Safety Risk Assessment Method and System for Photovoltaic-Charging Pile-Variable Frequency Load System

Abstract

This invention belongs to the field of power grid risk assessment technology, and provides a method and system for assessing the safety risks of a photovoltaic-charging pile-inverter load system. The method includes: constructing static safety risk indicators, dynamic safety risk indicators, and safety monitoring risk indicators for the customer-side photovoltaic-charging pile-inverter load system; calculating the subjective and objective weights of each indicator, and obtaining the combined weights of each indicator using a linear weighting method; based on the combined weights and quantified values ​​of each indicator, obtaining a comprehensive safety risk evaluation of the customer-side photovoltaic-charging pile-inverter load system using a matter-element extension model, thereby determining the safety risk level of the customer-side photovoltaic-charging pile-inverter load system. This invention can effectively support the operation, maintenance, and replacement of equipment, ensuring the safe operation of the customer-side photovoltaic-charging pile-inverter load system.

Description

Technical Field

[0001] This invention belongs to the field of power grid risk assessment technology, and in particular relates to a method and system for safety risk assessment of a photovoltaic-charging pile-frequency conversion load system. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] To contribute to achieving the goals of "carbon peaking" and "carbon neutrality," it is crucial to vigorously promote grid-connected photovoltaic systems on the customer side, drive the substitution of electricity at the customer end with electric vehicles, and promote the use of energy-saving variable frequency load equipment. The proportion of new energy-saving variable frequency load equipment such as variable frequency air conditioners and variable frequency motors in industrial, commercial, and residential customer sectors is also increasing year by year. Against this backdrop, a new energy consumption pattern of distributed photovoltaic systems, charging piles, and variable frequency loads is gradually forming on the customer side.

[0004] However, with the increasing proportion of customer-side photovoltaic (PV) power, electric vehicle (EV) charging piles, and frequency converter loads connected to the grid, related customer-side safety hazards and accidents are occurring frequently. Aging components, cable ruptures, and loose contacts in PV grid-connected inverters can easily generate fault arcs, leading to serious fires. Overheating of charging lines and short circuits in the external DC system of batteries are causing frequent spontaneous combustion and fires during EV charging. Voltage fluctuations, current exceeding limits, and other power quality issues can lead to abnormal disconnection of load equipment from the grid, affecting customers' normal living needs and economic production activities. Frequent safety hazards seriously affect the reliability of the power grid, threaten the personal safety of electricity customers, and cause huge losses to both customers and the power grid. Summary of the Invention

[0005] To address the technical problems existing in the background art, the present invention provides a safety risk assessment method and system for a photovoltaic-charging pile-inverter load system. By comprehensively evaluating the safety risks of the customer-side photovoltaic-charging pile-inverter load system and classifying them into risk levels, the present invention provides support for the operation, maintenance, and replacement of equipment, thereby ensuring the safe operation of the power system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The first aspect of the present invention provides a method for safety risk assessment of a photovoltaic-charging pile-frequency conversion load system.

[0008] Safety risk assessment methods for photovoltaic-charging pile-inverter load systems include:

[0009] Construct static safety risk indicators, dynamic safety risk indicators, and safety monitoring risk indicators for the customer-side photovoltaic-charging pile-inverter load system; wherein, the static safety risk indicators include: average pass rate of photovoltaic grid-connected node voltage, average pass rate of photovoltaic grid-connected node current, average pass rate of photovoltaic grid-connected node frequency, charging pile output voltage error, charging pile output current error, charging pile ripple coefficient, charging pile body temperature assessment index, inverter load AC side voltage deviation, and inverter load AC side frequency deviation.

[0010] The subjective and objective weights of each indicator are calculated, and the combined weights of each indicator are obtained by combining them with the linear weighting method.

[0011] Based on the combined weights of various indicators and their quantitative values, a comprehensive safety risk assessment of the customer-side photovoltaic-charging pile-inverter load system is obtained using the matter-element extension model, thereby determining the safety risk level of the customer-side photovoltaic-charging pile-inverter load system.

[0012] Furthermore, the process of calculating the subjective weights of each indicator specifically includes: determining the quantitative values ​​between each indicator based on expert experience, and calculating the subjective weights of each indicator based on the quantitative values ​​between each indicator.

[0013] Furthermore, the process of calculating the objective weights of each indicator specifically includes: standardizing each indicator; calculating the inverse entropy value of each indicator after standardization; and determining the entropy weight of each indicator based on its inverse entropy value, which is the objective weight of each indicator.

