A power grid parameter identification method and device and a storage medium

Abstract

The present application relates to a kind of power grid parameter identification method based on differential evolution algorithm and polynomial mixed measurement, device and storage medium, wherein the method comprises the following steps: obtaining the grid structure of power grid and carrying out topological analysis;Based on the state and acquisition value of remote signaling and telemetering, update topology;Determine the multi-working condition decision variable based on node state, power grid parameter;Determine multi-working condition node equation group and multi-working condition branch equation group;Determine multi-objective function based on multi-working condition node equation group and multi-working condition branch equation group;Multi-objective optimization is carried out based on differential evolution algorithm;The optimized result of power grid parameter identification is checked, if the number of parameters that need to be optimized again in this round is greater than or equal to the last round, or this round is the first round of optimization, then the next round of optimization is carried out, otherwise, the optimized power grid identification parameter is output.Compared with prior art, the present application can realize power grid parameter identification under multi-working condition, multi-time scale and multi-source measurement operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system automation, and in particular to a power grid parameter identification method and device based on a differential evolution algorithm and polynomial hybrid measurement, and a storage medium. BACKGROUND

[0002] In recent years, with the rapid development of China's national economy, the power grid in economically developed areas is becoming increasingly dense, with highly concentrated power sources and loads, large system short-circuit capacity, and close to the limit that the power grid can withstand. At the same time, the safety, reliability, and power supply quality requirements of the power system are gradually increasing. In order to improve the quality of service to society, various multi-condition analyses, checks, and optimizations of the power grid are needed, and on this basis, power production, maintenance, and other work can be properly arranged. The accuracy of the power grid parameters is an important factor affecting the accuracy of the analysis and checking, and therefore, the power grid parameters need to be identified to obtain more reliable parameters.

[0003] At present, there is a lack of a power grid parameter identification method under multi-condition, multi-time scale, and multi-source measurement operation, and it is not possible to accurately simulate and check the multi-condition power grid. SUMMARY

[0004] The purpose of the present application is to provide a power grid parameter identification method and device based on a differential evolution algorithm and polynomial hybrid measurement, and a storage medium, which realizes power grid parameter identification under multi-condition, multi-time scale, and multi-source measurement operation.

[0005] The purpose of the present application can be achieved by the following technical solutions:

[0006] A power grid parameter identification method based on a differential evolution algorithm and polynomial hybrid measurement, comprising the following steps:

[0007] Step 1) Obtain the grid structure of the power grid and perform topological analysis;

[0008] Step 2) Obtain the state and collected values of remote signaling and remote measurement, perform rough detection of the remote signaling position based on the topological analysis results and simple rules, and update the topology;

[0009] Step 3) Determine multi-condition decision variables based on node states and power grid parameters, the multi-condition decision variables being expressed in per unit values;

[0010] Step 4) Determine a multi-condition node equation set based on branch equivalent admittance and node self-admittance, the multi-condition node equation set including a multi-condition node voltage equation, an active power injection equation, and a reactive power injection equation;

[0011] Step 5) determining a multi-working condition branch equation group based on the branch current and active and reactive power measurement, the multi-working condition branch equation group comprising a branch current equation, an active power measurement equation and a reactive power measurement equation;

[0012] Step 6) determining a multi-objective function based on the multi-working condition node equation group and the multi-working condition branch equation group;

[0013] Step 7) performing multi-objective optimization on the multi-objective function based on a differential evolution algorithm to obtain a power grid parameter identification result to be optimized;

[0014] Step 8) checking the power grid parameter identification result to be optimized, counting the number of parameters that need to be optimized again, if the number of parameters that need to be optimized again in the current round is greater than or equal to the number of parameters that need to be optimized again in the last round, or if the current round is the first round of optimization, then re-executing steps 3) to 8) to perform the next round of optimization, otherwise, outputting the optimized power grid identification parameters.

[0015] The simple rules comprise:

[0016] If the state quantity identifies that the branch is not connected, but the telemetering quantity identifies that the branch has current or active power, it is considered that the telesignalling quantity and the telemetering quantity contradict each other, and at least one error exists between them, the telesignalling quantity and the telemetering quantity are checked until the checking result shows that there is no error;

[0017] If the state quantity identifies that the branch is connected, but the telemetering quantity identifies that the branch has no current or active power, it is considered that the telesignalling quantity or the telemetering quantity is suspicious, and is listed in a suspicious list.

