pvpumpingsystem.pump._curves_coeffs_theoretical_variable_efficiency

pvpumpingsystem.pump._curves_coeffs_theoretical_variable_efficiency(specs, data_completeness, elec_archi)

Compute curve-fitting coefficient following theoretical analysis of motor architecture.

This kind of approach is used in [1], [2].

Nevertheless, following function takes some liberties with the model of function f2 described in the mentionned papers, in order not to rely on K_p and K_t that are assumed to be unavailable in pump datasheet.

It uses a equation of the form V = R_a*i + beta(H)*np.sqrt(i) to model V(I, TDH) and an equation of the form Q = (a + b*H) * (c + d*P) to model Q(P, TDH) from the data.

Parameters:specs (pd.DataFrame) – DataFrame with specs. Returns:Coefficients resulting from linear regression under keys ‘coeffs_f1’ and ‘coeffs_f2’, and statistical figures on goodness of fit (keys: ‘rmse_f1’, ‘nrmse_f1’, ‘r_squared_f1’, ‘adjusted_r_squared_f1’, ‘rmse_f2’, ‘nrmse_f2’, ‘r_squared_f2’, ‘adjusted_r_squared_f2’) Return type:dict

References

[1] Mokkedem & al, 2011, ‘Performance of a directly-coupled PV water

pumping system’, Energy Conversion and Management

[2] Khatib & Elmenreich, 2016, ‘Modeling of Photovoltaic Systems Using MATLAB’, Wiley