Pandapower_Converter: corrected total 3ph_line loading with max of loading of 3 phases to align with pandapower. corrected line input producing NaN with c_nf_per_km=0, tan value should be 0 in this case

Signed-off-by: furqan463 <[email protected]>

Author: furqan463

src/power_grid_model_io/converters/pandapower_converter.py

@@ -422,11 +422,11 @@ def _create_pgm_input_lines(self):
         pgm_lines["x1"] = self._get_pp_attr("line", "x_ohm_per_km", expected_type="f8") * multiplier
         pgm_lines["c1"] = c_nf_per_km * length_km * parallel * 1e-9
         # The formula for tan1 = R_1 / Xc_1 = (g * 1e-6) / (2 * pi * f * c * 1e-9) = g / (2 * pi * f * c * 1e-3)
-        pgm_lines["tan1"] = (
-            self._get_pp_attr("line", "g_us_per_km", expected_type="f8", default=0)
-            / c_nf_per_km
-            / (2 * np.pi * self.system_frequency * 1e-3)
-        )
+        pgm_lines["tan1"] = np.divide(
+            self._get_pp_attr("line", "g_us_per_km", expected_type="f8", default=0),
+            c_nf_per_km * (2 * np.pi * self.system_frequency * 1e-3),
+        where=np.not_equal(c_nf_per_km, 0.0))
+        pgm_lines["tan1"][np.equal(c_nf_per_km, 0.0)] = 0.0
         pgm_lines["i_n"] = (
             (self._get_pp_attr("line", "max_i_ka", expected_type="f8", default=np.nan) * 1e3)
             * self._get_pp_attr("line", "df", expected_type="f8", default=1)
@@ -435,11 +435,11 @@ def _create_pgm_input_lines(self):
         pgm_lines["r0"] = self._get_pp_attr("line", "r0_ohm_per_km", expected_type="f8", default=np.nan) * multiplier
         pgm_lines["x0"] = self._get_pp_attr("line", "x0_ohm_per_km", expected_type="f8", default=np.nan) * multiplier
         pgm_lines["c0"] = c0_nf_per_km * length_km * parallel * 1e-9
-        pgm_lines["tan0"] = (
-            self._get_pp_attr("line", "g0_us_per_km", expected_type="f8", default=0)
-            / c0_nf_per_km
-            / (2 * np.pi * self.system_frequency * 1e-3)
-        )
+        pgm_lines["tan0"] = np.divide(
+            self._get_pp_attr("line", "g0_us_per_km", expected_type="f8", default=0),
+            c0_nf_per_km * (2 * np.pi * self.system_frequency * 1e-3)
+        ,where=np.not_equal(c0_nf_per_km, 0.0))
+        pgm_lines["tan0"][np.equal(c0_nf_per_km, 0.0)] = 0.0
         assert ComponentType.line not in self.pgm_input_data
         self.pgm_input_data[ComponentType.line] = pgm_lines
 
@@ -1942,7 +1942,8 @@ def _pp_lines_output_3ph(self):
         pp_output_lines_3ph["loading_c_percent"] = (
             np.maximum(pp_output_lines_3ph["i_c_from_ka"], pp_output_lines_3ph["i_c_to_ka"]) / pgm_input_lines["i_n"]
         ) * 1e5
-        pp_output_lines_3ph["loading_percent"] = pgm_output_lines["loading"] * 1e2
+        pp_output_lines_3ph["loading_percent"] = np.maximum(np.maximum(pp_output_lines_3ph["loading_a_percent"], pp_output_lines_3ph["loading_b_percent"]),
+                                                            pp_output_lines_3ph["loading_c_percent"])
 
         assert "res_line_3ph" not in self.pp_output_data
         self.pp_output_data["res_line_3ph"] = pp_output_lines_3ph

tests/data/pandapower/pp_validation.py

@@ -208,11 +208,11 @@ def pp_net_3ph_minimal_trafo():
         2,
         0.33,
         0.34,
-        0.001,
+        0.00,
         600,
         r0_ohm_per_km=0.66,
         x0_ohm_per_km=0.65,
-        c0_nf_per_km=0.001,
+        c0_nf_per_km=0.00,
         g_us_per_km=0,
         g0_us_per_km=0,
     )
@@ -235,5 +235,5 @@ def pp_net_3ph_minimal_trafo():
         mag0_rx=0,
         si0_hv_partial=0.9,
     )
-    pp.create_asymmetric_load(net, 2, 0.2, 0.19, 0.195, 0.05, 0.045, 0.035, 0, type="wye")
+    pp.create_asymmetric_load(net, 2, 0.2, 0.2, 0.2, 0.05, 0.05, 0.05, 0, type="wye")
     return net

tests/validation/converters/test_pandapower_converter_output.py

@@ -153,6 +153,13 @@ def check_result(net):
         np.testing.assert_allclose(
             np.maximum(i_c_hv / i_max_hv, i_c_lv / i_max_lv) * 100, net.res_trafo_3ph.loading_c_percent
         )
+        np.testing.assert_allclose(
+            np.maximum(np.maximum(net.res_trafo_3ph.loading_a_percent, net.res_trafo_3ph.loading_b_percent),
+                       net.res_trafo_3ph.loading_c_percent), net.res_trafo_3ph.loading_percent)
+        np.testing.assert_allclose(
+            np.maximum(np.maximum(net.res_line_3ph.loading_a_percent, net.res_line_3ph.loading_b_percent),
+                       net.res_line_3ph.loading_c_percent), net.res_line_3ph.loading_percent)
+
 
     def compare_result(actual, expected, *, rtol):
         np.testing.assert_allclose(actual.trafo.vn_hv_kv, expected.trafo.vn_hv_kv, rtol=rtol)
@@ -192,7 +199,7 @@ def compare_result(actual, expected, *, rtol):
     pp.runpp_3ph(pp_net)
     check_result(pgm_net)
     check_result(pp_net)
-    compare_result(pgm_net, pp_net, rtol=0.04)
+    compare_result(pgm_net, pp_net, rtol=0.004)
 
 
 def test_output_data(output_data: Tuple[PandaPowerData, PandaPowerData]):