CN115392735B - Photovoltaic power station working performance monitoring method, system, equipment and medium - Google Patents
Photovoltaic power station working performance monitoring method, system, equipment and medium Download PDFInfo
- Publication number
- CN115392735B CN115392735B CN202211044614.0A CN202211044614A CN115392735B CN 115392735 B CN115392735 B CN 115392735B CN 202211044614 A CN202211044614 A CN 202211044614A CN 115392735 B CN115392735 B CN 115392735B
- Authority
- CN
- China
- Prior art keywords
- evaluation factor
- weight
- performance
- evaluation
- value
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 71
- 238000012544 monitoring process Methods 0.000 title claims abstract description 31
- 238000011156 evaluation Methods 0.000 claims abstract description 190
- 239000013598 vector Substances 0.000 claims abstract description 39
- 238000005457 optimization Methods 0.000 claims abstract description 10
- 230000036541 health Effects 0.000 claims description 47
- 239000011159 matrix material Substances 0.000 claims description 39
- 238000010248 power generation Methods 0.000 claims description 32
- 238000004364 calculation method Methods 0.000 claims description 30
- 230000008569 process Effects 0.000 claims description 19
- 238000012545 processing Methods 0.000 claims description 18
- 238000010606 normalization Methods 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 15
- 238000003860 storage Methods 0.000 claims description 13
- 238000012423 maintenance Methods 0.000 claims description 7
- 238000004519 manufacturing process Methods 0.000 claims description 6
- 238000013441 quality evaluation Methods 0.000 claims description 5
- 238000012360 testing method Methods 0.000 claims description 4
- 238000013507 mapping Methods 0.000 claims description 3
- 238000011002 quantification Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 7
- 238000004458 analytical method Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 239000003086 colorant Substances 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 2
- 230000004075 alteration Effects 0.000 description 2
- 229910052799 carbon Inorganic materials 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013016 damping Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
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
- 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
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method, a system, equipment and a medium for monitoring the working performance of a photovoltaic power station, which are characterized in that at least one performance index is established; respectively selecting corresponding evaluation factors under each performance index; calculating a weight value and a weight vector of each evaluation factor, and obtaining a boundary condition of each evaluation factor according to the weight vector; optimizing the weight value of each evaluation factor in the boundary condition of each evaluation factor to obtain an optimized weight value of each evaluation factor; interpolation optimization is carried out on the optimized weight value of each evaluation factor to obtain the weight coefficient of the corresponding performance index; and obtaining the performance numerical value of the performance index according to the weight coefficient of the performance index, and obtaining the working performance of the photovoltaic power station according to the performance numerical value. According to the invention, a multi-dimensional bottom evaluation index system of the photovoltaic power station can be established through performance indexes of multiple dimensions, the performance monitoring is more integral and objective, and meanwhile, the normalized evaluation can be realized for the characteristic types of different photovoltaic power stations.
Description
Technical Field
The application relates to the field of photovoltaic power generation, in particular to a method, a system, equipment and a medium for monitoring working performance of a photovoltaic power station.
Background
With the development of technology, the utilization of energy is getting more and more important. The role of photovoltaic power generation technology in life is also becoming more and more prominent. The photovoltaic power generation technology can convert light energy into electric energy, and the energy in the nature is fully utilized.
The carbon reaching peak carbon neutralizes the construction of a novel power system, the generation permeability of new energy is rapidly improved, the installed capacity scale of the photovoltaic storage power station reaches 300GW, the new installation faces the double-layer pressure of construction cost and the internet power price, and the value of how to excavate the photovoltaic storage power station becomes a new research direction. Although some published patents refer to health evaluation indexes of certain power grid systems, such as CN112565007A, CN114066160A, CN106384186A, etc., a unified and feasible evaluation index system is not available for a large number of photovoltaic power stations, and a large number of photovoltaic power stations are low-efficiency and even off-grid for a long time, so that power loss is serious. Meanwhile, because the storage power station lacks clear operation grading indexes, the actual operation condition of the power station, such as the key data of generated energy, system efficiency and the like deviate from theoretical design value information, the information is unclear, the daily maintenance of the power station is easily influenced, and serious consequences are more likely to be caused.
Disclosure of Invention
In order to solve one of the technical problems, the invention provides a method, a system, equipment and a medium for monitoring the working performance of a photovoltaic power station.
