CN117310333A - High-low voltage overall process fault studying and judging method based on key factor filtering method - Google Patents
High-low voltage overall process fault studying and judging method based on key factor filtering method Download PDFInfo
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Abstract
The method for researching and judging faults in the whole process of high and low voltage based on a key factor filtering method. Relates to the technical field of power distribution networks. The method comprises the following steps: s1, receiving user report and repair, and calling a relay state of a user ammeter by a system; s2, if the relay call result is on, judging that the fault type is the internal fault of the user; s3, if the relay calling result is broken or failed, calling the relay state of the adjacent ammeter under the same ammeter box again; s4, if the result of the adjacent ammeter is on, judging that the single household low-voltage fault exists; s5, if the result of the adjacent ammeter is a break or a calling failure, calling the relay state of the upper-level distribution transformer ammeter again; s6, if the result of the distribution transformer is on, judging that the low-voltage line fails; s7, if the distribution transformer meter is broken or the calling fails, judging that the distribution transformer meter is a high-voltage fault; the invention improves the working medium efficiency, saves the research and judgment time, improves the fault research and judgment accuracy and provides better support for the rush repair work.
Description
Technical Field
The invention relates to the technical field of power distribution networks, in particular to a high-low voltage overall process fault studying and judging method based on a key factor filtering method.
Background
At present, for low-voltage faults, the fault range research and judgment basis adopted generally is to collect power failure reporting information of each technical scheme. Four mature low-pressure acquisition technical schemes are available: the method comprises the steps of type II concentrator acquisition, narrowband equipment acquisition, HPLC equipment acquisition and carrier electric energy meter acquisition. But is limited by historical factors and cost reasons, only carrier electric energy meter collection and HPLC equipment collection with super capacitors can realize the accurate power failure reporting function of the platform side and the user side. Taking Jiangsu province as an example, the current equipment coverage user number is 4502.25 thousands of users, 245.23 thousands of users with the accurate power failure information reporting function at the user side can be realized, and the coverage ratio is only 5.45%. Therefore, the comprehensive research and judgment capability for low-voltage faults is not available at present.
When the fault range extends to the high-voltage side, aiming at distribution transformer faults, the conventional TTU (namely a distribution transformer monitoring terminal), a fusion terminal, a distribution transformer summary table and the like can monitor multi-source distribution transformer power loss information, but the problems of signal acquisition deviation, harmonic waves and the like are commonly existed, and confidence deviation exists in multi-source data due to the non-penetration of the system. For example, the TTU-fusion terminal data is sent up in seconds, but the accuracy is low, and the total data of the distribution table is accurate, but the data is usually reported once in 15 minutes, and when the power failure information is often found, the customer has already made a repair call. Therefore, it is necessary to perform a range finding work quickly by preferentially screening timely and effective information.
Aiming at line faults, FA feeder automation is always an effective means for range study and judgment and fault isolation, but the FA starting conditions are complex, the overcurrent signals are monitored when the main network breaker is broken due to accident, and on the basis, large-range distribution automation coverage is realized, so that the economic cost is greatly improved.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art.
The technical scheme of the invention is as follows: the high-low pressure overall process fault research and judgment method based on the key factor filtering method is characterized by comprising the following steps of:
s1, receiving user report and repair, and calling a relay state of a user ammeter by a system;
s2, if the relay call result is on, judging that the fault type is the internal fault of the user;
s3, if the relay calling result is broken or failed, calling the relay state of the adjacent ammeter under the same ammeter box again;
s4, if the result of the adjacent ammeter is on, judging that the single household low-voltage fault exists;
s5, if the result of the adjacent ammeter is a break or a calling failure, calling the relay state of the upper-level distribution transformer ammeter again;
s6, if the result of the distribution transformer is on, judging that the low-voltage line fails;
s7, if the distribution transformer meter is broken or the calling fails, judging that the distribution transformer meter is a high-voltage fault;
s8, if the repair user locates the distribution transformer T 1 Judging that the power distribution transformer is lost, and analyzing a high-voltage fault range;
s9, acquiring n pieces of distribution transformer power-losing information by adopting a key factor filtering method, and summarizing and judging;
s10, if the same line is divided into T 1 If the power loss of other distribution transformers is not monitored, judging that the distribution transformer is a single distribution transformer fault;
s11, if N including the distribution transformer T1 is monitored on the same line 1 The power of the station distribution transformer is lost, wherein N 1 >And 1, analyzing a power distribution power supply path with possible faults by combining a line topology structure, and initiating a high-voltage line fault rush-repair process.
