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| Numerical Model Decoupling Acceleration Method with Numerical Integration Parallelism Construction Based on Computation Front in Real-Time Simulation |
| He Shaomin1, Zhang Zhe1, Lu Yiping1, Yang Huan1, Shen Jie2 |
1. College of Electrical Engineering Zhejiang University Hangzhou 310027 China;
2. Leadrive Technology (Shanghai) Co., Ltd. Shanghai 201315 China |
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Abstract The computation speed is the key performance of numerical simulation, which is the foundation and underlying core technology of intelligent computing. However, with the application of high switching-frequency power devices in electric drive system, the constraint of short computation time has led to the great challenges for computation speed in real-time or online numerical simulation. At the same time, with the trend of parallel architecture in digital platform, parallel computing is considered as an effective way to optimize the computation speed with short time constraint. However, the state-of-the-art numerical models and numerical algorithms are based on serial computation, which are lack of redundant parallelism. To address this issue, the computation front design methodology is proposed to construct the redundant parallelism for numerical integration, so the numerical model can be decoupled and parallel computed based on the redundant parallelism to accelerate the numerical simulation.
Firstly, the relationship between numerical integration and algorithm structure in power electronic simulation is discussed, which is the key to numerical solution of ordinary differential equation (ODE). The computation front involved in numerical integration optimization is proposed and the redundant parallelism construction process of optimization method is described based on predictor-corrector method. Secondly, the influence of proposed method on numerical performance are evaluated from numerical precision and numerical stability. The parallel computing resources are utilized in proposed method to improve the numerical precision without increasing the computation time. Although the numerical stability domain is changed during optimization process, the influence on numerical simulation is limited especially in the applications with small step size. Thirdly, the possibility of general application is discussed from multistep-step integration method and high-order integration method, which proves the computation front can be extended to different types of integration methods. Finally, the parallel numerical model is designed and applied to the electrical drive system based on the redundant parallelism constructed by proposed method, as a result, the model-level numerical acceleration is achieved.
The real-time simulation environment of hardware in the loop (HIL) is demonstrated to confirm the validity, fidelity and numerical acceleration of proposed method. The real-time simulation with serial computing model is compared with offline simulation in Simulink, so the effectiveness of self-defined HIL based on NI PXI is verified. Then, the real-time simulation with parallel computing model is compared between latency-insertion method and proposed method. The results show that the relative error with proposed method is reduced to less than 3%, and the numerical precision of parallel simulation is improved. Moreover, the speedup ratio and parallel efficiency are 1.72 and 57.2% respectively, which expands the step size selection range of real-time simulation.
The following conclusions can be drawn from the real-time simulation analysis: 1) The redundant parallelism of numerical integration can be constructed based on computation front to realize parallel computation. 2) The local step size of numerical integration is changed potentially during the optimization process, which may affect the numerical performance. Thus, the proposed method with accuracy compensation is designed based on predictor-corrector method, which can reduce the simulation error. 3) The parallel acceleration can be achieved without accuracy reduction, but the parallel efficiency is limited due to unbalanced computation load, which could be discussed in future research.
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