CN113780775A - Method and system for evaluating theoretical line loss calculation result of power grid - Google Patents
Method and system for evaluating theoretical line loss calculation result of power grid Download PDFInfo
- Publication number
- CN113780775A CN113780775A CN202111003942.1A CN202111003942A CN113780775A CN 113780775 A CN113780775 A CN 113780775A CN 202111003942 A CN202111003942 A CN 202111003942A CN 113780775 A CN113780775 A CN 113780775A
- Authority
- CN
- China
- Prior art keywords
- line loss
- theoretical line
- data
- power grid
- calculation result
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004364 calculation method Methods 0.000 title claims abstract description 72
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000004458 analytical method Methods 0.000 claims abstract description 32
- 238000013210 evaluation model Methods 0.000 claims abstract description 29
- 238000011156 evaluation Methods 0.000 claims abstract description 13
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 11
- 238000007781 pre-processing Methods 0.000 claims abstract description 11
- 238000010276 construction Methods 0.000 claims description 10
- 238000003066 decision tree Methods 0.000 claims description 6
- 230000002159 abnormal effect Effects 0.000 claims description 5
- 230000003044 adaptive effect Effects 0.000 claims description 4
- 238000013500 data storage Methods 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 4
- 238000013139 quantization Methods 0.000 claims description 4
- 239000000126 substance Substances 0.000 claims description 4
- 238000013215 result calculation Methods 0.000 claims description 3
- 238000007726 management method Methods 0.000 description 6
- 238000012360 testing method Methods 0.000 description 6
- 238000012423 maintenance Methods 0.000 description 4
- 238000004088 simulation Methods 0.000 description 3
- 238000004140 cleaning Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 230000016507 interphase Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Data Mining & Analysis (AREA)
- Educational Administration (AREA)
- Mathematical Analysis (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Pure & Applied Mathematics (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- Computational Mathematics (AREA)
- Mathematical Physics (AREA)
- Mathematical Optimization (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Probability & Statistics with Applications (AREA)
- Life Sciences & Earth Sciences (AREA)
- Algebra (AREA)
- Primary Health Care (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a method and a system for evaluating a theoretical line loss calculation result of a power grid. The evaluation method of the present invention includes: acquiring parameter data related to theoretical line loss of a power grid in real time, preprocessing the parameter data, and establishing a typical parameter database for the preprocessed data; calculating theoretical line loss by using a self-adaptive enhancement algorithm to obtain a theoretical line loss calculation result; and constructing a theoretical line loss analysis and evaluation model, checking the theoretical line loss calculation result, and if an unreasonable result appears, performing reverse checking and positioning unreasonable reasons. The invention improves the calculation precision of the theoretical line loss, eliminates the problem of lack of practicability and operability, and greatly reduces the limitation.
Description
Technical Field
The invention relates to the technical field of line loss of a power distribution network, in particular to a method and a system for evaluating a theoretical line loss calculation result of a power grid.
Background
The line loss is generated in the electric energy transmission process, and the line loss rate is an index for measuring the operation management level of the power grid and is also an important reference basis in the planning and construction of the intelligent power grid. The theoretical line loss calculation can comprehensively reflect the planning and design level of a power grid, the construction level of the power grid, the technical progress level and the production operation and operation management level, and is also an important technical management means for power supply enterprises. Compared with other work, line loss management has certain particularity, data acquisition and information maintenance in a maintenance period are required to be continuous and uninterrupted, workload is uniformly distributed on the whole time axis, and meanwhile, a calculation period with large loss calculation and result analysis workload exists, namely, obvious large-scale sudden calculation amount exists, and particularly large-scale power grid performance is more prominent. Power supply enterprises are often influenced by factors such as large fluctuation of line loss rate, large abnormal values of line loss rate and the like in the analysis of line loss statistics, and reasonable loss reduction measures cannot be provided only by analyzing and counting the line loss rate. The theoretical line loss calculation of the power grid is one of important technical means adopted by a power grid company for analyzing and deciding a power system, and various indexes of the power grid are analyzed through the theoretical line loss calculation, so that the problems existing in the power grid at present, such as the defects of a grid structure of the power grid and the operation of a system, are found out, and a theoretical basis is provided for energy conservation and loss reduction of the power grid.
