CN109409770A - A kind of rural power grids level of intelligence evaluation method neural network based - Google Patents
A kind of rural power grids level of intelligence evaluation method neural network based Download PDFInfo
- Publication number
- CN109409770A CN109409770A CN201811343591.7A CN201811343591A CN109409770A CN 109409770 A CN109409770 A CN 109409770A CN 201811343591 A CN201811343591 A CN 201811343591A CN 109409770 A CN109409770 A CN 109409770A
- Authority
- CN
- China
- Prior art keywords
- index
- rate
- level
- neural network
- weight
- 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
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 35
- 238000011156 evaluation Methods 0.000 title claims abstract description 32
- 238000000034 method Methods 0.000 claims abstract description 37
- 230000008569 process Effects 0.000 claims abstract description 14
- 230000006870 function Effects 0.000 claims description 17
- 238000011161 development Methods 0.000 claims description 15
- 239000011159 matrix material Substances 0.000 claims description 14
- 238000012797 qualification Methods 0.000 claims description 12
- 238000005516 engineering process Methods 0.000 claims description 10
- 238000004891 communication Methods 0.000 claims description 9
- 230000005484 gravity Effects 0.000 claims description 9
- 238000012549 training Methods 0.000 claims description 8
- 230000005611 electricity Effects 0.000 claims description 7
- 230000007613 environmental effect Effects 0.000 claims description 7
- 230000002452 interceptive effect Effects 0.000 claims description 7
- 238000009434 installation Methods 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 6
- 238000003062 neural network model Methods 0.000 claims description 6
- 230000008439 repair process Effects 0.000 claims description 6
- 230000001131 transforming effect Effects 0.000 claims description 6
- 238000013139 quantization Methods 0.000 claims description 5
- 230000009466 transformation Effects 0.000 claims description 5
- 230000007812 deficiency Effects 0.000 claims description 4
- 230000015572 biosynthetic process Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 239000004020 conductor Substances 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 3
- 230000006872 improvement Effects 0.000 claims description 3
- 238000009413 insulation Methods 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 239000013307 optical fiber Substances 0.000 claims description 3
- 230000035699 permeability Effects 0.000 claims description 3
- 238000002945 steepest descent method Methods 0.000 claims description 3
- 238000003786 synthesis reaction Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims 2
- 238000012545 processing Methods 0.000 claims 1
- 230000001681 protective effect Effects 0.000 claims 1
- 230000035945 sensitivity Effects 0.000 claims 1
- 238000012546 transfer Methods 0.000 claims 1
- 238000004458 analytical method Methods 0.000 abstract description 5
- 238000010276 construction Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 238000011160 research Methods 0.000 description 2
- 210000001367 artery Anatomy 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007813 chromatographic assay Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 210000004218 nerve net Anatomy 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- 210000003462 vein Anatomy 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas 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 discloses a kind of rural power grids level of intelligence evaluation methods neural network based, comprising the following steps: (1) rural power grids System of Comprehensive Evaluation of the building containing intelligent index, including first class index and two-level index corresponding with first class index；(2) weight is calculated using entropy assessment；(3) weight is calculated using analytic hierarchy process (AHP)；(4) weight is calculated using neural network；(5) method of weighting that the result for the analytic hierarchy process (AHP) weight that entropy assessment weight that step (2) obtains, step (3) obtain is obtained with step (4) is compared, verifies the feasibility of new method.It is more scientific, comprehensive analysis more reasonably is done to rural power grids intelligent level based on new evaluation method the present invention is directed to propose Novel rural net assessment indicator system.
Description
Technical field
The present invention relates to a kind of integrated evaluating methods, more particularly, to a kind of rural power grids intelligence neural network based
Level evaluation method.
Background technique
Agricultural civilization is modern civilization, the foundation of urban civilization, is cultural root arteries and veins and spiritual home.In modernization construction
Obvious to all, faith missing, culture sand are impacted in the contemporary society of rapid development, industrialization, urbanization caused by traditional culture
Desert allows modern not know where is returned in township, looks for less than " happiness ".The beautiful rural area of creation is exactly in this social economy and culture
The new issue proposed under historical background, and in beautiful rural forming process, electric energy is indispensable link, is rural area
It is that rural environment is promoted, use can experience the important step promoted under electric energy substitutes to an important ring for beautiful rural transformation.
On the basis of pushing forward rural power " transformation, reform and same price " comprehensively, " new countryside, new electric power, new service " is vigorously implemented
Rural power development strategy, accelerates building and upgrading of rural power grids paces, implements " being powered in every family " engineering, it is electrified to carry out new rural village
Construction, starting upgrade of rural power grids upgrade engineering, are made that positive contribution for the fast development of rural economy society.
