MAI Power Systems
MAI Renewable Energy
Modeling $ Testing
Modeling is not trivial and is the most important part of the stability analysis and performance enhancement. Unfortunately, manufacturers consider their models as intellectual property and do not provide insightful details. We develop MATLAB and PSCAD models that satisfy the grid operator requirements.
Data Colection & Analysis
We collect real data and produce synthetic data from developed models. Data is used to in the optimization process and for training deep networks.
Optimization
We use convex programming and well-established heuristic algorithms such as GA and PSO, to achieve optimal performance, e.g., in parameter tuning, etc.
AI $ Digital Twin
Utilizing the developed models to produce synthetic data, collected real data, and optimization results, we train deep networks. Based on the type of data and its attributes, we exploit and train CNN and LSTM deep networks for intelligent decision-making. The virtual models, deep networks, and physical model/real data are synchronized to create a digital twin of the system.
Distributed Generation
Renewable energy resources, micro-sources and battery energy storage systems are connected to the (micro) grids via power electronics interfaces so-called inverters.
Inverter-interfaced energy resources
Grid-connection stability analysis
Low-voltage ride-through
Autonomous Microgrids
The microgrid is an emerging technology that facilitates the integration of renewable resources into power systems, and definitely, would be the cornerstone of future/modern power systems
Energy management system
Dynamic performance and stability
Microgrids
Microgrid stability
Modern power systems have been developing through high penetration of the power electronic-based renewable energy resources and distributed generation units into conventional power systems. Mixing up the power systems and the power electronics technologies along with the smart grid facilities, the microgrid concept has gained full attention for addressing the resiliency issue of modern power systems through autonomous operation capability. However, it is not an easy task to stabilize an autonomous microgrid due to the dominated inverter-interfaced generation units and complex power flow.
Microgrid control and protection (MG-CAP)
Voltage and frequency control in autonomous mode
Inertia emulation and impedance shaping
Fault (low-voltage) ride-through transients
Sub-Consultancy
Intelligent inverter stability analysis tool to expedite grid-connection issues
A grid-connected inverter may work at different operating modes like grid-feeding/forming/supporting modes. Besides, the inverter works under different operating conditions in a weak grid (with a high impedance), with a low X/R ratio, low voltage/faults ride-through, etc. Consultant firms work with PSCAD/PSSE models, mostly generated by the manufacturers, to carry out the associated R0, R1 and R2 tests and obtain verification results. However, it is a time-consuming process to run tests at various operating points, particularly considering the encrypted models provided by the manufacturers, which demand a load of communications with the manufacturer/grid operator without achieving promising results. We provide the following services, particularly in the context of digital twin, to expedite issues with inverters
A generalized inverter Matlab/PSCAD model for grid-connection feasibility analysis
A reliable framework for testing the inverter in different operating modes and conditions
A compatible Matlab model based on the PSS/E *.dyr and PSCAD models
Intelligent process for automated parameter tuning under various operating points
Confident and reliable simulation-based reports to communicate with the grid operator
Leverage the power of MAI in your business to cope with technical problems you face that may put your business at risk. We in particular provide research-based (sub) consultancy services for consultant companies and investors willing to boost their assets in the technology sector