From 9335a74b17d55319f5640994ae77b329796db75d Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Wed, 2 Oct 2024 12:58:04 -0700 Subject: [PATCH] overhaul example, remove horizon --- docs/examples/snow-detection/snow-mode.py | 650 +++++------- pvanalytics/data/snow_config.json | 71 -- pvanalytics/data/snow_data.csv | 1154 ++++++++++----------- pvanalytics/data/snow_horizon.csv | 361 ------- pvanalytics/features/snow.py | 87 +- pvanalytics/tests/features/test_snow.py | 9 - 6 files changed, 844 insertions(+), 1488 deletions(-) delete mode 100644 pvanalytics/data/snow_config.json delete mode 100644 pvanalytics/data/snow_horizon.csv diff --git a/docs/examples/snow-detection/snow-mode.py b/docs/examples/snow-detection/snow-mode.py index de36c06e..74ac9c53 100644 --- a/docs/examples/snow-detection/snow-mode.py +++ b/docs/examples/snow-detection/snow-mode.py @@ -16,14 +16,16 @@ below the lower bound of the inverter's MPPT range but the voltage modeled using measured irradiance and ideal transmission is above the lower bound of the inverter's MPPT range. - * Mode 1: Indicates periods when the system has non-uniform snow and - both operating voltage and current are decreased. Operating voltage is - reduced when bypass diodes activate and current is decreased due to - decreased irradiance. - * Mode 2: Indicates periods when the operating voltage is reduced but - current is consistent with snow-free conditions. - * Mode 3: Indicates periods when the operating voltage is consistent with - snow-free conditions but current is reduced. + * Mode 1: Indicates periods when the system has non-uniform snow which + affects all strings. Mode 1 is assigned when both operating voltage and + current are reduced. Operating voltage is reduced when snow causes + mismatch and current is decreased due to reduced transmission. + * Mode 2: Indicates periods when the system has non-uniform snow which + causes mismatch for some modules, but doesn't reduce light transmission + to other modules. + * Mode 3: Indicates periods when the the system has snow that reduces + light transmission but doesn't create mismatch. Operating voltage is + consistent with snow-free conditions but current is reduced. * Mode 4: Voltage and current are consistent with snow-free conditions. * Mode -1: Indicates periods where it is unknown if or how snow impacts @@ -56,327 +58,209 @@ # %% Import packages import pathlib -import json import pandas as pd import numpy as np -import re + import pvlib import matplotlib.pyplot as plt -from matplotlib.dates import DateFormatter +import matplotlib.dates as mdates import matplotlib.patches as mpatches from matplotlib.lines import Line2D import pvanalytics + # Functions needed for the analysis procedure from pvanalytics.features import snow -# %% Load in system configuration parameters (dict) pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent -data_file = pvanalytics_dir / 'data' / 'snow_data.csv' -snowfall_file = pvanalytics_dir / 'data' / 'snow_snowfall.csv' -horizon_file = pvanalytics_dir / 'data' / 'snow_horizon.csv' -config_file = pvanalytics_dir / 'data' / 'snow_config.json' - -with open(config_file) as json_data: - config = json.load(json_data) - -# %% Retrieve and print system inverter specs and electrical configuration - -cec_inverter_db = pvlib.pvsystem.retrieve_sam('CECInverter') -my_inverter = cec_inverter_db[config['inverter']] - -max_ac_power = my_inverter['Paco']*0.001 # originally in W, convert to kW -mppt_low_voltage = my_inverter['Mppt_low'] # [V] -mppt_high_voltage = my_inverter['Mppt_high'] # [V] - -print(f"Inverter AC power rating: {max_ac_power} kW") -print(f"Inverter MPPT range: {mppt_low_voltage} V - {mppt_high_voltage} V") -num_str_per_cb = config['num_str_per_cb']['INV1 CB1'] -num_mods_per_str = config['num_mods_per_str']['INV1 CB1'] -print(f"There are {num_mods_per_str} modules connected in series in each" - f" string and there are {num_str_per_cb} strings connected in" - f" parallel at each combiner") # %% -# Read in 15-minute sampled DC voltage and current time series data, AC power, -# module temperature collected by a BOM sensor and plane-of-array -# irradiance data collected by a heated pyranometer. This sample is provided -# by an electric utility. +# Read in 15-minute DC voltage, DC current and AC power data. +# DC voltage and DC current are measured at the input to the inverter from +# one combiner box. AC power is measured at the inverter output. +# Module temperature is collected by a back-of-module sensor. +# Plane-of-array irradiance data is collected by a heated pyranometer. +# Data sample was provided by an electric utility. Solar position and other +# data were added by Sandia to avoid publishing a geographic location. # Load in utility data -data = pd.read_csv(data_file, index_col='Timestamp') -data.set_index(pd.DatetimeIndex(data.index, ambiguous='infer'), inplace=True) -data = data[~data.index.duplicated()] +data_file = pvanalytics_dir / 'data' / 'snow_data.csv' +data = pd.read_csv(data_file, index_col='Timestamp', parse_dates=True) # Explore utility datatset print('Utility-scale dataset') print('Start: {}'.format(data.index[0])) print('End: {}'.format(data.index[-1])) -print('Frequency: {}'.format(data.index.inferred_freq)) print('Columns : ' + ', '.join(data.columns)) -# Identify current, voltage, and AC power columns -dc_voltage_cols = [c for c in data.columns if 'Voltage' in c] -dc_current_cols = [c for c in data.columns if 'Current' in c] -ac_power_cols = [c for c in data.columns if 'AC' in c] +voltage_col = 'INV1 CB2 Voltage [V]' +power_col = 'INV1 AC Power [kW]' +current_col = 'INV1 CB2 Current [A]' -# Set negative or Nan current, voltage, AC power values to zero. This is -# allows us to calculate losses later. -data.fillna(0, inplace=True) +# %% Retrieve and print system inverter specs and DC electrical configuration -# %% -# Plot DC voltage for each combiner input relative to inverter nameplate +cec_inverter_db = pvlib.pvsystem.retrieve_sam('CECInverter') +my_inverter = cec_inverter_db["Yaskawa_Solectria_Solar__PVI_60TL_480__480V_"] + +max_ac_power = my_inverter['Paco']*0.001 # convert from W to kW +mppt_low_voltage = my_inverter['Mppt_low'] # [V] +mppt_high_voltage = my_inverter['Mppt_high'] # [V] + +print(f"Inverter AC power rating: {max_ac_power} kW") +print(f"Inverter MPPT range: {mppt_low_voltage} V - {mppt_high_voltage} V") + +num_str_per_cb = 4 +num_mods_per_str = 18 + + +# %% Plot DC voltage at the combiner input relative to inverter nameplate # limits. We are looking for periods where DC voltage is less than the -# inverter's turn-on voltage, as these values are recorded outside of MPP -# operating conditions. +# inverter's MPPT voltage range, as these values are outside of MPP +# operating conditions. If we find these periods, we fig, ax = plt.subplots(figsize=(10, 6)) -date_form = DateFormatter("%m/%d") -ax.xaxis.set_major_formatter(date_form) -for v in dc_voltage_cols: - ax.scatter(data.index, data[v], s=0.5, label=v) - ax.plot(data[v], alpha=0.2) -ax.axhline(mppt_high_voltage, c='r', ls='--', - label='Maximum MPPT voltage: {} V'.format(mppt_high_voltage)) -ax.axhline(mppt_low_voltage, c='g', ls='--', - label='Minimum MPPT voltage: {} V'.format(mppt_low_voltage)) +locator = mdates.DayLocator() +#ax.xaxis.set_minor_locator(mdates.DayLocator(interval=0.25)) +ax.xaxis.set_major_locator(mdates.DayLocator(interval=1)) +ax.