# Application of Internet of Things Energy System in Dynamic Risk Assessment of Composite Fault of Transmission Line

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

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## Abstract

In order to study the application of Internet of things energy system in complex fault risk dynamic assessment of transmission line. Firstly, the concept of power grid dynamic risk assessment is introduced, and the process of power grid dynamic risk assessment system based on Internet of things is designed. Then, it puts forward how to use the ubiquitous Internet of things multisource data to solve the key problems such as dynamic perception of fault probability, dynamic selection of fault set, dynamic generation of post fault state, and dynamic risk assessment of operation process. Finally, taking the maximum operation mode of a provincial power grid in summer 2013 as an example, this paper selects key 500 kV transmission lines for risk assessment, and the actual power grid example shows that. The power grid comprehensive risk assessment system considering the fault characteristics of transmission lines can effectively predict the fault probability of transmission lines; distinguish the two risks of high loss, low probability, and low loss and high probability; and provide guidance for operators. It is practical and effective.


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## Body

## 1. Introduction

Power grid risk assessment refers to the comprehensive measurement of the possibility and severity of accidents according to various uncertain factors faced by the power system. Power grid risk assessment usually includes four aspects: determining the component outage model (calculating the component failure rate), selecting the power grid state (i.e., selecting the expected fault set), calculating their probability, evaluating the consequences of the selected state, and calculating the risk index. Moreover, high voltage AC transmission has the characteristics of high voltage level, large transmission capacity, and long transmission distance. The implementation of real-time online monitoring of its line is a necessary measure to ensure the safe, stable, and reliable operation of EHV line [1].Based on the total energy allocation information of ubiquitous power Internet of things, the impact of power grid dynamic process on consequence assessment after expected fault can be effectively considered (Figure1). It is pointed out in the literature that the result of risk assessment is for the reference of dispatchers to adjust the power grid, so it is necessary to give the power grid state after the expected fault. In actual operation, after the failure occurs, the dispatcher gives the adjustment measures before the operation of the automatic device of the power grid, such as automatic reclosing and automatic switching. Considering the state of the grid after automatic operation of these devices is the power grid state that the dispatcher really faces after the failure. Traditional risk assessment often does not take into account the impact of automatic action after failure. Based on the system configuration information collected by the ubiquitous power Internet of things, the automatic switching attributes of automation devices and switches are obtained, and their action logic is simulated under the expected fault, so as to obtain the correct post fault system state and conduct consequence evaluation [2].Figure 1
Line fault system.Based on the above analysis, this paper proposes a power grid dynamic risk assessment scheme based on ubiquitous power Internet of things multienergy information. Firstly, through the analysis of the system operation process, different system operation states are divided, and the process of system dynamic risk assessment system for multioperation states of power grid is designed. Secondly, for different operation states, the key technologies and methods such as dynamic perception of system fault probability, dynamic selection of expected fault set, dynamic risk assessment in the process of system state transformation, and fault consequence assessment considering the system dynamic process after fault are introduced. On this basis, the software and functional architecture of the system are designed. Finally, the application of the proposed system in the actual power grid is introduced [3].


## 2. Literature Review

Wan and others believe that the Internet of things technology has been applied to the smart grid, making the smart grid more information-based than the traditional power grid operation, with three characteristics of information, automation, and interaction. The application of Internet of things technology plays an important role in improving the information collection, intelligent processing, and two-way communication of smart grid in the five links of power generation, transmission, transformation, distribution, and power consumption [4]. Fu and others believe that the introduction of Internet of things applications can realize detailed monitoring, state prediction, and regulation of the state of power generation equipment from the perspective of power generation links, and provide more timely and effective maintenance for generator set equipment. At the same time, through the detection and analysis of different data, we can realize accident early warning, reduce the occurrence of major accidents, and improve the operation life and efficiency of power generation equipment [5]. Yang and others believe that for the transmission link, the functions of real-time monitoring of the overall transmission line and location of damaged areas can be realized by deploying sensors on transmission lines, towers, or other equipment, so as to improve the all-round operation and maintenance management of transmission equipment [6]. Xiao and others believe that at the same time, intelligent interaction can be realized by monitoring the information of power field operators, equipment, and environment through sensors, so as to reduce the risk of misoperation, reduce potential safety hazards, and improve the efficiency and safety of off-site operations [7]. T. Y. Kim and E. J. Kim believe that most of the faults of transmission lines are single-phase grounding short circuit. The traditional method is to judge whether single-phase grounding fault occurs by detecting the value of zero sequence voltage on the bus of substation. In case of grounding fault, the method of manually switching off one line by one shall be adopted to judge which line has fault, and then the fault point shall be checked manually along the fault line [8]. Chen and others believe that at present, the monitoring of the operating conditions of distribution lines basically focuses on the data of substation bus voltage, outlet line current, active power, reactive power, and end load power consumption. A large number of lines lack measurement points, so it is difficult to obtain real-time monitoring data, resulting in opaque and invisible system operation [9]. Kopylov and others believe that the harmonic of distribution network comes from a large number of nonlinear loads of end users and iron core equipment with ferromagnetic saturation characteristics in the system, such as transformer and other nonlinear inductance equipment. The large number and wide distribution of this equipment make the harmonic current continuously inject into the power grid, resulting in serious distortion of power system waveform, which not only makes the equipment connected to the power grid of other users unable to work normally but also causes faults [10].


