Power distribution network reliability evaluation method, device, equipment and storage medium

Abstract

The invention provides a power distribution network reliability evaluation method and device, equipment and a storage medium. The method comprises the steps that a sensor data error rate is calculated according to collected current and voltage data and actual values of the collected current and voltage data; obtaining meteorological data probability deviation based on rainfall and wind speed prediction values and actual values; the correlation between the two is analyzed to determine an uncertainty coupling coefficient. Obtaining power grid topology parameters to calculate power grid topology complexity; obtaining energy storage response delay; calculating sensitive load hidden accumulated loss according to the sensitive and non-sensitive load data; and calculating the original power supply reliability. And calculating the real power supply reliability of the power distribution network through coupling correction by combining the original power supply reliability, the hidden cumulative loss, the sensor error rate, the meteorological deviation, the uncertainty coupling coefficient, the topology complexity and the energy storage response delay. According to the method, the power supply reliability is evaluated more accurately through the uncertainty and hidden loss of the multi-source information, and the deviation caused by simple superposition and missed calculation in the prior art is overcome.

Description

Technical Field

[0001] This application belongs to the field of power grid assessment, and in particular relates to a method, apparatus, equipment and storage medium for assessing the reliability of distribution networks. Background Technology

[0002] Distribution network reliability assessment is primarily used for disaster mitigation planning and power supply assurance decisions in extreme disaster scenarios to ensure the safety of residential electricity use and the continuity of industrial production. It typically involves collecting basic distribution network parameters, load data, and meteorological data to calculate the initial power supply reliability rate, providing a basis for power grid planning.

[0003] Currently, existing technologies specifically include collecting line parameters, switching equipment parameters, and topology parameters; calculating the direct impact range of a fault using a fixed radius; and statistically analyzing load losses under complete power outage conditions. During the calculation process, sensor data errors and meteorological data deviations are processed using a simple superposition method to obtain preliminary reliability assessment results, which are then used for decision support in routine operating scenarios.

[0004] In the existing technology, the "fixed radius calculation and simple superposition method" process does not consider the impact of the coupling effect of initial fault intensity, line disturbance immunity, load density and meteorological conditions on fault propagation, nor does it quantify the correlation coupling effect between sensor data error and meteorological data deviation. Therefore, there is a significant defect in the evaluation results that deviate significantly from the actual situation. In particular, the uncertainty processing of multi-source information is not accurate, which makes it impossible for the real power supply reliability rate to accurately reflect the power grid status under extreme scenarios. Summary of the Invention

[0005] The purpose of this application is to overcome the deficiencies in the prior art and provide a method, apparatus, equipment and storage medium for evaluating the reliability of power distribution networks.

[0006] This application provides a method for assessing the reliability of a power distribution network, including:

[0007] Acquire current and voltage data from the power distribution network, and calculate the sensor data error rate based on the current data, voltage data, actual current data, and actual voltage data;

[0008] Obtain the predicted precipitation probability and wind speed, and calculate the probability deviation of meteorological data based on the predicted precipitation probability, wind speed, actual precipitation probability, and actual wind speed.

[0009] The uncertainty coupling coefficient is determined based on the correlation between the sensor data error rate and the meteorological data probability deviation.

[0010] Obtain the topology parameters of the distribution network, including the number of ring networks and the number of tie lines, and calculate the topology complexity of the power grid based on the topology parameters;

[0011] The energy storage response delay is obtained from the energy storage devices in the power distribution network;

[0012] Sensitive load data and non-sensitive load data are obtained from the smart meters of the power distribution network, and the implicit cumulative loss of sensitive loads is calculated based on the sensitive load data and non-sensitive load data.

[0013] The power distribution network basic parameters, load data and meteorological data are obtained from the power distribution network SCADA system, the smart meters and the meteorological monitoring station, and the original power supply reliability rate is calculated based on the power distribution network basic parameters, load data and meteorological data;

[0014] The actual power supply reliability of the distribution network is calculated through coupling correction based on the original power supply reliability rate, the implicit cumulative loss of sensitive loads, the sensor data error rate, the meteorological data probability deviation, the uncertainty coupling coefficient, the power grid topology complexity, and the energy storage response delay.

[0015] Optionally, the uncertainty coupling coefficient is determined based on the correlation between the sensor data error rate and the meteorological data probability deviation:

[0016] The value of the uncertainty coupling coefficient is positively correlated with the correlation strength between the sensor data error rate and the meteorological data probability deviation. In the case of strong correlation, the value of the uncertainty coupling coefficient increases monotonically, while in the case of weak correlation, the value of the uncertainty coupling coefficient decreases monotonically.

