Solar-LEAP is an open-source decision support tool to implement prognostics-based maintennce in real-world solar inverter O&M schedules.
from inv_simulation import inv_simulation, dist_prep, dmc_prep
from draw_plot import draw_plot_f1, draw_plot_f2, draw_plot_f3
# simulation of inverter operation
plot_data [f'i{inv_num}'], [maint_day, maint_type, maint_na_days, maint_cost, ft_sample, day_it] = inv_simulation (inv_num, input_scale_dict, input_c_p, input_c_f, na_days_pm, na_days_cm, simulation_end_days, simulation_step_days, day, day_it, maint_day, maint_type, maint_na_days, maint_cost, ft_sample)
# through (1) failure time prediction; (2) dynamic maintennace cost and (3) maintenance metric summary
draw_plot_f1(inv_num, canvas_fig_ax_dict, day, day_it, plot_data)
draw_plot_f2(inv_num, canvas_fig_ax_dict, day, day_it, plot_data)
draw_plot_f3(inv_num, canvas_fig_ax_dict, day, day_it, plot_data, maint_na_days, maint_cost, eur_proposed)
# GUI developed using PysimpleGUI package.
layout1 = ['input module']
layout2 = ['output module - visulization' ]
layout3 = ['output module - quick results skipping visualization' ]
- Shijia Zhao (Argonne National Laboratory)
- Liming Liu (Iowa State University)
- Murat Yildirim (Wayne State University)
- This tool is based upon work supported by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy (EERE) under the Solar Energy Technologies Office Award Number 39640..
If you use Solar-LEAP in your research (instances, models or algorithms), we kindly request that you cite the package as follows:
- Yi Luo, Liming Liu, Yuxuan Yuan, Zhaoyu Wang, Murat Yildirim, Feng Qiu, Shijia Zhao. “Data-Driven Assessment of Operational and Harsh Environmental Stress Factors for Solar Inverters using Field Measurements”, PES-General Meeting, 2024
Solar-LEAP: A Python optimization package for the inspection and maintenance scheduling of marine energy systems.
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