An embodiment of the present invention provides a method for monitoring working performance of a photovoltaic power station, where the method includes:
establishing at least one performance index;
Selecting corresponding evaluation factors under each performance index respectively;
Calculating a weight value and a weight vector of each evaluation factor, and obtaining a boundary condition of each evaluation factor according to the weight vector;
Optimizing the weight value of each evaluation factor in the boundary condition of each evaluation factor to obtain an optimized weight value of each evaluation factor;
performing interpolation optimization on the optimized weight value of each evaluation factor to obtain a weight coefficient of a corresponding performance index;
and obtaining the performance numerical value of the performance index according to the weight coefficient of the performance index, and obtaining the working performance of the photovoltaic power station according to the performance numerical value.
Preferably, the process of calculating the weight value and the weight vector of each evaluation factor and obtaining the boundary condition of each evaluation factor according to the weight vector includes:
calculating the relative weight of each evaluation factor and the criterion corresponding to the evaluation factor;
Defining an entropy value vector, and calculating to obtain the entropy weight of each evaluation factor according to the entropy value vector;
Calculating a variation coefficient of each evaluation factor, and calculating to obtain a standard deviation rate weight of each evaluation factor according to the variation coefficient;
And obtaining a weight value and a weight vector of each evaluation factor according to the relative weight, the entropy weight and the standard deviation rate weight, and obtaining the boundary condition of each evaluation factor according to the weight vector.
Preferably, the process of calculating the relative weight of each evaluation factor and the criterion corresponding to the evaluation factor includes:
establishing a judgment matrix corresponding to each evaluation factor, and assigning values to elements in the judgment matrix according to the criterion of the judgment matrix;
performing hierarchical sorting on the assigned judgment matrix, and performing normalization calculation on the sorted judgment matrix;
consistency test is carried out on the judgment matrix after normalization calculation;
And carrying out relative weight calculation on the judgment matrix after consistency test.
Preferably, the method further comprises:
setting a health grade and a health score boundary of the health grade;
calculating the health score of the photovoltaic power station according to the performance numerical value of the performance index;
And obtaining the health grade of the photovoltaic power station according to the health score boundary of the health grade and the health score of the photovoltaic power station.
Preferably, the method further comprises:
and associating and displaying the corresponding health code color according to the health grade.
Preferably, before said calculating the weight value and the weight vector of each evaluation factor, the method further comprises: and carrying out data quantification on the photovoltaic power station according to the evaluation factors, wherein the specific process comprises the following steps:
establishing a data variable matrix according to the evaluation factors;
and carrying out normalization processing on the data variable matrix, and mapping elements in the data variable matrix to between [0,1 ].
Preferably, the performance index includes one or more of a power station safety operation index, a power generation efficiency discrete rate, a system efficiency, a failure outage loss coefficient, a device health degree or a network-related performance, wherein an evaluation factor of the power station safety operation index is a power station safety production index and an operation and maintenance work order processing time rate, an evaluation factor of the power generation efficiency discrete rate is an actual power generation hour number and a power generation hour number regional rank, an evaluation factor of the system efficiency is an actual efficiency deviation rate and an attenuation degree, an evaluation factor of the failure outage loss coefficient is a failure outage hour number and a failure outage frequency, an evaluation factor of the device health degree is a main device failure rate and a device loss electric quantity, and an evaluation factor of the network-related performance is an adjustment performance evaluation value, an electric energy quality evaluation value and a reward and punishment record.
A second aspect of an embodiment of the present invention provides a photovoltaic power station operating performance monitoring system, the system including:
the index establishing unit is used for establishing at least one performance index;
the evaluation factor selecting unit is used for selecting corresponding evaluation factors under each performance index respectively;
A weight calculation unit for calculating a weight value and a weight vector of each evaluation factor, and obtaining a boundary condition of each evaluation factor according to the weight vector;
The weight optimizing unit is used for optimizing the weight value of each evaluation factor in the boundary condition of each evaluation factor to obtain the optimized weight value of each evaluation factor;
The weight coefficient calculation unit is used for carrying out interpolation optimization on the optimized weight value of each evaluation factor to obtain a weight coefficient of the corresponding performance index;
the working performance obtaining unit is used for obtaining the performance numerical value of the performance index according to the weight coefficient of the performance index and obtaining the working performance of the photovoltaic power station according to the performance numerical value.
A third aspect of an embodiment of the present invention provides an electronic device, including:
a memory for storing a computer program;
and the processor is used for realizing the photovoltaic power station working performance monitoring method according to the first aspect of the embodiment of the invention when executing the computer program.
According to a fourth aspect of the embodiment of the present invention, there is provided a computer readable storage medium, in which computer executable instructions are stored, the computer executable instructions, when loaded and executed by a processor, implement the method for monitoring the working performance of a photovoltaic power station according to the first aspect of the embodiment of the present invention.
