WO2017028632A1 - 一种配电网运行可靠性的预测方法 - Google Patents
一种配电网运行可靠性的预测方法 Download PDFInfo
- Publication number
- WO2017028632A1 WO2017028632A1 PCT/CN2016/087815 CN2016087815W WO2017028632A1 WO 2017028632 A1 WO2017028632 A1 WO 2017028632A1 CN 2016087815 W CN2016087815 W CN 2016087815W WO 2017028632 A1 WO2017028632 A1 WO 2017028632A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- distribution network
- sequence
- power
- monthly
- sample set
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 57
- YHXISWVBGDMDLQ-UHFFFAOYSA-N moclobemide Chemical compound C1=CC(Cl)=CC=C1C(=O)NCCN1CCOCC1 YHXISWVBGDMDLQ-UHFFFAOYSA-N 0.000 claims abstract description 55
- 101100083492 Fritillaria agrestis PETE gene Proteins 0.000 claims abstract description 7
- 238000005311 autocorrelation function Methods 0.000 claims description 13
- 230000008569 process Effects 0.000 claims description 11
- 239000000284 extract Substances 0.000 claims description 8
- 238000011058 failure modes and effects analysis Methods 0.000 claims description 8
- 230000008859 change Effects 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000000611 regression analysis Methods 0.000 claims description 5
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000000638 solvent extraction Methods 0.000 claims description 2
- GNFTZDOKVXKIBK-UHFFFAOYSA-N 3-(2-methoxyethoxy)benzohydrazide Chemical compound COCCOC1=CC=CC(C(=O)NN)=C1 GNFTZDOKVXKIBK-UHFFFAOYSA-N 0.000 claims 1
- FGUUSXIOTUKUDN-IBGZPJMESA-N C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 Chemical compound C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 FGUUSXIOTUKUDN-IBGZPJMESA-N 0.000 claims 1
- 238000001914 filtration Methods 0.000 claims 1
- 238000013461 design Methods 0.000 abstract description 6
- 238000012423 maintenance Methods 0.000 abstract description 6
- 230000001419 dependent effect Effects 0.000 abstract description 3
- 230000000694 effects Effects 0.000 abstract description 2
- 238000011084 recovery Methods 0.000 abstract description 2
- 230000003247 decreasing effect Effects 0.000 abstract 1
- 238000005070 sampling Methods 0.000 description 6
- 238000004422 calculation algorithm Methods 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 238000011156 evaluation Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000008439 repair process Effects 0.000 description 4
- 230000000875 corresponding effect Effects 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 230000002596 correlated effect Effects 0.000 description 2
- 238000005192 partition Methods 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 238000012614 Monte-Carlo sampling Methods 0.000 description 1
- 238000005314 correlation function Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000012731 temporal analysis Methods 0.000 description 1
- 238000000700 time series analysis Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/001—Methods to deal with contingencies, e.g. abnormalities, faults or failures
- H02J3/0012—Contingency detection
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/30—State monitoring, e.g. fault, temperature monitoring, insulator monitoring, corona discharge
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/20—Information technology specific aspects, e.g. CAD, simulation, modelling, system security
Definitions
- the invention relates to the field of reliability assessment of distribution network operation, and particularly relates to a prediction method for reliability of distribution network operation.
- the distribution network is directly connected to the user at the end of the power system to distribute electric energy. Large-scale and long-term power outages not only cause huge losses but also threaten social order. The reliability of the distribution network directly affects whether users obtain qualified electric energy. Power companies continue to improve the quality of power supply and uninterrupted power supply capacity, enabling the grid to reduce the possibility of power outages within a reasonable investment range.
- the structure of the distribution network is complex and huge. It consists of overhead lines, cables connecting a large number of power points, loads, transformers, switches and various primary and secondary equipment.
- the network structure is often due to faults, load transfer or operational adjustments.
- the change has caused the equipment outage model of the distribution network to be complex, the dynamic characteristics of the system to be strong and the consequences of the failure to change, which brings certain difficulties to the reliability assessment of the distribution network in real-time operation.
- Existing methods for reliability assessment of distribution networks can be divided into two types: reliability assessment of distribution networks based on traditional analytical methods and reliability assessment based on Monte Carlo sampling.
- the mathematical model of reliability assessment of distribution network based on traditional analytical method is used to describe the reliability relationship between components and systems.
- the final reliability index is obtained through the whole process life of input components and systems, including failure mode consequence analysis method.
- the minimum path algorithm, the network equivalence method, the feeder partitioning algorithm, etc., the analytical method has strict mathematical logic but increases exponentially with the expansion of the system model.
- Reliability evaluation of distribution network based on Monte Carlo simulation algorithm is based on probability and statistics theory.
- Computer sampling method is used to evaluate system reliability. Since the sampling algorithm is based on the large number theorem, there are fewer system components or fault frequencies. In less cases, the sample variance is too large and the final result is inaccurate.
- Operational reliability assessment is different from planning and design Reliability assessment, with the large coverage of measurement equipment, the distribution network has detailed statistics and real-time operation and maintenance strategies, in addition to providing the average failure rate and repair rate of components, it can also provide repair time, transfer Program, real-time/historical remote telemetry, historical power outage data, etc.
- the state change of the system has memory, that is, the time when the event occurs does not satisfy the exponential distribution, and the equipment component outage events are not independent but have certain correlation.
- the reliability index is essentially a random variable that depends on various factors such as network topology, operation mode, system load, random outage of components, and random repair. As the amount of feedback, there is still uncertainty.
- embodiments of the present invention are expected to provide a method for predicting operational reliability of a distribution network, which can effectively and accurately guide the planning, design, operation, and maintenance of the distribution network in the future, and improve the reliability of the distribution network. Predict the accuracy of the assessment, achieve safe and stable operation of the distribution network, reduce the frequency of power outages, and reduce the scope of power outages.
- a method of forecasting distribution network operational reliability is based autoregressive integrated moving average model (Autoregressive Integrated Moving Average Model, ARIMA ), said method comprising the steps of:
- Step 1 Extract and pre-process the number of monthly power outages of users in the historical data of the distribution network system, and obtain a sequence of monthly average power outages in a time period;
- Step 2 determining whether the monthly average power outage sequence is a stationary sequence
- Step 3 correcting the sequence of the monthly average power outages; returning to step 2;
- Step 4 according to the monthly average power outage sequence; obtain the values of the autoregressive items and the moving average items of the ARIMA;
- Step 5 Establish and modify the ARIMA model
- Step 6 Predict the number of monthly power outages of the distribution network system in the most recent year according to the ARIMA model; obtain the operational reliability index of the distribution network throughout the year.
- the step 1 includes:
- Y t ⁇ 1 Y t-1 + ⁇ 2 Y t-2 +...+ ⁇ p Y tp +e t - ⁇ 1 e t-1 - ⁇ 2 e t-2 -...- ⁇ q e Tq (1)
- Y t is the sequence value of the monthly average power outage times from 0 to t
- p is the number of autoregressive terms of the model
- q is the moving average number of the model
- e is the white noise of each sequence change
- ⁇ and ⁇ is a different unknown parameter to be sought
- t is the number of months included in the calculation window.
- the step 2 includes:
- a n is a constant, representing the mean of the number of monthly power outages; ⁇ t,tk ⁇ 0,k indicates that the covariances of the sequences Y tk , Y t and the sequences Y and Y k are approximate; k is the presence of a parameter k
- the method for correcting the monthly average power outage sequence in the step 3 is to modify the time frequency of the monthly average power outage sequence, or perform logarithmic difference on the monthly average power outage sequence.
- the step 4 includes:
- n is the number of samples with monthly average power outage
- ⁇ is the sum of squared residuals
- p is the number of autoregressive terms for the model
- q is the moving average number of the model
- the step 5 includes:
- the initial type is the final type of the ARIMA model; the ARIMA model is established;
- the step 6 includes:
- the 6-1 includes:
- TLOC criteria and PLOC criteria based on the real-time operation of the distribution network system to partition the distribution network system and establish an accident mode affecting the FMEA table;
- the 6-2 includes:
- w' is the number of times of all e ⁇ S elements
- min(w') is the minimum value of the number of occurrences of e
- e is the accident component
- w is the accident probability weight
- the 6-3 includes:
- the present invention provides a prediction method for reliability of distribution network operation based on ARIMA model, and predicts the number of monthly power outages of users by establishing an ARIMA model, and converts the time series of non-stationary component failure times into a smooth Time series, then the dependent variable only regresses its lag value and the present value and lag value of the random error term to establish the user monthly power outage model; sample the outage point according to the prediction result, while considering the real-time load operation based on the TLOC criterion and The PLOC criterion establishes a failure mode impact table; calculates the system recovery time for each device outage and finally obtains the annual reliability indicator.
- the prediction method proposed by the invention comprehensively considers the real-time operating state and adjustment strategy of the distribution network, and deeply exploits the system risk characteristics in the historical data, effectively and accurately guides the planning, design, operation and maintenance of the future distribution network, and improves the allocation.
- the accuracy of power grid operation reliability prediction and evaluation realizes safe and stable operation of distribution network, reduces power failure frequency, reduces power outage range, strengthens the weak link of reliability in the system, and proposes effective improvement measures to accurately mine the inherent risks of the system.