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d')) +ax.plot(data[voltage_col], '.-', alpha=0.5, fillstyle='full', + label='DC voltage') +ax.axhline(mppt_high_voltage, c='r', ls='--') +ax.text(0.02, mppt_high_voltage + 20, 'Maximum MPPT voltage: {} V'.format(mppt_high_voltage), + transform=ax.get_yaxis_transform()) +ax.axhline(mppt_low_voltage, c='g', ls='--') +ax.text(0.02, mppt_low_voltage + 20, 'Minimum MPPT voltage: {} V'.format(mppt_low_voltage), + transform=ax.get_yaxis_transform()) ax.set_xlabel('Date', fontsize='large') ax.set_ylabel('Voltage [V]', fontsize='large') +ax.set_ylim([0, 1.2*mppt_high_voltage]) ax.legend(loc='lower left') fig.tight_layout() plt.show() -# %% -# Plot AC power relative to inverter nameplate limits. We are looking for -# periods of clipping, as these values are recorded outside of MPP operating -# conditions. +# %% Plot AC power relative to inverter nameplate limits. We are looking for +# periods of clipping, as these values are outside of MPP operating +# conditions. In these data, there is no clipping so no need to filter +# points out. fig, ax = plt.subplots(figsize=(10, 6)) -date_form = DateFormatter("%m/%d") -ax.xaxis.set_major_formatter(date_form) -for a in ac_power_cols: - ax.scatter(data.index, data[a], s=0.5, label=a) - ax.plot(data[a], alpha=0.2) -ax.axhline(max_ac_power, c='r', ls='--', - label='Maximum allowed AC power: {} kW'.format(max_ac_power)) +ax.xaxis.set_major_locator(mdates.DayLocator(interval=1)) +ax.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d')) +ax.plot(data[power_col], '.-', alpha=0.5, fillstyle='full', + label='AC Power [kW]') +ax.axhline(max_ac_power, c='r', ls='--') +ax.text(0.02, max_ac_power - 5, 'Maximum AC power: {} kW'.format(max_ac_power), + transform=ax.get_yaxis_transform()) ax.set_xlabel('Date', fontsize='large') ax.set_ylabel('AC Power [kW]', fontsize='large') -ax.legend(loc='upper left') -fig.tight_layout() -plt.show() - -# %% Filter data. -# Identify periods where the system is operating off of its maximum power -# point (MPP), and correct or mask. Conditions outside of the MPP cannot -# be accurately modeled without external information on the system's -# operating point. To allow us to make a valid comparison between system -# measurements and modeled power at MPP, we set measurements collected below -# the MPPT minimum voltage to zero, which emulates the condition where the -# inverter turns off when it cannot meet the turn-on voltage. - -ac_power_cols_repeated = ac_power_cols + ac_power_cols + ac_power_cols -for v, i, a in zip(dc_voltage_cols, dc_current_cols, ac_power_cols_repeated): - - # Data where V < MPPT minimum - data.loc[data[v] < mppt_low_voltage, v] = 0 - data.loc[data[v] < mppt_low_voltage, i] = 0 - data.loc[data[v] < mppt_low_voltage, a] = 0 - - # Data where system is at Voc - data.loc[data[i] == 0, v] = 0 - -# %% Plot DC voltage for each combiner input with inverter nameplate limits - -fig, ax = plt.subplots(figsize=(10, 10)) -date_form = DateFormatter("%m/%d") -ax.xaxis.set_major_formatter(date_form) -for v in dc_voltage_cols: - ax.scatter(data.index, data[v], s=0.5, label=v) - ax.plot(data[v], alpha=0.2) -ax.axhline(mppt_high_voltage, c='r', ls='--', - label='Maximum MPPT voltage: {} V'.format(mppt_high_voltage)) -ax.axhline(mppt_low_voltage, c='g', ls='--', - label='Minimum MPPT voltage: {} V'.format(mppt_low_voltage)) -ax.set_xlabel('Date', fontsize='large') -ax.set_ylabel('Voltage [V]', fontsize='large') -ax.legend(loc='lower left') -plt.show() - -# %% -# We want to exclude periods where array voltage is affected by horizon -# shading. Load in and apply horizon profiling created using approach described -# in [1]_. -# -# .. [1] J. L. Braid and B. G. Pierce, "Horizon Profiling Methods for Photovoltaic -# Arrays," 2023 IEEE 50th Photovoltaic Specialists Conference (PVSC), -# San Juan, PR, USA, 2023, pp. 1-7. :doi:`10.1109/PVSC48320.2023.10359914` - -horizon = pd.read_csv(horizon_file, index_col='Unnamed: 0').squeeze("columns") - -data['Horizon Mask'] = snow._get_horizon_mask(horizon, data['azimuth'], - data['elevation']) - -# %% -# Plot horizon mask - -fig, ax = plt.subplots() -ax.scatter(data['azimuth'], data['elevation'], s=0.5, label='data', - c=data['Horizon Mask']) -ax.scatter(horizon.index, horizon, s=0.5, label='mask') ax.legend() -ax.set_xlabel(r'Azimuth [$\degree$]') -ax.set_ylabel(r'Elevation [$\degree$]') +fig.tight_layout() plt.show() -# %% -# Exclude data collected while the sun is below the horizon -data = data[data['Horizon Mask']] - # %% # Define coefficients for modeling transmission and voltage. User can either # use the SAPM to calculate transmission or an approach based on the ratio # between measured current and nameplate current. For modeling voltage, the # user can use the SAPM or a single diode equivalent. -sapm_coeffs = config['sapm_coeff'] -cec_module_db = pvlib.pvsystem.retrieve_sam('cecmod') -sde_coeffs = cec_module_db[config['panel']] +# Data from CFV Solar Test Lab, tested 2013. +sapm_coeffs = { + "Cells_in_Series": 72, + "Isco": 9.36992857142857, + "Voco": 46.78626811224489, + "Impo": 8.895117736670294, + "Vmpo": 37.88508962264151, + "Aisc": 0.0002, + "Aimp": -0.0004, + "C0": 1.0145, + "C1": -0.0145, + "Bvoco": -0.1205, + "Mbvoc": 0, + "Bvmpo": -0.1337, + "Mbvmp": 0, + "N": 1.0925, + "C2": -0.4647, + "C3": -11.900781, + "FD": 1, + "A": -3.4247, + "B": -0.0951, + "C4": np.nan, + "C5": np.nan, + "IXO": np.nan, + "IXXO": np.nan, + "C6": np.nan, + "C7": np.nan, + } + -# %% # Model cell temperature using the SAPM model. irrad_ref = 1000 data['Cell Temp [C]'] = pvlib.temperature.sapm_cell_from_module( - data['Module Temp [C]'], data['POA [W/m²]'], 3) - -# %% -# Plot cell temperature -fig, ax = plt.subplots(figsize=(10, 6)) -date_form = DateFormatter("%m/%d") -ax.xaxis.set_major_formatter(date_form) -ax.scatter(data.index, data['Cell Temp [C]'], s=0.5, c='b') -ax.plot(data['Cell Temp [C]'], alpha=0.3, c='b') -ax.set_ylabel('Cell Temp [C]', c='b', fontsize='xx-large') -ax.set_xlabel('Date', fontsize='xx-large') -fig.tight_layout() -plt.show() + data['Module Temp [C]'], data['POA [W/m²]'], deltaT=3) # %% -# For one combiner, demonstrate the transmission calculation using two -# different approaches to modeling effective irradiance from measured Imp. - -# Choose one combiner box -j = 0 - -# Get key for configuration dict -matched = re.match(r'INV(\d+) CB(\d+)', dc_current_cols[j]) -inv_cb = matched.group(0) +# Demonstrate the transmission calculation and plot the result. -# Number of strings connected in parallel to combiner. -# Used to scale measured current down to the resolution -# of a single string connected in series, which should -# be the same current as a single module. +# We scale measured current at the combiner to that +# of a single string, assuming all strings have the same current. +# The string current should be the same current as a single module. -i_scaling_factor = int(config['num_str_per_cb'][f'{inv_cb}']) # String current -imp = data[dc_current_cols[j]] / i_scaling_factor +imp = data[current_col] / num_str_per_cb -# Approach 1 using SAPM +# Model effective irradiance using SAPM modeled_e_e1 = snow.get_irradiance_sapm( data['Cell Temp [C]'], imp, sapm_coeffs['Impo'], sapm_coeffs['C0'], sapm_coeffs['C1'], sapm_coeffs['Aimp']) T1 = snow.get_transmission(data['POA [W/m²]'], modeled_e_e1, imp) -# Approach 2 using a linear irradiance-Imp model -modeled_e_e2 = snow.get_irradiance_imp(imp, sapm_coeffs['Impo']) - -T2 = snow.get_transmission(data['POA [W/m²]'], modeled_e_e2, imp) - -# %% -# Plot transmission calculated using two different approaches fig, ax = plt.subplots(figsize=(10, 6)) -date_form = DateFormatter("%m/%d \n%H:%M") +date_form = mdates.DateFormatter("%m/%d \n%H:%M") ax.xaxis.set_major_formatter(date_form) -ax.scatter(T1.index, T1, s=0.5, c='b', label='SAPM') -ax.plot(T1, alpha=0.3, c='b') +ax.plot(T1, '.-', alpha=0.5, c='b', fillstyle='full') -ax.scatter(T2.index, T2, s=0.3, c='g', label='Linear model') -ax.plot(T2, alpha=0.3, c='g') - -ax.legend() ax.set_ylabel('Transmission', fontsize='xx-large') ax.set_xlabel('Date + Time', fontsize='large') fig.tight_layout() plt.show() # %% -# Model voltage using calculated transmission (two different approaches) +# Model and plot the voltage using calculated transmission. -# Number of modules in series in a string -v_scaling_factor = int(config['num_mods_per_str'][inv_cb]) - -# Approach 1. Reduce measured POA using the transmission. modeled_vmp_sapm = pvlib.pvsystem.sapm(data['POA [W/m²]']*T1, data['Cell Temp [C]'], sapm_coeffs)['v_mp'] -modeled_vmp_sapm *= v_scaling_factor - -# Approach 2 %TODO not sure we need this -# Code borrowed from pvlib-python/docs/examples/iv-modeling/plot_singlediode.py - -# adjust the reference parameters according to the operating -# conditions using the De Soto model: -IL, I0, Rs, Rsh, nNsVth = pvlib.pvsystem.calcparams_desoto( - data['POA [W/m²]']*T1, - data['Cell Temp [C]'], - alpha_sc=sde_coeffs['alpha_sc'], - a_ref=sde_coeffs['a_ref'], - I_L_ref=sde_coeffs['I_L_ref'], - I_o_ref=sde_coeffs['I_o_ref'], - R_sh_ref=sde_coeffs['R_sh_ref'], - R_s=sde_coeffs['R_s'], - ) - -# plug the parameters into the SDE and solve for IV curves: -SDE_params = { - 'photocurrent': IL, - 'saturation_current': I0, - 'resistance_series': Rs, - 'resistance_shunt': Rsh, - 'nNsVth': nNsVth -} -modeled_vmp_sde = pvlib.pvsystem.singlediode(**SDE_params)['v_mp'] -modeled_vmp_sde *= v_scaling_factor +modeled_vmp_sapm *= num_mods_per_str -# %% -# Plot modeled and measured voltage fig, ax = plt.subplots(figsize=(10, 6)) -date_form = DateFormatter("%H:%M") +date_form = mdates.DateFormatter("%H:%M") ax.xaxis.set_major_formatter(date_form) -ax.scatter(modeled_vmp_sapm.index, modeled_vmp_sapm, s=1, c='b', label='SAPM') -ax.scatter(modeled_vmp_sde.index, modeled_vmp_sde, s=1, c='g', label='SDE') +ax.plot(modeled_vmp_sapm, '.-', c='b', fillstyle='full', label='SAPM') -ax.scatter(data.index, data[inv_cb + ' Voltage [V]'], s=1, c='r', - label='Measured') -ax.legend(fontsize='xx-large') -ax.set_ylabel('Voltage [V]', fontsize='xx-large') +ax.scatter(data.index, data[voltage_col], s=1, c='r', label='Measured') +ax.legend(fontsize='large') +ax.set_ylabel('Voltage [V]', fontsize='large') ax.set_xlabel('Date', fontsize='large') plt.show() fig.tight_layout() # %% -# Function to do analysis so we can loop over combiner boxes +# We write a function to assign snow categories, so that we could loop over +# additional combiner boxes. -def wrapper(voltage, current, temp_cell, effective_irradiance, - coeffs, config, temp_ref=25, irrad_ref=1000): +def assign_snow_modes(voltage, current, temp_cell, effective_irradiance, + coeffs, min_dcv, max_dcv, threshold_vratio, + threshold_transmission, num_mods_per_str, + num_str_per_cb, temp_ref=25, irrad_ref=1000): ''' Categorizes each data point as Mode 0-4 based on transmission and the @@ -402,22 +286,21 @@ def wrapper(voltage, current, temp_cell, effective_irradiance, Snow-free POA irradiance measured by a heated pyranometer. [W/m²] coeffs : dict A dict defining the SAPM parameters, used for pvlib.pvsystem.sapm. - config : dict - min_dcv : float - The lower voltage bound on the inverter's maximum power point - tracking (MPPT) algorithm. [V] - max_dcv : numeric - Upper bound voltage for the MPPT algorithm. [V] - threshold_vratio : float - The lower bound on vratio that is found under snow-free conditions, - determined empirically. - threshold_transmission : float - The lower bound on transmission that is found under snow-free - conditions, determined empirically. - num_mods_per_str : int - Number of modules in series in each string. - num_str_per_cb : int - Number of strings in parallel at the combiner. + min_dcv : float + The lower voltage bound on the inverter's maximum power point + tracking (MPPT) algorithm. [V] + max_dcv : numeric + Upper bound voltage for the MPPT algorithm. [V] + threshold_vratio : float + The lower bound on vratio that is found under snow-free conditions, + determined empirically. + threshold_transmission : float + The lower bound on transmission that is found under snow-free + conditions, determined empirically. + num_mods_per_str : int + Number of modules in series in each string. + num_str_per_cb : int + Number of strings in parallel at the combiner. Returns ------- @@ -429,53 +312,41 @@ def wrapper(voltage, current, temp_cell, effective_irradiance, # Calculate transmission modeled_e_e = snow.get_irradiance_sapm( - temp_cell, current/config['num_str_per_cb'], coeffs['Impo'], + temp_cell, current / num_str_per_cb, coeffs['Impo'], coeffs['C0'], coeffs['C1'], coeffs['Aimp']) T = snow.get_transmission(effective_irradiance, modeled_e_e, - current/config['num_str_per_cb']) + current / num_str_per_cb) # Model voltage for a single module, scale up to array modeled_voltage_with_calculated_transmission =\ pvlib.pvsystem.sapm(effective_irradiance*T, temp_cell, - coeffs)['v_mp'] * config['num_mods_per_str'] + coeffs)['v_mp'] * num_mods_per_str modeled_voltage_with_ideal_transmission =\ pvlib.pvsystem.sapm(effective_irradiance, temp_cell, - coeffs)['v_mp'] * config['num_mods_per_str'] + coeffs)['v_mp'] * num_mods_per_str mode, vmp_ratio = snow.categorize( T, voltage, modeled_voltage_with_calculated_transmission, - modeled_voltage_with_ideal_transmission, config['min_dcv'], - config['max_dcv'], config['threshold_vratio'], - config['threshold_transmission']) + modeled_voltage_with_ideal_transmission, min_dcv, max_dcv, + threshold_vratio, threshold_transmission) - my_dict = {'transmission': T, + result = pd.DataFrame(index=voltage.index, data={'transmission': T, 'modeled_voltage_with_calculated_transmission': modeled_voltage_with_calculated_transmission, 'modeled_voltage_with_ideal_transmission': modeled_voltage_with_ideal_transmission, 'vmp_ratio': vmp_ratio, - 'mode': mode} + 'mode': mode}) - return my_dict + return result # %% # Demonstrate transmission, modeled voltage calculation and mode categorization # on voltage, current pair -j = 0 -v = dc_voltage_cols[j] -i = dc_current_cols[j] - -# Used to get key for configuration dict -matched = re.match(r'INV(\d+) CB(\d+)', i) -inv_cb = matched.group(0) - -# Number of strings connected in parallel at the combiner -i_scaling_factor = int(config['num_str_per_cb'][f'{inv_cb}']) - -# threshold_vratio and threshold-transmission were empirically determined +# threshold_vratio and threshold_transmission were empirically determined # using data collected on this system over five summers. Transmission and # vratio were calculated for all data collected in the summer (under the # assumption that there was no snow present at this time), and histograms @@ -486,81 +357,44 @@ def wrapper(voltage, current, temp_cell, effective_irradiance, # is the same value, but for transmission calculated from data recorded # during the summer. -threshold_vratio = config['threshold_vratio'] -threshold_transmission = config['threshold_transmission'] - -my_config = {'threshold_vratio': threshold_vratio, - 'threshold_transmission': threshold_transmission, - 'min_dcv': mppt_low_voltage, - 'max_dcv': mppt_high_voltage, - 'num_str_per_cb': int(config['num_str_per_cb'][f'{inv_cb}']), - 'num_mods_per_str': int(config['num_mods_per_str'][f'{inv_cb}'])} - -out = wrapper(data[v], data[i], - data['Cell Temp [C]'], - data['POA [W/m²]'], sapm_coeffs, - my_config) - -# %% -# Calculate the transmission, model the voltage, and categorize into modes for -# all combiners. Use the SAPM to calculate transmission and model the voltage. - -for v_col, i_col in zip(dc_voltage_cols, dc_current_cols): +threshold_vratio = 0.933 +threshold_transmission = 0.598 - matched = re.match(r'INV(\d+) CB(\d+) Current', i_col) - inv_num = matched.group(1) - cb_num = matched.group(2) - inv_cb = f'INV{inv_num} CB{cb_num}' +# Calculate the transmission, model the voltage, and categorize snow mode. +# Use the SAPM to calculate transmission and model the voltage. - v_scaling_factor = int(config['num_mods_per_str'][inv_cb]) - i_scaling_factor = int( - config['num_str_per_cb'][f'INV{inv_num} CB{cb_num}']) +inv_cb = 'INV1 CB2' - my_config = { - 'threshold_vratio': threshold_vratio, - 'threshold_transmission': threshold_transmission, - 'min_dcv': mppt_low_voltage, - 'max_dcv': mppt_high_voltage, - 'num_str_per_cb': int(config['num_str_per_cb'][f'{inv_cb}']), - 'num_mods_per_str': int(config['num_mods_per_str'][f'{inv_cb}'])} - - out = wrapper(data[v_col], data[i_col], - data['Cell Temp [C]'], - data['POA [W/m²]'], sapm_coeffs, - my_config) - - for k, v in out.items(): - data[inv_cb + ' ' + k] = v +snow_results = assign_snow_modes(data[voltage_col], data[current_col], + data['Cell Temp [C]'], + data['POA [W/m²]'], sapm_coeffs, + mppt_low_voltage, mppt_high_voltage, + threshold_vratio, + threshold_transmission, + num_mods_per_str, + num_str_per_cb) +# Plot transmission -# %% -# Look at transmission for all DC inputs - -transmission_cols = [c for c in data.columns if 'transmission' in c and - 'voltage' not in c] fig, ax = plt.subplots(figsize=(10, 6)) -date_form = DateFormatter("%m/%d") +date_form = mdates.DateFormatter("%m/%d") ax.xaxis.set_major_formatter(date_form) -temp = data['2022-01-06 07:45:00': '2022-01-09 17:45:00'] -for c in transmission_cols: - ax.scatter(temp.index, temp[c], s=0.5, label=c) -ax.set_xlabel('Date', fontsize='xx-large') +ax.plot(snow_results['transmission'], '.-', + label='INV1 CB2 Transmission') +ax.set_xlabel('Date', fontsize='large') ax.legend() fig.tight_layout() plt.show() # %% -# Look at voltage ratios for all DC inputs +# Look at voltage ratio -vratio_cols = [c for c in data.columns if "vmp_ratio" in c] fig, ax = plt.subplots(figsize=(10, 6)) -date_form = DateFormatter("%m/%d") +date_form = mdates.DateFormatter("%m/%d") ax.xaxis.set_major_formatter(date_form) -temp = data['2022-01-06 07:45:00': '2022-01-09 17:45:00'] -for c in vratio_cols: - ax.scatter(temp.index, temp[c], s=0.5, label=c) +ax.plot(snow_results['vmp_ratio'], label='INV1 CB2 Voltage Ratio') ax.set_xlabel('Date', fontsize='xx-large') ax.set_ylabel('Voltage Ratio (measured/modeled)', fontsize='xx-large') @@ -570,28 +404,26 @@ def wrapper(voltage, current, temp_cell, effective_irradiance, plt.show() # %% -# Calculate all power losses - snow and non-snow +# Calculate total DC power loss as the difference between modeled and measured +# power. Total power loss includes losses caused by snow and by other factors, +# e.g., shading from structures. + +model_results = pvlib.pvsystem.sapm(data['POA [W/m²]'], + data['Cell Temp [C]'], + sapm_coeffs) -modeled_df = pvlib.pvsystem.sapm(data['POA [W/m²]'], - data['Cell Temp [C]'], - sapm_coeffs) -for v_col, i_col in zip(dc_voltage_cols, dc_current_cols): - matched = re.match(r'INV(\d+) CB(\d+) Current', i_col) - inv_num = matched.group(1) - cb_num = matched.group(2) - inv_cb = f'INV{inv_num} CB{cb_num}' - i_scaling_factor = int( - config['num_str_per_cb'][f'INV{inv_num} CB{cb_num}']) - v_scaling_factor = int(config['num_mods_per_str'][inv_cb]) +modeled_power = model_results['p_mp'] * num_str_per_cb * num_mods_per_str +measured_power = data['INV1 CB2 Voltage [V]'] * data['INV1 CB2 Current [A]'] +loss_total = np.maximum(modeled_power - measured_power, 0) - modeled_power = modeled_df['p_mp']*v_scaling_factor*i_scaling_factor - name_modeled_power = inv_cb + ' Modeled Power [W]' - data[name_modeled_power] = modeled_power +# Calculate snow losses. Snow loss occurs when the mode is 0, 1, 2, or 3. +# When the snow loss occurs we assume that all the DC power loss is due to +# snow. +snow_loss_filter = snow_results['mode'].isin([0, 1, 2, 3]) +loss_snow = loss_total.copy() +loss_snow[~snow_loss_filter] = 0.0 - name_loss = inv_cb + ' Loss [W]' - loss = np.maximum(data[name_modeled_power] - data[i_col]*data[v_col], 0) - data[name_loss] = loss # %% # Plot measured and modeled power, color by mode @@ -600,39 +432,33 @@ def wrapper(voltage, current, temp_cell, effective_irradiance, alpha = 0.5 cmap = plt.get_cmap('plasma', N) -loss_cols = [c for c in data.columns if "Loss" in c] -mode_cols = [c for c in data.columns if "mode" in c and "modeled" not in c] -modeled_power_cols = [c for c in data.columns if "Modeled