The beneficial effects of the invention are as follows: according to the photovoltaic power station working performance monitoring method provided by the invention, at least one performance index is established; selecting corresponding evaluation factors under each performance index respectively; calculating a weight value and a weight vector of each evaluation factor, and obtaining a boundary condition of each evaluation factor according to the weight vector; optimizing the weight value of each evaluation factor in the boundary condition of each evaluation factor to obtain an optimized weight value of each evaluation factor; performing interpolation optimization on the optimized weight value of each evaluation factor to obtain a weight coefficient of a corresponding performance index; and obtaining the performance numerical value of the performance index according to the weight coefficient of the performance index, and obtaining the working performance of the photovoltaic power station according to the performance numerical value. According to the invention, a multi-dimensional bottom evaluation index system of the photovoltaic power station can be established through performance indexes of multiple dimensions, the performance monitoring is more integral and objective, and meanwhile, the normalized evaluation can be realized for the characteristic types of different photovoltaic power stations.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
Fig. 1 is a flowchart of a method for monitoring the working performance of a photovoltaic power station according to embodiment 1 of the present invention;
FIG. 2 is a schematic view of the evaluation factor weights according to embodiment 1 of the present invention;
FIG. 3 is a health score radar chart of a photovoltaic power plant according to example 1 of the present invention;
Fig. 4 is a schematic diagram of a photovoltaic power station operation performance monitoring system according to embodiment 2 of the present invention.
Detailed Description
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following detailed description of exemplary embodiments of the present application is provided in conjunction with the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present application and not exhaustive of all embodiments. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
Example 1
As shown in fig. 1, this embodiment proposes a method for monitoring the working performance of a photovoltaic power station, where the method includes:
s101, establishing at least one performance index, and respectively selecting corresponding evaluation factors under each performance index.
Specifically, the embodiment establishes six performance indexes of a power station safety operation index, a power generation efficiency discrete rate, a system efficiency, a failure outage loss coefficient, equipment health degree or network-related performance. And meanwhile, the actual power generation hours, the actual efficiency deviation rate, the electric energy quality evaluation value, the main equipment failure rate, the failure shutdown hours and the power station safety production index are selected as 6 dominant factors according to the dominant factor principle and the comprehensive integrity of the evaluation factors, and the operation and maintenance work order processing time rate, the power generation hours region ranking, the attenuation degree, the failure shutdown frequency, the equipment loss electric quantity, the adjustment performance evaluation value and the reward and punishment record are used as 7 secondary factors. The dominant factors and the minor factors are used as evaluation factors of six performance indexes in the photovoltaic power station working performance monitoring method. The evaluation factors of the power station safety operation index are the power station safety production index and the operation and maintenance work order processing time rate, the evaluation factors of the power generation efficiency discrete rate are the actual power generation hours and the power generation hours, the evaluation factors of the system efficiency are the actual efficiency deviation rate and the attenuation degree, the evaluation factors of the failure outage loss coefficient are the failure outage hours and the failure outage frequency, the evaluation factors of the equipment health degree are the main equipment failure rate and the equipment loss electric quantity, and the evaluation factors of the network performance are the regulation performance evaluation value, the electric energy quality evaluation value and the reward and punishment record. Table 1 is a table of five basic data representing photovoltaic power plants for a selected representative area.
TABLE 1
After the performance index is established and the corresponding evaluation factor is selected, the photovoltaic power station can be subjected to data quantization so as to facilitate subsequent calculation. Specifically, the evaluation factor data of five photovoltaic power stations in the above table are selected and defined as a data variable matrix x= { X ij } (i=1, 2, …, m; j=1, 2, …, n), m represents the number of power stations, and n represents the number of evaluation factor types.
Carrying out normalization processing on the data variable matrix to eliminate the influence of dimension on a calculation result, and mapping evaluation factor data in the data variable matrix to a [0,1] interval, wherein the normalization processing process is as follows:
Wherein max is the maximum value of j-th evaluation factor data in m power stations; min is the minimum value of the j-th evaluation factor data in m power stations. x i′,j is the j-th evaluation factor data in the original i-th power station, and x i,j is the j-th evaluation factor data in the i-th power station after normalization processing.
S102, calculating a weight value and a weight vector of each evaluation factor, and obtaining the boundary condition of each evaluation factor according to the weight vector.
In this embodiment, in order to ensure objectivity and rationality of monitoring working performance, the present embodiment uses bounded uniform random variables to represent weights, and uses a hierarchical analysis method in a supervisor weighting method, an entropy weighting method in an objective weighting method, and a standard deviation rate method to calculate and obtain a weight value and a weight vector of each evaluation factor, and boundary conditions of each evaluation factor.
