# Research on Dynamic Assessment System of Composite Fault Risk of Transmission Line Based on Blockchain Energy

Authors: Jie Liu; Boyan Jia; Likun Ding; Zhimeng Zhang; Cuiying Sun
Journal: Wireless Communications and Mobile Computing
 (2022)

---

## Abstract

In order to study the dynamic assessment system of composite fault risk of transmission line based on blockchain energy and in order to study the transmission line compound fault risk dynamic assessment system based on blockchain, firstly, according to the coupling relationship between power grid and natural disasters, the information resources such as data collected by power grid intelligent devices and natural meteorology are excavated, and the overall architecture of power grid disaster early warning and decision-making system supported by blockchain is built. Then, from the perspective of risk, combined with analytic hierarchy process, an index system for reasonable evaluation of distribution network fault benchmark risk is established. Quantitative assessment and risk classification shall be carried out for the failure probability, failure impact consequence, and comprehensive failure risk, so as to facilitate the adoption of risk response measures. Finally, taking several 220 kV lines in the northwest and central part of a city as examples, the icing prediction analysis verifies the feasibility and effectiveness of the proposed power grid disaster early warning decision system based on blockchain to predict the icing thickness. The experimental results show that taking the icing disaster as an example, the MPC method is used to modify the icing thickness prediction model, improve the accuracy of the icing prediction model, and verify the feasibility and effectiveness of the prediction and early warning system based on blockchain.


---

## Body

## 1. Introduction

In recent years, the violent global climate change, the frequent occurrence of severe weather, and extreme natural disasters have brought great impact to the power system, resulting in large-scale power outage and damage to power transmission and transformation and other related equipment, so that the safe and stable operation of the power grid under extreme natural disasters and the corresponding disaster early warning and disaster prevention strategies have been widely studied. The focus of power grid early warning and disaster prevention is to reduce the probability and frequency of equipment failure. With the improvement of equipment manufacturing technology and equipment operation and maintenance level, the power grid failure rate is mainly caused by meteorological factors such as lightning, mountain fire, and ice disaster (Figure1). Therefore, the research on power grid early warning decision-making should focus on the disaster caused by extreme natural meteorological disasters [1]. At present, unreasonable distribution network structure, frequent faults, and low power supply security are common. With the rapid development of economy and social progress, the construction of distribution network has attracted more and more attention, especially how to reduce the risk level of power failure and outage and improve the safe operation level of distribution network.Figure 1
Integrated fault location of transmission line.Power grid security refers to the ability of the power grid to resist disturbance events such as faults, which directly reflects the strength of the power grid and the ability of uninterrupted power supply to users. The safe operation of distribution network is an indispensable part of the safe operation of the whole power grid, and it is also the key to improve the operation level of power supply system. Some data show that about 80% of the power outage accidents in the power system are caused by the failure of the distribution system. Therefore, it is of great theoretical and practical significance to accurately evaluate the failure and outage risk of distribution network, locate the weak links, and take measures to improve it in order to improve the security of power supply [2].In view of this, this study will strive to establish a set of scientific and comprehensive evaluation method of distribution network failure and outage risk, try to reflect the overall failure risk level and technical management of distribution network, point out the focus and direction of distribution network to enhance fault risk resistance, and lay a good foundation for the development of urban distribution network in the future [3].