Power" in c] - -col = 1 # plot one combiner box -los = loss_cols[col] -mod = mode_cols[col] -pwr = modeled_power_cols[col] - fig, ax = plt.subplots(figsize=(10, 6)) -date_form = DateFormatter("%m/%d") +date_form = mdates.DateFormatter("%m/%d") ax.xaxis.set_major_formatter(date_form) -temp = data[~data[mod].isna()] +exclude = modeled_power.isna() | snow_results['mode'].isna() +temp = data[~exclude] # Plot each day individually so we are not exaggerating losses days_mapped = temp.index.map(lambda x: x.date()) -days = np.unique(days_mapped) -grouped = temp.groupby(days_mapped) +days = np.unique(temp.index.date) +grouped = temp.groupby(temp.index.date) for d in days: temp_grouped = grouped.get_group(d) - ax.plot(temp_grouped[pwr], c='k', ls='--') - ax.plot(temp_grouped[pwr] - temp_grouped[los], c='k') - ax.fill_between(temp_grouped.index, temp_grouped[pwr] - temp_grouped[los], - temp_grouped[pwr], color='k', alpha=alpha) + model_power = modeled_power[temp_grouped.index] + meas_power = measured_power[temp_grouped.index] + mode = snow_results['mode'][temp_grouped.index] + ax.plot(model_power, c='k', ls='--') + ax.plot(meas_power, c='k') + ax.fill_between(temp_grouped.index, meas_power, + model_power, color='k', alpha=alpha) - chng_pts = np.ravel(np.where(temp_grouped[mod].values[:-1] - - temp_grouped[mod].values[1:] != 0)) + chng_pts = np.ravel(np.where(mode.values[:-1] + - mode.values[1:] != 0)) if len(chng_pts) == 0: ax.axvspan(temp_grouped.index[0], temp_grouped.index[-1], - color=cmap.colors[temp_grouped.at[temp_grouped.index[-1], - mod]], + color=cmap.colors[mode.at[temp_grouped.index[-1]]], alpha=alpha) else: set1 = np.append([0], chng_pts) @@ -642,15 +468,14 @@ def wrapper(voltage, current, temp_cell, effective_irradiance, my_index = temp_grouped.index[start:end] ax.axvspan( temp_grouped.index[start], temp_grouped.index[end], - color=cmap.colors[temp_grouped.at[temp_grouped.index[end], - mod]], + color=cmap.colors[mode.at[temp_grouped.index[end]]], alpha=alpha, ec=None) # Add different colored intervals to legend handles, labels = ax.get_legend_handles_labels() -modeled_line = Line2D([0], [0], label='Modeled', color='k', ls='--') -measured_line = Line2D([0], [0], label='Measured', color='k') +modeled_line = Line2D([0], [0], label='Modeled power', color='k', ls='--') +measured_line = Line2D([0], [0], label='Measured power', color='k') handles.append(measured_line) handles.append(modeled_line) @@ -660,64 +485,59 @@ def wrapper(voltage, current, temp_cell, effective_irradiance, alpha=alpha) handles.append(my_patch) -ax.set_xlabel('Date', fontsize='xx-large') -ax.set_ylabel('DC Power [W]', fontsize='xx-large') -ax.legend(handles=handles, fontsize='xx-large', loc='upper left') -ax.set_title('Measured and modeled production for INV1 CB2', - fontsize='xx-large') +ax.set_xlabel('Date', fontsize='large') +ax.set_ylabel('DC Power [W]', fontsize='large') +ax.legend(handles=handles, fontsize='large', loc='upper left') +ax.set_title('Snow modes for INV1 CB2', + fontsize='large') fig.tight_layout() plt.show() # %% -# Calculate daily losses occuring while the system is operating in one of the -# four modes that are associated with the presence of snow. +# Calculate daily DC energy loss due to snow as a percentage of potential +# (modeled) DC power. +# Plot daily DC energy loss and daily snowfall totals. -# Daily snowfall is measured at 7:00 am of each day -snow = pd.read_csv(snowfall_file, index_col='DATE') # originally in [mm] -snow['SNOW'] *= 1/(10*2.54) # convert to [in] +# DataFrame so that we can group by days. +loss_df = pd.DataFrame(data={'modeled_power': modeled_power, + 'loss_snow': loss_snow}) -loss_cols = [c for c in data.columns if "Loss" in c] -mode_cols = [c for c in data.columns if "mode" in c and "modeled" not in c] -modeled_power_cols = [c for c in data.columns if "Modeled Power" in c] +# We will also show daily snowfall totals for context. +# Daily snowfall is measured at 7:00 am of each day. -days_mapped = data.index.map(lambda x: x.date()) -days = np.unique(days_mapped) -data_gped = data.groupby(days_mapped) +snowfall_file = pvanalytics_dir / 'data' / 'snow_snowfall.csv' +snowfall = pd.read_csv(snowfall_file, index_col='DATE', parse_dates=True) +snowfall['SNOW'] *= 1/(10*2.54) # convert from mm depth to inches +snowfall.index = snowfall.index + pd.Timedelta('7H') -columns = [re.match(r'INV(\d) CB(\d)', c).group(0) for c in loss_cols] -snow_loss = pd.DataFrame(index=days, columns=columns) +days_mapped = loss_df.index.map(lambda x: x.date()) +days = np.unique(days_mapped) +loss_df_gped = loss_df.groupby(days_mapped) -for d in days: - temp = data_gped.get_group(d) +snow_loss_daily = pd.Series(index=days, dtype=float) - for c, m, l, p in zip(columns, mode_cols, loss_cols, modeled_power_cols): - snow_loss_filter = ~(temp[m].isna()) & (temp[m] != 4) & (temp[m] != -1) - daily_snow_loss = 100*temp[snow_loss_filter][l].sum()/temp[p].sum() - snow_loss.at[d, c] = daily_snow_loss +for d in days: + temp = loss_df_gped.get_group(d) + snow_loss_daily.at[d] = 100 * temp['loss_snow'].sum() / temp['modeled_power'].sum() fig, ax = plt.subplots(figsize=(10, 6)) -date_form = DateFormatter("%m/%d") +ax2 = ax.twinx() +snow_loss_daily.plot(kind='bar', ax=ax, width=0.4, align='edge', + label='Snow loss (%)') +snowfall['SNOW'].plot(kind='bar', ax=ax2, width=-0.4, alpha=0.2, + align='edge', + label='Snow fall in prior 24 hours') -days_mapped = data.index.map(lambda x: x.date()) -days = np.unique(days_mapped) - -xvals = np.arange(0, len(days), 1) -xwidth = 0.05 -colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] - -for i, c in enumerate(columns): - ax.bar(xvals + xwidth*i, snow_loss[c], width=xwidth, color=colors[i], - ec='k', label=c) - -ax.legend() -ax.set_ylabel('[%]', fontsize='xx-large') -ax.set_xticks(xvals, days) +ax.legend(loc='upper left') +ax2.legend(loc='upper right') +ax.set_ylabel('DC energy loss [%]', fontsize='large') +ax2.set_ylabel('Inches') +date_form = mdates.DateFormatter("%m/%d") ax.xaxis.set_major_formatter(date_form) -ax.set_title('Losses incurred in modes 0, 1, 2, 3', fontsize='xx-large') +ax.set_title('Daily energy loss and snowfall', fontsize='large') fig.tight_layout() plt.show() -# %% diff --git a/pvanalytics/data/snow_config.json b/pvanalytics/data/snow_config.json deleted file mode 100644 index a08d2964..00000000 --- a/pvanalytics/data/snow_config.json +++ /dev/null @@ -1,71 +0,0 @@ -{ "panel": "REC_Solar_REC340TP_72_BLK", - "inverter" : "Yaskawa_Solectria_Solar__PVI_60TL_480__480V_", - "threshold_vratio" : 0.933, - "threshold_transmission" : 0.598, - "num_mods_per_str": { - "INV1 CB1": "18", - "INV1 CB2": "18", - "INV1 CB3": "18", - "INV2 CB1": "18", - "INV2 CB2": "18", - "INV2 CB3": "18", - "INV3 CB1": "18", - "INV3 CB2": "18", - "INV3 CB3": "18" - }, - "num_str_per_cb": { - "INV1 CB1": "4", - "INV1 CB2": "4", - "INV1 CB3": "4", - "INV2 CB1": "4", - "INV2 