[0014] Furthermore, the specific process of using the matter-element extension model in computer analysis includes: treating the user-side photovoltaic-charging pile-inverter load system to be evaluated as a matter element, combining the distances of various indicators with the classical domain and the distances of various indicators with the node domain, calculating the correlation degree of each indicator, and thereby determining the safety risk level of the customer-side photovoltaic-charging pile-inverter load system.

[0015] A second aspect of the present invention provides a safety risk assessment system for a photovoltaic-charging pile-frequency conversion load system.

[0016] A safety risk assessment system for photovoltaic-charging pile-inverter load systems includes:

[0017] The indicator construction module is configured to: construct static safety risk indicators, dynamic safety risk indicators, and safety monitoring risk indicators for the customer-side photovoltaic-charging pile-inverter load system; wherein, the static safety risk indicators include: average pass rate of photovoltaic grid-connected node voltage, average pass rate of photovoltaic grid-connected node current, average pass rate of photovoltaic grid-connected node frequency, charging pile output voltage error, charging pile output current error, charging pile ripple coefficient, charging pile body temperature assessment index, inverter load AC side voltage deviation, and inverter load AC side frequency deviation;

[0018] The weight calculation module is configured to: calculate the subjective and objective weights of each indicator, and combine them with the linear weighting method to obtain the combined weights of each indicator;

[0019] The safety risk assessment module is configured to: obtain a comprehensive safety risk assessment of the customer-side photovoltaic-charging pile-inverter load system based on the combined weights of various indicators and the quantitative values ​​of each indicator, and thereby determine the safety risk level of the customer-side photovoltaic-charging pile-inverter load system.

[0020] A third aspect of the present invention provides a computer-readable storage medium.

[0021] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the safety risk assessment method for a photovoltaic-charging pile-frequency conversion load system as described in the first aspect above.

[0022] A fourth aspect of the present invention provides a computer device.

[0023] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the safety risk assessment method for a photovoltaic-charging pile-frequency conversion load system as described in the first aspect above.

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] This invention constructs a multi-level safety risk assessment index system based on novel safety hazards and operational risks of customer-side photovoltaic-charging pile-inverter load systems. The first layer is the overall safety risk assessment index for the customer-side photovoltaic-charging pile-inverter load system. The second layer establishes static safety risk indicators, dynamic safety risk indicators, and safety monitoring risk indicators to evaluate the system's safe operation risks from multiple perspectives. The third layer refines the second layer, identifying 24 evaluation indicators. The relative importance of each indicator is determined through expert experience. The subjective weights of each indicator are quantified using the analytic hierarchy process (AHP). Objective weights are obtained based on the distribution characteristics of the indicator data and the inverse entropy weighting method. Then, a linear weighting method is used to process the subjective and objective weights of each indicator to obtain a combined weight. Finally, based on the combined weights and quantified values ​​of each indicator, a comprehensive safety risk assessment of the photovoltaic-charging-load system is obtained using a matter-element extension model, thereby determining the system's safety risk level. This invention effectively supports the operation, maintenance, and replacement of equipment, ensuring the safe operation of customer-side photovoltaic-charging pile-inverter load systems. Attached Figure Description

[0026] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0027] Figure 1 This is a flowchart illustrating the safety risk assessment method for a photovoltaic-charging pile-frequency conversion load system according to Embodiment 1 of the present invention;

[0028] Figure 2 This is the safety risk assessment index system for the customer-side photovoltaic-charging pile-frequency conversion load system shown in Embodiment 1 of the present invention. Detailed Implementation

[0029] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0030] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0031] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the term "comprising" is used in this specification, it indicates the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0032] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of this disclosure. It should be noted that each block in a flowchart or block diagram may represent a module, segment, or portion of code, which may include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, may be implemented using a dedicated hardware-based system that performs the specified functions or operations, or using a combination of dedicated hardware and computer instructions.

[0033] Example 1

[0034] like Figure 1 As shown, this embodiment provides a safety risk assessment method for a photovoltaic-charging pile-frequency conversion load system. This embodiment uses the application of this method to a server as an example for illustration. It is understood that this method can also be applied to terminals, and can also be applied to systems including terminals, servers, and other components, and can be implemented through interaction between the terminal and the server. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communication, middleware services, domain name services, CDN security services, and big data and artificial intelligence platforms. The terminal can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited herein. In this embodiment, the method includes the following steps:

[0035] Construct static safety risk indicators, dynamic safety risk indicators, and safety monitoring risk indicators for the customer-side photovoltaic-charging pile-inverter load system; wherein, the static safety risk indicators include: average pass rate of photovoltaic grid-connected node voltage, average pass rate of photovoltaic grid-connected node current, average pass rate of photovoltaic grid-connected node frequency, charging pile output voltage error, charging pile output current error, charging pile ripple coefficient, charging pile body temperature assessment index, inverter load AC side voltage deviation, and inverter load AC side frequency deviation.

[0036] The subjective and objective weights of each indicator are calculated, and the combined weights of each indicator are obtained by combining them with the linear weighting method.

[0037] Based on the combined weights of various indicators and their quantitative values, a comprehensive safety risk assessment of the customer-side photovoltaic-charging pile-inverter load system is obtained using the matter-element extension model, thereby determining the safety risk level of the customer-side photovoltaic-charging pile-inverter load system.

[0038] The specific solution for this embodiment can be implemented by referring to the following content:

[0039] A multi-level safety risk assessment index system is constructed based on the new safety hazards and operational risks of the customer-side photovoltaic-charging pile-frequency conversion load system.

[0040] The relative importance of each indicator is determined by expert experience, and the subjective weight of each indicator is obtained by quantification using the analytic hierarchy process. The objective weight of the indicator is obtained by combining the distribution characteristics of the indicator data with the inverse entropy weight method.

[0041] The subjective and objective weights of each indicator are processed using a linear weighting method to obtain the combined weights of each indicator;

[0042] Based on the combined weights and quantitative values ​​of various indicators, a comprehensive safety risk assessment of the customer-side photovoltaic-charging pile-frequency conversion load system is obtained using the matter-element extension model, thereby determining the system's safety risk level.

[0043] As one or more implementation methods, such as Figure 2 As shown, the multi-level safety risk assessment index system constructed based on the new safety hazards and operational risks of the customer-side photovoltaic-charging pile-frequency converter load system includes:

[0044] Based on the failure patterns, aging patterns, and changes in equipment attribute parameters and operating parameters, static safety risk indicators are constructed for the customer-side photovoltaic-charging pile-inverter load system. Dynamic safety risk indicators are constructed based on the broadband dynamic characteristics of the customer-side photovoltaic-charging pile-inverter load system. Based on the analysis of historical fault records and safety hazard investigation records of photovoltaic, charging pile, and inverter load equipment and the entire system, safety monitoring risk indicators for the customer-side photovoltaic-charging pile-inverter load system are constructed.

[0045] Static safety risk indicators include the probability of repairable failure, the probability of aging failure, the average failure rate of photovoltaic grid-connected point voltage, the average failure rate of photovoltaic grid-connected point current, the average failure rate of photovoltaic grid-connected point frequency, the output voltage error of charging pile, the output current error of charging pile, the ripple coefficient of charging pile, the temperature assessment index of charging pile body, the voltage deviation of AC side of variable frequency load, and the frequency deviation of AC side of variable frequency load.

[0046] Dynamic safety risk indicators include voltage sag / sag indicators, frequency offset, three-phase imbalance, voltage broadband harmonic content, current broadband harmonic content, and power broadband harmonic content.

[0047] Safety monitoring risk indicators include equipment failure rate, equipment maintenance rate, system failure power outage rate, average power outage time, rate of high-age equipment, severe weather indicators, and safety hazard investigation.

[0048] As one or more implementation methods, the specific meanings and calculation formulas of each safety risk assessment indicator are as follows.

[0049] 1. Static security risk indicators

[0050] (1) Probability of repairable failure

[0051] Component failure is a significant cause of system safety issues. Component failure can be categorized into repairable failure and aging failure. The probability of a component's repairable failure refers to the probability that a device will experience a repairable fault. This probability generally exhibits memorylessness, meaning that whether a fault has occurred previously is irrelevant to whether a fault will occur in the future; this characteristic is also known as the Markov property.

[0052] The probability of repairable failure can be expressed by the following formula:

[0053]

[0054] In the formula, MTTF is the mean time to failure (h), and MTTR is the mean repair time (h).

[0055] (2) Probability of aging failure

[0056] Unlike repairable failure, aging failure is also known as irreparable failure. The aging failure of equipment generally follows a lifespan basin curve, meaning that the probability of aging failure is relatively high in the early stages of equipment use and after long-term use, while the probability of aging failure is relatively low in the middle stages of equipment use.

[0057] The probability of aging failure refers to the probability that a component will experience irreparable failure at time T+t, given that it has been used for time T. Its expression is:

[0058]

[0059] In the formula, P l Let f(t) represent the probability of failure due to aging, and let f(t) represent the normal distribution failure probability density function.

[0060] (3) Average failure rate of photovoltaic grid connection point voltage

[0061] The average voltage failure rate at the grid connection point of photovoltaic (PV) systems primarily reflects the voltage failure rate at the grid connection point of the PV equipment, thereby reflecting the performance of the PV equipment and its adverse effects on the system. The average voltage failure rate can be expressed by the following formula:

[0062]

[0063] In the formula, U g This indicates the average failure rate of the grid-connected voltage of photovoltaic equipment, where T0 represents the voltage detection time, and t... g This indicates the time during which the voltage was not up to standard.

[0064] (4) Average failure rate of photovoltaic grid-connected current

[0065] The average failure rate of the grid-connected current in photovoltaic systems primarily reflects the non-compliance of the current at the grid connection point of the photovoltaic equipment. It can also be used to reflect the performance of the photovoltaic equipment and its adverse effects on the system. The average failure rate of the current can be expressed by the following formula:

[0066]

[0067] In the formula, I g This represents the average failure rate of the grid-connected current of photovoltaic equipment, where T1 represents the current detection time, and t... g1 This indicates the time during which the current was not up to standard.

[0068] (5) Average failure rate of photovoltaic grid connection point frequency

[0069] The average frequency failure rate at the grid connection point of photovoltaic (PV) systems primarily reflects the frequency non-compliance of the PV equipment's grid connection points. It can also reflect the performance of the PV equipment and its adverse effects on the system. The average frequency failure rate can be expressed by the following formula:

[0070]

[0071] In the formula, f g T2 represents the average frequency failure rate at the grid connection point of photovoltaic equipment, and t represents the frequency detection time. g2 This indicates the time during which the frequency is out of range.

[0072] (6) Output voltage error of charging pile

[0073] The output voltage error of a charging pile represents the difference between the actual output voltage and the set output voltage. The output voltage error of a charging pile can effectively reflect its health status; a charging pile in a healthy operating state typically has an output voltage error of no more than ±0.5%. The output voltage error of a charging pile can be expressed by the following formula:

[0074]

[0075] In the formula, ξ U U represents the output voltage error of the charging pile. r U represents the actual output voltage of the charging station. s This indicates the set charging voltage.

[0076] (7) Output current error of charging pile

[0077] The output current error of a charging pile represents the difference between the actual output current and the set output current. The output current error of a charging pile can effectively reflect its health status; a charging pile operating in a healthy state typically has an output current error of no more than ±1%. The output current error of a charging pile can be expressed by the following formula:

[0078]

[0079] In the formula, ξ I I represents the output current error of the charging pile. r I represents the actual output current of the charging pile. s This indicates the set charging current.

[0080] (8) Ripple coefficient of charging pile

[0081] The DC power supplied to charging stations is derived from AC power through rectification and voltage regulation. Inevitably, some AC components are mixed in; this AC superimposed on the DC is called ripple. The ripple factor characterizes the magnitude of the ripple; a larger ripple factor may indicate a greater safety risk to the DC charging station. The ripple factor of a healthy electric vehicle charging station should not exceed 1%. The ripple factor of a charging station can be expressed by the following formula:

[0082]

[0083] In the formula, δ represents the ripple coefficient of the charging pile, U P U represents the average DC voltage. f U represents the peak value of the DC voltage ripple. g This indicates the pulsation valley value in DC voltage.

[0084] (9) Charging pile body temperature evaluation index

[0085] When the temperature rise of a charging pile exceeds the limit, it can lead to reduced charging efficiency or cessation of output, resulting in charging pile malfunction. Therefore, the temperature difference between the charging pile body and the ambient temperature is an important indicator for safety risk assessment of charging pile equipment. The charging pile body temperature assessment index can be expressed by the following formula:

[0086] ΔT=T z-T s (9)

[0087] In the formula, T z This indicates the temperature of the charging pile body when it is working, in T. s Indicates ambient temperature.

[0088] (10) Voltage deviation on AC side of variable frequency load

[0089] The basic structure of a variable frequency load generally consists of an induction motor and a frequency converter. Excessive or insufficient AC voltage may cause motor stall or frequency converter malfunction, leading to abnormal off-grid disconnection of the variable frequency load. Therefore, the AC voltage deviation needs to be selected as a safety risk assessment indicator for the variable frequency load. It is expressed by the following formula:

[0090]

[0091] In the formula, U represents the voltage deviation on the AC side of the variable frequency load. bN U represents the effective value of the rated voltage on the AC side. b1 This indicates the effective value of the actual voltage on the AC side.

[0092] (11) Frequency deviation on the AC side of the variable frequency load

[0093] Excessively high or low frequency on the AC side of a variable frequency load can also pose safety hazards. The frequency deviation can be expressed by the following formula:

[0094]

[0095] In the formula, Indicates the frequency deviation on the AC side of the variable frequency load, f bN Indicates the rated frequency on the AC side, f b1 This indicates the actual frequency on the AC side.

[0096] 2. Dynamic security risk indicators

[0097] (1) Voltage sag / sag index

[0098] The safety risks of the customer-side photovoltaic-charging pile-inverter load system studied may not only originate from within the system but also from the external power grid. The voltage sag / slippage index is established to address the impact of voltage sags / slippages from the external power grid on the customer-side photovoltaic-charging pile-inverter load system. This index can be expressed by the following formula:

[0099]

[0100] In the formula, ξ U U represents the voltage sag / droop index. mNU represents the rated voltage amplitude at the connection point between the system and the external power grid. m1 It indicates the maximum or minimum voltage amplitude at that location over a period of time.

[0101] (2) Frequency offset index

[0102] A frequency offset index is established to address the impact of external power grid frequency offset on the customer-side photovoltaic-charging pile-inverter load system. It can be expressed by the following formula:

[0103]

[0104] In the formula, ξ f The frequency offset index, f N f1 represents the rated frequency at the connection point between the system and the external power grid, and f1 represents the value at that point that deviates most from the rated frequency over a period of time.

[0105] (3) Three-phase imbalance

[0106] Three-phase imbalance can be used to reflect the quality of power supplied by the external power grid to the customer-side photovoltaic-charging pile-frequency converter load system. If the power quality is too poor, it will pose a safety risk to the system. Therefore, three-phase imbalance is selected as a safety risk assessment index, which can be calculated using the following formula:

[0107]

[0108] In the formula, LVUR represents the three-phase unbalance, V ab V bc V ca This represents the effective value of the three-phase line voltage, V. Lavg This represents the average value of the three-phase phase voltage.

[0109] (4) Voltage wideband harmonic content

[0110] Because the customer-side photovoltaic-charging pile-frequency converter load system uses a large number of power electronic devices connected to the grid, it injects a large number of harmonic components into the system, causing waveform distortion and affecting power quality. Furthermore, the harmonic frequencies can reach the MHz level, exhibiting wideband characteristics. This characteristic easily leads to system resonance, causing safety risks such as overvoltage and overcurrent. The formula for calculating the wideband harmonic content of voltage is as follows:

[0111]

[0112] In the formula, C U U represents the wideband harmonic content of the voltage. (1) This indicates the fundamental frequency content in the waveform, and U represents the effective value of the cycle voltage.

[0113] (5) Current wideband harmonic content

[0114] Similarly, the formula for calculating the broadband harmonic content of current is as follows:

[0115]

[0116] In the formula, C I I represents the broadband harmonic content of the current. (1) This indicates the fundamental frequency content in the waveform, and I represents the effective value of the cycle current.

[0117] (6) Power broadband harmonic content

[0118] Similarly, the formula for calculating the power broadband harmonic content is as follows:

[0119]

[0120] In the formula, C P P represents the power broadband harmonic content. (1) This indicates the fundamental frequency content in the waveform, and P represents the effective value of the cycle power.

[0121] 3. Safety Inspection Risk Indicators

[0122] (1) Equipment failure rate

[0123] Equipment failure rate refers to the ratio of the number of equipment failures to the number of monitoring days during the monitoring period, and its expression is:

[0124]

[0125] In the formula, r e n represents the equipment failure rate. e This indicates the number of equipment failures that occurred during the monitoring period, where T represents the number of monitoring days.

[0126] (2) Equipment maintenance rate

[0127] Equipment maintenance rate refers to the ratio of the number of times equipment is repaired to the number of monitoring days during the monitoring period, and its expression is:

[0128]

[0129] In the formula, r w n represents the equipment maintenance rate. w This indicates the number of times the equipment was repaired during the monitoring period, and T represents the number of monitoring days.

[0130] (3) System failure power outage rate

[0131] The system outage rate refers to the ratio of the number of system outages to the number of monitoring days during the monitoring period. Its expression is:

[0132]

[0133] In the formula, r t n represents the system failure power outage rate. t This indicates the number of system failures and power outages that occurred during the monitoring period, where T represents the number of monitoring days.

[0134] (4) Average power outage time

[0135] Average outage time refers to the average duration of each outage within the monitoring period, and its expression is:

[0136]

[0137] In the formula, t represents the average power outage time. i Let n represent the duration of the i-th power outage. t This indicates the number of power outages during the monitoring period.

[0138] (5) High-age equipment rate

[0139] The long service life and numerous years of equipment use are a significant factor contributing to safety risks in customer-side photovoltaic-charging pile-inverter load systems. Here, equipment used for more than twenty years is defined as high-age equipment. The high-age equipment rate is the proportion of high-age equipment to the total number of equipment, expressed as:

[0140]

[0141] In the formula, r g Indicates the rate of high-life equipment, n g This represents the number of older equipment units, and N represents the total number of equipment units.

[0142] (6) Severe weather rate

[0143] Severe weather conditions can easily cause safety risks to the customer-side photovoltaic-charging pile-inverter load system. The severe weather rate refers to the proportion of days with severe weather to the total number of monitoring days within the monitoring period, expressed as:

[0144]

[0145] In the formula, r l n represents the severe weather rate. l This indicates the number of days with severe weather during the monitoring period, where T represents the number of monitoring days.

[0146] (7) Safety hazard investigation rate

[0147] The safety hazard detection rate refers to the proportion of safety hazards discovered during the safety hazard detection process of the customer-side photovoltaic-charging pile-inverter load system out of the total number of detections. Its expression is:

[0148]

[0149] In the formula, r a n represents the rate of safety hazard detection. a N represents the number of times safety hazards were discovered during the inspection process. a This indicates the total number of safety hazard inspections.

[0150] As one or more implementation methods, the step of determining the relative importance of each indicator through expert experience and quantifying the subjective weights of each indicator using the analytic hierarchy process includes:

[0151] Step 211: Construct the judgment matrix:

[0152]

[0153] Based on expert experience, the relative importance of each indicator was determined, and quantification was performed using a 1-9 scale. Wherein, a... ij This represents the quantitative value indicating the relative importance between indicator i and indicator j, where n represents the number of evaluation indicators; in this method, n = 24. The meaning of the selected values ​​is shown in Table 1.

[0154] Table 1. Quantitative values ​​and descriptions of relative importance

[0155]

[0156]

[0157] Step 212: Matrix consistency check:

[0158] λ max If the largest eigenvalue of matrix A is the consistency index, then...

[0159] Consistency ratio R CR The consistency index is defined as the ratio of the consistency index of a matrix to the average random consistency index of the same order. That is:

[0160] Step 213: Calculation of Subjective Weight Vector

[0161]

[0162] in, Represents the subjective weight vector. In the formula, n represents the number of safety risk assessment indicators; in this method, n = 24. It is the nth root of the product of each element in the i-th row.

[0163] As one or more implementation methods, obtaining the objective weight of the indicator based on the distribution characteristics of the indicator data combined with the inverse entropy weight method includes:

[0164] Step 221: Standardization of indicator data for each group

[0165] The safety risk indicator data for several sets of customer-side photovoltaic-charging pile-inverter load systems need to be standardized. For example, the i-th indicator y in the j-th data set... ij The proportion p ij for:

[0166]

[0167] In the formula, n represents the number of safety risk assessment indicators, and m represents the number of customer-side photovoltaic-charging pile-frequency conversion load systems to be assessed.

[0168] Step 222: Calculate the inverse entropy value e of the i-th safety risk assessment indicator. i :

[0169]

[0170] Step 223: Calculate the entropy weight of the i-th safety risk assessment index.

[0171]

[0172] As one or more implementation methods, the method of using linear weighting to process the subjective and objective weights of each indicator to obtain the combined weights of each safety risk assessment indicator is as follows:

[0173] The formula for calculating the portfolio weights using the linear weighting method is as follows:

[0174]

[0175] In the formula, α represents the weight allocation coefficient, and its value range is 0 < α < 1. In this method, we simply take α = 0.5.

[0176] As one or more implementation methods, based on the combined weights and quantified values ​​of various indicators, a comprehensive safety risk assessment of the optical-charging-load system is obtained using the matter-element extension model, thereby determining the system's safety risk level, including:

[0177] Step 41: Treat all user-side photovoltaic-charging pile-inverter load systems to be evaluated as a single object element.

[0178] The object element to be evaluated is:

[0179] Where R represents the object to be evaluated, N represents the object to be evaluated, C represents each indicator in the evaluation index system, and V represents the quantitative value of each indicator.

[0180] Classical domain matter-element matrix R ck for:

[0181] Among them, G k This represents the kth level in the safety risk level assessment established for the customer-side photovoltaic-charging pile-inverter load system, which is divided into four levels in total: "Excellent", "Good", "Medium" and "Poor". nk ,b nk This represents the upper and lower limits of the values ​​of each evaluation indicator at the k-th level;

[0182] Sectional matter-element matrix R p for:

[0183] Among them, G p V represents the overall rating of the object being evaluated. p This represents the sum of the value ranges of each evaluation index in a classical domain matter element;

[0184] Step 42: Calculate the distance between the index and the classical domain

[0185]

[0186] Step 43: Calculate the distance between the index and the section.

[0187]

[0188] Among them, v ij V represents the specific value of the i-th evaluation indicator for the j-th evaluation object; ijk Let i be the value range of the i-th evaluation index at the k-th risk level (a jk ,b jk ),V ijp Indicates the range of values ​​for the i-th evaluation index (a j1 ,b jp ).

[0189] Step 44: Calculate the correlation between each evaluation indicator.

[0190] According to the definition of extension theory, the correlation between the i-th indicator of the j-th evaluation object and the k-th level is as follows:

[0191]

[0192] Step 45: Calculate the correlation between the evaluation objects​

[0193]

[0194] Among them, K k (N i ) represents the object to be evaluated, N. i The correlation degree w for the k-th evaluation level j This represents the combined weight coefficient of the j-th evaluation indicator;

[0195] Step 46: Calculate the eigenvalues ​​of the ordinal variables

[0196] By K k' (N i )=max{K k (N i )} We have the object element N to be evaluated. i Belonging to level k', further:

[0197]

[0198]

[0199] Where, k * It represents the characteristic value of the rank variable, which can reflect the degree to which the object being evaluated is biased towards adjacent ranks.

[0200] Example 2

[0201] This embodiment provides a safety risk assessment system for a photovoltaic-charging pile-frequency conversion load system.

[0202] A safety risk assessment system for photovoltaic-charging pile-inverter load systems includes:

[0203] The indicator construction module is configured to: construct static safety risk indicators, dynamic safety risk indicators, and safety monitoring risk indicators for the customer-side photovoltaic-charging pile-inverter load system; wherein, the static safety risk indicators include: average pass rate of photovoltaic grid-connected node voltage, average pass rate of photovoltaic grid-connected node current, average pass rate of photovoltaic grid-connected node frequency, charging pile output voltage error, charging pile output current error, charging pile ripple coefficient, charging pile body temperature assessment index, inverter load AC side voltage deviation, and inverter load AC side frequency deviation;

[0204] The weight calculation module is configured to: calculate the subjective and objective weights of each indicator, and combine them with the linear weighting method to obtain the combined weights of each indicator;

[0205] The safety risk assessment module is configured to: obtain a comprehensive safety risk assessment of the customer-side photovoltaic-charging pile-inverter load system based on the combined weights of various indicators and the quantitative values ​​of each indicator, and thereby determine the safety risk level of the customer-side photovoltaic-charging pile-inverter load system.

[0206] It should be noted that the above-mentioned indicator construction module, weight calculation module, and security risk assessment module are the same examples and application scenarios implemented in the steps of Embodiment 1, but are not limited to the content disclosed in Embodiment 1. It should also be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0207] Example 3

[0208] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the safety risk assessment method for a photovoltaic-charging pile-frequency conversion load system as described in Embodiment 1 above.

[0209] Example 4

[0210] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the safety risk assessment method for the photovoltaic-charging pile-frequency conversion load system as described in Embodiment 1 above.

[0211] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0212] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0213] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0214] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0215] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0216] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A safety risk assessment method for a photovoltaic-charging pile-frequency conversion load system, characterized in that, include: Static safety risk indicators, dynamic safety risk indicators, and safety monitoring risk indicators are constructed for the customer-side photovoltaic-charging pile-inverter load system. The static safety risk indicators include: average pass rate of photovoltaic grid-connected node voltage, average pass rate of photovoltaic grid-connected node current, average pass rate of photovoltaic grid-connected node frequency, charging pile output voltage error, charging pile output current error, charging pile ripple coefficient, charging pile body temperature assessment index, inverter load AC side voltage deviation, and inverter load AC side frequency deviation. The dynamic safety risk indicators include: voltage sag or dip index, frequency offset, three-phase imbalance, voltage broadband harmonic content, current broadband harmonic content, and power broadband harmonic content. The safety monitoring risk indicators include: equipment failure rate, equipment maintenance rate, system failure power outage rate, average power outage time, high-age equipment rate, severe weather index, and safety hazard investigation rate. Calculate the subjective and objective weights of each indicator, and then combine them with the linear weighting method to obtain the combined weights of each indicator. Based on the combined weights and quantitative values ​​of each indicator, a comprehensive safety risk assessment of the customer-side photovoltaic-charging pile-inverter load system is obtained using the matter-element extension model, thereby determining the safety risk level of the customer-side photovoltaic-charging pile-inverter load system. The specific process of using the matter-element extension model includes: treating the customer-side photovoltaic-charging pile-inverter load system to be evaluated as a matter element, combining the distances of each indicator to the classical domain and the distances of each indicator to the node domain, calculating the correlation degree of each indicator, thereby determining the safety risk level of the customer-side photovoltaic-charging pile-inverter load system.

2. The safety risk assessment method for a photovoltaic-charging pile-frequency conversion load system according to claim 1, characterized in that, The static safety risk indicators also include: the probability of repairable failure and the probability of aging failure.

3. The safety risk assessment method for a photovoltaic-charging pile-frequency conversion load system according to claim 1, characterized in that, The process of calculating the subjective weights of each indicator specifically includes: determining the quantitative values ​​between each indicator based on expert experience, and calculating the subjective weights of each indicator based on the quantitative values ​​between each indicator.

4. The safety risk assessment method for a photovoltaic-charging pile-frequency conversion load system according to claim 1, characterized in that, The process of calculating the objective weights of each indicator includes: standardizing each indicator; calculating the inverse entropy value of each indicator after standardization; and determining the entropy weight of each indicator based on the inverse entropy value, which is the objective weight of each indicator.

5. A safety risk assessment system for a photovoltaic-charging pile-frequency conversion load system, characterized in that, include: The indicator construction module is configured to construct static safety risk indicators, dynamic safety risk indicators, and safety monitoring risk indicators for the customer-side photovoltaic-charging pile-inverter load system. The static safety risk indicators include: average pass rate of photovoltaic grid-connected node voltage, average pass rate of photovoltaic grid-connected node current, average pass rate of photovoltaic grid-connected node frequency, charging pile output voltage error, charging pile output current error, charging pile ripple coefficient, charging pile body temperature assessment index, inverter load AC side voltage deviation, and inverter load AC side frequency deviation. The dynamic safety risk indicators include: voltage sag or dip index, frequency offset, three-phase imbalance, voltage broadband harmonic content, current broadband harmonic content, and power broadband harmonic content. The safety monitoring risk indicators include: equipment failure rate, equipment maintenance rate, system outage rate, average outage time, high-age equipment rate, severe weather index, and safety hazard investigation rate. The weight calculation module is configured to calculate the subjective and objective weights of each indicator, and then combine them with a linear weighting method to obtain the combined weights of each indicator. The safety risk assessment module is configured to: obtain a comprehensive safety risk evaluation of the customer-side photovoltaic-charging pile-inverter load system based on the combined weights and quantitative values ​​of various indicators and the matter-element extension model, thereby determining the safety risk level of the customer-side photovoltaic-charging pile-inverter load system; the specific process of using the matter-element extension model includes: treating the customer-side photovoltaic-charging pile-inverter load system to be evaluated as a matter element, combining the distances of various indicators to the classical domain and the distances of various indicators to the node domain, calculating the correlation degree of each indicator, thereby determining the safety risk level of the customer-side photovoltaic-charging pile-inverter load system.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the safety risk assessment method for a photovoltaic-charging pile-frequency conversion load system as described in any one of claims 1-4.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the safety risk assessment method for a photovoltaic-charging pile-frequency conversion load system as described in any one of claims 1-4.

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