Safety influence assessment method and device based on data fuzzy hybrid modeling

Abstract

The embodiment of the invention provides a safety influence assessment method and device based on data fuzzy hybrid modeling, and the method comprises the steps: analyzing the load characteristics of a power utilization system of a station, and determining an analysis result; constructing a three-phase power flow model of the typical station power utilization system under non-full-phase energy taking based on an analysis result; based on the three-phase power flow model, constructing a three-phase unbalance degree analytic expression of the unbalanced power flow of the station power utilization system; building a multi-dimensional safety evaluation index of the station power utilization system based on the three-phase power flow model; based on the three-phase unbalance degree analytic expression and the safety evaluation index, constructing a model for evaluating the influence of the power utilization unbalance power flow of the station on the safety of the system under the data-fuzzy hybrid modeling; and generating an evaluation model based on the station power utilization system parameters and the safety influence evaluation model, and determining an evaluation result based on the evaluation model. The method achieves the accurate multi-dimensional evaluation of the influence of the unbalanced power flow of the station power utilization on the safety of the system, and can provide guidance for the management of unbalanced energy taking of a station power utilization system.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of power technology, and in particular to a method for assessing the safety impact based on data fuzzy hybrid modeling. Background Technology

[0002] With the current goal of unmanned substations, numerous new technologies such as "one-click sequential control," "intelligent inspection," and "digital twins" have rapidly developed. Traditional substations, in order to achieve unmanned operation, have gradually entered a phase of technological transformation, installing intelligent cameras throughout the station, upgrading lighting systems, and installing auxiliary equipment. All of these facilities require support from the substation's power supply system. Currently, due to the presence of a large number of fluctuating loads within the substation, initial load planning cannot guarantee symmetrical system operation in real time. Furthermore, years of continuous equipment upgrades have resulted in significant differences between the current system's operating power and the initial design, leading to severe asymmetrical operation of the substation's power supply system. In this context, considering that the equipment used for unmanned transformation consists of single-phase power extraction devices in large numbers with high total power extraction, the three-phase power flow imbalance of the substation's power supply system is exacerbated. When the system experiences a decrease in safety under three-phase unbalanced power flow operation, is it due to the power flow imbalance? To what extent does it affect the safety of the system? Is imbalance mitigation necessary to provide guidance? Therefore, how to accurately assess the impact of unbalanced power flow on the power supply system of a substation in a multi-dimensional manner to provide support for subsequent management of power supply imbalance has become an issue to be addressed.

[0003] Currently, the assessment of the impact of three-phase imbalance on system safety is mainly divided into empirical assessment and data assessment. Empirical assessment typically involves on-site personnel making a subjective evaluation of the current system's safety status based on their work experience and the operating data of on-site equipment. This evaluation is then combined with the severity of the unbalanced power flow to determine its impact on system safety. The accuracy of this method relies entirely on human experience. Its advantage lies in the ability of experienced personnel to quickly assess the current system status and promptly activate contingency plans to prevent system failures. However, its disadvantages include reliance on human experience, strong subjectivity, poor interpretability, difficulty in guaranteeing the accuracy of the assessment values, and a tendency to misjudge the causes of system safety problems, leading to incorrect guidance in fault handling. Data assessment typically involves theoretical calculations and item-by-item evaluation of system voltage, current, equipment parameters, etc. While the physical mechanism of this method is clear, all assessment indicators reference uniform international standards and lack specificity. Considering that different substations have different equipment types, power consumption methods, and power quality, uniform assessment indicators cannot accurately depict the actual operating state of the system. Therefore, how to combine human experience and physical mechanisms to more accurately conduct a multi-dimensional assessment of the impact of unbalanced power flow at substations on system safety has become an unresolved issue. Summary of the Invention

[0004] In view of this, embodiments of this specification provide a security impact assessment method based on data fuzzy hybrid modeling. One or more embodiments of this specification also relate to a security impact assessment apparatus based on data fuzzy hybrid modeling, a computing device, a computer-readable storage medium, and a computer program, to address the technical deficiencies existing in the prior art.

[0005] According to a first aspect of the embodiments of this specification, a security impact assessment method based on data fuzzy hybrid modeling is provided, comprising: Analyze the load characteristics of the station's power system and determine the analysis results; Based on the analysis results, a three-phase power flow model for a typical station power system under non-full-phase energy extraction was constructed. An analytical expression for the three-phase unbalance of the station power supply system is constructed based on the three-phase power flow model. Constructing a multi-dimensional safety assessment index for the station power system based on a three-phase power flow model; A data-fuzzy hybrid model is constructed to assess the impact of station power imbalance on system safety based on the analytical expression of three-phase unbalance and safety evaluation indicators. An assessment model is generated based on the parameters of the station's power supply system and the safety impact assessment model, and the assessment results are determined based on the assessment model.