CB2": "4", - "INV2 CB3": "4", - "INV3 CB1": "4", - "INV3 CB2": "4", - "INV3 CB3": "4" - }, - "sapm_coeff": { - "Vintage": "2013", - "Area": 1.64, - "Material": "c-Si", - "Cells_in_Series": 72, - "Parallel_Strings": 1, - "Isco": 9.36992857142857, - "Voco": 46.78626811224489, - "Impo": 8.895117736670294, - "Vmpo": 37.88508962264151, - "Aisc": 0.0002, - "Aimp": -0.0004, - "C0": 1.0145, - "C1": -0.0145, - "Bvoco": -0.1205, - "Mbvoc": 0, - "Bvmpo": -0.1337, - "Mbvmp": 0, - "N": 1.0925, - "C2": -0.4647, - "C3": -11.900781, - "A0": 0.9731, - "A1": 0.02966, - "A2": -0.01024, - "A3": 0.001793, - "A4": -0.0001286, - "B0": 1, - "B1": -0.0154, - "B2": 0.001572, - "B3": -5.525e-05, - "B4": 8.04e-07, - "B5": -4.202e-09, - "DTC": 3.05, - "FD": 1, - "A": -3.4247, - "B": -0.0951, - "C4": NaN, - "C5": NaN, - "IXO": NaN, - "IXXO": NaN, - "C6": NaN, - "C7": NaN, - "Notes": "Source: CFV Solar Test Lab. Tested 2013. Module 13022-11" - } -} \ No newline at end of file diff --git a/pvanalytics/data/snow_data.csv b/pvanalytics/data/snow_data.csv index aab894d3..bbc081a5 100644 --- a/pvanalytics/data/snow_data.csv +++ b/pvanalytics/data/snow_data.csv @@ -1,577 +1,577 @@ -Timestamp,POA [W/m²],INV1 CB1 Voltage [V],INV1 CB2 Voltage [V],INV1 CB3 Voltage [V],INV2 CB1 Voltage [V],INV2 CB2 Voltage [V],INV2 CB3 Voltage [V],INV3 CB1 Voltage [V],INV3 CB2 Voltage [V],INV3 CB3 Voltage [V],Module Temp [C],Ambient Temp [C],INV1 CB1 Current [A],INV1 CB2 Current [A],INV1 CB3 Current [A],INV2 CB1 Current [A],INV2 CB2 Current [A],INV2 CB3 Current [A],INV3 CB1 Current [A],INV3 CB2 Current [A],INV3 CB3 Current [A],INV1 AC Power [kW],INV2 AC Power [kW],INV3 AC Power [kW],apparent_zenith,zenith,apparent_elevation,elevation,azimuth,equation_of_time,aoi -2022-01-05 00:00:00,1.069027,,,,,,,,,,-11.67601,-8.526217,,,,,,,,,,,,,160.00998020281045,160.00998020281045,-70.00998020281045,-70.00998020281045,3.16402318811231,-5.244508918804058,156.33263130086794 -2022-01-05 00:15:00,1.218347,,,,,,,,,,-10.71838,-7.358494,,,,,,,,,,,,,159.61658636000453,159.61658636000453,-69.61658636000455,-69.61658636000455,13.138328308505265,-5.249180891598371,159.64158725879298 -2022-01-05 00:30:00,0.5797986,,,,,,,,,,-9.996516,-6.536161,,,,,,,,,,,,,158.76703492902075,158.76703492902075,-68.76703492902075,-68.76703492902075,22.592518191033605,-5.253852054578601,162.85994049254788 -2022-01-05 00:45:00,0.6020421,,,,,,,,,,-9.189205,-5.791483,,,,,,,,,,,,,157.51396864859737,157.51396864859737,-67.51396864859738,-67.51396864859738,31.26630311690792,-5.258522407337296,165.92338777433648 -2022-01-05 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17:45:00,-2.712758,,,,,,,,,,-11.73181,-7.986522,,,,,,,,,,,,,102.34779701609074,102.34779701609074,-12.347797016090745,-12.347797016090745,251.56324082488544,-7.680690714281809,70.97437960641993 -2022-01-10 18:00:00,-0.195458,,,,,,,,,,-11.89225,-8.36065,,,,,,,,,,,,,104.98258119404761,104.98258119404761,-14.982581194047615,-14.982581194047615,253.9024593813728,-7.684835284067958,74.13974428293308 -2022-01-10 18:15:00,1.497619,,,,,,,,,,-10.66256,-7.672122,,,,,,,,,,,,,107.64855459060838,107.64855459060838,-17.648554590608384,-17.648554590608384,256.2327072941455,-7.688978768306697,77.34880028143749 -2022-01-10 18:30:00,0.9736995,,,,,,,,,,-9.324455,-7.27885,,,,,,,,,,,,,110.34124595526882,110.34124595526882,-20.34124595526882,-20.34124595526882,258.5638830684694,-7.693121166768833,80.59611514585634 -2022-01-10 18:45:00,0.8054042,,,,,,,,,,-8.122817,-7.230422,,,,,,,,,,,,,113.0562511406697,113.0562511406697,-23.05625114066971,-23.05625114066971,260.9066639733185,-7.697262479225174,83.87701639335744 -2022-01-10 19:00:00,0.2351132,,,,,,,,,,-7.357272,-7.237372,,,,,,,,,,,,,,,,,,, -2022-01-10 19:15:00,-2.423988,,,,,,,,,,-7.488067,-7.472116,,,,,,,,,,,,,,,,,,, -2022-01-10 19:30:00,-5.39413,,,,,,,,,,-8.607639,-8.047572,,,,,,,,,,,,,,,,,,, -2022-01-10 19:45:00,-6.316714,,,,,,,,,,-10.22274,-9.069683,,,,,,,,,,,,,,,,,,, -2022-01-10 20:00:00,-5.722897,,,,,,,,,,-11.68762,-10.1064,,,,,,,,,,,,,,,,,,, -2022-01-10 20:15:00,-3.769511,,,,,,,,,,-12.80632,-10.84562,,,,,,,,,,,,,,,,,,, -2022-01-10 20:30:00,-3.452007,,,,,,,,,,-13.68255,-11.46631,,,,,,,,,,,,,,,,,,, -2022-01-10 20:45:00,-2.206551,,,,,,,,,,-13.97515,-11.35991,,,,,,,,,,,,,,,,,,, -2022-01-10 21:00:00,0.2080746,,,,,,,,,,-13.39726,-10.4194,,,,,,,,,,,,,,,,,,, -2022-01-10 21:15:00,0.2080833,,,,,,,,,,-12.32311,-9.299583,,,,,,,,,,,,,,,,,,, -2022-01-10 21:30:00,-1.345389,,,,,,,,,,-11.53553,-8.764911,,,,,,,,,,,,,,,,,,, -2022-01-10 21:45:00,-1.42481,,,,,,,,,,-11.19843,-8.651484,,,,,,,,,,,,,,,,,,, -2022-01-10 22:00:00,-0.2970335,,,,,,,,,,-11.02538,-8.529866,,,,,,,,,,,,,,,,,,, -2022-01-10 22:15:00,-0.04924034,,,,,,,,,,-11.01999,-8.61165,,,,,,,,,,,,,,,,,,, -2022-01-10 22:30:00,0.02700322,,,,,,,,,,-10.84902,-8.547039,,,,,,,,,,,,,,,,,,, -2022-01-10 22:45:00,0.06671461,,,,,,,,,,-10.64079,-8.525689,,,,,,,,,,,,,,,,,,, -2022-01-10 23:00:00,0.0603609,,,,,,,,,,-10.38599,-8.548889,,,,,,,,,,,,,,,,,,, -2022-01-10 23:15:00,-0.01747335,,,,,,,,,,-10.26169,-8.659889,,,,,,,,,,,,,,,,,,, -2022-01-10 23:30:00,-0.01747335,,,,,,,,,,-10.3576,-8.866811,,,,,,,,,,,,,,,,,,, -2022-01-10 23:45:00,0.0,,,,,,,,,,-10.43267,-9.007706,,,,,,,,,,,,,,,,,,, +Timestamp,POA [W/m²],INV1 CB2 Voltage [V],INV1 CB2 Current [A],INV1 AC Power [kW],Module Temp [C],Ambient Temp [C],apparent_zenith,zenith,apparent_elevation,elevation,azimuth,equation_of_time,aoi +1/5/2022 0:00,1.069027,,,,-11.67601,-8.526217,160.0099802,160.0099802,-70.0099802,-70.0099802,3.164023188,-5.244508919,156.3326313 +1/5/2022 0:15,1.218347,,,,-10.71838,-7.358494,159.6165864,159.6165864,-69.61658636,-69.61658636,13.13832831,-5.249180892,159.6415873 +1/5/2022 0:30,0.5797986,,,,-9.996516,-6.536161,158.7670349,158.7670349,-68.76703493,-68.76703493,22.59251819,-5.253852055,162.8599405 +1/5/2022 0:45,0.6020421,,,,-9.189205,-5.791483,157.5139686,157.5139686,-67.51396865,-67.51396865,31.26630312,-5.258522407,165.9233878 +1/5/2022 1:00,0.2589272,,,,-8.529516,-5.298972,155.9217677,155.9217677,-65.92176773,-65.92176773,39.04886195,-5.263191949,168.7032133 +1/5/2022 1:15,0.02859372,,,,-8.239856,-5.170183,154.0546547,154.0546547,-64.05465466,-64.05465466,45.94790818,-5.267860679,170.9328509 +1/5/2022 1:30,0.02541689,,,,-8.140345,-4.9114,151.9697154,151.9697154,-61.96971537,-61.96971537,52.04068144,-5.272528597,172.1285747 +1/5/2022 1:45,-0.08419616,,,,-8.281861,-5.063289,149.7144041,149.7144041,-59.71440411,-59.71440411,57.43305431,-5.277195701,171.8228698 +1/5/2022 2:00,-0.393975,,,,-8.922639,-5.946322,147.3267452,147.3267452,-57.32674522,-57.32674522,62.23423946,-5.281861993,170.1546961 +1/5/2022 2:15,-0.3066016,,,,-9.644333,-6.723111,144.8366899,144.8366899,-54.83668992,-54.83668992,66.54437534,-5.28652747,167.6654856 +1/5/2022 2:30,0.003177027,,,,-10.00414,-7.043106,142.2677294,142.2677294,-52.26772938,-52.26772938,70.4500626,-5.291192133,164.7529653 +1/5/2022 