In step S11, the method further includes the step of studying and judging the fault tripping path, including:
analyzing substation to N 1 Power supply path of station power-off distribution transformer, extracting N 1 Common power supply path L of station transformer 1 Reverse lookup of the power-on-line power-loss-free distribution transformer list N 2 Stage, path L 1 Subtracting from N 2 The overlapping part of the power supply paths of the station transformer is N 1 Self-power supply path L of station transformer 2 L is then 2 Trip the line segment for the fault.
Also included is a step S12 of,
fault power supply path L after research 2 Analyzing power failure equipment and setting a fault power supply path L 2 The following coexist in N 3 A station transformer, wherein N 3 ≥N 1 And N 1 ∈N 3 ；
If a new distribution transformer T is monitored 2 Power loss information, T 2 ∈N 3 The previous research and judgment result is considered to be accurate, and the fault power supply path is still L 2 ；
If it isStep S11 is repeated to obtain T 2 Incorporating the power-loss distribution transformer set, updating the non-power-loss distribution transformer list N in the line 2 ' calculating to obtain a new fault power supply path L 2 ’。
And step S13, randomly extracting power failure information, judging whether the fault tripping line section is in the fault tripping line section or not, and performing reverse verification.
In step S9, firstly collecting the power-losing information of the multi-source distribution transformer on the same line, and then adopting a key factor filtering method to process multi-source data;
establishing m samples and q key indexes to obtain a traditional data matrix X= (X) ab ) m×q A=1, 2 …, m, b=1, 2 … q, where x ab A b-th index value representing a-th sample;
taking X into formula (1), and calculating X:
wherein:
available data matrix
Simultaneously, a correlation matrix R= (R) is calculated according to the formula (2) a'b ) q×q
Wherein: a', b=1, 2 … q, k=1, 2..m;
calculating eigenvalue lambda of correlation matrix R a' And feature vector e j And lambda is 1 ≥λ 2 ≥…≥λ q Not less than 0, feature vector e j Corresponds to lambda a' Is a normal unit vector of (a): e, e j ＝(e 1a' ,e 2a' ,…e qa' ) T After the required characteristic values and the characteristic vectors are obtained, main key indexes are screened, and a projection matrix is obtained;
the contribution degree of the a' th main key index is defined as lambda a' And q, wherein the comprehensive contribution degree of the first n main key indexes in the q key indexes is as follows:
if the contribution degree is not less than 80%, adopting the first n main key indexes to replace the influence range of q key indexes,
obtaining n main key indexes through a formula (4),
wherein x is a' Is a key factor, e a’h As unit vector, z h Is the h main key index;
related principal parameters are z 1 ,z 2 ,…z n Representing that the construction of the linear structure based on complex parameter conditions is:
f＝β 0 +β 1 z 1 +β 2 z 2 +…β n z n (5)
wherein: f represents the power-off time of a certain distribution transformer, beta 1 ，β 2 ，…β n Representing a linear coefficient;
let the target actual data of m samples be (y a ) m×1 The optimal solution can be obtained by adopting the objective function operation:
wherein: y is a Representing target actual data corresponding to the a-th sample;
taking n main key indexes as parameters, and constructing the following linear regression model:
calculating a regression coefficient by adopting a genetic algorithm, and obtaining the actual power-off time of the distribution transformer by a formula (5);
when f >0, the power loss of the power distribution transformer is determined.
In operation, the invention researches and judges the hierarchy of the fault equipment according to the real-time electrified states of the self ammeter of the repairing user, the adjacent ammeter under the same ammeter box and the upper-level distribution transformer ammeter.
If the high-voltage faults are involved, judging whether the faults are single distribution transformer faults or not, then judging and analyzing the line section which is possibly tripped by the faults, and pushing the conclusion to the auxiliary field inspection of the rush repair team to improve the rush repair efficiency, and meanwhile, taking the conclusion as an assessment basis of the accuracy of the reason of the fault feedback of the rush repair single.
The invention utilizes the existing resources to realize the complete penetration of the transformer station-line-transformer-user. The effective information is integrated, the flow fault judging mode can be automatically completed by the system, the human working medium effect is greatly improved, the judging time is saved, the fault judging accuracy is improved, and better support is provided for the rush repair work.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. In the drawings, the various elements are not necessarily drawn to scale.