[0018] The multi-working condition decision variable comprises a node decision variable and a branch decision variable, wherein the branch decision variable comprises a conductor type branch decision variable and a transformer winding type branch decision variable,

[0019] The node decision variable is related to the multi-working condition, comprising a node voltage real part e and a node voltage imaginary part f, the value range of the node decision variable is 0.75≤e≤1.25 and 0.75≤f≤1.25; wherein the node decision variable being related to the multi-working condition means that the number of working conditions of the multi-working condition is consistent with the number of node decision variable groups;

[0020] The conductor type branch decision variable is obtained based on a π type equivalent circuit and is irrelevant to the multi-working condition, comprising a branch resistance r, a branch reactance x, a half ground conductance g and a half susceptance b, wherein the half ground conductance g is half of the ground conductance, the half ground conductance g is ignored under the condition of steady state analysis, the half susceptance b is half of the ground susceptance, and the initial value range of the conductor type branch decision variable is 0.000001≤r≤0.05, 0.000001≤x≤0.1 and 0.000001≤b≤1.0;

[0021] The branch decision variable of the transformer winding type is irrelevant to multiple working conditions, including copper loss equivalent resistance r, leakage reactance x, iron loss equivalent conductance g, and excitation admittance b. The equivalent conductance g and excitation admittance b are ignored under the condition of steady-state analysis. The initial value range of the branch decision variable of the transformer winding type is 0.000001≤r≤0.05 and 0.000001≤x≤1.0.

[0022] The step 4) comprises:

[0023] Step 4-1) determining branch equivalent admittance

[0024]

[0025] wherein, Z ij is the equivalent impedance of the branch between node i and node j, r ij is the resistance of the branch between node i and node j, x ij is the reactance of the branch between node i and node j, Y ij is the admittance of the branch between node i and node j, G ij is the conductance of the branch between node i and node j, B ij is the susceptance of the branch between node i and node j; wherein, r ij , x ij are based on the branch type corresponding to r and x in the branch decision variable of the conductor type or r and x in the branch decision variable of the transformer winding type in step 3);

[0026] Step 4-2) determining node self-admittance

[0027]

[0028] wherein, Y ii is the self-admittance of node i, M is the branch set connected with node i and having ground admittance, N is the branch set connected with node i, b ij is the half-susceptance of the branch between node i and node j, corresponding to b in the branch decision variable of the conductor type in step 3, G ii is the self-conductance of node i, B ij is the self-susceptance of node i;

[0029] Step 4-3) determining multiple working condition node equation set

[0030]

[0031] wherein, subscript w represents working condition, N is the branch set connected with node i, is the voltage square deviation of node i, e iw is the real part of the voltage of node i, f iwU is the voltage imaginary part of node i, corresponding to e and f in the node decision variable of step 3) iw ΔP is the collected voltage amplitude of node i iw e is the active deviation of node i jw f is the voltage real part of node j jw P is the voltage imaginary part of node j iw ΔQ is the collected active of node i iw Q is the reactive deviation of node i iw is the collected reactive of node i.

[0032] The multi-working-condition branch equation set is:

[0033]

[0034] wherein, subscript w represents a working condition, ΔP is the current square deviation of branch i-j ΔP is the square of the current measurement amplitude of branch i-j ijw P is the active deviation of branch i-j ijw ΔQ is the active measurement of branch i-j ijw Q is the reactive deviation of branch i-j ijw is the collected reactive of node i.

[0035] The multi-objective function is:

[0036]

[0037] wherein, represent all working conditions, f(u) represents the node voltage objective function, f(pn) represents the node active objective function, f(qn) represents the node reactive objective function, f(i) represents the branch current objective function, f(pl) represents the branch active objective function, and f(ql) represents the branch reactive objective function, ΔP is the voltage square deviation of node i iw ΔQ is the active deviation of node i iw Q is the reactive deviation of node i ΔP is the current square deviation of branch i-j ijw ΔQ is the active deviation of branch i-j ijw Q is the reactive deviation of branch i-j.

[0038] The step 8) is specifically:

[0039] Step 8-1) statistics of the multi-objective optimization obtained parameter identification to be optimized result in each parameter in the pre-configuration number of individuals Distribution and the value range of the multi-working-condition decision variable, the distribution includes maximum, minimum, average, variance;

[0040] Step 8-2) set the number of parameters that need to be optimized again to 0;

[0041] Step 8-3) for each parameter, calculate the deviation percentage of the average value and the variance in the current round of statistical results based on the corresponding last round of statistical results, if one of the deviation percentages of the average value or the variance exceeds the preconfigured threshold value, or the current round is the first round of optimization, record the maximum value and the minimum value of the current round as the value range of the multi-condition decision variable in the next round of optimization, and the number of parameters that need to be optimized again is increased by 1; otherwise, do not perform any operation;

[0042] Step 8-4) if the number of parameters that need to be optimized again in all parameters is greater than or equal to the number of parameters that need to be optimized again in the last round, or the current round is the first round of optimization, re-execute steps 3) to 8) to perform the next round of optimization, wherein the value range of the multi-condition decision variable in step 3) is updated to the value range of the multi-condition decision variable in the next round of optimization in step 8-3); otherwise, go to step 8-5);

[0043] Step 8-5) output the optimized power grid identification parameters and known parameters, wherein the known parameters are obtained based on actual measurement results.

[0044] The method further comprises:

[0045] Step 9) based on the optimized power grid identification parameters, perform state estimation calculation or power flow examination calculation, if the telemetry result reaches the preconfigured standard value for judging qualified telemetry, or the difference between the measurement result and the collected value is less than the preconfigured threshold value of the power flow examination, set the upper limit and the lower limit of the initial value range of the multi-condition decision variable to the corresponding optimized power grid identification parameters when identifying the power grid parameters again based on steps 1) to 8).

[0046] A power grid parameter identification device based on a differential evolution algorithm and a polynomial hybrid measurement, comprising a memory, a processor, and a program stored in the memory, and the processor implements the method as described above when executing the program.

[0047] A storage medium having a program stored thereon, and the program implements the method as described above when executed.

[0048] Compared with the prior art, the present application has the following beneficial effects:

[0049] (1) The power grid parameter identification method based on the differential evolution algorithm and the polynomial hybrid measurement is adopted in the present application, which realizes the power grid parameter identification under the conditions of multi-condition, multi-time scale and multi-source measurement operation, better copes with the multi-condition situation often occurring in actual application, and fills the gap in the prior art.

[0050] (2) The application adopts a differential evolution algorithm, compared with traditional evolution calculation, reduces the complexity of evolution calculation operation, is faster in calculation speed, and is more suitable for the field of power grid parameter identification. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 The method flowchart of the application. DETAILED DESCRIPTION

[0052] The application will be described in detail below in combination with the drawings and specific embodiments. The embodiments are implemented on the premise of the technical solution of the application, and detailed implementation modes and specific operation processes are given, but the protection scope of the application is not limited to the following embodiments.

[0053] A power grid parameter identification method based on a differential evolution algorithm and a polynomial mixed measurement, as shown in FIG. 1, comprises the following steps: Figure 1

[0054] Step 1) Obtain the grid structure of the power grid and perform topological analysis.

[0055] Obtain the parameters of each device of the power grid and the connection relationship between each device, do not consider the running state of the open and closed device, perform topological analysis, and prepare data for the subsequent steps. Whether the connection relationship of the device is correct is also one of the contents of parameter identification, but in this step, it is assumed that the connection relationship of the device is correct, if the connection relationship is incorrect, it will be shown in step 2), and listed in the separation result.

[0056] Step 2) Obtain the state and collection value of the remote signaling and remote measurement, based on the topological analysis result of step 1), put the remote measurement and remote signaling position into the device running data, perform rough detection of the remote signaling position based on simple rules, and update the topology.

[0057] The simple rules include:

[0058] If the state quantity identifies that the branch is not connected, but the remote measurement identifies that the branch has current or active power, it is considered that the remote signaling quantity and the remote measurement are contradictory, and at least one error exists between them, the remote signaling quantity and the remote measurement are checked until the checking result shows that there is no error, only when the checking result shows that there is no error, step 3) can be entered;

[0059] If the state quantity identifies that the branch is connected, but the remote measurement identifies that the branch has no current or active power, it is considered that the remote signaling quantity or the remote measurement is suspicious, and is listed in the suspicious list, but does not affect the step flow to step 3).