[0006] In one possible implementation, the load characteristics of the station's power supply system are analyzed, and the analysis results are determined, including: The station's power load energy extraction characteristics, power characteristics, and fluctuation characteristics were analyzed, and the analysis results were determined.

[0007] In one possible implementation, a three-phase power flow model for a typical station power system under non-full-phase energy extraction is constructed based on the analysis results, including: Based on the analysis results, the characteristics of the three-phase power flow equations of the traditional power system and the network characteristics of the station power system are analyzed to determine the three-phase time-series power flow equations of the station power system. The three-phase power flow model is determined by modeling based on the three-phase time-series power flow equation.

[0008] In one possible implementation, an analytical expression for the three-phase unbalance of the station power supply system is constructed based on a three-phase power flow model, including: Based on the three-phase power flow model, we construct analytical expressions for the three-phase current imbalance of the station power system and analytical expressions for the three-phase voltage imbalance of the station power system. The analytical expression for three-phase unbalance is determined based on the analytical expressions for three-phase current unbalance and three-phase voltage unbalance.

[0009] In one possible implementation, a multi-dimensional security assessment index for the station power system is constructed based on a three-phase power flow model, including: A multi-dimensional safety assessment model for the station power system was established based on a three-phase power flow model. Establish a multi-factor system security level matching table based on a multi-dimensional security assessment model; A multi-dimensional safety assessment index for station power systems is constructed based on a multi-factor system safety level matching table.

[0010] In one possible implementation, a data-fuzzy hybrid model is constructed to assess the impact of station power imbalance on system security based on the analytical expression of three-phase imbalance and security assessment indicators, including: A time-series fuzzy rule table is established based on the analytical expression of three-phase imbalance and safety assessment indicators; A security impact assessment model is established based on a temporal fuzzy rule table.

[0011] In one possible implementation, an assessment model is generated based on the station power system parameters and a safety impact assessment model, including: Based on the actual station power system parameters, the three-phase unbalanced power flow of the station power system is calculated, and a fuzzy database is constructed. Typical data from the database were selected to correct the membership function of the safety impact assessment model, resulting in an assessment model based on data-fuzzy hybrid modeling.

[0012] According to a second aspect of the embodiments of this specification, a security impact assessment apparatus based on data fuzzy hybrid modeling is provided, comprising: The load characteristics module is configured to analyze the load characteristics of the station's power system and determine the analysis results; The power flow model module is configured to construct a three-phase power flow model for a typical station power system under non-full-phase energy extraction based on the analysis results. The analytical construction module is configured to construct analytical expressions for the unbalanced three-phase unbalance of the station power system based on the three-phase power flow model. The indicator construction module is configured to construct multi-dimensional safety assessment indicators for the station power system based on a three-phase power flow model. The model building module is configured to construct a data-fuzzy hybrid modeling model for assessing the impact of station power unbalanced power flow on system safety based on the analytical expression of three-phase unbalance and safety assessment indicators. The model training module is configured to generate an evaluation model based on the station power system parameters and the safety impact assessment model, and to determine the evaluation results based on the evaluation model.

[0013] According to a third aspect of the embodiments of this specification, a computing device is provided, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the security impact assessment method based on data fuzzy hybrid modeling described above are implemented.

[0014] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided that stores computer-executable instructions, which, when executed by a processor, implement the steps of the security impact assessment method based on data fuzzy hybrid modeling described above.

[0015] According to a fifth aspect of the embodiments of this specification, a computer program is provided, wherein when the computer program is executed in a computer, it causes the computer to perform the steps of the security impact assessment method based on data fuzzy hybrid modeling described above.