Figure 1 is a flow chart of the method of the present invention,
fig. 2 is a high voltage fault diagnosis chart in the present invention.
Detailed Description
The invention discloses a high-low pressure whole-process fault judging method based on a key factor filtering method, which is shown in figures 1-2 and comprises the following steps of:
s1, receiving user report and repair, and calling a relay state of a user ammeter by a system;
s2, if the relay call result is on, judging that the fault type is the internal fault of the user;
s3, if the relay calling result is broken or failed, calling the relay state of the adjacent ammeter under the same ammeter box again;
s4, if the result of the adjacent ammeter is on, judging that the single household low-voltage fault exists;
s5, if the result of the adjacent ammeter is a break or a calling failure, calling the relay state of the upper-level distribution transformer ammeter again;
s6, if the result of the distribution transformer is on, judging that the low-voltage line fails;
s7, if the distribution transformer meter is broken or the calling fails, judging that the distribution transformer meter is a high-voltage fault;
s8, if the repair user locates the distribution transformer T 1 Judging that the power distribution transformer is lost, and analyzing a high-voltage fault range;
s9, acquiring n pieces of distribution transformer power-losing information by adopting a key factor filtering method, and summarizing and judging;
s10, if the same line is divided into T 1 If the power loss of other distribution transformers is not monitored, judging that the distribution transformer is a single distribution transformer fault;
s11, if N including the distribution transformer T1 is monitored on the same line 1 The power of the station distribution transformer is lost, wherein N 1 >And 1, analyzing a power distribution power supply path with possible faults by combining a line topology structure, and initiating a high-voltage line fault rush-repair process.
In operation, the invention researches and judges the hierarchy of the fault equipment according to the real-time electrified states of the self ammeter of the repairing user, the adjacent ammeter under the same ammeter box and the upper-level distribution transformer ammeter.
If the high-voltage faults are involved, judging whether the faults are single distribution transformer faults or not, then judging and analyzing the line section which is possibly tripped by the faults, and pushing the conclusion to the auxiliary field inspection of the rush repair team to improve the rush repair efficiency, and meanwhile, taking the conclusion as an assessment basis of the accuracy of the reason of the fault feedback of the rush repair single.
The invention utilizes the existing resources to realize the complete penetration of the transformer station-line-transformer-user. The effective information is integrated, the flow fault judging mode can be automatically completed by the system, the human working medium effect is greatly improved, the judging time is saved, the fault judging accuracy is improved, and better support is provided for the rush repair work.
In step S11, the method further includes the step of studying and judging the fault tripping path, including:
analyzing substation to N 1 Power supply path of station power-off distribution transformer, extracting N 1 Common power supply path L of station transformer 1 Reverse lookup of the power-on-line power-loss-free distribution transformer list N 2 Stage, path L 1 Subtracting from N 2 The overlapping part of the power supply paths of the station transformer is N 1 Self-power supply path L of station transformer 2 L is then 2 Trip the line segment for the fault. Thus, path L 2 Any line or switch failure within range may cause the N 1 The power of the station transformer, i.e. L 2 High-voltage fault emergency repair personnel for fault trip line sectionImportant point inspection L 2 Line segments.
By extracting N 1 Common power supply path L of station transformer 1 And then the reverse searching is matched with the power-losing distribution transformer, so that the fault tripping line section is determined.
Because the existing national grid system push system D5000 and the power distribution automation system can accurately judge the power loss circuit range caused by the tripping of the automatic switch, but the automatic switch accounts for less than 10% in the whole circuit, no actual means can realize the fault monitoring of the tripping of the non-automatic switch at present.
According to the invention, topology deduction is realized by optimizing the research and judgment algorithm, and the precise analysis of the event research and judgment and the power failure range of the tripping operation of the non-automatic switch can be completed without increasing hardware cost.
Also included is a step S12 of,
fault power supply path L after research 2 Analyzing power failure equipment and setting a fault power supply path L 2 The following coexist in N 3 A station transformer, wherein N 3 ≥N 1 And N 1 ∈N 3 The method comprises the steps of carrying out a first treatment on the surface of the And storing the data.
If a new distribution transformer T is monitored 2 Power loss information, T 2 ∈N 3 The previous research and judgment result is considered to be accurate, and the fault power supply path is still L 2 ；
If it isStep S11 (i.e., T) is repeated 1 Decision logic), T 2 Incorporating the power-loss distribution transformer set, updating the non-power-loss distribution transformer list N in the line 2 ' calculating to obtain a new fault power supply path L 2 ' thus forming a closed loop grinding logic.