[0017] Optionally, sensitive load data and non-sensitive load data are obtained from smart meters in the distribution network, and the implicit cumulative loss of sensitive loads is calculated based on the sensitive load data and non-sensitive load data, including:

[0018] Calculate the load density of the evaluation area based on the sensitive load data and the non-sensitive load data;

[0019] Obtain the line disturbance rejection coefficient from the basic parameters of the distribution network;

[0020] The scope of fault cascading propagation is calculated based on the load density of the assessment area and the line immunity coefficient.

[0021] The implicit cumulative loss of the sensitive load is calculated based on the scope of the cascading effect of the fault.

[0022] Optionally, the fault cascading propagation impact range is calculated based on the load density of the assessment area and the line immunity coefficient, including:

[0023] The spatiotemporal attenuation coefficient is obtained from the meteorological data;

[0024] Obtain the tie-line transmission efficiency from the aforementioned distribution network basic parameters;

[0025] The spatiotemporal attenuation coefficient and the tie-line transmission efficiency are used as exponential terms for calculating the scope of the fault cascading propagation effect.

[0026] Optionally, in calculating the implicit accumulation of sensitive loads based on the scope of the fault cascading propagation, the calculation of the implicit accumulation loss of sensitive loads is proportional to the square of the scope of the fault cascading propagation.

[0027] Optionally, calculating the implicit cumulative loss of the sensitive load based on the scope of the fault cascading propagation further includes:

[0028] The system rated power and reference influence range are obtained from the basic parameters of the distribution network.

[0029] The sensitive load voltage tolerance threshold and cumulative time coefficient are obtained from the sensitive load data;

[0030] The voltage sag ratio is calculated based on the system rated voltage, the fault cascading propagation range, the baseline impact range, and the sensitive load voltage tolerance threshold.

[0031] Using the voltage sag ratio as the base and the cumulative time coefficient as the exponent, a voltage sag degree term is constructed;

[0032] The voltage sag term is incorporated into the formula for calculating the implicit accumulation of the sensitive load.

[0033] Optionally, in calculating the actual power supply reliability of the distribution network through coupling correction, the coupling correction adopts an exponential function form;

[0034] The exponent of the exponential function includes the product of the implicit cumulative loss of the sensitive load and the correction factor.

[0035] The correction factor is: 1 + (sensor data error rate × meteorological data probability deviation × uncertainty coupling coefficient) + (grid topology complexity × energy storage response delay).

[0036] This application also provides a power distribution network reliability assessment device, comprising:

[0037] The sensing module acquires current and voltage data from the power distribution network and calculates the sensor data error rate based on the current, voltage, actual current, and actual voltage data.

[0038] The meteorological module acquires the predicted precipitation probability and wind speed, and calculates the probability deviation of the meteorological data based on the predicted precipitation probability, wind speed, actual precipitation probability, and actual wind speed.

[0039] The relevant module determines the uncertainty coupling coefficient based on the correlation between the sensor data error rate and the meteorological data probability deviation;

[0040] The topology module obtains the topology parameters of the distribution network, including the number of ring networks and the number of tie lines, and calculates the topology complexity of the power grid based on the topology parameters.

[0041] The energy storage module obtains the energy storage response delay from the energy storage devices in the power distribution network;

[0042] The loss module obtains sensitive load data and non-sensitive load data from the smart meters of the distribution network, and calculates the implicit cumulative loss of sensitive loads based on the sensitive load data and non-sensitive load data;

[0043] The calculation module obtains basic parameters of the power distribution network, load data, and meteorological data from the power distribution network SCADA system, the smart meter, and the meteorological monitoring station, and calculates the original power supply reliability rate based on the basic parameters of the power distribution network, load data, and meteorological data.

[0044] The correction module calculates the actual power supply reliability of the distribution network through coupling correction based on the original power supply reliability rate, the implicit cumulative loss of sensitive loads, the sensor data error rate, the meteorological data probability deviation, the uncertainty coupling coefficient, the power grid topology complexity, and the energy storage response delay.

[0045] This application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0046] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the above-described method.

