Research on Comprehensive Evaluation and Early Warning of Transmission Lines’ Operation Status Based on Dynamic Cloud Computing
Abstract
:1. Introduction
2. Method for Evaluating ETL Status
2.1. Improved HSA Method of Subjective Hierarchical Analysis
 (1)
 Build a model
 (2)
 Construction of the discrimination matrix
 (3)
 Consistency check
 (4)
 Index weight calculation
2.2. Objective Weight Calculation
 (1)
 The standard deviation of each indicator is calculated to reflect the varying extent of each indicator, as shown in Equation (5):$${\mathsf{\sigma}}_{\mathrm{j}}=\sqrt{\frac{1}{\mathrm{N}}{\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{N}}{\left({\mathrm{x}}_{\mathrm{i}}\mathsf{\mu}\right)}^{2}}\text{}$$
 (2)
 The correlation coefficient ${\mathrm{R}}_{\mathrm{ij}}$ of each indicator is calculated, and the correlation quantification equation $\sum}_{\mathrm{i}=1}^{\mathrm{n}}\left(1{\mathrm{R}}_{\mathrm{ij}}\right)$ is obtained.
 (3)
 The amount of information for each indicator is comprehensively calculated, as shown in Equation (6):$${\mathrm{C}}_{\mathrm{j}}={\mathsf{\sigma}}_{\mathrm{j}}\ast {\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{N}}\left(1{\mathrm{R}}_{\mathrm{ij}}\right)\text{}$$
 (4)
 The index weight β is calculated by Equation (7):$${\mathsf{\beta}}_{\mathrm{j}}=\frac{{\mathrm{C}}_{\mathrm{j}}}{{{\displaystyle \sum}}_{\mathrm{j}=1}^{\mathrm{N}}{\mathrm{C}}_{\mathrm{j}}}\text{}$$
2.3. Subjective and Objective Evaluation
2.4. Calculation of Evaluation Results
3. Weight Analysis of Evaluation Indices Based on Expert Experience
3.1. Calculation of Subjective Weight
 (1)
 Construct regular discriminant matrix and calculate weight
 (2)
 Appraise layer of the discriminant matrix construct and the weight calculation
 (3)
 Overall weight calculation of each indicator of the appraise layer
3.2. Objective Weight Calculation
3.3. Calculation of the Weight
4. Evaluation of Cloud Model Establishment and Verification
4.1. Applicability of the Cloud Model in Early Warning of ETL Operation Status
4.2. Evaluation of the Cloud Model and the Platform’s Establishment
 (1)
 Theory of the cloud
 (2)
 Digital Characteristics of Clouds
 Expectation (Ex): This refers to the expectation of cloud droplet distribution in the universe of discourse, and it is also the core of a cloud, meaning the most probable point of a qualitative concept in the universe of discourse.
 Entropy (En): This measures the randomness of qualitative concepts, which reflects the extent of dispersion of a cloud drop. Furthermore, it reflects the acceptable range of cloud drop values in the universe of discourse. Overall, the value of En directly determines the width of a cloud.
 Super entropy (He): This reflects the uncertainty of entropy, or the entropy of entropy, and its value determines the thickness of a cloud. A high value of He corresponds to high dispersion and viscosity of the cloud.
 (3)
 Clouds computing model platform establishment
 Generate a standard random number En’ with En as the expectation and He as the standard deviation;
 Generate a regular random number xi with Ex as the expectation and En’ as the standard deviation;
 Calculate the cloud titre value using Equation (17):$$\mathsf{\mu}\left({\mathrm{x}}_{\mathrm{i}}\right)={\mathrm{e}}^{{\left({\mathrm{x}}_{\mathrm{i}}\mathrm{E}\mathrm{x}\right)}^{2}/2{(\mathrm{E}\mathrm{n})}^{2}}$$Then, (xi,$\mathsf{\mu}\left(\mathrm{x}\right)$) is a cloud droplet, which realises the conversion of qualitative concepts into quantitative concepts;
 Repeat steps a–c n times to generate a sufficient number of cloud droplets.
 Input n cloud droplets xi, and calculate the mean value of this group of cloud droplets—that is, the cloud model digital feature expectation Ex and the sample variance S2—using Equations (18) and (19):$$\overline{\mathrm{X}}=\frac{1}{\mathrm{n}}{\sum}_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{x}}_{\mathrm{i}}$$$${\mathrm{S}}^{2}=\frac{1}{\mathrm{n}1}{\sum}_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{x}}_{\mathrm{i}}\overline{\mathrm{X}}\right)}^{2}$$
 Calculate the digital feature entropy En of the cloud model using Equation (20).$$\mathrm{E}\mathrm{n}=\sqrt{\frac{\pi}{2}}\xb7\frac{1}{\mathrm{n}}\xb7{\sum}_{\mathrm{i}=1}^{\mathrm{n}}\left{\mathrm{x}}_{\mathrm{i}}\overline{\mathrm{X}}\right$$
 Calculate the digital feature super entropy (He) of the cloud model using Equation (21).$$\mathrm{H}\mathrm{e}={\left({\mathrm{S}}^{2}\mathrm{E}{\mathrm{n}}^{2}\right)}^{\frac{1}{2}}\text{}$$
4.3. Evaluate the Impact of Cloud Model Dynamic Weight
 (1)
 Determine the index and evaluate the cloud
 (2)
 Determining the criteria layer of the dynamic index evaluation cloud
 (3)
 Determine the dynamic, comprehensive evaluation cloud
5. Results and Discussion
5.1. Evaluation Indices’ Dynamic Weight Determination Based on Expert Experience
5.2. Analysis of Sensitive Influencing Factors of Some Key Evaluation Indices, including Data Timeliness
5.3. Determining the Dynamic Combination Weight of Transmission Lines’ Operating Condition Evaluation Index
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Importance  NineLevel Scale Value K  Exponential Scaling Value K1 (a = 1.316) 