Specifically, the relative weight of each evaluation factor and the criterion corresponding to the evaluation factor is calculated in the analytic hierarchy process. Firstly, establishing a judgment matrix corresponding to each evaluation factor, and assigning values to elements in the judgment matrix according to the criteria of the judgment matrix. Expert opinion may be added during this process. And comparing and distinguishing importance degrees of two elements in the judgment matrix according to the criterion of the judgment matrix, and assigning values to the elements in the judgment matrix according to the importance degrees. After assignment, the judgment matrixes can be subjected to hierarchical sequencing, normalization calculation is performed on each judgment matrix, consistency check is performed on the judgment matrix, the judgment matrix is kept to have transitivity and consistency, and then the relative weight of each evaluation factor aiming at the corresponding criterion is obtained, and the calculation process is as follows:
Wherein i=1, 2, …, m; j=1, 2, …, n, m represents the number of representative power stations, n represents the number of evaluation factor types, x i,j is the j-th evaluation factor data in the i-th power station after normalization processing, and w j is the relative weight vector of the j-th evaluation factor.
The entropy weight method needs to define an entropy value vector, and the entropy weight of each evaluation factor is obtained through calculation according to the entropy value vector. Specifically, on the premise that the sample data volume is sufficient and accurate, the entropy weight method realizes information concentration according to entropy weight calculation so as to perform weight analysis, and the result can be better explained. In statistical analysis, the more scattered the data, the smaller the entropy weight, and the greater the corresponding evaluation factor weight. In this embodiment, the entropy vector is defined as follows:
The entropy weight of the evaluation factor is:
Wherein i=1, 2, …, m; j=1, 2, …, n, m represents the number of representative power stations, n represents the number of evaluation factor types, x i,j is the j-th evaluation factor data in the i-th power station after normalization, H j is the entropy value of the j-th evaluation factor, and w j is the entropy weight of the j-th evaluation factor.
In the standard deviation method, the variation coefficient of each evaluation factor is calculated, and the standard deviation weight of each evaluation factor is obtained according to the variation coefficient calculation. Specifically, calculating the variation coefficient of each evaluation factor can objectively evaluate the variation degree of each evaluation factor, and the indexes with large variation difference and large deviation degree have higher weights. However, the weight calculated by the standard deviation method cannot represent the importance of the evaluation factor, but only improves the resolution of the evaluation factor, and the calculation process of the variation coefficient is as follows:
The weight of the evaluation factor is:
Wherein i=1, 2, …, m; j=1, 2, …, n, m represents the number of plants, n represents the number of evaluation factor classes, σ i represents the standard deviation of the j-th evaluation factor, For the average value of the j-th evaluation factor, V j represents the coefficient of variation of the j-th evaluation factor, and w j represents the weight of the j-th evaluation factor.
And carrying out data processing, analysis and solving on the evaluation factors at different angles through the analytic hierarchy process, the entropy weight process and the standard deviation rate process, and calculating to obtain weight vectors corresponding to each evaluation factor. From the characteristics of the weights, the vector w＝[w1,w2,w3,w4,w5,w6,w7,w8,w9,w10,w11,w12,w13]T with the sum of the elements of each evaluation factor being 1 can be used as the weight, the weights are represented by bounded uniform random variables, and boundary conditions of the evaluation factors are calculated by a hierarchical analysis method, an entropy weight method and a standard deviation rate method, and the results are shown in table 2:
TABLE 2
S103, optimizing the weight value of each evaluation factor in the boundary condition of each evaluation factor to obtain the optimized weight value of each evaluation factor.
After the weight of each evaluation factor is obtained, probability fusion is performed by Monte Carlo. Specifically, the present embodiment sets the train weight data variable matrix w= { W ij } (i=1, 2, …, m; j=1, 2, …, n) within the boundary condition of each evaluation factor. The optimized weight value is obtained by calculating fine deviation optimization weight assignment, and the calculation result is shown in fig. 2, wherein rectangular blocks in each method in fig. 2 are sequentially, from left to right, a power station safety production index, an operation and maintenance work order processing time rate, an actual power generation hour number, a power generation hour number region ranking, an actual efficiency deviation rate, a damping degree, a main equipment failure rate, a failure outage hour number, a failure outage frequency, equipment loss electric quantity, an adjustment performance evaluation value, an electric energy quality evaluation value and a reward record.
And S104, carrying out interpolation optimization on the optimized weight value of each evaluation factor to obtain a weight coefficient of the corresponding performance index.