By studying rural power grids overall merit, the assessment such as new technology, new energy is focused on, is conducive to
The construction of power grid itself, and be conducive to the development of entire power industry, national economy sustainable health development and society
It can harmonious stable development.
Summary of the invention
In view of this, in view of the deficiencies of the prior art, it is an object of the present invention to provide a kind of rural areas neural network based
Power grid level of intelligence evaluation method can accurately make scientific and reasonable analysis to rural power grids intelligent level.
In order to achieve the above objectives, the invention adopts the following technical scheme:
A kind of rural power grids level of intelligence evaluation method neural network based, comprising the following steps:
(1) index system of rural power grids overall merit, including first class index and corresponding with first class index two are constructed
Grade index；
(2) weight is calculated using entropy assessment；
(3) weight is calculated using analytic hierarchy process (AHP)；
(4) weight is calculated using neural network；
(5) result and step for the analytic hierarchy process (AHP) weight for obtaining entropy assessment weight that step (2) obtains, step (3)
(4) method of weighting obtained compares, and verifies the feasibility of neural network.
Further, the first class index include power supply quality, power supply capacity, grid equipment, operation of power networks, informationization,
Automation, interactive, DG/ microgrid access, new technology and clean and environmental protection.
Further, the power supply quality in first class index includes rate of qualified voltage, power supply reliability and rate of qualified voltage etc.
Two-level index；
Power supply capacity in first class index includes capacity load ratio of network, substation's load factor qualification rate, main transformer " N-1 " qualification
Rate, middle pressure and low-voltage circuit load factor qualification rate, middle pressure and low-voltage circuit power supply capacity deficiency ratio, medium-voltage line can turn to supply
Rate qualification rate and 10kV outgoing line interval utilization rate；
Grid equipment in first class index includes on-load voltage regulation main transformer ratio, low-voltage distribution equipment one kind equipment ratio, opens
Close oil-free rate, energy-saving ratio-change, middle pressure and low-voltage circuit one kind equipment ratio, line insulation rate, the line conductor of matching is cut
Face standardized rate；
Operation of power networks in first class index includes medium-voltage line and low-voltage circuit high load rate route specific gravity, high load rate
Main transformer and distribution transforming specific gravity, three-phase imbalance platform area accounting；
Informationization in first class index includes substation and power supply station's optical fiber coverage rate, communication line percentage reserve, has letter
Company's ratio at county level, the power distribution station ratio with communication function of breathization Horizontal communication；
The corresponding two-level index of automation in first class index includes the unattended rate of electric substation, dispatch automated system
Realize that the route ratio of distribution automation, substation have five distant function ratios, substation is idle automatic throwing in practical, county town
Cutting apparatus coverage rate, 100kVA and above customer charge monitoring ratio, relay equipment popularize installation rate, platform area and user's low pressure
Aftercurrent protecting equipment operational percentage；
Interactive corresponding two-level index in first class index includes that successfully repair rate, self-help charging rate, electricity consumption are complained
Rate, the functionization of 95598 customer service systems and intelligent electric meter utility ratio；
It includes generation of electricity by new energy permeability, distributed generation resource that DG/ microgrid in first class index, which accesses corresponding two-level index,
Utilization rate and new energy installation rate, distributed generation resource Capacity Margin, distributed generation resource rate of qualified voltage, electric car low ebb fill
Electric rate, electric car peak discharge rate；
The corresponding two-level index of new technology in first class index includes realizing that the control of whole network voltage reactive comprehensive, development are set
Standby on-line monitoring and repair based on condition of component, development 10kV power distribution live-wire, development harmonic synthesis improvement and power distribution network self-healing control；
The corresponding two-level index of environmental index in first class index include clean energy resource Generation Rate, polluted gas discharge amount,
Controllable load occupation ratio.