[0047] The beneficial effects of this application are:

[0048] This application provides a method for assessing the reliability of a distribution network, comprising: acquiring current and voltage acquisition data from the distribution network; calculating sensor data error rate based on the current, voltage, actual current, and actual voltage data; acquiring precipitation probability prediction and wind speed prediction; calculating meteorological data probability deviation based on the precipitation probability prediction, wind speed prediction, actual precipitation probability, and actual wind speed; determining an uncertainty coupling coefficient based on the correlation between the sensor data error rate and the meteorological data probability deviation; acquiring the topology parameters of the distribution network, including the number of ring networks and the number of tie lines, and calculating the network topology complexity based on the topology parameters; and obtaining energy storage data from the distribution network. The device acquires the energy storage response delay; it acquires sensitive load data and non-sensitive load data from the smart meters of the distribution network, and calculates the implicit cumulative loss of sensitive loads based on the sensitive load data and non-sensitive load data; it acquires basic parameters of the distribution network, load data, and meteorological data from the distribution network SCADA system, the smart meters, and the meteorological monitoring station, and calculates the original power supply reliability rate based on the basic parameters of the distribution network, load data, and meteorological data; based on the original power supply reliability rate, the implicit cumulative loss of sensitive loads, the sensor data error rate, the meteorological data probability deviation, the uncertainty coupling coefficient, the grid topology complexity, and the energy storage response delay, it calculates the actual power supply reliability rate of the distribution network through coupling correction. This application achieves accurate evaluation of the actual power supply reliability rate of the distribution network by acquiring the sensor data error rate, meteorological data probability deviation, uncertainty coupling coefficient, grid topology complexity, energy storage response delay, and implicit cumulative loss of sensitive loads, and performing coupling correction calculations, overcoming the evaluation bias problems caused by the simple superposition of uncertainties from multiple sources and the omission of implicit losses in the prior art. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the power distribution network reliability assessment process in this application;

[0050] Figure 2 This is a schematic diagram of the power distribution network reliability assessment device in this application. Detailed Implementation

[0051] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that various forms of implementation of the present disclosure are intended and should not be limited to the embodiments set forth herein. Rather, the embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0052] Please refer to Figure 1This application provides a method for assessing the reliability of a distribution network, applied in the field of distribution networks. It addresses the problems of traditional assessment methods that only calculate the direct impact range of a fault while ignoring the spatiotemporal decay law of fault cascading propagation, only count load losses from complete power outages while neglecting the implicit cumulative losses of sensitive loads caused by voltage dips, and simply superimpose sensor data errors and meteorological data deviations without reflecting the coupling effect, resulting in significantly biased assessment results that cannot provide accurate basis for distribution network disaster mitigation planning. The method includes:

[0053] S101. Acquire current and voltage acquisition data in the power distribution network, and calculate the sensor data error rate based on the current acquisition data, voltage acquisition data, actual current data, and actual voltage data;

[0054] The sensor data error rate is the deviation rate between the data collected by the current sensor and the voltage sensor and the actual data. The data collection error rate of the current sensor does not exceed 0.2, and the data collection error rate of the voltage sensor does not exceed 0.15.

[0055] Current and voltage sensors are deployed at key nodes in the power distribution network. They collect current and voltage values ​​in real time and compare them with the actual values ​​to calculate the deviation rate. For example, if the current sensor collects a value of 1.2kA and the actual value is 1.0kA, the error rate is |1.2-1.0| / 1.0=0.20; if the voltage sensor collects a value of 10.2kV and the actual value is 10.0kV, the error rate is |10.2-10.0| / 10.0=0.02, but does not exceed 0.15.

[0056] This calculation ensures that sensor data errors are quantifiable and traceable, providing a foundation for subsequent multi-source uncertainty coupling.

[0057] S102. Obtain the predicted precipitation probability and wind speed, and calculate the probability deviation of meteorological data based on the predicted precipitation probability, wind speed, actual precipitation probability, and actual wind speed.

[0058] The meteorological data probability deviation is the deviation rate between the predicted precipitation probability and the predicted wind speed and the actual precipitation probability and the actual wind speed, respectively. The deviation rate of the predicted precipitation probability during typhoon weather shall not exceed 0.30, and the deviation rate of the predicted wind speed during rainstorm weather shall not exceed 0.25.

[0059] Meteorological monitoring stations provide forecast data. For example, if the predicted probability of precipitation is 80% and the actual probability of precipitation is 70%, then the deviation rate is |80%-70%| / 70%≈0.14; if the predicted wind speed is 15m / s and the actual wind speed is 12m / s, then the deviation rate is |15-12| / 12=0.25.

[0060] This calculation quantifies the uncertainty of weather forecasts, avoiding the crude handling of meteorological factors in traditional methods.

[0061] S103. Determine the uncertainty coupling coefficient based on the correlation between the sensor data error rate and the meteorological data probability deviation;

[0062] The uncertainty coupling coefficient increases as the correlation between the sensor data error rate and the meteorological data probability deviation strengthens. In the strong correlation scenario, the uncertainty coupling coefficient is 3, and in the weak correlation scenario, the uncertainty coupling coefficient is 1.

[0063] The correlation strength is determined through statistical analysis of historical data. For example, during typhoon weather, sensor error and meteorological deviation often increase simultaneously, which is a strong correlation, so θ=3 is taken; during sunny weather, the correlation between the two is weak, so θ=1 is taken.

[0064] This coefficient quantifies the coupling amplification effect of multi-source uncertainties, avoiding simple superposition.

[0065] S104. Obtain the topology parameters of the distribution network, including the number of ring networks and the number of tie lines, and calculate the topology complexity of the power grid based on the topology parameters.

[0066] The power grid topology complexity is a dimensionless parameter with a value ranging from 0.2 to 0.9, reflecting the ease or difficulty of fault control in relation to the power grid structure.

[0067] The more ring networks there are and the fewer the number of connecting lines, the higher the topology complexity σ. For example, σ = 0.2 for a simple radial network and σ = 0.9 for a complex multi-ring network, which is calculated through normalization.

[0068] This parameter reflects the impact of the power grid's own characteristics on reliability.

[0069] S105. Obtain the energy storage response delay from the energy storage device of the power distribution network;

[0070] The energy storage response delay is the speed at which the energy storage device participates in power restoration, measured in seconds, and ranging from 0.1 to 10.

[0071] The response delay ω of energy storage devices, such as battery energy storage systems, is obtained through actual measurement. For example, for fast energy storage, ω=0.1s, and for slow energy storage, ω=10s.

[0072] This parameter captures the timeliness of energy storage devices in recovering from faults.

[0073] S106. Obtain sensitive load data and non-sensitive load data from the smart meters of the power distribution network, and calculate the implicit cumulative loss of sensitive loads based on the sensitive load data and non-sensitive load data;

[0074] The sensitive loads include semiconductor lithography machines, medical equipment, and precision industrial machine tools. The sensitive load data includes load values ​​and distribution locations. The non-sensitive load data includes ordinary load values.

[0075] When calculating the implicit cumulative loss of sensitive loads, the load density of the assessment area is first calculated based on the sensitive load data and the non-sensitive load data. The load density of the assessment area is the ratio of the total real-time load value of the area to the geographical area of ​​the area, in kW / km². For example, if the total load of the area collected by the smart meter is 5000kW and the area is 10km², then the load density ρ = 500kW / km².

[0076] Secondly, the line immunity coefficient is obtained from the basic parameters of the distribution network. This line immunity coefficient is determined by the line insulation level and lightning protection measures. The line immunity coefficient is 0.8 when the line insulation level is 35kV, 0.5 when the line insulation level is 10kV, and 0.2 when the line insulation level is 0.4kV. For example, if the regional lines are mainly 35kV, then... .

[0077] Then, the cascading propagation impact range of the fault is calculated based on the load density of the assessment area and the line immunity coefficient. During the calculation, a spatiotemporal attenuation coefficient is obtained from the meteorological data. The meteorological conditions are reflected by this coefficient, which is 0.4 / min during heavy rain, 0.35 / min during heavy snow, and 0.1 / min during clear weather. Simultaneously, the tie-line transmission efficiency is obtained from the distribution network basic parameters. This efficiency is the ratio of the actual transmission capacity to the rated transmission capacity of the tie-line, ranging from 0.3 to 1.0.

[0078] The spatiotemporal attenuation coefficient and the tie-line transmission efficiency are used as exponential terms for calculating the range of the fault cascading propagation effect.

[0079] The scope of the cascading impact of the fault is calculated using the following formula:

[0080]

[0081] Where S represents the range of impact of the fault cascading propagation, in km²; The initial fault strength is the short-circuit current amplitude, expressed in kA. The line interference immunity factor ranges from 0.2 to 0.9; ρ is the load density of the evaluation area, in kW / km². η is the maximum allowable load density of the area, in kW / km²; t is the fault propagation time, in min; η is the tie-line transmission efficiency, ranging from 0.3 to 1.0; α is the spatiotemporal attenuation coefficient, in 1 / min.

[0082] The formula for the scope of impact of fault cascading propagation quantifies the spatiotemporal decay law of fault propagation, for example:

[0083] .

[0084] The implicit cumulative loss of the sensitive load is calculated based on the range of influence of the fault cascading propagation. The calculation of the implicit cumulative loss of the sensitive load is proportional to the square of the range of influence of the fault cascading propagation. During the calculation, the system rated voltage and the reference influence range are obtained from the basic parameters of the distribution network; the system rated voltage is the standard operating voltage of the distribution network, in kV; the reference influence range is used to standardize the impact of the fault range on the voltage sag, in km².