After the optimized weight value is obtained through Monte Carlo simulation calculation in the boundary condition, the weight of the corresponding evaluation factor is extracted from the power station safety operation index, the power generation efficiency discrete rate, the system efficiency, the failure outage loss coefficient, the equipment health or the network performance 6 performance index, then interpolation method optimizing is carried out, and the best matched weight coefficient of the performance index corresponding to the evaluation factor is found. Specifically, taking the power generation efficiency discrete rate as an example, the evaluation factor of the power generation efficiency discrete rate is the actual power generation hours and the regional rank of the power generation hours. If the power generation efficiency discrete rate initialization value is 0.2, taking 0.1 as an interpolation interval, calculating a power generation efficiency score 1 when the power generation efficiency discrete rate is 0.2, calculating a power generation efficiency score 2 when the power generation efficiency discrete rate is 0.3, and performing expert evaluation on the score 1 and the score 2 respectively, wherein the evaluation opinion is assumed to be: and (3) performing secondary interpolation on the weight interval (0.2,0.3) with the value 1 being lower and the value 2 being higher, calculating the value 3 corresponding to the power generation efficiency discrete rate of 0.25 by taking 0.25 as an interpolation interval, comparing the values 1,2 and 3, if the uncertainty exists, continuing performing third interpolation until the weight coefficient passes expert evaluation, and determining the final weight coefficient.
S105, obtaining a performance numerical value of the performance index according to the weight coefficient of the performance index, and obtaining the working performance of the photovoltaic power station according to the performance numerical value.
Specifically, in this embodiment, the performance values of each performance index include two parts: the product of the index and the influence factor is the performance numerical value of the performance index. The index part is the integrated value of the evaluation factor data of the performance index, and the influence factor is the weight coefficient. Therefore, after the weight coefficient of the performance index is obtained, the performance value of the performance index can be obtained, and the higher the performance value is, the higher the working performance of the current photovoltaic power station is.
In addition, the working performance of the photovoltaic power station can be scored according to the embodiment. Specifically, a photovoltaic power station health division concept PV healthy is defined, 100 minutes is taken as a full division system, a measuring unit takes years as an assessment index, each index corresponds to different performance indexes, meanwhile, health divisions and health codes can be communicated, different health divisions correspond to health codes with different colors, and the health level of a power station can be reflected rapidly and intuitively. The health score calculation process is as follows:
Wherein y aq represents a power station safe operation index, and i aq represents a power station safe operation influence factor;
y fd represents a power generation efficiency dispersion index, i fd represents a power generation efficiency influence factor;
y xt represents a system efficiency index, i xt represents a system efficiency influencing factor;
y gz represents a loss of outage coefficient, i gz represents a loss of outage impact factor;
y sb represents the device health index, i sb represents the device health impact factor;
y sw represents the network performance index and i sw represents the network performance influencing factor.
And selecting five representative power stations in the typical area, calculating health scores and corresponding health code colors after normalizing parameters, wherein the health scores and the corresponding health code colors are shown in table 3. For a power station with green health codes, the operation level is good, and observation and continuous maintenance are recommended; the power station with yellow health code color indicates that certain sub-health items exist in the power station and needs to be adjusted and improved; the power station with the red health code color shows that the power station has serious problems and needs immediate improvement; the power station with black health code color shows that the power station has serious problems and needs special depth evaluation improvement.
TABLE 3 Table 3
The photovoltaic power plant health score may also be represented in the form of a radar chart, as shown in fig. 4.
The photovoltaic power station working performance monitoring method provided by the embodiment is characterized by establishing at least one performance index; selecting corresponding evaluation factors under each performance index respectively; calculating a weight value and a weight vector of each evaluation factor, and obtaining a boundary condition of each evaluation factor according to the weight vector; optimizing the weight value of each evaluation factor in the boundary condition of each evaluation factor to obtain an optimized weight value of each evaluation factor; performing interpolation optimization on the optimized weight value of each evaluation factor to obtain a weight coefficient of a corresponding performance index; and obtaining the performance numerical value of the performance index according to the weight coefficient of the performance index, and obtaining the working performance of the photovoltaic power station according to the performance numerical value. According to the embodiment, a multi-dimensional bottom evaluation index system of the photovoltaic power station can be established through performance indexes of multiple dimensions, performance monitoring is more integral and objective, and meanwhile normalized evaluation can be achieved for characteristic types of different photovoltaic power stations.