The existing theoretical line loss computing system usually adopts an independent design, independent construction and self-maintenance mode, and has the problems of long construction period, high construction cost, improper maintenance, inflexible expansion and the like; meanwhile, the calculation results of a plurality of branch management departments are difficult to gather, and timely and comprehensive analysis cannot be performed. In addition, the currently adopted offline calculation and other manners influence the precision of theoretical line loss calculation to a certain extent, and with the development of line loss refinement work, the theoretical line loss level evaluation in different areas has great guiding significance on the level of the theoretical line loss in each solution area and the difference between the theoretical line loss levels of line loss practitioners, and is also convenient for management departments to develop the evaluation of line loss work.
Conventionally, the evaluation method of the theoretical line loss level of the region usually only depends on experience and lacks a scientific judgment basis, the problem of lack of practicability and operability exists in the evaluation of the theoretical line loss level of each region, and the existing evaluation method still has certain limitations.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects in the prior art and provide a method and a system for evaluating the theoretical line loss calculation result of a power grid so as to improve the theoretical line loss calculation precision, eliminate the problem of lack of practicability and operability and reduce the limitation of the theoretical line loss calculation result.
In order to solve the technical problems, the invention adopts the following technical scheme: a method for evaluating a theoretical line loss calculation result of a power grid comprises the following steps:
acquiring parameter data related to theoretical line loss of a power grid in real time, preprocessing the parameter data, and establishing a typical parameter database for the preprocessed data;
calculating theoretical line loss by using a self-adaptive enhancement algorithm to obtain a theoretical line loss calculation result;
and constructing a theoretical line loss analysis and evaluation model, checking the theoretical line loss calculation result, and if an unreasonable result appears, performing reverse checking and positioning unreasonable reasons.
Further, the parameter data includes current, voltage, power supply path, resistivity, line length, resistance, total line resistance, feeder power supply radius, operating voltage, loss constant, and load curve.
Further, the step of preprocessing the parameter data comprises:
carrying out feature construction, data grading and data quantization on data;
carrying out data statistics on the quantized data, and merging the data into a unified data storage;
and detecting and removing samples which are possibly abnormal in the stored data by adopting an outlier sample detection strategy based on clustering.
Further, the adaptive boosting algorithm includes:
defining the current and voltage of three-phase line of power network as IV、UV；
Load current unbalance of the three-phase line
Load voltage unbalance
Comprises the following steps:
wherein the content of the first and second substances,
respectively showing the phase current and the phase voltage,
is a number of 1, 2 or 3,
the total loss of the power grid is as follows:
Δp＝K(UV-U)2
wherein, L represents the total loss value, M represents the power grid structure coefficient, R represents the resistance, Δ p represents the single-phase corona loss power, t represents the temperature value, n represents the number of the power grid leads, K represents the correlation coefficient, U represents the initial voltage, and the numerical values are scalar quantities.
Further, the step of constructing the theoretical line loss analysis and evaluation model includes:
acquiring rule data information and establishing a data acquisition rule;
traversing the historical theoretical line loss calculation value and the data in the typical parameter database, analyzing the characteristic data of data information according to a decision tree, and acquiring the characteristic data for 2 times according to the data acquisition rule to obtain the normal proportion of the characteristic data;
calculating a proportional ratio of the feature data acquired 2 times;
if the proportion of the positive proportion is larger than a preset threshold value, the acquired feature data are invalid, and the historical theoretical line loss calculation value and the data in the typical parameter database are traversed again;
if the proportion of the positive proportion is smaller than or equal to a preset threshold value, the acquired feature data is valid;
and constructing a theoretical line loss analysis evaluation model according to the effective characteristic data and a least square method.