- Statistical characteristics and developmental regularity Highlight the trend, seasonality and periodicity of distribution network reliability.
- the ARIMA method is used to evaluate the reliability of the distribution network operation, comprehensively consider the real-time running state and adjustment strategy of the distribution network, and deeply exploit the system risk characteristics in the historical data to realize the distribution. Accurate and effective prediction and evaluation of network reliability; effectively and accurately guide the planning, design, operation and maintenance of the distribution network in the future, and improve the accuracy of the prediction and evaluation of the reliability of the distribution network.
- the technical solution provided by the embodiment of the present invention realizes the prediction of the number of power outages of the system is a scientific and effective short-term prediction method, which can accurately mine the statistical characteristics and development regularity of the inherent risk of the system. Highlight the trend, seasonality and periodicity of distribution network reliability.
- FIG. 1 is a schematic flow chart of a method for predicting operational reliability of a distribution network according to an embodiment of the present invention
- step 1 is a schematic flowchart of step 1 in a prediction method according to an embodiment of the present invention
- step 4 is a schematic flowchart of step 4 in a prediction method according to an embodiment of the present invention.
- step 5 is a schematic flowchart of step 5 in a prediction method according to an embodiment of the present invention.
- FIG. 5 is a schematic flowchart of step 6 in a prediction method according to an embodiment of the present invention.
- FIG. 6 is a schematic flowchart of an application example of a prediction method according to an embodiment of the present invention.
- FIG. 7 is a schematic flowchart of calculating a reliability index based on a historical fault set sampling according to an application example in the prediction method according to the embodiment of the present invention.
- an embodiment of the present invention provides a method for predicting operational reliability of a distribution network, which is based on an ARIMA model and uses the number of user historical power outages as an ARIMA model. Enter and predict the reliability indicators for the coming year. Because there are many nodes in the complex distribution network, the number of monthly power outages is highly correlated with time-related attributes such as load changes, seasons and weather, because load forecasting and weather forecasting can be solved using the autoregressive differential moving average model. The actual production data results in the autocorrelation coefficient after differential or frequency modulation is also greater than 0, 5, so the number of monthly power outages solved with a time series model is scientific;
- Step 1 Extract and pre-process the number of monthly power outages of users in the historical data of the distribution network, and obtain a sequence of monthly average power outages in a time period;
- Step 2 determining whether the sequence of monthly average power outages is a stationary sequence
- Step 3 correct the monthly average power outage sequence; return to step 2;
- Step 4 According to the monthly average power outage sequence; obtain the values of the autoregressive items and the moving average items of the ARIMA;
- Step 5 Establish and modify the ARIMA model; among them, the ARIMA model is also called the box-jenkins model and the Boks-Jenkins method.
- ARIMA(p,d,q) is called differential autoregressive moving average model, AR is autoregressive, p is autoregressive term; MA is moving average, q is the moving average, and d is the time series becomes smooth. The number of differences.
- the so-called ARIMA model refers to a model that converts a non-stationary time series into a stationary time series and then returns the dependent variable only to its lag value and the present and lag values of the random error term.
- the ARIMA model is based on whether the original sequence is stationary and the parts contained in the regression, including the moving average process (MA), the autoregressive process (AR), the autoregressive moving average process (ARMA), and the ARIMA process.
- Step 6 According to the ARIMA model, predict the number of system monthly power outages in the most recent year; obtain the operational reliability index of the distribution network throughout the year.
- step 1 including:
- Y t ⁇ 1 Y t-1 + ⁇ 2 Y t-2 +...+ ⁇ p Y tp +e t - ⁇ 1 e t-1 - ⁇ 2 e t-2 -...- ⁇ q e Tq (1)
- Y t is the sequence value of the monthly average power outage times from 0 to t
- p is the number of autoregressive terms of the model
- q is the moving average number of the model
- e is the white noise of each sequence change
- ⁇ and ⁇ is a different unknown parameter to be sought
- t is the number of months included in the calculation window.
- Step 2 including:
- a n is a constant, representing the mean of the number of monthly power outages; ⁇ t,tk ⁇ 0,k indicates that the covariances of the sequences Y tk , Y t and the sequences Y and Y k are approximate; k is the presence of a parameter k
- the method of correcting the monthly average power outage sequence in step 3 is to modify the time frequency of the monthly average power outage sequence or to logarithmically differentiate the monthly average power outage sequence.
- step 4 includes:
- n is the number of samples with monthly average power outage
- ⁇ is the sum of squared residuals
- p is the number of autoregressive terms for the model
- q is the moving average number of the model
- step 5 includes:
- the initial type is the final type of the ARIMA model; the ARIMA model is established;
- step 6 includes:
- the TLOC criterion and the PLOC criterion based on the real-time operation of the system partition the prediction system of the transmission network and establish an accident mode affecting the FMEA table; the TLOC criterion is that all the connectivity criteria are lost; and the PLOC criterion is a partial loss connectivity criterion;
- a Laplace smooth fault component sample set is established, and the number of power failures, power outage time, and power shortage of the power loss load point are obtained;
- Res l (e, w) sampling in the historical fault information sample set, if the number of times each device is drawn is 0, a minimum value is assigned, and vice versa, the device is sampled. frequency. Forming a Res l (e, w) set;
- w' is the number of times of all e ⁇ S elements
- min(w') is the minimum value of the number of occurrences of e
- e is the accident component
- w is the accident probability weight
- Reading the accident mode affects the FMEA table, and finds the power loss load point affected by the failed component of the power failure event in the event of a failure;
- an embodiment of the present invention provides a method for predicting operational reliability of a distribution network.
- the application example predicts the reliability index for the next year based on the number of user historical power outages as an input to the ARIMA model. Because there are many nodes in the complex distribution network, the number of monthly power outages is highly correlated with time-related attributes such as load changes, seasons and weather, because load forecasting and weather forecasting can be solved using the autoregressive differential moving average model.
- the actual production data results in the autocorrelation coefficient after differential or frequency modulation is also greater than 0, 5, so the number of monthly power outages with a time series model to solve is scientific.
- the invention includes two key steps of establishing an ARIMA model and calculating a reliability index based on historical fault set sampling, and the specific steps are as follows:
- the pre-processing of the time series analysis determines that the time series period is month, and extracts the observation value of the number of user monthly power outages from the historical data as the sequence Y.
- Y t is the sequence value of the monthly average power outage times from 0 to t
- p is the number of autoregressive terms of the model
- q is the moving average number of the model
- e is the white noise of each sequence change.
- the mean value A n of the number of monthly power outages in (3) is a constant, and the equation (4) indicates that the covariance of the sequences Y tk , Y t and the sequences Y and Y k are approximate for any t time.
- the ARIMA model order range is obtained, and then the AIC value is calculated separately from low to high, that is, the minimum order of the AIC is determined by the minimum information rule.
- the AIC function is defined as follows:
- n is the number of power outage samples
- ⁇ is the sum of squared residuals
- p and q are the orders of the ARIMA model.
- regression analysis is performed using the least squares method, and the ARIMA model is fitted, and the residual sequence of the model is detected as white noise. If the detection does not continue, regression analysis.
- the reliability index is calculated based on the historical fault set sampling in the application example, and includes the following steps:
- the system is partitioned based on the TLOC and PLOC criteria of the real-time operation of the system and the FMEA table (accident mode impact table) is established.
- the ARIMA model prediction result is used to establish a set of annual power outage events.
- a faulty component sample set is built based on Laplacian smoothing for each component based on a historical fault information sample set. Let the component set be E and the historical fault set be S, then the fault component sample set Res l (e, w) is established according to the following rules (where e represents the accident component and w represents the accident probability weight).
- w i is the number of occurrences. If it does not occur, the following formula:
- w' is the number of times of all e ⁇ S elements
- min(w') represents the minimum value of the number of occurrences of e.
- step 3 Read the FMEA table to find the load points affected by the failure of the power failure event in this power failure event, and record the number of power outages, power outage time, and power shortage. Determine whether the power failure event set is completed. If it is reached, go to step 4, otherwise return to step 2.
- Step 1 Extract and pre-process the number of monthly power outages of users in the historical data of the distribution network system, and obtain a sequence of monthly average power outages in a time period;
- Step 2 determining whether the monthly average power outage sequence is a stationary sequence
- Step 3 correct the sequence of the monthly average power outages, and return to step 2;
- Step 4 obtaining, according to the sequence of the average number of power outages in the month, the values of the number of autoregressive items and the moving average number of the ARIMA;
- Step 5 Establish and modify the ARIMA model
- Step 6 According to the ARIMA model, predict the number of monthly power outages of the distribution network system in the most recent year, and obtain the operational reliability index of the distribution network throughout the year. In this way, accurate prediction and assessment of the reliability of the distribution network is realized, which effectively and accurately guides the planning, design, operation and maintenance of the distribution network in the future, and improves the accuracy of the reliability prediction and assessment of the distribution network.