## 2. Literature Review

Relevant experts have done a lot of research on how to realize the fault early warning of power grid under the condition of extreme natural disasters. Yamashita and others analyzed the research status and technical requirements of power system security assessment under extreme ice and snow disasters, designed a power grid security assessment framework considering the impact of ice and snow disasters, proposed the concept of meteorological electrical hybrid simulation, and discussed the implementation of hybrid simulation [4]. Chen and others proposed the quantitative classification method of landslide influencing factors for the early warning of transmission towers under rainfall-induced landslide disaster and constructed the risk early warning method of transmission towers based on the two-factor hierarchical superposition method [5]. Strawn and others analyzed the relevant laws of the impact of freezing rain on the power grid failure rate, evaluated the change of transmission line failure rate online through the measured information of the line, and established the early warning model of ice disaster outage risk according to the dynamic law of electrical and system collapse of power flow transfer [6]. According to the way and mechanism of power equipment failure caused by mountain fire, Strawn combined mountain fire information with geographical environment and meteorological information to predict the temporal and spatial distribution of transmission line failure rate and improve the early warning ability of power grid outage prevention system for mountain fire disaster [7]. Underwood established a knowledge platform, data exploration, and comprehensive management mechanism by analyzing the causes and processes of disasters, based on advanced scientific research and historical data exploration, and built a disaster prevention and early warning and disaster relief decision support system [8]. In terms of the main network, Sikorski and others believe that the application of risk assessment method to solve different problems has achieved many results, such as the regional power grid security risk assessment system, the risk index of system vulnerability, the method of natural disaster risk assessment for the power grid, the risk assessment method of cascading faults of complex power system, the comprehensive transient stability index, and the risk-based low-voltage security early warning method of power system [9]. Kshetri believes that the above method can not be directly applied to distribution network. The distribution network has complex network structure, many types of equipment, large quantity, wide dispersion, and variable operation mode. It is easy to be affected by various external factors, and there are many factors involved in fault risk assessment. The energy failure risk assessment of the distribution network requires not only the overall assessment of the distribution network but also the disclosure of the weak links that cause risks at key points. The above methods are difficult to meet these requirements [10].


## 3. Method

### 3.1. Comprehensive Assessment Method and Index System of Transmission Line Fault Risk

The concept of power system risk assessment was clearly put forward for the first time by the international power grid conference in 1997. The conference pointed out that the purpose of power system risk assessment is to quantitatively analyze the uncertainty faced in the operation of power system. Power scholar McCalley and others comprehensively expounded the connotation and significance of power system risk assessment. Generally speaking, power system risk assessment involves identifying uncertain factors in power system operation, establishing risk index system, quantitatively evaluating risk, and studying reasonable decision-making in dispatching operation for risk control [11]. Power scholars define the concept of power system risk assessment with mathematical methods, that is, comprehensively measure the possibility and severity of uncertain factors faced by power system, and its expression is
(1)RiskXf=∑iPrEi×SevEi,Xf,whereXf represents the operation mode of power system, Ei represents the i-th accident, PrEi represents the probability of accident Ei, SevEi,Xf indicates the severity of loss caused by accident Ei under operation mode Xf, and RiskXf represents the risk index of power system under operation mode Xf.During the operation of distribution network, the event that the function of distribution lines and equipment fails and is forced to shut down due to various reasons is distribution network failure. There are common causes such as equipment failure caused by lightning strike, equipment failure caused by human factors, etc. Risk generally refers to the possibility and severity of potential loss. For distribution network, it is the combination of the probability of failure and the loss caused by failure. Risk assessment requires quantification of the likelihood of adverse events and the severity of the consequences [12].The distribution network fault risk assessment defined in this paper refers to the comprehensive quantitative assessment of various faults that may cause the function failure of distribution facilities and forced outage in the distribution network from the two aspects of probability and loss, so as to determine the overall risk level of the distribution network.In order to evaluate the fault risk of distribution network, the research of this subject starts from two aspects: the possibility of fault occurrence and the consequence caused by fault. These two aspects are quantified to obtain the comprehensive fault probability value and comprehensive fault consequence value and then obtain the fault value that can characterize the fault risk of distribution network. The quantitative method is to establish the fault benchmark risk index system and consider the influence of load importance, weather, time, and other factors. The evaluation method is based on the comprehensive failure probability value and comprehensive failure consequence value. According to the concept of distribution network fault risk assessment, the calculation method of the determined distribution network fault risk value is(2)Faultrisk=Comprehensivefaultprobability×Comprehensivefaultconsequences.Among them, the comprehensive failure probability value and comprehensive failure consequence value are calculated from the reference failure probability value, reference failure consequence value, and related influencing factors calculated in the failure risk energy assessment index system [13–16].Distribution network fault risk is random and dynamic. The failure risk not only depends on the power grid itself but also related to weather conditions, load structure, and the time of failure. Therefore, the influence factors for the above three factors are introduced. The framework of comprehensive evaluation method for distribution network fault risk is shown in Figure2.Figure 2
Framework of comprehensive evaluation method for transmission line fault risk.Among them, the benchmark failure probability value is calculated by the benchmark failure probability index in the failure risk assessment index system.The operation statistics of distribution network show that the fault probability level of distribution network in bad weather is significantly higher than that in general weather. This is because bad weather will affect the overall operation of distribution network, such as deteriorating the operating conditions of equipment, even damaging the power tower conductor, hindering normal operation and maintenance, etc. [17]. In order to characterize the amplification effect of meteorological factors on the fault probability of distribution network, meteorological influence factors are introduced. This factor is determined according to different meteorological types in the meteorological disaster early warning signal, and only the more serious yellow, orange, and red early warning levels are selected, as shown in Table 1. Each region can adjust the specific value of meteorological factors according to the operation statistical data of the region [18].Table 1
Meteorological influence factors.
TypeNormalTyphoonThunderstorm and galeHigh temperatureDense fogFreezeFactor value11-21-21-1.21-1.21-1.5When the factor value is 1-1.2, it is yellow warning, 1.2-1.5 is orange warning, and 1.5-2 is red warning. The yellow warning of thunderstorm and gale is 1-1.2, the orange warning is 1.2-1.5, and the red warning is 1.5-2; 1.1 is for high temperature orange warning, and 1.2 is for red warning; 1.1 is for orange early warning of heavy fog, and 1.2 is for red early warning. The value of icing depends on the weather and line icing [19].For distribution networks with various important loads, the greater the proportion of important loads, the greater the loss caused by failure. In addition, the losses caused by faults at different times are also different. Therefore, the load importance factor and time factor are introduced. The important factor of load will be determined according to the proportion of class I and II load, as shown in Table2.Table 2
Important factors of load.
Specific gravity of class I and II load30%60%80%100%Factor value0.91.11.11.2


### 3.2. Calculation of Transmission Line Failure Rate

In this paper, the transmission line is divided into different segments according to the surrounding environmental characteristics such as terrain, landform, and pollution degree. Assuming that the icing thickness, wind force, and pollution degree of the same division section are the same, the icing load expression of gear 1 is(3)I=10−6πρg0LdLd+DLi,wherego is the acceleration of gravity, Ld is the icing thickness of the line, Li is the line length of this gear, and D is the equivalent diameter of the line section.Considering the influence of micro terrain factors on the vertical wind load of the line, the wind load expression is constructed as follows:(4)Wa=va2+2Ld+DLi1600,a=1,2,wherea=1 represents the vertical wind load and a=2 represents horizontal wind load. The expression of the dead weight of the transmission line is
(5)G=10−3πD2ρ1goLi4,whereρ1 is the density of transmission conductor.Therefore, the expression of the total specific load of ice and wind of the conductor under freezing disaster is(6)f=I+G+W12+W221/2πD2Li/4.The exponential function is used to fit the relationship between the failure probabilityPice of line breaking caused by excessive icing and the maximum bearing force σmax of the conductor, and the expression is
(7)Pice=A1eσmax/B1,σmax<λσs.The real-time MPC algorithm has good robustness and does not require high accuracy of the model. This paper uses the real-time MPC algorithm to solve the prediction and early warning model. Read the information of weather environment, geographical environment, and operation conditions of power grid equipment from the equipment layer of blockchain intelligent prediction and early warning system, and segment the transmission line according to the characteristics of geographical environment [20].


## 3.1. Comprehensive Assessment Method and Index System of Transmission Line Fault Risk

The concept of power system risk assessment was clearly put forward for the first time by the international power grid conference in 1997. The conference pointed out that the purpose of power system risk assessment is to quantitatively analyze the uncertainty faced in the operation of power system. Power scholar McCalley and others comprehensively expounded the connotation and significance of power system risk assessment. Generally speaking, power system risk assessment involves identifying uncertain factors in power system operation, establishing risk index system, quantitatively evaluating risk, and studying reasonable decision-making in dispatching operation for risk control [11]. Power scholars define the concept of power system risk assessment with mathematical methods, that is, comprehensively measure the possibility and severity of uncertain factors faced by power system, and its expression is
(1)RiskXf=∑iPrEi×SevEi,Xf,whereXf represents the operation mode of power system, Ei represents the i-th accident, PrEi represents the probability of accident Ei, SevEi,Xf indicates the severity of loss caused by accident Ei under operation mode Xf, and RiskXf represents the risk index of power system under operation mode Xf.During the operation of distribution network, the event that the function of distribution lines and equipment fails and is forced to shut down due to various reasons is distribution network failure. There are common causes such as equipment failure caused by lightning strike, equipment failure caused by human factors, etc. Risk generally refers to the possibility and severity of potential loss. For distribution network, it is the combination of the probability of failure and the loss caused by failure. Risk assessment requires quantification of the likelihood of adverse events and the severity of the consequences [12].The distribution network fault risk assessment defined in this paper refers to the comprehensive quantitative assessment of various faults that may cause the function failure of distribution facilities and forced outage in the distribution network from the two aspects of probability and loss, so as to determine the overall risk level of the distribution network.In order to evaluate the fault risk of distribution network, the research of this subject starts from two aspects: the possibility of fault occurrence and the consequence caused by fault. These two aspects are quantified to obtain the comprehensive fault probability value and comprehensive fault consequence value and then obtain the fault value that can characterize the fault risk of distribution network. The quantitative method is to establish the fault benchmark risk index system and consider the influence of load importance, weather, time, and other factors. The evaluation method is based on the comprehensive failure probability value and comprehensive failure consequence value. According to the concept of distribution network fault risk assessment, the calculation method of the determined distribution network fault risk value is(2)Faultrisk=Comprehensivefaultprobability×Comprehensivefaultconsequences.Among them, the comprehensive failure probability value and comprehensive failure consequence value are calculated from the reference failure probability value, reference failure consequence value, and related influencing factors calculated in the failure risk energy assessment index system [13–16].Distribution network fault risk is random and dynamic. The failure risk not only depends on the power grid itself but also related to weather conditions, load structure, and the time of failure. Therefore, the influence factors for the above three factors are introduced. The framework of comprehensive evaluation method for distribution network fault risk is shown in Figure2.Figure 2
Framework of comprehensive evaluation method for transmission line fault risk.Among them, the benchmark failure probability value is calculated by the benchmark failure probability index in the failure risk assessment index system.The operation statistics of distribution network show that the fault probability level of distribution network in bad weather is significantly higher than that in general weather. This is because bad weather will affect the overall operation of distribution network, such as deteriorating the operating conditions of equipment, even damaging the power tower conductor, hindering normal operation and maintenance, etc. [17]. In order to characterize the amplification effect of meteorological factors on the fault probability of distribution network, meteorological influence factors are introduced. This factor is determined according to different meteorological types in the meteorological disaster early warning signal, and only the more serious yellow, orange, and red early warning levels are selected, as shown in Table 1. Each region can adjust the specific value of meteorological factors according to the operation statistical data of the region [18].Table 1
Meteorological influence factors.
TypeNormalTyphoonThunderstorm and galeHigh temperatureDense fogFreezeFactor value11-21-21-1.21-1.21-1.5When the factor value is 1-1.2, it is yellow warning, 1.2-1.5 is orange warning, and 1.5-2 is red warning. The yellow warning of thunderstorm and gale is 1-1.2, the orange warning is 1.2-1.5, and the red warning is 1.5-2; 1.1 is for high temperature orange warning, and 1.2 is for red warning; 1.1 is for orange early warning of heavy fog, and 1.2 is for red early warning. The value of icing depends on the weather and line icing [19].For distribution networks with various important loads, the greater the proportion of important loads, the greater the loss caused by failure. In addition, the losses caused by faults at different times are also different. Therefore, the load importance factor and time factor are introduced. The important factor of load will be determined according to the proportion of class I and II load, as shown in Table2.Table 2
Important factors of load.
Specific gravity of class I and II load30%60%80%100%Factor value0.91.11.11.2


## 3.2. Calculation of Transmission Line Failure Rate

In this paper, the transmission line is divided into different segments according to the surrounding environmental characteristics such as terrain, landform, and pollution degree. Assuming that the icing thickness, wind force, and pollution degree of the same division section are the same, the icing load expression of gear 1 is(3)I=10−6πρg0LdLd+DLi,wherego is the acceleration of gravity, Ld is the icing thickness of the line, Li is the line length of this gear, and D is the equivalent diameter of the line section.Considering the influence of micro terrain factors on the vertical wind load of the line, the wind load expression is constructed as follows:(4)Wa=va2+2Ld+DLi1600,a=1,2,wherea=1 represents the vertical wind load and a=2 represents horizontal wind load. The expression of the dead weight of the transmission line is
(5)G=10−3πD2ρ1goLi4,whereρ1 is the density of transmission conductor.Therefore, the expression of the total specific load of ice and wind of the conductor under freezing disaster is(6)f=I+G+W12+W221/2πD2Li/4.The exponential function is used to fit the relationship between the failure probabilityPice of line breaking caused by excessive icing and the maximum bearing force σmax of the conductor, and the expression is
(7)Pice=A1eσmax/B1,σmax<λσs.The real-time MPC algorithm has good robustness and does not require high accuracy of the model. This paper uses the real-time MPC algorithm to solve the prediction and early warning model. Read the information of weather environment, geographical environment, and operation conditions of power grid equipment from the equipment layer of blockchain intelligent prediction and early warning system, and segment the transmission line according to the characteristics of geographical environment [20].


## 4. Experimental Analysis

In order to verify the feasibility and effectiveness of the power grid natural disaster prediction and early warning model based on blockchain built in this paper, several 220 kV lines in the northwest and central part of a city are selected for icing prediction and analysis. See Table1 for the actual measurement data of icing thickness of a 220 kV North transmission line in a city for 5 consecutive days. The icing changes from the morning of January 6. With the increase of rainfall in the next five days, the icing thickness of the line will also increase, but the icing changes are different according to the changes of wind speed and temperature [21]. Based on the measured data in Table 3, compare the predicted ice thickness of the line between the original system and the system in this paper, as shown in Table 3. Through comparative analysis, the maximum relative error of the ice thickness prediction of the early warning system in this paper is 2.14%, and the minimum relative error is 0.41%, while the maximum relative error of the ice thickness measured by the original system is 7.46%, and the minimum relative error is 1.36%. Then, the comprehensive index weight is accumulated and calculated layer by layer, so as to obtain the benchmark failure probability value and benchmark failure consequence value. According to the working conditions of the distribution network, the values of meteorological influence factor, time factor, and load important factor are determined, and then, the comprehensive fault probability value, comprehensive fault consequence value, and distribution network fault risk value are calculated. Through comparison, it can be seen that the prediction and early warning system studied in this paper can effectively improve the accuracy of icing prediction [22].Table 3
Measured data of icing thickness of 220 kV North transmission line in a city.
NumberDateTimeRainfall/mmWind speed/(m∙S−1)Temperature/°CMeasure icing thickness/mm101-0607 : 491.790.2-2.00.8211 : 304.870.3-1.01.76316 : 286.691.0-1.02.07423 : 057.321.2-3.02.34501-0808 : 117.450.2-1.02.76615 : 079.370.2-1.03.27717 : 429.810.2-1.03.9822 : 2510.340.2-2.04.26901-0909 : 1512.720.4-1.04.671001-1000 : 2214.485.0-2.55.141103 : 1516.263.0-3.06.201207 : 2618.343.0-3.06.971310 : 0521.152.0-2.07.641421 : 3022.473.0-3.08.731501-1100 : 1524.762.0-3.59.141604 : 2626.841.0-4.09.86Figures3 and 4 show the variation trend between icing failure rate and rainfall in line sections 70, 60, 09, and 83. It can be seen from Figure 4 that the overall trend of the change of fault rate in the four line sections increases with the increase of rainfall, but there are certain differences in different line sections due to the influence of geographical environment factors, wind speed, temperature, and other meteorological factors [23].Figure 3
Comparison of predicted icing thickness of Beifa line.Figure 4
Variation trend of line failure rate caused by rainfall.The evaluation results in Table4 are as follows: the fault risk value of distribution network is 820.8, and the risk level is “large risk.” Among them, the comprehensive failure probability value is 24.0, and the level is “general possibility.” The comprehensive fault consequence value is 34.2, and the level is “large loss.” It can be seen that the score of fault impact consequence is high, which is the main aspect of fault risk.Table 4
Evaluation index value and score.
Basic level failure probability index (weight, score)Grass root indexWeightIndex valueScoreEquipment level (0.55, 15.0)Insulation rate of overhead line0.06758.056.5Proportion of overhead lines that do not meetlightning protection standards0.03917.022.7Proportion of overhead lines that do not meetantipollution standards0.03915.020.0Direct burial ratio of cable line0.03927.054.9Proportion of cables without compressionmeasures on roads0.01031.042.1Proportion of maintenance free equipment0.10777.023.0Proportion of less maintenance equipment0.10783.017.0Oilless rate of circuit breaker0.08986.025.0The automation level and user control ability are weak links in terms of fault impact consequences. The analysis shows that the distribution network automation coverage rate in the automation level is low, only 16%, and the index score is 67.1. The user control ability is insufficient, in which the proportion of multipower supply automatic switching users is 30% and the proportion of self-provided power supply is 11%. The above two proportions are low, resulting in the high scores of these two indicators. In addition, in the load isolation capacity, the low average number of sections of the line is 1.9, and the index score is 60.0. In the load transfer capacity, the average load loss ratio of line “n-1” is 26%, and the index score is 55.3 [24].Suggestions for improving the probability of failure are as follows: increase the number of warning signs along the cable line, especially in the section where external damage failure has occurred, and regularly clean the trees under the overhead line corridor. The trees grow luxuriantly in summer, and the cleaning time interval should be shortened. Insulated overhead lines shall be used as far as possible for new overhead lines and overhead lines to be reconstructed. On the basis of balancing safety and economy, the method of direct buried cable laying shall be minimized. If it is adopted, the cement protection board shall be laid in strict accordance with the construction requirements [25].


## 5. Conclusion

This paper studies the development of power system risk assessment at home and abroad and analyzes the relevant contents of comprehensive evaluation theory. Based on the detailed analysis of the influencing factors of transmission line fault risk, a scientific and reasonable comprehensive evaluation method of transmission line fault risk is proposed. The system can well reflect various factors affecting the fault risk of distribution network and adapt to the evaluation of transmission lines in various regions of China and can also be used for the evaluation and comparison with foreign advanced transmission lines. It plays a good guiding role in the construction and operation of transmission lines in the future. With the advancement of the research and construction of smart grid, the evaluation contents of distributed energy, microgrid operation, advanced protection, and control can be added on the basis of this evaluation method, in order to effectively evaluate the risk of transmission line failure and outage.Next, based on the existing research, the prediction and early warning function of mountain fires, typhoons, and other natural disasters will be added to enhance the prediction and early warning ability of the system to deal with natural disasters, so as to provide reference for the application of blockchain technology in power grid natural disaster early warning and prevention.



---

*Source: 7426559-2022-05-05.xml*