## 3. Method

### 3.1. Power Internet of Things Multisource Data Acquisition and Fusion

The data required for power grid energy dynamic risk assessment proposed in this paper mainly include three categories: full perception information, equipment status information, and various integrated system information. Full sensing information mainly includes voltage, current, active power, and reactive power collected by various measuring devices. Information and temperature, humidity, wind speed, and other information were collected by various environmental sensors. At present, the collection of such information is relatively complete at the main network side, but at the distribution network side [11], due to the influence of factors such as low degree of distribution network automation, high coverage cost, and communication, the measuring points are very rare, which can not meet the needs of power grid dynamic risk assessment. With the development of ubiquitous power Internet of things, with the listing of low-cost and low-power measurement devices and the promotion of 5 g communication, the coverage of power IOT sensing terminals will be greatly improved to provide full sensing information for dynamic risk assessment. Equipment status information mainly includes equipment status, defects, service life, maintenance records, and other information. At present, this kind of information is mainly maintained manually, and the information is incomplete and wrong. The first mock exam is based on the unified power grid model. The device can be automatically updated with the information of the equipment ledger, thus providing accurate information about equipment status for dynamic risk assessment. All kinds of system integration information mainly refer to the internal information of the asset management system, GIS system, operation ticket management system, disaster system, and other systems that have been built at present. At present, each system is independent of each other, and the data cannot interact. It is very difficult to obtain the data required for dynamic risk assessment from each system. Based on the unified data model of ubiquitous power Internet of things, break through the barriers of various systems, form a unified Internet of things data platform, integrate multisource data, and provide a good data basis for dynamic risk assessment [12].


### 3.2. Dynamic Calculation of System Failure Probability of Accurate Equipment Status Information of Power Internet of Things

The acquisition of system failure probability is the basic work of system risk assessment, but the system failure probability is not invariable [13]. It is affected not only by the service life and load rate of the component itself but also by the external environment. The component failure probability under different operating states is generally different. The formula of component failure probability is
(1)P=Pmteλ1Z1s,dg+λ2Z2de+λ3Z3dte1,e2,⋯,en,where P is the component dynamic failure probability, Pm is the base failure probability, Zi is the dynamic influence factor of failure probability, i=1,2,3,4, and λi is the covariance coefficient, which can be obtained according to the maximum likelihood estimation method [14].The relationship between the benchmark failure probabilityPm and the operation time of the equipment conforms to the bathtub curve, and the probability density can be expressed by Weibull distribution, as shown in
(2)Pmt=β∂t∂β−1,where t is the service time of the equipment and ∂,β are the scale parameter and shape parameter of Weibull distribution, respectively.The disaster state correction factor is shown in formula (3), which is related to the disaster level s and the geographical distance dg between the current element and the disaster center [15].
(3)Z1S,dg=esdg.The repair state correction factor is shown in formula (3), which is inversely proportional to the electrical distance de.
(4)Z2de=1de.The scheduling state correction factorZ3 is related to the topological distance dtei between the element and the element involved in the scheduling operation, as shown in
(5)Z3=1mindte1,dte2,⋯dten.The fault state correction factor is shown in equation (6), which is related to the topological distance dt between the current element and the fault element.
(6)Z4dt=1dt.


### 3.3. System Structure Model

At present, the industry generally believes that the Internet of things should be divided into three levels: perception layer, network layer, and application layer. The three-tier architecture corresponding to the power Internet of things and the Internet of things is the collection, transmission, and processing of power information. The transmission line intelligent monitoring system adopts the architecture of power Internet of things, which is divided into three layers (see Figure2).
(1)
The perception layer is the perception of the material world, that is, the collection of power information [16]. Through sensors, information acquisition equipment, and other technical means, the information collection of various links such as environmental information, line information, and tower status of transmission line is realized(2)
The network layer realizes the transmission and control of information. In view of the power grid’s requirements for network security, reliability, and real time, network transmission mainly relies on power communication network and power wireless private network. In the special environment without conditions, after encryption, information transmission and control can be realized with the help of public telecommunication network [17](3)
The application layer is the processing and application of information, including the platform, middleware, and various business applications that provide basic services for applications [18]. Realize the business functions of intelligent monitoring, analysis, and decision-making, on-site monitoring, intelligent line patrol, intelligent early warning, line detection, and so onFigure 2
System hierarchy.


#### 3.3.1. System Layered Design

The sensing layer is composed of edge intelligent terminal and information acquisition equipment, in which the acquisition equipment is responsible for the information acquisition of transmission line, and the edge intelligent terminal is responsible for the management and control, data collection, intelligent analysis, and communication of acquisition equipment. The sensing layer structure is shown in Figure3.Figure 3
Perception layer structure.The edge intelligent terminal manages the collection equipment, summarizes, intelligently processes and stores the collected data, encrypts the data and uploads it to the cloud, and parses and executes the remote control commands. The acquisition equipment includes microweather station, inclination sensor, wire vibration sensor, wire temperature sensor, and HD camera [19].
(1)
Microweather station: it realizes the collection of environmental and meteorological data of transmission line, which is composed of wind speed, wind direction, ambient temperature, ambient humidity, air pressure, and rainfall sensors [20].(2)
Conductor vibration sensor: use the optical phase change caused by the stress deformation of optical cable vibration to locate the vibration intensity and position of conductor.(3)
Conductor temperature sensor: the temperature information at each position in the optical fiber is calculated by using the scattering effect of temperature on light, which can accurately reflect the temperature and position of transmission line conductor.(4)
Inclination sensor: also known as inclinometer, it is a kind of monitoring equipment to monitor the inclination of tower [21].(5)
HD camera: realize the real-time video monitoring of the transmission line, and realize the real-time intelligent analysis and perception of the line through the intelligent image processing algorithm.


#### 3.3.2. Data Processing and Strategy

(1)
Intelligent data processing: the edge computing terminal summarizes the data collected by the sensor and makes a comprehensive analysis according to the alarm threshold and data model set by the system, so as to improve the accuracy of the data and reduce unnecessary alarms caused by false positives. After the video data and image data are collected to the edge computing terminal, the image detection and recognition based on artificial intelligence can automatically identify large machinery, foreign object intrusion, man-made damage, and so on(2)
Data reporting strategy: the edge intelligent terminal adopts different data reporting strategies according to the data processing results and back-end settings. In order to ensure the safety and reliability of the data, all the data of the system is encrypted by the encryption module and uploaded to the cloud system by the communication module (see Figure4).Figure 4
Data reporting strategy.When the system starts the real-time monitoring function, in order to ensure the real-time performance of the system, the data is directly reported without being processed by the edge intelligent terminal. In normal mode, the data is analyzed and identified by the edge intelligent terminal first. In normal mode, the data is reported periodically, and in abnormal mode, the data and abnormal information are reported immediately.(3)
Data storage strategy: the data storage strategy adopts the combination of local storage and cloud storage. The edge intelligent terminal adopts the method of cyclic coverage to store the collected data in real time. For sensordatastorage≥30 days and video datastorage≥24 hours. Generally, the cloud stores the normal data reported periodically and can store real-time data as needed [22]. In order to realize the degree of attention to different line areas and different periods, the system can flexibly set the data reporting cycle and can also synchronize the data stored in the edge intelligent terminal.


## 3.1. Power Internet of Things Multisource Data Acquisition and Fusion

The data required for power grid energy dynamic risk assessment proposed in this paper mainly include three categories: full perception information, equipment status information, and various integrated system information. Full sensing information mainly includes voltage, current, active power, and reactive power collected by various measuring devices. Information and temperature, humidity, wind speed, and other information were collected by various environmental sensors. At present, the collection of such information is relatively complete at the main network side, but at the distribution network side [11], due to the influence of factors such as low degree of distribution network automation, high coverage cost, and communication, the measuring points are very rare, which can not meet the needs of power grid dynamic risk assessment. With the development of ubiquitous power Internet of things, with the listing of low-cost and low-power measurement devices and the promotion of 5 g communication, the coverage of power IOT sensing terminals will be greatly improved to provide full sensing information for dynamic risk assessment. Equipment status information mainly includes equipment status, defects, service life, maintenance records, and other information. At present, this kind of information is mainly maintained manually, and the information is incomplete and wrong. The first mock exam is based on the unified power grid model. The device can be automatically updated with the information of the equipment ledger, thus providing accurate information about equipment status for dynamic risk assessment. All kinds of system integration information mainly refer to the internal information of the asset management system, GIS system, operation ticket management system, disaster system, and other systems that have been built at present. At present, each system is independent of each other, and the data cannot interact. It is very difficult to obtain the data required for dynamic risk assessment from each system. Based on the unified data model of ubiquitous power Internet of things, break through the barriers of various systems, form a unified Internet of things data platform, integrate multisource data, and provide a good data basis for dynamic risk assessment [12].


## 3.2. Dynamic Calculation of System Failure Probability of Accurate Equipment Status Information of Power Internet of Things

The acquisition of system failure probability is the basic work of system risk assessment, but the system failure probability is not invariable [13]. It is affected not only by the service life and load rate of the component itself but also by the external environment. The component failure probability under different operating states is generally different. The formula of component failure probability is
(1)P=Pmteλ1Z1s,dg+λ2Z2de+λ3Z3dte1,e2,⋯,en,where P is the component dynamic failure probability, Pm is the base failure probability, Zi is the dynamic influence factor of failure probability, i=1,2,3,4, and λi is the covariance coefficient, which can be obtained according to the maximum likelihood estimation method [14].The relationship between the benchmark failure probabilityPm and the operation time of the equipment conforms to the bathtub curve, and the probability density can be expressed by Weibull distribution, as shown in
(2)Pmt=β∂t∂β−1,where t is the service time of the equipment and ∂,β are the scale parameter and shape parameter of Weibull distribution, respectively.The disaster state correction factor is shown in formula (3), which is related to the disaster level s and the geographical distance dg between the current element and the disaster center [15].
(3)Z1S,dg=esdg.The repair state correction factor is shown in formula (3), which is inversely proportional to the electrical distance de.
(4)Z2de=1de.The scheduling state correction factorZ3 is related to the topological distance dtei between the element and the element involved in the scheduling operation, as shown in
(5)Z3=1mindte1,dte2,⋯dten.The fault state correction factor is shown in equation (6), which is related to the topological distance dt between the current element and the fault element.
(6)Z4dt=1dt.


## 3.3. System Structure Model

At present, the industry generally believes that the Internet of things should be divided into three levels: perception layer, network layer, and application layer. The three-tier architecture corresponding to the power Internet of things and the Internet of things is the collection, transmission, and processing of power information. The transmission line intelligent monitoring system adopts the architecture of power Internet of things, which is divided into three layers (see Figure2).
(1)
The perception layer is the perception of the material world, that is, the collection of power information [16]. Through sensors, information acquisition equipment, and other technical means, the information collection of various links such as environmental information, line information, and tower status of transmission line is realized(2)
The network layer realizes the transmission and control of information. In view of the power grid’s requirements for network security, reliability, and real time, network transmission mainly relies on power communication network and power wireless private network. In the special environment without conditions, after encryption, information transmission and control can be realized with the help of public telecommunication network [17](3)
The application layer is the processing and application of information, including the platform, middleware, and various business applications that provide basic services for applications [18]. Realize the business functions of intelligent monitoring, analysis, and decision-making, on-site monitoring, intelligent line patrol, intelligent early warning, line detection, and so onFigure 2
System hierarchy.


### 3.3.1. System Layered Design

The sensing layer is composed of edge intelligent terminal and information acquisition equipment, in which the acquisition equipment is responsible for the information acquisition of transmission line, and the edge intelligent terminal is responsible for the management and control, data collection, intelligent analysis, and communication of acquisition equipment. The sensing layer structure is shown in Figure3.Figure 3
Perception layer structure.The edge intelligent terminal manages the collection equipment, summarizes, intelligently processes and stores the collected data, encrypts the data and uploads it to the cloud, and parses and executes the remote control commands. The acquisition equipment includes microweather station, inclination sensor, wire vibration sensor, wire temperature sensor, and HD camera [19].
(1)
Microweather station: it realizes the collection of environmental and meteorological data of transmission line, which is composed of wind speed, wind direction, ambient temperature, ambient humidity, air pressure, and rainfall sensors [20].(2)
Conductor vibration sensor: use the optical phase change caused by the stress deformation of optical cable vibration to locate the vibration intensity and position of conductor.(3)
Conductor temperature sensor: the temperature information at each position in the optical fiber is calculated by using the scattering effect of temperature on light, which can accurately reflect the temperature and position of transmission line conductor.(4)
Inclination sensor: also known as inclinometer, it is a kind of monitoring equipment to monitor the inclination of tower [21].(5)
HD camera: realize the real-time video monitoring of the transmission line, and realize the real-time intelligent analysis and perception of the line through the intelligent image processing algorithm.


### 3.3.2. Data Processing and Strategy

(1)
Intelligent data processing: the edge computing terminal summarizes the data collected by the sensor and makes a comprehensive analysis according to the alarm threshold and data model set by the system, so as to improve the accuracy of the data and reduce unnecessary alarms caused by false positives. After the video data and image data are collected to the edge computing terminal, the image detection and recognition based on artificial intelligence can automatically identify large machinery, foreign object intrusion, man-made damage, and so on(2)
Data reporting strategy: the edge intelligent terminal adopts different data reporting strategies according to the data processing results and back-end settings. In order to ensure the safety and reliability of the data, all the data of the system is encrypted by the encryption module and uploaded to the cloud system by the communication module (see Figure4).Figure 4
Data reporting strategy.When the system starts the real-time monitoring function, in order to ensure the real-time performance of the system, the data is directly reported without being processed by the edge intelligent terminal. In normal mode, the data is analyzed and identified by the edge intelligent terminal first. In normal mode, the data is reported periodically, and in abnormal mode, the data and abnormal information are reported immediately.(3)
Data storage strategy: the data storage strategy adopts the combination of local storage and cloud storage. The edge intelligent terminal adopts the method of cyclic coverage to store the collected data in real time. For sensordatastorage≥30 days and video datastorage≥24 hours. Generally, the cloud stores the normal data reported periodically and can store real-time data as needed [22]. In order to realize the degree of attention to different line areas and different periods, the system can flexibly set the data reporting cycle and can also synchronize the data stored in the edge intelligent terminal.


## 3.3.1. System Layered Design

The sensing layer is composed of edge intelligent terminal and information acquisition equipment, in which the acquisition equipment is responsible for the information acquisition of transmission line, and the edge intelligent terminal is responsible for the management and control, data collection, intelligent analysis, and communication of acquisition equipment. The sensing layer structure is shown in Figure3.Figure 3
Perception layer structure.The edge intelligent terminal manages the collection equipment, summarizes, intelligently processes and stores the collected data, encrypts the data and uploads it to the cloud, and parses and executes the remote control commands. The acquisition equipment includes microweather station, inclination sensor, wire vibration sensor, wire temperature sensor, and HD camera [19].
(1)
Microweather station: it realizes the collection of environmental and meteorological data of transmission line, which is composed of wind speed, wind direction, ambient temperature, ambient humidity, air pressure, and rainfall sensors [20].(2)
Conductor vibration sensor: use the optical phase change caused by the stress deformation of optical cable vibration to locate the vibration intensity and position of conductor.(3)
Conductor temperature sensor: the temperature information at each position in the optical fiber is calculated by using the scattering effect of temperature on light, which can accurately reflect the temperature and position of transmission line conductor.(4)
Inclination sensor: also known as inclinometer, it is a kind of monitoring equipment to monitor the inclination of tower [21].(5)
HD camera: realize the real-time video monitoring of the transmission line, and realize the real-time intelligent analysis and perception of the line through the intelligent image processing algorithm.


## 3.3.2. Data Processing and Strategy

(1)
Intelligent data processing: the edge computing terminal summarizes the data collected by the sensor and makes a comprehensive analysis according to the alarm threshold and data model set by the system, so as to improve the accuracy of the data and reduce unnecessary alarms caused by false positives. After the video data and image data are collected to the edge computing terminal, the image detection and recognition based on artificial intelligence can automatically identify large machinery, foreign object intrusion, man-made damage, and so on(2)
Data reporting strategy: the edge intelligent terminal adopts different data reporting strategies according to the data processing results and back-end settings. In order to ensure the safety and reliability of the data, all the data of the system is encrypted by the encryption module and uploaded to the cloud system by the communication module (see Figure4).Figure 4
Data reporting strategy.When the system starts the real-time monitoring function, in order to ensure the real-time performance of the system, the data is directly reported without being processed by the edge intelligent terminal. In normal mode, the data is analyzed and identified by the edge intelligent terminal first. In normal mode, the data is reported periodically, and in abnormal mode, the data and abnormal information are reported immediately.(3)
Data storage strategy: the data storage strategy adopts the combination of local storage and cloud storage. The edge intelligent terminal adopts the method of cyclic coverage to store the collected data in real time. For sensordatastorage≥30 days and video datastorage≥24 hours. Generally, the cloud stores the normal data reported periodically and can store real-time data as needed [22]. In order to realize the degree of attention to different line areas and different periods, the system can flexibly set the data reporting cycle and can also synchronize the data stored in the edge intelligent terminal.


## 4. Experimental Analysis

### 4.1. Example Introduction

Taking the maximum operation mode of a provincial power grid in summer 2013 as an example, this paper selects key 500 kV transmission lines for risk assessment. Firstly, according to the average failure probability and health index of key 500 kV transmission lines in the region in the past 10 years, the failure probability of transmission lines is calculated by using cloud prediction model. Then, the risk indicators of system operation are calculated, respectively, to obtain the comprehensive risk indicators of power grid.


### 4.2. Calculation of Transmission Line Fault Probability

Take lianbei line I as an example to predict its failure probability. Table1 shows the average value of health index and failure probability of condition evaluation of transmission line of lianbei line I in recent ten years. The normal cloud prediction model of transmission line fault probability is established through one-dimensional reverse cloud generator. It is obtained that the expected values of Ex, entropy En, and excess entropy He of the cloud prediction model are 0.168 1, 0.079 7 and 0.005 9, respectively. Because En/He=13.51>10, the absolute error of En is less than 1%, the relative error of En is less than 2%, and the relative error of He is less than 10%. Meet the accuracy requirements. On this basis, using the generated normal cloud model to generate a certain number of cloud droplets, the predicted value of failure probability per 100 km of lianbei line I in 2013 is 0.168 1 times/A. Figure 5 shows the sampling distribution of fault probability of lianbei line I based on a normal cloud model [23].Table 1
Status data of lianbei line I.
Particular yearHealth indexFailure probability2002100.1052003150.1642004110.1852005120.1182006130.1402007240.2052008100.2232009170.3052010110.087Figure 5
Distribution diagram of fault probability prediction of lianbei line I based on a cloud model.According to the above method, the predicted value of failure probability per 100 km of key 500 kV transmission lines in the region in 2013 is calculated and then multiplied by the length of transmission lines to obtain the predicted value of failure probability of each line, as shown in Table2.Table 2
Failure probability prediction results of main transmission lines.
Line namePredicted value of failure probability per 100 km/(times·a-1)Actual length/kmFailure probability pre/(times·a-1)Lianbei line I0.168245.6320.0765Lianji line0.129554.3590.0715Peng Lian line I0.150368.4320.1023Pengguang I line0.118782.8530.0648Guanglin line0.118673.7560.0888Guangxin line0.138475.6530.1063Beiqing line I0.1354105.2970.1046Qingbao line I0.145643.1320.0666The subjective and objective weights between indexes are calculated by using analytic hierarchy process and entropy method. Among them, in the subjective weight, this paper believes that the four indicators are equally important. The calculation results of subjective and objective weights are shown in Table3.Table 3
Index weight of transmission line risk assessment.
Risk indicatorsObjective weightSubjective weightCombination weightLoad loss0.29540.250.2562Potential interlock failure0.41260.250.3212Voltage out of limit0.13580.250.1856Stability index0.15260.250.2015It can be seen from the above calculation results although the failure probability of Qingcang line is significantly higher than that of Lianji line. However, the fault severity is lower than that of Lianji line. When the Lianji line finally exits the operation, the comprehensive operation risk of the system is significantly higher than that of other lines, which requires key maintenance and management [24]. At the same time, it also shows that the utility function theory is used to evaluate the operation risk of power grid. It can effectively distinguish the two risks of high loss and low probability from low loss and high probability and provide better guidance for operators.


## 4.1. Example Introduction

Taking the maximum operation mode of a provincial power grid in summer 2013 as an example, this paper selects key 500 kV transmission lines for risk assessment. Firstly, according to the average failure probability and health index of key 500 kV transmission lines in the region in the past 10 years, the failure probability of transmission lines is calculated by using cloud prediction model. Then, the risk indicators of system operation are calculated, respectively, to obtain the comprehensive risk indicators of power grid.


## 4.2. Calculation of Transmission Line Fault Probability

Take lianbei line I as an example to predict its failure probability. Table1 shows the average value of health index and failure probability of condition evaluation of transmission line of lianbei line I in recent ten years. The normal cloud prediction model of transmission line fault probability is established through one-dimensional reverse cloud generator. It is obtained that the expected values of Ex, entropy En, and excess entropy He of the cloud prediction model are 0.168 1, 0.079 7 and 0.005 9, respectively. Because En/He=13.51>10, the absolute error of En is less than 1%, the relative error of En is less than 2%, and the relative error of He is less than 10%. Meet the accuracy requirements. On this basis, using the generated normal cloud model to generate a certain number of cloud droplets, the predicted value of failure probability per 100 km of lianbei line I in 2013 is 0.168 1 times/A. Figure 5 shows the sampling distribution of fault probability of lianbei line I based on a normal cloud model [23].Table 1
Status data of lianbei line I.
Particular yearHealth indexFailure probability2002100.1052003150.1642004110.1852005120.1182006130.1402007240.2052008100.2232009170.3052010110.087Figure 5
Distribution diagram of fault probability prediction of lianbei line I based on a cloud model.According to the above method, the predicted value of failure probability per 100 km of key 500 kV transmission lines in the region in 2013 is calculated and then multiplied by the length of transmission lines to obtain the predicted value of failure probability of each line, as shown in Table2.Table 2
Failure probability prediction results of main transmission lines.
Line namePredicted value of failure probability per 100 km/(times·a-1)Actual length/kmFailure probability pre/(times·a-1)Lianbei line I0.168245.6320.0765Lianji line0.129554.3590.0715Peng Lian line I0.150368.4320.1023Pengguang I line0.118782.8530.0648Guanglin line0.118673.7560.0888Guangxin line0.138475.6530.1063Beiqing line I0.1354105.2970.1046Qingbao line I0.145643.1320.0666The subjective and objective weights between indexes are calculated by using analytic hierarchy process and entropy method. Among them, in the subjective weight, this paper believes that the four indicators are equally important. The calculation results of subjective and objective weights are shown in Table3.Table 3
Index weight of transmission line risk assessment.
Risk indicatorsObjective weightSubjective weightCombination weightLoad loss0.29540.250.2562Potential interlock failure0.41260.250.3212Voltage out of limit0.13580.250.1856Stability index0.15260.250.2015It can be seen from the above calculation results although the failure probability of Qingcang line is significantly higher than that of Lianji line. However, the fault severity is lower than that of Lianji line. When the Lianji line finally exits the operation, the comprehensive operation risk of the system is significantly higher than that of other lines, which requires key maintenance and management [24]. At the same time, it also shows that the utility function theory is used to evaluate the operation risk of power grid. It can effectively distinguish the two risks of high loss and low probability from low loss and high probability and provide better guidance for operators.


## 5. Conclusion

This paper presents a dynamic risk assessment method of power grid based on multi-source data of ubiquitous power Internet of things. The dynamic, accuracy, and practicability of power grid risk assessment are improved from the aspects of system state probability calculation, system state selection, and system state assessment. Based on this idea, a software system is designed and implemented. The preliminary application practice in the actual power grid shows that the system can effectively evaluate the dynamic risk of the system under each operation state, provide risk prompt and auxiliary decision-making for dispatchers and operators, and provide technical support for power grid operation risk management and control. In the future, with the deepening of the construction of ubiquitous power Internet of things and the transformation of power grid, based on the traditional risk assessment for the power grid itself, how to conduct a more lean power grid risk assessment will become the focus of the next research.



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*Source: 6523337-2022-05-05.xml*