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1.增加Unittest 

2.考虑利用实际地闪密度和雷电流曲线。
3.版本号v1.2.0
This commit is contained in:
n3040 2022-01-21 15:50:59 +08:00
parent ee2d6477ee
commit 791f7c281f
4 changed files with 91 additions and 34 deletions
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26
core.py
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@ -21,6 +21,9 @@ class Parameter:
altitude: int # 海拔，单位米
max_i: float # 最大尝试电流，单位kA
rated_voltage: float # 额定电压
ng: float # 地闪密度 次/(每平方公里·每年)
Ip_a: float # 概率密度曲线系数a
Ip_b: float # 概率密度曲线系数b
para = Parameter()
@ -186,7 +189,7 @@ def solve_circle_line_intersection(
def min_i(string_len, u_ph):
# 海拔修正
altitude = para.altitude
k_a = math.exp((altitude-1000) / 8150) # 气隙海拔修正
k_a = math.exp((altitude - 1000) / 8150) # 气隙海拔修正
u_50 = 1 / k_a * (530 * string_len + 35) # 50045 上附录的公式，实际应该用负极性电压的公式
z_0 = 300 # 雷电波阻抗
z_c = 251 # 导线波阻抗
@ -195,13 +198,20 @@ def min_i(string_len, u_ph):
return r
def thunder_density(i): # l雷电流幅值密度函数
td = para.td
def thunder_density(i, td, ip_a, ip_b): # 雷电流幅值密度函数
# td = para.td
r = None
if td == 20:
r = -(10 ** (-i / 44)) * math.log(10) * (-1 / 44) # 雷暴日20d
if td == 40:
r = -(10 ** (-i / 88)) * math.log(10) * (-1 / 88) # 雷暴日40d
# ip_a = para.Ip_a
# ip_b = para.Ip_b
if ip_a > 0 and ip_b > 0:
r = -(
-ip_b / ip_a / ((1 + (i / ip_a) ** ip_b) ** 2) * ((i / ip_a) ** (ip_b - 1))
)
else:
if td == 20:
r = -(10 ** (-i / 44)) * math.log(10) * (-1 / 44) # 雷暴日20d
if td == 40:
r = -(10 ** (-i / 88)) * math.log(10) * (-1 / 88) # 雷暴日40d
return r
@ -433,7 +443,7 @@ def tangent_line_k(line_x, line_y, center_x, center_y, radius, init_k=10.0):
return np.array(k_candidate)[np.max(k_angle) == k_angle].tolist()[-1]
def func_ng(td): # 地闪密度
def func_ng(td): # 地闪密度，通过雷暴日计算
return 0.023 * (td ** 1.3)

68
main.py
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@ -8,30 +8,29 @@ import timeit
# 打印参数
def parameter_display(para: Parameter):
logger.info(f"额定电压 kV {para.rated_voltage}")
logger.info(f"导线弧垂 m {para.h_c_sag}")
logger.info(f"地线弧垂 m {para.h_g_sag}")
logger.info(f"全塔高 m {para.h_arm[0]}")
logger.info(f"串绝缘距离 m {para.insulator_c_len}")
logger.info(f"导线串长 m {para.string_c_len}")
logger.info(f"地线串长 m {para.string_g_len}")
logger.info(f"挂点垂直坐标 m {para.h_arm}")
logger.info(f"挂点水平坐标 m {para.gc_x}")
logger.info(f"地面倾角 ° {[an*180/math.pi for an in para.ground_angels]}")
logger.info(f"海拔高度 m {para.altitude}")
logger.info(f"雷暴日 d {para.td}")
def egm():
if len(sys.argv) < 2:
toml_file_path = r"article.toml"
def parameter_display(para_dis: Parameter):
logger.info(f"额定电压 kV {para_dis.rated_voltage}")
logger.info(f"导线弧垂 m {para_dis.h_c_sag}")
logger.info(f"地线弧垂 m {para_dis.h_g_sag}")
logger.info(f"全塔高 m {para_dis.h_arm[0]}")
logger.info(f"串绝缘距离 m {para_dis.insulator_c_len}")
logger.info(f"导线串长 m {para_dis.string_c_len}")
logger.info(f"地线串长 m {para_dis.string_g_len}")
logger.info(f"挂点垂直坐标 m {para_dis.h_arm}")
logger.info(f"挂点水平坐标 m {para_dis.gc_x}")
logger.info(f"地面倾角 ° {[an * 180 / math.pi for an in para_dis.ground_angels]}")
logger.info(f"海拔高度 m {para_dis.altitude}")
if para_dis.ng > 0:
logger.info("不采用雷暴日计算地闪密度和雷电流密度")
logger.info(f"地闪密度 次/(每平方公里·每年) {para_dis.ng}")
logger.info(f"概率密度曲线系数a {para_dis.Ip_a}")
logger.info(f"概率密度曲线系数b {para_dis.Ip_b}")
pass
else:
toml_file_path = sys.argv[1]
if not os.path.exists(toml_file_path):
logger.info(f'无法找到数据文件{toml_file_path}，程序退出。')
sys.exit(0)
logger.info(f"读取文件{toml_file_path}")
logger.info(f"雷暴日 d {para_dis.td}")
def read_parameter(toml_file_path):
with open(toml_file_path, "rb") as toml_fs:
toml_dict = tomli.load(toml_fs)
toml_parameter = toml_dict["parameter"]
@ -49,9 +48,25 @@ def egm():
para.h_arm = toml_parameter["h_arm"]
para.altitude = toml_parameter["altitude"]
para.rated_voltage = toml_parameter["rated_voltage"]
toml_advance = toml_dict["advance"]
para.ng = toml_advance["ng"] # 地闪密度
para.Ip_a = toml_advance["Ip_a"] # 概率密度曲线系数a
para.Ip_b = toml_advance["Ip_b"] # 概率密度曲线系数b
toml_optional = toml_dict["optional"]
para.voltage_n = toml_optional["voltage_n"] # 工作电压分成多少份来计算
para.max_i = toml_optional["max_i"]
def egm():
if len(sys.argv) < 2:
toml_file_path = r"default.toml"
else:
toml_file_path = sys.argv[1]
if not os.path.exists(toml_file_path):
logger.info(f"无法找到数据文件{toml_file_path}，程序退出。")
sys.exit(0)
logger.info(f"读取文件{toml_file_path}")
read_parameter(toml_file_path)
#########################################################
# 以上是需要设置的参数
parameter_display(para)
@ -223,7 +238,7 @@ def egm():
** 0.5
)
if distance > rc:
logger.info("暴露弧已经完全被屏蔽")
logger.info(f"电流为{i_bar}kV时，暴露弧已经完全被屏蔽")
exposed_curve_shielded = True
break
# if phase_conductor_foo == 2:
@ -273,6 +288,9 @@ def egm():
i_min, i_max, curt_segment_n + 1, retstep=True
)
bd_area_vec = np.vectorize(bd_area)
td = para.td
ip_a = para.Ip_a
ip_b = para.Ip_b
cal_bd_np = (
bd_area_vec(
i_curt_samples,
@ -286,7 +304,7 @@ def egm():
ground_angel,
rg_type,
)
* thunder_density(i_curt_samples)
* thunder_density(i_curt_samples, td, ip_a, ip_b)
)
calculus = np.sum(cal_bd_np[:-1] + cal_bd_np[1:]) / 2 * d_curt
# for i_curt in i_curt_samples[:-1]:

2
metadata.yml
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@ -1,4 +1,4 @@
Version: 1.1.0
Version: 1.2.0
#CompanyName: My Imaginary Company
#FileDescription: Simple App
#InternalName: Simple App

29
untest.py Normal file
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@ -0,0 +1,29 @@
import unittest
from core import thunder_density
from main import read_parameter
import numpy as np
import sympy
class Testing(unittest.TestCase):
@classmethod
def setUpClass(cls):
# read_parameter('default.toml')
pass
def test_thunder_density(self):
i = sympy.symbols("i")
a = np.random.random()
b = np.random.random()
p = 1-1 / (1 + (i / a) ** b)
d_p = sympy.diff(p, i)
random_i = np.random.randint(10, 100)
v_from_thunder_density = thunder_density(random_i, 0, a, b)
v_from_diff = d_p.evalf(subs={i: random_i})
self.assertTrue(
abs(v_from_thunder_density - v_from_diff) < 1e-5, "与自动微分结果不一致"
) # add assertion here
if __name__ == "__main__":
unittest.main()