[0016] This specification provides a method and apparatus for assessing the safety impact of station power supply systems based on data fuzzy hybrid modeling. The method includes: analyzing the load characteristics of a station power system and determining the analysis results; constructing a three-phase power flow model for a typical station power system under non-full-phase energy extraction based on the analysis results; constructing an analytical expression for the three-phase unbalance of the station power system based on the three-phase power flow model; constructing multi-dimensional safety assessment indicators for the station power system based on the three-phase power flow model; constructing an assessment model for the impact of unbalanced power flow on system safety under data-fuzzy hybrid modeling based on the analytical expression for three-phase unbalance and the safety assessment indicators; generating an assessment model based on the station power system parameters and the safety impact assessment model; and determining the assessment results based on the assessment model. This method achieves a relatively accurate multi-dimensional assessment of the impact of unbalanced power flow on system safety and can provide guidance for the management of unbalanced energy extraction in station power systems. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a security impact assessment method based on data fuzzy hybrid modeling, provided in one embodiment of this specification. Figure 2 This is an architecture diagram of a security impact assessment method based on data fuzzy hybrid modeling, provided in one embodiment of this specification. Figure 3 This is a schematic diagram of the structure of a security impact assessment device based on data fuzzy hybrid modeling, provided in one embodiment of this specification; Figure 4 This is a structural block diagram of a computing device provided in one embodiment of this specification. Detailed Implementation

[0018] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0019] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0020] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0021] This specification provides a security impact assessment method based on data fuzzy hybrid modeling. This specification also relates to a security impact assessment device based on data fuzzy hybrid modeling, a computing device, and a computer-readable storage medium, which will be described in detail in the following embodiments.

[0022] See Figure 1 , Figure 1 A flowchart of a security impact assessment method based on data fuzzy hybrid modeling, according to an embodiment of this specification, is shown, specifically including the following steps.

[0023] Step 101: Analyze the load characteristics of the station's power system and determine the analysis results; In one possible implementation, the load characteristics of the station's power system are analyzed, and the analysis results are determined, including: performing energy extraction characteristic analysis, power characteristic analysis, and fluctuation characteristic analysis of the station's power load, and determining the analysis results.

[0024] In practical applications, see Figure 2Based on the energy extraction characteristics of the station's power load, the period can be divided into symmetrical energy extraction loads and asymmetrical energy extraction loads. Symmetrical energy extraction loads are usually three-phase energy extraction, such as three-phase motors, UPS power supplies, and three-phase rectifier inverters. Taking phase A as the reference, its load energy extraction characteristics are usually manifested as a 120-degree angle difference in power between each phase, and its timing expression is shown in equation (1) below. For asymmetrical energy extraction loads, they are usually single-phase energy extraction or two-phase energy extraction, such as single-phase motors, station lighting systems, and station domestic power loads. Taking single-phase energy extraction of phase A or two-phase energy extraction of phases AB as examples, its energy extraction power expression is shown in equations (2) and (3).

[0025] (1) (2) (3) In the formula, P A , P B , P C These represent the three-phase power of loads A, B, and C, respectively. U AN This refers to the phase voltage amplitude of phase A. I A This represents the amplitude of the A-phase line current. f The load impedance angle; P AB Let A and B be the power of the two-phase energy-harvesting load. From the above energy harvesting characteristics, it can be seen that the power angles of each phase of the three-phase energy-harvesting load are consistent with those of a symmetrical three-phase power supply. In single-phase energy harvesting, the power amplitude angle of the harvesting phase conforms to the corresponding phase of the three-phase power supply, while the power amplitude and angle of the non-harvesting phase do not conform to the symmetrical three-phase power supply. In two-phase energy harvesting, the amplitudes and angles of the two harvesting phases and the non-harvesting phase do not conform to the symmetrical three-phase power supply.

[0026] Based on the power characteristics of the station's electrical load, it can be divided into resistive loads, such as indoor and outdoor lighting systems, security facilities, and canteen power supplies; and impedance loads, such as UPS power supplies, DC chargers, indoor air conditioners, and fire pumps. For resistive loads, their power characteristics are shown in equation (4), and for impedance loads, their power characteristics are shown in equation (5).

[0027] (4) (5) For resistive loads, their apparent power characteristics are dominated by active power, with reactive power being negligible. For impedance loads, their apparent power characteristics are... P +j Q The complex power form.

[0028] Based on the fluctuating characteristics of the station's power load, it can be divided into stable loads, such as AC feeder power supply loads for communication power supplies, air conditioning loads for protection rooms, and UPS power supply loads; and fluctuating loads, such as fire pumps, voltage stabilizing pumps, fans, and indoor and outdoor lighting systems. The power expression characterizing the load fluctuation characteristics is shown in equation (6). (6) In the formula, U AN ( t () represents the amplitude of phase voltage A at time t; I A ( t () represents the amplitude of the A-phase line current at time t; (t) is a function that changes 0-1 with time step. When the load is a steady load, (t) is always 1 when the load is a fluctuating load. (t) changes stepwise with load fluctuation characteristics.