Example 2
Corresponding to embodiment 1, as shown in fig. 4, the present embodiment proposes a photovoltaic power station operation performance monitoring system, which includes:
the index establishing unit is used for establishing at least one performance index;
the evaluation factor selecting unit is used for selecting corresponding evaluation factors under each performance index respectively;
A weight calculation unit for calculating a weight value and a weight vector of each evaluation factor, and obtaining a boundary condition of each evaluation factor according to the weight vector;
The weight optimizing unit is used for optimizing the weight value of each evaluation factor in the boundary condition of each evaluation factor to obtain the optimized weight value of each evaluation factor;
The weight coefficient calculation unit is used for carrying out interpolation optimization on the optimized weight value of each evaluation factor to obtain a weight coefficient of the corresponding performance index;
the working performance obtaining unit is used for obtaining the performance numerical value of the performance index according to the weight coefficient of the performance index and obtaining the working performance of the photovoltaic power station according to the performance numerical value.
The specific working principle and process of the photovoltaic power station working performance monitoring system provided in this embodiment can be described with reference to embodiment 1, and the description of this embodiment is omitted.
Example 3
The embodiment of the invention also provides electronic equipment, which comprises:
a memory for storing a computer program;
And the processor is used for realizing the steps of the photovoltaic power station working performance monitoring method when executing the computer program.
Since the embodiments of the electronic device portion correspond to the embodiments of the photovoltaic power station operation performance monitoring method portion, the embodiments of the electronic device portion are referred to the description of the embodiments of the photovoltaic power station operation performance monitoring method portion, and are not repeated herein.
Example 4
The embodiment of the invention also provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the steps of the photovoltaic power station working performance monitoring method are realized when the computer program is executed by a processor.
Since the embodiments of the computer readable storage medium portion and the embodiments of the photovoltaic power station operation performance monitoring method portion correspond to each other, the embodiments of the storage medium portion are referred to the description of the embodiments of the photovoltaic power station operation performance monitoring method portion, and are not repeated herein.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be realized by adopting various computer languages, such as object-oriented programming language Java, an transliteration script language JavaScript and the like.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In the present application, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; may be mechanically connected, may be electrically connected or may communicate with each other; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art according to the specific circumstances.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. A method for monitoring the working performance of a photovoltaic power station, the method comprising:
establishing at least one performance index;
Selecting corresponding evaluation factors under each performance index respectively; establishing a judgment matrix corresponding to each evaluation factor, and assigning values to elements in the judgment matrix according to the criterion of the judgment matrix; performing hierarchical sorting on the assigned judgment matrix, and performing normalization calculation on the sorted judgment matrix; consistency test is carried out on the judgment matrix after normalization calculation; and calculating relative weights of the judgment matrixes after consistency test, wherein the calculation process of the relative weights is as follows:
Wherein i=1, 2, …, m; j=1, 2, …, n, m represents the number of representative power stations, n represents the number of evaluation factor types, x i,j is the j-th evaluation factor data in the i-th power station after normalization processing, and w j is the relative weight vector of the j-th evaluation factor;
Defining an entropy value vector, and calculating to obtain the entropy weight of each evaluation factor according to the entropy value vector;
calculating a variation coefficient of each evaluation factor, and calculating to obtain a standard deviation rate weight of each evaluation factor according to the variation coefficient, wherein the calculation process of the variation coefficient is as follows:
Wherein i=1, 2, …, m; j=1, 2, …, n, m represents the number of plants, n represents the number of evaluation factor classes, σ i represents the standard deviation of the j-th evaluation factor, V j represents the variation coefficient of the j-th evaluation factor as the average value of the j-th evaluation factor;
Obtaining a weight value and a weight vector of each evaluation factor according to the relative weight, the entropy weight and the standard deviation rate weight, and obtaining a boundary condition of each evaluation factor according to the weight vector;
Optimizing the weight value of each evaluation factor in the boundary condition of each evaluation factor to obtain an optimized weight value of each evaluation factor;
performing interpolation optimization on the optimized weight value of each evaluation factor to obtain a weight coefficient of a corresponding performance index;
and obtaining the performance numerical value of the performance index according to the weight coefficient of the performance index, and obtaining the working performance of the photovoltaic power station according to the performance numerical value.
2. The method according to claim 1, wherein the method further comprises:
setting a health grade and a health score boundary of the health grade;
calculating the health score of the photovoltaic power station according to the performance numerical value of the performance index;
And obtaining the health grade of the photovoltaic power station according to the health score boundary of the health grade and the health score of the photovoltaic power station.
3. The method according to claim 2, wherein the method further comprises:
and associating and displaying the corresponding health code color according to the health grade.