[0060] Step 3) Determine the multi-working condition decision variable based on the node state and the power grid parameter, and the multi-working condition decision variable is expressed in per unit value. ​

[0061] The reference value of the unit value adopts the unified assessment reference value issued by State Grid, for example, the voltage assessment reference value of 110kv voltage is 132kv, and the active / reactive power assessment reference value is 305kw.

[0062] The multi-working condition decision variable includes a node decision variable and a branch decision variable, wherein the branch decision variable includes a conductor type branch decision variable and a transformer winding type branch decision variable.

[0063] The node decision variable is related to the multi-working condition, including a node voltage real part e and a node voltage imaginary part f, and the value range of the node decision variable is 0.75≤e≤1.25 and 0.75≤f≤1.25; wherein the node decision variable being related to the multi-working condition means that the number of working conditions is consistent with the number of node decision variable groups, that is, there are as many groups of node decision variables as there are working conditions.

[0064] The conductor type branch decision variable is obtained based on a π type equivalent circuit and is irrelevant to the multi-working condition, including a branch resistance r, a branch reactance x, a half ground conductance g and a half ground susceptance b, wherein the half ground conductance g is half of the ground conductance, the half ground susceptance b is half of the ground susceptance, and the initial value range of the conductor type branch decision variable is 0.000001≤r≤0.05, 0.000001≤x≤0.1 and 0.000001≤b≤1.0; if it is not the first round of optimization, the conductor type branch decision variable is updated to the maximum value and the minimum value of the current round according to step 8-3); if the condition in step 9) is met, it means that the parameter is set as credible, and when the power grid parameters are identified again based on steps 1)-8), the upper limit and the lower limit of the conductor type branch decision variable are set to the corresponding optimized power grid identification parameters, that is, the parameter is not optimized again.

[0065] The transformer winding type branch decision variable is irrelevant to the multi-working condition, including a copper loss equivalent resistance r, a leakage reactance x, a iron loss equivalent conductance g and a excitation susceptance b, wherein the iron loss equivalent conductance g and the excitation susceptance b are ignored under the condition of steady state analysis; the initial value range of the transformer winding type branch decision variable is 0.000001≤r≤0.05 and 0.000001≤x≤1.0; if it is not the first round of optimization, the transformer winding type branch decision variable is updated to the maximum value and the minimum value of the current round according to step 8-3); if the condition in step 9) is met, it means that the parameter is set as credible, and when the power grid parameters are identified again based on steps 1)-8), the upper limit and the lower limit of the transformer winding type branch decision variable are set to the corresponding optimized power grid identification parameters, that is, the parameter is not optimized again.

[0066] Step 4) determining a multi-working condition node equation group based on the branch equivalent admittance and the node self-admittance, the multi-working condition node equation group comprising a multi-working condition node voltage equation, an active power injection equation, and a reactive power injection equation.

[0067] Step 4-1) determining the branch equivalent admittance

[0068]

[0069] wherein, Z ij is the equivalent impedance of the branch between node i and node j, r ij is the resistance of the branch between node i and node j, x ij is the reactance of the branch between node i and node j, Y ij is the admittance of the branch between node i and node j, G ij is the conductance of the branch between node i and node j, B ij is the susceptance of the branch between node i and node j; wherein, r ij , x ij determining r and x in the branch decision variable of the conductor type or r and x in the branch decision variable of the transformer winding type based on the branch type;

[0070] Step 4-2) determining the node self-admittance

[0071]

[0072] wherein, Y ii is the self-admittance of node i, M is the set of branches connected with node i and having a ground admittance, N is the set of branches connected with node i, b ij is the half-susceptance of the branch between node i and node j, corresponding to b in the branch decision variable of the conductor type, G ii is the self-conductance of node i, B ij is the self-susceptance of node i;

[0073] Step 4-3) determining the multi-working condition node equation group

[0074]

[0075] wherein, subscript w represents a working condition, N is the set of branches connected with node i, is the voltage square deviation of node i, e iw is the real part of the voltage of node i, f iw is the imaginary part of the voltage of node i, corresponding to e and f in the node decision variable of step 3, U iw is the collected voltage amplitude of node i, ΔP iw is the active power deviation of node i, e jwthe real part of the voltage of node j, f jw the imaginary part of the voltage of node j, P iw the collected active of node i, ΔQ iw the reactive deviation of node i, Q iw the collected reactive of node i.