[0029] Step 102: Based on the analysis results, construct a three-phase power flow model for a typical station power system under non-full-phase energy harvesting conditions; In one possible implementation, a three-phase power flow model for a typical station power system under non-full-phase energy extraction is constructed based on the analysis results. This includes: performing characteristic analysis of the three-phase power flow equations of the traditional power system and network characteristic analysis of the station power system based on the analysis results to determine the three-phase time-series power flow equations of the station power system; and modeling based on the three-phase time-series power flow equations to determine the three-phase power flow model.

[0030] In practical applications, during the steady-state operation of a three-phase power system, the system's operating state can be grasped by analyzing the voltage, phase angle, injected active and reactive power of each phase node. Traditional three-phase operating state perception analysis of the system is achieved by combining the network topology of each phase, the known parameters of different types of nodes, and network parameters to compose a set of node power equations under Kirchhoff's laws, as shown in the following equation, and then solving it iteratively: (7) In the formula, P i d For electrical networks i node d Phase-injected active power; Q i d For electrical networks i node d Phase-injected reactive power; U i d and U jd For each system i Nodes and j node d Phase voltage amplitude; i i x for i node x Phase voltage phase angle; i i d for i node d Phase voltage phase angle; i j x for j node x Phase voltage phase angle; i j d for j node d Phase voltage phase angle; y ij dx for i , j Between nodes x , d The modulus of the corresponding admittance matrix elements; α ij dx for i , j Between nodes x , d The phase angle of the corresponding admittance matrix elements.

[0031] For the station's power supply system, its network topology is a single-bus segmented, hierarchical, radial operation, which has the advantages of simple structure and low cost. Loads are divided into two categories: centralized power supply and distributed power supply. Centralized power supply, such as UPS loads, station DC power supplies, and switchgear distribution panels, is characterized by centralized power supply through a single bus, followed by hierarchical downward distribution. Therefore, this type of load can be equivalently replaced by a composite load model using Thevenin's model. For distributed power supply, such as fire pumps and main transformer fans, they can be directly used as load nodes. Through equivalent replacement, the hierarchical load network of the station's power supply system can be simplified into a backbone radial network.

[0032] In the simplified station power system, based on the characteristic energy extraction analysis of each load, the traditional power flow equations are modified to construct a three-phase time-series power flow equation, as shown in the following equation:

[0033] In the above power flow equations, the power equations for each node are still based on the traditional three-phase power flow equations. Since the time-series evolution of a large amount of load power and station transformer voltage in the station service system is relatively clear (station transformer voltage is constant, stable load power is constant, and the main fluctuation law of fluctuating loads is obvious, allowing for the construction of time-series evolution), the voltage and power parameters are represented by time-series functions. Since the station service transformer system load is not equipped with a power measurement unit, the injected power for the relevant nodes is... P i d and Q i d The node admittance parameters can be obtained by equations (1) to (6), where the network admittance parameters after the Thevenin equivalent simplification in the above steps can be used as a reference.

[0034] Step 103: Construct an analytical expression for the three-phase unbalance of the station power supply system based on the three-phase power flow model; In one possible implementation, an analytical expression for the three-phase unbalance of the station power system is constructed based on a three-phase power flow model, including: constructing an analytical expression for the three-phase current unbalance of the station power system based on the three-phase power flow model, and constructing an analytical expression for the three-phase voltage unbalance of the station power system; and determining the analytical expression for the three-phase unbalance based on the analytical expressions for the three-phase current unbalance and the three-phase voltage unbalance.

[0035] In practical applications, considering that the three-phase current of a three-phase load changes in real time with the grid status during operation, in order to accurately extract the maximum imbalance of the three-phase current over a certain period of time, the range statistical method is used to construct the analytical expression for the three-phase current imbalance of the station power system, as shown below.

[0036] In the formula, CUF ( t ( ) represents the three-phase current of the station's power supply system. t Imbalance at all times I max ( t ( ) is the three-phase current of the station's power supply system t Maximum single-phase current amplitude at any given time I min ( t ( ) is the three-phase current of the station's power supply system t Minimum single-phase current amplitude at any given time MAX {} represents the function to find the maximum value. CUF The three-phase current imbalance of the power supply system of the station.