4. The method of claim 1, wherein prior to said calculating the weight value and weight vector for each evaluation factor, the method further comprises: and carrying out data quantification on the photovoltaic power station according to the evaluation factors, wherein the specific process comprises the following steps:
establishing a data variable matrix according to the evaluation factors;
and carrying out normalization processing on the data variable matrix, and mapping elements in the data variable matrix to between [0,1 ].
5. The method of claim 1, wherein the performance indicators comprise one or more of a plant safety operation index, a power generation efficiency discrete rate, a system efficiency, a failure loss coefficient, a plant health degree, or a grid-related performance, wherein the evaluation factor of the plant safety operation index is a plant safety production index and an operation and maintenance work order processing time rate, the evaluation factor of the power generation efficiency discrete rate is an actual power generation hour number and a power generation hour number regional rank, the evaluation factor of the system efficiency is an actual efficiency deviation rate and a attenuation degree, the evaluation factor of the failure loss coefficient is a failure loss hour number and a failure loss frequency, the evaluation factor of the plant health degree is a main plant failure rate and a plant loss electric quantity, and the evaluation factor of the grid-related performance is an adjustment performance evaluation value, a power quality evaluation value, and a reward and punishment record.
6. A photovoltaic power plant operational performance monitoring system, the system comprising:
the index establishing unit is used for establishing at least one performance index;
the evaluation factor selecting unit is used for selecting corresponding evaluation factors under each performance index respectively;
the weight calculation unit is used for establishing a judgment matrix corresponding to each evaluation factor, assigning values to elements in the judgment matrix according to the criteria of the judgment matrix, carrying out hierarchical ordering on the assigned judgment matrix, carrying out normalization calculation on the ordered judgment matrix, carrying out consistency check on the judgment matrix after normalization calculation, and carrying out relative weight calculation on the judgment matrix after consistency check, wherein the calculation process of the relative weight is as follows Wherein i=1, 2, …, m; j=1, 2, …, n, m represents the number of representative power stations, n represents the number of evaluation factor types, x i,j is the j-th evaluation factor data in the i-th power station after normalization processing, and w j is the relative weight vector of the j-th evaluation factor; the method is also used for defining entropy value vectors, and calculating and obtaining the entropy weight of each evaluation factor according to the entropy value vectors; the method is also used for calculating the variation coefficient of each evaluation factor and obtaining the standard deviation weight of each evaluation factor according to the calculation of the variation coefficient, wherein the calculation process of the variation coefficient is/>Wherein i=1, 2, …, m; j=1, 2, …, n, m represents the number of representative plants, n represents the number of evaluation factor classes, σ i represents the standard deviation of the j-th evaluation factor,/>V j represents the variation coefficient of the j-th evaluation factor as the average value of the j-th evaluation factor; the method is also used for obtaining a weight value and a weight vector of each evaluation factor according to the relative weight, the entropy weight and the standard deviation rate weight, and obtaining a boundary condition of each evaluation factor according to the weight vector;
The weight optimizing unit is used for optimizing the weight value of each evaluation factor in the boundary condition of each evaluation factor to obtain the optimized weight value of each evaluation factor;
The weight coefficient calculation unit is used for carrying out interpolation optimization on the optimized weight value of each evaluation factor to obtain a weight coefficient of the corresponding performance index;
the working performance obtaining unit is used for obtaining the performance numerical value of the performance index according to the weight coefficient of the performance index and obtaining the working performance of the photovoltaic power station according to the performance numerical value.
7. An electronic device, comprising:
a memory for storing a computer program;
A processor for implementing the photovoltaic power plant operating performance monitoring method according to any one of claims 1 to 5 when executing the computer program.
8. A computer readable storage medium, wherein computer executable instructions are stored in the computer readable storage medium, which when loaded and executed by a processor, implement the method for monitoring the operation performance of a photovoltaic power plant according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211044614.0A CN115392735B (en) | 2022-08-30 | 2022-08-30 | Photovoltaic power station working performance monitoring method, system, equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211044614.0A CN115392735B (en) | 2022-08-30 | 2022-08-30 | Photovoltaic power station working performance monitoring method, system, equipment and medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115392735A CN115392735A (en) | 2022-11-25 |
| CN115392735B true CN115392735B (en) | 2024-04-23 |
Family
ID=84122553
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211044614.0A Active CN115392735B (en) | 2022-08-30 | 2022-08-30 | Photovoltaic power station working performance monitoring method, system, equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115392735B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116468253B (en) * | 2023-06-09 | 2023-09-19 | 山西中能运维新能源技术有限公司 | Multi-energy scheduling management and control method and system combined with intelligent energy management platform |
| CN116579500B (en) * | 2023-06-20 | 2023-10-13 | 国家电投集团综合智慧能源科技有限公司 | Photovoltaic power station health degree prediction method, system and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017008180A1 (en) * | 2015-07-16 | 2017-01-19 | 广东产品质量监督检验研究院 | Photovoltaic module failure risk determination method |
| CN107808239A (en) * | 2017-09-30 | 2018-03-16 | 中国科学院遥感与数字地球研究所 | A kind of photovoltaic plant builds suitability evaluation methods and system |
| CN109034653A (en) * | 2018-08-16 | 2018-12-18 | 广东电网有限责任公司 | A kind of power source planning schemes synthesis evaluation method |
| CN109599899A (en) * | 2018-12-14 | 2019-04-09 | 国网山东省电力公司经济技术研究院 | A kind of setting method of new energy running simulation boundary condition |
| CN112381422A (en) * | 2020-11-17 | 2021-02-19 | 国网青海省电力公司清洁能源发展研究院 | Method and device for determining performance of photovoltaic power station |
| CN113902219A (en) * | 2021-11-08 | 2022-01-07 | 贵州电网有限责任公司 | Analysis method of main transformer load influence factor analysis model |
-
2022
- 2022-08-30 CN CN202211044614.0A patent/CN115392735B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017008180A1 (en) * | 2015-07-16 | 2017-01-19 | 广东产品质量监督检验研究院 | Photovoltaic module failure risk determination method |
| CN107808239A (en) * | 2017-09-30 | 2018-03-16 | 中国科学院遥感与数字地球研究所 | A kind of photovoltaic plant builds suitability evaluation methods and system |
| CN109034653A (en) * | 2018-08-16 | 2018-12-18 | 广东电网有限责任公司 | A kind of power source planning schemes synthesis evaluation method |
| CN109599899A (en) * | 2018-12-14 | 2019-04-09 | 国网山东省电力公司经济技术研究院 | A kind of setting method of new energy running simulation boundary condition |
| CN112381422A (en) * | 2020-11-17 | 2021-02-19 | 国网青海省电力公司清洁能源发展研究院 | Method and device for determining performance of photovoltaic power station |
| CN113902219A (en) * | 2021-11-08 | 2022-01-07 | 贵州电网有限责任公司 | Analysis method of main transformer load influence factor analysis model |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115392735A (en) | 2022-11-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115392735B (en) | Photovoltaic power station working performance monitoring method, system, equipment and medium | |
| CN106447205A (en) | Method for evaluating state of distribution automation terminal based on analytic hierarchy process | |
| CN106327062A (en) | State evaluation method of power distribution network equipment | |
| CN113592359B (en) | Method and device for evaluating health degree of power transformer | |
| CN105303331A (en) | Transformer repair risk decision-making method | |
| CN206312210U (en) | State evaluation system of power distribution network equipment | |
| CN109255514B (en) | Method for evaluating independent power supply capacity of intelligent power distribution network partitions | |
| CN116993227B (en) | Heat supply analysis and evaluation method, system and storage medium based on artificial intelligence | |
| CN111582630A (en) | Method and system for determining low-voltage transformer area line loss rate evaluation value | |
| CN113327047B (en) | Power marketing service channel decision method and system based on fuzzy comprehensive model | |
| CN117828498A (en) | Method and system for evaluating health state of measurement and control device based on triangle membership function | |
| CN113191627A (en) | Improved composite weighting-based multi-dimensional comprehensive evaluation method for power distribution network construction | |
| CN116629632A (en) | Comprehensive evaluation method and device for power quality of power distribution network and storage medium | |
| CN116091106A (en) | Multifunctional financial cost evaluation system | |
| CN115660175A (en) | Wind power plant comprehensive evaluation method and device based on analytic hierarchy process | |
| CN114611877A (en) | Safety evaluation method and system for light-charging energy storage source station | |
| CN114764672A (en) | Multi-factor-fused power grid equipment risk assessment method under joint learning framework | |
| CN112232661A (en) | Limited output priority evaluation method considering offshore wind power grid-connected friendliness | |
| CN118367554B (en) | Communication mode adaptation method and device for power load management system | |
| CN110717725A (en) | Power grid project selection method based on big data analysis | |
| CN113283702B (en) | Power flow state evaluation method and device integrating safety and stability characteristics of power system | |
| CN114325141B (en) | Base station electric quantity abnormality monitoring method and device and readable storage medium | |
| CN116307860B (en) | Photovoltaic power station monitoring and alarming method, device, equipment and storage medium | |
| CN115907293A (en) | Distributed photovoltaic power quality evaluation method and system based on time probability distribution | |
| CN118800346A (en) | Australian nut protein extraction process optimization method and system |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |