Several aspects must be considered when dealing with power electronic design. Within the system we can define various elements such as heat dissipation, electrical properties, control and protection systems. The development process is an important one that many testing and metrics companies use as a tool for the development process. In an interview with the developers behind the new simulation tool SIMBA, we analyzed different perspectives of the power electronics simulation. SIMBA is a new simulation environment for power electronics. “Our ambition is to create a platform that is simple enough for students and hobbyists but fast and robust enough for the most complex use cases,” said Emmanuel Rutovic, lead developer of SIMBA. Power electronics engineers use simulation tools to develop transducers and drive systems for motors. The model provides the ability to evaluate different device configurations, explore the effects of different sets of parameters and understand, for example, how component properties affect efficiency and response time. A model can have component variables at different resolutions, allowing you to start with simple linear representations and work your way toward complex nonlinear behaviors. “We also offer a standalone Python package that contains hundreds of functions that provide direct access to SIMBA such as circuit creation, parameter modification, simulation run, and results retrieval. This Python module opens this door to a new workflow for power electronics transformer design based on advanced modular analysis and machine learning. Emmanuel Rotovich said. What is the model? A model is a representation of a physical object or an entire system. Simulation is the process of knowing how a model-based system works under certain conditions. The purpose of building a model is to represent as faithfully as possible a certain real phenomenon in order to be able to make predictions about the future state of the system. Since it is a simplified version, only aspects of the phenomenon analyzed are considered in the model. Therefore, a mathematical model describes the development of a phenomenon or system: by providing input data, the model returns the output data. Thus, the model would be effective if the resultant is close to the measurements made in observing the true phenomenon. Mathematical models are often represented by equations of various types that must be solved by known mathematical methods. In such equations we can find both parameters, i.e. the quantities that cannot be processed, and the variables. Simulation models in power electronics can be divided into static and dynamic. The latter is used to evaluate most technical performance problems of classical energy systems from a planning and operating point of view. For modeling, simulations are equivalent to traditional design prototypes. In addition to permitting an evaluation of the behavior of the model system, which is difficult to obtain in real systems, thanks to its ability to be reconfigured, simulations allow the study of the system in a wide range of conditions and an understanding of the extent to which the model represents the system it refers to. Gaining confidence in the accuracy of power system models is essential because these models are very reliable for developing and operating the system itself. “We are including a new generation of simulation engines called predictive time solver. How much time have you spent tuning simulation solution parameters (time step, tolerance …) to find the best possible compromise between simulation speed and accuracy? Predictive Time Step” It automatically searches for the optimal time step and uses it to simulate all time constants and system events without compromising accuracy. This innovative approach provides the highest level of accuracy and performance. Emanuel Rotovich said. Predictive time step is a new type of transient solution that has been developed to overcome the challenges of simulating power electronics such as Analyze a wide range of time constants (transient switching, frequency switching, control system, thermal …), stop and volume events from the model itself. Figure 1: Predictive time-step algorithm (source: Simba) Figure 2: SIMBA modeling interface (Source: Simba) It contains a variety of time constants that are constantly evolving during simulation. The time challenge in this type of analysis is an important consideration that designers must bear in mind. Correct intent while switching events to good modeling and simulation activity. “We have developed an OTSF (Optimal Time Step Finder) algorithm which is called at the start of the transient simulation and after each switching event. This innovative algorithm analyzes each model and the entire circuit to determine what is the optimal time step to use at a given time,” Rotovich said. There is another crucial aspect. Required for reliable simulation of electronic energy is the accuracy of the time-lapse event. In SIMBA, non-continuity is a switching event or control event such as a variable comparison event. It is extremely important to simulate these events exactly as they occur. “The innovative NDETE algorithm works in parallel with the main solution. Its aim is to reduce the time leading up to control or switch events.” The SIMBA simulation engine is based on modified nodal analysis. Compared to classical nodal analysis, modified nodal analysis allows modeling of ideal voltage sources and switches with advantages of speed and accuracy. Another advantage of nodal analysis is that it measures well. Other approaches, such as country area analysis, which is used in other tools, do not use sparse matrices. This results in a quadratic relationship between system size (number of nodes) and calculation time. On the contrary, the nodal analysis matrix is sparse (filled with zeros) and if an efficient sparse matrix analyzer is used, the computation time grows linearly with the number of nodes, which is a major advantage. Modeling is an essential initial stage of simulation because it allows, through mathematical, logical, statistical, linguistic methodologies, etc., to provide the simulation system with the necessary functional mechanisms that simulate the behavior of the system being designed. The flexibility in terms of modification and reconfiguration of the model allows the simulation to operate the model system in all possible conditions, to study its behavior and verify its correctness for production purposes. .