CN114844114A - Distribution network system based on distributed optical storage and remote safe operation and maintenance method - Google Patents

Distribution network system based on distributed optical storage and remote safe operation and maintenance method Download PDF

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CN114844114A
CN114844114A CN202210697549.5A CN202210697549A CN114844114A CN 114844114 A CN114844114 A CN 114844114A CN 202210697549 A CN202210697549 A CN 202210697549A CN 114844114 A CN114844114 A CN 114844114A
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photovoltaic
storage battery
power
module
light intensity
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CN114844114B (en
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郭睿
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Beijing Xinyunzhu Technology Co ltd
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    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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
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    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
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    • 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
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    • 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
    • H02J13/00006—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 information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
    • H02J13/00022—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 information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission
    • 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
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0029—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with safety or protection devices or circuits
    • H02J7/00302—Overcharge protection
    • 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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0029—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with safety or protection devices or circuits
    • H02J7/00306—Overdischarge protection
    • 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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
    • H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
    • 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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0068—Battery or charger load switching, e.g. concurrent charging and load supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
    • H02M3/00—Conversion of dc power input into dc power output
    • H02M3/02—Conversion of dc power input into dc power output without intermediate conversion into ac
    • H02M3/04—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters
    • H02M3/10—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode
    • H02M3/145—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal
    • H02M3/155—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only
    • H02M3/156—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only with automatic control of output voltage or current, e.g. switching regulators
    • H02M3/158—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only with automatic control of output voltage or current, e.g. switching regulators including plural semiconductor devices as final control devices for a single load
    • H02M3/1582—Buck-boost converters
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • 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
    • Y04S10/12—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation
    • Y04S10/123—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation the energy generation units being or involving renewable energy sources

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Abstract

The invention discloses a power distribution network system based on distributed optical storage, which comprises a distributed optical storage structure and a control device, wherein the control device comprises a calculation end and a collection end, the calculation end comprises a computer, a comparator, a load power collection module and a photovoltaic power prediction module, and the collection end comprises a microcontroller, a PWM pulse control module, a light intensity sensor, a temperature sensor and a wind speed sensor. The invention also discloses a distributed light storage-based remote safe operation and maintenance method for the power distribution network, which comprises the steps of predicting the output power of the photovoltaic cell; step two, load power detection; step three, remotely controlling the charging and discharging of the storage battery; and step four, predicting and controlling the SOC of the storage battery pack. When the output power of the photovoltaic cell is calculated, the light intensity function and the temperature function are fused, the light intensity factor and the temperature factor are adjusted in a following mode by adopting variable weight, the output power of the photovoltaic cell is predicted by adopting a neural network prediction model, and the prediction result is clear and accurate.

