Gain tuning of proportional integral controller based on multiobjective optimization and controller hardware-in-loop microgrid setup

Proportional integral (PI) control is a commonly used industrial controller framework. This PI controller needs to be tuned to obtain desired response from the process under control. Tuning methods available in literature by and large need sophisticated mathematical modelling, and simplifications in the plant model to perform gain tuning. The process of obtaining approximate plant model conceivably become time consuming and produce less accurate results. This is due to the simplifications desired by the power system applications especially when power electronics based inverters are used in it. Optimal gain selection for PI controllers becomes crucial for microgrid application. Because of the presence of inverter based distributed energy resources. In the proposed approach, a multi-objective genetic algorithm is used to tune the controller to obtain expected step response characteristics. The proposed approach do not need simplified mathematical models. This prevents the need for obtaining unfailing plant models to maintain the fidelity of modelling. Microgrid system and the PI controller are modelled in different software, hardware platform and tuned using the proposed approach. Gain values for PI controller in these different platform are tuned using the same objective function and multi-objective optimization. This proves the re-usability, scalability, and modularity of the proposed tuning algorithm. Three different combination of software, hardware platform are proposed. First, the process and the PI controller are modelled in a computer based hardware. In order to increase the speed of the multi-objective optimization in the computer based hardware parallel computing is employed. This is a natural fit for paralleling the GA based optimization. Second, both the plant and control representation are modelled in the real time digital simulator (RTDS). Finally, a controller hardware in loop platform is used. In this platform, the plant will be modelled in RTDS and the PI controller will be modelled in an FPGA based hardware platform. Results indicate that the proposed approach has promising potentials since it does not need to simplify the switching model and can effectively solve the complicated tuning procedure using parallel computing. Similar advantage could be said for RTDS based tuning because RTDS simulates the models in real time

Fast Quasi-Static Time-Series Simulation for Accurate PV Inverter Semiconductor Fatigue Analysis with a Long-Term Solar Profile

Power system simulations with long-term data typically have large time steps, varying from one second to a few minutes. However, for PV inverter semiconductors in grid-connected applications, the minimum thermal stress cycle occurs over the fundamental grid frequency (50 or 60 Hz). This requires the time step of the fatigue simulation to be approximately 100 μs. This small time step requires long computation times to process yearly power production profiles. In this paper, we propose a fast fatigue simulation for inverter semiconductors using the quasi-static time-series simulation concept. The proposed simulation calculates the steady state of the semiconductor junction temperature using a fast Fourier transform. The small thermal cycling during a switching period and even over the fundamental waveform is disregarded to further accelerate the simulation speed. The resulting time step of the fatigue simulation is 15 min, which is consistent with the solar dataset. The error of the proposed simulation is 0.16% compared to the fatigue simulation results using the complete thermal stress profile. The error of the proposed method is significantly less than the conventional averaged thermal profile. A PV inverter that responds to a transactive energy system is simulated to demonstrate the use of the proposed fatigue simulation. The proposed simulation has the potential to cosimulate with system-level simulation tools that also adopt the quasi-static time-series concept