[0037] In a three-phase unbalanced power flow, the three-phase voltages of the system lose symmetry. By employing the symmetrical component method, the voltage can be decomposed into positive-sequence, negative-sequence, and zero-sequence components. These three components can accurately characterize the degree of amplitude and phase asymmetry between phases A, B, and C of the three-phase voltage. Therefore, the analytical expression for the three-phase voltage imbalance of the station power system based on the symmetrical component method is as follows:

[0038] In the formula, VUF ( t ( ) refers to the three-phase voltage of the station's power supply system. T Imbalance at all times U 1 ( t ), U 2 ( t ), U 0 ( t These are the station's power supply systems. t The positive, negative, and zero sequence components of the voltage at any given time. VUF The three-phase voltage imbalance of the power supply system of the station.

[0039] Step 104: Construct multi-dimensional safety assessment indicators for the station power system based on the three-phase power flow model; In one possible implementation, a multi-dimensional safety assessment index for the station power system is constructed based on a three-phase power flow model, including: establishing a multi-dimensional safety assessment model for the station power system based on the three-phase power flow model; establishing a multi-factor system safety level matching table based on the multi-dimensional safety assessment model; and constructing the multi-dimensional safety assessment index for the station power system based on the multi-factor system safety level matching table.

[0040] In practical applications, under three-phase unbalanced operation in station power systems, circulating currents will be generated between phases, causing the lines to heat up. The magnitude of system network loss reflects the degree of heating. When the network loss is too large, the overheating of the lines will damage the line insulation, and in severe cases, it will lead to line combustion. Therefore, system network loss is used as one of the system safety assessment indicators, and its normalized assessment value is shown below.

[0041] In the formula, l loss ( t ) for system t Normalized evaluation value of network loss at any time. P loss ( t ) for system t Network loss at all times P loss.max For the system's maximum network loss, P loss.min To minimize network loss in the system, t The evaluation period in which it falls is the interval between the maximum and minimum values.

[0042] In the substation's power supply system, the AC input load of the DC charger, the UPS load, and the communication load are crucial to the controllable, safe, and stable operation of the entire substation. These loads require uninterrupted operation in actual production. Therefore, the operating status of these critical loads is considered one of the system safety assessment indicators. The assessment values ​​for the status of these critical loads are shown below.

[0043] In the formula, l equ ( t ) for system t Real-time critical load operating status assessment values; U PA , U PB , U PC These are the rated voltage amplitudes of the three phases of loads A, B, and C, respectively. U A ( t ), U B ( t ), U C ( t The loads are respectively the three phases A, B, and C. t Voltage amplitude at time When the station's power supply system operates with three-phase imbalance, the neutral point carries a zero-sequence current due to the YN connection on the low-voltage side of the station transformer. When the imbalance is excessive, the zero-sequence current at the neutral point increases, leading to increased eddy current excitation losses in the station transformer and severe overheating at the neutral point. Therefore, the neutral point current and temperature of the station transformer are considered as one of the system safety assessment indicators. The assessed value for the neutral point temperature of the station transformer is shown in the following formula.

[0044] In the formula, l Temp.N ( t ) for system t Normalized evaluation value of neutral point temperature at the station. Temp ( t ) for system T The station uses a variable neutral point temperature at all times. Temp max The maximum temperature of the station's variable neutral point. Temp min To determine the minimum temperature of the station's variable neutral point, T The evaluation period in which it falls is the interval between the maximum and minimum values.

[0045] The evaluation value of the neutral point current of the station service transformer is shown in the following formula.

[0046] In the formula, l IN ( T ) for system T Normalized evaluation value of the neutral current at the station. I N ( T ) for system T The station uses a variable neutral point current at all times. I N.max For the station's neutral point maximum current, I N.min To minimize the neutral point current of the station service transformer, T The evaluation period in which it falls is the interval between the maximum and minimum values.

[0047] Based on the weighted coefficients, a weighted summation of various safety assessment indicators of the station power system is used to construct a multi-dimensional safety assessment model for the station power system, as shown in the following formula.

[0048] In the formula, l SEC ( t This represents the multi-dimensional safety assessment value of the station's power supply system. k 1. k 2. k 3. k 4 represents the weighting coefficient for each of the above evaluation values. A multi-factor system security level matching table is established as follows: Table 1

[0049] In the table, NB, NM, NS, ZO, PS, PM, and PB represent security risk levels from low to high, with lower numbers indicating a more secure system.

[0050] Step 105: Construct an assessment model for the impact of station power imbalance on system safety based on the analytical expression of three-phase unbalance and safety assessment indicators; In one possible implementation, a data-fuzzy hybrid model is constructed to assess the impact of station power imbalance on system safety based on the analytical expression of three-phase imbalance and safety assessment indicators. This includes: establishing a time-series fuzzy rule table based on the analytical expression of three-phase imbalance and safety assessment indicators; and establishing a safety impact assessment model based on the time-series fuzzy rule table.

[0051] In practical applications, a temporal fuzzy rule table is established.

[0052] The current imbalance is classified into fuzzy levels, as shown in the table below: Table 2

[0053] The voltage unbalance degree is classified into fuzzy levels, as shown in the table below: Table 3

[0054] Based on the three-phase time-series power flow equations of the station power system, the voltage and current imbalance of the current station power system and the current system safety assessment value can be solved in real time. On this basis, combined with the fuzzy level classification table and the system safety level matching table, the time-series fuzzy rules between the current voltage and current imbalance level and the system safety level can be obtained. The rule function is shown in the following formula.

[0055]

[0056] In the formula, Level [*] The function represents the solution to the level corresponding to parameter * based on a level classification table or matching table; Ψ( X , t The three-phase time-series power flow equations for the station's power supply system are as follows: X The electrical parameter matrix represents the power flow equation; A←B indicates that level B corresponds to level A. The data-fuzzy combined timing rule table is constructed as shown in the table below: Table 4

[0057] Furthermore, an evaluation model is established based on a time-series fuzzy table.

[0058] Using trigonometric membership functions VUF ( t ), CUF ( t ), l SEC ( t The image is blurred as shown below:

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065] In the formula, λ NB , λ NM , λ NS , λ ZO , λ PS , λ PM , λ PB Membership functions corresponding to NB, NM, NS, ZO, PS, PM, and PB levels, respectively, χ( t ) are the input parameters for the membership function, where χ∈{ VUF,CUF,λ SEC}

[0066] The system voltage and current imbalance and system safety assessment value are fuzzified into fuzzy subsets by using membership functions. Then, based on the fuzzy assessment rule table and the Mamdani algorithm as shown in the following formula, fuzzy assessment is performed. Finally, the fuzzy values ​​are clarified by the maximum membership average method to obtain the final assessment value.

[0067]

[0068] In the formula, This represents the fuzzy evaluation function; n represents the number of activated rules; The membership function output value is represented by α ∈ {NB, NM, NS, ZO, PS, PM, PB}; (A^B) o C represents the inverse mapping value of values ​​A and B in function C.

[0069] Step 106: Generate an assessment model based on the station power system parameters and safety impact assessment model, and determine the assessment results based on the assessment model.

[0070] In one possible implementation, an evaluation model is generated based on the station power system parameters and the safety impact assessment model, including: calculating the three-phase unbalanced power flow of the station power system based on the actual station power system parameters, and constructing a fuzzy database; selecting typical data from the database to correct the membership function of the safety impact assessment model, thereby obtaining an evaluation model based on data-fuzzy hybrid modeling.

[0071] In practical applications, based on the actual power system parameters of the station, three-phase unbalanced power flow calculations are performed, a fuzzy database is constructed, and then typical data from the database are selected to determine the membership function. a 、 b 、 c 、 d 、e 、 f 、 g The parameters are rolled over to improve the accuracy of the evaluation model, and finally a multi-dimensional real-time evaluation model of the impact of station power imbalance on system safety based on data-fuzzy hybrid modeling is obtained.

[0072] Corresponding to the above method embodiments, this specification also provides embodiments of a security impact assessment device based on data fuzzy hybrid modeling. Figure 3 This specification illustrates a schematic diagram of a security impact assessment device based on data fuzzy hybrid modeling, according to one embodiment of this specification. Figure 3 As shown, the device includes: According to a second aspect of the embodiments of this specification, a security impact assessment apparatus based on data fuzzy hybrid modeling is provided, comprising: The load characteristic module 301 is configured to analyze the load characteristics of the station's power system and determine the analysis results; The power flow model module 302 is configured to construct a three-phase power flow model for a typical station power system under non-full-phase energy extraction based on the analysis results. The analytical construction module 303 is configured to construct an analytical expression for the unbalanced three-phase unbalance of the station power system based on the three-phase power flow model. The indicator construction module 304 is configured to construct multi-dimensional safety assessment indicators for the station power system based on a three-phase power flow model. The model building module 305 is configured to construct a data-fuzzy hybrid modeling model for evaluating the impact of station power unbalanced power flow on system safety based on the analytical expression of three-phase unbalance and safety assessment indicators. The model training module 306 is configured to generate an evaluation model based on the station power system parameters and safety impact assessment model, and determine the evaluation results based on the evaluation model.

[0073] In one possible implementation, the load characteristics of the station's power supply system are analyzed, and the analysis results are determined, including: The station's power load energy extraction characteristics, power characteristics, and fluctuation characteristics were analyzed, and the analysis results were determined.

[0074] In one possible implementation, a three-phase power flow model for a typical station power system under non-full-phase energy extraction is constructed based on the analysis results, including: Based on the analysis results, the characteristics of the three-phase power flow equations of the traditional power system and the network characteristics of the station power system are analyzed to determine the three-phase time-series power flow equations of the station power system. The three-phase power flow model is determined by modeling based on the three-phase time-series power flow equation.

[0075] In one possible implementation, an analytical expression for the three-phase unbalance of the station power supply system is constructed based on a three-phase power flow model, including: Based on the three-phase power flow model, we construct analytical expressions for the three-phase current imbalance of the station power system and analytical expressions for the three-phase voltage imbalance of the station power system. The analytical expression for three-phase unbalance is determined based on the analytical expressions for three-phase current unbalance and three-phase voltage unbalance.

[0076] In one possible implementation, a multi-dimensional security assessment index for the station power system is constructed based on a three-phase power flow model, including: A multi-dimensional safety assessment model for the station power system was established based on a three-phase power flow model. Establish a multi-factor system security level matching table based on a multi-dimensional security assessment model; A multi-dimensional safety assessment index for station power systems is constructed based on a multi-factor system safety level matching table.

[0077] In one possible implementation, a data-fuzzy hybrid model is constructed to assess the impact of station power imbalance on system security based on the analytical expression of three-phase imbalance and security assessment indicators, including: A time-series fuzzy rule table is established based on the analytical expression of three-phase imbalance and safety assessment indicators; A security impact assessment model is established based on a temporal fuzzy rule table.

[0078] In one possible implementation, an assessment model is generated based on the station power system parameters and a safety impact assessment model, including: Based on the actual station power system parameters, the three-phase unbalanced power flow of the station power system is calculated, and a fuzzy database is constructed. Typical data from the database were selected to correct the membership function of the safety impact assessment model, resulting in an assessment model based on data-fuzzy hybrid modeling.

[0079] The above is a schematic scheme of a security impact assessment device based on data fuzzy hybrid modeling according to this embodiment. It should be noted that the technical solution of this security impact assessment device based on data fuzzy hybrid modeling belongs to the same concept as the technical solution of the security impact assessment method based on data fuzzy hybrid modeling described above. For details not described in detail in the technical solution of the security impact assessment device based on data fuzzy hybrid modeling, please refer to the description of the technical solution of the security impact assessment method based on data fuzzy hybrid modeling described above.

[0080] Figure 4A structural block diagram of a computing device 400 according to one embodiment of this specification is shown. The components of the computing device 400 include, but are not limited to, a memory 410 and a processor 420. The processor 420 is connected to the memory 410 via a bus 430, and a database 450 is used to store data.

[0081] The computing device 400 also includes an access device 440, which enables the computing device 400 to communicate via one or more networks 460. Examples of these networks include Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or combinations of communication networks such as the Internet. The access device 440 may include one or more of any type of wired or wireless network interface (e.g., a network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Wi-MAX (Worldwide Interoperability for Microwave Access) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, or a Near Field Communication (NFC) interface.

[0082] In one embodiment of this specification, the aforementioned components of the computing device 400 and Figure 4 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 4 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0083] The computing device 400 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 400 can also be a mobile or stationary server.

[0084] The processor 420 executes the following computer-executable instructions, which, when executed by the processor, implement the steps of the aforementioned security impact assessment method based on data fuzzy hybrid modeling. The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the aforementioned security impact assessment method based on data fuzzy hybrid modeling belong to the same concept. Details not described in detail in the technical solution of the computing device can be found in the description of the aforementioned security impact assessment method based on data fuzzy hybrid modeling.

[0085] An embodiment of this specification also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the security impact assessment method based on data fuzzy hybrid modeling described above.

[0086] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium belongs to the same concept as the technical solution of the security impact assessment method based on data fuzzy hybrid modeling described above. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the security impact assessment method based on data fuzzy hybrid modeling described above.

[0087] An embodiment of this specification also provides a computer program, wherein when the computer program is executed in a computer, it causes the computer to perform the steps of the security impact assessment method based on data fuzzy hybrid modeling described above.

[0088] The above is an illustrative example of a computer program in this embodiment. It should be noted that the technical solution of this computer program belongs to the same concept as the aforementioned security impact assessment method based on fuzzy data hybrid modeling. Details not described in detail in the computer program's technical solution can be found in the description of the aforementioned security impact assessment method based on fuzzy data hybrid modeling.

[0089] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0090] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0091] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0092] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0093] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A security impact assessment method based on data fuzzy hybrid modeling, characterized in that, include: Analyze the load characteristics of the station's power system and determine the analysis results; Based on the analysis results, a three-phase power flow model for a typical station power system under non-full-phase energy extraction is constructed. Based on the three-phase power flow model, construct an analytical expression for the three-phase unbalance of the station power supply system. Based on the three-phase power flow model, a multi-dimensional safety assessment index for the station power system is constructed. Based on the analytical expression of the three-phase unbalance degree and the safety assessment index, a data-fuzzy hybrid model is constructed to assess the impact of station power unbalance flow on system safety. An evaluation model is generated based on the station's power system parameters and the aforementioned safety impact assessment model, and the evaluation results are determined based on the evaluation model.

2. The method according to claim 1, characterized in that, The load characteristics of the station's power system were analyzed, and the analysis results were determined, including: The station's power load energy extraction characteristics, power characteristics, and fluctuation characteristics were analyzed, and the analysis results were determined.

3. The method according to claim 1, characterized in that, Based on the analysis results, a three-phase power flow model for a typical station power system under non-full-phase energy harvesting is constructed, including: Based on the analysis results, the characteristics of the traditional three-phase power flow equations and the network characteristics of the station power system are analyzed to determine the three-phase time-series power flow equations of the station power system. The three-phase power flow model is determined based on the aforementioned three-phase time-series power flow equations.

4. The method according to claim 1, characterized in that, Based on the aforementioned three-phase power flow model, an analytical expression for the three-phase unbalance of the station power supply system is constructed, including: Based on the aforementioned three-phase power flow model, an analytical expression for the three-phase current imbalance of the station power system and an analytical expression for the three-phase voltage imbalance of the station power system are constructed. The analytical formula for three-phase unbalance is determined based on the analytical formula for three-phase current unbalance and the analytical formula for three-phase voltage unbalance.

5. The method according to claim 1, characterized in that, Based on the aforementioned three-phase power flow model, a multi-dimensional security assessment index for the station power system is constructed, including: A multi-dimensional safety assessment model for the station power system is established based on the aforementioned three-phase power flow model. A multi-factor system security level matching table is established based on the aforementioned multi-dimensional security assessment model; Based on the multi-factor system safety level matching table, a multi-dimensional safety assessment index for the station power system is constructed.

6. The method according to claim 1, characterized in that, Based on the analytical expression for the three-phase unbalance and the safety assessment index, a data-fuzzy hybrid model is constructed to assess the impact of station power imbalance flow on system safety, including: A time-series fuzzy rule table is established based on the analytical expression of the three-phase imbalance and the safety assessment index. A security impact assessment model is established based on the aforementioned temporal fuzzy rule table.

7. The method according to claim 1, characterized in that, An assessment model is generated based on the station power system parameters and the aforementioned safety impact assessment model, including: Based on the actual station power system parameters, the three-phase unbalanced power flow of the station power system is calculated, and a fuzzy database is constructed. Typical data from the database were selected to correct the membership function of the safety impact assessment model, resulting in an assessment model based on data-fuzzy hybrid modeling.

8. A security impact assessment device based on data fuzzy hybrid modeling, characterized in that, include: The load characteristics module is configured to analyze the load characteristics of the station's power system and determine the analysis results; The power flow model module is configured to construct a three-phase power flow model for a typical station power system under non-full-phase energy extraction based on the analysis results. The analytical construction module is configured to construct an analytical expression for the unbalanced three-phase power flow of the station power system based on the three-phase power flow model. The indicator construction module is configured to construct multi-dimensional safety assessment indicators for the station power system based on the three-phase power flow model. The model building module is configured to construct an assessment model of the impact of station power unbalanced power flow on system safety under data-fuzzy hybrid modeling based on the analytical expression of the three-phase unbalance degree and the safety assessment index. The model training module is configured to generate an evaluation model based on the station power system parameters and the safety impact assessment model, and to determine the evaluation results based on the evaluation model.

9. A computing device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the security impact assessment method based on data fuzzy hybrid modeling as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the security impact assessment method based on data fuzzy hybrid modeling as described in any one of claims 1 to 7.

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