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Data Mining & Analysis (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Operations Research (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Power Engineering (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Health & Medical Sciences (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Physics (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Probability & Statistics with Applications (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Algebra (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Software Systems (AREA)
- Primary Health Care (AREA)
- Life Sciences & Earth Sciences (AREA)
- Databases & Information Systems (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
一种配电网运行可靠性的预测方法,所述方法基于ARIMA模型,通过建立ARIMA模型预测用户月停电次数,将非平稳元件失效次数时间序列转化为平稳时间序列,然后将因变量仅对它的滞后值以及随机误差项的现值和滞后值进行回归建立用户月停电次数模型;根据预测结果抽样停运点,同时考虑实时负荷运行状况基于TLOC准则和PLOC准则建立故障模式影响表;计算每次设备停运的***恢复时间最终获得全年可靠性指标。所述预测方法,有效且准确的指导未来配电网的规划、设计、运行和维修,提高了配电网运行可靠性预测评估的准确性,实现了配电网安全稳定运行降低停电频率、缩小停电范围。
Description
本发明涉及配电网运行可靠性评估领域,具体涉及一种配电网运行可靠性的预测方法。
配电网处于电力***末端与用户直接相连起分配电能作用,大规模和长时间的停电不仅造成巨大的损失而且威胁到社会秩序,配电网的可靠性直接影响用户是否获得合格的电能,所以电力公司不断提高供电质量和不间断的电力供应能力,使得电网能够在合理的投资范围内减少停电的可能。
配电网的结构复杂庞大设备繁多,由架空线路、电缆连接大量的电源点、负荷、变压器、开关以及各类一次及二次设备等元件组成,网络结构由于故障、负荷转移或是运行调整经常发生变化,导致配电网一些设备停运模型复杂、***动态特性较强和失效后果多变,给实时运行中的配电网可靠性评估带来了一定的难度。现有对配电网进行可靠性评估的方法主要可以分两种:基于传统解析法的配电网可靠性评估和基于蒙特卡洛抽样的可靠性评估。基于传统解析法的配电网可靠性评估用数学模型来描述元件与***间的可靠性关系,通过输入元件和***的全过程寿命求得最终的可靠性指标,主要包括故障模式后果分析法、最小路算法、网络等值法、馈线分区算法等,解析法具有严格的数学逻辑但随着***模型的扩大时间复杂度呈指数型增加。基于蒙特卡洛等模拟算法的配电网可靠性评估以概率统计理论为数学基础,使用计算机抽样方法对***可靠性进行评估,由于抽样算法基于大数定理,所以在***元件较少或者故障频率较少的情况下样本方差过大导致最终结果不准确。运行可靠性评估不同于规划和设计可
靠性评估,随着量测设备的大量覆盖,配电网运行中具有详实的统计资料和实时的运行维修策略,除了可以提供元件的平均失效率和修复率,还能提供修复时间,转供方案、实时/历史遥信遥测量,历史停电数据等。其次在实际生产环节中,***的状态改变存在记忆性即事件发生的时间不满足指数分布,设备元件发生停运事件并非相互独立而是具有一定的相关性。再次可靠性指标本质上是一个取决于网络拓扑、运行方式、***负荷和元件随机停运及随机修复等诸多相关因素的随机变量,作为反馈的信息量还是存在不确定性。
综上所述,如何利用好历史或实时数据实现配电网可靠性的精确预测评估成为当前亟待解决的问题。
发明内容
有鉴于此,本发明实施例期望提供一种配电网运行可靠性的预测方法,该方法能够有效且准确的指导未来配电网的规划、设计、运行和维修,提高配电网运行可靠性预测评估的准确性,实现配电网安全稳定运行降低停电频率、缩小停电范围。
本发明的目的是通过以下技术方案实现的:
一种配电网运行可靠性的预测方法,所述方法基于自回归积分滑动平均模型(Autoregressive Integrated Moving Average Model,ARIMA),所述方法包括如下步骤:
步骤1、提取并预处理所述配电网***的历史数据中的用户的月停电次数,得到一个时间段内的月平均停电次数序列;
步骤2、判断所述月平均停电次数序列是否为平稳序列;
若是,则进入步骤4;
若否,则进入步骤3;
步骤3、修正所述月平均停电次数序列;返回步骤2;
步骤4、根据所述月平均停电次数序列;得到ARIMA的自回归项数及移动平均项数的值;
步骤5、建立并修正ARIMA模型;
步骤6、根据ARIMA模型预测最近一年的所述配电网***月停电次数;获得所述配电网全年的运行可靠性指标。
在一实施例中,所述步骤1,包括:
1-1、提取所述配电网的历史数据中的用户的月停电次数序列Y;
1-2、预处理所述月停电次数序列Y,得到一个时间段内的月平均停电次数序列Yt:
Yt=φ1Yt-1+φ2Yt-2+...+φpYt-p+et-θ1et-1-θ2et-2-...-θqet-q (1)
式中,Yt为0至t时刻的月平均停电次数序列值,p为该模型的自回归项数,q为该模型的移动平均项数,e为每次序列变化的白噪声;φ与θ为不同的未知待求参数;t是计算窗口中所含的月数。
在一实施例中,所述步骤2,包括:
根据公式(2)和公式(3)判断所述月平均停电次数序列是否为平稳序列:
γt,t-k≈γ0,k (3)
式中,An为常数、表示月停电次数的均值;γt,t-k≈γ0,k表示序列Yt-k、Yt和序列Y、Yk的协方差是近似的;k是存在一个参数k使得任意时刻公式(3)都成立;K表示一个常数;t是计算窗口中所含的月数。
若公式(2)和公式(3)均成立,则进入步骤4;
若公式(2)或公式(3)中的任一不成立,则进入步骤3;
在一实施例中,所述步骤3中修正所述月平均停电次数序列的方法为修改所述月平均停电次数序列的时间频率、或对所述月平均停电次数序列进行对数差分。
在一实施例中,所述步骤4,包括:
4-1、建立所述月平均停电次数序列的自相关函数及偏自相关函数;
4-2、求解所述月平均停电次数序列的自相关函数及偏自相关函数;得到ARIMA模型的自回归项数及移动平均项数的值集;
4-3、建立最小信息准则AIC函数:
AIC=nlogσ2+2(p+q) (4)
式中,n为月平均停电的样本数,σ是残差平方和,p为该模型的自回归项数,q为该模型的移动平均项数;
4-4、筛选出得出使得AIC的值最小的pm和qm作为所述自回归项数p及移动平均项数q的最终取值;pm是所述自回归项数p的所述值集中的某一个值;qm是所述移动平均项数q的所述值集中的某一个值。
在一实施例中,所述步骤5,包括:
5-1、根据最小二乘法对所述自回归项数及移动平均项数的值进行回归分析,得到所述ARIMA模型的初型;
5-2、检测所述初型的残差序列;
若所述残差序列为白噪声,则所述初型即为所述ARIMA模型的最终型;所述ARIMA模型建立完成;
若所述残差序列不为白噪声,则返回5-1。
在一实施例中,所述步骤6,包括:
6-1、根据所述ARIMA模型,预测并提取最近一年的所述配电网***月停电次数;并建立历史故障信息样本集;
6-2、根据历史故障信息样本集,建立拉普拉斯平滑的故障元件样本集,
并得到失电负荷点的停电次数、停电时间和缺供电量;
6-3、获得所述配电网全年的运行可靠性指标。
在一实施例中,所述6-1,包括:
a、基于所述配电网***实时运行情况的TLOC准则及PLOC准则对所述配电网***进行分区并建立事故模式影响FMEA表;
b、求解所述ARIMA模型,得到预测的全年的所述配电网***月停电次数;并建立历史故障信息样本集。
在一实施例中,所述6-2,包括:
c、根据所述历史故障信息样本集,建立拉普拉斯平滑的故障元件样本集Resl(e,w):
设元件样本集为E,历史故障集合为S,则故障元件样本集合Resl(e,w)按以下的规则建立;
对于某元件ei,若在历史故障集合S中出现的次数为wi若没有发生,则有以下公式:
上式中w'为所有e∈S元件的次数,min(w')表示其中e出现过次数的最小值,e表示事故元件,w表示事故概率权重;
d、循环读取所述故障元件样本集中的每一次停电事件,同时在所述故障元件样本集中抽取对应的失效元件;
e、读取所述事故模式影响FMEA表,并查找本次停电事件的所述失效元件在故障时所影响的失电负荷点;
f、记录所述失电负荷点的停电次数、停电时间和缺供电量;
g、判断所述故障元件样本集中的全部停电事件是否均循环完成;
若是,则跳转到6-3;
若否,则返回到d。
在一实施例中,所述6-3,包括:
h、统计各个所述失电负荷点的停电次数、停电时间和缺供电量;
i、计算出整个所述配电网***的全年的运行可靠性指标整个所述配电网***的可靠性指标。
从上述的技术方案可以看出,本发明提供了一种基于ARIMA模型的配电网运行可靠性的预测方法,通过建立ARIMA模型预测用户月停电次数,将非平稳元件失效次数时间序列转化为平稳时间序列,然后将因变量仅对它的滞后值以及随机误差项的现值和滞后值进行回归建立用户月停电次数模型;根据预测结果抽样停运点,同时考虑实时负荷运行状况基于TLOC准则和PLOC准则建立故障模式影响表;计算每次设备停运的***恢复时间最终获得全年可靠性指标。本发明提出的预测方法,综合考虑配电网实时运行状态和调整策略,深度挖掘历史数据中的***风险特性,有效且准确的指导未来配电网的规划、设计、运行和维修,提高了配电网运行可靠性预测评估的准确性,实现了配电网安全稳定运行降低停电频率、缩小停电范围,加强了***中可靠性的薄弱环节,并提出有效的改进措施,能够精确挖掘***内在风险的统计特性和发展规律性。突出配电网可靠性的趋势性、季节性和周期性。
与最接近的现有技术比,本发明实施例提供的技术方案具有以下优异效果:
1、本发明实施例所提供的技术方案中,采用ARIMA方法进行配电网运行可靠性评估,综合考虑配电网实时运行状态和调整策略,深度挖掘历史数据中的***风险特性,实现配电网可靠性的准确有效预测评估;有效且准确的指导未来配电网的规划、设计、运行和维修,提高了配电网运行可靠性预测评估的准确性。
2、本发明实施例所提供的技术方案,其考虑历史数据和实时运行条件
能够真实反映***中负荷和网架的动态变化对配电网可靠性的影响,算法高效且不因数据噪音过多而影响结果精度。
3、本发明实施例所提供的技术方案,实现的对***停电次数的预测是一种科学有效的短期预测方法,能够精确挖掘***内在风险的统计特性和发展规律性。突出配电网可靠性的趋势性、季节性和周期性。
4、本发明实施例提供的技术方案,应用广泛,具有显著的社会效益和经济效益。
图1是本发明实施例的配电网运行可靠性的预测方法的流程示意图;
图2是本发明实施例的预测方法中的步骤1的流程示意图;
图3是本发明实施例的预测方法中的步骤4的流程示意图;
图4是本发明实施例的预测方法中的步骤5的流程示意图;
图5是本发明实施例的预测方法中的步骤6的流程示意图;
图6是本发明实施例的预测方法的应用例的流程示意图;
图7是本发明实施例的预测方法中的应用例的基于历史故障集抽样计算可靠性指标的流程示意图。
下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。
如图1所示,本发明实施例提供一种配电网运行可靠性的预测方法,所述方法基于ARIMA模型,依据用户历史停电次数作为ARIMA模型的输
入,预测未来一年的可靠性指标。因为复杂配电网的节点较多,月停电次数与负荷变化、季节和天气这些与时间相关的属性关联度较大,因为负荷预测、天气预测都可以使用自回归差分滑动平均模型来解决,基于实际的生产数据结果差分或调频后的自相关系数也大于0、5,所以月停电次数用时间序列模型来解决具有一定的科学性;
包括如下步骤:
步骤1、提取并预处理配电网的历史数据中的用户的月停电次数,得到一个时间段内的月平均停电次数序列;
步骤2、判断月平均停电次数序列是否为平稳序列;
若是,则进入步骤4;
若否,则进入步骤3;
步骤3、修正月平均停电次数序列;返回步骤2;
步骤4、根据月平均停电次数序列;得到ARIMA的自回归项数及移动平均项数的值;
步骤5、建立并修正ARIMA模型;其中,ARIMA模型又称为box-jenkins模型、博克思-詹金斯法。其中ARIMA(p,d,q)称为差分自回归移动平均模型,AR是自回归,p为自回归项;MA为移动平均,q为移动平均项数,d为时间序列成为平稳时所做的差分次数。所谓ARIMA模型,是指将非平稳时间序列转化为平稳时间序列,然后将因变量仅对它的滞后值以及随机误差项的现值和滞后值进行回归所建立的模型。ARIMA模型根据原序列是否平稳以及回归中所含部分的不同,包括移动平均过程(MA)、自回归过程(AR)、自回归移动平均过程(ARMA)以及ARIMA过程。
步骤6、根据ARIMA模型预测最近一年的***月停电次数;获得配电网全年的运行可靠性指标。
如图2所示,步骤1,包括:
1-1、提取配电网的历史数据中的用户的月停电次数序列Y;
1-2、预处理月停电次数序列Y,得到一个时间段内的月平均停电次数序列Yt:
Yt=φ1Yt-1+φ2Yt-2+...+φpYt-p+et-θ1et-1-θ2et-2-...-θqet-q (1)
式中,Yt为0至t时刻的月平均停电次数序列值,p为该模型的自回归项数,q为该模型的移动平均项数,e为每次序列变化的白噪声;φ与θ为不同的未知待求参数;t是计算窗口中所含的月数。
步骤2,包括:
根据公式(2)和公式(3)判断月平均停电次数序列是否为平稳序列:
γt,t-k≈γ0,k (3)
若公式(2)和公式(3)均成立,则进入步骤4;
若公式(2)或公式(3)中的任一不成立,则进入步骤3;
式中,An为常数、表示月停电次数的均值;γt,t-k≈γ0,k表示序列Yt-k、Yt和序列Y、Yk的协方差是近似的;k是存在一个参数k使得任意时刻公式(3)都成立;K表示一个常数;t是计算窗口中所含的月数。
步骤3中修正月平均停电次数序列的方法为修改月平均停电次数序列的时间频率、或对月平均停电次数序列进行对数差分。
如图3所示,步骤4,包括:
4-1、建立月平均停电次数序列的自相关函数及偏自相关函数;
4-2、求解月平均停电次数序列的自相关函数及偏自相关函数;得到ARIMA模型的自回归项数及移动平均项数的值集;
4-3、建立最小信息准则AIC函数,将值集中的值依次代入AIC函数求解AIC的值:
AIC=nlogσ2+2(p+q) (4)
式中,n为月平均停电的样本数,σ是残差平方和,p为该模型的自回归项数,q为该模型的移动平均项数;
4-4、筛选出得出使得AIC的值最小的pm和qm作为自回归项数p及移动平均项数q的最终取值。
如图4所示,步骤5,包括:
5-1、根据最小二乘法对自回归项数及移动平均项数的值进行回归分析,得到ARIMA模型的初型;
5-2、检测初型的残差序列;
若残差序列为白噪声,则初型即为ARIMA模型的最终型;ARIMA模型建立完成;
若残差序列不为白噪声,则返回5-1。
如图5所示,步骤6,包括:
6-1、根据ARIMA模型,预测并提取最近一年的***月停电次数;并建立历史故障信息样本集;包括:
a、基于***实时运行情况的TLOC准则及PLOC准则对输电网的预测***进行分区并建立事故模式影响FMEA表;TLOC准则为全部失去连通性准则;PLOC准则为部分失去连通性准则;
b、求解ARIMA模型,得到预测的全年的***月停电次数;并建立历史故障信息样本集。
6-2、根据历史故障信息样本集,建立拉普拉斯平滑的故障元件样本集,并得到失电负荷点的停电次数、停电时间和缺供电量;包括:
c、根据历史故障信息样本集,建立拉普拉斯平滑的故障元件样本集
Resl(e,w):
其中,Resl(e,w)的求解过程为:在历史故障信息样本集中进行抽样,每一个设备被抽到的次数若为0,则赋一个最小值,反则增加该设备被抽样到的次数。形成Resl(e,w)集合;
设元件样本集为E,历史故障集合为S,则故障元件样本集合Resl(e,w)按以下的规则建立;
对于某元件ei,若在历史故障集合S中出现的次数为wi若没有发生,则有以下公式:
上式中w'为所有e∈S元件的次数,min(w')表示其中e出现过次数的最小值,e表示事故元件,w表示事故概率权重;
d、循环读取故障元件样本集中的每一次停电事件,同时在故障元件样本集中抽取对应的失效元件;
e、读取事故模式影响FMEA表,并查找本次停电事件的失效元件在故障时所影响的失电负荷点;
f、记录失电负荷点的停电次数、停电时间和缺供电量;
g、判断故障元件样本集中的全部停电事件是否均循环完成;
若是,则跳转到6-3;
若否,则返回到d。
6-3、获得配电网全年的运行可靠性指标,包括:
h、统计各个失电负荷点的停电次数、停电时间和缺供电量;
i、计算出整个输电网***的全年的运行可靠性指标整个***的可靠性指标。
如图6所示,本发明实施例提供一种配电网运行可靠性的预测方法的
应用例,依据用户历史停电次数作为ARIMA模型的输入,预测未来一年的可靠性指标。因为复杂配电网的节点较多,月停电次数与负荷变化、季节和天气这些与时间相关的属性关联度较大,因为负荷预测、天气预测都可以使用自回归差分滑动平均模型来解决,基于实际的生产数据结果差分或调频后的自相关系数也大于0、5,所以月停电次数用时间序列模型来解决具有一定的科学性。本发明包括建立ARIMA模型和基于历史故障集抽样计算可靠性指标两个关键步骤,具体步骤如下:
1、时间序列分析的预处理,确定时间序列周期为月,从历史数据中提取用户月停电次数观测值为序列Y。建立自回归滑动模型:
Yt=φ1Yt-1+φ2Yt-2+...+φpYt-p+et-θ1et-1-θ2et-2-...-θqet-q (2)
上式中,Yt为0至t时刻的月平均停电次数序列值,p为该模型的自回归项数,q为该模型的移动平均项数,e为每次序列变化的白噪声。
2、检测由用户月停电次数组成的时间序列是否满足平稳条件,即满足:
γt,t-k≈γ0,k (4)
其中(3)中表示月停电次数的均值An为常数,式(4)表示对于任意t时刻,序列Yt-k、Yt和序列Y、Yk的协方差是近似的。
3、首先基于平稳时间序列自相关函数和偏相关函数求得ARIMA模型阶数范围,然后低至高分别计算其AIC值也就是利用最小信息定则确定使得AIC最小的阶数。AIC函数定义如下:
AIC=nlogσ2+2(p+q) (5)
式中,n为停电样本数,σ是残差平方和,p、q是ARIMA模型的阶数。
4、基于步骤3确定的模型阶数使用最小二乘法进行回归分析,拟合ARIMA模型,同时检测模型的残差序列是否为白噪声,若检测不通过继续
回归分析。
5、利用生成的ARIMA模型预测最近一年的***月停电次数,提取***历史故障信息使用拉普拉斯平滑建立故障点样本集,循环读取每一次停电事件进行抽样求出每一次停电所故障修复时间、故障隔离时间和负荷转供时间。最终获得全年可靠性指标。
如图7所示,该应用例中的基于历史故障集抽样计算可靠性指标,包括以下步骤:
1、基于***实时运行情况的TLOC、PLOC准则对***进行分区并建立FMEA表(事故模式影响表),利用ARIMA模型预测结果建立全年停电事件集。基于历史故障信息样本集对每一个元件基于拉普拉斯平滑建立故障元件样本集。设元件集合为E,历史故障集合为S,则故障元件样本集合Resl(e,w)按以下的规则建立(其中e表示事故元件,w表示事故概率权重)。
对于某元件ei,若在集合S中出现过则wi为其出现的次数,若没有发生,则有以下公式:
上式中w'为所有e∈S元件的次数,min(w')表示其中e出现过次数的最小值。
2、循环读取每一次停电事件,同时在样本集中抽取对应失效元件。
3、读取FMEA表,查找本次停电事件失效元件故障时影响的负荷点,记录这些失电负荷点的停电次数、停电时间、缺供电量等信息。判断停电事件集是否循环完成,若达到则跳转到步骤4,否则返回到步骤2。
4、循环结束,统计各个模拟年的负荷点和***的可靠性指标,进而计算整个***的可靠性指标。
以上实施例仅用以说明本发明的技术方案而非对其限制,尽管参照上述实施例对本发明进行了详细的说明,所属领域的普通技术人员依然可以
对本发明的具体实施方式进行修改或者等同替换,而这些未脱离本发明精神和范围的任何修改或者等同替换,其均在申请待批的本发明的权利要求保护范围之内。
本发明实施例提供的配电网运行可靠性的预测方法包括:
步骤1、提取并预处理所述配电网***的历史数据中的用户的月停电次数,得到一个时间段内的月平均停电次数序列;
步骤2、判断所述月平均停电次数序列是否为平稳序列;
若是,则进入步骤4;
若否,则进入步骤3;
步骤3、修正所述月平均停电次数序列,返回步骤2;
步骤4、根据所述月平均停电次数序列,得到ARIMA的自回归项数及移动平均项数的值;