Further, the theoretical line loss analysis and evaluation model is as follows:
wherein L, B represents theoretical line loss and actual line loss, respectively, J (L, B) represents an output value, J represents a constant coefficient, m represents an iteration coefficient, ω represents a weight coefficient, siCorresponding characteristic data values representing i lines, bjAnd the actual line loss coefficients corresponding to the j lines are represented, mu represents a characteristic dimension, and R represents a real number.
And furthermore, checking the theoretical line loss calculation result, wherein the criterion for judging whether the checking result is reasonable comprises the following steps:
obtaining an output value of the theoretical line loss analysis and evaluation model, and judging whether a check result is reasonable or not based on the optimal solution of the output value:
and when the tau belongs to [0,0.135], judging that the checking result is reasonable, and outputting the theoretical line loss.
Further, the step of reverse checking comprises:
and analyzing the theoretical line loss output result, comparing the real-time theoretical line loss with a historical theoretical line loss value and an actual line loss value, positioning a place with a comparison error larger than a preset value, outputting the positioning result, and obtaining the unreasonable reason from expert database data.
The other technical scheme adopted by the invention is as follows: a power grid theoretical line loss calculation result evaluation system comprises:
typical parameter database establishment unit: acquiring parameter data related to theoretical line loss of a power grid in real time, preprocessing the parameter data, and establishing a typical parameter database for the preprocessed data;
theoretical line loss result calculation unit: calculating theoretical line loss by using a self-adaptive enhancement algorithm to obtain a theoretical line loss calculation result;
and a theoretical line loss calculation result checking unit: and constructing a theoretical line loss analysis and evaluation model, checking the theoretical line loss calculation result, and if an unreasonable result appears, performing reverse checking and positioning unreasonable reasons.
Further, the step of constructing the theoretical line loss analysis and evaluation model includes:
acquiring rule data information and establishing a data acquisition rule;
traversing the historical theoretical line loss calculation value and the data in the typical parameter database, analyzing the characteristic data of data information according to a decision tree, and acquiring the characteristic data for 2 times according to the data acquisition rule to obtain the normal proportion of the characteristic data;
calculating a proportional ratio of the feature data acquired 2 times;
if the proportion of the positive proportion is larger than a preset threshold value, the acquired feature data are invalid, and the historical theoretical line loss calculation value and the data in the typical parameter database are traversed again;
if the proportion of the positive proportion is smaller than or equal to a preset threshold value, the acquired feature data is valid;
constructing a theoretical line loss analysis evaluation model according to the effective characteristic data and a least square method;
the theoretical line loss analysis and evaluation model comprises the following steps:
wherein L, B represents theoretical line loss and actual line loss, respectively, J (L, B) represents an output value, J represents a constant coefficient, m represents an iteration coefficient, ω represents a weight coefficient, siCorresponding characteristic data values representing i lines, bjAnd the actual line loss coefficients corresponding to the j lines are represented, mu represents a characteristic dimension, and R represents a real number.
The invention has the beneficial effects that: by designing an intelligent theoretical line loss calculation and evaluation method, the method improves the theoretical line loss calculation precision, eliminates the problem of lack of practicability and operability, and greatly reduces the limitation.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a basic flow diagram of a method for evaluating a theoretical line loss calculation result of a power grid according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Example 1
Referring to fig. 1, an embodiment of the present invention provides a method for evaluating a theoretical line loss calculation result of a power grid, which includes the following steps:
s1: acquiring parameter data related to theoretical line loss of a power grid in real time, preprocessing the parameter data, and establishing a typical parameter database for the preprocessed data; it should be noted that:
the parameter data includes current, voltage, supply path, resistivity, line length, resistance, total line resistance, feeder supply radius, operating voltage, loss constant, and load curve.