Further, it in the step (2), is calculated using entropy assessment as follows:
(1) original index data matrix is constructed
X in matrixijFor the evaluation of estimate of j-th of project under i-th of index, wherein i≤m, j≤n, m are the total of evaluation object
Quantity, n are the total quantity of the project under i-th of evaluation object；
(2) two-level index carries out unison quantization under each scale, calculates the specific gravity p of the i-th scheme index value under jth item indexij
(3) the entropy e of jth item index is calculatedj
In formula: k > 0, ln are natural logrithm；
(4) objective weight for calculating jth item index, for given j, xijThe smaller then e of othernessjIt is bigger；Work as xijEntirely
When portion is equal, ej=emax=1, at this point for the comparison of scheme, index xjAlmost without effect；When each scheme index value difference is got over
When big, ejSmaller, this index is bigger for project plan comparison role；Objective weight value vjWith ejIt is inversely proportional, so jth
The weight of item index can be by 1-ejTo measure；The objective weight of each index acquired by entropy assessment is
Further, in the step (3), the method for calculating weight using analytic hierarchy process (AHP) is as follows:
(1) it is filled in by expert as follows for a certain application form for assessing object:
B in upper tableijReflect significance level of the index j relative to index i, wherein m be evaluation index number, i, j≤
m；
(2) judgment matrix is established according to application form
(3) the maximum eigenvalue λ of judgment matrix B is soughtmaxAnd its corresponding feature vector v
Bv=λmaxv
(4) each index weights are sought
(5) consistency check
Consistency ratio CR is at random
In formula, CI is coincident indicator；RI is Aver-age Random Consistency Index；
Coincident indicator CI can be determined according to following formula
In formula, m is judgment matrix order, works as m=1, and 2 when is not required to examine.
Further, in the step (4), the method for calculating two-level index weight using neural network is as follows:
A. neural network is a kind of Multi-layered Feedforward Networks by Back Propagation Algorithm training, is most widely used at present
One of general neural network model.BP network can learn and store a large amount of input-output mode map relationship, without thing
It is preceding to disclose the math equation for describing this mapping relations.Its learning rules be using steepest descent method, by backpropagation come
The constantly weight and threshold value of adjustment network, utilizes the error sum of squares minimum of network.Neural network model is divided into input layer
(input), hidden layer (hide layer) and output layer (output layer).
B. neural network three layer model defines
Input layer: X=xi, i=1,2 ..., n
Wherein xiFor the index raw value of input.
Transforming function transformation function f (x) is continuous, guidable unipolarity Sigmoid function:
F ' (x)=f (x) [1-f (x)]
Hidden layer: yj=f (netj), j=1,2 ..., m
netj=∑nvijxi, j=1,2 ..., m
Output layer:
Wherein, yjThe output of the linear combiner of input signal, wjk、vjkFor weighted value, OkFor output signal.
C. algorithm and step
Hidden layer:
Output layer:
One error signal is respectively defined to hidden layer and output layer, is had
With
Again by netj=∑nvijxi,Adjust weighted value:
With
ByIn summary method obtains:
The training step for concluding neural network model is as follows:
(1) initial value is assigned to all weights.Randomly initial value is accompanied by the threshold value of whole weights and neuron.
(2) training sample output and input is given.
(3) each layer reality output is calculated.
(4) weight is corrected.According to the deviation of output calculated value and actual value, weight is reversely successively adjusted from output layer, directly
Deviation E is fallen within setting value by correcting each weight to input layer.
(5) reach deviation precision or cycle-index requirement, output is as a result, otherwise, return step 2.
The beneficial effects of the present invention are:
The invention proposes the multinomial novel indexes about rural power grids intelligent level, including grid automation, information
Change, support technology and the indexs such as clean, more comprehensively predicts and cover the developing direction of rural power grids from now on, energy
It is enough that there is certain reference in the following power grid construction timing；Secondly, proposing mind by the research to single evaluation method
It is applied in evaluation through network algorithm, avoids entirely subjective or completely objective extreme end value, utilize the excellent of neural network
Gesture constructs the Rating Model based on first class index, is evaluated finally by the smart grid level in a certain area, verifies
The science and reasonability of neural network method provide strong branch to the problems such as investment decision person's lookup electrical network weak link
Support, while being of great significance to the operation of Intelligent rural network.
Other features and advantages of the present invention will be illustrated in the following description, also, partly becomes from specification
It obtains it is clear that understand through the implementation of the invention.The objectives and other advantages of the invention can be by written explanation
Specifically noted structure is achieved and obtained in book, claims and attached drawing.
Detailed description of the invention
Fig. 1 is flow diagram of the invention；
Fig. 2 is the index system of rural area smart grid Integrated Assessment On The Level in the present invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
Attached drawing, the technical solution of the embodiment of the present invention is clearly and completely described.Obviously, described embodiment is this hair
Bright a part of the embodiment, instead of all the embodiments.Based on described the embodiment of the present invention, the common skill in this field
Art personnel every other embodiment obtained, shall fall within the protection scope of the present invention.