And step S13, randomly extracting power failure information, judging whether the fault tripping line section is in the fault tripping line section or not, and performing reverse verification.
Substituting the screened non-main power failure information into the closed loop research judgment strategy to realize reverse verification and judge whether the power failure information is accurate; and the reliability of research and judgment is ensured by matching with topology deduction.
In step S9, firstly collecting the power-losing information of the multi-source distribution transformer on the same line, and then adopting a key factor filtering method to process multi-source data;
the method comprises the following steps: the plurality of searched influencing factors are named as key factors, the key factors contain the multi-element information of the traditional matrix, and the multi-element information quantity has no intersection.
Firstly, normalizing a traditional data matrix:
assuming that the total number of the established samples is m, the key index q terms are used to obtain a traditional data matrix X= (X) ab ) m×q A=1, 2 …, m, b=1, 2 … q, where x ab A b-th index value representing a-th sample;
taking X into formula (1), and calculating X:
wherein:
available data matrix
Simultaneously, a correlation matrix R= (R) is calculated according to the formula (2) a'b ) q×q
Wherein: a', b=1, 2 … q, k=1, 2..m;
calculating eigenvalue lambda of correlation matrix R a' And feature vector e j And lambda is 1 ≥λ 2 ≥…≥λ q Not less than 0, feature vector e j Corresponds to lambda a' Is a normal unit vector of (a): e, e j ＝(e 1a' ,e 2a' ,…e qa' ) T After obtaining the required characteristic value and the characteristic vector, the method is toScreening main key indexes to obtain a projection matrix;
here, the contribution degree of the a' th main key index is defined as lambda a' And q, wherein the comprehensive contribution degree of the first n main key indexes in the q key indexes is as follows:
if the contribution degree is not less than 80%, the influence range of the traditional q key indexes is replaced by the first n main key indexes,
obtaining n main key indexes through a formula (4),
wherein x is a' Is a key factor, e a’h As unit vector, z h Is the h main key index;
related principal parameters are z 1 ,z 2 ,…z n Representing that the construction of the linear structure based on complex parameter conditions is:
f＝β 0 +β 1 z 1 +β 2 z 2 +…β n z n (5)
wherein: f represents the power-off time (namely a target calculation formula) of a certain distribution transformer, beta 1 ，β 2 ，…β n Representing a linear coefficient;
let the target actual data of m samples be (y a ) m×1 The optimal solution can be obtained by adopting the objective function operation:
wherein: y is a Representing target actual data corresponding to the a-th sample;
taking n main key indexes as parameters, and constructing the following linear regression model of a matched target calculation formula:
and calculating a regression coefficient by adopting a genetic algorithm, and reasonably setting an initial population number and a genetic algebra. Constructing data required by a genetic algorithm, and acquiring the actual power-off time (namely a target calculation formula) of the distribution transformer through a formula (5);
when f >0, the power loss of the power distribution transformer is determined.
The invention presumes that q key parameters for judging the power failure of a certain distribution transformer are provided, and the reference samples obtained by a test (simulation verification) method are m in total, and X= (X) can be obtained by calculation ab ) n×q In the formula, subscripts respectively represent a sample and an influence parameter, and a distribution transformer power-losing time length matrix can be written as Y= (Y) a ) m×1 Wherein y is a Representing the actual power loss time of sample a.
Q key parameters are reduced to n by key factor filtering, where n is less than q. Related principal parameters are z 1 ,z 2 ,…z n And (3) representing. On the basis, a linear structure based on complex parameter conditions can be constructed: f=β 0 +β 1 z 1 +β 2 z 2 +…β n z n F represents the power-off time of a certain distribution transformer, beta 1 ，β 2 ，…β n Representing the linear coefficient.
The optimal solution can be obtained by adopting objective function operation:
the data required by the genetic algorithm is constructed, and the calculation formula of the actual power-off time of the distribution transformer can be obtained through the formula.
The key factor filtering method can be used for multi-source data screening and complex model simplification, and traditional data order is reduced. The method comprises the steps of refining four key factors of power failure time recorded by a distribution transformer summary table, last calling time of a transformer area, power failure time reported by an ammeter box II type collector and power failure time reported by a TTU-fusion terminal through a key factor method, constructing a distribution transformer power failure time length linear calculation model, and judging that the distribution transformer is powered down when the power failure time length is greater than 0.