Specifically, preprocessing the parameter data includes:
cleaning vacancy values, format contents, logic errors and non-demand data;
carrying out feature construction, data grading and data quantization on data;
carrying out data statistics on the quantized data, and merging the data into a unified data storage;
and detecting and removing samples which are possibly abnormal in the stored data samples by adopting an outlier sample detection strategy based on clustering.
S2: calculating theoretical line loss by using a self-adaptive enhancement algorithm to obtain a theoretical line loss calculation result; it should be noted that:
the adaptive enhancement algorithm comprises:
defining the current and voltage of three-phase line of power network as IV、UV；
Load current unbalance of three-phase line
Load voltage unbalance
Comprises the following steps:
wherein the content of the first and second substances,
respectively showing the phase current and the phase voltage,
the total loss of the grid is:
Δp＝K(UV-U)2
wherein, L represents the total loss value, M represents the power grid structure coefficient, R represents the resistance, Δ p represents the single-phase corona loss power, t represents the temperature value, n represents the number of the power grid leads, K represents the correlation coefficient, U represents the initial voltage, and the numerical values are scalar quantities.
S3: constructing a theoretical line loss analysis and evaluation model, checking a theoretical line loss calculation result, and if an unreasonable result appears, performing reverse checking and positioning unreasonable reasons; it should be noted that:
the method for constructing the theoretical line loss analysis and evaluation model comprises the following steps:
acquiring rule data information and establishing a data acquisition rule;
traversing the historical theoretical line loss calculation value and data in the typical parameter database, analyzing the characteristic data of the data information according to the decision tree, and acquiring the characteristic data for 2 times according to the data acquisition rule to obtain the proportional proportion of the characteristic data;
calculating a proportional ratio of the feature data acquired 2 times;
if the positive proportion ratio is larger than the preset threshold, the acquired feature data are invalid, and the historical theoretical line loss calculation value and the data in the typical parameter database are traversed again;
if the proportional ratio is smaller than or equal to the preset threshold, the acquired feature data is valid;
and constructing a theoretical line loss analysis evaluation model according to the effective characteristic data and a least square method.
The theoretical line loss analysis and evaluation model comprises the following steps:
wherein L, B represents theoretical line loss and actual line loss, respectively, J (L, B) represents an output value, J represents a constant coefficient, n represents an iteration coefficient, ω represents a weight coefficient, siCorresponding characteristic data values representing i lines, bjAnd the actual line loss coefficients corresponding to the j lines are represented, mu represents a characteristic dimension, and R represents a real number.
The standard for checking the theoretical line loss calculation result and judging whether the checking result is reasonable comprises the following steps:
obtaining an output value of a theoretical line loss analysis evaluation model, and judging whether a check result is reasonable or not based on an optimal solution of the output value:
and when the tau belongs to [0,0.135], judging that the checking result is reasonable and outputting the theoretical line loss.
More specifically, the step of reverse checking includes: and analyzing the theoretical line loss output result, comparing the real-time theoretical line loss with the historical theoretical line loss value and the actual line loss value, positioning the place with the comparison error larger than the preset value, outputting the positioning result, and obtaining unreasonable reasons from expert database data.
Example 2
The embodiment provides a theoretical line loss calculation result evaluation system of power grid, including:
typical parameter database establishment unit: acquiring parameter data related to theoretical line loss of a power grid in real time, preprocessing the parameter data, and establishing a typical parameter database for the preprocessed data;
theoretical line loss result calculation unit: calculating theoretical line loss by using a self-adaptive enhancement algorithm to obtain a theoretical line loss calculation result;
and a theoretical line loss calculation result checking unit: and constructing a theoretical line loss analysis and evaluation model, checking the theoretical line loss calculation result, and if an unreasonable result appears, performing reverse checking and positioning unreasonable reasons.
In the typical parameter database building unit, the parameter data includes current, voltage, power supply path, resistivity, line length, resistance, total line resistance, feeder power supply radius, operating voltage, loss constant, and load curve.