As shown in Figure 1, a kind of rural power grids level of intelligence evaluation method neural network based, comprising the following steps:
(1) index system of rural power grids overall merit, including first class index and corresponding with first class index two are constructed
Grade index；
(2) weight is calculated using entropy assessment；
(3) weight is calculated using analytic hierarchy process (AHP)；
(4) weight is calculated using neural network；
(5) result and step for the analytic hierarchy process (AHP) weight for obtaining entropy assessment weight that step (2) obtains, step (3)
(4) method of weighting obtained compares, and verifies the feasibility of neural network.
As shown in Fig. 2, the first class index includes power supply quality, power supply capacity, grid equipment, operation of power networks, information
Change, automation, interactive, DG/ microgrid access, new technology and environmental index.
Power supply quality in first class index includes the two-level index such as rate of qualified voltage, power supply reliability and rate of qualified voltage；
Power supply capacity in first class index includes capacity load ratio of network, substation's load factor qualification rate, main transformer " N-1 " qualification
Rate, middle pressure and low-voltage circuit load factor qualification rate, middle pressure and low-voltage circuit power supply capacity deficiency ratio, medium-voltage line can turn to supply
Rate qualification rate and 10kV outgoing line interval utilization rate；
Grid equipment in first class index includes on-load voltage regulation main transformer ratio, low-voltage distribution equipment one kind equipment ratio, opens
Close oil-free rate, energy-saving ratio-change, middle pressure and low-voltage circuit one kind equipment ratio, line insulation rate, the line conductor of matching is cut
Face standardized rate；
Operation of power networks in first class index includes medium-voltage line and low-voltage circuit high load rate route specific gravity, high load rate
Main transformer and distribution transforming specific gravity, three-phase imbalance platform area accounting；
Informationization in first class index includes substation and power supply station's optical fiber coverage rate, communication line percentage reserve, has letter
Company's ratio at county level, the power distribution station ratio with communication function of breathization Horizontal communication；
The corresponding two-level index of automation in first class index includes the unattended rate of electric substation, dispatch automated system
Realize that the route ratio of distribution automation, substation have five distant function ratios, substation is idle automatic throwing in practical, county town
Cutting apparatus coverage rate, 100kVA and above customer charge monitoring ratio, relay equipment popularize installation rate, platform area and user's low pressure
Aftercurrent protecting equipment operational percentage；
Interactive corresponding two-level index in first class index includes that successfully repair rate, self-help charging rate, electricity consumption are complained
Rate, the functionization of 95598 customer service systems and intelligent electric meter utility ratio；
It includes generation of electricity by new energy permeability, distributed generation resource that DG/ microgrid in first class index, which accesses corresponding two-level index,
Utilization rate and new energy installation rate, distributed generation resource Capacity Margin, distributed generation resource rate of qualified voltage, electric car low ebb fill
Electric rate, electric car peak discharge rate；
The corresponding two-level index of new technology in first class index includes realizing that the control of whole network voltage reactive comprehensive, development are set
Standby on-line monitoring and repair based on condition of component, development 10kV power distribution live-wire, development harmonic synthesis improvement and power distribution network self-healing control；
The corresponding two-level index of environmental index in first class index include clean energy resource Generation Rate, polluted gas discharge amount,
Controllable load occupation ratio.
In the step (2), the method for calculating the entropy assessment weight of each two-level index using entropy assessment is as follows:
(1) original index data matrix is constructed
X in matrixijFor the evaluation of estimate of j-th of project under i-th of index, wherein i≤m, j≤n, m are the sum of index
Amount, n are the total quantity of the project under i-th of index；
(2) unison quantization is carried out under each scale, calculates the specific gravity p of the i-th scheme index value under jth item indexij
(3) the entropy e of jth item index is calculatedj
In formula: k > 0, ln are natural logrithm；
(4) objective weight for calculating jth item index, for given j, xijThe smaller then e of othernessjIt is bigger；Work as xijEntirely
When portion is equal, ej=emax=1, at this point for the comparison of scheme, index xjAlmost without effect；When each scheme index value difference is got over
When big, ejSmaller, this index is bigger for project plan comparison role；Objective weight value vjWith ejIt is inversely proportional, so jth
The weight of item index can be by 1-ejTo measure；The objective weight of each index acquired by entropy assessment is
As shown in table 1, it is two-level index entropy assessment weight analysis table:
Table 1:
In the step (3), the method for the analytic hierarchy process (AHP) weight of each two-level index is calculated using analytic hierarchy process (AHP)
It is as follows:
1) judgment matrix is established according to application form, wherein bjiSignificance level of the index j relative to index i is reflected, wherein
M is the number of evaluation index, i, j≤m；
2) the maximum eigenvalue λ of judgment matrix B is soughtmaxAnd its corresponding feature vector v
Bv=λmaxv
3) weight is sought
As shown in table 2, it is two-level index analytic hierarchy process (AHP) weight analysis table:
In the step (4), the method for calculating weight using neural network is as follows:
A. neural network is a kind of Multi-layered Feedforward Networks by Back Propagation Algorithm training, is most widely used at present
One of general neural network model.BP network can learn and store a large amount of input-output mode map relationship, without thing
It is preceding to disclose the math equation for describing this mapping relations.Its learning rules be using steepest descent method, by backpropagation come
The constantly weight and threshold value of adjustment network, utilizes the error sum of squares minimum of network.Neural network model is divided into input layer
(input), hidden layer (hide layer) and output layer (output layer).