步骤5、建立并修正ARIMA模型;
步骤6、根据ARIMA模型预测最近一年的所述配电网***月停电次数,获得所述配电网全年的运行可靠性指标。如此,实现了配电网可靠性的准确预测评估,有效且准确的指导未来配电网的规划、设计、运行和维修,提高了配电网运行可靠性预测评估的准确性。
Claims (10)
- 一种配电网运行可靠性的预测方法,所述方法基于自回归积分滑动平均模型ARIMA,所述方法包括如下步骤:步骤1、提取并预处理所述配电网***的历史数据中的用户的月停电次数,得到一个时间段内的月平均停电次数序列;步骤2、判断所述月平均停电次数序列是否为平稳序列;若是,则进入步骤4;若否,则进入步骤3;步骤3、修正所述月平均停电次数序列,返回步骤2;步骤4、根据所述月平均停电次数序列,得到ARIMA的自回归项数及移动平均项数的值;步骤5、建立并修正ARIMA模型;步骤6、根据ARIMA模型预测最近一年的所述配电网***月停电次数,获得所述配电网全年的运行可靠性指标。
- 如权利要求1所述的方法,其中,所述提取并预处理所述配电网***的历史数据中的用户的月停电次数,得到一个时间段内的月平均停电次数序列,包括:步骤1-1、提取所述配电网的历史数据中的用户的月停电次数序列Y;步骤1-2、预处理所述月停电次数序列Y,得到一个时间段内的月平均停电次数序列Yt:Yt=φ1Yt-1+φ2Yt-2+...+φpYt-p+et-θ1et-1-θ2et-2-...-θqet-q (1)式中,Yt为0至t时刻的月平均停电次数序列值,p为该模型的自回归项数,q为该模型的移动平均项数,e为每次序列变化的白噪声;φ与θ为不同的未知待求参数;t是计算窗口中所含的月数。
- 如权利要求3所述的方法,其中,所述修正所述月平均停电次数序列,包括:修改所述月平均停电次数序列的时间频率、或对所述月平均停电次数序列进行对数差分。
- 如权利要求4所述的方法,其中,所述根据所述月平均停电次数序列,得到ARIMA的自回归项数及移动平均项数的值,包括:步骤4-1、建立所述月平均停电次数序列的自相关函数及偏自相关函数;步骤4-2、求解所述月平均停电次数序列的自相关函数及偏自相关函数,得到ARIMA模型的自回归项数及移动平均项数的值集;步骤4-3、建立最小信息准则AIC函数:AIC=nlogσ2+2(p+q) (4)式中,n为月平均停电的样本数,σ是残差平方和,p为该模型的自回 归项数,q为该模型的移动平均项数;步骤4-4、筛选出得出使得AIC的值最小的pm和qm作为所述自回归项数p及移动平均项数q的最终取值;pm是所述自回归项数p的所述值集中的某一个值;qm是所述移动平均项数q的所述值集中的某一个值。
- 如权利要求5所述的方法,其中,所述建立并修正ARIMA模型,包括:步骤5-1、根据最小二乘法对所述自回归项数及移动平均项数的值进行回归分析,得到所述ARIMA模型的初型;步骤5-2、检测所述初型的残差序列;若所述残差序列为白噪声,则所述初型即为所述ARIMA模型的最终型;所述ARIMA模型建立完成;若所述残差序列不为白噪声,则返回步骤5-1。
- 如权利要求6所述的方法,其中,所述根据ARIMA模型预测最近一年的所述配电网***月停电次数,获得所述配电网全年的运行可靠性指标,包括:步骤6-1、根据所述ARIMA模型,预测并提取最近一年的所述配电网***月停电次数,并建立历史故障信息样本集;步骤6-2、根据历史故障信息样本集,建立拉普拉斯平滑的故障元件样本集,并得到失电负荷点的停电次数、停电时间和缺供电量;步骤6-3、获得所述配电网全年的运行可靠性指标。
- 如权利要求7所述的方法,其中,所述根据所述ARIMA模型,预测并提取最近一年的所述配电网***月停电次数,并建立历史故障信息样本集,包括:步骤a、基于所述配电网***实时运行情况的TLOC准则及PLOC准则对所述配电网***进行分区并建立事故模式影响FMEA表;步骤b、求解所述ARIMA模型,得到预测的全年的所述配电网***月停电次数,并建立历史故障信息样本集。
- 如权利要求8所述的方法,其中,所述根据历史故障信息样本集,建立拉普拉斯平滑的故障元件样本集,并得到失电负荷点的停电次数、停电时间和缺供电量,包括:步骤c、根据所述历史故障信息样本集,建立拉普拉斯平滑的故障元件样本集Resl(e,w):设元件样本集为E,历史故障集合为S,则故障元件样本集合Resl(e,w)按以下的规则建立;对于某元件ei,若在历史故障集合S中出现的次数为wi若没有发生,则有以下公式:上式中w'为所有e∈S元件的次数,min(w')表示其中e出现过次数的最小值,e表示事故元件,w表示事故概率权重;步骤d、循环读取所述故障元件样本集中的每一次停电事件,同时在所述故障元件样本集中抽取对应的失效元件;步骤e、读取所述事故模式影响FMEA表,并查找本次停电事件的所述失效元件在故障时所影响的失电负荷点;步骤f、记录所述失电负荷点的停电次数、停电时间和缺供电量;步骤g、判断所述故障元件样本集中的全部停电事件是否均循环完成;若是,则跳转到步骤6-3;若否,则返回到步骤d。
- 如权利要求9所述的方法,其中,所述获得所述配电网全年的运行可靠性指标,包括:步骤h、统计各个所述失电负荷点的停电次数、停电时间和缺供电量;步骤i、计算出整个所述配电网***的全年的运行可靠性指标整个所述配电网***的可靠性指标。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US15/752,030 US10825112B2 (en) | 2015-08-19 | 2016-06-30 | Method of predicting distribution network operation reliability |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510511780.0A CN105069535B (zh) | 2015-08-19 | 2015-08-19 | 一种基于arima模型的配电网运行可靠性的预测方法 |
CN201510511780.0 | 2015-08-19 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2017028632A1 true WO2017028632A1 (zh) | 2017-02-23 |
Family
ID=54498895
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2016/087815 WO2017028632A1 (zh) | 2015-08-19 | 2016-06-30 | 一种配电网运行可靠性的预测方法 |
Country Status (3)
Country | Link |
---|---|
US (1) | US10825112B2 (zh) |
CN (1) | CN105069535B (zh) |
WO (1) | WO2017028632A1 (zh) |
Cited By (21)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108335057A (zh) * | 2018-04-18 | 2018-07-27 | 国网天津市电力公司电力科学研究院 | 农村分散式煤改电供暖可靠性评估方法 |
CN109657420A (zh) * | 2019-02-21 | 2019-04-19 | 中国人民解放军战略支援部队航天工程大学 | 一种基于航天任务的装备保障特性仿真建模方法 |
CN109977577A (zh) * | 2019-04-03 | 2019-07-05 | 合肥工业大学 | 考虑天然气输配管网不确定性的气-电耦合***可靠性评估方法 |
CN110457651A (zh) * | 2019-07-05 | 2019-11-15 | 中国地质大学(武汉) | 一种利用数据同化改进的滑坡位移预测方法 |
CN110598082A (zh) * | 2019-09-17 | 2019-12-20 | 安徽信息工程学院 | 一种基于pm2.5数据采集预测方法 |
CN110598899A (zh) * | 2019-07-31 | 2019-12-20 | 国网上海市电力公司 | 一种基于数据预测的地方电能表申校服务规划方法及*** |
CN111427753A (zh) * | 2020-03-23 | 2020-07-17 | 上海新炬网络信息技术股份有限公司 | 基于arima模型的容量预测装置及其控制方法 |
CN111461786A (zh) * | 2020-04-03 | 2020-07-28 | 中南大学 | 基于Prophet-CEEMDAN-ARIMA的商品销量预测方法及装置 |
CN111581772A (zh) * | 2020-03-31 | 2020-08-25 | 国网经济技术研究院有限公司 | 一种用于配电网的配置方案优化方法和*** |
CN111626498A (zh) * | 2020-05-25 | 2020-09-04 | 成都中电熊猫显示科技有限公司 | 设备运行状态预测方法、装置、设备及存储介质 |
CN111861141A (zh) * | 2020-06-29 | 2020-10-30 | 国网上海市电力公司 | 一种基于模糊故障率预测的配电网可靠性评估方法 |
CN112345972A (zh) * | 2020-11-02 | 2021-02-09 | 国网江苏省电力有限公司电力科学研究院 | 基于停电事件的配电网线变关系异常诊断方法、装置及*** |
CN112350331A (zh) * | 2020-10-21 | 2021-02-09 | 广东电网有限责任公司 | 一种中压馈线预安排停电优化方法和装置 |
CN112365087A (zh) * | 2020-11-26 | 2021-02-12 | 深圳供电局有限公司 | 一种预测年度不停电作业需求量的方法 |
CN112446619A (zh) * | 2020-11-27 | 2021-03-05 | 国网北京市电力公司 | 配电网抢修处理方法和装置 |
CN112561312A (zh) * | 2020-12-14 | 2021-03-26 | 国网河南省电力公司电力科学研究院 | 一种基于电能质量因素的配电网线损计算方法及*** |
CN113095534A (zh) * | 2019-12-23 | 2021-07-09 | 南京理工大学 | 一种ARIMA与改进Elman神经网络组合的风电功率预测方法 |
CN113129161A (zh) * | 2019-12-31 | 2021-07-16 | 国网山东省电力公司威海供电公司 | 基于小波分解和神经网络的架空配电***中动物故障分析方法 |
CN113642766A (zh) * | 2021-07-08 | 2021-11-12 | 南方电网科学研究院有限责任公司 | 一种电力***站点停电数的预测方法、装置、设备及介质 |
CN114034978A (zh) * | 2021-11-11 | 2022-02-11 | 四川中电启明星信息技术有限公司 | 一种用于配网类项目的自动模型检测方法及*** |
CN114205190A (zh) * | 2021-12-03 | 2022-03-18 | 中国长江三峡集团有限公司 | 一种用于物联网关的自主监测协调方法 |
Families Citing this family (69)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105069535B (zh) | 2015-08-19 | 2020-07-24 | 中国电力科学研究院 | 一种基于arima模型的配电网运行可靠性的预测方法 |
CN105844346A (zh) * | 2016-03-17 | 2016-08-10 | 福州大学 | 基于arima模型的航班延误预测方法 |
CN106446016A (zh) * | 2016-08-29 | 2017-02-22 | 中国电力科学研究院 | 一种基于并行关联规则挖掘的配电网运行可靠性预测方法 |
CN107993033A (zh) * | 2017-11-14 | 2018-05-04 | 广东电网有限责任公司物流服务中心 | 一种电力物资预测方法 |
CN108664741B (zh) * | 2018-05-14 | 2022-02-08 | 平顶山学院 | 基于时间序列模型特征的变电站故障检测方法 |
CN108960520A (zh) * | 2018-07-12 | 2018-12-07 | 南方电网科学研究院有限责任公司 | 一种电力负荷预测方法、***、计算机设备、介质 |
CN109447388A (zh) * | 2018-09-03 | 2019-03-08 | 广东电网有限责任公司 | 考虑多时段特性的配电网供电可靠性动态综合评估方法 |
CN109165873B (zh) * | 2018-09-20 | 2021-11-05 | 沈阳鼓风机集团股份有限公司 | 一种UMDs***可靠性评估方法 |
CN109325622A (zh) * | 2018-09-26 | 2019-02-12 | 巢湖学院 | 一种电力***负荷预测的方法 |
CN109086889B (zh) * | 2018-09-30 | 2021-05-11 | 广东电网有限责任公司 | 基于神经网络的终端故障诊断方法、装置和*** |
CN109657835A (zh) * | 2018-10-31 | 2019-04-19 | 中国电力科学研究院有限公司 | 一种配电网区域故障次数预测方法及*** |
CN111199492B (zh) * | 2018-11-16 | 2023-09-08 | 华北电力大学 | 一种基于云计算的新能源电力***可靠性快速求解方法 |
CN109596913B (zh) * | 2018-11-26 | 2021-06-15 | 国网冀北电力有限公司 | 充电桩故障原因诊断方法及装置 |
CN109638959B (zh) * | 2018-12-06 | 2022-06-17 | 杭州意能电力技术有限公司 | 基于ar和深度学习的电力设备遥信功能调试方法及*** |
CN109711715A (zh) * | 2018-12-25 | 2019-05-03 | 国家电网有限公司 | 电力通信网风险评估方法 |
CN110232203B (zh) * | 2019-04-22 | 2020-03-03 | 山东大学 | 知识蒸馏优化rnn短期停电预测方法、存储介质及设备 |
CN110188998B (zh) * | 2019-05-13 | 2021-03-16 | 湖北省电力勘测设计院有限公司 | 配电网的风电机组和储能的时序建设可靠性的评估方法 |
CN110188122B (zh) * | 2019-05-15 | 2021-12-10 | 电子科技大学 | 一种用于不同线损行为间的关联关系分析方法 |
CN110212519B (zh) * | 2019-05-23 | 2023-04-07 | 山西金晖隆开关股份有限公司 | 用于中低压配电网的数据处理方法 |
CN110310048B (zh) * | 2019-07-10 | 2023-07-04 | 云南电网有限责任公司电力科学研究院 | 一种配网规划全过程评估方法及装置 |
CN110490471B (zh) * | 2019-08-23 | 2023-07-11 | 广西电网有限责任公司电力科学研究院 | 一种配电网供电可靠性差异化的改造方法 |
CN112633618B (zh) * | 2019-09-24 | 2023-05-16 | 电力规划总院有限公司 | 中压配电网的可靠性评估方法及装置 |
CN110766295B (zh) * | 2019-10-09 | 2024-02-09 | 深圳供电局有限公司 | 一种分析配电***可靠性提升方案的方法 |
CN110739691B (zh) * | 2019-11-06 | 2021-05-14 | 南方电网科学研究院有限责任公司 | 一种电网支路故障预测方法及装置 |
CN111126656B (zh) * | 2019-11-10 | 2023-07-04 | 国网浙江省电力有限公司温州供电公司 | 一种电能表故障数量预测方法 |
CN110912737A (zh) * | 2019-11-14 | 2020-03-24 | 国网浙江省电力有限公司信息通信分公司 | 一种基于混合模型的动态感知性能预告警方法 |
CN110941642B (zh) * | 2019-11-20 | 2022-12-23 | 贵州电网有限责任公司电力科学研究院 | 基于Lucene全文检索的配电网数据处理方法、装置 |
CN110866739B (zh) * | 2019-11-29 | 2022-08-12 | 国网四川省电力公司电力科学研究院 | 一种考虑故障排查路径的配电网综合停电时间表示方法 |
CN111008793B (zh) * | 2019-12-24 | 2023-10-31 | 天津相和电气科技有限公司 | 面向用户体验的电-气-热综合用能可靠性评估方法 |
CN111143776B (zh) * | 2019-12-27 | 2022-06-28 | 新奥数能科技有限公司 | 一种电量负荷预测方法及装置 |
CN111369159A (zh) * | 2020-03-11 | 2020-07-03 | 四川大学 | 一种电力交通耦合网络可靠性评估方法 |
CN111539844B (zh) * | 2020-04-20 | 2023-06-16 | 海南电网有限责任公司 | 一种基于k-l信息量和arima误差修正的月度电量预测方法 |
CN111799773B (zh) * | 2020-05-26 | 2022-05-20 | 中国电力科学研究院有限公司 | 一种用于预测电网连锁故障集的方法及*** |
CN111860940B (zh) * | 2020-06-02 | 2024-03-19 | 湖北凌云电力建设有限公司 | 一种基于逻辑回归算法的煤矿配电网络停电事故预测方法 |
CN111666697B (zh) * | 2020-06-19 | 2022-07-05 | 国网福建省电力有限公司电力科学研究院 | 基于序贯蒙特卡洛法的有源配电网可靠性评估方法及*** |
CN111797530B (zh) * | 2020-07-07 | 2023-10-31 | 重庆大学 | 一种电力***薄弱环节确定方法、装置、设备及介质 |
CN112668249B (zh) * | 2020-07-17 | 2023-05-02 | 国网山东省电力公司电力科学研究院 | 电网一次设备大修技改方案在线构建方法及*** |
CN111913803B (zh) * | 2020-07-21 | 2023-12-29 | 哈尔滨工程大学 | 一种基于akx混合模型的服务负载细粒度预测方法 |
CN111967747A (zh) * | 2020-08-06 | 2020-11-20 | 广东电网有限责任公司 | 一种电力用户停电影响评估方法、装置及存储介质 |
CN112101742B (zh) * | 2020-08-26 | 2023-07-21 | 华南理工大学 | 一种计及负荷特性的配电网一二次可靠性评估的方法 |
CN112084640B (zh) * | 2020-08-28 | 2022-07-08 | 华能澜沧江水电股份有限公司 | 一种调频市场下不同调频容量水电机组的开停机仿真模型 |
CN114154667A (zh) * | 2020-09-07 | 2022-03-08 | 思维实创(哈尔滨)科技有限公司 | 一种基于大数据的混合式时间序列电梯运行参数预测方法 |
CN112290542A (zh) * | 2020-10-19 | 2021-01-29 | 北京中恒博瑞数字电力科技有限公司 | 基于多源数据融合的配网故障停电损失分析展示方法 |
CN112258030A (zh) * | 2020-10-21 | 2021-01-22 | 国网天津市电力公司 | 配电网智能开关站的选址方法、***及信息数据处理终端 |
CN112232580B (zh) * | 2020-10-26 | 2023-04-14 | 广东电网有限责任公司广州供电局 | 一种供电中断损失分析方法和装置 |
CN112163723B (zh) * | 2020-11-02 | 2023-09-12 | 西安热工研究院有限公司 | 基于情景划分的水电站中长期径流预测方法、介质及设备 |
CN112383059A (zh) * | 2020-11-27 | 2021-02-19 | 国网北京市电力公司 | 交直流混联配电网可靠性的评估方法、装置 |
CN112613638A (zh) * | 2020-11-30 | 2021-04-06 | 国网北京市电力公司 | 配网故障数量总体趋势预测方法、预测装置与处理器 |
CN112688313A (zh) * | 2020-12-15 | 2021-04-20 | 国网辽宁省电力有限公司经济技术研究院 | 一种基于矩阵建模的有源配电网可靠性评估方法 |
CN112712203B (zh) * | 2020-12-29 | 2023-09-15 | 湖南大学 | 一种配电网日最高负荷预测方法和*** |
CN112801815B (zh) * | 2020-12-30 | 2024-03-29 | 国网江苏省电力公司信息通信分公司 | 一种基于联邦学习的电力通信网络故障预警方法 |
CN112836871A (zh) * | 2021-01-27 | 2021-05-25 | 广东电网有限责任公司广州供电局 | 一种配电网可靠性预测方法、装置、设备和介质 |
CN112865090B (zh) * | 2021-01-30 | 2023-09-26 | 上海电力大学 | 一种基于生物体免疫机制的智能配电网故障恢复方法 |
CN112906251B (zh) * | 2021-04-16 | 2023-05-02 | 云南电网有限责任公司 | 一种配电网可靠性影响要素的分析方法及*** |
CN113361856B (zh) * | 2021-05-07 | 2022-10-28 | 广东电网有限责任公司电力调度控制中心 | 一种直流输电***运行风险评估方法和装置 |
CN113723677A (zh) * | 2021-08-26 | 2021-11-30 | 国网河北省电力有限公司 | 一种基于大数据分析的电力***风险预测*** |
CN113902583B (zh) * | 2021-09-22 | 2024-06-14 | 国网安徽省电力有限公司电力科学研究院 | 利用低压网络设备数据的配网侧运维方法及*** |
CN114037220B (zh) * | 2021-10-19 | 2024-06-04 | 华北电力大学(保定) | 一种配电网可靠性等级划分标准及评估方法 |
CN113988415B (zh) * | 2021-10-28 | 2024-06-04 | 河北工业大学 | 一种中长期电力负荷预测方法 |
CN113866708B (zh) * | 2021-12-06 | 2022-03-25 | 青岛鼎信通讯股份有限公司 | 一种基于智能断路器的电能表计量误差分析方法 |
CN114295940B (zh) * | 2022-01-04 | 2022-07-15 | 珠海市鸿瑞信息技术股份有限公司 | 一种基于智慧城市的配网故障状态监测***及方法 |
CN114489944B (zh) * | 2022-01-24 | 2022-12-20 | 合肥工业大学 | 基于Kubernetes的预测式弹性伸缩方法 |
CN115221468B (zh) * | 2022-09-14 | 2023-04-18 | 天津大学 | 配电网可靠性指标解析计算方法、***、服务器和终端 |
CN115347570B (zh) * | 2022-10-17 | 2023-01-24 | 国网浙江省电力有限公司宁波供电公司 | 一种基于主配协同的区域停电范围分析方法 |
CN115347573A (zh) * | 2022-10-20 | 2022-11-15 | 北京智盟信通科技有限公司 | 一种基于变电设备可靠性的线路运行评估方法 |
CN116933981B (zh) * | 2023-09-15 | 2023-12-08 | 安徽方能电气技术有限公司 | 基于停复电事件数据监测的区域电力稳定性分析评价方法 |
CN117294019B (zh) * | 2023-10-11 | 2024-03-22 | 中铁十四局集团建筑工程有限公司 | 一种基于物联网的绿色建筑能耗监测方法及*** |
CN117526318B (zh) * | 2024-01-05 | 2024-05-10 | 珠海许继电气有限公司 | 配电网状态评估方法、装置、电子设备、存储介质 |
CN117878926B (zh) * | 2024-03-11 | 2024-06-18 | 国网上海市电力公司 | 一种韧性电网的监测预警和应急处理方法和*** |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102545214A (zh) * | 2012-01-13 | 2012-07-04 | 重庆大学 | 一种含电池储能设备的风电场可靠性评估方法 |
US20120185414A1 (en) * | 2010-12-15 | 2012-07-19 | Vaisala, Inc. | Systems and methods for wind forecasting and grid management |
CN103198235A (zh) * | 2013-04-27 | 2013-07-10 | 国家电网公司 | 基于风功率纵向时刻概率分布的风功率预测值预评估方法 |
CN104318334A (zh) * | 2014-10-29 | 2015-01-28 | 上海工程技术大学 | 一种基于长相关farima模型的短期电力负荷预测方法 |
CN104766175A (zh) * | 2015-04-16 | 2015-07-08 | 东南大学 | 一种基于时间序列分析的电力***异常数据辨识与修正方法 |
CN105069535A (zh) * | 2015-08-19 | 2015-11-18 | 中国电力科学研究院 | 一种基于arima模型的配电网运行可靠性的预测方法 |
Family Cites Families (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9484747B1 (en) * | 2012-01-25 | 2016-11-01 | Siemens Aktiengesellschaft | Holistic optimization of distribution automation using survivability modeling |
US10443577B2 (en) * | 2015-07-17 | 2019-10-15 | General Electric Company | Systems and methods for improved wind power generation |
-
2015
- 2015-08-19 CN CN201510511780.0A patent/CN105069535B/zh active Active
-
2016
- 2016-06-30 US US15/752,030 patent/US10825112B2/en active Active
- 2016-06-30 WO PCT/CN2016/087815 patent/WO2017028632A1/zh active Application Filing
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20120185414A1 (en) * | 2010-12-15 | 2012-07-19 | Vaisala, Inc. | Systems and methods for wind forecasting and grid management |
CN102545214A (zh) * | 2012-01-13 | 2012-07-04 | 重庆大学 | 一种含电池储能设备的风电场可靠性评估方法 |
CN103198235A (zh) * | 2013-04-27 | 2013-07-10 | 国家电网公司 | 基于风功率纵向时刻概率分布的风功率预测值预评估方法 |
CN104318334A (zh) * | 2014-10-29 | 2015-01-28 | 上海工程技术大学 | 一种基于长相关farima模型的短期电力负荷预测方法 |
CN104766175A (zh) * | 2015-04-16 | 2015-07-08 | 东南大学 | 一种基于时间序列分析的电力***异常数据辨识与修正方法 |
CN105069535A (zh) * | 2015-08-19 | 2015-11-18 | 中国电力科学研究院 | 一种基于arima模型的配电网运行可靠性的预测方法 |
Non-Patent Citations (1)
Title |
---|
WANG, YOUCHUN ET AL.: "Study on Microgrid Short-term Load Combination Forecasting Based on ARIMA-SVM Model", SHAANXI ELECTRIC POWER, vol. 42, no. 3, 31 December 2014 (2014-12-31), pages 19 - 23, ISSN: 1673-7598 * |
Cited By (37)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108335057B (zh) * | 2018-04-18 | 2021-08-20 | 国网天津市电力公司电力科学研究院 | 农村分散式煤改电供暖可靠性评估方法 |
CN108335057A (zh) * | 2018-04-18 | 2018-07-27 | 国网天津市电力公司电力科学研究院 | 农村分散式煤改电供暖可靠性评估方法 |
CN109657420B (zh) * | 2019-02-21 | 2023-10-13 | 中国人民解放军战略支援部队航天工程大学 | 一种基于航天任务的装备保障特性仿真建模方法 |
CN109657420A (zh) * | 2019-02-21 | 2019-04-19 | 中国人民解放军战略支援部队航天工程大学 | 一种基于航天任务的装备保障特性仿真建模方法 |
CN109977577A (zh) * | 2019-04-03 | 2019-07-05 | 合肥工业大学 | 考虑天然气输配管网不确定性的气-电耦合***可靠性评估方法 |
CN109977577B (zh) * | 2019-04-03 | 2023-03-24 | 合肥工业大学 | 考虑天然气输配管网不确定性的气-电耦合***可靠性评估方法 |
CN110457651A (zh) * | 2019-07-05 | 2019-11-15 | 中国地质大学(武汉) | 一种利用数据同化改进的滑坡位移预测方法 |
CN110457651B (zh) * | 2019-07-05 | 2022-11-15 | 中国地质大学(武汉) | 一种利用数据同化改进的滑坡位移预测方法 |
CN110598899A (zh) * | 2019-07-31 | 2019-12-20 | 国网上海市电力公司 | 一种基于数据预测的地方电能表申校服务规划方法及*** |
CN110598899B (zh) * | 2019-07-31 | 2023-09-15 | 国网上海市电力公司 | 一种基于数据预测的地方电能表申校服务规划方法及*** |
CN110598082A (zh) * | 2019-09-17 | 2019-12-20 | 安徽信息工程学院 | 一种基于pm2.5数据采集预测方法 |
CN110598082B (zh) * | 2019-09-17 | 2022-09-27 | 安徽信息工程学院 | 一种基于pm2.5数据采集预测方法 |
CN113095534A (zh) * | 2019-12-23 | 2021-07-09 | 南京理工大学 | 一种ARIMA与改进Elman神经网络组合的风电功率预测方法 |
CN113129161A (zh) * | 2019-12-31 | 2021-07-16 | 国网山东省电力公司威海供电公司 | 基于小波分解和神经网络的架空配电***中动物故障分析方法 |
CN111427753B (zh) * | 2020-03-23 | 2024-04-23 | 上海新炬网络信息技术股份有限公司 | 基于arima模型的容量预测装置及其控制方法 |
CN111427753A (zh) * | 2020-03-23 | 2020-07-17 | 上海新炬网络信息技术股份有限公司 | 基于arima模型的容量预测装置及其控制方法 |
CN111581772A (zh) * | 2020-03-31 | 2020-08-25 | 国网经济技术研究院有限公司 | 一种用于配电网的配置方案优化方法和*** |
CN111581772B (zh) * | 2020-03-31 | 2023-06-02 | 国网经济技术研究院有限公司 | 一种用于配电网的配置方案优化方法和*** |
CN111461786A (zh) * | 2020-04-03 | 2020-07-28 | 中南大学 | 基于Prophet-CEEMDAN-ARIMA的商品销量预测方法及装置 |
CN111626498A (zh) * | 2020-05-25 | 2020-09-04 | 成都中电熊猫显示科技有限公司 | 设备运行状态预测方法、装置、设备及存储介质 |
CN111626498B (zh) * | 2020-05-25 | 2023-10-24 | 成都京东方显示科技有限公司 | 设备运行状态预测方法、装置、设备及存储介质 |
CN111861141A (zh) * | 2020-06-29 | 2020-10-30 | 国网上海市电力公司 | 一种基于模糊故障率预测的配电网可靠性评估方法 |
CN111861141B (zh) * | 2020-06-29 | 2024-02-13 | 国网上海市电力公司 | 一种基于模糊故障率预测的配电网可靠性评估方法 |
CN112350331A (zh) * | 2020-10-21 | 2021-02-09 | 广东电网有限责任公司 | 一种中压馈线预安排停电优化方法和装置 |
CN112350331B (zh) * | 2020-10-21 | 2023-01-24 | 广东电网有限责任公司 | 一种中压馈线预安排停电优化方法和装置 |
CN112345972A (zh) * | 2020-11-02 | 2021-02-09 | 国网江苏省电力有限公司电力科学研究院 | 基于停电事件的配电网线变关系异常诊断方法、装置及*** |
CN112365087B (zh) * | 2020-11-26 | 2023-08-25 | 深圳供电局有限公司 | 一种预测年度不停电作业需求量的方法 |
CN112365087A (zh) * | 2020-11-26 | 2021-02-12 | 深圳供电局有限公司 | 一种预测年度不停电作业需求量的方法 |
CN112446619A (zh) * | 2020-11-27 | 2021-03-05 | 国网北京市电力公司 | 配电网抢修处理方法和装置 |
CN112446619B (zh) * | 2020-11-27 | 2024-04-26 | 国网北京市电力公司 | 配电网抢修处理方法和装置 |
CN112561312A (zh) * | 2020-12-14 | 2021-03-26 | 国网河南省电力公司电力科学研究院 | 一种基于电能质量因素的配电网线损计算方法及*** |
CN113642766B (zh) * | 2021-07-08 | 2024-01-30 | 南方电网科学研究院有限责任公司 | 一种电力***站点停电数的预测方法、装置、设备及介质 |
CN113642766A (zh) * | 2021-07-08 | 2021-11-12 | 南方电网科学研究院有限责任公司 | 一种电力***站点停电数的预测方法、装置、设备及介质 |
CN114034978A (zh) * | 2021-11-11 | 2022-02-11 | 四川中电启明星信息技术有限公司 | 一种用于配网类项目的自动模型检测方法及*** |
CN114034978B (zh) * | 2021-11-11 | 2023-11-10 | 四川中电启明星信息技术有限公司 | 一种用于配网类项目的自动模型检测方法及*** |
CN114205190B (zh) * | 2021-12-03 | 2023-07-14 | 中国长江三峡集团有限公司 | 一种用于物联网关的自主监测协调方法 |
CN114205190A (zh) * | 2021-12-03 | 2022-03-18 | 中国长江三峡集团有限公司 | 一种用于物联网关的自主监测协调方法 |
Also Published As
Publication number | Publication date |
---|---|
US10825112B2 (en) | 2020-11-03 |
US20180240202A1 (en) | 2018-08-23 |
CN105069535B (zh) | 2020-07-24 |
CN105069535A (zh) | 2015-11-18 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2017028632A1 (zh) | 一种配电网运行可靠性的预测方法 | |
CN105740975B (zh) | 一种基于数据关联关系的设备缺陷评估与预测方法 | |
CN107547269B (zh) | 基于farima的智能变电站通信流量阈值模型的构建方法 | |
WO2015196133A2 (en) | Energy infrastructure sensor data rectification using regression models | |
Jiang et al. | Stochastic subspace identification‐based approach for tracking inter‐area oscillatory modes in bulk power system utilising synchrophasor measurements | |
CN107832927B (zh) | 基于灰色关联分析法的10kV线路线变关系评价方法 | |
CN108684051B (zh) | 一种基于因果诊断的无线网络性能优化方法、电子设备及存储介质 | |
CN110930052A (zh) | 一种变电设备故障率预测方法,***,设备及可读存储介质 | |
CN112906251B (zh) | 一种配电网可靠性影响要素的分析方法及*** | |
Tsai et al. | Polynomial-Fourier series model for analyzing and predicting electricity consumption in buildings | |
CN110826228A (zh) | 一种地区电网运行品质极限评估方法 | |
CN110084403B (zh) | 架空导线载流量的多时段联合概率密度预测方法及*** | |
CN111091223A (zh) | 一种基于物联网智能感知技术的配变短期负荷预测方法 | |
CN117767283A (zh) | 一种基于多策略融合的电网资源趋势预警分析方法及*** | |
CN112950048A (zh) | 基于模糊综合评价的国家高等教育体系健康评价 | |
CN116796906A (zh) | 基于数据融合的电力配电网投资预测分析***及方法 | |
Wang et al. | Forecast and analyze the telecom income based on arima model | |
CN115267641A (zh) | 同塔双回输电线路中电流互感器误差异常识别方法、*** | |
CN112232580B (zh) | 一种供电中断损失分析方法和装置 | |
Pavasson et al. | Variation mode and effect analysis compared to FTA and FMEA in product development | |
CN114118759A (zh) | 一种配变台区负荷过载状态评估方法及装置 | |
Wang et al. | Research of software reliability combination model based on neural net | |
Dadi et al. | Estimation of transmission reliability margin using statistical methods and intelligent techniques | |
CN111565121A (zh) | 人员及技术对it支撑度评估方法及装置 | |
CN117154723B (zh) | 基于多源数据和模型融合的台区短期负荷预测方法及*** |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 16836496 Country of ref document: EP Kind code of ref document: A1 |
|
WWE | Wipo information: entry into national phase |
Ref document number: 15752030 Country of ref document: US |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 16836496 Country of ref document: EP Kind code of ref document: A1 |