[0076] Step 5) determining a multi-working-condition branch equation group based on the branch current and the active and reactive measurement, the multi-working-condition branch equation group comprising a branch current equation, an active measurement equation and a reactive measurement equation.

[0077] The multi-working-condition branch equation group is:

[0078]

[0079] wherein subscript w represents a working condition, the square deviation of the current of branch i-j, ΔP the square of the measured amplitude of the current of branch i-j, ΔP ijw the active deviation of branch i-j, ΔQ ijw the active measurement of branch i-j, ΔQ ijw the reactive deviation of branch i-j, Q ijw the reactive measurement of branch i-j.

[0080] Step 6) determining a multi-objective function based on the multi-working-condition node equation group and the multi-working-condition branch equation group.

[0081] The multi-objective function is:

[0082]

[0083] wherein, representing all working conditions, f(u) representing a node voltage objective function, f(pn) representing a node active objective function, f(qn) representing a node reactive objective function, f(i) representing a branch current objective function, f(pl) representing a branch active objective function, and f(ql) representing a branch reactive objective function, the square deviation of the voltage of node i, ΔP iw the active deviation of node i, ΔQ iw the reactive deviation of node i, the square deviation of the current of branch i-j, ΔP ijw the active deviation of branch i-j, ΔQ ijw the reactive deviation of branch i-j.

[0084] Step 7) performing multi-objective optimization on the multi-objective function based on a differential evolution algorithm to obtain a power grid parameter identification result to be optimized.

[0085] The differential evolution is selected as the evolutionary operator to perform multi-objective optimization on the multi-objective function in step 6), the population size is set to 10 times the number of nodes, and the maximum evolutionary generation is set to 1000 generations. The specific implementation of the differential evolution algorithm belongs to the conventional setting in the art, and in order not to obscure the purpose of the present application, it will not be described here.

[0086] Step 8)

[0087] Step 8-1) Statistics of the distribution of each parameter in the pre-configured number of individuals obtained by multi-objective optimization of the power grid parameter identification to be optimized and the value range of the multi-working-condition decision variable, the distribution includes maximum, minimum, average, and variance.

[0088] Step 8-2) Set the number of parameters that need to be optimized again to 0.

[0089] Step 8-3) For each parameter, calculate the deviation percentage of the average and variance in the current round of statistics based on the corresponding last round of statistical results. If one of the deviation percentages of the average or variance exceeds ±25%, or if it is the first round of optimization, record the maximum and minimum values in the current round as the value range of the multi-working-condition decision variable in the next round of optimization, and the number of parameters that need to be optimized again is increased by 1. Otherwise, no operation is performed.

[0090] Step 8-4) If the number of parameters that need to be optimized again in all parameters is greater than or equal to the number of parameters that need to be optimized again in the last round, or if it is the first round of optimization, re-execute steps 3) to 8) to perform the next round of optimization, wherein the value range of the multi-working-condition decision variable in step 3) is updated to the value range of the multi-working-condition decision variable in the next round of optimization in step 8-3); otherwise, go to step 8-5).

[0091] Step 8-5) Output the optimized power grid identification parameters and known parameters, wherein the known parameters are obtained based on actual measurement results.

[0092] The known parameters may be affected by various factors, resulting in inaccurate measurement results. The optimized power grid identification parameters can provide a reference for them to achieve optimization.

[0093] Step 9) Based on the optimized power grid identification parameters, perform state estimation calculation or power flow assessment calculation. If the telemetry result meets the pre-configured standard value for determining telemetry qualification, or the difference between the measurement result and the collected value is less than the pre-configured threshold value for power flow assessment, then in the identification of the power grid parameters based on steps 1) to 8) again, the upper and lower limits of the initial value range of the multi-working-condition decision variable are set to the corresponding optimized power grid identification parameters.

[0094] The purpose of step 9) is to identify the grid parameters again based on the method described in the application, and the parameters that are satisfactory to the optimization result can no longer be optimized to reduce the amount of calculation and improve the calculation speed. Therefore, step 9) can be manually selected to be executed or not executed.

[0095] The above functions, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the prior art that essentially contributes or the part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0096] The preferred embodiments of the present application are described in detail above. It should be understood that those skilled in the art can make many modifications and changes without creative labor based on the concept of the present application. Therefore, any technical solution that can be obtained by logical analysis, reasoning, or limited experiments by those skilled in the art based on the concept of the present application on the basis of the prior art should be within the protection scope determined by the claims.