B. neural network three layer model defines
Input layer: X=xi, i=1,2 ..., n
Wherein xiFor the index raw value of input.
Transforming function transformation function f (x) is continuous, guidable unipolarity Sigmoid function:
F ' (x)=f (x) [1-f (x)]
Hidden layer: yj=f (netj), j=1,2 ..., m
netj=∑nvijxi, j=1,2 ..., m
Output layer:
Wherein, yjThe output of the linear combiner of input signal, wjk、vjkFor weighted value, OkFor output signal.
C. algorithm and step
Hidden layer:
Output layer:
One error signal is respectively defined to hidden layer and output layer, is had
With
Again by netj=∑nvijxi,Adjust weighted value:
With
ByIn summary method obtains:
As shown in table 3, it is two-level index neural network weight analysis table:
It is each two-level index enabling legislation weight contrast table such as table 4:
Table 4:
As shown in table 4, this patent, can be more by proposing that neural network algorithm applies the new strategy in power grid evaluation
Clearly find out, the weights of evaluation are more scientific, compared to single subjective estimate method and objective evaluation, have higher
Science, by the comparison to weighted value, the value that neural network algorithm obtains mostly among chromatographic assays and entropy assessment,
Do not occur against scientific extremum, by verifying, method can be used in evaluation procedure.
It is the final scoring of each first class index such as table 5:
Table 5
| First class index | Scoring | First class index | Scoring |
| Power supply quality | 96.64 | Power supply capacity | 70.77 |
| Grid equipment | 35.05 | The level of IT application | 58.5 |
| Automatization level | 69.65 | It is interactive | 80.83 |
| Distribution access | 54.97 | Clean and environmental protection | 52.94 |
As shown in table 5, the scoring of rural power grids constitution element is obtained, can clearly find weak link at this stage.
The invention proposes the multinomial novel index about rural area intelligent level, including grid automation, informationization,
It support technology and the indexs such as cleans, more comprehensively predicts and cover the developing direction of rural power grids from now on, Neng Gou
The following power grid construction timing has certain reference；Secondly, proposing nerve net by the research to single evaluation method
Network algorithm is applied in evaluation, and entirely subjective or objective extreme end value completely is avoided, using the advantage of neural network,
The Rating Model based on first class index is constructed, is evaluated finally by the smart grid level in a certain area, verifying nerve
The science and reasonability of network method provide strong support to the problems such as investment decision person's lookup electrical network weak link, together
When be of great significance to the operation of Intelligent rural network.
Finally, it is stated that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, this field is common
Other modifications or equivalent replacement that technical staff makes technical solution of the present invention, without departing from the technology of the present invention side
The spirit and scope of case, are intended to be within the scope of the claims of the invention.
Claims (6)
1. a kind of rural power grids level of intelligence evaluation method neural network based, which comprises the following steps:
(1) index system of rural power grids level of intelligence overall merit, including first class index and corresponding with first class index are constructed
Two-level index；
(2) weight is calculated using entropy assessment；
(3) weight is calculated using analytic hierarchy process (AHP)；
(4) weight is calculated using neural network；
(5) result for the analytic hierarchy process (AHP) weight for obtaining entropy assessment weight that step (2) obtains, step (3) and step (4)
To the method for weighting compare, verify the feasibility of neural network.
2. a kind of integrated evaluating method of rural power grids intelligent level according to claim 1, it is characterised in that: described
First class index includes power supply quality, power supply capacity, grid equipment, operation of power networks, informationization, automation, interactive, DG/ microgrid
Access, new technology and environmental index.