The preprocessing parameter data includes:
cleaning vacancy values, format contents, logic errors and non-demand data;
carrying out feature construction, data grading and data quantization on data;
carrying out data statistics on the quantized data, and merging the data into a unified data storage;
and detecting and removing samples which are possibly abnormal in the stored data samples by adopting an outlier sample detection strategy based on clustering.
In the theoretical line loss result calculating unit, the adaptive enhancement algorithm comprises:
defining the current and voltage of three-phase line of power network as IV、UV；
Load of three-phase lineDegree of current imbalance
Load voltage unbalance
Comprises the following steps:
wherein the content of the first and second substances,
respectively showing the phase current and the phase voltage,
the total loss of the grid is:
Δp＝K(UV-U)2
wherein, L represents the total loss value, M represents the power grid structure coefficient, R represents the resistance, Δ p represents the single-phase corona loss power, t represents the temperature value, n represents the number of the power grid leads, K represents the correlation coefficient, U represents the initial voltage, and the numerical values are scalar quantities.
In the theoretical line loss calculation result checking unit, the construction of the theoretical line loss analysis and evaluation model comprises the following steps:
acquiring rule data information and establishing a data acquisition rule;
traversing the historical theoretical line loss calculation value and data in the typical parameter database, analyzing the characteristic data of the data information according to the decision tree, and acquiring the characteristic data for 2 times according to the data acquisition rule to obtain the proportional proportion of the characteristic data;
calculating a proportional ratio of the feature data acquired 2 times;
if the positive proportion ratio is larger than the preset threshold, the acquired feature data are invalid, and the historical theoretical line loss calculation value and the data in the typical parameter database are traversed again;
if the proportional ratio is smaller than or equal to the preset threshold, the acquired feature data is valid;
and constructing a theoretical line loss analysis evaluation model according to the effective characteristic data and a least square method.
The theoretical line loss analysis and evaluation model comprises the following steps:
wherein L, B represents theoretical line loss and actual line loss, respectively, J (L, B) represents an output value, J represents a constant coefficient, n represents an iteration coefficient, ω represents a weight coefficient, siCorresponding characteristic data values representing i lines, bjAnd the actual line loss coefficients corresponding to the j lines are represented, mu represents a characteristic dimension, and R represents a real number.
Checking the theoretical line loss calculation result, and judging whether the checking result is reasonable or not according to the following standards: obtaining an output value of a theoretical line loss analysis evaluation model, and judging whether a check result is reasonable or not based on an optimal solution of the output value:
and when the tau belongs to [0,0.135], judging that the checking result is reasonable and outputting the theoretical line loss.
The reverse checking step comprises the following steps: and analyzing the theoretical line loss output result, comparing the real-time theoretical line loss with the historical theoretical line loss value and the actual line loss value, positioning the place with the comparison error larger than the preset value, outputting the positioning result, and obtaining unreasonable reasons from expert database data.
Application example
The application example provides a verification test of the evaluation method of the theoretical line loss calculation result of the power grid, and in order to verify and explain the technical effect adopted in the method, the application example adopts the traditional technical scheme and the method of the invention to carry out comparison test, compares the test result by means of scientific demonstration, and verifies the real effect of the method of the invention.
The traditional technical scheme is as follows: the theoretical line loss calculation precision is low, the practicability and the operability are low, and the limitation is high. In order to verify that the method has higher calculation precision compared with the traditional method. In the application example, the theoretical line loss rate of the simulation power grid is measured and compared in real time by adopting the traditional line loss level evaluation method based on the power grid characteristic difference and the method provided by the invention.
And (3) testing environment: 110kv, 220kv, 10kv and 500kv grid lines are selected for measurement, the structure of the line is 4-LGL-300 split line, the diameter is 24.26mm, the split distance is 450mm, three items are horizontally arranged, the inter-phase distance is 13m, the circuit length is 300km, and the line parameter table is as follows:
table 1: circuit parameter table
| Line model | Reactance omega/km | Susceptance s/km | Radius of wire cm | Geometric mean distance cm | Resistance omega/km |
| 4*LGL-300 | 0.281 | 3.956*10-6 | 1.213 | 1638 | 0.02625 |
The traditional method and the method of the invention are respectively utilized to start the automatic test equipment and utilize MATLB software programming to realize the simulation test of the two methods, and the simulation data is obtained according to the experimental result. The results are shown in the following table.