Claims

1. A power grid parameter identification method based on differential evolution algorithm and polynomial hybrid measurement, characterized in that, The method comprises the following steps: Step 1) obtaining the network structure of the power grid and performing topology analysis; Step 2) obtaining the state and collected value of remote signaling and remote measurement, performing rough detection of the remote signaling position based on the topology analysis result and simple rules, and updating the topology; Step 3) determining multi-working-condition decision variables based on node states and power grid parameters, wherein the multi-working-condition decision variables are expressed in per unit values; The multi-working-condition decision variables comprise node decision variables and branch decision variables, wherein the branch decision variables comprise branch decision variables of a conductor type and branch decision variables of a transformer winding type; the node decision variables comprise a real part e of a node voltage and an imaginary part f of the node voltage; the branch decision variables of the conductor type comprise a branch resistance r, a branch reactance x and a half susceptance b; and the branch decision variables of the transformer winding type comprise a copper loss equivalent resistance r, a leakage reactance x and a field excitation admittance b; Step 4) determining a multi-working-condition node equation set based on branch equivalent admittance and node self-admittance, wherein the multi-working-condition node equation set comprises a multi-working-condition node voltage equation, an active power injection equation and a reactive power injection equation; Step 5) determining a multi-working-condition branch equation set based on branch current and active and reactive power measurement, wherein the multi-working-condition branch equation set comprises a branch current equation, an active power measurement equation and a reactive power measurement equation; Step 6) determining a multi-objective function based on the multi-working-condition node equation set and the multi-working-condition branch equation set; Step 7) performing multi-objective optimization on the multi-objective function based on a differential evolution algorithm to obtain power grid parameter identification results to be optimized; Step 8) checking the power grid parameter identification results to be optimized, counting the number of parameters that need to be optimized again, and if the number of parameters that need to be optimized again in the current round is greater than or equal to the number of parameters that need to be optimized again in the last round or the current round is the first round of optimization, then the next round of optimization is performed by re-executing steps 3) to 8), otherwise, the optimized power grid identification parameters are outputted; The step 4) comprises: Step 4-1) determining branch equivalent admittance wherein, Z ij is the equivalent impedance of the branch between node i and node j, r ij is the resistance of the branch between node i and node j, x ij is the reactance of the branch between node i and node j, Y ij is the admittance of the branch between node i and node j, G ij is the conductance of the branch between node i and node j, B ij is the susceptance of the branch between node i and node j; wherein, r ij , x ij are determined based on the branch type, respectively, r and x in the branch decision variable of the conductor type in step 3) or r and x in the branch decision variable of the transformer winding type. Step 4-2) determining node self-admittance where Y ii is the self-admittance of node i, M is the set of branches connected to node i and having a ground admittance, N is the set of branches connected to node i, b ij is the half-admittance of the branch between node i and node j, corresponding to b in the decision variable of the branch of wire type in step 3), G ii is the self-conductance of node i, B ij is the self-admittance of node i; Step 4-3) determining a multi-working-condition node equation set where subscript w represents the working condition, N is the set of branches connected to node i, is the voltage square deviation of node i, is the real part of the voltage of node i, is the imaginary part of the voltage of node i, corresponding to e and f in the node decision variable of step 3), is the collected voltage amplitude of node i, is the active deviation of node i, is the real part of the voltage of node j, is the imaginary part of the voltage of node j, is the collected active of node i, is the reactive deviation of node i, is the collected reactive of node i; The step 8) specifically comprises: Step 8-1) counting the distribution of each parameter in the power grid parameter identification results to be optimized obtained by multi-objective optimization in a pre-configured number of individuals and the value range of the multi-working-condition decision variables, wherein the distribution comprises a maximum value, a minimum value, an average value and a variance; Step 8-2) setting the number of parameters that need to be optimized again to 0; Step 8-3) for each parameter, the deviation percentages of the average value and the variance in the current round of statistical results are calculated based on the last round of statistical results corresponding to the parameter, if one of the deviation percentages of the average value or the variance exceeds a pre-configured threshold value, or the current round is the first round of optimization, then the maximum value and the minimum value in the current round are recorded as the value range of the multi-working-condition decision variables in the next round of optimization, and the number of parameters that need to be optimized again is increased by 1; Step 8-4) if the number of parameters that need to be optimized again in all parameters is greater than or equal to the number of parameters that need to be optimized again in the last round or the current round is the first round of optimization, then the next round of optimization is performed by re-executing steps 3) to 8), otherwise, step 8-5) is performed; Step 8-5) output the optimized power grid identification parameters and known parameters, wherein the known parameters are obtained based on actual measurement results.

2. The power grid parameter identification method based on differential evolution algorithm and polynomial hybrid measurement of claim 1, wherein, The simple rules include: If the state quantity identifies that the branch is not open, but the telemetry quantity identifies that the branch has current or active power, it is considered that the telesignalling quantity and the telemetry quantity contradict each other, and at least one error exists between the two, the telesignalling quantity and the telemetry quantity are checked until the checking result shows that there is no error; If the state quantity identifies that the branch is open, but the telemetry quantity identifies that the branch has no current or active power, it is considered that the telesignalling quantity or the telemetry quantity is suspicious, and is listed in a suspicious list.

3. The power grid parameter identification method based on differential evolution algorithm and polynomial hybrid measurement of claim 1, wherein, The node decision variable is related to multiple working conditions, including a real part e and an imaginary part f of a node voltage, and a value range of the node decision variable is 0.75≤e≤1.25 and 0.75≤f≤1.25; wherein the node decision variable being related to multiple working conditions means that a working condition number of the multiple working conditions is consistent with a group number of the node decision variable; The branch decision variable of the conductor type is obtained based on a π-type equivalent circuit and is irrelevant to multiple working conditions, and includes a branch resistance r, a branch reactance x, a half ground conductance g and a half ground susceptance b, wherein the half ground conductance g is half of the ground conductance, the half ground susceptance b is half of the ground susceptance, and initial value ranges of the branch decision variable of the conductor type are 0.000001≤r≤0.05, 0.000001≤x≤0.1 and 0.000001≤b≤1.0; The branch decision variable of the transformer winding type is irrelevant to multiple working conditions, and includes a copper loss equivalent resistance r, a leakage reactance x, a iron loss equivalent conductance g and a magnetizing conductance b, wherein the iron loss equivalent conductance g and the magnetizing conductance b are ignored under a steady-state analysis condition; and initial value ranges of the branch decision variable of the transformer winding type are 0.000001≤r≤0.05 and 0.000001≤x≤1.

0.

4. The power grid parameter identification method based on differential evolution algorithm and polynomial hybrid measurement of claim 1, wherein, The multiple working condition branch equation set is: wherein subscript w denotes a working condition, is a current square deviation of the branch i-j, is a square of a current measurement amplitude of the branch i-j, is an active deviation of the branch i-j, is an active measurement of the branch i-j, is a reactive deviation of the branch i-j, is a reactive measurement of the branch i-j.

5. The power grid parameter identification method based on differential evolution algorithm and polynomial hybrid measurement of claim 1, wherein, The multiple objective function is: in, Represents all operating conditions. The objective function representing the node voltage. The active power objective function of the representative node. The objective function representing the reactive power of the node. The objective function representing the branch current, The objective function representing the active power of the branch is... The objective function representing the reactive power of the branch is... Let be the squared voltage deviation at node i. Let be the active power deviation at node i. Let be the reactive power deviation at node i. Let be the squared deviation of the current in branch ij. The active power deviation of branch ij, The reactive power deviation of branch ij is denoted as .

6. The power grid parameter identification method based on differential evolution algorithm and polynomial hybrid measurement of claim 1, wherein, The method further includes: Step 9) based on the optimized power grid identification parameters, state estimation calculation or power flow examination calculation is performed, if the telemetry result reaches a pre-configured standard value of judging telemetry qualification, or a difference between the measurement result and the collected value is less than a pre-configured threshold value of the power flow examination, upper and lower limits of the initial value range of the multiple working condition decision variable are set as the corresponding optimized power grid identification parameters when the power grid parameters are identified again based on steps 1) to 8). 7.A power grid parameter identification device based on a differential evolution algorithm and a polynomial hybrid measurement, comprising a memory, a processor, and a program stored in the memory, characterized in that, The processor implements the method of any one of claims 1-6 when executing the program.

8. A storage medium having stored thereon a program, characterized by The program is executed to implement the method of any one of claims 1-6.

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