3. a kind of rural power grids level of intelligence evaluation method neural network based according to claim 2, feature exist
In:
Power supply quality in first class index includes the two-level index such as rate of qualified voltage, power supply reliability and rate of qualified voltage；
Power supply capacity in first class index include capacity load ratio of network, substation's load factor qualification rate, main transformer " N-1 " qualification rate, in
Pressure and low-voltage circuit load factor qualification rate, middle pressure and low-voltage circuit power supply capacity deficiency ratio, medium-voltage line can turn for rate qualification
Rate and 10kV outgoing line interval utilization rate；
Grid equipment in first class index includes on-load voltage regulation main transformer ratio, low-voltage distribution equipment one kind equipment ratio, switch nothing
Oiling rate, energy-saving ratio-change, middle pressure and low-voltage circuit one kind equipment ratio, line insulation rate, the line conductor section of matching are marked
Quasi- rate；
Operation of power networks in first class index include medium-voltage line and low-voltage circuit high load rate route specific gravity, high load rate main transformer and
Distribution transforming specific gravity, three-phase imbalance platform area accounting；
Informationization in first class index includes substation and power supply station's optical fiber coverage rate, communication line percentage reserve, has informationization
Company's ratio at county level, the power distribution station ratio with communication function of Horizontal communication；
The corresponding two-level index of automation in first class index includes that the unattended rate of electric substation, dispatch automated system are practical
Change, the route ratio of distribution automation is realized at county town, substation has five distant function ratios, substation is idle automatic switching device
Coverage rate, 100kVA and above customer charge monitoring ratio, relay equipment popularize installation rate, platform area and user's low pressure residual current
Protective device operational percentage；
Interactive corresponding two-level index in first class index include successfully repair rate, self-help charging rate, electricity consumption the rate of complaints,
95598 customer service system functionizations and intelligent electric meter utility ratio；
It includes generation of electricity by new energy permeability, distributed generation resource utilization that DG/ microgrid in first class index, which accesses corresponding two-level index,
Rate and new energy installation rate, distributed generation resource Capacity Margin, distributed generation resource rate of qualified voltage, electric car low ebb charge rate,
Electric car peak discharge rate；
The corresponding two-level index of new technology in first class index includes realizing the control of whole network voltage reactive comprehensive, carrying out equipment on-line
Monitoring and repair based on condition of component, development 10kV power distribution live-wire, development harmonic synthesis improvement and power distribution network self-healing control；
The corresponding two-level index of environmental index in first class index includes clean energy resource Generation Rate, polluted gas discharge amount, adjustable
Control load occupation ratio.
4. a kind of rural power grids level of intelligence evaluation method neural network based according to claim 3, feature exist
In: in the step (2), calculated using entropy assessment as follows:
(1) original index data matrix is constructed
X in matrixijFor the evaluation of estimate of j-th of project under i-th of index, wherein i≤m, j≤n, m are the total quantity of evaluation object,
N is the total quantity of the project under i-th of evaluation object；
(2) two-level index carries out unison quantization under each scale, calculates the specific gravity p of the i-th scheme index value under jth item indexij
(3) the entropy e of jth item index is calculatedj
In formula: k > 0, ln are natural logrithm；
(4) objective weight for calculating jth item index, for given j, xijThe smaller then e of othernessjIt is bigger；Work as xijWhole phases
Whens equal, ej=emax=1, at this point for the comparison of scheme, index xjAlmost without effect；When each scheme index value difference is bigger,
ejSmaller, this index is bigger for project plan comparison role；Objective weight value vjWith ejIt is inversely proportional, so jth item index
Weight can be by 1-ejTo measure；The objective weight of each index acquired by entropy assessment is
。
5. a kind of rural power grids level of intelligence evaluation method neural network based according to claim 4, feature exist
In: in the step (3), the method for calculating weight using analytic hierarchy process (AHP) is as follows:
(1) it is filled in by expert as follows for a certain application form for assessing object:
B in upper tableijSignificance level of the index j relative to index i is reflected, wherein m is the number of evaluation index, i, j≤m；
(2) judgment matrix is established according to application form
(3) the maximum eigenvalue λ of judgment matrix B is soughtmaxAnd its corresponding feature vector v
Bv=λmaxv
(4) each index weights are sought
(5) consistency check
Consistency ratio CR is at random
In formula, CI is coincident indicator；RI is Aver-age Random Consistency Index；
Coincident indicator CI can be determined according to following formula
In formula, m is judgment matrix order, works as m=1, and 2 when is not required to examine.
6. a kind of rural power grids level of intelligence evaluation method neural network based according to claim 5, feature exist
In: in the step (4), the method for calculating two-level index weight using neural network is as follows:
A. neural network is a kind of Multi-layered Feedforward Networks by Back Propagation Algorithm training, is current most widely used mind
Through one of network model；BP network can learn and store a large amount of input-output mode map relationship, retouch without disclosing in advance
State the math equation of this mapping relations；Its learning rules are constantly to be adjusted using steepest descent method by backpropagation
The weight and threshold value of network utilize the error sum of squares minimum of network；Neural network model be divided into input layer (input),
Hidden layer (hide layer) and output layer (output layer)；
BP network obtains knowledge by the study to sample, and for a network, the acquisition of sample is particularly important；For rural area electricity
Net intelligent development new height overall merit, sample are divided into training sample and test sample；Training sample and test sample it is defeated
Entering is each index actual data value, is exported as the score value of each index；
B. neural network three layer model defines
Input layer: X=xi, i=1,2 ..., n
Wherein xiFor the index raw value of input；
Transforming function transformation function f (x) is continuous, guidable unipolarity Sigmoid function:
F ' (x)=f (x) [1-f (x)]
Hidden layer: yj=f (netj), j=1,2 ..., m
netj=∑nvijxi, j=1,2 ..., m
Output layer:
Wherein, yjThe output of the linear combiner of input signal, wjk、vjkFor weighted value, OkFor output signal；
C. algorithm and step
By assessment software obtain data, ununified dimension, thus primary data is carried out quantization and it is normalized
Processing；The hidden layer of BP neural network generally uses Sigmoid transfer function, to improve training speed and sensitivity and effectively keeping away
The saturation region of Sigmoid function is opened, therefore, is limited in input between 0~1 input data quantization and normalized；
Standardization to data:
Hidden layer:
Output layer:
One error signal is respectively defined to hidden layer and output layer, is had
With
Again byAdjust weighted value:
With
ByIn summary method obtains:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811343591.7A CN109409770A (en) | 2018-11-13 | 2018-11-13 | A kind of rural power grids level of intelligence evaluation method neural network based |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811343591.7A CN109409770A (en) | 2018-11-13 | 2018-11-13 | A kind of rural power grids level of intelligence evaluation method neural network based |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109409770A true CN109409770A (en) | 2019-03-01 |
Family
ID=65473161
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811343591.7A Pending CN109409770A (en) | 2018-11-13 | 2018-11-13 | A kind of rural power grids level of intelligence evaluation method neural network based |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109409770A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110097282A (en) * | 2019-04-30 | 2019-08-06 | 中国人民解放军海军工程大学 | A kind of supply chain quality performance appraisal procedure based on LMBP model |
| CN110766301A (en) * | 2019-10-12 | 2020-02-07 | 南京理工大学 | AC/DC power grid autonomous capacity evaluation method based on NARX neural network |
| CN111353574A (en) * | 2019-11-04 | 2020-06-30 | 清华大学 | Group intelligent level calculation method and system based on action relation network |
| CN111582626A (en) * | 2020-03-17 | 2020-08-25 | 上海博英信息科技有限公司 | Power grid planning adaptability method based on big data |
| CN111798082A (en) * | 2020-05-11 | 2020-10-20 | 国网江西省电力有限公司电力科学研究院 | Method for constructing rural electrification comprehensive evaluation index system |
| CN111898929A (en) * | 2020-08-19 | 2020-11-06 | 浙江金淳信息技术有限公司 | Rural multi-production data index evaluation system based on machine learning |
| CN112561252A (en) * | 2020-11-30 | 2021-03-26 | 郑州轻工业大学 | Reactive power combination evaluation method for power grid in new energy-containing region |
| CN112907124A (en) * | 2021-03-22 | 2021-06-04 | 国家电网有限公司大数据中心 | Data link abnormity evaluating method and device, electronic equipment and storage medium |
| CN113256041A (en) * | 2020-02-11 | 2021-08-13 | 中国农业大学 | Method and system for calculating energy efficiency of rural micro-energy network |
-
2018
- 2018-11-13 CN CN201811343591.7A patent/CN109409770A/en active Pending
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110097282A (en) * | 2019-04-30 | 2019-08-06 | 中国人民解放军海军工程大学 | A kind of supply chain quality performance appraisal procedure based on LMBP model |
| CN110766301A (en) * | 2019-10-12 | 2020-02-07 | 南京理工大学 | AC/DC power grid autonomous capacity evaluation method based on NARX neural network |
| CN110766301B (en) * | 2019-10-12 | 2022-08-16 | 南京理工大学 | AC/DC power grid autonomous capability evaluation method based on NARX neural network |
| CN111353574B (en) * | 2019-11-04 | 2021-04-30 | 清华大学 | Group intelligent level calculation method and system based on action relation network |
| CN111353574A (en) * | 2019-11-04 | 2020-06-30 | 清华大学 | Group intelligent level calculation method and system based on action relation network |
| CN113256041A (en) * | 2020-02-11 | 2021-08-13 | 中国农业大学 | Method and system for calculating energy efficiency of rural micro-energy network |
| CN111582626A (en) * | 2020-03-17 | 2020-08-25 | 上海博英信息科技有限公司 | Power grid planning adaptability method based on big data |
| CN111798082A (en) * | 2020-05-11 | 2020-10-20 | 国网江西省电力有限公司电力科学研究院 | Method for constructing rural electrification comprehensive evaluation index system |
| CN111898929A (en) * | 2020-08-19 | 2020-11-06 | 浙江金淳信息技术有限公司 | Rural multi-production data index evaluation system based on machine learning |
| CN112561252A (en) * | 2020-11-30 | 2021-03-26 | 郑州轻工业大学 | Reactive power combination evaluation method for power grid in new energy-containing region |
| CN112561252B (en) * | 2020-11-30 | 2023-06-16 | 郑州轻工业大学 | Reactive power combination evaluation method for power grid in new energy-containing region |
| CN112907124A (en) * | 2021-03-22 | 2021-06-04 | 国家电网有限公司大数据中心 | Data link abnormity evaluating method and device, electronic equipment and storage medium |
| CN112907124B (en) * | 2021-03-22 | 2023-10-31 | 国家电网有限公司大数据中心 | Data link abnormity evaluation method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109409770A (en) | A kind of rural power grids level of intelligence evaluation method neural network based | |
| Macedo et al. | Demand side management using artificial neural networks in a smart grid environment | |
| CN103903058A (en) | Assessment method of efficient operation of intelligent power distribution network | |
| CN104376371B (en) | A kind of distribution based on topology is layered load forecasting method | |
| CN106127377A (en) | A kind of intelligent grid multiple-energy-source comprehensive coordination level evaluation method | |
| CN104636988A (en) | Active power distribution network assessment method | |
| CN108520362A (en) | A kind of integrated evaluating method of rural area intelligent grid level | |
| CN106651225A (en) | Method and system for comprehensively evaluating smart power grid demonstration project | |
| CN109214713B (en) | Planning method for active power distribution network containing distributed power supply | |
| CN110210670A (en) | A kind of prediction technique based on power-system short-term load | |
| CN106451418A (en) | Online clustering equivalent modeling method of photovoltaic power plant | |
| CN113269371A (en) | Method and system for evaluating comprehensive performance of power supply of intelligent power distribution network | |
| Oskuee et al. | Multi-stage planning of distribution networks with application of multi-objective algorithm accompanied by DEA considering economical, environmental and technical improvements | |
| CN111612244A (en) | QRA-LSTM-based method for predicting nonparametric probability of photovoltaic power before day | |
| CN114742283A (en) | Medium-voltage line loop closing current estimation and risk assessment method based on deep learning network | |
| CN106096849A (en) | Distributed photovoltaic accesses electric network synthetic and evaluates system and method | |
| CN111091223A (en) | Distribution transformer short-term load prediction method based on Internet of things intelligent sensing technology | |
| CN103606931B (en) | A kind of AVC system definite value method of adjustment based on probability statistics feature | |
| Xiang et al. | Coordinated DG-Tie planning in distribution networks based on temporal scenarios | |
| CN112633762A (en) | Building energy efficiency obtaining method and equipment | |
| Jahan et al. | Intelligent system for power load forecasting in off-grid platform | |
| CN104036366A (en) | Method for evaluating power grid development supporting degree through electric power telecommunication network | |
| CN104361453A (en) | Decision-making method of intelligentization building projects of intelligent power distribution network | |
| Hansen et al. | Keys to success for wind power in isolated power systems | |
| CN103886393A (en) | Power grid investment optimization method based on simulation investment benefit analysis and learning automatons |
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 | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20200702 Address after: 450052 C building, No. 87, Songshan office, South Zhengzhou Road, 27 District, Henan 1-10, China Applicant after: ECONOMIC TECHNOLOGY RESEARCH INSTITUTE OF STATE GRID HENAN ELECTRIC POWER Co. Address before: 100031 Xicheng District West Chang'an Avenue, No. 86, Beijing Applicant before: STATE GRID CORPORATION OF CHINA Applicant before: ECONOMIC TECHNOLOGY RESEARCH INSTITUTE OF STATE GRID HENAN ELECTRIC POWER Co. |
|
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190301 |