A distribution network reliability assessment system and method

Abstract

The present invention relates to the field of reliability assessment technology, specifically to a distribution network reliability assessment system and method, the system comprising an equipment health status analysis module, a load status analysis module, a fault probability prediction module and a reliability analysis module. The present invention monitors the working environment temperature and humidity of the distribution network equipment, combines the equipment service life and fault history data, makes the health status assessment more forward-looking and adaptable, monitors and evaluates the equipment load in real time, compares it with the standard load, identifies and quantifies load anomalies, and enhances the accuracy and timeliness of fault prediction by evaluating the risk of equipment running outside the standard load and the potential failure risk caused by load anomalies. It also combines the failure probability with the importance of the equipment, and dynamically evaluates the overall reliability of the distribution network, providing real-time data support for the operation and maintenance of power facilities, and reducing the economic losses and power outage risks caused by equipment failures.

Description

Technical Field

[0001] The present invention relates to the technical field of reliability assessment, and in particular to a distribution network reliability assessment system and method. Background Art

[0002] The field of reliability assessment technology focuses on analyzing and improving the performance stability and durability of various systems, equipment or processes. This field has a wide range of applications, covering multiple industries from industrial products to information systems and energy facilities. Technical means include using statistical data, failure data and operation history to predict potential failures and performance degradation, and implementing preventive measures to improve the overall efficiency and safety of the system. Through methods such as failure mode and effect analysis, root cause analysis and life data analysis, reliability assessment aims to identify possible risk points and implement cost-effective system optimization and maintenance strategies to extend system life and improve operational reliability.

[0003] Among them, the distribution network reliability assessment system is a technical solution applied in the field of power system management, which aims to evaluate and ensure the stable operation and service reliability of the distribution network. The system analyzes the operation data, historical fault records and environmental factors of the power grid, and uses mathematical models and statistical methods to predict and evaluate possible faults in the power grid. The main uses of the system include fault diagnosis, risk management and resource optimization, helping power companies optimize maintenance and investment plans, thereby reducing power outages and improving the quality and efficiency of power supply.

[0004] Traditional assessment systems rely on intermittent data collection and historical fault analysis, lacking real-time and comprehensive consideration of environmental factors. In a dynamically changing operating environment, it is difficult to promptly respond to immediate changes in equipment status, resulting in delayed fault prevention measures. For example, under extreme climatic conditions, traditional systems cannot accurately predict equipment failures, resulting in delayed or inadequate maintenance measures, increasing the system's operation and maintenance costs and failure rate. In addition, the lack of analysis of load fluctuations makes it difficult to foresee equipment losses caused by load anomalies, which is particularly insufficient when facing rapidly changing market demand and grid load. It is impossible to effectively predict and prevent large-scale power outages, affecting the overall reliability of the power system and user satisfaction. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a distribution network reliability assessment system and method.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a distribution network reliability assessment system, the system comprising:

[0007] The equipment health status analysis module is based on the working environment of the distribution network equipment. According to the temperature and humidity of the current working environment, it evaluates the deviation between the current working environment and the standard environment. Combined with the service life and fault records of the distribution network equipment, the fault records include the number of faults and the severity of the faults, it evaluates the current health status of the distribution network equipment and obtains the equipment health status information.

[0008] The load status analysis module is based on the load records of distribution network equipment. By analyzing the load data over a period of time, the load fluctuation of distribution network equipment is evaluated, and compared with the allowed standard load, the load excess is identified, and the load abnormality is evaluated based on the load excess duration to obtain load status information;

[0009] The fault probability prediction module predicts the fault probability of the distribution network equipment under the current condition based on the load condition information and the equipment health condition information, according to the load fluctuation condition and the load abnormality degree, combined with the failure probability of the distribution network equipment under the standard load and the standard fluctuation range under the normal health condition, and obtains the fault probability prediction result;

[0010] The reliability analysis module evaluates the reliability of the distribution network based on the fault probability prediction result and the importance of differentiated distribution network equipment to obtain a distribution network reliability evaluation result.

