A method and system for detecting risk of electric power equipment

Abstract

The present invention relates to the field of power grid technology, and specifically discloses a method and system for detecting risks of electric power equipment, which obtain current state data of electric power equipment during operation, establish a current operation curve according to the current state data, and obtain a current operation area; the current operation area includes a normal area and an abnormal area; based on the current operation area, the dynamic behavior data of the electric power equipment during operation is obtained, and the negative state value of the operation state of the electric power equipment within a monitoring period is obtained according to the dynamic behavior data; the abnormality level signal of the electric power equipment within the monitoring period is completed according to the negative state value of the operation state; when the electric power equipment is abnormal, the present invention processes the temperature, noise and the number of connected electronic components corresponding to the electronic components in the electric power equipment during the abnormal process, obtains the behavior verification cardinality of all electronic components, arranges the electronic components in the period in descending order according to the behavior verification cardinality, and completes the priority processing of the abnormal electronic components.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to a method and system for detecting risks of power equipment. Background Art

[0002] In the power system, the operation and maintenance of power equipment is an important measure to ensure the normal operation of the equipment. In order to improve the accuracy of operation and maintenance and reduce resource waste, it is usually necessary to conduct risk assessment of power equipment.

[0003] In the existing technology, for normal risks, the failure probability of equipment is mainly evaluated based on the equipment failure aging model or the "reliability index" issued by the State Electricity Regulatory Commission. For latent and abnormal failure risks, risk assessment is carried out based on the failure mode.

[0004] However, the existing risk assessment methods have low accuracy and cannot effectively assess the real-time status of power equipment during operation, and cannot accurately provide corresponding decision-making references for operation and maintenance work. Summary of the invention

[0005] In order to make up for the deficiencies of the existing technology and solve the problem that the real-time status of the power equipment during operation cannot be effectively evaluated in the existing technology and cannot accurately provide corresponding decision-making references for operation and maintenance work.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A method for detecting risk of electric power equipment comprises the following steps:

[0008] Acquire the current state data of the power equipment during operation, establish the current operation curve according to the current state data, and obtain the current operation area;

[0009] Among them, the current operation area includes a normal area and an abnormal area;

[0010] Acquire the dynamic behavior data of the power equipment during operation based on the current operation area, and obtain the negative value of the operation state of the power equipment during the monitoring period according to the dynamic behavior data;

[0011] Complete the abnormality level signal of the power equipment within the monitoring period according to the negative value of the operating status;

[0012] Based on the abnormality level of the power equipment, the abnormality level of the electronic components in the power equipment is identified, and priority processing of the abnormal electronic components is completed.

[0013] As a further solution of the present invention: dividing the monitoring period into a number of time sub-units, obtaining the current value at the middle moment of each time sub-unit;

[0014] With time as the X-axis and current value as the Y-axis, a plane coordinate system is constructed;

[0015] According to the time sequence, the current value corresponding to each time sub-unit is used to construct a current operation curve in the plane coordinate system;

[0016] Obtain the rated current range of the power equipment during operation, and construct an upper standard line and a lower standard line for the two end values ​​of the rated current range in a plane coordinate system;

[0017] The area enclosed by the curve segment of the current operating curve above the upper standard line is obtained and recorded as the upper abnormal area;

[0018] The area enclosed by the curve segment of the current operation curve below the lower standard line is obtained and recorded as the lower abnormal area;

[0019] The upper abnormal area and the lower abnormal area are recorded together as abnormal areas;

[0020] The area between the lower standard line and the upper standard line of the current operation curve is obtained and recorded as the normal area.

[0021] As a further solution of the present invention: the behavioral dynamic data includes the hetero-unit number ratio, hetero-current bias ratio and positive hetero-region ratio

[0022] The ratio of the number of heteromeric units is denoted as Ys;

[0023] The current bias ratio of the hetero-particle is recorded as Yd;

[0024] The positive isotropic ratio is denoted as Zy;

[0025] By formula The negative state value Yi of the operating state of the power equipment during the monitoring period is calculated, where a1 and a2 are both preset proportional factors, and yi is the abnormal precipitation factor.

[0026] As a further solution of the present invention: the time corresponding to the abnormal area is integrated to obtain an abnormal time period, and the time subunit located in the abnormal time period is recorded as a time abnormal subunit;

[0027] The time corresponding to the normal area is integrated to obtain the normal time period, and the time subunit in the normal time period is recorded as the time positive subunit;

[0028] The ratio of the number of temporal heterosubunits to the number of temporal subunits is calculated to obtain the heterosubunit number ratio;

[0029] The area value of the abnormal area and the area value of the normal area are obtained, and the ratio of the area value of the normal area to the area value of the abnormal area is calculated to obtain the positive abnormal area ratio.

[0030] As a further solution of the present invention: The current values in the time anomaly sub-units during the abnormal time period are respectively subtracted from the rated current standard value and the absolute value is taken to obtain the current deviation value of the time anomaly sub-unit;

[0031] The current deviation values of all time anomaly sub-units are summed and averaged to obtain the current anomaly value during the abnormal time period;

[0032] The current values in the time positive sub-units during the normal time period are summed and averaged to obtain the current positive value during the normal time period;

[0033] The current anomaly value during the abnormal time period is divided by the current positive value during the normal time period to obtain the abnormal sub-current deviation ratio.

[0034] As a further solution of the present invention: The limit values of the preset operating state negative value threshold are Yi1 and Yi2, where Yi1 < Yi2;

[0035] When Fy < Yi1, an abnormal degree level 1 signal of the power equipment is obtained;

[0036] When Yi1 ≤ Fy < Yi2, an abnormal degree level 2 signal of the power equipment is obtained;

[0037] When Fy ≥ Yi2, an abnormal degree level 3 signal of the power equipment is obtained.

[0038] As a further solution of the present invention: The process of obtaining the abnormal precipitation factor is as follows:

[0039] The temperature deviation rate is obtained through temperature processing in the time anomaly sub-unit;

[0040] The noise deviation rate obtained through noise processing in the time anomaly sub-unit;

[0041] The total risk analysis value obtained through the abnormal analysis of electronic components in the time anomaly sub-unit;

[0042] The temperature deviation rate of the time anomaly sub-unit is denoted as Swi;

[0043] The noise deviation rate of the time anomaly sub-unit is denoted as Szi;

[0044] The total risk analysis value of the time anomaly sub-unit is denoted as Sfi;

[0045] The temperature deviation rate Swi, the noise deviation rate Szi, and the total risk analysis value Sfi of the obtained time anomaly sub-unit are quantitatively processed and numerically calculated;

[0046] That is, through the formula The abnormal precipitation factor yi is calculated.

[0047] As a further solution of the present invention: based on the abnormality signal of the power equipment, any electronic component in the power equipment is recorded as a target device, and the risk summary value of the target device in the time abnormality sub-unit is obtained;

[0048] The time difference sub-unit corresponding to the risk summary value of the target device being greater than or equal to the risk summary threshold of the target device is recorded as the validation sub-unit;

[0049] Calculate the ratio of the number of verification subunits matched by the target device to the number of time-different subunits to obtain the verification ratio of the target device;

[0050] Performing weighted processing on the temperature deviation rate and noise deviation rate corresponding to the target device in the verification subunit to obtain the temperature noise deviation value of the target device in the verification subunit;

[0051] The temperature noise variation values ​​in all the valid verification sub-units of the target device are summed and averaged to obtain the valid temperature noise value of the target device;

[0052] The verification ratio of the target period is multiplied by the verification temperature-drying value of the target period to obtain the verification temperature-drying behavior value of the target device.

[0053] As a further solution of the present invention: obtaining an electronic component directly connected to the target device, recorded as a bypass device;

[0054] The bypass device temperature-dryness behavior value of the bypass device is obtained by multiplying the number ratio of the bypass devices by the mean value of the effective temperature-dryness behavior of the bypass devices.

[0055] The verification temperature and dryness behavior value of the target device is recorded as Xwz;

[0056] The bypass temperature behavior value of the bypass device is recorded as Pwz;

[0057] By formula The behavior validation base XP of the target device is calculated, where: is the preset proportional coefficient;

[0058] Arrange the electronic components in descending order according to the behavior verification cardinality within the cycle. The larger the behavior verification cardinality, the higher the priority level.

[0059] As a further solution of the present invention: a power equipment risk detection system, comprising:

[0060] The regional division module is used to obtain the current status data of the power equipment during operation, establish the current operation curve according to the current status data, obtain the current operation area status, and upload the current operation area status to the cloud management and control platform;

[0061] Among them, the current operation area includes a normal area and an abnormal area;

[0062] The state analysis module receives the current operation area status transmitted by the cloud management and control platform. The state analysis module obtains the dynamic behavior data of the power equipment during operation based on the current operation area, obtains the negative value of the operation state of the power equipment during the monitoring period according to the dynamic behavior data, and uploads the negative value of the operation state to the cloud management and control platform;

[0063] The detection and evaluation module receives the negative value of the operating status transmitted by the cloud management and control platform. The detection and evaluation module completes the abnormality level signal of the power equipment within the monitoring period according to the negative value of the operating status, and uploads the abnormality level signal to the cloud management and control platform;

[0064] The screening processing module receives the abnormality degree signal transmitted by the cloud management and control platform. Based on the abnormality degree of the power equipment, the screening processing module identifies the abnormality degree of the electronic components in the power equipment and completes the priority processing of the abnormal electronic components.

[0065] Beneficial effects of the present invention:

[0066] The present invention establishes a current operation curve according to the current state data within the monitoring cycle, and obtains the abnormal current time period and the corresponding abnormal area, the normal current time period and the corresponding normal area by identifying the current operation curve, and obtains the dynamic behavior data (the ratio of the number of abnormal sub-units, the abnormal sub-current deviation ratio and the positive abnormal area ratio) of the power equipment during operation by comparing and processing the abnormal time period with the normal time period, and obtains the negative state value of the operation state of the power equipment by processing the dynamic behavior data, thereby completing the identification of the operation state of the power equipment, that is, the present invention realizes the state identification of the power equipment from multiple dimensions such as the time period length corresponding to the abnormal area of ​​the power equipment within the monitoring cycle, the current deviation degree in the abnormal time period and the number of abnormal time sub-units, with high accuracy;

[0067] In the process of identifying the operating status of the power equipment, the present invention identifies and processes the temperature status, noise status and working status of the electronic components of the power equipment in the time difference sub-unit corresponding to the abnormal time period, obtains the temperature deviation rate obtained by temperature processing, the noise deviation rate obtained by noise processing, and the risk summary value obtained by abnormal analysis of the electronic components in the time difference sub-unit, and further improves the accuracy of identifying the power equipment by combining the monitoring status of external factors of the power equipment and the status of internal electronic components;

[0068] When the power equipment is abnormal, the present invention processes the temperature, noise and the number of connected electronic components corresponding to the electronic components in the power equipment during the abnormal process, thereby obtaining the behavior verification cardinality of all electronic components, and arranges the electronic components in a descending order according to the behavior verification cardinality within a cycle, thereby completing the priority processing of the abnormal electronic components. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The present invention will be further described below in conjunction with the accompanying drawings.

[0070] Figure 1 is a flow chart of a method for detecting risk of electric power equipment according to an embodiment of the present invention;

[0071] Figure 2 This is a flow chart of abnormality degree identification in a method for detecting risk of electric power equipment according to an embodiment of the present invention;

[0072] Figure 3 It is a flowchart of a power equipment risk detection system in an embodiment of the present invention. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] Example 1, please refer to Figure 1 As shown, the present invention is a method for detecting risk of electric power equipment, comprising the following steps:

[0075] Acquire the current state data of the power equipment during operation, establish the current operation curve according to the current state data, and obtain the current operation area;

[0076] Among them, the current operation area includes a normal area and an abnormal area;

[0077] Acquire the dynamic behavior data of the power equipment during operation based on the current operation area, and obtain the negative value of the operation state of the power equipment during the monitoring period according to the dynamic behavior data;

[0078] Complete the abnormality level signal of the power equipment within the monitoring period according to the negative value of the operating status;

[0079] Based on the abnormality level of the power equipment, the abnormality level of the electronic components in the power equipment is identified, and priority processing of the abnormal electronic components is completed.

[0080] It should be noted that the monitoring cycle is preset, and the current state data is obtained by monitoring and processing the current data of the power equipment within the monitoring cycle;

[0081] In a specific embodiment:

[0082] Divide the monitoring period into several time sub-units, and obtain the current value at the middle moment of each time sub-unit;

[0083] With time as the X-axis and current value as the Y-axis, a plane coordinate system is constructed;

[0084] In chronological order, the current value corresponding to each time sub-unit is plotted in the plane coordinate system, and all points are connected in sequence from left to right in the coordinate system with a smooth curve to obtain the current operation curve within the monitoring period;

[0085] Obtain the rated current range of the power equipment during operation, and record the maximum value of the rated current range as the current upper limit value;

[0086] The minimum value of the rated current range is recorded as the current lower limit value;

[0087] Draw an upper standard line parallel to the X-axis for the current upper limit value;

[0088] Draw a lower standard line parallel to the X-axis for the lower limit value of the current;

[0089] Compare and analyze the current operating curve with the upper standard line and the lower standard line;

[0090] The area enclosed by the curve segment of the current operating curve above the upper standard line is obtained and recorded as the upper abnormal area;

[0091] The area enclosed by the curve segment of the current operation curve below the lower standard line is obtained and recorded as the lower abnormal area;

[0092] The upper abnormal area and the lower abnormal area are recorded together as abnormal areas;

[0093] The area between the lower standard line and the upper standard line of the current operation curve is obtained and recorded as the normal area.

[0094] The time corresponding to the abnormal area is integrated to obtain the time period corresponding to the abnormal area, which is recorded as the abnormal time period, and the time subunit located in the abnormal time period is recorded as the time abnormal subunit;

[0095] The time corresponding to the normal area is integrated to obtain the time period corresponding to the normal area, which is recorded as the normal time period, and the time subunit in the normal time period is recorded as the time positive subunit;

[0096] The number of time-differentiated subunits is obtained, and the ratio of the number of time-differentiated subunits to the number of time subunits is calculated to obtain the ratio of the number of time-differentiated subunits;

[0097] The current value in the time anomaly subunit in the abnormal time period is subtracted from the rated current standard value and the absolute value is taken to obtain the current deviation value of the time anomaly subunit;

[0098] The current deviation values ​​of all time-abnormal sub-units are summed and averaged to obtain the current abnormal value in the abnormal time period;

[0099] Among them, the rated current standard value is the average of the current upper limit value and the current lower limit value;

[0100] The current values ​​in the positive subunits in the normal time period are summed and averaged to obtain the positive current value in the normal time period;

[0101] The current abnormal value in the abnormal time period is calculated by ratio with the positive current value in the normal time period to obtain the abnormal current bias ratio;

[0102] Obtain the area value of the abnormal area and the area value of the normal area, calculate the ratio of the area value of the normal area to the area value of the abnormal area, and obtain the positive abnormal area ratio;

[0103] The ratio of the number of heteromeric units is denoted as Ys;

[0104] The current bias ratio of the hetero-particle is recorded as Yd;

[0105] The positive isotropic ratio is denoted as Zy;

[0106] By formula The negative state value Yi of the operating state of the power equipment during the monitoring period is calculated, where a1 and a2 are both preset proportional factors, and a1 and a2 are both greater than zero, and yi is the abnormal precipitation factor.

[0107] Through the above formula for obtaining the negative value of the operating state of the power equipment in the monitoring period, it can be known that the larger the ratio of the number of abnormal sub-units, the more the number of abnormal sub-time units in the monitoring period, the longer the time period corresponding to the abnormal area, the larger the negative value of the operating state obtained, and the worse the operating state of the power equipment;

[0108] The larger the deviation ratio of the current in the abnormal time period is, the larger the deviation of the current value in the abnormal time period is compared with the current value in the normal time period, indicating that the current instability in the abnormal time period is high. The larger the negative value of the operating state is, the worse the operating state of the power equipment is.

[0109] The larger the positive-to-abnormal area ratio is, it indicates that within the monitoring period, the proportion of the normal area (normal operation time, number of normal operation units) is higher than that of the abnormal area, which means that the probability of the power equipment operating normally is high, the negative value of the operating state obtained is smaller, and the operating state of the power equipment is better.

[0110] The limit values of the preset negative value threshold of the operating state are Yi1 and Yi2, where Yi1 < Yi2;

[0111] Among them, the limit values Yi1 and Yi2 of the negative value threshold of the operating state are an empirical value, obtained according to experience:

[0112] In the actual obtaining process, there are many groups of ratios of abnormal sub-units, ratios of abnormal sub-currents, and positive-to-abnormal area ratios. Process these many groups of ratios of abnormal sub-units, ratios of abnormal sub-currents, and positive-to-abnormal area ratios to obtain the corresponding group of negative values of the operating state. The staff identify the state level of the power equipment based on these many groups of negative values of the operating state, so as to obtain a corresponding relationship between the negative value of the operating state and the state level of the power equipment. Furthermore, deduce and divide the negative value threshold of the operating state according to the power equipment state of the negative value of the operating state, so as to obtain the limit values Yi1 and Yi2 of the negative value threshold of the operating state. By comparing the limit values of the negative value threshold of the operating state, the identification of the state level of the power equipment corresponding to the negative value of the operating state is completed;

[0113] Refer to Figure 2 , when Fy < Yi1, it indicates that the abnormal degree of the power equipment is low, and the first-level signal of the abnormal degree of the power equipment is obtained;

[0114] When Yi1 ≤ Fy < Yi2, it indicates that the abnormal degree of the power equipment is medium, and the second-level signal of the abnormal degree of the power equipment is obtained;

[0115] When Fy ≥ Yi2, it indicates that the abnormal degree of the power equipment is high, and the third-level signal of the abnormal degree of the power equipment is obtained;

[0116] The higher the signal level of the abnormal degree of the power equipment, the worse the operating state of the power equipment and the greater the risk, realizing the visual management of the power equipment.

[0117] Among them, the process of obtaining the abnormal precipitation factor is:

[0118] Collect the temperature data of the power equipment in the time abnormal sub-unit, obtain the maximum temperature in the time abnormal sub-unit, calculate the difference between the maximum temperature and the rated temperature value of the power equipment to obtain the temperature deviation value of the time abnormal sub-unit;

[0119] Process the ratio of the temperature deviation value of the time abnormal sub-unit to the rated temperature value of the power equipment to obtain the temperature deviation rate of the time abnormal sub-unit;

[0120] Among them, the rated temperature value of the power equipment is an empirical value, which is set by the staff based on experience, and when the temperature of the power equipment during operation is the rated temperature value, the power equipment is in the best operating state;

[0121] It should be noted that the temperature in the time-dependent subunit is the temperature of the outer surface shell of the power equipment, which is collected by a temperature sensor.

[0122] The sound data of the electric equipment in the time difference sub-unit is collected to obtain the maximum value of the noise in the time difference sub-unit, and the difference between the maximum value of the noise and the rated decibel value of the electric equipment is calculated to obtain the noise deviation value of the time difference sub-unit;

[0123] The sound deviation value of the time difference sub-unit is processed by ratio with the rated decibel value of the power equipment to obtain the noise deviation rate of the time difference sub-unit;

[0124] Among them, the rated decibel value of the power equipment is an empirical value, which is set by the staff based on experience, and when the sound of the power equipment during operation is at the rated decibel value, the power equipment is in the best operating state;

[0125] It should be noted that the noise in the time-differentiated subunit is the sound value when the power equipment is running, which is collected by the sound sensor;

[0126] Collect risk data of power equipment in the time-differentiated sub-unit and monitor the status of electronic components when the power equipment in the time-differentiated sub-unit is running;

[0127] Record the items that need to be monitored for electronic components as monitoring items;

[0128] Collect the monitoring value of each monitoring item in the electronic components, and compare the monitoring value of the corresponding monitoring item with the preset requirement range of the monitoring item;

[0129] The monitoring items in the electronic components whose monitoring values ​​do not meet the preset requirement range of the monitoring items are recorded as risk items;

[0130] The monitoring items in the electronic components whose monitoring values ​​meet the preset requirement range of the monitoring items are recorded as safe items;

[0131] Calculate the ratio of the number of risk items to the number of monitoring items in the electronic components to obtain the risk number ratio in the electronic components;

[0132] The deviation of the monitoring value of the risk item from the preset required range of the corresponding monitoring item is recorded as the measured risk difference of the corresponding risk item;

[0133] Calculate the ratio of the measured risk difference to the preset standard value of the monitoring item to obtain the risk deviation ratio of the risk item;

[0134] Sum the risk deviation ratios of all risk items of electronic components to obtain the risk deviation extreme value of the electronic components;

[0135] Among them, the preset standard value of the monitoring item is the middle value of the preset range of the monitoring item;

[0136] For example, if the preset requirement range of the monitoring item is [Amax, Amin], the middle value of the preset requirement range of the monitoring item is the average of the value Amax and the value Amin;

[0137] The risk number ratio in the electronic components is multiplied by the risk deviation extreme value of the electronic components to obtain the risk extreme value of the electronic components;

[0138] The risk extreme values ​​of all electronic components of the electronic equipment in the time-differentiated sub-unit are summed up to obtain the risk summary value of the time-differentiated sub-unit;

[0139] Among them, power equipment includes but is not limited to power transmission equipment (transformers, circuit breakers, generator sets), power distribution equipment (distribution boxes, power stabilizers) and secondary equipment (voltmeters, ammeters, power meters);

[0140] Electronic components of power equipment include, but are not limited to, resistors, relays, optocoupler devices, capacitors and transformers;

[0141] The monitoring items of electronic components include but are not limited to current value, voltage value, temperature value, vibration frequency, etc. The monitoring content of the monitoring items is determined according to the corresponding usage scenarios of the electronic components. The content of the monitoring items is generated by the corresponding electronic components when they are working. For example, the current value and temperature value are the actual current value and temperature value when the monitoring items are working.

[0142] The temperature deviation rate of the time isocell is denoted as Swi;

[0143] The noise deviation rate of the temporal heterosubunit is denoted as Szi;

[0144] The risk summary value of the time heterogeneous subunit is recorded as Sfi;

[0145] The temperature deviation rate Swi of the time-differentiated sub-unit, the noise deviation rate Szi of the time-differentiated sub-unit and the risk summary value Sfi of the time-differentiated sub-unit are quantified and numerically calculated;

[0146] That is, through the formula The abnormal precipitation factor yi is calculated;

[0147] Among them, i=1, 2, ..., i; where is the number of time-differentiated subunits.

[0148] Embodiment 2, based on the three-level abnormality signal of the power equipment, any electronic component in the power equipment is recorded as a target device, and the risk summary value of the target device in the time abnormality sub-unit is obtained;

[0149] Compare the target device's risk summary value to the target device's risk summary threshold:

[0150] The time difference sub-unit corresponding to the risk summary value of the target device being greater than or equal to the risk summary threshold of the target device is recorded as the validation sub-unit;

[0151] Calculate the ratio of the number of verification subunits matched by the target device to the number of time-different subunits to obtain the verification ratio of the target device;

[0152] Obtaining the temperature deviation rate and noise deviation rate of the power equipment corresponding to the target device in the verification subunit;

[0153] The temperature deviation rate and the noise deviation rate are weighted to obtain the temperature noise variation value of the target device in the verification subunit;

[0154] Exemplarily: the weight ratio of the temperature deviation rate Swi is assigned to k1, and the weight ratio of the noise deviation rate Szi is assigned to k2;

[0155] That is, through the formula Calculate the temperature variation value WZ of the target device in the verification subunit, where k1+k2=1, and k2>k1>0;

[0156] The temperature noise variation values ​​in all the valid verification sub-units of the target device are summed and averaged to obtain the valid temperature noise value of the target device;

[0157] The verification ratio of the target period is multiplied by the verification temperature and noise value of the target period to obtain the verification temperature and noise behavior value of the target device;

[0158] Obtain electronic components directly connected to the target device, recorded as bypass devices;

[0159] Obtaining the number ratio of bypass devices and the mean value of the verification temperature-drying behavior corresponding to the bypass devices, and multiplying the number ratio of bypass devices by the mean value of the verification temperature-drying behavior of the bypass devices to obtain the bypass temperature-drying behavior value of the bypass devices;

[0160] The number of bypass devices is compared with the number of electronic components that need to be monitored during the operation of the power equipment to obtain a bypass device quantity ratio;

[0161] The verification temperature-dryness behavior values ​​of all bypass devices are summed up and averaged to obtain the mean value of the verification temperature-dryness behavior of the bypass devices;

[0162] The verification temperature and dryness behavior value of the target device is recorded as Xwz;

[0163] The bypass temperature behavior value of the bypass device is recorded as Pwz;

[0164] By formula The behavior validation base XP of the target device is calculated, where: is the preset proportional coefficient;

[0165] There are m groups of historical data, each group of historical data includes a verification temperature and dryness behavior value Xwz of the target device, a bypass temperature and dryness behavior value Pwz of the bypass device, and a behavior verification base number XP of the target device;

[0166] Use a linear model to fit m groups of historical data, bring the prepared historical data into the selected fitting model for fitting, and get the mean of the fitting coefficients as the preset proportional coefficient .

[0167] Obtain the behavior verification cardinal numbers of all electronic components, arrange the electronic components in descending order according to the behavior verification cardinal numbers within the cycle, record the electronic component corresponding to the largest behavior verification cardinal number as the first component to be processed, record the electronic component corresponding to the second largest behavior verification cardinal number as the second component to be processed, and so on, to complete the detection and processing of the electronic components.

[0168] Example 3, please refer to Figure 3 As shown, the present invention is a power equipment risk detection system, comprising:

[0169] The regional division module is used to obtain the current status data of the power equipment during operation, establish the current operation curve according to the current status data, obtain the current operation area status, and upload the current operation area status to the cloud management and control platform;

[0170] Among them, the current operation area includes a normal area and an abnormal area;

[0171] The state analysis module receives the current operation area status transmitted by the cloud management and control platform. The state analysis module obtains the dynamic behavior data of the power equipment during operation based on the current operation area, obtains the negative value of the operation state of the power equipment during the monitoring period according to the dynamic behavior data, and uploads the negative value of the operation state to the cloud management and control platform;

[0172] The detection and evaluation module receives the negative value of the operating status transmitted by the cloud management and control platform. The detection and evaluation module completes the abnormality level signal of the power equipment within the monitoring period according to the negative value of the operating status, and uploads the abnormality level signal to the cloud management and control platform;

[0173] The screening processing module receives the abnormality degree signal transmitted by the cloud management and control platform. Based on the abnormality degree of the power equipment, the screening processing module identifies the abnormality degree of the electronic components in the power equipment and completes the priority processing of the abnormal electronic components.

[0174] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for detecting risk of electric power equipment, characterized in that: The following steps are involved: Acquire the current state data of the power equipment during operation, establish the current operation curve according to the current state data, and obtain the current operation area; Among them, the current operation area includes a normal area and an abnormal area; Acquire the dynamic behavior data of the power equipment during operation based on the current operation area, and obtain the negative value of the operation state of the power equipment during the monitoring period according to the dynamic behavior data; Complete the abnormality level signal of the power equipment within the monitoring period according to the negative value of the operating status; Based on the abnormality of the power equipment, the abnormality of the electronic components in the power equipment is identified, and priority processing of the abnormal electronic components is completed; Behavioral dynamics data include heteroatom unit number ratio, heteroatom current bias ratio and positive heteroatom area ratio The ratio of the number of heteromeric units is denoted as Ys; The current bias ratio of the hetero-particle is recorded as Yd; The positive isotropic ratio is denoted as Zy; By formula The negative state value Yi of the operating state of the power equipment during the monitoring period is calculated, where a1 and a2 are both preset proportional factors, and yi is the abnormal precipitation factor; The process of obtaining the abnormal precipitation factor is as follows: The temperature deviation rate is obtained by temperature processing in the time isocell; The noise deviation rate obtained by noise processing within the temporal heterosubunit; The overall risk value obtained by analyzing the abnormalities of electronic components in the time-dependent sub-unit; The temperature deviation rate of the time isocell is denoted as Swi; The noise deviation rate of the temporal heterosubunit is denoted as Szi; The risk summary value of the time heterogeneous subunit is recorded as Sfi; The temperature deviation rate Swi of the time-differentiated sub-unit, the noise deviation rate Szi of the time-differentiated sub-unit and the risk summary value Sfi of the time-differentiated sub-unit are quantified and numerically calculated; That is, through the formula The abnormal precipitation factor yi is calculated; Based on the abnormality signal of the power equipment, any electronic component in the power equipment is recorded as a target device, and the risk summary value of the target device in the time abnormality sub-unit is obtained; The time difference sub-unit corresponding to the risk summary value of the target device being greater than or equal to the risk summary threshold of the target device is recorded as the validation sub-unit; Calculate the ratio of the number of verification subunits matched by the target device to the number of time-different subunits to obtain the verification ratio of the target device; Performing weighted processing on the temperature deviation rate and noise deviation rate corresponding to the target device in the verification subunit to obtain the temperature noise deviation value of the target device in the verification subunit; The temperature noise variation values ​​in all the valid verification sub-units of the target device are summed and averaged to obtain the valid temperature noise value of the target device; The verification ratio of the target period is multiplied by the verification temperature and noise value of the target period to obtain the verification temperature and noise behavior value of the target device; Obtain electronic components directly connected to the target device, recorded as bypass devices; The bypass device temperature-dryness behavior value of the bypass device is obtained by multiplying the number ratio of the bypass devices by the mean value of the effective temperature-dryness behavior of the bypass devices. The verification temperature and dryness behavior value of the target device is recorded as Xwz; The bypass temperature behavior value of the bypass device is recorded as Pwz; By formula Calculate the behavior validation base XP of the target device, where: is the preset proportional coefficient; Arrange the electronic components in descending order according to the behavior verification cardinality within the cycle. The larger the behavior verification cardinality, the higher the priority level.

2. A method for detecting risk of electric power equipment according to claim 1, characterized in that: Divide the monitoring period into several time sub-units, and obtain the current value at the middle moment of each time sub-unit; With time as the X-axis and current value as the Y-axis, a plane coordinate system is constructed; Construct a current operation curve in a plane coordinate system for the current values corresponding to each time sub-unit in chronological order; Obtain the rated current range during the operation of the power equipment, and construct an upper standard line and a lower standard line in the plane coordinate system for the two end values of the rated current range; Obtain the area enclosed by the curve segments of the current operation curve above the upper standard line, and denote it as the upper abnormal area; Obtain the area enclosed by the curve segments of the current operation curve below the lower standard line, and denote it as the lower abnormal area; Collectively denote the upper abnormal area and the lower abnormal area as the abnormal area; Obtain the area between the lower standard line and the upper standard line of the current operation curve, and denote it as the normal area; 3. A method for detecting risk of electric power equipment according to claim 1, characterized in that: Integrate the time corresponding to the abnormal area to obtain the abnormal time period, and denote the time sub-units within the abnormal time period as time abnormal sub-units; Integrate the time corresponding to the normal area to obtain the normal time period, and denote the time sub-units within the normal time period as time positive sub-units; Calculate the ratio of the number of time abnormal sub-units to the number of time sub-units to obtain the ratio of abnormal sub-units; Obtain the area value of the abnormal area and the area value of the normal area, and calculate the ratio of the area value of the normal area to the area value of the abnormal area to obtain the ratio of the positive and abnormal areas; 4. A method for detecting risk of electric power equipment according to claim 3, characterized in that: Calculate the difference between the current values within the time abnormal sub-units in the abnormal time period and the rated current standard value respectively and take the absolute value to obtain the current deviation value of the time abnormal sub-units; Sum up and average the current deviation values of all time abnormal sub-units to obtain the current abnormal value of the abnormal time period; Sum up and average the current values within the time positive sub-units in the normal time period to obtain the current positive value of the normal time period; Calculate the ratio of the current abnormal value of the abnormal time period to the current positive value of the normal time period to obtain the ratio of abnormal sub-unit current deviation; 5. A method for detecting risk of electric power equipment according to claim 1, characterized in that: Preset the limit values of the negative state value threshold of the operating state as Yi1 and Yi2, where Yi1 < Yi2; When Fy < Yi1, obtain the first-level signal of the abnormal degree of the power equipment; When Yi1 ≤ Fy < Yi2, obtain the second-level signal of the abnormal degree of the power equipment; When Fy ≥ Yi2, obtain the third-level signal of the abnormal degree of the power equipment; 6. A power equipment risk detection system, characterized in that: Including: A region division module, which is used to obtain the current state data during the operation of the power equipment, establish a current operation curve based on the current state data, obtain the current operation region state, and upload the current operation region state to the cloud control platform; Among them, the current operation region includes a normal region and an abnormal region; The state analysis module receives the current operation region state transmitted by the cloud control platform. The state analysis module obtains the dynamic behavior data during the operation of the power equipment based on the current operation region, and obtains the negative state value of the operation state of the power equipment during the monitoring period according to the dynamic behavior data, and uploads the negative state value of the operation state to the cloud control platform; The detection and evaluation module receives the negative state value of the operation state transmitted by the cloud control platform. The detection and evaluation module completes the signal of the abnormal degree level of the power equipment during the monitoring period according to the negative state value of the operation state, and uploads the signal of the abnormal degree level to the cloud control platform; The screening processing module receives the abnormality degree signal transmitted by the cloud management and control platform. Based on the abnormality degree of the power equipment, the screening processing module identifies the abnormality degree of the electronic components in the power equipment and completes the priority processing of the abnormal electronic components; Behavioral dynamics data include heteroatom unit number ratio, heteroatom current bias ratio and positive heteroatom area ratio The ratio of the number of heteromeric units is denoted as Ys; The current bias ratio of the hetero-particle is recorded as Yd; The positive isotropic ratio is denoted as Zy; By formula The negative state value Yi of the operating state of the power equipment during the monitoring period is calculated, where a1 and a2 are both preset proportional factors, and yi is the abnormal precipitation factor; The process of obtaining the abnormal precipitation factor is as follows: The temperature deviation rate is obtained by temperature processing in the time isocell; The noise deviation rate obtained by noise processing within the temporal heterosubunit; The overall risk value obtained by analyzing the abnormalities of electronic components in the time-dependent sub-unit; The temperature deviation rate of the time isocell is denoted as Swi; The noise deviation rate of the temporal heterosubunit is denoted as Szi; The risk summary value of the time heterogeneous subunit is recorded as Sfi; The temperature deviation rate Swi of the time-differentiated sub-unit, the noise deviation rate Szi of the time-differentiated sub-unit and the risk summary value Sfi of the time-differentiated sub-unit are quantified and numerically calculated; That is, through the formula The abnormal precipitation factor yi is calculated; Based on the abnormality signal of the power equipment, any electronic component in the power equipment is recorded as a target device, and the risk summary value of the target device in the time abnormality sub-unit is obtained; The time difference sub-unit corresponding to the risk summary value of the target device being greater than or equal to the risk summary threshold of the target device is recorded as the validation sub-unit; Calculate the ratio of the number of verification subunits matched by the target device to the number of time-different subunits to obtain the verification ratio of the target device; Performing weighted processing on the temperature deviation rate and noise deviation rate corresponding to the target device in the verification subunit to obtain the temperature noise deviation value of the target device in the verification subunit; The temperature noise variation values ​​in all the valid verification sub-units of the target device are summed and averaged to obtain the valid temperature noise value of the target device; The verification ratio of the target period is multiplied by the verification temperature and noise value of the target period to obtain the verification temperature and noise behavior value of the target device; Obtain electronic components directly connected to the target device, recorded as bypass devices; The bypass device temperature-dryness behavior value of the bypass device is obtained by multiplying the number ratio of the bypass devices by the mean value of the effective temperature-dryness behavior of the bypass devices. The verification temperature and dryness behavior value of the target device is recorded as Xwz; The bypass temperature behavior value of the bypass device is recorded as Pwz; By formula The behavior validation base XP of the target device is calculated, where: is the preset proportional coefficient; Arrange the electronic components in descending order according to the behavior verification cardinality within the cycle. The larger the behavior verification cardinality, the higher the priority level.

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