2:45,0.1096092,,,,-10.09863,-7.008945,139.6383646,139.6383646,-49.63836461,-49.63836461,74.02398846,-5.29585598,161.6182219 +1/5/2022 3:00,0.1064322,,,,-10.28779,-7.137044,136.9633118,136.9633118,-46.96331177,-46.96331177,77.32630553,-5.300519011,158.3587062 +1/5/2022 3:15,0,,,,-10.70247,-7.4041,134.2544414,134.2544414,-44.25444143,-44.25444143,80.40655734,-5.305181226,155.0244272 +1/5/2022 3:30,0,,,,-11.10306,-7.509205,131.5214919,131.5214919,-41.52149194,-41.52149194,83.30559608,-5.309842624,151.6430417 +1/5/2022 3:45,0,,,,-11.41071,-7.805861,128.7726081,128.7726081,-38.77260809,-38.77260809,86.05727438,-5.314503204,148.2309676 +1/5/2022 4:00,0,,,,-11.81071,-8.3981,126.0147472,126.0147472,-36.01474716,-36.01474716,88.68985754,-5.319162966,144.7985898 +1/5/2022 4:15,0,,,,-12.17561,-8.920567,123.2539857,123.2539857,-33.25398571,-33.25398571,91.22716995,-5.323821909,141.352862 +1/5/2022 4:30,0,,,,-12.04299,-8.910183,120.4957554,120.4957554,-30.4957554,-30.4957554,93.68951073,-5.328480032,137.8986929 +1/5/2022 4:45,0.0873676,,,,-11.5078,-8.678711,117.745026,117.745026,-27.74502603,-27.74502603,96.09438101,-5.333137335,134.4397265 +1/5/2022 5:00,0.0873676,,,,-10.52836,-8.186017,115.006449,115.006449,-25.00644895,-25.00644895,98.45706135,-5.337793817,130.9788028 +1/5/2022 5:15,0.3177018,,,,-9.649034,-7.738228,112.2844733,112.2844733,-22.28447328,-22.28447328,100.7910688,-5.342449479,127.5182442 +1/5/2022 5:30,1.345465,,,,-8.792339,-7.476972,109.5834414,109.5834414,-19.58344138,-19.58344138,103.1085198,-5.347104318,124.0600414 +1/5/2022 5:45,2.12069,,,,-7.662806,-7.020505,106.907668,106.907668,-16.90766795,-16.90766795,105.4204193,-5.351758334,120.6059778 +1/5/2022 6:00,2.10169,,,,-6.689061,-6.506834,104.2615091,104.2615091,-14.26150905,-14.26150905,107.7368895,-5.356411528,117.1577195 +1/5/2022 6:15,1.979401,,,,-5.890139,-5.942461,101.6494226,101.6494226,-11.6494226,-11.6494226,110.0673501,-5.361063898,113.716882 +1/5/2022 6:30,1.512287,,,,-5.264884,-5.4799,99.07602147,99.07602147,-9.076021475,-9.076021475,112.4206586,-5.365715443,110.2850834 +1/5/2022 6:45,1.440562,,,,-4.775061,-5.103017,96.54612261,96.54612261,-6.546122613,-6.546122613,114.8052157,-5.370366163,106.8639894 +1/5/2022 7:00,2.279092,256.2846,0,0,-4.283155,-4.373294,94.06479171,94.06479171,-4.064791715,-4.064791715,117.2290397,-5.375016058,103.4553554 +1/5/2022 7:15,2.822341,387.1234,0.009913795,0.03965517,-3.770317,-3.570317,91.63738283,91.63738283,-1.637382831,-1.637382831,119.6998123,-5.379665127,100.0610659 +1/5/2022 7:30,3.26234,496.5162,0.05814388,0.2396552,-3.314744,-2.990978,88.88434591,89.26957455,1.115654086,0.730425447,122.224897,-5.384313369,96.6831757 +1/5/2022 7:45,4.394756,502.8697,0.09823009,0.4480469,-2.867678,-2.717878,86.74411978,86.96740093,3.255880221,3.032599069,124.8113275,-5.388960784,93.32395469 +1/5/2022 8:00,6.341689,530.4985,0.1273504,0.5377905,-2.22215,-2.649089,84.58420803,84.73727491,5.415791971,5.262725087,127.46577,-5.39360737,89.9859364 +1/5/2022 8:15,9.50158,561.6168,0.2346538,0.7470469,-1.911483,-2.950828,82.47008808,82.58600432,7.529911917,7.413995675,130.1944527,-5.398253129,86.67197452 +1/5/2022 8:30,17.47532,602.7585,0.4330846,1.234256,-1.847694,-3.278228,80.42740483,80.52079695,9.572595166,9.479203048,133.0030643,-5.402898057,83.38530861 +1/5/2022 8:45,27.68384,637.2244,0.7064382,1.875128,-1.20765,-3.001067,78.47080161,78.54925099,11.52919839,11.45074901,135.8966212,-5.407542157,80.12963996 +1/5/2022 9:00,54.13859,673.6044,1.579364,3.752917,-0.5778555,-2.383795,76.61142434,76.6793292,13.38857566,13.3206708,138.8793022,-5.412185426,76.90922295 +1/5/2022 9:15,82.93109,704.5355,2.593245,5.989615,-0.2303111,-1.635106,74.85917941,74.9193122,15.14082059,15.0806878,141.9542537,-5.416827864,73.72897367 +1/5/2022 9:30,85.68211,706.6226,2.719244,6.307815,-0.05290556,-1.241533,73.22350256,73.27772668,16.77649744,16.72227332,145.1233734,-5.42146947,70.59459794 +1/5/2022 9:45,81.89581,697.9531,2.58131,5.977227,-0.04693333,-1.089439,71.71361241,71.76324678,18.28638759,18.23675322,148.3870801,-5.426110245,67.51274519 +1/5/2022 10:00,95.24964,703.9135,2.981795,6.816117,0.08261666,-0.8457611,70.3385461,70.38456601,19.6614539,19.61543399,151.7440837,-5.430750186,64.49119034 +1/5/2022 10:15,112.4451,709.1207,3.439897,7.802028,1.059355,-0.5234833,69.10708379,69.15023865,20.89291621,20.84976135,155.191178,-5.435389295,61.53904574 +1/5/2022 10:30,90.24828,685.5192,2.701441,6.158053,2.073883,-0.2815389,68.02760802,68.06849424,21.97239198,21.93150576,158.723076,-5.440027569,58.667007 +1/5/2022 10:45,74.26115,672.4007,2.174359,4.964887,2.179517,-0.1622222,67.10792152,67.14702967,22.89207848,22.85297033,162.3323147,-5.444665009,55.88762932 +1/5/2022 11:00,83.69518,683.1378,2.574858,5.837101,2.522033,0.09223889,66.3550408,66.39278752,23.6449592,23.60721248,166.0092571,-5.449301614,53.21562444 +1/5/2022 11:15,99.909,696.4601,3.292742,7.416766,3.147772,0.3917778,65.77498303,65.81173347,24.22501697,24.18826653,169.7422076,-5.453937383,50.668161 +1/5/2022 11:30,98.77921,693.6614,3.262419,7.335231,3.411294,0.6336111,65.37256281,65.40864715,24.62743719,24.59135285,173.5176551,-5.458572316,48.26512977 +1/5/2022 11:45,83.96643,682.2324,2.69451,6.107737,3.670956,0.9635667,65.15121599,65.1869423,24.84878401,24.8130577,177.3206424,-5.463206412,46.02931048 +1/5/2022 12:00,75.41232,674.7064,2.398623,5.462979,3.766117,1.2543,65.11286688,65.14853175,24.88713312,24.85146825,181.1352427,-5.467839671,43.98634836 +1/5/2022 12:15,60.06609,663.1763,1.897936,4.347672,3.567539,1.436278,65.25785116,65.29374924,24.74214884,24.70625076,184.9451127,-5.472472091,42.16441147 +1/5/2022 12:30,60.27884,662.4284,1.920557,4.386229,3.597144,1.699072,65.58490222,65.62133555,24.41509778,24.37866445,188.7340819,-5.477103673,40.59338117 +1/5/2022 12:45,55.42746,659.1337,1.733645,3.990028,3.739917,2.029994,66.0912023,66.12849042,23.9087977,23.87150958,192.486725,-5.481734416,39.30345001 +1/5/2022 13:00,60.47979,663.8647,1.877099,4.311738,3.987206,2.356395,66.77249324,66.81098491,23.22750676,23.18901509,196.1888756,-5.486364319,38.32308946 +1/5/2022 13:15,57.50068,661.16,1.802328,4.182762,4.210311,2.586161,67.62323636,67.66332436,22.37676364,22.33667564,199.8280439,-5.490993381,37.67653018 +1/5/2022 13:30,35.39885,646.2947,1.074775,2.640988,3.990505,2.598294,68.63680638,68.67894713,21.36319362,21.32105287,203.3937128,-5.495621602,37.38113534 +1/5/2022 13:45,28.48105,637.3043,0.8426348,2.128207,3.593911,2.434733,69.80570244,69.8504434,20.19429756,20.1495566,206.8775064,-5.500248982,37.44523577 +1/5/2022 14:00,20.0643,606.9224,0.5859998,1.553771,3.281367,2.274872,71.12175953,71.16977904,18.87824047,18.83022096,210.2732375,-5.50487552,37.86700599 +1/5/2022 14:15,12.03206,576.1127,0.338365,1.001472,2.953317,2.202833,72.57634264,72.62851036,17.42365736,17.37148964,213.5768501,-5.509501215,38.63471555 +1/5/2022 