Impact
Integrating advanced simulation capabilities into hydro-wind-solar control centers significantly boosts efficiency, accuracy, and security across critical operations, driving substantial improvements in energy management and system resilience.
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Simulation Efficiency Improvement
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Prediction Accuracy for Pressure-Pulsation Signals
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Hydropower Generation Increase
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Photovoltaic Fault Diagnosis Accuracy
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Key Insights in Simulation Platform Management
This section explores how simulation platforms for hydro-wind-solar control centers are advancing through technological integration, optimized dispatch strategies, cybersecurity, and intelligent O&M.
30%
Increase in simulation efficiency with Hardware-in-the-Loop (HIL) technology for microgrids.
Enterprise Process Flow
PV String Inverter System - 1...n
→
FTP/Fileserver Server
→
Central PV Monitoring System & Data Warehouse
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Manual Import of Measurement Data
| Dimension | Current Characteristics | Future Trends | Existing Shortcomings | Breakthrough Directions |
|---|---|---|---|---|
| Technology Convergence | Cross-Application of Digital Twins/Heterogeneous Computing/Large Language Models | System-wide Real-Time Simulation | Refined modeling lacks unified standards |
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| Multi-functional Complementary Dispatch | Single-Energy Optimization for Full-System Synergy | Distributed Optimization Scheduling | Limited robustness under extreme conditions |
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| Safety Protection | Passive Defense → Active Immunity | Intelligent Security Protection | Weak knowledge transfer capabilities |
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Case Study: Digital Twin for Pump-Turbine System
A digital twin system for pump-turbines achieved a real-time response of 728.6 ms and a prediction accuracy exceeding 96% for pressure-pulsation signals using an intrinsic orthogonal decomposition-based reduced-order model combined with Open3D visualization and LSTM-based forecasting. This significantly improves precision and predictive capabilities. [9]
97.9%
Accuracy in photovoltaic module fault diagnosis using deep generative digital twin framework.
Key Insights in Routine Operation Training
This section outlines how simulation platforms enhance operator training through intelligent monitoring, advanced architectures, and realistic scenario simulations for integrated hydro-wind-solar control centers.
90-1186
Minutes in advance for wind turbine gearbox failure prediction using Vine-Copula and BiLSTM algorithms.
Case Study: BiLSTM Network for Wind Turbine Health Prediction
Li et al. developed a BO-BiLSTM network model to construct a health-index trajectory and predict the degradation trend of wind-turbine bearing conditions. This leverages Bayesian optimization for hyperparameter selection, achieving high precision in status monitoring and evaluation for core units. [56]
5.23-8.59%
Increase in wind and solar power utilization rates due to dual-loop optimization framework.
Key Insights in Operational Strategy Optimization
This section covers advancements in optimization algorithms, digital twin technology, market mechanisms, and stability control techniques for integrated hydro-wind-solar systems.
4.39%
Increase in average hydraulic head for hydropower generation by optimizing flood-season scheduling.
Enterprise Process Flow
Electric Power Supply
→
Renewable Energy Source
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Gas Supply
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Heating System
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Cooling System
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Electric/Geotherm/Solar Energy/Natural Gas
→
ES/HS/HE
| Current Research Framework and Trends | Core Challenges and Limitations Faced | Key Breakthrough Areas of This Study | Ultimate Goal |
|---|---|---|---|
| Algorithm Fusion: Distributed-Parallelization-Reinforcement Learning | Limited generalization ability | Development Strategy Online Verification Module Digital Twin-Driven "Simulation-Decision-Feedback" Closed-Loop System | To provide theoretical tools and practical paradigms for the intelligent upgrade of integrated control centers for hydropower, wind power, and solar power. |
| Technology Architecture Upgrade: Real-Time Closed-Loop "Perception-Decision-Control" System | Insufficient scale coordination | Designing Distributed Parallel Algorithms Based on Federated Learning to Enhance Solution Efficiency | |
| Expanding Research Perspectives on Technology-Market-Policy Synergy | Solving high-dimensional constraints is inefficient | Developing a Multi-Scale Strategy Optimization Model Coupling Meteorological, Hydrological, and Market Dynamics |
Case Study: Distributed Multi-strategy PSO for Economic Dispatch
Ren et al. proposed a distributed multi-strategy PSO algorithm for multi-region interconnected economic dispatch problems on an IEEE 39-node system. This approach partitions the population through a competition mechanism, enhancing global search capabilities while preserving regional privacy, making it highly effective for integrated hydro-wind-solar optimization. [80]
Key Insights in Complex Exception Handling
This section delves into innovations in anomaly detection, intelligent diagnostic algorithms, and AGC control optimization crucial for maintaining system stability and reliability.
40%
Reduction in data processing load at the dispatch center by offloading tasks to edge nodes.
Enterprise Process Flow
Data stream 1,2,3
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Standardized processing
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Load detection rules / Historical abnormal feature information
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Get historical exception information / Autocorrelation Matching
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Abnormal data flow / Normal data flow
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Detection rule configuration
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Early Warning Distribution
| Current Research Findings and Characteristics | Existing Core Limitations and Challenges | Future New Explorations and Breakthrough Directions | Ultimate Goal |
|---|---|---|---|
| Testing and Inspection Methods: Intelligent Fault Injection and Edge Detection | Insufficient generalization ability | Developing a Cross-Device Generalization Federated Learning Diagnostic Framework | To provide critical technological support for the stable operation of the central control center under high-penetration renewable energy integration |
| Fault Diagnosis Algorithm Deepening: Driven by Deep Learning and Digital Twins | Weakness in volatility management | Robust Adaptive Control Algorithm for AGC Considering Uncertainty in Wind and Solar Power | |
| Detailed Analysis of AGC Control Strategy Incidents and Application of Control Theory | Dynamic coupling scenario coverage is insufficient | Establishing a Multi-Energy Coupling Simulation and Testing Platform |
Case Study: SVM for Transformer Fault Diagnosis
A transformer mechanical fault diagnosis model based on vibration signals with u-SVM achieved effective identification of winding deformation and core loosening through small-sample training. This demonstrates SVM's capability for precise fault diagnosis even with limited data. [104]
Key Insights in Emergency Drill
This section highlights how 3D visualization, interactive logic, and system integration are transforming emergency drills into dynamic, realistic, and collaborative experiences for control center operators.
Case Study: VR-based Emergency Drill for Lithium Battery Fires
Wu Yu et al. developed an integrated VR-based 'training and assessment' platform for emergency response to lithium battery fires in aircraft cabins. Leveraging Unity3D and 3Dmax, it created a highly immersive and realistic environment, enabling multi-person collaborative, script-free full-process emergency drills and objective quantitative evaluation of participants' operations, overcoming traditional drill limitations. [119]
3D
Visualization and scriptless modeling enable dynamic, realistic emergency scenarios.
Calculate Your Potential AI ROI
See how integrating AI-powered simulation and monitoring can translate into tangible operational savings and efficiency gains for your enterprise.
Estimated Annual Savings
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Productive Hours Reclaimed Annually
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Your AI Implementation Roadmap
A phased approach to integrate advanced simulation, monitoring, and control systems for optimal performance and security in hydro-wind-solar operations.
Phase 01: Needs Assessment & Custom Modeling
Conduct a thorough analysis of existing systems and operational challenges. Develop high-precision, hybrid digital twin models for hydro, wind, and solar assets, focusing on multi-energy coupling mechanisms and real-time dynamic response characteristics.
Phase 02: Platform Integration & Intelligent Algorithms
Integrate models into a cloud-based simulation platform, leveraging heterogeneous computing (CPU-GPU-FPGA) for efficient, real-time simulation. Implement advanced AI/ML algorithms for multi-objective optimization, robust scheduling under uncertainty, and federated learning for fault diagnosis.
Phase 03: Security Hardening & Training System Development
Deploy a comprehensive cybersecurity framework based on national cryptographic standards and trusted computing. Develop an immersive, 3D visualization-based operator training system with dynamic scenario generation and adaptive assessment for routine operations and emergency drills.
Phase 04: Real-time Deployment & Continuous Optimization
Transition validated simulation models and optimization strategies to real-time control. Establish a digital twin-driven online verification module for continuous feedback and self-updating. Implement intelligent O&M with predictive fault detection and automated anomaly handling.
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