1+ """
2+ Function to compute the Joule law for power grid use case
3+ """
4+ from typing import Union
5+ import numpy as np
6+
7+ from lightsim2grid import LightSimBackend
8+ from grid2op .Backend import PandaPowerBackend
9+
10+ from ...logger import CustomLogger
11+
12+ def verify_joule_law (predictions : dict ,
13+ log_path : Union [str , None ]= None ,
14+ result_level : int = 0 ,
15+ ** kwargs ):
16+ """
17+ This function compute Joule's law which is ``$PL = R \t imes I^2$``
18+
19+ To compute the PowerLoss(PL), we use : ``$p_ex + p_or$``
20+ The resistance values are deduced from backends.
21+ The currents are computed as follows : ``$\f rac{a_or + a_ex}{2}$``
22+
23+ TODO : consider the line disconnections
24+
25+ Parameters
26+ ----------
27+ predictions: ``dict``
28+ Predictions made by an augmented simulator
29+ log_path: ``str``, optional
30+ the path where the logs should be saved, by default None
31+ **kwargs: ``dict``
32+ It should contain `observations` and `config` to load the tolerance
33+
34+ Returns
35+ -------
36+ ``dict``
37+ a dictionary with useful information about the verification
38+
39+ These informations are:
40+ - mae_per_obs: `array`
41+ The Absolute error (difference) between two sides of Joule equation per observation
42+
43+ - mae_per_obj: `array`
44+ The Absolute Error (difference) between two sides of Joule equation per line for each observation
45+
46+ - mae: `scalar`
47+ the Mean Absolute Error (difference) between two sides of the Joule equation
48+
49+ - wmape: `scalar`
50+ weighted mean absolute percentage error between two sides of the Joule equation
51+
52+ - violation_proportion: `scalar`
53+ The percentage of violation of Joule law over all the observations
54+ """
55+ # logger
56+ logger = CustomLogger ("PhysicsCompliances(Joule_law)" , log_path ).logger
57+
58+ try :
59+ env = kwargs ["env" ]
60+ except KeyError :
61+ logger .error ("The requirements were not satisiftied to verify_joule_law function" )
62+ raise
63+
64+ try :
65+ config = kwargs ["config" ]
66+ except KeyError :
67+ try :
68+ tolerance = kwargs ["tolerance" ]
69+ except KeyError :
70+ logger .error ("The tolerance could not be found for verify_joule_law function" )
71+ raise
72+ else :
73+ tolerance = float (tolerance )
74+ else :
75+ tolerance = float (config .get_option ("eval_params" )["JOULE_tolerance" ])
76+
77+ if isinstance (env .backend , LightSimBackend ):
78+ # extract the impedance values which are in p.u
79+ z_pu = [el .r_pu for el in env .backend ._grid .get_lines ()]
80+ z_pu_trafo = [el .r_pu for el in env .backend ._grid .get_trafos ()]
81+ # enumerate lines and trafos
82+ n_lines = len (z_pu )
83+ n_trafo = len (z_pu_trafo )
84+ # transform pu to Ohm and keep only lines
85+ z_base = np .power (env .backend .lines_or_pu_to_kv , 2 ) / env .backend ._grid .get_sn_mva ()
86+ r_ohm = z_pu * z_base [:n_lines ]
87+
88+ elif isinstance (env .backend , PandaPowerBackend ):
89+ # n_lines
90+ n_lines = len (env .backend ._grid .line )
91+ # n_transformers
92+ n_transformers = len (env .backend ._grid .trafo )
93+ # get the resistance
94+ r_ohm = np .array (env .backend ._grid .line ["r_ohm_per_km" ]).reshape (1 ,- 1 )
95+
96+ verifications = dict ()
97+ mean_current = _get_average_currents (predictions )
98+ mean_current_squared = np .power (mean_current , 2 )
99+ pl_mw = _get_power_loss (predictions )
100+ # MAE between PL and R.I^2
101+ left_array = pl_mw [:, :n_lines ]/ 3
102+ right_array = mean_current_squared [:, :n_lines ] * r_ohm
103+ mae_per_obj = np .abs (left_array - right_array )
104+ mae_per_obs = mae_per_obj .mean (axis = 1 )
105+ mae = np .mean (mae_per_obs )
106+ wmape = np .mean (np .abs (left_array - right_array )) / np .mean (np .abs (left_array ))
107+ violation_prop = np .sum (mae_per_obj > tolerance ) / mae_per_obj .size
108+
109+ logger .info ("Joule law violation proportion: %.3f" , violation_prop )
110+ logger .info ("MAE for Joule law: %.3f" , mae )
111+ logger .info ("WMAPE for Joule law: %.3f" , wmape )
112+
113+ if result_level > 0 :
114+ verifications ["mae_per_obs" ] = mae_per_obs
115+ verifications ["mae_per_obj" ] = mae_per_obj
116+ verifications ["violation_proportion" ] = violation_prop
117+ verifications ["mae" ] = mae
118+ verifications ["wmape" ] = wmape
119+
120+ return verifications
121+
122+ def _get_average_currents (data : dict ) -> np .ndarray :
123+ """Compute the average current using current from both extremities of a power line
124+
125+ Parameters
126+ ----------
127+ data : ``dict``
128+ A python dictionary including currents as keys
129+
130+ Returns
131+ -------
132+ np.array
133+ average matrix of currents
134+ """
135+ current_avg = np .abs (data .get ("a_or" ) + data .get ("a_ex" ))/ 2
136+ current_avg_kA = current_avg / 1000
137+ return current_avg_kA
138+
139+ def _get_power_loss (data : dict ) -> np .ndarray :
140+ """Compute the power loss from powers at two extremities of power lines
141+
142+ Parameters
143+ ----------
144+ data : ``dict``
145+ A python dictionary including powers as keys
146+
147+ Returns
148+ -------
149+ ``np.ndarray``
150+ Power losses array at the line level for all the observations
151+ """
152+ pl_mw = np .abs (data .get ("p_or" ) + data .get ("p_ex" ))
153+ return pl_mw
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