[0011] The present invention is improved in that the method for evaluating the deviation between the current working environment and the standard environment is:

[0012] Based on the working environment of distribution network equipment, collect the temperature and humidity data of the current working environment of distribution network equipment to obtain real-time environmental data;

[0013] Based on the real-time environment data, compared with the standard environment, the formula is:

[0014]

[0015] Calculate the environmental deviation index D and obtain the environmental deviation assessment result, where T c Represents the current ambient temperature, T s Represents standard ambient temperature, H c Represents the current ambient humidity, H s Represents standard ambient humidity, α D is the weight coefficient of temperature deviation, β D is the weight coefficient of humidity deviation, and D is the environmental deviation index.

[0016] The present invention is improved in that the step of obtaining the device health status information is:

[0017] Based on the environmental deviation assessment results, the service life and fault records of the distribution network equipment are collected, the fault records include the number of faults and the severity of the faults, and the equipment health status related data is obtained;

[0018] Based on the equipment health status associated data, by formula:

[0019]

[0020] Calculate the health score H of the device and obtain the health status information of the device, where H is the health score of the device, γ H is the adjustment coefficient for the effect of age, y H is the current equipment age, Y max is the theoretical maximum service life of the equipment, κ H is the annual impact index, D is the environmental deviation index, λ H is the weight coefficient of environmental deviation, δ H is the weight coefficient of the number of failures, n H is the number of failures, θ H is the weight coefficient of fault severity, is the average severity of the fault.

[0021] The present invention is improved in that the method for evaluating the load fluctuation of the distribution network equipment is:

[0022] Based on the load records of distribution network equipment, extract the load data of distribution network equipment within a period of time, including the load size and the corresponding time point;

[0023] Based on the load data of the distribution network equipment, the formula:

[0024]

[0025] Calculate the load fluctuation index B and obtain the load fluctuation evaluation result, where x i is the load value at the i-th time point, μ B is the weighted average of the load data, w i is the weight factor of time point i, N B is the total number of data points, and B is the load fluctuation index.

[0026] The present invention is improved in that the step of acquiring the load status information is:

[0027] Based on the load records of distribution network equipment, the load data of distribution network equipment within a period of time is extracted, compared with the allowed standard load, the data exceeding the standard load is identified, and the corresponding exceeding time is recorded to obtain the load abnormality related data;

[0028] Based on the load anomaly correlation data, the formula is:

[0029]

[0030] Calculate the load abnormality degree E, where L f is the actual load in the fth time period, L std is the allowable standard load, T f is the duration of the fth time period, T ref is the observation time length, α E is the adjustment factor, N E is the number of time periods during which the standard load is exceeded.

[0031] Based on the load abnormality degree E, the load fluctuation evaluation result is integrated to obtain the load status information.

[0032] The present invention is improved in that the steps of obtaining the failure probability prediction result are:

[0033] Based on the load status information and the equipment health status information, extract the equipment health score, load fluctuation index and load abnormality degree to obtain fault-related data;

[0034] Based on the fault correlation data, combined with the failure probability and allowable fluctuation range of the distribution network equipment under standard conditions, the formula is:

[0035]

[0036] Calculate the current failure probability P of the distribution network equipment and obtain the failure probability prediction result, where P std is the failure probability under standard conditions, H std is the normal health score, H is the health score of the current device, and B max is the allowable fluctuation range, B is the current load fluctuation index, E is the load abnormality degree, β P , γ P and δ P is the adjustment factor.

[0037] The present invention is improved in that the steps of obtaining the distribution network reliability evaluation result are as follows:

[0038] Based on the failure probability prediction result, the failure probability of each distribution network device and the importance of the device are extracted to obtain distribution network reliability correlation data;

[0039] Based on the distribution network reliability correlation data, the formula is:

[0040]

[0041] Calculate the reliability R of the distribution network, where P jis the failure probability of the jth device, W j is the importance of the jth device, α R is the adjustment factor, N R is the total number of devices in the distribution network, and R is the reliability of the distribution network;

[0042] Based on the reliability R of the distribution network, it is compared with the preset reliability standard to determine whether the reliability of the current distribution network meets expectations, and obtain the distribution network reliability evaluation result.