14:30,11.54156,573.7137,0.3043655,0.9327453,2.747917,2.1629,74.16050709,74.21797976,15.83949291,15.78202024,216.7862801,-5.514126066,39.72827872 +1/5/2022 14:45,11.7781,569.4493,0.3009109,0.9341089,2.681978,2.153795,75.8651053,75.92948624,14.1348947,14.07051376,219.9012636,-5.518750074,41.12165469 +1/5/2022 15:00,10.06,560.8682,0.2601818,0.8278282,2.672578,2.213839,77.68080531,77.7544261,12.31919469,12.2455739,222.9231137,-5.523373237,42.78549423 +1/5/2022 15:15,7.197282,540.0151,0.1728114,0.6499579,2.686544,2.323233,79.59794876,79.6844036,10.40205124,10.3155964,225.8544879,-5.527995555,44.68951649 +1/5/2022 15:30,5.768328,514.1613,0.1070335,0.5219374,2.7563,2.476383,81.60605832,81.71131395,8.393941679,8.288686046,228.6991653,-5.532617027,46.8043233 +1/5/2022 15:45,5.069704,493.3478,0.1075626,0.4881411,2.870844,2.608283,83.69241156,83.82739972,6.30758844,6.172600281,231.4618417,-5.537237653,49.1025799 +1/5/2022 16:00,3.499427,502.7438,0.06652238,0.2983333,3.047011,2.931589,85.83759391,86.02528487,4.162406094,3.974715128,234.1479513,-5.541857432,51.55964113 +1/5/2022 16:15,1.945011,434.3824,0.01136363,0.065,3.282778,3.387633,87.99919082,88.29799103,2.000809179,1.70200897,236.7635209,-5.546476363,54.15376525 +1/5/2022 16:30,0.5525505,340.6613,0,0,3.454261,3.5978,90.06162196,90.63893768,-0.061621963,-0.638937682,239.315055,-5.551094447,56.86605663 +1/5/2022 16:45,0,,,,3.559789,3.683561,93.04193034,93.04193034,-3.041930344,-3.041930344,241.8094527,-5.555711682,59.68025419 +1/5/2022 17:00,0,,,,3.618305,3.767767,95.50114027,95.50114027,-5.501140274,-5.501140274,244.2539569,-5.560328067,62.58244916 +1/5/2022 17:15,0,,,,3.639333,3.789972,98.01107599,98.01107599,-8.011075994,-8.011075994,246.6561323,-5.564943603,65.56078245 +1/5/2022 17:30,0,,,,3.5844,3.761444,100.566549,100.566549,-10.56654901,-10.56654901,249.0238707,-5.569558289,68.60515264 +1/5/2022 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-354,0.0 -355,0.0 -356,0.0 -357,0.0 -358,0.0 -359,0.0 diff --git a/pvanalytics/features/snow.py b/pvanalytics/features/snow.py index 0881f92d..724c68c2 100644 --- a/pvanalytics/features/snow.py +++ b/pvanalytics/features/snow.py @@ -1,33 +1,6 @@ import numpy as np -def _get_horizon_mask(horizon, azimuth, elevation): - - """ - Determines if a given (azimuth, elevation) pair is above a horizon profile. - - Parameters - ---------- - horizon : Series - Series with numeric index of 0 - 359 (represents azimuth) and float - values (represents elevation [deg] of horizon profile). - azimuth : array-like - Solar azimuth angle. [deg] - elevation : array-like - Solar elevation angle. [deg] - - Returns - ------- - out : array-like - Array of bool and NaN values, where True indicates that the - (azimuth, elevation) pair is above the horizon profile. NaN if the sun - position inputs contain a NaN. - """ - yp = np.interp(azimuth, horizon.index, horizon.values, period=360) - out = elevation >= yp - return out - - def get_irradiance_sapm(temp_cell, i_mp, imp0, c0, c1, alpha_imp, irrad_ref=1000, temp_ref=25): """ @@ -160,34 +133,6 @@ def categorize(transmission, measured_voltage, """ Categorizes electrical behavior into a snow-related mode. - Modes are defined in [1]_: - - * Mode 0: Indicates periods with enough opaque snow that the system is not - producing power. Specifically, Mode 0 is when the measured voltage is - below the lower bound of the inverter's MPPT range but the voltage - modeled using measured irradiance and ideal transmission is above the - lower bound of the inverter's MPPT range. - * Mode 1: Indicates periods when the system has non-uniform snow that - decreases both operating voltage and current. Operating Voltage is - reduced when bypass diodes activate and current is decreased due to - decreased irradiance. - * Mode 2: Indicates periods when the operating voltage is reduced but - current is consistent with snow-free conditions. - * Mode 3: Indicates periods when the operating voltage is consistent with - snow-free conditions but current is reduced. - * Mode 4: Voltage and current are consistent with snow-free conditions. - * Mode -1: Indicates periods where it is unknown if or how snow impacts - power output. Mode -1 includes periods when: - - 1. Voltage modeled using measured irradiance and ideal transmission - is outside the inverter's MPPT range, OR - 2. measured voltage exceeds the upper bound of the inverter's MPPT - algorithm. - - Mode -1 is added in this function to cover a case that was not addressed - in [1]_. - - Parameters ---------- transmission : array-like @@ -224,6 +169,38 @@ def categorize(transmission, measured_voltage, Ratio between measured DC voltage and DC voltage modeled with calculated transmission. + Notes + ----- + Modes are defined in [1]_: + + * Mode 0: Indicates periods with enough opaque snow that the system is not + producing power. Specifically, Mode 0 is when the measured voltage is + below the lower bound of the inverter's MPPT range but the voltage + modeled using measured irradiance and ideal transmission is above the + lower bound of the inverter's MPPT range. + * Mode 1: Indicates periods when the system has non-uniform snow which + affects all strings. Mode 1 is assigned when both operating voltage and + current are reduced. Operating voltage is reduced when snow causes + mismatch and current is decreased due to reduced transmission. + * Mode 2: Indicates periods when the system has non-uniform snow which + causes mismatch for some modules, but doesn't reduce light transmission + to other modules. + * Mode 3: Indicates periods when the the system has snow that reduces + light transmission but doesn't create mismatch. Operating voltage is + consistent with snow-free conditions but current is reduced. + * Mode 4: Voltage and current are consistent with snow-free conditions. + + * Mode -1: Indicates periods where it is unknown if or how snow impacts + power output. Mode -1 includes periods when: + + 1. Voltage modeled using measured irradiance and ideal transmission + is outside the inverter's MPPT range, OR + 2. measured voltage exceeds the upper bound of the inverter's MPPT + algorithm. + + Mode -1 is added in this function to cover a case that was not addressed + in [1]_. + References ---------- .. [1] E. C. Cooper, J. L. Braid and L. M. Burnham, "Identifying the diff --git a/pvanalytics/tests/features/test_snow.py b/pvanalytics/tests/features/test_snow.py index a6987583..4b5d1c72 100644 --- a/pvanalytics/tests/features/test_snow.py +++ b/pvanalytics/tests/features/test_snow.py @@ -11,15 +11,6 @@ from pvanalytics.features import snow -def test_get_horizon_mask(): - horizon = pd.Series(index=range(0, 360), data=0) - horizon[5:10] = 10 - result = snow._get_horizon_mask(horizon, azimuth=np.array([4, 5, 7]), - elevation=np.array([-1, 11, np.nan])) - expected = np.array([False, True, False]) - assert_array_equal(result, expected) - - def test_get_irradiance_sapm(): # solve Ee^2 + 2 Ee - 3 = 0 c0, c1 = (2, 1)