Table 2: experimental results comparison table
Compared with the traditional method, the method has better calculation precision, thereby reflecting the effectiveness of the method.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. A method for evaluating a theoretical line loss calculation result of a power grid is characterized by comprising the following steps:
acquiring parameter data related to theoretical line loss of a power grid in real time, preprocessing the parameter data, and establishing a typical parameter database for the preprocessed data;
calculating theoretical line loss by using a self-adaptive enhancement algorithm to obtain a theoretical line loss calculation result;
and constructing a theoretical line loss analysis and evaluation model, checking the theoretical line loss calculation result, and if an unreasonable result appears, performing reverse checking and positioning unreasonable reasons.
2. A power grid theoretical line loss calculation result evaluation method as claimed in claim 1, wherein the parameter data comprises current, voltage, power supply path, resistivity, line length, resistance, line total resistance, feeder power supply radius, operating voltage, loss constant, and load curve.
3. A method for evaluating theoretical line loss calculation results of a power grid as claimed in claim 1, wherein the step of preprocessing the parameter data comprises:
carrying out feature construction, data grading and data quantization on data;
carrying out data statistics on the quantized data, and merging the data into a unified data storage;
and detecting and removing samples which are possibly abnormal in the stored data by adopting an outlier sample detection strategy based on clustering.
4. The method for evaluating the theoretical line loss calculation result of the power grid according to claim 1, wherein the adaptive enhancement algorithm comprises:
defining the current and voltage of three-phase line of power network as IV、UV；
Load current unbalance of the three-phase line
Load voltage unbalance
Comprises the following steps:
wherein the content of the first and second substances,
respectively showing the phase current and the phase voltage,
is a number of 1, 2 or 3,
the total loss of the power grid is as follows:
Δp＝K(UV-U)2
wherein, L represents the total loss value, M represents the power grid structure coefficient, R represents the resistance, Δ p represents the single-phase corona loss power, t represents the temperature value, n represents the number of the power grid leads, K represents the correlation coefficient, U represents the initial voltage, and the numerical values are scalar quantities.
5. The method for evaluating the calculation result of the theoretical line loss of the power grid according to claim 1, wherein the step of constructing the theoretical line loss analysis and evaluation model comprises the following steps:
acquiring rule data information and establishing a data acquisition rule;
traversing the historical theoretical line loss calculation value and the data in the typical parameter database, analyzing the characteristic data of data information according to a decision tree, and acquiring the characteristic data for 2 times according to the data acquisition rule to obtain the normal proportion of the characteristic data;
calculating a proportional ratio of the feature data acquired 2 times;
if the proportion of the positive proportion is larger than a preset threshold value, the acquired feature data are invalid, and the historical theoretical line loss calculation value and the data in the typical parameter database are traversed again;
if the proportion of the positive proportion is smaller than or equal to a preset threshold value, the acquired feature data is valid;
and constructing a theoretical line loss analysis evaluation model according to the effective characteristic data and a least square method.
6. The method for evaluating the theoretical line loss calculation result of the power grid according to claim 5, wherein the theoretical line loss analysis and evaluation model is as follows:
wherein L, B represents theoretical line loss and actual line loss, respectively, J (L, B) represents an output value, J represents a constant coefficient, m represents an iteration coefficient, ω represents a weight coefficient, siCorresponding characteristic data values representing i lines, bjAnd the actual line loss coefficients corresponding to the j lines are represented, mu represents a characteristic dimension, and R represents a real number.
7. The method for evaluating the theoretical line loss calculation result of the power grid according to claim 6, wherein the theoretical line loss calculation result is checked, and the criterion for judging whether the check result is reasonable comprises:
obtaining an output value of the theoretical line loss analysis and evaluation model, and judging whether a check result is reasonable or not based on the optimal solution of the output value:
and when the tau belongs to [0,0.135], judging that the checking result is reasonable, and outputting the theoretical line loss.
8. The method for evaluating the calculation result of the theoretical line loss of the power grid according to any one of claims 1 to 7, wherein the step of reversely checking comprises the following steps of:
and analyzing the theoretical line loss output result, comparing the real-time theoretical line loss with a historical theoretical line loss value and an actual line loss value, positioning a place with a comparison error larger than a preset value, outputting the positioning result, and obtaining the unreasonable reason from expert database data.
9. A power grid theoretical line loss calculation result evaluation system is characterized by comprising:
typical parameter database establishment unit: acquiring parameter data related to theoretical line loss of a power grid in real time, preprocessing the parameter data, and establishing a typical parameter database for the preprocessed data;
theoretical line loss result calculation unit: calculating theoretical line loss by using a self-adaptive enhancement algorithm to obtain a theoretical line loss calculation result;
and a theoretical line loss calculation result checking unit: and constructing a theoretical line loss analysis and evaluation model, checking the theoretical line loss calculation result, and if an unreasonable result appears, performing reverse checking and positioning unreasonable reasons.
10. The system for evaluating the calculation result of the theoretical line loss of the power grid as claimed in claim 9, wherein the step of constructing the theoretical line loss analysis and evaluation model comprises:
acquiring rule data information and establishing a data acquisition rule;
traversing the historical theoretical line loss calculation value and the data in the typical parameter database, analyzing the characteristic data of data information according to a decision tree, and acquiring the characteristic data for 2 times according to the data acquisition rule to obtain the normal proportion of the characteristic data;
calculating a proportional ratio of the feature data acquired 2 times;
if the proportion of the positive proportion is larger than a preset threshold value, the acquired feature data are invalid, and the historical theoretical line loss calculation value and the data in the typical parameter database are traversed again;
if the proportion of the positive proportion is smaller than or equal to a preset threshold value, the acquired feature data is valid;
constructing a theoretical line loss analysis evaluation model according to the effective characteristic data and a least square method;
the theoretical line loss analysis and evaluation model comprises the following steps:
wherein L, B represents theoretical line loss and actual line loss, respectively, J (L, B) represents an output value, J represents a constant coefficient, m represents an iteration coefficient, ω represents a weight coefficient, siCorresponding characteristic data values representing i lines, bjAnd the actual line loss coefficients corresponding to the j lines are represented, mu represents a characteristic dimension, and R represents a real number.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111003942.1A CN113780775A (en) | 2021-08-30 | 2021-08-30 | Method and system for evaluating theoretical line loss calculation result of power grid |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111003942.1A CN113780775A (en) | 2021-08-30 | 2021-08-30 | Method and system for evaluating theoretical line loss calculation result of power grid |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113780775A true CN113780775A (en) | 2021-12-10 |
Family
ID=78840040
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111003942.1A Pending CN113780775A (en) | 2021-08-30 | 2021-08-30 | Method and system for evaluating theoretical line loss calculation result of power grid |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113780775A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114994401A (en) * | 2022-07-18 | 2022-09-02 | 广东电网有限责任公司佛山供电局 | Line loss abnormity detection method and device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106156892A (en) * | 2016-07-13 | 2016-11-23 | 国网福建省电力有限公司 | A kind of method for building up of grid line loss rate forecast model |
