Journal of Shandong University(Engineering Science) ›› 2021, Vol. 51 ›› Issue (3): 76-83.doi: 10.6040/j.issn.1672-3961.0.2020.527

• Electrical Engineering • Previous Articles     Next Articles

Prediction method of power grid emergency supplies under meteorological disasters

Qingfa CHAI1(),Shoujing SUN1,*(),Jifu QIU2,Ming CHEN2,Zhen WEI2,Wei CONG1   

  1. 1. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University), Jinan 250061, Shandong, China
    2. Qingdao Power Supply Company, State Grid Shandong Electric Power Company, Qingdao 266002, Shandong, China
  • Received:2020-12-17 Online:2021-06-20 Published:2021-06-24
  • Contact: Shoujing SUN E-mail:chaiqingfasdu@163.com;sjingjing@sdu.edu.cn

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Abstract:

In order to improve the efficiency of grid emergency repairs, a method for predicting emergency supplies under the conditions of power grid meteorological disasters combining case-based reasoning, deep belief network and deep learning was proposed. Based on meteorological data, power grid maintenance data and geographic environment data, this method was used case-based reasoning to determine the appropriate input and output structure of the prediction model, and different methods was used to process and quantify according to the characteristics of disagreeing input factors. Deep belief networks were used to complete case adaptation, and integrate accident scale information was used to establish a dynamic power grid emergency supplies prediction model. The analysis results showed that the emergency material prediction method proposed in this paper could comprehensively analyze various characteristic factors, and combined the scale of the accident to establish the relationship between the emergency material demand of the power grid, and accurately predicted the material demand for the emergency response of the power grid under the weather disaster. and provided a scientific reference for emergency decision-making of power grids.

Key words: meteorological disaster, characteristic factor, emergency materials prediction, case-based reasoning, deep learning

CLC Number: 

  • TM73

Table 1

Technical status parameter value"

状态分级 赋值范围
一级 0~2.99
二级 3~4.99
三级 5~6.99
四级 7~10

Table 2

Operating defect parameter value table"

线路电压/kV 赋值 运行时间/a 赋值
35 0.608 ≤1 0.321
110 7.010 2~5 3.419
220 2.031 6~10 3.116
330 0.064 11~15 1.532
≥500 0.287 ≥15 1.613

Table 3

Classification standard of relief height and terrain  m"

地形 平地 丘陵 山地 高山
T1 0~20 20~50 50~150 150~250

Table 4

Terrain standard deviation and terrain classification standard"

地形 平地 草地 丘陵 山区
T2 0~10 10~30 30~50 ≥50

Fig.1

Emergency material forecast model structure"

Fig.2

Parallel classification output structure"

Table 5

Correlation coefficient and correlation degree"

R 相关程度
0.8~1.0 极强
0.6~0.79 强
0.4~0.59 中等
0.2~0.39 弱
0~0.19 极弱

Fig.3

Correlation between material quantity and characteristic factors"

Table 6

Correlation coefficient between the correction value of characteristic factors and the amount of materials"

特征因素 案例组
20 40 60
X1 0.517 0.489 0.431
X′1 0.823 0.757 0.771
X2 0.271 0.332 0.282
X′2 0.568 0.581 0.578
F1 0.895 0.908 0.893
F2 0.621 0.563 0.561
T1 0.332 0.292 0.399
T2 0.374 0.421 0.506
T3 0.469 0.349 0.472

Table 7

Grid accident case caused by typhoon disaster"

类别 输入因素 输入量 输出因素 预测输出/个 实际需求/个 准确率/%
应急人员 风力修正 2.5 抢修人员 1324 1132 85.52
降雨修正 17.5 后勤人员 275 315 78.21
降雪修正 0 运输人员 212 174 82.32
风向 1.0 其他 — — —
应急资源 云量 6.4 安全帽 1552 1319 85.14
温度 16.5 安全带 314 363 86.84
湿度 22.0 安全绳 445 368 83.13
雷击密度 0.022 急救箱 124 93 76.31
主成分F1 4.96 其他 — — —
应急工具 主成分F2 2.17 发电机 92 73 79.21
地形T1 11.5 抢修车 103 91 88.32
地形T2 6.2 冲锋舟 13 11 85.71
地形T3 7.1 工器具 4314 3565 82.68
10 kV断线 196处 其他 — — —
设备元件 10 kV倒杆 124基 导线 5531 4222 76.38
35 kV断线 1处 仝杆 85 76 89.48
35 kV倒杆 1基 横担 123 151 81.25
110 kV断线 0处 绝缘子 312 156 82.35
110 kV倒杆 2基 其他 — — —

Table 8

Forecast results of different networks  %"

模型 d1 d2 d3 d4
DBN 78.60 81.56 84.56 88.73
MLP1 63.31 63.67 64.76 71.55
MLP2 66.73 68.05 73.43 75.48
SVM 75.21 77.03 80.02 83.25
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