Transmission line safety risk assessment calculation model based on over-limit perception normalization

Abstract

The present invention discloses a transmission line safety risk assessment calculation model based on normalized over-limit perception, and the construction process is as follows: 1. Constructing a normalized quantitative scoring model for the severity of temperature rise exceeding the corresponding safety threshold; 2. Constructing a normalized quantitative scoring model for the severity of dancing amplitude exceeding the corresponding safety threshold; 3. Constructing a normalized quantitative scoring model for the severity of tower tilt displacement exceeding the corresponding safety threshold; 4. Constructing a normalized quantitative scoring model for the severity of tower settlement displacement exceeding the corresponding safety threshold; 5. Normalizing and fusing the over-limit states of the perception parameters of each dimension to construct a line overall operation safety risk assessment model; 6. According to the score value calculated by the line overall operation safety risk assessment model, determine the line safety risk severity level. The present invention can generate a systematic scoring value that can be used to comprehensively evaluate the overall operation safety risk of the line, thereby determining the severity level of the line safety risk.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission line operation and maintenance, and in particular to a power transmission line safety risk assessment calculation model based on over-limit perception normalization. Background Art

[0002] With the rapid development of the national economy, the requirements for the power supply quality of the power grid are getting higher and higher. Due to the changes in climate factors and the continuous increase in long-distance transmission lines, the overtemperature of transmission lines and the dancing of wires are common. In addition, due to the structural deformation of the line towers and other influences, the operation safety of long-distance high-voltage transmission lines is particularly important. Failures caused by transmission line operation safety problems can cause damage to line equipment and affect transmission and power supply, or even cause line short circuits or tower collapses, causing huge economic losses. The most direct way to solve this problem is to deploy multiple types of status monitoring sensors on the transmission lines to grasp the status information of temperature rise, dancing, tower structure changes, etc. of the transmission lines in real time, and use this to evaluate the severity of the transmission line operation safety risks.

[0003] Every transmission line is a nerve of the power system, and the operating status of the transmission line determines the safety of the power system. As overhead transmission lines are in the wild for a long time, under the influence of natural environmental conditions, there are huge safety hazards in the transmission lines. Only by scientifically evaluating the operating risks of the lines can we timely grasp the operating status of the lines, correctly take adjustment measures, and achieve predictive status maintenance.

[0004] Transmission line operation safety risk assessment is a very complex task, but in traditional line risk assessment, subjective factors often lead to poor assessment accuracy. At the same time, there are a series of difficulties in line operation safety risk assessment, mainly manifested as: the assessment method is simple and the results are not credible; the assessment information is not comprehensive and the equipment situation cannot be truly reflected; the condition monitoring and risk assessment are not adapted, and the assessment and maintenance work are out of touch; the assessment method causes the assessment results to be incorrect and thus cannot correctly guide the implementation of maintenance decisions.

[0005] Therefore, existing transmission lines generally lack scientific methods to assess their safety risk levels, especially the comprehensive utilization of multiple types of sensor data used for line monitoring is low, and there is a lack of methods for comprehensively assessing the safety risks of line operation when multiple different types of sensor values ​​exceed the threshold. Summary of the invention

[0006] In view of this, the present invention provides a transmission line safety risk assessment calculation model based on over-limit perception normalization, which can normalize and quantify the severity of the sensor values ​​of various types of multi-parameters such as line temperature rise, dancing, inclination, settlement displacement, etc. exceeding the corresponding safety threshold, and on this basis, normalize and fuse the over-limit status of multi-dimensional perception parameters to generate a systematic scoring value that can be used to comprehensively evaluate the overall operation safety risk of the line, so as to determine the severity level of the line safety risk, so as to greatly improve the accuracy and scientificity of the transmission line operation safety risk assessment, so as to truly and reliably reflect the actual situation of the line operation, and finally achieve the purpose of predictive status maintenance guiding the operation and maintenance of the transmission line, and provide guidance for arranging a reasonable maintenance plan.

[0007] A calculation model for transmission line safety risk assessment based on normalized over-limit perception is proposed. The construction process of the calculation model is as follows:

[0008] Step 1: Obtain the perceived data of line temperature rise and construct a normalized quantitative scoring model for the severity of line temperature rise exceeding the corresponding safety threshold;

[0009] Step 2: Obtain the sensory data of the line vibration amplitude and construct a normalized quantitative scoring model for the severity of the line vibration amplitude exceeding the corresponding safety threshold;

[0010] Step 3: Acquire the perceived data of the tilt displacement of the line tower and construct a normalized quantitative scoring model for the severity of the tilt displacement of the line tower exceeding the corresponding safety threshold;

[0011] Step 4: Obtain the sensing data of the settlement displacement of the line tower, and construct a normalized quantitative scoring model for the severity of the settlement displacement of the line tower exceeding the corresponding safety threshold;

[0012] Step 5: Normalize and integrate the over-limit status of the perception parameters in each dimension to build a line overall operation safety risk assessment model;

[0013] Step 6: Determine the severity level of the line safety risk based on the score calculated by the line overall operation safety risk assessment model.

[0014] Furthermore, the process of constructing a normalized quantitative scoring model for the severity of line temperature rise exceeding the corresponding safety threshold in step 1 is as follows:

[0015] The conductor temperature T = {t1, t2, t3...}, which is the average temperature set of the line temperature sensor in a certain period of time, represented by an array vector. Among them, t1 is the average temperature value of temperature sensor 1 in a certain period of time, and the time period can be counted in hours. Tsd_T is the temperature rise threshold allowed by the line;

[0016] First, filter out the temperature sensing data that exceeds the threshold, and record them as: T = {T1, T2, ..., T N}, use the cross-limit mean square value T of all temperature sensor data exceeding the temperature rise threshold ave To measure the statistical average value of abnormal temperature rise exceeding the limit:

[0017]

[0018] The normalized quantitative scoring model A for the severity of line temperature rise exceeding the temperature rise safety threshold is as follows:

[0019]

[0020] Furthermore, the process of constructing a normalized quantitative scoring model for the severity of the line dancing amplitude exceeding the corresponding safety threshold in step 2 is as follows:

[0021] Let Gpe = {gpe1, gpe2, gpe3...} be the maximum dancing amplitude set in the e direction within a certain period of time, represented by an array vector, wherein gpe1 is the maximum dancing amplitude in the e direction within the first acquisition time window of the period of time, wherein gpe2 is the maximum dancing amplitude in the e direction within the second acquisition time window of the period of time, and so on; let tsd_Gpe be the maximum dancing amplitude threshold allowed in the e direction;

[0022] First, the dancing sensor data exceeding the dancing amplitude threshold in the e direction are screened out and recorded as: Ge = {Ge1, Ge2, ..., Ge M}, use the cross-limit mean square value Ge of all dancing sensor data exceeding the e-direction dancing amplitude threshold ave To measure the statistical average value of abnormal e-direction dancing amplitude exceeding the limit:

[0023]

[0024] Normalized quantitative scoring model B for the severity of exceeding the safety threshold of e-direction dancing amplitude e as follows:

[0025]

[0026] Similarly, let Gpn = {gpn1, gpn2, gpn3...} be the set of maximum dancing amplitudes in n directions within a certain period of time, represented by an array vector; where gpn1 is the maximum dancing amplitude in n directions within the first acquisition time window of the period of time, where gpn2 is the maximum dancing amplitude in n directions within the second acquisition time window of the period of time, and so on. Let tsd_Gpn be the maximum dancing amplitude threshold allowed in n directions;

[0027] It is necessary to first filter out the dancing sensor data that exceeds the n-direction dancing amplitude threshold, which are recorded as: Gn = {Gn1, Gn2, ..., Gn M}, use the cross-limit mean square value Gn of all dancing sensor data exceeding the n-direction dancing amplitude threshold ave To measure the statistical average value of the abnormal n-direction dancing amplitude exceeding the limit

[0028]

[0029] Normalized quantitative scoring model B for the severity of exceeding the safety threshold of n-directional dancing amplitude n as follows:

[0030]

[0031] Let Gpu = {gpu1, gpu2, gpu3...} be the maximum dancing amplitude set in the u direction within a certain period of time, represented by an array vector. Among them, gpu1 is the maximum dancing amplitude in the u direction within the first acquisition time window of the period of time, and gpu2 is the maximum dancing amplitude in the u direction within the second acquisition time window of the period of time, and so on. Let tsd_Gpu be the maximum dancing amplitude threshold allowed in the u direction. It is necessary to first filter out the dancing sensor data that exceeds the dancing amplitude threshold in the u direction, which are recorded as: Gu = {Gu1, Gu2,..., Gu M}, use the over-limit mean square value Gu of all dancing sensor data exceeding the dancing amplitude threshold in the u direction ave To measure the statistical average value of the abnormal exceeding of the u-direction dancing amplitude

[0032]

[0033] Normalized quantitative scoring model B for the severity of exceeding the safety threshold of the u-direction dancing amplitude u as follows:

[0034]

[0035] The normalized quantitative scores of the safety threshold severity of the three axial dancing amplitudes e, n, and u are integrated to obtain the normalized quantitative scoring model B for dancing amplitude exceeding the limit as follows:

[0036] B=α1·B e +α2×B n +α3×B u (9)

[0037] Among them, α1, α2, and α3 are normalized influencing factors, which are determined according to the voltage level and current load of the line, and α1+α2+α3=1.

[0038] Furthermore, the process of constructing a normalized quantitative scoring model for the severity of the transmission line tower inclination exceeding the corresponding safety threshold in step 3 is as follows:

[0039] Sde = {sde1, sde2, sde3...} is the set of tilt displacements in the e direction within a certain period of time, represented by an array vector. Among them, sde1 is the tilt displacement in the e direction in the first acquisition time window of the period of time, sde2 is the tilt displacement in the e direction in the second acquisition time window of the period of time, and so on;

[0040] Sdn = {sdn1, sdn2, sdn3...} is a set of n-direction tilt displacements in a certain period of time, represented by an array vector. Among them, sdn1 is the n-direction tilt displacement in the first acquisition time window of the period of time, sdn2 is the n-direction tilt displacement in the second acquisition time window of the period of time, and so on;

[0041] Sdu = {sdu1, sdu2, sdu3...} is a set of u-direction tilt displacements in a certain period of time, represented by an array vector. Among them, sdu1 is the u-direction tilt displacement in the first acquisition time window of the period of time, sdu2 is the u-direction tilt displacement in the second acquisition time window of the period of time, and so on;

[0042] Let tsd_Sde be the maximum tilt displacement threshold allowed in the e direction. First, filter out the tilt sensing data that exceeds the tilt displacement threshold in the e direction, and record them as: Se = {Se1, Se2, ..., Se L}, use the cross-limit mean square value Se of all tilt sensor data exceeding the tilt displacement threshold in the e direction ave To measure the statistical average value of the abnormal displacement in the e direction:

[0043]

[0044] Normalized quantitative scoring model C for the severity of exceeding the safety threshold of e-direction tilt displacement e as follows:

[0045]

[0046] Let tsd_Sdn be the maximum tilt displacement threshold allowed in the n direction. First, the tilt sensing data exceeding the tilt displacement threshold in the n direction are screened out and recorded as: Sn = {Sn1, Sn2, ..., Sn L}, use the cross-limit mean square value Sn of all tilt sensor data exceeding the n-direction tilt displacement threshold ave To measure the statistical average value of the abnormal displacement exceeding the limit in n direction:

[0047]

[0048] Normalized quantitative scoring model C for the severity of exceeding the safety threshold of n-directional tilt displacement n as follows:

[0049]

[0050] Let tsd_Sdu be the maximum tilt displacement threshold allowed in the u direction. First, filter out the tilt sensing data that exceeds the tilt displacement threshold in the u direction, and record them as: Su = {Su1, Su2, ..., Su L}, use the over-limit mean square value Su of all tilt sensor data exceeding the u-direction tilt displacement threshold ave To measure the statistical average value of the abnormal displacement exceeding the limit in the u direction:

[0051]

[0052] Normalized quantitative score C of the severity of exceeding the safety threshold of u-direction tilt displacement u as follows:

[0053]

[0054] The normalized quantitative scores of the severity of the three axial tilt displacement safety thresholds e, n, and u are integrated to obtain the normalized quantitative scoring model C of tilt displacement exceeding the limit as follows:

[0055] C=β1·C e +β2×C n +β3×C u (16)

[0056] Among them, β1, β2, and β3 are normalized influencing factors, which are usually determined according to the voltage level and current load of the line, and β1+β2+β3=1.

[0057] Furthermore, the process of constructing a normalized quantitative scoring model for the severity of the transmission line tower settlement exceeding the corresponding safety threshold in step 4 is as follows:

[0058] Let Dde = {dde1, dde2, dde3...} be the set of settlement displacements in the e direction within a certain period of time, represented by an array vector, where dde1 is the settlement displacement in the e direction within the first acquisition time window of the period of time, dde2 is the settlement displacement in the e direction within the second acquisition time window of the period of time, and so on;

[0059] Let ddn = {ddn1, ddn2, ddn3...} be the set of settlement displacements in n directions within a certain period of time, represented by an array vector, where ddn1 is the settlement displacement in n directions within the first acquisition time window of the period of time, ddn2 is the settlement displacement in n directions within the second acquisition time window of the period of time, and so on;

[0060] Let dddu = {ddu1, ddu2, ddu3...} be the set of u-direction settlement displacements in a certain period of time, represented by an array vector, where ddu1 is the u-direction settlement displacement in the first acquisition time window of the period of time, ddu2 is the u-direction settlement displacement in the second acquisition time window of the period of time, and so on;

[0061] Let tsd_Dde be the maximum settlement displacement threshold allowed in the e direction. It is necessary to first filter out the settlement sensing data that exceeds the settlement displacement threshold in the e direction, which are recorded as: De = {De1, De2, ..., De K}, use the over-limit mean square value De of all settlement sensor data exceeding the settlement displacement threshold in the e direction ave To measure the statistical average value of the abnormal displacement of the e-direction settlement:

[0062]

[0063] Normalized quantitative scoring model D for the severity of exceeding the safety threshold of e-direction settlement displacement e as follows:

[0064]

[0065] Let tsd_Ddn be the maximum settlement displacement threshold allowed in the n direction. First, filter out the settlement sensing data that exceeds the settlement displacement threshold in the n direction, and record them as: Dn = {Dn1, Dn2, ..., Dn K}, use the over-limit mean square value Dn of all settlement sensor data exceeding the settlement displacement threshold in n directions ave To measure the statistical average value of the abnormal displacement of n-direction settlement:

[0066]

[0067] Normalized quantitative scoring model D for the severity of exceeding the safety threshold of n-dimensional settlement displacement n as follows:

[0068]

[0069] Let tsd_Ddu be the maximum settlement displacement threshold allowed in the u direction. First, filter out the settlement sensing data that exceeds the settlement displacement threshold in the u direction, and record them as: Du = {Du1, Du2, ..., DuK}, use the over-limit mean square value Du of all settlement sensor data exceeding the u-direction settlement displacement threshold ave To measure the statistical average value of the abnormal displacement of u-direction settlement:

[0070]

[0071] Normalized quantitative score D of the severity of exceeding the safety threshold of u-direction settlement displacement u as follows:

[0072]

[0073] The normalized quantitative scores of the severity of the three axial settlement displacement safety thresholds e, n, and u are integrated to obtain the normalized quantitative scoring model D of settlement displacement exceeding the limit as follows:

[0074] D=γ1·D e +γ2×D n +γ3×D u (twenty three)

[0075] Among them, γ1, γ2, and γ3 are normalized influencing factors, which are usually determined according to the voltage level and current load of the line, and γ1+γ2+γ3=1.

[0076] Furthermore, in step 5, the process of normalizing and fusing the over-limit states of the perception parameters of each dimension and calculating the overall line operation safety risk score value is as follows:

[0077] According to the above-mentioned calculation and obtained normalized quantitative score set of the perceived quantity exceeding the limit in each dimension, combined with the weight distribution of each type of perceived parameter, the overall operation safety risk comprehensive assessment score F of the line is obtained by normalization fusion:

[0078] F=μA+τB+ρC+ηD (24)

[0079] Among them, μ, τ, ρ, η are weight factors, which are usually determined according to the voltage level and current load of the line. They are also related to the influencing factors of temperature rise, dancing, inclination and settlement, and μ+τ+ρ+η=1.

[0080] Furthermore, in step 6, the process of determining the severity level of the line safety risk according to the overall line operation safety risk score value is as follows:

[0081] According to the overall safety risk score of the transmission line operation, the safety risk severity level Safe_Level is determined according to the three levels of normal, slight and severe:

[0082]

[0083] Among them, the threshold for distinguishing normal from mild risk is S1, and the threshold for distinguishing mild from severe risk is S2.

[0084] Beneficial effects:

[0085] 1. The present invention aims at the practical problem that it is difficult to effectively and comprehensively evaluate the safety risks of power transmission line operation when various types of sensor data exceed normal thresholds. Taking into account the coupling change influencing mechanism of temperature rise, dancing, inclination, settlement and other aspects during line operation, the present invention can normalize and quantify the severity of the sensor values ​​of various types of multi-parameters such as line temperature rise, dancing, inclination, settlement and displacement exceeding the corresponding safety threshold, and on this basis, normalize and fuse the over-limit states of multi-dimensional perception parameters to generate a systematic scoring value that can be used to comprehensively evaluate the overall safety risks of line operation, thereby determining the severity level of the safety risks of the line.

[0086] 2. The present invention can effectively solve the problem of safety risk assessment of power transmission line operation. It can evaluate the serious characteristics of line operation safety risk by normalizing and fusing the over-limit status of perception parameters in various dimensions to generate a systematic scoring value, which greatly improves the accuracy and scientificity of transmission line operation safety risk assessment, thereby being able to truly and reliably reflect the actual status of line operation, so as to achieve the purpose of predictive status maintenance guiding transmission line operation and maintenance, and provide guidance for arranging reasonable maintenance plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 This is a schematic diagram of the installation locations of temperature, vibration and tilt displacement sensors on transmission lines and towers;

[0088] Figure 2 The present invention is a flowchart of the construction process of the power transmission line safety risk assessment calculation model. DETAILED DESCRIPTION

[0089] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0090] As attached Figure 1 As shown, the following example uses a 220kV transmission line to obtain the operation safety risk assessment severity level using a transmission line operation safety risk assessment model based on over-limit perception normalization fusion:

[0091] Four temperature sensors are deployed on the line, a pulsation sensor is set in the middle of the line, and a tilt displacement sensor is set on the tower. A certain period of 60 minutes is taken as the generation period of safety risk assessment. The time windows for pulsation collection, tilt displacement collection, and settlement displacement collection in this time period are all three sections (each 20 minutes is a time window). The sensor data collected within the risk assessment generation period (60 minutes) is used as input, including:

[0092] (1) The average temperature measurement data set of the four temperature measurement points on the line within the time period is T = {t1, t2, t3, t4} = [30 80 100 87];

[0093] The maximum dancing amplitude set in the e direction Gpe = {gpe1, gpe2, gpe3} = [530, 408, 550];

[0094] The maximum dancing amplitude set in n direction Gpn = {gpn1, gpn2, gpn3} = [240, 450, 467];

[0095] The maximum dancing amplitude set in u direction Gpu = {gpu1, gpu2, gpu3} = [900, 1020, 1300];

[0096] The set of tilt displacements in the e direction Sde = {sde1, sde2, sde3} = [23, 14, 55];

[0097] The set of tilt displacements in n directions Sdn = {sdn1, sdn2, sdn3} = [23, 33, 20];

[0098] The u-direction tilt displacement set Sdu = {sdu1, sdu2, sdu3} = [28, 26, 17];

[0099] e-direction settlement displacement set Dde = {dde1, dde2, dde3} = [25, 17, 44];

[0100] Settlement displacement in n direction Ddn = {ddn1, ddn2, ddn3} = [20, 17, 40];

[0101] u-direction settlement displacement set Ddu = {ddu1, ddu2, ddu3} = [18, 12, 22];

[0102] (2) The corresponding safety thresholds are set as follows:

[0103] The allowed temperature rise threshold of the circuit is tsd_T = 80°C;

[0104] The maximum allowed dancing amplitude threshold in the e direction tsd_Gpe=500mm

[0105] The maximum allowed dancing amplitude threshold in direction n tsd_Gpn=500mm

[0106] The maximum allowed swing amplitude threshold in the u direction is tsd_Gpu=1000mm

[0107] The maximum tilt displacement threshold allowed in the e direction tsd_Sde=24mm

[0108] The maximum tilt displacement threshold allowed in the n direction tsd_Sdn=24mm

[0109] The maximum tilt displacement threshold allowed in the u direction tsd_Sdu=24mm

[0110] The maximum settlement displacement threshold allowed in the e direction tsd_Dde=24mm

[0111] The maximum settlement displacement threshold allowed in the n direction tsd_Ddn=24mm

[0112] The maximum settlement displacement threshold allowed in the u direction tsd_Ddu=24mm

[0113] The specific steps for establishing the evaluation model are as follows: Figure 2 As shown:

[0114] Step 1: Construct a normalized quantitative scoring model for the severity of line temperature rise exceeding the temperature rise safety threshold.

[0115] Filter out the temperature sensor data that exceeds the temperature rise threshold T = {100, 87}, so there is a temperature rise over-limit mean square value T ave Calculated as:

[0116]

[0117] Thus, we get the normalized quantitative score A of the severity of the line temperature rise exceeding the temperature rise safety threshold:

[0118]

[0119] Step 2: Construct a normalized quantitative scoring model for the severity of line vibration exceeding the corresponding safety threshold.

[0120] First, select the dancing sensor data Ge that exceeds the dancing amplitude threshold in the e direction, Ge = {Ge1, Ge2, ..., Ge M}={530,550}, so the e-direction dancing amplitude exceeds the limit mean square value Ge ave :

[0121]

[0122] The normalized quantitative scoring model B for the severity of exceeding the safety threshold of the e-direction dancing amplitude is obtained e :

[0123]

[0124] Next, the dancing sensor data Gn={Gn1, Gn2, ..., Gn M}={}, so there is an e-direction dancing amplitude exceeding the limit mean square value Ge ave =0,

[0125] Obtain a normalized quantitative scoring model B for the severity of exceeding the safety threshold of the n-directional dancing amplitude n :

[0126]

[0127] Then the dancing sensor data Gu={Gu1,Gu2,...,Gu M}={1020,1300}, so the u-direction dancing amplitude exceeds the limit mean square value Ge ave :

[0128]

[0129] Get the normalized quantitative score B of the severity of exceeding the safety threshold of the u-direction dancing amplitude u :

[0130]

[0131] Finally, the normalized quantitative scores of the severity of the safety thresholds of the three axial dancing amplitudes e, n, and u are integrated, and the normalized influence factors α1 = 0.3, α2 = 0.3, and α3 = 0.4 are taken to obtain the normalized quantitative scoring model B for the dancing amplitude exceeding the limit:

[0132] B=α1·B e +α2×B n +α3×B u =0.3*8.25+0.3*0+0.4*21.26=10.98

[0133] Step 3: Construct a normalized quantitative scoring model for the severity of line tilt displacement exceeding the corresponding safety threshold.

[0134] First, select the tilt sensing data Se that exceeds the tilt displacement threshold in the e direction = {Se1, Se2, ..., Se L}={55}, so the e-direction tilt displacement exceeds the limit mean square value Se ave :

[0135]

[0136] Get the normalized quantitative score C of the severity of exceeding the safety threshold of the tilt displacement in the e direction e :

[0137]

[0138] Next, the tilt sensing data Sn={Sn1, Sn2,..., Sn L}={33}, so there is an n-direction tilt displacement exceeding the limit mean square value Gn ave :

[0139]

[0140] Get the normalized quantitative score C of the severity of exceeding the safety threshold of the n-direction tilt displacement n :

[0141]

[0142] Then the tilt sensing data Su={Su1,Su2,...,Su L}={28,26}, so the u-direction tilt displacement exceeds the limit mean square value Su ave :

[0143]

[0144] Get the normalized quantitative score C of the severity of exceeding the safety threshold of the u-direction tilt displacement u :

[0145]

[0146] Finally, the normalized quantitative scores of the severity of the three axial tilt displacement safety thresholds e, n, and u are integrated, and the normalized influence factors β1 = 0.3, β2 = 0.3, and β3 = 0.4 are taken to obtain the normalized quantitative score C of the tilt displacement exceeding the limit:

[0147] C=β1·C e +β2×C n +β3×C u =0.3*129.17+0.3*37.5+0.4*13.2=55.27

[0148] Step 4: Construct a normalized quantitative scoring model for the severity of line settlement displacement exceeding the corresponding safety threshold.

[0149] First, filter out the settlement sensing data that exceeds the settlement displacement threshold in the e direction De={De1,De2,...,DeK}={25,44}, so the e-direction settlement displacement exceeds the limit mean square value De ave :

[0150]

[0151] The normalized quantitative score D of the severity of exceeding the safety threshold of the e-direction settlement displacement is obtained. e :

[0152]

[0153] Next, the settlement sensing data Dn={Dn1, Dn2, ..., Dn L}={40}, so there is an over-limit mean square value Dn of the settlement displacement in n directions ave :

[0154]

[0155] Get the normalized quantitative score D of the severity of exceeding the safety threshold of settlement displacement in n directions n :

[0156]

[0157] Then filter out the settlement sensing data Du={Du1, Du2, ..., Du K}={}, so the u-direction settlement displacement exceeds the limit mean square value Du ave =0:

[0158] Get the normalized quantitative score C of the severity of exceeding the safety threshold of the u-direction tilt displacement u :

[0159]

[0160] Finally, the normalized quantitative scores of the severity of the three axial settlement displacement safety thresholds e, n, and u are integrated, and the normalized influence factors γ1 = 0.3, γ2 = 0.3, and γ3 = 0.4 are taken to obtain the normalized quantitative score D of settlement displacement exceeding the limit:

[0161] D=γ1·D e +γ2×D n +γ3×D u =0.3*59+0.3*66.67+0.4*0=37.7

[0162] Step 5: Normalize and fuse the over-limit status of the perception parameters of each dimension. According to the voltage level of the line of 220kV and the current load of 240A, and considering the influencing factors of temperature rise, dancing, tilt and settlement, take the weight factors μ=0.3, τ=0.3, ρ=0.2, η=0.2, and calculate the overall operation safety risk score F of the line as follows:

[0163] F=μA+τB+ρC+ηD=0.3*18.7+0.3*10.98+0.2*55.27+0.2*37.7=27.5

[0164] Step 6: Based on the line overall operation safety risk score value calculated above, determine the severity of the line's current safety risk according to the following line overall operation safety risk classification standards:

[0165]

[0166] Therefore, the severity level of the line safety risk is judged to be slight. Based on this, it can provide guidance for arranging a reasonable maintenance plan. By reducing the line current load, timely power outages and installing wind hammers to reduce dancing and reduce the tensile tension on the tower, the safety risk assessment value can be effectively reduced. At the same time, it also proves the feasibility and effectiveness of the transmission line operation safety risk assessment method based on over-limit perception normalization fusion.

[0167] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A transmission line safety risk assessment calculation model based on over-limit perception normalization is characterized by: The construction process of the calculation model is as follows: Step 1: Obtain the perceived data of line temperature rise and construct a normalized quantitative scoring model for the severity of line temperature rise exceeding the corresponding safety threshold; Step 2: Obtain the sensory data of the line vibration amplitude and construct a normalized quantitative scoring model for the severity of the line vibration amplitude exceeding the corresponding safety threshold; Step 3: Acquire the perceived data of the tilt displacement of the line tower and construct a normalized quantitative scoring model for the severity of the tilt displacement of the line tower exceeding the corresponding safety threshold; Step 4: Obtain the sensing data of the settlement displacement of the line tower, and construct a normalized quantitative scoring model for the severity of the settlement displacement of the line tower exceeding the corresponding safety threshold; Step 5: Normalize and integrate the over-limit status of the perception parameters in each dimension to build a line overall operation safety risk assessment model; Step 6: Determine the severity level of line safety risk based on the score calculated by the line overall operation safety risk assessment model; The process of constructing a normalized quantitative scoring model for the severity of line temperature rise exceeding the corresponding safety threshold in step 1 is as follows: The conductor temperature T={t1, t2, t3...} is the average temperature set of the line temperature sensor in a certain period of time, which is represented by an array vector; t1 is the average temperature value of temperature sensor 1 in a certain period of time, and the time period is counted in hours. Tsd_T is the temperature rise threshold allowed by the line; First, filter out the temperature sensing data that exceeds the threshold, and record them as: , using the mean square value of all temperature sensor data exceeding the temperature rise threshold To measure the statistical average value of abnormal temperature rise exceeding the limit: (1) The normalized quantitative scoring model A for the severity of line temperature rise exceeding the temperature rise safety threshold is as follows: (2)。 2. The transmission line safety risk assessment calculation model based on over-limit perception normalization according to claim 1 is characterized in that: The process of constructing a normalized quantitative scoring model for the severity of line dancing amplitude exceeding the corresponding safety threshold in step 2 is as follows: Let Gpe={gpe1, gpe2, gpe3...} be the set of maximum dancing amplitudes in the e direction within a certain period of time, represented by an array vector, where gpe1 is the maximum dancing amplitude in the e direction within the first acquisition time window of the period of time, where gpe2 is the maximum dancing amplitude in the e direction within the second acquisition time window of the period of time, and so on; let tsd_Gpe be the maximum dancing amplitude threshold allowed in the e direction; First, the dancing sensor data exceeding the dancing amplitude threshold in the e direction are screened out and recorded as: , using the mean square value of all dancing sensor data exceeding the e-direction dancing amplitude threshold To measure the statistical average value of abnormal e-direction dancing amplitude exceeding the limit: (3) Normalized quantitative scoring model for the severity of exceeding the safety threshold of e-direction dancing amplitude as follows: (4) Similarly, let Gpn={gpn1, gpn2, gpn3...} be the set of maximum dancing amplitudes in n directions within a certain period of time, represented by an array vector; where gpn1 is the maximum dancing amplitude in n directions within the first acquisition time window of the period of time, where gpn2 is the maximum dancing amplitude in n directions within the second acquisition time window of the period of time, and so on; let tsd_Gpn be the maximum dancing amplitude threshold allowed in n directions; It is necessary to first filter out the dancing sensor data that exceeds the n-direction dancing amplitude threshold, which are recorded as: , using the mean square value of all dancing sensor data exceeding the n-direction dancing amplitude threshold To measure the statistical average value of the abnormal n-direction dancing amplitude exceeding the limit (5) Normalized quantitative scoring model for the severity of exceeding the safety threshold of n-directional dancing amplitude as follows: (6) Let Gpu={gpu1,gpu2,gpu3...} be the set of maximum dancing amplitudes in the u direction within a certain period of time, represented by an array vector; where gpu1 is the maximum dancing amplitude in the u direction within the first acquisition time window of the period of time, where gpu2 is the maximum dancing amplitude in the u direction within the second acquisition time window of the period of time, and so on; let tsd_Gpu be the maximum dancing amplitude threshold allowed in the u direction; it is necessary to first filter out the dancing sensor data that exceeds the dancing amplitude threshold in the u direction, which are recorded as: , using the mean square value of all dancing sensor data exceeding the u-direction dancing amplitude threshold To measure the statistical average value of the abnormal exceeding of the u-direction dancing amplitude (7) Normalized quantitative scoring model for the severity of exceeding the safety threshold of u-direction dancing amplitude as follows: (8) The normalized quantitative scores of the safety threshold severity of the three axial dancing amplitudes e, n, and u are integrated to obtain the normalized quantitative scoring model B for dancing amplitude exceeding the limit as follows: (9) in, , , They are normalized impact factors, which are determined according to the voltage level and current load of the line, and there are .

3. The transmission line safety risk assessment calculation model based on over-limit perception normalization as claimed in claim 2 is characterized in that: The process of constructing a normalized quantitative scoring model for the severity of the transmission line tower inclination exceeding the corresponding safety threshold in step 3 is as follows: Sde={sde1, sde2, sde3...} is the set of tilt displacements in the e direction within a certain period of time, represented by an array vector; among them, sde1 is the tilt displacement in the e direction in the first acquisition time window of the period of time, sde2 is the tilt displacement in the e direction in the second acquisition time window of the period of time, and so on; Sdn={sdn1,sdn2,sdn3...} is a set of n-directional tilt displacements in a certain period of time, represented by an array vector; sdn1 is the n-directional tilt displacement in the first acquisition time window of the period of time, sdn2 is the n-directional tilt displacement in the second acquisition time window of the period of time, and so on; Sdu={sdu1,sdu2,sdu3...} is the set of tilt displacements in the u direction within a certain period of time, represented by an array vector; where sdu1 is the tilt displacement in the u direction within the first acquisition time window of the period of time, sdu2 is the tilt displacement in the u direction within the second acquisition time window of the period of time, and so on; Let tsd_Sde be the maximum tilt displacement threshold allowed in the e direction. First, filter out the tilt sensing data that exceeds the tilt displacement threshold in the e direction and record them as: , using the mean square value of all tilt sensor data exceeding the e-direction tilt displacement threshold To measure the statistical average value of the abnormal displacement in the e direction: (10) Normalized quantitative scoring model for the severity of exceeding the safety threshold of e-direction tilt displacement as follows: (11) Let tsd_Sdn be the maximum tilt displacement threshold allowed in the n direction. First, filter out the tilt sensing data that exceeds the n-direction tilt displacement threshold and record them as: , using the mean square value of all tilt sensor data exceeding the n-direction tilt displacement threshold To measure the statistical average value of the abnormal displacement exceeding the limit in n direction: (12) Normalized quantitative scoring model for the severity of exceeding the safety threshold of n-directional tilt displacement as follows: (13) Let tsd_ Sdu be the maximum tilt displacement threshold allowed in the u direction. First, filter out the tilt sensing data that exceeds the tilt displacement threshold in the u direction and record them as: , using the mean square value of all tilt sensor data exceeding the u-direction tilt displacement threshold To measure the statistical average value of the abnormal displacement exceeding the limit in the u direction: (14) Normalized quantitative score for the severity of exceeding the safety threshold of u-direction tilt displacement as follows: (15) The normalized quantitative scores of the severity of the three axial tilt displacement safety thresholds e, n, and u are integrated to obtain the normalized quantitative scoring model C of tilt displacement exceeding the limit as follows: (16) in, , , They are normalized impact factors, which are usually determined according to the voltage level and current load of the line, and have .

4. The transmission line safety risk assessment calculation model based on over-limit perception normalization as claimed in claim 3 is characterized in that: The process of constructing a normalized quantitative scoring model for the severity of transmission line tower settlement exceeding the corresponding safety threshold in step 4 is as follows: Let Dde={dde1, dde2, dde3...} be the set of settlement displacements in the e direction within a certain period of time, represented by an array vector, where dde1 is the settlement displacement in the e direction within the first acquisition time window of the period of time, dde2 is the settlement displacement in the e direction within the second acquisition time window of the period of time, and so on; Let Ddn={ddn1,ddn2,ddn3...} be the set of settlement displacements in n directions within a certain period of time, represented by an array vector, where ddn1 is the settlement displacement in n directions within the first acquisition time window of the period of time, ddn2 is the settlement displacement in n directions within the second acquisition time window of the period of time, and so on; Let Ddu={ddu1,ddu2,ddu3...} be the set of settlement displacements in the u direction within a certain period of time, represented by an array vector, where ddu1 is the settlement displacement in the u direction within the first acquisition time window of the period of time, ddu2 is the settlement displacement in the u direction within the second acquisition time window of the period of time, and so on; Let tsd_Dde be the maximum settlement displacement threshold allowed in the e direction. It is necessary to first filter out the settlement sensing data that exceeds the settlement displacement threshold in the e direction, which are recorded as: , using the over-limit mean square value of all settlement sensor data exceeding the settlement displacement threshold in the e direction To measure the statistical average value of the abnormal displacement of the e-direction settlement: (17) Normalized quantitative scoring model for the severity of exceeding the safety threshold of e-direction settlement displacement as follows: (18) Let tsd_Ddn be the maximum settlement displacement threshold allowed in the n direction. First, filter out the settlement sensing data that exceeds the settlement displacement threshold in the n direction and record them as: , using the mean square value of all settlement sensor data exceeding the settlement displacement threshold in n directions To measure the statistical average value of the abnormal displacement of n-direction settlement: (19) Normalized quantitative scoring model for the severity of exceeding the safety threshold of n-dimensional settlement displacement as follows: (20) Let tsd_Ddu be the maximum settlement displacement threshold allowed in the u direction. First, filter out the settlement sensing data that exceeds the settlement displacement threshold in the u direction and record them as: , using the mean square value of all settlement sensor data exceeding the u-direction settlement displacement threshold To measure the statistical average value of the abnormal displacement of u-direction settlement: (21) Normalized quantitative score of the severity of exceeding the safety threshold of u-direction settlement displacement as follows: (22) The normalized quantitative scores of the severity of the three axial settlement displacement safety thresholds e, n, and u are integrated to obtain the normalized quantitative scoring model D of settlement displacement exceeding the limit as follows: (23) in, , , They are normalized impact factors, which are usually determined according to the voltage level and current load of the line, and have .

5. The transmission line safety risk assessment calculation model based on over-limit perception normalization according to claim 4 is characterized in that: In step 5, the process of normalizing and fusing the over-limit status of the perception parameters of each dimension and calculating the overall line operation safety risk score value is as follows: According to the above-mentioned calculation and obtained normalized quantitative score set of the perceived quantity exceeding the limit in each dimension, combined with the weight distribution of each type of perceived parameter, the overall operation safety risk comprehensive assessment score F of the line is obtained by normalization fusion: (24) in, is a weight factor, which is usually determined according to the voltage level and current load of the line. It is also related to the influencing factors of temperature rise, dancing, tilt and settlement, and has .

6. The transmission line safety risk assessment calculation model based on over-limit perception normalization according to claim 5 is characterized in that: In step 6, the process of determining the severity level of line safety risk according to the overall line operation safety risk score value is as follows: According to the overall operation safety risk score of the transmission line, the safety risk severity level is determined according to the three levels of normal, slight and severe. : （7） The threshold for distinguishing normal from mild risk is , the threshold for distinguishing between mild and severe risk levels is .

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