| CN109165764A (en) * | 2018-06-26 | 2019-01-08 | 昆明理工大学 | A kind of line loss calculation method of genetic algorithm optimization BP neural network |
| CN110110887A (en) * | 2019-03-22 | 2019-08-09 | 国网浙江省电力有限公司信息通信分公司 | To the prediction technique of low-voltage platform area line loss per unit |
| US20200366092A1 (en) * | 2019-05-15 | 2020-11-19 | University Of Electronic Science And Technology Of China | Method for analyzing correlation between different line loss actions |
-
2021
- 2021-08-30 CN CN202111003942.1A patent/CN113780775A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106156892A (en) * | 2016-07-13 | 2016-11-23 | 国网福建省电力有限公司 | A kind of method for building up of grid line loss rate forecast model |
| CN109165764A (en) * | 2018-06-26 | 2019-01-08 | 昆明理工大学 | A kind of line loss calculation method of genetic algorithm optimization BP neural network |
| CN110110887A (en) * | 2019-03-22 | 2019-08-09 | 国网浙江省电力有限公司信息通信分公司 | To the prediction technique of low-voltage platform area line loss per unit |
| US20200366092A1 (en) * | 2019-05-15 | 2020-11-19 | University Of Electronic Science And Technology Of China | Method for analyzing correlation between different line loss actions |
Non-Patent Citations (1)
| Title |
|---|
| 文锦霞;: "基于理论线损计算的结果数据质量评价方法研究", 机电信息, no. 30, pages 4 - 6 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114994401A (en) * | 2022-07-18 | 2022-09-02 | 广东电网有限责任公司佛山供电局 | Line loss abnormity detection method and device |
| CN114994401B (en) * | 2022-07-18 | 2023-04-18 | 广东电网有限责任公司佛山供电局 | Line loss abnormity detection method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109687458B (en) | Grid planning method considering regional distribution network risk bearing capacity difference | |
| CN104134999B (en) | Distribution network based on multi-data source measures the practical method of calculation of efficiency analysis | |
| US20140236513A1 (en) | Region-based security evaluation method for the electric power distribution system | |
| CN110264112B (en) | Bidirectional weighted gray correlation-based power distribution network reliability influence factor analysis method | |
| CN109598435A (en) | A kind of power distribution network cable evaluation of running status method and system | |
| CN109034461A (en) | A kind of voltage dip Stochastic prediction method based on actual electric network monitoring information | |
| CN107741578B (en) | Original meter reading data processing method for remote calibration of running error of intelligent electric energy meter | |
| CN106503885A (en) | A kind of method that health state evaluation is carried out to cable run | |
| CN115018139A (en) | Current transformer error state online identification method and system based on interphase characteristics | |
| CN106503886A (en) | A kind of modeling method for carrying out health state evaluation to power equipment | |
| CN114626769B (en) | Operation and maintenance method and system for capacitor voltage transformer | |
| Tarigan | A Novelty Method Subjectif of Electrical Power Cable Retirement Policy | |
| CN112508232A (en) | Short-circuit current limitation measure evaluation method based on multi-level fuzzy comprehensive evaluation model | |
| CN111027886B (en) | Low-voltage treatment scheme evaluation method considering unit cost effectiveness | |
| CN113033617A (en) | Deep mining analysis method based on line loss data of big data transformer area | |
| CN112116276A (en) | Transformer substation operation risk assessment method considering time-varying state of electrical main equipment | |
| CN112084678A (en) | Wire loss rate processing method and device based on multiple regression | |
| CN113904322A (en) | Low-voltage distribution network topology generation method based on current and voltage | |
| CN113780775A (en) | Method and system for evaluating theoretical line loss calculation result of power grid | |
| CN111428754A (en) | Optimal design method of line loss rate benchmark value based on ground state correction | |
| CN111342454A (en) | Method and system for analyzing big data of low voltage cause at platform area outlet | |
| CN114742415A (en) | Operation effect evaluation method, device and system suitable for charging station | |
| CN111160576A (en) | Quantitative evaluation method, device, equipment and medium for health degree of distribution transformer | |
| CN113313371B (en) | Power distribution network risk assessment method, device, computer equipment and storage medium | |
| CN102590652A (en) | Electric-information-based equipment performance evaluation system and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |