CN112580993B - Power grid equipment fault probability analysis method - Google Patents
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Abstract
The invention relates to a power grid equipment fault probability analysis method, and belongs to the technical field of power regulation and control. The method comprises the following steps: taking the health state of the equipment as a center, and carrying out multi-dimensional data acquisition and structural analysis; cross identification and cleaning of data; respectively establishing an equipment health state evaluation model in each dimension; setting weight coefficients of all dimension factors based on expert experience; automatic optimization adjustment of multidimensional data weight coefficients based on historical data; analyzing and calculating the equipment tripping probability by combining historical data; and handling based on different tripping probabilities. The method can effectively calculate and analyze the fault probability of the power grid equipment, carries out corresponding processing, and is easy to popularize and apply.
Description
Technical Field
The invention belongs to the technical field of power regulation and control, and particularly relates to a power grid equipment fault probability analysis method.
Background
With the continuous expansion of the scale of the power grid, the alternating current and direct current power transmission hybrid and the distributed power supply are inrush, and the wiring mode and the operation mode of the power grid become more and more complex. On the other hand, the popularization of a large operation and regulation integrated mode, hundreds of substations and related lines are intensively regulated by a small number of operators, and the working pressure and the mental pressure of the operators are increased day by day.
In the unattended mode of the transformer substation, a regulation and control person monitors the running state of the power grid equipment by means of information such as remote signaling and remote measurement; because the number of the administration equipment is large, massive information of various types is received every day, so that operating personnel can be tired of coping. How to excavate the fault risk of the power grid equipment from various information by means of a technical method becomes the key point and the difficulty of the work of each level of regulation personnel. In the process of power grid operation management, problems and deterioration trends of equipment can be fed back by remote signaling alarm, remote measurement out-of-limit, power transmission and transformation online monitoring, field inspection, test data and the like, and regulation and control related departments in various regions try to mine the health state of the equipment by analyzing related data. However, the following disadvantages are common:
1. many of various data used for analyzing the state of the equipment are unstructured data, and a certain amount of error data, repeated data, missing data and contradictory data exist, so that the quality of the state analysis of the equipment is influenced.
2. The dimensions influencing the running state of the equipment are numerous, the equipment is divided into different sub-factors and severity levels under the same dimension, and a unified judging system is difficult to form among the dimensions for comprehensive evaluation in the evaluation process of the states of various kinds of equipment.
3. In the past, too much judgment is carried out by depending on expert experience, and the number of samples, the adaptability to different operation modes and the adaptability to new characteristics of the current power grid have problems, so that judgment deviation is easily caused.
Therefore, how to overcome the defects of the prior art is a problem which needs to be solved urgently in the technical field of power regulation at present.
Disclosure of Invention
The invention aims to solve the defects of the prior art and provides a power grid equipment fault probability analysis method combining expert experience and historical data, wherein the method is used for modeling multi-dimensional feature points, building, analyzing and perfecting a causal relationship model by combining artificial experience and historical data, performing cross identification on various information on the basis, and performing duplication removal, completion supplement and correction processing on the data; based on the corrected data, an analytic model of the health state of the equipment is built by using an analytic hierarchy process related theory; and based on the historical health evaluation result of the equipment and the record of unplanned power failure, performing self-adaptive adjustment on the equipment health evaluation model and the failure probability model.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
a power grid equipment fault probability analysis method comprises the following steps:
step (1), taking the health state of equipment as a center, and carrying out multi-dimensional data acquisition and structured analysis;
step (2), cross identification and cleaning of data;
step (3), establishing an equipment health state evaluation model in each dimension;
step (4), setting the weight coefficient of each dimension factor based on expert experience;
step (5), automatic optimization and adjustment of multi-dimensional data weight coefficients based on historical data;
step (6), analyzing and calculating the equipment trip probability by combining historical data;
and (7) dealing processing based on different tripping probabilities.
Further, preferably, the data collected in the step (1) includes four dimensions of equipment defects, historical operating conditions, operating years and family hidden dangers;
the family hidden danger is the average number of the defects of the corresponding type of the model of the manufacturer;
and respectively carrying out structured analysis on the acquired data.
Further, it is preferable that the specific method of the step (2) is: cleaning data by building a cross identification model among multi-dimensional data; the method comprises the following specific steps:
(2.1) telemetry data quality identification: identifying the hop count and the error count in the remote measurement based on the normal range and the change speed limit value of the remote measurement; extracting the telemetering unchanged value based on the historical data of the telemetering value; based on topology and power balance, performing power balance check on active power and reactive power of each side of a main transformer, a line and a bus, and identifying abnormal data; cleaning hop count, error count, unchanged telemetering data and abnormal data in telemetering;
and (2.2) remote signaling and telemetry data cross identification: identifying the operation mode of each interval through remote signaling and equipment topology; establishing a characteristic point model of operation interval remote measurement and a characteristic point model of non-operation interval remote measurement; cross identification is carried out through interval state characteristic points corresponding to remote signaling and remote sensing, a remote signaling alarm sent by mistake is cleaned, and a remote sensing abnormal data list is extracted and provided for a worker to carry out inspection processing;
the characteristic point model of the operation interval remote measurement is that the active power or the reactive power of the operation interval is not 0 except the condition of the line empty charge; the characteristic point model of the non-operation interval remote measurement is that the active power, the reactive power and the current of the non-operation interval are 0.
Further, it is preferable that the specific method of step (3) is: and (3) respectively establishing an equipment health state evaluation model aiming at each dimension factor by adopting the data cleaned in the step (2), and evaluating the equipment health state, wherein the evaluation specifically comprises the following steps:
(3.1) for defect recording: establishing a defect deduction standard table according to the type and defect classification of the defective equipment, and deducting the defects existing in the equipment; setting a universal deduction standard according to the grade of the defect, wherein the emergency defect is deducted by 41 points, the major defect is deducted by 11 points, and the general defect is deducted by 3 points;
the final score of the defect factor is recorded as S 1 ；
(3.2) for historical operating conditions: dividing historical operating conditions into short circuit impact accumulation, line heavy overload, main transformer heavy overload and oil temperature out-of-limit;
the short circuit impact is accumulated, 11 minutes of impact is deducted each time, tripping is accumulated once, the reclosing is unsuccessful, deduction is carried out according to the interval from reclosing success to tripping again, and 11 minutes of deduction is accumulated once every 1 second;
secondly, the calculation formula of the corresponding deduction for the damage to the equipment caused by the heavy overload of the line and the heavy overload of the main transformer is as follows:
the deduction value is:
wherein L is A Is the average value of this time out-of-limit, L M Is the maximum value of the current out-of-limit, and T (L) is the duration time of the current out-of-limit;
and thirdly, considering the out-of-limit temperature and the accumulated time length for the out-of-limit oil temperature, and the corresponding deduction calculation formula for the damage of the main transformer is as follows:
the deduction value is: (e) (t-75)×0.1 -1)×T(t)
Wherein t is the highest oil temperature during the oil temperature out-of-limit period, and T (t) is the time of the current out-of-limit duration;
short circuit impact, overload of a line or a main transformer and out-of-limit of oil temperature, accumulating the deduction calculated by the three models by taking equipment as a unit, and taking the accumulated deduction as the integral deduction of historical operation working conditions of the equipment;
the final deduction value of the historical operating condition factors is recorded as S 2 ；
(3.3) for the operation age factor, making the standard service life of the equipment be Y, and making the current operation time of the equipment be Y years;
for arrangements with run times exceeding 90% of the standard lifeAnd the deduction value is as follows:
equipment with the running time not exceeding 90% of the standard service life is not deducted;
the final deduction value of the operating age factor is recorded as S 3 ；
(3.4) for the family hidden danger, the deduction value is as follows:
wherein F is a certain type of defect, P (F) is the average probability of the same device type and voltage class appearing in the defect type D (F) Is the average probability of such defects occurring for a certain manufacturer model; s 1 (F) The deduction value corresponding to the defect F in the defect deduction standard table.
The final score of the family hidden danger factors is recorded as S 4 。
Further, it is preferable that the specific method of step (4) is: the initial coefficients of all factors are appointed by experts, namely, under the conditions of certain defects, equipment weight overload, equipment operation age and family hidden dangers, the proportion of the influence of all the factors on equipment health is appointed, the average value of multiple experts is taken, then, the calculation of all the factors is carried out, the average proportion of all the factors is corresponded, and effective coefficients are calculated;
in the process of initial setting of parameters, an analytic hierarchy process is adopted to complete the method, and the specific method comprises the following steps:
(4.1) extracting the combination of different factor cases: respectively selecting typical samples according to defect records, historical operating conditions, operating years and family hidden dangers, and combining the typical samples to obtain a combination case of a plurality of factors;
(4.2) the expert evaluates the importance relation of each factor: selecting 3 or more experts, comparing and evaluating the weights of different factors in the selected case combination on equipment trip contribution, and inputting the weights as evaluation matrix parameters;
(4.3) calculation of respective factors by analytic hierarchy ProcessThe weights contributing to the equipment trip determination are respectively marked as L n (ii) a Wherein n is 1, 2, 3 and 4, which respectively correspond to four factors;
(4.4) sequentially adjusting the coefficients of the evaluation model established in the step (3): aiming at four types of factors, the obtained deductions are respectively S through model calculation n N is 1, 2, 3 and 4, which respectively correspond to four types of factors; combining the results calculated in (5.3) to obtain the coefficient R calculated from the set of cases n The calculation formula of (2) is as follows:
the coefficient R n Is the result of the combined calculation for this kind of case;
(4.5) setting initial coefficients according to the combination of a plurality of groups of cases: for defect recording, each group of cases is analyzed to obtain a coefficient R 1 This sequence is denoted as
M is the serial number of case combination, the value is from 1 to M, and M is the number of case combination; the coefficient corresponding to the defect record is evaluated by experts, and the corresponding coefficient calculation formula is as follows:
this parameter will be used as the initial value for parameter adjustment;
and sequentially carrying out corresponding calculation aiming at the other three factors to obtain initial values of all the factors.
Further, preferably, in the step (5), on the basis of setting the weight coefficients of different dimensional factors according to expert experience, the weight coefficients are adjusted, and consistency check is performed on different weight combinations by combining historical data to obtain an optimal coefficient combination;
the method comprises the following specific steps:
(5.1) extracting equipment which has been subjected to unplanned power failure in the analysis range;
(5.2) for each initial coefficient, n is 1, 2, 3, 4, corresponding to four types of factors, and the coefficient is determined by
25% of the total amount of the composition is the step length, to
Traversing different combinations of the four coefficients for an effective interval, extracting an optional coefficient combination list, and performing coarse adjustment on the optimal coefficient by adopting the steps (5.3) to (5.5);
(5.3) selecting historical time intervals with complete defects and operating conditions, calculating the deduction of each device in the power grid by taking each coefficient combination in the coefficient combination list as a unit in days according to the evaluation method in the step (3) and taking the selected coefficient combination as a coefficient;
(5.4) detecting the consistency of each group of coefficients respectively: sorting each device from big to small according to the deduction value of each day; traversing the deduction value sequence from front to back for each day, and if the unplanned power failure does not occur in a certain day but the deduction value of n unplanned power failures is smaller than the current deduction value, adding n to the inconsistency; the total number of inconsistencies, denoted U C ；
(5.5) selecting an optimal effective coefficient: for coefficient combination (R) 1 ,R 2 ,R 3 ,R 4 ) Calculated number of inconsistencies U C The inconsistency coefficient is calculated according to the following formula:
(5.6) two groups of coefficient combinations with smaller inconsistent coefficients in the coarse adjustment process are selected, and each coefficient in each group of coefficient combinations is pointed to
In steps of 0.01, in
Traversing different combinations of the four coefficients for an effective interval, extracting an optional coefficient combination list, and finely adjusting the optimal coefficient according to the method from the step (5.3) to the step (5.5);
taking the parameter combination with the minimum inconsistent coefficient in the fine adjustment result as the optimal coefficient combination, and recording the optimal coefficient combination as the optimal coefficient combination
Further, preferably, the step (6) is to comprehensively evaluate the failure probability of the equipment by combining the time nodes of the equipment with failure trip and unplanned power failure in history and combining the health evaluation result of the equipment;
the specific method comprises the following steps: and (4) performing deduction calculation on each device, wherein the optimal coefficient combination obtained in the step (5) is used as an effective coefficient for calculation, and the total deduction value of the devices is as follows:
calculating for each type of equipment independently, and counting the corresponding effective fault number of tripping, unscheduled maintenance and scheduled maintenance;
in the effective fault number calculation process, the trip, the unplanned maintenance and the planned maintenance are respectively counted according to the coefficients of 1, 0.5 and 0.25, namely:
the number of effective faults is equal to the trip number, the number of other unplanned overhauls is multiplied by 0.5, the number of planned overhauls is multiplied by 0.25
Firstly, counting the fault probability of equipment without deduction time;
then removing the influence of the fault probability when no deduction exists: the effective number of equipment trips, which is increased by equipment due to health problems, is calculated by subtracting the expected value of equipment failure when no deduction exists from the total effective failure number:
deduction influence effective quantity is equal to effective quantity-deduction-free equipment fault probability multiplied by equipment operation total station day;
if the calculated value is negative, 0 is taken;
and (3) calculating the probability of the corresponding failure of the deduction:
further, preferably, the step (7) is based on different tripping probabilities of the equipment, and adopts different coping processing schemes of power failure overhaul, load transfer and planning of a predetermined plan, and the specific method comprises the following steps:
if the equipment has a trip probability of more than 10%, if a power failure condition exists, the equipment needs to be overhauled by power failure;
the equipment with the fault probability of more than 5 percent and less than or equal to 10 percent transfers the carried load to equipment with a relatively healthy running state as far as possible to supply power;
the equipment with the fault probability of more than 0.1 percent and less than or equal to 5 percent is subjected to a plan when the equipment is in fault, and the inspection of the equipment is enhanced;
and the failure probability is less than or equal to 0.1 percent, and no special measures are taken.
The structured resolution described in the present invention is divided into two aspects: firstly, associating with equipment, carrying out standardized analysis on a text (namely carrying out uniform processing on figures in the text and writing methods of equipment types), and carrying out association matching through keywords of a station, a voltage level, the equipment types and equipment names; secondly, classifying the equipment defects according to the equipment types and the defect types, establishing a keyword model for text matching (maintaining keywords corresponding to each classification, and combining the keywords through an OR/AND/NOT relationship), and corresponding the defects to the corresponding defect classifications.
Compared with the prior art, the invention has the following beneficial effects:
the invention has reasonable design, provides a method for realizing the integral analysis of the health state and the trip probability of the equipment by comprehensively analyzing the multidimensional data related to the equipment, and has the following realization effects: through the establishment of a data cross identification correlation model, rules are automatically perfected based on historical data, and abnormal data are removed and completed. And respectively establishing equipment state evaluation models based on different dimensions to evaluate the health state of the equipment. And establishing a consistency comparison equation among all dimensions. And (4) setting the coefficient according to expert experience, and automatically learning and adjusting parameters based on historical data. And qualitatively and quantitatively evaluating the health state of the equipment by integrating various factors.
Compared with the prior equipment state evaluation method, the method provided by the invention can establish a set of consistency comparison equations among different dimensions, and better conforms to the real probability of equipment problems in history on the basis of conforming to the expedition expectation of experts.
Drawings
Fig. 1 is a flowchart of a method for analyzing a failure probability of a power grid device according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples.
It will be appreciated by those skilled in the art that the following examples are illustrative of the invention only and should not be taken as limiting the scope of the invention. The examples do not specify particular techniques or conditions, and are performed according to the techniques or conditions described in the literature in the art or according to the product specifications. The materials or equipment used are not indicated by manufacturers, and all are conventional products available by purchase.
As shown in fig. 1, a method for analyzing a failure probability of a power grid device by combining expert experience with historical data includes the following steps:
step (1), taking the health state of equipment as a center, and carrying out multi-dimensional data acquisition and structured analysis;
step (2), cross identification and cleaning of data;
step (3), establishing an equipment health state evaluation model in each dimension;
step (4), setting the weight coefficient of each dimension factor based on expert experience;
step (5), automatic optimization and adjustment of multidimensional data weighting coefficients based on historical data;
step (6), analyzing and calculating the equipment trip probability by combining historical data;
and (7) dealing processing based on different tripping probabilities.
The specific implementation method of the step (1) comprises the following steps:
the health state of the equipment mainly comprises the following aspects:
1) equipment defect: the method comprises the steps of manually recording defects (from a defect processing flow or a log) and analyzing the defects from alarm signals (extracting information reflecting the abnormal operation of a transformer, a switch and secondary equipment after removing signals sent in the normal operation management processes of feedback operation, maintenance debugging and the like from remote signaling alarm signals); classifying the defects according to the types of the defects; carrying out duplicate elimination treatment on defects from different sources;
2) historical operating conditions: extracting out-of-limit information (main transformer oil temperature out-of-limit, line and main transformer load out-of-limit and bus voltage out-of-limit) from the remote signaling data, extracting active power, reactive power, current, voltage and main transformer oil temperature measuring data from the remote sensing data, and acquiring waveform information of voltage and current during fault from fault recording; extracting information from historical short circuit impact accumulation, line heavy overload, main transformer heavy overload and oil temperature out-of-limit dimensions based on the data;
3) the service life is as follows: judging whether the equipment enters an old stage or not according to the standard service life corresponding to the equipment type and whether the real running time exceeds 90% of the standard service life or not; the equipment entering the old age stage deducts points according to the relation between the current running time and the standard service life;
4) the hidden danger of the family: the average number of various defects of the equipment and the number of the defects of corresponding types of the manufacturer + model are extracted, and the family hidden danger is counted for the manufacturer model with the defect probability more than 5 times higher than the average defect probability.