[0043] A distribution network reliability assessment method, which is performed based on the above distribution network reliability assessment system, comprises the following steps:

[0044] S1: Based on the working environment of the distribution network equipment, according to the temperature and humidity of the current working environment, evaluate the deviation between the current working environment and the standard environment, and combine the service life and fault records of the distribution network equipment. The fault records include the number of faults and the severity of the faults. Evaluate the current health status of the distribution network equipment and obtain the equipment health status information;

[0045] S2: Based on the load records of distribution network equipment, by analyzing the load data over a period of time, the load fluctuation of the distribution network equipment is evaluated to obtain the load fluctuation evaluation result;

[0046] S3: Based on the load fluctuation assessment result, by comparing the load data within a period of time with the allowed standard load, identifying the load excess situation, and evaluating the load abnormality degree in combination with the load excess duration, to obtain load status information;

[0047] S4: Based on the load status information and the equipment health status information, according to the load fluctuation and load abnormality, combined with the failure probability of the distribution network equipment under the standard load and standard fluctuation range under the normal health state, predict the failure probability of the distribution network equipment under the current state to obtain the failure probability prediction result;

[0048] S5: Based on the fault probability prediction result and combined with the importance of differentiated distribution network equipment, the reliability of the distribution network is evaluated to obtain a distribution network reliability evaluation result.

[0049] Compared with the prior art, the advantages and positive effects of the present invention are:

[0050] In the present invention, by monitoring the working environment temperature and humidity of the distribution network equipment, combined with the equipment service life and fault history data, the health status assessment is made more forward-looking and adaptable, and potential faults can be foreseen earlier. By real-time monitoring and evaluating the equipment load conditions and comparing them with the standard load, load anomalies are identified and quantified to avoid equipment damage caused by overload. By evaluating the risk of equipment running outside the standard load and the potential failure risks caused by load anomalies, the accuracy and timeliness of fault prediction are enhanced, and the failure probability is combined with the importance of the equipment to dynamically evaluate the overall reliability of the distribution network, providing real-time data support for the operation and maintenance of power facilities, and reducing the economic losses and power outage risks caused by equipment failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a system flow chart of the present invention;

[0052] Figure 2 A flow chart for evaluating the deviation of the current working environment from the standard environment for the present invention;

[0053] Figure 3 A flowchart of the present invention for obtaining device health status information;

[0054] Figure 4 A flow chart for evaluating load fluctuation of distribution network equipment according to the present invention;

[0055] Figure 5 A flow chart of obtaining load status information according to the present invention;

[0056] Figure 6 A flowchart for obtaining a fault probability prediction result of the present invention;

[0057] Figure 7 The present invention is a flow chart for obtaining distribution network reliability assessment results. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0059] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0060] See also Figure 1 The present invention provides a technical solution: a distribution network reliability assessment system, the system comprising:

[0061] The equipment health status analysis module is based on the working environment of the distribution network equipment. According to the temperature and humidity of the current working environment, it evaluates the deviation between the current working environment and the standard environment. Combined with the service life and fault records of the distribution network equipment, the fault records include the number of faults and the severity of the faults, it evaluates the current health status of the distribution network equipment and obtains the equipment health status information.

[0062] The load status analysis module is based on the load records of distribution network equipment. By analyzing the load data over a period of time, the load fluctuation of distribution network equipment is evaluated, and compared with the allowed standard load, the load excess is identified, and the load abnormality is evaluated based on the load excess duration to obtain load status information;

[0063] The fault probability prediction module is based on the load status information and equipment health status information, according to the load fluctuation and load abnormality, combined with the failure probability of the distribution network equipment under the standard load and standard fluctuation range under the normal health state, to predict the failure probability of the distribution network equipment under the current condition and obtain the failure probability prediction result;

[0064] The reliability analysis module evaluates the reliability of the distribution network based on the failure probability prediction results and the importance of differentiated distribution network equipment to obtain the distribution network reliability assessment results.

[0065] Equipment health status information includes the degree of environmental deviation, equipment aging index and equipment health index. Load status information includes load fluctuation index, standard load deviation value and overload duration information. Fault probability prediction results include fault possibility rating, expected fault type and impact range prediction. Distribution network reliability assessment results include reliability score and key area risk level.

[0066] See also Figure 2 , the method to evaluate the deviation between the current working environment and the standard environment is:

[0067] Based on the working environment of distribution network equipment, collect the temperature and humidity data of the current working environment of distribution network equipment to obtain real-time environmental data;

[0068] Based on real-time environment data, compared with the standard environment, the formula is:

[0069]

[0070] Calculate the environmental deviation index D and obtain the environmental deviation assessment result, where T c Represents the current ambient temperature, T s Represents standard ambient temperature, H c Represents the current ambient humidity, H s Represents standard ambient humidity, α D is the weight coefficient of temperature deviation, β D is the weight coefficient of humidity deviation, and D is the environmental deviation index.