[0085] The sensitive load voltage withstand threshold and cumulative time coefficient are obtained from the sensitive load data. The sensitive load voltage withstand threshold is for different types of loads. The voltage withstand threshold for the semiconductor lithography machine is 0.3kV, the voltage withstand threshold for the medical equipment is 0.25kV, and the voltage withstand threshold for the precision industrial machine tool is 0.2kV. The cumulative time coefficient reflects the degree of nonlinear growth of loss with sag time and has a value range of 1 to 5.

[0086] The voltage sag ratio is calculated based on the system rated voltage, the fault cascading propagation range, the baseline range, and the sensitive load voltage withstand threshold. A voltage sag degree term is constructed using the voltage sag ratio as the base and the cumulative time coefficient as the exponent. Simultaneously, actual temperature deviation is considered, where the actual temperature deviation is the difference between the actual ambient temperature and the rated 25°C. When the deviation is positive, the voltage withstand threshold of the sensitive load decreases linearly with increasing actual temperature deviation, with a decrease slope of 0.002 kV / °C.

[0087] The implicit cumulative loss of the sensitive load is calculated using the following formula:

[0088]

[0089] Where L is the implicit cumulative loss of sensitive load, in RMB 10,000; v is the value coefficient of sensitive load, in RMB 10,000 / (kW·h); β is the proportion of sensitive load within the scope of fault cascading propagation, ranging from 0.1 to 0.8; S is the scope of fault cascading propagation, in km²; τ is the duration of voltage sag, in seconds. This is the system's rated voltage, expressed in kV. The baseline influence range is expressed in km². is the sensitive load voltage withstand threshold, in kV; is the cumulative time coefficient, ranging from 1 to 5; T is the actual temperature deviation, in °C. The maximum permissible temperature deviation is expressed in °C.

[0090] For example:

[0091] v = 0.01 million yuan / (kW·h), β = 0.5, S = 2.5 km², τ = 10 s =10kV, =1km², =0.3kV, γ=3, ΔT=5℃, =10℃, then L≈12,000 yuan.

[0092] The above formula is used to accurately quantify the nonlinear growth of latent losses.

[0093] S107. Obtain basic parameters of the power distribution network, load data and meteorological data from the power distribution network SCADA system, the smart meter and the meteorological monitoring station, and calculate the original power supply reliability rate based on the basic parameters of the power distribution network, load data and meteorological data;

[0094] The basic parameters of the distribution network include line parameters, switchgear parameters, and topology parameters. The line parameters include line length, conductor cross-sectional area, and impedance value. The switchgear parameters include switch operating time and rated current. The topology parameters include the number of ring networks and the number of tie lines. The load data includes real-time load values ​​on the user side, distinguishing between sensitive and non-sensitive loads and their distribution locations. The meteorological data includes precipitation intensity, wind speed, and ambient temperature data, with a data acquisition frequency set to 1 minute / time.

[0095] The original power supply reliability rate is a basic reliability rate that does not take into account fault propagation attenuation and hidden losses. It ranges from 0.95 to 0.9999 and is obtained through conventional reliability calculation methods, such as calculation based on historical fault statistics and load data.

[0096] The power distribution network SCADA system interacts with the smart meters and the meteorological monitoring station via industrial Ethernet to ensure real-time performance.

[0097] S108. Based on the original power supply reliability rate, the implicit cumulative loss of sensitive loads, the sensor data error rate, the meteorological data probability deviation, the uncertainty coupling coefficient, the power grid topology complexity, and the energy storage response delay, the actual power supply reliability rate of the distribution network is calculated through coupling correction.

[0098] The coupling correction adopts an exponential function form, wherein the exponent of the exponential function includes the product of the implicit cumulative loss of the sensitive load and the correction factor. The correction factor is 1 plus the product of the sensor data error rate, the meteorological data probability deviation and the uncertainty coupling coefficient, plus the product of the grid topology complexity and the energy storage response delay.

[0099] The actual power supply reliability of the distribution network is calculated using the following formula:

[0100]

[0101] Where R is the corrected power supply reliability of the distribution network, with a value ranging from 0.90 to 0.9999; The original power supply reliability rate ranges from 0.95 to 0.9999. This is the loss correction ratio, in units of The value ranges from 0.001 to 0.01; L represents the implicit cumulative loss of sensitive loads, in ten thousand yuan. δ is the baseline loss, in ten thousand yuan; δ is the sensor data error rate, ranging from 0.01 to 0.20; δ is the meteorological data probability deviation, ranging from 0.05 to 0.30; δ is the uncertainty coupling coefficient, ranging from 1 to 3; δ is the grid topology complexity, ranging from 0.2 to 0.9; ω is the energy storage response delay, in seconds, ranging from 0.1 to 10.

[0102] For example:

[0103] =0.99, =0.005, L=12,000 yuan, =01,000 yuan, ε=0.15, δ=0.14, θ=3, σ=0.5, ω=2s, then R≈0.96.

[0104] The above formula achieves multi-factor coupling correction, making the reliability rate more in line with reality.

[0105] Furthermore, when determining the uncertainty coupling coefficient, the value of the uncertainty coupling coefficient is positively correlated with the correlation strength between the sensor data error rate and the meteorological data probability deviation. Specifically, the uncertainty coupling coefficient increases monotonically in the strong correlation scenario and decreases monotonically in the weak correlation scenario.

[0106] Monotonically increasing means that when both the sensor data error rate and the meteorological data probability deviation increase simultaneously, the uncertainty coupling coefficient increases accordingly. For example, during typhoon weather, if the sensor error rate increases from 0.10 to 0.15 and the meteorological deviation rate increases from 0.20 to 0.25, the uncertainty coupling coefficient may increase from 2.5 to 3.0.

[0107] Monotonically decreasing means that when the correlation between the two is weak, the coefficient decreases as the error or deviation increases. For example, in sunny weather, the error rate increases but the meteorological deviation changes little, and the coefficient decreases from 1.2 to 1.0.

[0108] This design quantifies the dynamic characteristics of multi-source uncertainty coupling.

[0109] When calculating the line immunity factor, if there are multiple insulation classes of lines within the assessment area, the line immunity factor is determined using a weighted average method, with the weights being the length proportion or load contribution of each insulation class of lines. For example, if there are 35kV lines in the area (accounting for 40% of the total length), the line immunity factor is determined using a weighted average method. ) and 10kV lines (60% of the total length) Then the weighted average .

[0110] This calculation reflects the overall line's anti-interference capability in the region, avoiding deviations caused by a single value.

[0111] Furthermore, initial fault strength The short-circuit current amplitude is obtained through either measurement or calculation. Measurement methods utilize current sensors in the distribution network SCADA system to collect real-time current data at the time of the fault, directly calculating the short-circuit current amplitude, for example, by analyzing the fault current waveform. When data is missing, the calculation method derives the current based on distribution network topology parameters such as line impedance, power supply capacity, and fault type, using a short-circuit current calculation model such as the symmetrical component method. This ensures... The accuracy and feasibility of [the measures].

[0112] Furthermore, the sensitive load value coefficient *v* is obtained through industry research and data conversion. For example, if a semiconductor lithography machine has an hourly output value of 100,000 yuan and a rated power of 500 kW, then *v* = 10 / 500 = 0.02 million yuan / (kW·h). If there are multiple sensitive loads in the assessment area, *v* is calculated using a power-weighted average.

[0113]

[0114] in , Value coefficients for different loads, , This corresponds to real-time power. This processing method is suitable for mixed load scenarios.

[0115] The proportion of sensitive loads, β, is calculated using smart meter data. The formula is β = total real-time power of sensitive loads within the assessment area / total real-time power of the area. For example, if the total power of sensitive loads is 2000kW and the total power of the area is 5000kW, then β = 0.4. This calculation accurately reflects the spatial distribution of sensitive loads.

[0116] Furthermore, the voltage sag duration τ is defined as the time interval (in seconds) from when the voltage drops below the tolerance threshold to when it recovers above the threshold after the current fault-induced voltage sag event. Only the current sag directly related to the current fault is considered; historical sags are not included in the calculation. This ensures the timeliness and accuracy of implicit loss calculation.

[0117] Furthermore, the fault propagation time t is defined as the time interval from the occurrence of the fault to the stable termination of fault propagation. It is directly related to the action delay of the tie line switch and the response speed of the protection device. For example, if the switch delay is 2 minutes and the protection response is 1 minute, then t = 3 minutes. This parameter captures the cumulative effect of fault propagation over time.

[0118] Furthermore, after obtaining the actual power supply reliability rate of the distribution network, the process also includes a step of formulating distribution network optimization strategies based on the actual power supply reliability rate. When the actual power supply reliability rate is below 0.95, the optimization strategies include increasing tie line redundancy, deploying voltage stabilization devices for sensitive loads, and improving the insulation level of lines. The voltage stabilization accuracy of the voltage stabilization devices for sensitive loads is controlled within ±1%. When the actual power supply reliability rate is between 0.95 and 0.99, the optimization strategies include adjusting the deployment of energy storage devices and optimizing the parameters of the self-healing control algorithm. The parameters of the self-healing control algorithm include fault isolation time thresholds and power supply restoration priorities. When the actual power supply reliability rate is above 0.99, the optimization strategies include periodically verifying sensor accuracy and updating meteorological data prediction models. The sensor accuracy verification cycle is set to once every 3 months. This optimization strategy ensures that the distribution network dynamically adjusts according to its reliability status, thereby improving its disaster resilience.

[0119] Furthermore, the execution of the power distribution network reliability assessment method relies on a power distribution network reliability assessment system. This system includes a data acquisition unit, a data processing unit, and a decision output unit. The data acquisition unit includes current sensors, voltage sensors, smart meters, and a meteorological monitoring terminal. The current sensors' measurement range covers current values ​​under both normal and fault conditions of the power distribution network. The data processing unit includes an FPGA processor (Xilinx Kintex-7) and a memory. The memory stores various acquired data and preset algorithm programs, including fault propagation range calculation programs, implicit loss calculation programs, and reliability rate correction programs. The decision output unit includes a touchscreen display and a communication module. The communication module transmits the optimization strategy to the power distribution network dispatch center via a 5G network. The touchscreen display shows the actual power supply reliability rate and various assessment parameters of the power distribution network in real time. This system provides hardware support for the assessment method, ensuring the efficiency of data acquisition, processing, and output.

[0120] Please refer to Figure 2 As shown, this application also provides a power distribution network reliability assessment device, comprising:

[0121] The sensing module 201 acquires current and voltage data from the power distribution network and calculates the sensor data error rate based on the current, voltage, actual current, and actual voltage data.

[0122] Meteorological module 202 acquires precipitation probability prediction and wind speed prediction, and calculates meteorological data probability deviation based on the precipitation probability prediction, wind speed prediction, actual precipitation probability and actual wind speed.

[0123] The relevant module 203 determines the uncertainty coupling coefficient based on the correlation between the sensor data error rate and the meteorological data probability deviation;

[0124] Topology module 204 obtains the topology parameters of the distribution network, including the number of ring networks and the number of tie lines, and calculates the topology complexity of the power grid based on the topology parameters;

[0125] Energy storage module 205 obtains the energy storage response delay from the energy storage device of the power distribution network;

[0126] The loss module 206 obtains sensitive load data and non-sensitive load data from the smart meters of the distribution network, and calculates the implicit cumulative loss of sensitive loads based on the sensitive load data and non-sensitive load data.

[0127] The calculation module 207 obtains basic parameters of the power distribution network, load data and meteorological data from the power distribution network SCADA system, the smart meter and the meteorological monitoring station, and calculates the original power supply reliability rate based on the basic parameters of the power distribution network, load data and meteorological data;

[0128] The correction module 208 calculates the actual power supply reliability of the distribution network through coupling correction based on the original power supply reliability rate, the implicit cumulative loss of sensitive loads, the sensor data error rate, the meteorological data probability deviation, the uncertainty coupling coefficient, the power grid topology complexity, and the energy storage response delay.

[0129] This application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0130] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the above-described method.

[0131] The above description of the embodiments is provided to enable those skilled in the art to understand and apply this application. Those skilled in the art will readily make various modifications to the above embodiments and apply the general principles described herein to other embodiments without inventive effort. Therefore, this application is not limited to the above embodiments, and any improvements and modifications made to this application based on the disclosure thereof should be within the scope of protection of this application.

Claims

1. A method for assessing the reliability of a power distribution network, characterized in that, include: Acquire current and voltage data from the power distribution network, and calculate the sensor data error rate based on the current, voltage, actual current, and actual voltage data. Obtain the predicted precipitation probability and wind speed, and calculate the probability deviation of meteorological data based on the predicted precipitation probability, wind speed, actual precipitation probability, and actual wind speed. The uncertainty coupling coefficient is determined based on the correlation between the sensor data error rate and the meteorological data probability deviation. Obtain the topology parameters of the distribution network, including the number of ring networks and the number of tie lines, and calculate the topology complexity of the power grid based on the topology parameters; The energy storage response delay is obtained from the energy storage devices in the power distribution network; Sensitive load data and non-sensitive load data are obtained from the smart meters of the power distribution network, and the implicit cumulative loss of sensitive loads is calculated based on the sensitive load data and non-sensitive load data. The power distribution network basic parameters, load data and meteorological data are obtained from the power distribution network SCADA system, the smart meters and the meteorological monitoring station, and the original power supply reliability rate is calculated based on the power distribution network basic parameters, load data and meteorological data; The actual power supply reliability of the distribution network is calculated through coupling correction based on the original power supply reliability rate, the implicit cumulative loss of sensitive loads, the sensor data error rate, the meteorological data probability deviation, the uncertainty coupling coefficient, the power grid topology complexity, and the energy storage response delay.