And respectively carrying out structured analysis on the acquired data. Structured parsing is divided into two aspects: firstly, associating with equipment, namely performing standardized analysis on a text (performing unified treatment on figures and equipment type writing methods in the text), and performing association matching through keywords of a station, a voltage level, an equipment type and an equipment name; secondly, classifying the equipment defects according to the equipment types and the defect types, establishing a keyword model for text matching (maintaining keywords corresponding to each classification, and combining the keywords through an OR/AND/NOT relationship), and corresponding the defects to the corresponding defect classifications.
The specific implementation method of the step (2) comprises the following steps: and cleaning the data by building a cross identification model among the multi-dimensional data. The method for building and cleaning the model comprises the following contents:
1) and (3) telemetry data quality identification: identifying hop count and error count in remote measurement based on the normal range and the change speed limit value of the remote measurement; extracting the telemetering unchanged value based on the historical data of the telemetering value; based on topology and power balance, performing power balance check on active power and reactive power of each side of a main transformer, a line and a bus, and identifying abnormal data; cleaning hop count, error count, unchanged telemetering data and abnormal data in telemetering;
2) cross identification of remote signaling and telemetry data: identifying the operation mode of each interval through remote signaling + equipment topology; establishing a characteristic point model for operation interval remote measurement (except the condition that the line is empty and charged, the active power or the reactive power is not 0) and a characteristic point model for non-operation interval remote measurement (the active power, the reactive power and the current of the non-operation interval are 0); and cross identification is carried out through interval state characteristic points corresponding to remote signaling and remote measurement, the remote signaling alarm sent by mistake is cleaned, and a remote measurement abnormal data list is extracted and provided for a worker to carry out inspection processing.
The specific implementation method of the step (3) comprises the following steps: starting from the aspects of defect record, historical operating condition, operating age and family hidden danger, the health state of the equipment is analyzed. And respectively establishing a deduction system aiming at each type of factor to evaluate the health state of the equipment.
1) Recording defects: including manually recorded defects, defects analyzed from within the alarm signal. And filtering out related signals of maintenance debugging, operation companion and AVC, and extracting effective alarm to obtain corresponding equipment defects. Establishing a defect deduction standard table according to the type and defect classification of the defective equipment, and deducting the defects existing in the equipment; the default deduction standard table is set according to the grade of the defects (41 points for critical defect deduction, 11 points for major defect deduction and 3 points for general defect deduction), and partial types of defects can be adjusted according to actual conditions.
The deduction criteria table creation process is as follows:
setting general deduction standards according to the grades of the defects (41 points for critical defect deduction, 11 points for major defect deduction and 3 points for general defect deduction);
secondly, according to actual conditions, specially setting defect types with larger difference between the severity of partial defects and the general deduction standard of the defect grades, and independently setting deduction values;
checking the deduction value set separately for the defect type in practical use; and if the corresponding deduction value is not set, selecting the corresponding general deduction standard according to the defect grade.
2) Historical operating conditions: and the method is subdivided into short circuit impact accumulation, heavy line overload, main transformer heavy overload and oil temperature out-of-limit, and evaluation models are respectively built in a plurality of dimensions to evaluate the health state of the equipment.
The short circuit impact accumulation considers the impact of equipment tripping to the equipment in history, considers the impact times and the impact duration, and deducts the points according to the following rules: and (4) buckling 11 minutes each time, accumulating the trip once, and buckling 11 minutes every 1 second according to the interval from the successful superposition to the re-trip time when the superposition is unsuccessful, wherein the above buckled points are used as the accumulated buckled points of the short circuit impact.
The equipment overload takes the equipment load rate and the accumulated time into consideration, and the calculation formula of the corresponding deduction for the damage to the equipment is as follows:
wherein L is A Is the average value of this time out-of-limit, L M Is the maximum value of the current violation, and T (L) is the duration of the current violation.
For the out-of-limit oil temperature, the out-of-limit temperature and the accumulated time length are considered, and a deduction calculation formula corresponding to the damage to the main transformer is as follows:
(e (t-75)×0.1 -1)×T(t)
wherein t is the highest oil temperature during the oil temperature out-of-limit period, and T (t) is the time of the current out-of-limit duration.
Short circuit impact, overload of a line or a main transformer and out-of-limit of oil temperature, and the deduction calculated by the three models is accumulated by taking equipment as a unit and is used as the integral deduction of historical operation working conditions of the equipment.
3) And the operation time limit is that the standard service life of the equipment is Y, and the current equipment operation time is Y years. For devices with a run time that exceeds 90% of the standard life, the deduction value is:
(note: equipment with operating life of no more than 90% of standard life, no deduction is made).
4) The hidden danger of the family: and (4) researching the total average probability of various defects and the average probability of each manufacturer type by taking the manufacturer + type as a standard. The defect rate of the equipment is calculated by dividing the number of defects by the total time period (in days) for which the equipment is operated. The conditions of all the devices and the conditions of the vendor + model are calculated respectively. For a certain manufacturer and model, if the corresponding defect rate exceeds the defect rate of the same defect of the same type of equipment by more than 5 times, the deduction formula for the equipment of the relevant manufacturer model is as follows:
wherein F is a certain type of defect, P (F) is the average probability of the same device type and voltage class to have the defect, P (F) is the average probability of the defect D (F) Is the average probability of a certain manufacturer model for the defect; s 1 (F) The deduction value corresponding to the defect F in the defect deduction standard table.
The above four types of models are weighted by a constant coefficient in actual calculation, and a method of setting the coefficient will be described later.
The specific implementation method of the step (4) is as follows: the initial coefficients of all the factors are appointed by experts (under the conditions of appointing a certain defect, equipment heavy overload, equipment operation age and family hidden trouble, the proportion of the various factors influencing the equipment health is taken as the average value of the experts, then the effective coefficients are calculated by calculating the scores of the various factors and corresponding to the average proportion of the various factors finally). In the process of initial setting of the parameters, an analytic hierarchy process is adopted to complete the process, and the specific steps are as follows:
1) and (3) combining and extracting different factor cases: and respectively selecting typical examples according to the defect records, the historical operating conditions, the operating years and the family hidden dangers, and combining the typical examples to obtain a combination case of a plurality of factors. For example, for a main transformer, an oil temperature abnormal alarm (defect record) can be extracted, the load rate is 98%, the operation time is 24 hours (historical operation condition), the operation time reaches a standard operation year (operation year), and the probability of the cold zone device full stop alarm of the main transformer of the same manufacturer is 10 times of the average probability of the main transformers of the same voltage class (family hidden danger), and the case combination is used.
2) Experts assess the importance relationship of each factor: and selecting 3 or more experts, comparing and evaluating the weights of different factors in the selected case combination on equipment trip contribution, and inputting the weights as evaluation matrix parameters.
3) Calculating the weight of each factor contributing to equipment trip judgment through an analytic hierarchy process, and respectively recording the weight as L n (n is 1, 2, 3, 4, respectively corresponding to four types of factors).
4) Sequentially adjusting coefficients of the evaluation model established in the step (3): aiming at four types of factors, the obtained deductions are respectively S through model calculation n (n is 1, 2, 3, 4, corresponding to four types of factors, respectively); combining the results calculated in (5.3) to obtain the coefficient R calculated from the set of cases n The calculation formula of (2) is as follows:
note that the above calculation results are results calculated for one case combination.
5) Setting initial coefficients according to a plurality of groups of case combinations: for defect recording, each group of cases is analyzed to obtain a coefficient R 1 This sequence is denoted as
(M is the number of case combinations, and takes values from 1 to M, where M is the number of case combinations). The coefficient corresponding to the defect record is evaluated by experts, and the corresponding coefficient calculation formula is as follows:
this parameter will be used as the initial value of parameter adjustment, and further automatic adjustment will be subsequently required in combination with the historical data. And sequentially carrying out corresponding calculation aiming at the four factors to obtain initial values of all the factors.
The specific implementation method of the step (5) is as follows: and automatically adjusting each type of coefficient to ensure that an evaluation system is more reasonable, and the method comprises the following steps:
1) and extracting the equipment which has been subjected to the unplanned power failure within the analysis range. The analysis range is to take a period of time when the defect record, the remote signaling and the remote measuring information are complete and take data of more than one year, wherein the time is not too long from the present, for example, data from 1 month and 1 day of the last year to the present can be taken.
2) For each initial coefficient
(n is from 1 to 4, and represents four kinds of factors), by respective coefficients
25% of the total amount of the composition is the step length, to
For the valid interval, different combinations of the four coefficients are traversed (e.g. the four coefficients are taken separately
) Extracting an optional coefficient combination list, and performing coarse adjustment on the optimal coefficient, wherein the specific process is shown in the following steps 3) to 5);
3) selecting historical time intervals with complete defects and operating conditions, calculating the deduction of each device in the power grid by taking the selected coefficient combination as a coefficient according to the evaluation method in the step (3) by taking days as a unit for each coefficient combination in the coefficient combination list;
4) and respectively detecting the consistency of each group of coefficients: sorting each device from big to small according to the deduction value of each day; traversing the deduction value sequence of each day from front to back, and if the unplanned power failure does not occur in a certain day but the deduction value of n times of unplanned power failure is smaller than the current deduction value, adding n to the inconsistency. The total number of inconsistencies, denoted U C 。
5) Selecting the optimal effective coefficient: for coefficient combination R n Calculated number of inconsistencies U C The disparity coefficient is calculated according to the following formula:
selecting two groups of coefficient combinations with smaller inconsistent coefficients in the coarse adjustment process, and aiming at each coefficient in each group of coefficient combinations
In steps of 0.01, in
And traversing different combinations of the four coefficients for an effective interval, extracting an optional coefficient combination list, and finely adjusting the optimal coefficient according to the methods from 3) to 5).
Taking the parameter combination with the minimum inconsistent coefficient in the fine adjustment result as the optimal coefficient combination, and recording the optimal coefficient combination as the optimal coefficient combination
The specific implementation method of the step (6) is as follows: and (4) calculating the deduction of each device by taking the optimal coefficient combination obtained in the step (5) as an effective coefficient, wherein the total deduction value of the devices is as follows:
and (4) calculating each type of equipment independently, and counting the corresponding effective fault number of tripping, unscheduled maintenance and scheduled maintenance.
In the effective fault number calculation process, the trip, the unplanned maintenance and the planned maintenance are respectively counted according to the coefficients of 1, 0.5 and 0.25, namely:
the number of effective faults is equal to the trip number, the number of other unplanned overhauls is multiplied by 0.5, the number of planned overhauls is multiplied by 0.25
Firstly, counting the failure probability of equipment without deduction (namely, whether deduction exists or not, according to the calculation process in the step (3), the deduction values of all parts are overlapped, and the day when the deduction is 0 in total is marked as the non-deduction);
then, eliminating the influence of the fault probability when no deduction exists: the effective number of equipment trips, which is increased by equipment due to health problems (corresponding to equipment deduction), is calculated by subtracting the expected value of equipment failure without deduction from the total effective failure number (when the equipment has no health problems (namely, has no deduction), the equipment also has a certain probability of failure):
effective number of deduction influence, i.e. effective number-probability of failure of deduction-free equipment x total station and day of equipment operation
If the calculated value is negative, 0 is taken.
And (4) calculating the probability of the corresponding failure of the deduction:
the following examples are given: assuming that there are 100 main transformers, 40 of which are operated accumulatively for 300 days, and 60 of which are operated accumulatively for 500 days, the accumulated operation time is as follows: 40 × 300+60 × 500 ═ 42000 stations/day.
The deduction conditions and the corresponding conditions of fault and maintenance in the period are as follows:
Then:
effective number of no deduction (on the day) 6 × 1+12 × 0.5+24 × 0.25 ═ 18
The effective amount of the compound (on the same day) is 5 multiplied by 1+10 multiplied by 0.5+20 multiplied by 0.25 to 15
Total number of active faults 18+15 33
Effective failure probability of deduction influence 33-0.000486 × 42000 ≈ 33-25.3 ≈ 7.7
If the deduction of a certain main transformer in a certain day is 10 points, the corresponding fault probability is as follows:
the specific implementation method of the step (7) is as follows: according to the fault probability level of the power equipment, different measures are taken for treatment.
If the equipment with the failure probability larger than 10% has a power failure condition, the equipment needs to be overhauled by power failure; the equipment with the fault probability of more than 5 percent and the equipment with the fault probability of less than or equal to 10 percent transfers the carried load (especially important load) to the equipment with a relatively healthy operation state as far as possible for supplying power; the equipment with the fault probability of more than 0.1 percent and the equipment with the fault probability of less than or equal to 5 percent are used for making a plan when the equipment is in fault, strengthening the inspection of the equipment and taking the next step of measures according to the development trend and the change speed of the health state of the equipment; and the failure probability is less than or equal to 0.1 percent, and no special measures are taken.
The specific proportion is specifically adjusted according to the actual load level conditions of the power grids in different places.
Examples of the applications
The following description will take an example of an analysis process of a power grid in a certain area. The data range analyzed was from 1/2017 to 1/2020 for a total of 3 years. Data before 7, month and 1 in 2019 are used as learning data, and relevant parameters are subjected to self-adaptive adjustment; and (4) taking data from 7/month 1 in 2019 to 1/month 1 in 2020 as test data, and verifying the validity of the algorithm. The analysis process is mainly based on the analysis of the main transformer related data.
1) Taking the health state of the equipment as the center, carrying out multi-dimensional data acquisition and structured analysis
And accessing equipment information, topology information, remote signaling and remote measuring information from the D5000 system. The remote signaling information comprises accident, abnormity, deflection and informing information, and the out-of-limit information is independently obtained. Telemetry information was measured for 5 minutes and one cross-section. The equipment ledger information is supplemented by docking in the PMS.
And (4) butting with the OMS to acquire information of maintenance, defect record and trip record.
And (4) acquiring mountain fire information (the recorded information comprises influenced lines and towers) by the external environment.
And structurally analyzing the overhaul, defect record and trip record in the OMS data, establishing association between the relevant data and the equipment model in the D5000 through keyword matching, and classifying the defect information according to the defect type.
2) Cross identification and cleaning of data
The main problems found by the data subjected to structured analysis and cross identification are shown in table 1:
TABLE 1
3) Respectively establishing equipment health state evaluation models in all dimensions
According to the method, an equipment health state evaluation model with four dimensions of equipment defects, historical operating conditions, operating years and family hidden dangers is established.
4) Setting the weight coefficient of each dimension factor based on expert experience;
and analyzing the specific gravity of each factor by using an analytic hierarchy process. The initialization process of the weight corresponding to each factor will be described below by taking the main change as an example. The details and evaluation results are shown in Table 2:
TABLE 2
5) Automatic optimization adjustment of multi-dimensional data weight coefficients based on historical data
Taking a main transformer as an example, the tripping and unplanned power failure conditions of the main transformer between the date 1/month 1 in 2017 and the date 1/month 7 in 2019 are analyzed, and the related parameters of the fault probability are calculated by combining the deduction conditions in the corresponding period. During this period, an unplanned power outage occurred 36 times in total (including a fault trip).
By automatically adjusting the coefficients of the factors of the 4 types of equipment (respectively, defect record, historical operating conditions, operating age and family hidden danger), the initial step length is set to be 25% of each factor. After finding the roughly optimal coefficient combination, the coefficients for each factor are finely adjusted using 0.01 as the step size. The results of the adjustments are shown in table 3:
TABLE 3
| Factors of the fact | Coarse and poor optimum combination | Precise optimal combination |
| Defect recording | 0.80 | 0.81 |
| Short circuit impact accumulation | 0.59 | 0.59 |
| Operating life | 0.51 | 0.52 |
| Hidden danger of family | 0.62 | 0.62 |
| Probability of inconsistency | 3.91％ | 2.32％ |
6) Analyzing and calculating the equipment tripping probability by combining historical data;
the data from 7/1/2019 to 2020/1 are analyzed, and the number of unplanned blackouts in which the primary transformer occurs is 9 in total.
The probability of inconsistency during this period was calculated to be 2.69%. The overall calculation result is in accordance with expectations.
7) Real-time analysis of equipment trip probability
Carrying out online analysis on the failure probability of equipment in the power grid from 5 months and 1 day in 2020; in month 5, finding case 2 with failure probability more than 10% (together, the line is influenced by mountain fire, together, the main transformer is analyzed from the alarm signal to have defects); the main reason for the case 9 with the failure probability between 0.1% and 5% is the influence of defect record and historical operating conditions.
8) Subsequent disposition based on different trip probabilities
Aiming at the condition that the failure probability is more than 10 percent, power failure maintenance is adopted; and aiming at the condition that the fault probability is between 0.1% and 5%, checking a corresponding plan when the equipment is in fault, and strengthening the inspection of the equipment.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are given by way of illustration of the principles of the present invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, and such changes and modifications are within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. A power grid equipment fault probability analysis method is characterized by comprising the following steps:
step (1), taking the health state of equipment as a center, and carrying out multi-dimensional data acquisition and structured analysis;
step (2), cross identification and cleaning of data;
step (3), establishing an equipment health state evaluation model in each dimension;
step (4), setting the weight coefficient of each dimension factor based on expert experience;
step (5), automatic optimization and adjustment of multi-dimensional data weight coefficients based on historical data;
step (6), analyzing and calculating the equipment trip probability by combining historical data;
step (7), dealing processing based on different tripping probabilities;
the specific method of the step (4) comprises the following steps: the initial coefficients of all factors are appointed by experts, namely, under the conditions of certain defects, equipment weight overload, equipment operation age and family hidden dangers, the proportion of the influence of all the factors on equipment health is appointed, the average value of multiple experts is taken, then, the calculation of all the factors is carried out, the average proportion of all the factors is corresponded, and effective coefficients are calculated;
in the process of initial setting of parameters, an analytic hierarchy process is adopted to complete the method, and the specific method comprises the following steps:
(4.1) extracting the combination of different factor cases: respectively selecting typical samples according to defect records, historical operating conditions, operating years and family hidden dangers, and combining the typical samples to obtain a combination case of a plurality of factors;
(4.2) the expert evaluates the importance relation of each factor: selecting 3 or more experts, comparing and evaluating the weights of different factors in the selected case combination on equipment trip contribution, and inputting the weights as evaluation matrix parameters;
(4.3) calculating the weight of contribution of each factor to equipment trip judgment through an analytic hierarchy process, and recording the weight asL n (ii) a Wherein, n is 1, 2, 3 and 4, which respectively correspond to four factors;
(4.4) sequentially adjusting the coefficients of the evaluation model established in the step (3): aiming at four types of factors, the deduction obtained by model calculation is S n N is 1, 2, 3 and 4, which respectively correspond to four types of factors; combining the results calculated in (4.3) and the coefficient R calculated from the set of case combinations n The calculation formula of (2) is as follows:
the coefficient R n Is the result of the combined calculation for this set of cases;
(4.5) setting initial coefficients according to the combination of a plurality of groups of cases: for defect recording, each group of cases is analyzed to obtain a coefficient R 1 This sequence is denoted as
M is the serial number of case combination, and takes values from 1 to M, wherein M is the number of case combination; the coefficient corresponding to the defect record is evaluated by experts, and the corresponding coefficient calculation formula is as follows:
this parameter will be used as the initial value for parameter adjustment;
and sequentially carrying out corresponding calculation aiming at the other three factors to obtain initial values of all the factors.
2. The power grid equipment fault probability analysis method according to claim 1, wherein the data collected in the step (1) comprises four dimensions of equipment defects, historical operating conditions, operating years and family hidden dangers;
the family hidden danger is the average number of corresponding defects in a certain model of a certain manufacturer;
and respectively carrying out structured analysis on the acquired data.
3. The method for analyzing the fault probability of the power grid equipment according to claim 1, wherein the specific method in the step (2) is as follows: cleaning data by building a cross identification model among multi-dimensional data; the method comprises the following specific steps:
and (2.1) telemetry data quality identification: identifying hop count and error count in remote measurement based on the normal range and the change speed limit value of the remote measurement; extracting the telemetering unchanged value based on the historical data of the telemetering value; based on topology and power balance, performing power balance check on active power and reactive power of each side of a main transformer, a line and a bus, and identifying abnormal data; cleaning hop count, error count, unchanged telemetering data and abnormal data in telemetering;
and (2.2) remote signaling and telemetry data cross identification: identifying the operation mode of each interval through remote signaling and equipment topology; establishing a characteristic point model of operation interval remote measurement and a characteristic point model of non-operation interval remote measurement; cross identification is carried out through interval state characteristic points corresponding to remote signaling and remote sensing, a remote signaling alarm sent by mistake is cleaned, and a remote sensing abnormal data list is extracted and provided for a worker to carry out inspection processing;
the characteristic point model of the operation interval remote measurement is that the active power or the reactive power of the operation interval is not 0 except the condition of the line empty charge; the characteristic point model of the non-operation interval remote measurement is that the active power, the reactive power and the current of the non-operation interval are 0.
4. The method for analyzing the fault probability of the power grid equipment according to claim 1, wherein the specific method in the step (3) is as follows: and (3) respectively establishing an equipment health state evaluation model aiming at each dimension factor by adopting the data cleaned in the step (2), and evaluating the equipment health state, wherein the evaluation specifically comprises the following steps:
(3.1) for defect recording: establishing a defect deduction standard table according to the type and defect classification of the defective equipment, and deducting the defects existing in the equipment; setting a universal deduction standard according to the grade of the defect, wherein the emergency defect is deducted by 41 points, the major defect is deducted by 11 points, and the general defect is deducted by 3 points;
the final score of the defect factor is recorded as S 1 ；
(3.2) for historical operating conditions: dividing historical operating conditions into short circuit impact accumulation, line heavy overload, main transformer heavy overload and oil temperature out-of-limit;
the short circuit impact is accumulated, 11 minutes of impact is deducted each time, tripping is accumulated once, the reclosing is unsuccessful, deduction is carried out according to the interval from reclosing success to tripping again, and 11 minutes of deduction is accumulated once every 1 second;
secondly, for heavy overload of the line and heavy overload of the main transformer, a calculation formula of corresponding deduction for damage to equipment is as follows:
the deduction value is:
wherein L is A Is the average value of this time out-of-limit, L M Is the maximum value of the current out-of-limit, and T (L) is the duration time of the current out-of-limit;
and thirdly, considering the out-of-limit temperature and the accumulated time length for the out-of-limit oil temperature, and the corresponding deduction calculation formula for the damage of the main transformer is as follows:
the deduction value is: (e) (t-75)×0.1 -1)×T(t)
Wherein t is the highest oil temperature during the oil temperature out-of-limit period, and T (t) is the time of the current out-of-limit duration;
short circuit impact, overload of a line or a main transformer and out-of-limit of oil temperature, accumulating the deduction calculated by the three models by taking equipment as a unit, and taking the accumulated deduction as the integral deduction of historical operation working conditions of the equipment;
the final deduction value of the historical operating condition factors is recorded as S 2 ；
(3.3) for the operation age factor, making the standard service life of the equipment be Y, and making the current operation time of the equipment be Y years;
for devices with a run time that exceeds 90% of the standard life, the deduction value is:
equipment with the running time not exceeding 90% of the standard service life is not deducted;
the final deduction value of the operating age factor is recorded as S 3 ；
(3.4) for the family hidden danger, the deduction value is as follows:
wherein F is a certain type of defect, P (F) is the average probability of the same device type and voltage class appearing in the defect type D (F) Is the average probability of such defects occurring for a certain manufacturer model; s. the 1 (F) The deduction value is the corresponding deduction value of the defect F in the defect deduction standard table;
the final score of the family hidden danger factors is recorded as S 4 。
5. The method for analyzing the fault probability of the power grid equipment according to claim 1, wherein the step (5) is to adjust the weight coefficients based on the initial setting of the weight coefficients of different dimensional factors according to expert experience, and perform consistency check on different weight combinations by combining historical data to obtain an optimal coefficient combination;
the method comprises the following specific steps:
(5.1) extracting equipment which has been subjected to unplanned power failure in the analysis range;
(5.2) for each initial coefficient
Corresponding to four types of factors, respectively, in terms of respective coefficients
Is the step size, in
Traversing different combinations of the four coefficients for effective intervals, extracting an optional coefficient combination list, and adopting the steps (5.3) to (5)(5.5) performing coarse adjustment of the optimal coefficient;
(5.3) selecting historical time intervals with complete defects and operating conditions, calculating the deduction of each device in the power grid by taking each coefficient combination in the coefficient combination list as a unit in days according to the evaluation method in the step (3) and taking the selected coefficient combination as a coefficient;
(5.4) detecting the consistency of each group of coefficients respectively: sorting each device from big to small according to the per-day deduction | value; traversing the deduction value sequence from front to back for each day, and if the unplanned power failure does not occur in a certain day but the deduction value of n unplanned power failures is smaller than the current deduction value, adding n to the inconsistency; the total number of inconsistencies, denoted U C ；
(5.5) selecting an optimal effective coefficient: for coefficient combination (R) 1 ，R 2 ，R 3 ，R 4 ) Calculated number of inconsistencies U C The disparity coefficient is calculated according to the following formula:
(5.6) two groups of coefficient combinations with smaller inconsistent coefficients in the coarse adjustment process are selected, and each coefficient in each group of coefficient combinations is targeted
In steps of 0.01, and
traversing different combinations of the four coefficients for an effective interval, extracting an optional coefficient combination list, and finely adjusting the optimal coefficient according to the method from the step (5.3) to the step (5.5);
taking the parameter combination with the minimum inconsistent coefficient in the fine adjustment result as the optimal coefficient combination, and recording the optimal coefficient combination as the optimal coefficient combination
6. The method for analyzing the fault probability of the power grid equipment, according to the claim 5, is characterized in that the step (6) is to comprehensively evaluate the fault probability of the equipment by combining the time nodes of the equipment with fault tripping and unplanned power failure in history and combining the health evaluation result of the equipment;
the specific method comprises the following steps: and (4) calculating the deduction of each device by taking the optimal coefficient combination obtained in the step (5) as an effective coefficient, wherein the total deduction value of the devices is as follows:
calculating separately for each type of equipment, and counting the corresponding effective fault number of tripping, unscheduled maintenance and scheduled maintenance;
in the effective fault number calculation process, the trip, the unplanned maintenance and the planned maintenance are respectively counted according to the coefficients of 1, 0.5 and 0.25, namely:
the number of effective faults is equal to the trip number, the number of other unplanned overhauls is multiplied by 0.5, the number of planned overhauls is multiplied by 0.25
Firstly, counting the fault probability of equipment without deduction;
then, eliminating the influence of the fault probability when no deduction exists: the effective number of equipment trips, which is increased by equipment due to health problems, is calculated by subtracting the expected value of equipment failure when no deduction exists from the total effective failure number:
deduction influence effective quantity is equal to effective quantity-deduction-free equipment fault probability multiplied by equipment operation total station day;
if the calculated value is negative, 0 is selected;
and (3) calculating the probability of the corresponding failure of the deduction:
7. the method for analyzing the fault probability of the power grid equipment according to claim 1, wherein the step (7) is to adopt different treatment schemes of power failure overhaul, load transfer and planning of different plans based on different tripping probabilities of the equipment, and the specific method is as follows:
if the equipment has a trip probability of more than 10%, if a power failure condition exists, the equipment needs to be overhauled by power failure;
the equipment with the fault probability of more than 5 percent and less than or equal to 10 percent transfers the carried load to equipment with a relatively healthy running state as far as possible to supply power;
the equipment with the fault probability of more than 0.1 percent and less than or equal to 5 percent is subjected to a plan when the equipment is in fault, and the inspection of the equipment is enhanced;
and the failure probability is less than or equal to 0.1 percent, and no special measures are taken.
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