Equally important  1  a^{0} = 1 
More than equally important but less than slightly important  2  a^{1} = 1.316 
Slightly important  3  a^{2} = 1.732 
More than slightly important but less than Important  4  a^{3} = 2.279 
Important  5  a^{4} = 3 
More than obviously important but less than strongly important  6  a^{5} = 3.947 
Strongly important  7  a^{6} = 5.194 
More than strongly important but less than extremely important  8  a^{7} = 6.836 
Extremely important  9  a^{8} = 9 
m  1  2  3  4  5  6  7  8  9  10  11  12 

R_{I}  0  0  0.52  0.89  1.12  1.26  1.36  1.41  1.46  1.49  1.52  1.54 
The Scoring Standard for Line Unit Status [3]  Score 

Normal status I  5 
General state II  4 
Attention status III  3 
Abnormal state IV  2 
Severe state V  1 
Index  ${\mathbf{T}}_{1}$  ${\mathbf{T}}_{2}$  ${\mathbf{T}}_{3}$  ${\mathbf{T}}_{4}$  ${\mathbf{T}}_{5}$  ${\mathbf{T}}_{6}$  ${\mathbf{T}}_{7}$  ${\mathbf{T}}_{8}$ 

Weights  0.1078  0.3234  0.0948  0.1867  0.1419  0.0622  0.0473  0.0359 
Index  ${\mathbf{T}}_{51}$  ${\mathbf{T}}_{52}$  ${\mathbf{T}}_{53}$  ${\mathbf{T}}_{54}$  ${\mathbf{T}}_{55}$  ${\mathbf{T}}_{56}$  ${\mathbf{T}}_{57}$  ${\mathbf{T}}_{58}$  ${\mathbf{T}}_{59}$  ${\mathbf{T}}_{510}$  ${\mathbf{T}}_{511}$  ${\mathbf{T}}_{512}$ 

Weights  0.1378  0.1378  0.1378  0.1378  0.1378  0.0918  0.047  0.047  0.047  0.047  0.0157  0.0157 
Index 
$${\mathbf{T}}_{51}$$

$${\mathbf{T}}_{52}$$

$${\mathbf{T}}_{53}$$

$${\mathbf{T}}_{54}$$

$${\mathbf{T}}_{55}$$

$${\mathbf{T}}_{56}$$

$${\mathbf{T}}_{57}$$

$${\mathbf{T}}_{58}$$

$${\mathbf{T}}_{59}$$

$${\mathbf{T}}_{510}$$

$${\mathbf{T}}_{511}$$

$${\mathbf{T}}_{512}$$


Weights  0.0196  0.0196  0.0196  0.0196  0.0196  0.0130  0.0067  0.0067  0.0067  0.0067  0.0022  0.0022 
Index  Foundation  Pole and Tower  Guide Ground  Insulator String  Gold Tools  Earthing Device  Ancillary Facilities  Channel Environment  Meteorological Environment 

Weights  0.113  0.304  0.096  0.175  0.133  0.058  0.044  0.036  0.039 
Early Warning Method  Principle  Characteristic 

BP neural network  A selflearning network early warning method continuously updates the data optimisation model through selflearning until it reaches the optimal state [33]  These networks have good adaptability and can handle more complex problems, making them suitable for a wide range of applications [34] 
Supportvector machine  According to statistical learning theory and the structural risk minimisation principles, limited samples strive to find the best balance between model complexity and learning ability in order to achieve the best generalisation ability [35]  They are particularly useful for solving small, nonlinear problems [36] 
AHPfuzzy comprehensive  For the index system, the AHP principle is used to determine the weight by comparing the importance of each index layer by layer, and the overall warning level is obtained by synthesising multiple index values using the membership theory in fuzzy mathematics [37]  Precise results for nondeterministic problems that are difficult to quantify [38] 
Cloud model  The approach combines experts’ qualitative linguistic value descriptions with scientific quantitative calculation, allowing qualitative information expressed through linguistic values to be transformed into quantitative data or precise numerical values that can be effectively converted into appropriate qualitative linguistic values for analysis [39]  Taking into account randomness and ambiguity to effectively solve complex and fuzzy system problems [40] 
Early Warning Level  Critical State  Abnormal State  Alert Status  Attention Status  Normal Status 

Scoring interval  [0, c1]  [c1, c2]  [c2, c3]  [c3, c4]  [c4, 10] 
Early Warning Level  Scoring Interval  Cloud Model Digital Eigenvalues 

Critical state  [0, 2]  (1, 0.33, 0.08) 
Abnormal state  [2, 4]  (3, 0.33, 0.08) 
Alert state  [4, 6]  (5, 0.33, 0.08) 
Attention state  [6, 8]  (7, 0.33, 0.08) 
Normal state  [8, 10]  (9, 0.33, 0.08) 
Line  Standard Specification  HSA  Improved HSA  CRITIC  Improved HSA–CRITIC  

Evaluation Statue  Sort  Evaluation Score  Sort  Evaluation Score  Sort  Evaluation Score  Sort  Evaluation Score  Sort  
#1  Notice  1  4.958  1  4.930  1  4.771  1  4.882  1 
#2  Abnormal  5  4.861  4  4.834  4  4.120  4  4.620  3 
#3  Notice  1  4.936  2  4.902  2  4.615  2  4.816  2 
#4  Notice  1  4.669  6  4.688  6  4.233  3  4.551  5 
#5  Notice  1  4.907  3  4.872  3  4.021  5  4.617  4 
#6  Serious  6  4.753  5  4.731  5  3.798  6  4.451  6 
Criterion Layer  Evaluation Layer  Combined Weight Value 

Foundation T_{1}  Surface damage of tower foundation T_{11}  0.040 
Foundation settlement T_{12}  0.045  
Pole and tower T_{2}  Tilt of the tower T_{21}  0.130 
Bending of the main wood T_{22}  0.059  
Crack condition of tower rod T_{23}  0.066  
Guide ground T_{3}  Corrosion, broken strands, damage, and flashover burns T_{31}  0.035 
Foreign body hanging condition T_{32}  0.037  
Abnormal vibration, dancing, and icing T_{33}  0.012  
Arcing T_{34}  0.008  
Insulator string T_{4}  Corrosion of iron cap and steel pin of insulator T_{41}  0.030 
Insulator string tilt condition T_{42}  0.029  
Breakage of insulators T_{43}  0.050  
Zero value of porcelain insulator and selfdetonation of glass insulator T_{44}  0.038  
Lock pin defect T_{45}  0.036  
Gold tools T_{5}  The defect of the instrument T_{51}  0.051 
The condition of the fittings T_{52}  0.039  
The displacement of the instrument T_{53}  0.030  
Earthing device T_{6}  Grounding lead down connection T_{61}  0.032 
Ground resistance value T_{62}  0.040  
Grounding depth T_{63}  0.027  
Ancillary facilities T_{7}  The defect of the lever plate T_{71}  0.032 
Damage to bird control facilities T_{72}  0.032  
Ladder and guardrail damage T_{73}  0.007  
Channel environment T_{8}  Crossing distance T_{81}  0.021 
Trees and buildings in the passageway T_{82}  0.015  
Meteorological environment T_{9}  Temperature T_{91}  0.031 
Humidity T_{92}  0.013  
Wind speed T_{93}  0.008  
Rainfall T_{94}  0.006 
Line Name  Inspection Time  Traditional Manual Scoring  Evaluation Score  Precedence Ranking 

66 kV Chengbao line  2021.2  4  4.176  3 
2022.11  4  4.129  4  
2021.3  4  4.064  5  
66 kV Chenglan line  2021.3  4  4.509  1 
2020.6  3  3.566  8  
2021.3  3  3.512  10  
66 kV Chengtai line  2020.4  4  3.541  9 
2022.3  4  3.759  7  
2022.4  4  4.333  2  
66 kV Town line  2022.3  4  3.892  6 
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Wang, M.; Li, C.; Wang, X.; Piao, Z.; Yang, Y.; Dai, W.; Zhang, Q. Research on Comprehensive Evaluation and Early Warning of Transmission Lines’ Operation Status Based on Dynamic Cloud Computing. Sensors 2023, 23, 1469. https://doi.org/10.3390/s23031469
Wang M, Li C, Wang X, Piao Z, Yang Y, Dai W, Zhang Q. Research on Comprehensive Evaluation and Early Warning of Transmission Lines’ Operation Status Based on Dynamic Cloud Computing. Sensors. 2023; 23(3):1469. https://doi.org/10.3390/s23031469
Chicago/Turabian StyleWang, Minzhen, Cheng Li, Xinheng Wang, Zheyong Piao, Yongsheng Yang, Wentao Dai, and Qi Zhang. 2023. "Research on Comprehensive Evaluation and Early Warning of Transmission Lines’ Operation Status Based on Dynamic Cloud Computing" Sensors 23, no. 3: 1469. https://doi.org/10.3390/s23031469