2. The method according to claim 1, characterized in that, Based on the correlation between the sensor data error rate and the meteorological data probability deviation, the uncertainty coupling coefficient is determined as follows: The value of the uncertainty coupling coefficient is positively correlated with the correlation strength between the sensor data error rate and the meteorological data probability deviation. In the case of strong correlation, the value of the uncertainty coupling coefficient increases monotonically, while in the case of weak correlation, the value of the uncertainty coupling coefficient decreases monotonically.

3. The method according to claim 1, characterized in that, Sensitive load data and non-sensitive load data are obtained from smart meters in the distribution network. Based on this data, the implicit cumulative loss of sensitive loads is calculated, including: Calculate the load density of the evaluation area based on the sensitive load data and the non-sensitive load data; Obtain the line disturbance rejection coefficient from the basic parameters of the distribution network; The scope of fault cascading propagation is calculated based on the load density of the assessment area and the line immunity coefficient. The implicit cumulative loss of the sensitive load is calculated based on the scope of the cascading failure propagation.

4. The method according to claim 3, characterized in that, The fault cascading propagation impact range is calculated based on the load density of the assessment area and the line immunity coefficient, including: The spatiotemporal attenuation coefficient is obtained from the meteorological data; Obtain the tie-line transmission efficiency from the aforementioned basic parameters of the distribution network; The spatiotemporal attenuation coefficient and the tie-line transmission efficiency are used as exponential terms for calculating the scope of the fault cascading propagation effect.

5. The method according to claim 3, characterized in that, In the calculation of the implicit accumulation of sensitive loads based on the scope of the fault cascading propagation, the calculation of the implicit accumulation loss of sensitive loads is proportional to the square of the scope of the fault cascading propagation.

6. The method according to claim 5, characterized in that, The calculation of the implicit cumulative loss of the sensitive load based on the scope of the fault cascading propagation also includes: The system rated power and reference influence range are obtained from the basic parameters of the distribution network. The sensitive load voltage tolerance threshold and cumulative time coefficient are obtained from the sensitive load data; The voltage sag ratio is calculated based on the system rated voltage, the fault cascading propagation range, the baseline impact range, and the sensitive load voltage tolerance threshold. Using the voltage sag ratio as the base and the cumulative time coefficient as the exponent, a voltage sag degree term is constructed; The voltage sag term is incorporated into the formula for calculating the implicit accumulation of the sensitive load.

7. The method according to claim 1, characterized in that, In calculating the actual power supply reliability of the distribution network through coupling correction, the coupling correction adopts an exponential function form; The exponent of the exponential function includes the product of the implicit cumulative loss of the sensitive load and the correction factor. The correction factor is: 1 + (sensor data error rate × meteorological data probability deviation × uncertainty coupling coefficient) + (grid topology complexity × energy storage response delay).

8. A power distribution network reliability assessment device, characterized in that, include: The sensing module acquires current and voltage data from the power distribution network and calculates the sensor data error rate based on the current, voltage, actual current, and actual voltage data. The meteorological module acquires the predicted precipitation probability and wind speed, and calculates the probability deviation of the meteorological data based on the predicted precipitation probability, wind speed, actual precipitation probability, and actual wind speed. The relevant module determines the uncertainty coupling coefficient based on the correlation between the sensor data error rate and the meteorological data probability deviation; The topology module obtains the topology parameters of the distribution network, including the number of ring networks and the number of tie lines, and calculates the topology complexity of the power grid based on the topology parameters. The energy storage module obtains the energy storage response delay from the energy storage devices in the power distribution network; The loss module obtains sensitive load data and non-sensitive load data from the smart meters of the distribution network, and calculates the implicit cumulative loss of sensitive loads based on the sensitive load data and non-sensitive load data; The calculation module obtains basic parameters of the power distribution network, load data, and meteorological data from the power distribution network SCADA system, the smart meter, and the meteorological monitoring station, and calculates the original power supply reliability rate based on the basic parameters of the power distribution network, load data, and meteorological data. The correction module calculates the actual power supply reliability of the distribution network through coupling correction based on the original power supply reliability rate, the implicit cumulative loss of sensitive loads, the sensor data error rate, the meteorological data probability deviation, the uncertainty coupling coefficient, the power grid topology complexity, and the energy storage response delay.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method of any one of claims 1-7.

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