In specific application, the power distribution network rush-repair data of 2021 Yangzhou power supply company are substituted to check the correctness of the proposed algorithm. The key indexes for judging the power-off time of the distribution transformer are as follows: fault signal acquisition precision, data reporting channels, communication capability, fault user power supply reliability, fault user satisfaction, station operating conditions, distribution and voltage-transformation current signals and the like, the traditional matrix standardization and contribution degree are respectively realized through the formula (1) and the formula (2), and the characteristic value and the associated data are obtained.
As can be seen from Table 1, the total contribution of the first 4 key indexes exceeds 80%, which marks that the first 4 key indexes are effective key data and cover the main information of the traditional influencing factors, so that the first 4 key indexes can be used as important reference data for evaluating whether the power of the distribution transformer is lost. The 4 key indexes are built, and the absolute value of the characteristic vector can represent the contribution degree to the power-off time of the judgment. Table 2 shows the feature vectors of the respective factors.
Table 1 comprehensive contribution evaluation table
As can be seen from Table 2, the most important evaluation information is calculated with the power outage time (x 1 ) Time (x) of last calling station 2 ) Electric meter box II type collector reporting power failure time (x) 3 ) Power failure time (x) reported by TTU-fusion terminal 4 ) Closely related. Referring to the contribution degree of each key index given in table 1, it can be seen that in the modeling calculation for determining the actual power loss time of the power distribution, the power failure time recorded by the total table of the power distribution changes plays a decisive role: and (4) calculating the association degree between the 4 key indexes and the traditional data through the formula (4).
Table 2 characteristic vector table of each factor
And (3) taking the 4 key indexes as parameters, and building a linear regression model of the power-off duration of the distribution transformer through a formula (5). Regression coefficients were calculated using a genetic algorithm, where the initial population number in the algorithm was set to 30 and the genetic algebra was set to 850. The power-on and power-off time length linear model of the distribution transformer calculated in the method is as follows:
z 1 ＝0.4727x 1 +0.2417x 2 +…+·0.0372x 11 +0.2341x 12
z 2 ＝-0.1886x 1 +0.2107x 2 +…+0.5871x 11 -0.3013x 12
z 3 ＝0.2751x 1 +0.4464x 2 +…+0.1501x 11 -0.1731x 12
z 4 ＝0.0552x 1 -0.3785x 2 +…+0.2362x 11 +0.5335x 12
f＝-3.65487+0.77983z 1 +0.47044z 2 -0.30198z 3 -0.23809z 4
when f >0, the power loss of the power distribution transformer is determined.
After key factors are found out, the invention carries out calculation to judge whether the power is lost or not by the single power distribution transformer, and converts the judging standard of whether the power is lost or not into calculation of the power distribution transformer power loss time length linear model, and when the power loss time length is more than 0, the power loss of the power distribution transformer can be judged. Compared with the existing power failure research and judgment strategy of the distribution transformer, the method is more accurate in research and judgment and does not depend on the accuracy of a single data source.
For the purposes of this disclosure, the following points are also described:
(1) The drawings of the embodiments disclosed in the present application relate only to the structures related to the embodiments disclosed in the present application, and other structures can refer to common designs;
(2) The embodiments disclosed herein and features of the embodiments may be combined with each other to arrive at new embodiments without conflict;
the above is only a specific embodiment disclosed in the present application, but the protection scope of the present disclosure is not limited thereto, and the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (5)
1. The high-low pressure overall process fault research and judgment method based on the key factor filtering method is characterized by comprising the following steps of:
s1, receiving user report and repair, and calling a relay state of a user ammeter by a system;
s2, if the relay call result is on, judging that the fault type is the internal fault of the user;
s3, if the relay calling result is broken or failed, calling the relay state of the adjacent ammeter under the same ammeter box again;
s4, if the result of the adjacent ammeter is on, judging that the single household low-voltage fault exists;
s5, if the result of the adjacent ammeter is a break or a calling failure, calling the relay state of the upper-level distribution transformer ammeter again;
s6, if the result of the distribution transformer is on, judging that the low-voltage line fails;
s7, if the distribution transformer meter is broken or the calling fails, judging that the distribution transformer meter is a high-voltage fault;
s8, if the repair user locates the distribution transformer T 1 Judging that the power distribution transformer is lost, and analyzing a high-voltage fault range;
s9, acquiring n pieces of distribution transformer power-losing information by adopting a key factor filtering method, and summarizing and judging;
s10, if the same line is divided into T 1 If the power loss of other distribution transformers is not monitored, judging that the distribution transformer is a single distribution transformer fault;
s11, if N including the distribution transformer T1 is monitored on the same line 1 The power of the station distribution transformer is lost, wherein N 1 >1, analyzing a power distribution power supply path with possible faults by combining a line topology structure, and initiating high-voltage line fault rush-repair flowAnd (5) processing.
2. The method for judging high-low pressure overall process faults based on the key factor filtering method as claimed in claim 1, wherein,
in step S11, the method further includes the step of studying and judging the fault tripping path, including:
analyzing substation to N 1 Power supply path of station power-off distribution transformer, extracting N 1 Common power supply path L of station transformer 1 Reverse lookup of the power-on-line power-loss-free distribution transformer list N 2 Stage, path L 1 Subtracting from N 2 The overlapping part of the power supply paths of the station transformer is N 1 Self-power supply path L of station transformer 2 L is then 2 Trip the line segment for the fault.
3. The method for judging high-low pressure overall process faults based on the key factor filtering method as claimed in claim 2, wherein,
also included is a step S12 of,
fault power supply path L after research 2 Analyzing power failure equipment and setting a fault power supply path L 2 The following coexist in N 3 A station transformer, wherein N 3 ≥N 1 And N 1 ∈N 3 ；
If a new distribution transformer T is monitored 2 Power loss information, T 2 ∈N 3 The previous research and judgment result is considered to be accurate, and the fault power supply path is still L 2 ；
If it isStep S11 is repeated to obtain T 2 Incorporating the power-loss distribution transformer set, updating the non-power-loss distribution transformer list N in the line 2 ' calculating to obtain a new fault power supply path L 2 ’。
4. The method for judging high-low pressure overall process faults based on the key factor filtering method as claimed in claim 3, wherein,
and step S13, randomly extracting power failure information, judging whether the fault tripping line section is in the fault tripping line section or not, and performing reverse verification.
5. The method for judging high-low pressure overall process faults based on the key factor filtering method as claimed in claim 1, wherein,
in step S9, firstly collecting the power-losing information of the multi-source distribution transformer on the same line, and then adopting a key factor filtering method to process multi-source data;
establishing m samples and q key indexes to obtain a traditional data matrix X= (X) ab ) m×q A=1, 2 …, m, b=1, 2 … q, where x ab A b-th index value representing a-th sample;
taking X into formula (1), and calculating X:
wherein:
available data matrix
Simultaneously, a correlation matrix R= (R) is calculated according to the formula (2) a'b ) q×q
Wherein: a', b=1, 2 … q, k=1, 2..m;
calculating eigenvalue lambda of correlation matrix R a' And feature vector e j And lambda is 1 ≥λ 2 ≥…≥λ q Not less than 0, feature vector e j Corresponds to lambda a' Is a normal unit vector of (a): e, e j ＝(e 1a' ,e 2a' ,…e qa' ) T After the required characteristic values and the characteristic vectors are obtained, main key indexes are screened, and a projection matrix is obtained;
the contribution degree of the a' th main key index is defined as lambda a' And q, wherein the comprehensive contribution degree of the first n main key indexes in the q key indexes is as follows:
if the contribution degree is not less than 80%, adopting the first n main key indexes to replace the influence range of q key indexes,
obtaining n main key indexes through a formula (4),
wherein x is a' Is a key factor, e a’h As unit vector, z h Is the h main key index;
related principal parameters are z 1 ,z 2 ,…z n Representing that the construction of the linear structure based on complex parameter conditions is:
f＝β 0 +β 1 z 1 +β 2 z 2 +…β n z n (5) Wherein: f represents the power-off time of a certain distribution transformer, beta 1 ，β 2 ，…β n Representing a linear coefficient;
let the target actual data of m samples be (y a ) m×1 The optimal solution can be obtained by adopting the objective function operation:
wherein: y is a Representing target actual data corresponding to the a-th sample;
taking n main key indexes as parameters, and constructing the following linear regression model:
calculating a regression coefficient by adopting a genetic algorithm, and obtaining the actual power-off time of the distribution transformer by a formula (5);
when f >0, the power loss of the power distribution transformer is determined.
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