[0071] formula:

[0072]

[0073] The meaning and acquisition method of parameters:

[0074] Current ambient temperature T c and standard ambient temperature T s :Parameters are directly measured by environmental monitoring systems or sensors. c is the real-time temperature of the device, and T s It is the ideal temperature to be set when establishing the standard working environment of the equipment.

[0075] Current ambient humidity H c and standard ambient humidity H s :Two humidity values ​​are obtained through the humidity sensor, where H c is the real-time humidity of the current environment, H s It is the environmental standard value.

[0076] α D and β D :Weight coefficients are used to adjust the impact of temperature deviation and humidity deviation in the total deviation index. The coefficients can be obtained based on historical data analysis, and adjusting these coefficients can be optimized based on the different sensitivity of the device to temperature and humidity.

[0077] Calculation Example

[0078] Set the current ambient temperature T c The standard ambient temperature is 30°C. s The current ambient humidity is 25℃ and H c The standard ambient humidity is 60%. sSet the temperature weight α to 50%. D = 0.7 and humidity weight β D =0.3.

[0079] Calculate the deviation of temperature and humidity:

[0080] ΔT=T c -T s =30-25=5

[0081] ΔH=H c -H s =60-50=10

[0082] Calculate the environmental deviation index D:

[0083]

[0084] The calculated environmental deviation index D=0.2 indicates that the current environment has a certain deviation compared with the standard environment.

[0085] See also Figure 3 , the steps to obtain the device health status information are:

[0086] Based on the environmental deviation assessment results, the service life and fault records of distribution network equipment are collected. The fault records include the number of faults and the severity of the faults, and the equipment health status related data is obtained;

[0087] Based on the equipment health status correlation data, the formula is:

[0088]

[0089] Calculate the health score H of the device and obtain the health status information of the device, where H is the health score of the device, γ H is the adjustment coefficient for the effect of age, y H is the current equipment age, Y max is the theoretical maximum service life of the equipment, κ H is the annual impact index, D is the environmental deviation index, λ H is the weight coefficient of environmental deviation, δ H is the weight coefficient of the number of failures, n H is the number of failures, θ H is the weight coefficient of fault severity, is the average severity of the fault.

[0090] formula:

[0091]

[0092] The meaning and acquisition method of parameters:

[0093] In the device health assessment formula discussed previously, the following parameters were used:

[0094] γ H : An adjustment factor for the effects of age, used to adjust for the effects of age based on equipment type and expected durability, typically determined based on historical data and manufacturer guidance.

[0095] y H :The current service life of the equipment, calculated from the date of equipment installation or purchase to the current date, usually obtained from the equipment's operation and maintenance system.

[0096] Y max : The theoretical maximum useful life of the equipment, as set by the manufacturer’s design life or industry standards.

[0097] κ H : Age impact index, which increases or decreases the effect of the service life ratio and is adjusted according to the equipment durability characteristics and failure rate.

[0098] λ H : Weight coefficient of environmental deviation, based on analysis of the device’s sensitivity to environmental conditions.

[0099] D: Environmental deviation index, calculated by the previous step.

[0100] δ H and θ H : They are the weight coefficients of the number of faults and fault severity, respectively, which are set based on the statistical analysis of fault data, taking into account the fault frequency and the impact of each fault on equipment health.

[0101] n H : Number of failures, obtained from the equipment maintenance record.

[0102] s H :The average severity of the fault is calculated by counting the average severity of all faults in the records. The severity of each fault is evaluated and set by the maintenance personnel.

[0103] Calculation example:

[0104] Set the following parameters:

[0105] Current service life H = 10 years, theoretical maximum service life Y max = 20 years, adjustment factor for the effect of age γ H =10, influence index κ H =2, environmental deviation index D = 0.2, environmental deviation weight λ H =1.5, number of failures n H =3, failure number weight δ H=2.0, average severity of faults Fault severity weight θ H =1.0.

[0106] Calculate the health status H:

[0107]

[0108]

[0109] The calculation results show that the health status score of the equipment has dropped to 71.75. The evaluation results show that the equipment has experienced serious failures and has been in use for a long time, so its health status has declined significantly and may need to be replaced.

[0110] See also Figure 4 ,The method for evaluating the load fluctuation of distribution network equipment is:

[0111] Based on the load records of distribution network equipment, extract the load data of distribution network equipment within a period of time, including the load size and the corresponding time point;

[0112] Based on the load data of the distribution network equipment, the formula is:

[0113]

[0114] Calculate the load fluctuation index B and obtain the load fluctuation evaluation result, where x i is the load value at the i-th time point, μ B is the weighted average of the load data, w i is the weight factor of time point i, N B is the total number of data points, and B is the load fluctuation index.

[0115] formula:

[0116]

[0117] The meaning and acquisition method of parameters:

[0118] x i : represents the load value at the i-th time point, which is directly obtained from the load monitoring system of the distribution network equipment. The value is measured in real time and reflects the power consumption of the equipment at each moment.

[0119] μ B : The weighted average of the load data, which takes into account the importance or representativeness of each load value. This average is calculated using the following weighted average formula:

[0120]

[0121] wi : Weight factor, set according to the importance of the data point in the analysis. For example, you can give a higher weight to the load data during the peak period to reflect the importance of the load in different time periods.

[0122] N B : Total number of data points, which indicates the total number of data points considered during the analysis. The number determines the range of summing and averaging operations.

[0123] Calculation Example

[0124] There are three load data points: x1=150, x2=160, x3=170 (unit: KW). The corresponding weight factors are: w1=1, w2=2, w3=2. The total number of data points is N B =3.

[0125] Calculate the weighted average μ B :

[0126]

[0127] Calculate the load fluctuation index B:

[0128]

[0129] The calculated load fluctuation index B is approximately 9.64, which can be used to evaluate the degree of fluctuation of equipment load.

[0130] See also Figure 5 , the steps for obtaining load status information are:

[0131] Based on the load records of distribution network equipment, the load data of distribution network equipment within a period of time is extracted, compared with the allowed standard load, the data exceeding the standard load is identified, and the corresponding exceeding time is recorded to obtain the load abnormality related data;

[0132] Based on the load anomaly correlation data, the formula is:

[0133]

[0134] Calculate the load abnormality degree E, where L f is the actual load in the fth time period, L std is the allowable standard load, T f is the duration of the fth time period, T ref is the observation time length, α E is the adjustment factor, N E is the number of time periods during which the standard load is exceeded.

[0135] Based on the load abnormality degree E, the load fluctuation assessment results are integrated to obtain the load status information.

[0136] formula:

[0137]

[0138] The meaning and acquisition method of parameters:

[0139] L f : represents the actual load in the fth time period, which is the real-time data directly obtained from the load monitoring system of the distribution network equipment.

[0140] L std : The standard load allowed, usually based on equipment specifications or industry standards, used as a basis for comparison.

[0141] T f : The load exceeding duration in the fth time period is obtained from the system records, indicating the length of time the load exceeds the standard load during the time period.

[0142] T ref : Observation time length, indicating the target observation time.

[0143] α E : Adjustment factor used to weight the impact of time exceedance. This index can be adjusted according to the sensitivity of the equipment to load changes.

[0144] N E : Indicates the total number of time periods exceeding the standard load, the total count obtained by analyzing the data within the entire monitoring period.

[0145] Calculation example:

[0146] Set the following parameters: L f 110, 120 and 130 (in KW), standard load L std =100, load exceeds duration T f are 0.5 minutes, 0.75 minutes and 1 minute respectively, and the observation period T ref Set to 60 minutes, adjustment factor α E Set to 2, the total number of time periods is N E =3.

[0147] Calculation of load excess value:

[0148] ΔL f1 =110-100=10

[0149] ΔL f2 =120-100=20

[0150] ΔL f3 =130-100=30

[0151] Calculation of load abnormality:

[0152]

[0153] The calculation result E=0.31944 shows that during the target observation period, although the load exceeded the standard, the duration of the excess was very short and the degree of equipment load abnormality was low.

[0154] See also Figure 6 , the steps to obtain the failure probability prediction results are:

[0155] Based on the load status information and equipment health status information, the equipment health score, load fluctuation index and load abnormality degree are extracted to obtain fault-related data;

[0156] Based on the fault correlation data, combined with the failure probability and allowable fluctuation range of distribution network equipment under standard conditions, the formula is:

[0157]

[0158] Calculate the current failure probability P of the distribution network equipment and obtain the failure probability prediction result, where P std is the failure probability under standard conditions, H std is the normal health score, H is the health score of the current device, and B max is the allowable fluctuation range, B is the current load fluctuation index, E is the load abnormality degree, β P , γ P and δ P is the adjustment factor.

[0159] formula:

[0160]

[0161] The meaning and acquisition method of parameters:

[0162] P std : Failure probability under standard conditions, usually based on data provided by the manufacturer or obtained from historical operation and maintenance records, indicating the expected failure rate of the equipment when operating under standard load and fluctuation range.

[0163] H std : Normal health score, which represents the ideal health score of the device in perfect operating condition. It is usually the highest score set by the device manufacturer, such as 100 points.

[0164] H: The health score of the current device, calculated in the previous step.

[0165] B max: Allowable fluctuation range, the maximum fluctuation index under which the equipment can operate safely, usually defined by equipment specifications or empirical data.

[0166] B: Current load fluctuation index, calculated in the previous step.

[0167] E: Load abnormality degree, calculated by the previous step.

[0168] β P , γ P and δ P : Adjustment factor, which is used to adjust the sensitivity of failure probability to health score, fluctuation index and load abnormality. These values ​​are usually set according to equipment type and past experience data.

[0169] Calculation Example

[0170] Assume the following parameters: P std =0.02 (standard failure probability), H std =100 (normal health score), H = 85 (current health score), B max =10 (allowable fluctuation range), B = 12 (current fluctuation index), E = 0.3 (load abnormality degree), β P =2,γ P =1.5,δ P =0.05 (adjustment factor).

[0171] Calculate the failure probability P under the current state:

[0172]

[0173] The calculated result P = 0.037, indicating that under the current operating conditions, the expected failure probability of the equipment is 3.7%, which is higher than 2% under the standard state, mainly due to the decline in the equipment's health score and the fluctuation index exceeding the allowable range. The increased failure probability indicates that the equipment may require additional maintenance or monitoring measures to reduce the risk of future failures.

[0174] See also Figure 7 , the steps to obtain the distribution network reliability assessment results are as follows:

[0175] Based on the failure probability prediction results, the failure probability and importance of each distribution network equipment are extracted to obtain distribution network reliability correlation data;

[0176] Based on the distribution network reliability correlation data, the formula is:

[0177]

[0178] Calculate the reliability R of the distribution network, where P jis the failure probability of the jth device, W j is the importance of the jth device, α R is the adjustment factor, N R is the total number of devices in the distribution network, and R is the reliability of the distribution network;

[0179] Based on the reliability R of the distribution network, it is compared with the preset reliability standard to determine whether the reliability of the current distribution network meets expectations and obtain the distribution network reliability assessment result.

[0180] formula:

[0181]

[0182] Parameter meaning and acquisition method

[0183] P j : The failure probability of the jth device, calculated in the previous step.

[0184] W j : The importance score of the jth device, determined based on the role of the device in the distribution network and its importance to the overall system. The importance score is usually assigned by system engineers based on the function and location of the device and the potential impact of its failure on the system.

[0185] α R : Adjustment factor, used to adjust the weight of failure probability in reliability assessment. This factor is set based on past statistical data and equipment performance analysis to ensure that changes in failure probability have an appropriate impact on the final reliability score.

[0186] N R : The total number of devices in the distribution network, indicating the number of distribution network devices involved in the calculation. This number is obtained directly from the distribution network equipment inventory database.

[0187] Calculation example:

[0188] Assume that there are three devices in the distribution network, and the specific parameters are as follows:

[0189] Device 1: P1=0.1, W1=0.5.

[0190] Device 2: P2=0.2, W2=1.0.

[0191] Device 3: P3=0.05, W3=0.3.

[0192] Coefficient α R =10, N R =3.

[0193] Calculation steps:

[0194] For each device, calculate ln(1+α R ×P j ) value:

[0195] Device 1: ln(1+10×0.1)=log(2)≈0.693

[0196] Device 2: ln(1+10×0.2)=log(3)≈1.099

[0197] Device 3: ln(1+10×0.05)=log(1.5)≈0.405

[0198] Importance rating of using equipment j Weighted logarithmic value.

[0199] 0.5×0.693=0.3465

[0200] 1.0×1.099=1.099

[0201] 0.3×0.405=0.1215

[0202] Compute the sum and apply the exponential function:

[0203] Sum=0.3465+1.099+0.1215=1.567

[0204] R=exp(-1.567)≈0.209

[0205] The calculated reliability index R=0.209 indicates that the overall reliability of the entire distribution network is low under the current equipment status. The results suggest that attention should be paid to equipment with high failure rates or greater importance, and necessary maintenance, replacement or upgrade should be carried out to improve the stability and reliability of the overall network.

[0206] A distribution network reliability assessment method comprises the following steps:

[0207] S1: Based on the working environment of the distribution network equipment, according to the temperature and humidity of the current working environment, evaluate the deviation between the current working environment and the standard environment, and combine the service life and fault records of the distribution network equipment. The fault records include the number of faults and the severity of the faults. Evaluate the current health status of the distribution network equipment and obtain the equipment health status information;

[0208] S2: Based on the load records of distribution network equipment, by analyzing the load data over a period of time, the load fluctuation of the distribution network equipment is evaluated to obtain the load fluctuation evaluation result;

[0209] S3: Based on the load fluctuation assessment results, the load data within a period of time is compared with the allowed standard load to identify the load exceeding situation, and the load abnormality degree is evaluated in combination with the load exceeding duration to obtain the load status information;

[0210] S4: Based on the load status information and equipment health status information, according to the load fluctuation and load abnormality, combined with the failure probability of the distribution network equipment under the standard load and standard fluctuation range under the normal health state, the failure probability of the distribution network equipment under the current condition is predicted to obtain the failure probability prediction result;

[0211] S5: Based on the fault probability prediction results and combined with the importance of differentiated distribution network equipment, the reliability of the distribution network is evaluated to obtain the distribution network reliability evaluation results.

[0212] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A distribution network reliability assessment system, characterized in that: The system comprises: The equipment health status analysis module is based on the working environment of the distribution network equipment. According to the temperature and humidity of the current working environment, it evaluates the deviation between the current working environment and the standard environment. Combined with the service life and fault records of the distribution network equipment, the fault records include the number of faults and the severity of the faults, it evaluates the current health status of the distribution network equipment and obtains the equipment health status information. The load status analysis module is based on the load records of distribution network equipment. By analyzing the load data over a period of time, the load fluctuation of distribution network equipment is evaluated, and compared with the allowed standard load, the load excess is identified, and the load abnormality is evaluated based on the load excess duration to obtain load status information; The fault probability prediction module predicts the fault probability of the distribution network equipment under the current condition based on the load condition information and the equipment health condition information, according to the load fluctuation condition and the load abnormality degree, combined with the failure probability of the distribution network equipment under the standard load and the standard fluctuation range under the normal health condition, and obtains the fault probability prediction result; The reliability analysis module evaluates the reliability of the distribution network based on the fault probability prediction result and the importance of differentiated distribution network equipment to obtain a distribution network reliability evaluation result.

2. The distribution network reliability assessment system according to claim 1, characterized in that: The method for evaluating the deviation between the current working environment and the standard environment is: Based on the working environment of distribution network equipment, collect the temperature and humidity data of the current working environment of distribution network equipment to obtain real-time environmental data; Based on the real-time environment data, compared with the standard environment, the formula is: Calculate the environmental deviation index D and obtain the environmental deviation assessment result, where T c Represents the current ambient temperature, T s Represents standard ambient temperature, H c Represents the current ambient humidity, H s Represents standard ambient humidity, α D is the weight coefficient of temperature deviation, β D is the weight coefficient of humidity deviation, and D is the environmental deviation index.

3. The distribution network reliability assessment system according to claim 2, characterized in that: The steps for obtaining the device health status information are as follows: Based on the environmental deviation assessment results, the service life and fault records of the distribution network equipment are collected, the fault records include the number of faults and the severity of the faults, and the equipment health status related data is obtained; Based on the equipment health status associated data, by formula: Calculate the health score H of the device and obtain the health status information of the device, where H is the health score of the device, γ H is the adjustment coefficient for the effect of age, y H is the current equipment age, Y max is the theoretical maximum service life of the equipment, κ H is the annual impact index, D is the environmental deviation index, λ H is the weight coefficient of environmental deviation, δ H is the weight coefficient of the number of failures, n H is the number of failures, θ H is the weight coefficient of fault severity, is the average severity of the fault.

4. The distribution network reliability assessment system according to claim 1, characterized in that: The method for evaluating the load fluctuation of distribution network equipment is as follows: Based on the load records of distribution network equipment, extract the load data of distribution network equipment within a period of time, including the load size and the corresponding time point; Based on the load data of the distribution network equipment, the formula: Calculate the load fluctuation index B and obtain the load fluctuation evaluation result, where x i is the load value at the i-th time point, μ B is the weighted average of the load data, w i is the weight factor of time point i, N B is the total number of data points, and B is the load fluctuation index.

5. The distribution network reliability assessment system according to claim 4, characterized in that: The steps for acquiring the load status information are as follows: Based on the load records of distribution network equipment, the load data of distribution network equipment within a period of time is extracted, compared with the allowed standard load, the data exceeding the standard load is identified, and the corresponding exceeding time is recorded to obtain the load abnormality related data; Based on the load anomaly correlation data, by formula: Calculate the load abnormality degree E, where L f is the actual load in the fth time period, L std is the allowable standard load, T f is the duration of the fth time period, T ref is the observation time length, α E is the adjustment factor, N E is the number of time periods during which the standard load is exceeded. Based on the load abnormality degree E, the load fluctuation evaluation result is integrated to obtain the load status information.

6. The distribution network reliability assessment system according to claim 1, characterized in that: The steps for obtaining the failure probability prediction result are: Based on the load status information and the equipment health status information, extract the equipment health score, load fluctuation index and load abnormality degree to obtain fault-related data; Based on the fault correlation data, combined with the failure probability and allowable fluctuation range of the distribution network equipment under standard conditions, the formula is: Calculate the current failure probability P of the distribution network equipment and obtain the failure probability prediction result, where P std is the failure probability under standard conditions, H std is the normal health score, H is the health score of the current device, and B max is the allowable fluctuation range, B is the current load fluctuation index, E is the load abnormality degree, β P , γ P and δ P is the adjustment factor.

7. The distribution network reliability assessment system according to claim 1, characterized in that: The steps for obtaining the distribution network reliability assessment result are as follows: Based on the failure probability prediction result, the failure probability of each distribution network device and the importance of the device are extracted to obtain distribution network reliability correlation data; Based on the distribution network reliability correlation data, the formula is: Calculate the reliability R of the distribution network, where P j is the failure probability of the jth device, W j is the importance of the jth device, α R is the adjustment factor, N R is the total number of devices in the distribution network, and R is the reliability of the distribution network; Based on the reliability R of the distribution network, it is compared with the preset reliability standard to determine whether the reliability of the current distribution network meets expectations, and obtain the distribution network reliability evaluation result.

8. A distribution network reliability assessment method, characterized in that: The distribution network reliability assessment system according to any one of claims 1 to 7 is implemented, comprising the following steps: Based on the working environment of the distribution network equipment, according to the temperature and humidity of the current working environment, the deviation between the current working environment and the standard environment is evaluated. Combined with the service life and fault records of the distribution network equipment, the fault records include the number of faults and the severity of the faults, the current health status of the distribution network equipment is evaluated to obtain the equipment health status information; Based on the load records of distribution network equipment, by analyzing the load data over a period of time, the load fluctuation of distribution network equipment is evaluated to obtain the load fluctuation evaluation result; Based on the load fluctuation assessment result, by comparing the load data within a period of time with the allowed standard load, the load exceeding situation is identified, and the load abnormality degree is assessed in combination with the load exceeding duration to obtain the load condition information; Based on the load status information and the equipment health status information, according to the load fluctuation and load abnormality, combined with the failure probability of the distribution network equipment under the standard load and standard fluctuation range under the normal health state, the failure probability of the distribution network equipment under the current condition is predicted to obtain a failure probability prediction result; Based on the fault probability prediction result and combined with the importance of differentiated distribution network equipment, the reliability of the distribution network is evaluated to obtain a distribution network reliability evaluation result.

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