Description

Distribution network system based on distributed optical storage and remote safe operation and maintenance method
Technical Field
The invention belongs to the technical field, and particularly relates to a power distribution network system based on distributed optical storage and a remote safe operation and maintenance method.
Background
In order to achieve the substantial solution to the increasingly severe energy and pollution situation, extensive and intensive research is conducted on photovoltaics in the world, and the research center is mainly focused on independent, grid-connected and distributed types. Wherein, 1) independent photovoltaic, also called off-grid photovoltaic, mainly uses independent operation mode to supply power for remote areas, signal power supply, street lamps, etc.; 2) grid-connected photovoltaic, which is mainly a large national power station, collects the electric energy generated by each power station by a large power grid, and allocates the electric energy to each user for electricity utilization; 3) distributed photovoltaics (D photovoltaics), also known as Distributed power generation or Distributed energy supply, are generally power generation devices that are deployed close to the distribution network side or at the load, operate mainly in a self-sufficient manner on the user side, deliver residual electrical energy to the distribution network, and have regulation and stabilization features.
For some problems existing in grid-connected photovoltaic systems and independent photovoltaic systems, the distributed photovoltaic system has important application prospects and high research value. The distributed photovoltaic has a wide development prospect, and the application of the distributed photovoltaic can change the role played by a city in a brand new energy structure, namely, the production, consumption and transaction of energy are integrated. Any simple building can be used as an independent distributed photovoltaic power station, the distributed photovoltaic power station has self-generation and self-use, the dependence on a power grid can be eliminated, and the nearby supply of energy is realized.
Because the output power of the solar cell is continuously changed along with the change of the environment, the photovoltaic system added with the storage battery can well reduce the energy fluctuation and play a role in preventing light abandoning and electricity limiting. However, the price of energy storage devices such as storage batteries is expensive, and the energy storage devices usually account for a large part of the manufacturing cost of the whole photovoltaic system. And the photovoltaic system added with the storage battery has high requirements on the control aspect of energy management, so that the photovoltaic system can provide the working condition with stable rated load. Therefore, a designed charge and discharge control technology is another research hotspot in the optical storage system, and the damage to the energy storage device is reduced by reducing the fluctuation of the charge and discharge voltage and current of the storage battery, the working life of the storage battery is prolonged, and the voltage of the direct current bus is kept stable.
Because the generating efficiency of photovoltaic power generation is related to environmental factors such as ambient temperature, cloud, dust, etc., and the variation of the generating efficiency is likely to cause the output power of the photovoltaic module to be unstable and intermittent, a lot of uncertain problems are brought to planning, operation, scheduling and control of the power system by a large number of accesses. Therefore, considering the influence of multiple factors is also an important means for improving the power generation efficiency of the photovoltaic module.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a power distribution network system based on distributed optical storage and a remote safe operation and maintenance method aiming at the defects in the prior art, the power distribution network system based on distributed optical storage is simple in structure and reasonable in design, when the output power of a photovoltaic cell is calculated, an optical intensity function and a temperature function are fused, the optical intensity factor and the temperature factor are adjusted along with the optical intensity function and the temperature factor by adopting variable weight, the output power of the photovoltaic cell is predicted by adopting a neural network prediction model, and the prediction result is clear and accurate.
In order to solve the technical problems, the invention adopts the technical scheme that: a distribution network system based on distributed light stores up which characterized in that: the distributed optical storage structure comprises a storage battery set and a plurality of photovoltaic generator sets, wherein the storage battery set is connected with a direct current bus, a DC/DC converter is connected between the photovoltaic generator sets and the direct current bus, a converter is connected between the storage battery set and the direct current bus, an inverter is connected between the direct current bus and an alternating current bus, and the alternating current bus is connected with a plurality of alternating current loads; the utility model discloses a photovoltaic power generation set output power prediction module, including the computer, the output of computer still has connect overcharge warning circuit and overdischarge warning circuit, the collection end includes microcontroller, microcontroller's output termination has been used for controlling the PWM pulse control module of converter break-make, microcontroller's input termination has light intensity sensor, temperature sensor and wind speed sensor, it has first wireless communication module and second wireless communication module to connect gradually between computer and the microcontroller.
The power distribution network system based on distributed optical storage is characterized in that: the system also comprises a condenser and a condenser driving module, wherein the condenser driving module is connected with the output end of the microcontroller.
The power distribution network system based on distributed optical storage is characterized in that: the converter is a buck-boost converter.
The invention discloses a distributed optical storage-based remote safe operation and maintenance method for a power distribution network, which is characterized by comprising the following steps of: the method comprises the following steps:
step one, predicting the output power of a photovoltaic cellP pre-p ：
Step 101, constructing a light intensity functionm(s t ) Wherein
Figure 159522DEST_PATH_IMAGE001
，m(s t ) The output power of the photovoltaic generator set at the time t under the influence of light intensity is shown,s t representing the light intensity collected by the light intensity sensor at time t,A r indicates the area of the r-th photovoltaic power generation cell,η r represents the photoelectric conversion efficiency of the r-th photovoltaic power generation cell,ϕ T which represents the light intensity correction coefficient, is,βrepresenting a discount coefficient;
step 102, constructing a temperature function m (m:)T t ) Wherein
Figure 804130DEST_PATH_IMAGE002
，m(T t ) The output power of the photovoltaic generator set at the time t, which is influenced by the plate temperature of the photovoltaic component,T t the temperature of the photovoltaic module at the moment t is shown,T R representing the ambient temperature collected via the temperature sensor,T N the temperature of the photovoltaic module plate under the NOCT condition is shown,W t representing the wind speed collected via the wind speed sensor at time t,φ 1 、φ 2 、φ 3 、φ 4 all represent fitting coefficients;
step 103, calculating a variable weight: computer according to formula
Figure 135885DEST_PATH_IMAGE003
Calculating a variable weight, wherein
Figure 891352DEST_PATH_IMAGE004
，
Figure 421690DEST_PATH_IMAGE005
Representing the variable weight of the light intensity function,
Figure 611101DEST_PATH_IMAGE006
the initial weight of the light intensity function is represented,
Figure 289207DEST_PATH_IMAGE007
represents the variable weight of the temperature function,
Figure 723730DEST_PATH_IMAGE008
an initial weight representing a function of temperature;
step 104, for m (m) of the multiple history timess t ) And m: (T t ) Information fusion is carried out to obtain a fusion characteristic setM，M={m 1 ,…m t ,…,m n Will fuse feature setsMIs divided into a training set and a testing set,m t m (m) at time ts t ) And time t m: (T t ) Fusion value of 1. ltoreqt≤n；
105, constructing a neural network prediction model in a photovoltaic power prediction module;
step 106, training a prediction model through a training set: training a neural network by using training set data, and adjusting weight parameters of the convolutional neural network according to a method of minimizing errors;
step 107, model evaluation is performed through the test set: until the prediction accuracy of the test set meets the precision requirement, the precision requirement is more than 95%;
step 108, obtaining the light intensity at the current moment in real times t’ And temperatureT t’ Calculating the fusion value at the current time t' according to the step 101-104m t’ ；
Step 109, merging the fusion value of the current t' momentm t’ Inputting the power into a neural network prediction model, and outputting the photovoltaic cell output power predicted value at the current moment by the neural network prediction modelP pre-p ；
Step two, load power detection: the load power acquisition module is used for monitoring the sum of load power values of a plurality of alternating current loads at the current momentP tes-p And the sum of the monitored current load power valuesP tes-p Sending to a comparator;
step three, remotely controlling the charging and discharging of the storage battery:
the comparator predicts the output power of the photovoltaic cell at the current momentP pre-p And the sum of the current moment load power valuesP tes-p Make a comparison ifP pre-p >P tes-p If yes, charging the storage battery pack and entering step 301; if it isP pre-p <P tes-p If yes, discharging the storage battery pack and entering step 302;
301, the computer sends a PWM pulse instruction to the PWM pulse control module through the first wireless communication module and the second wireless communication module, a switch of the converter is turned off, a reverse electromotive force is generated on an inductor, a diode is turned on from a cut-off state, and a storage battery pack is charged;
step 302, the computer sends a PWM pulse instruction to a PWM pulse control module through a first wireless communication module and a second wireless communication module, a converter switch is turned on, an inductor stores energy, a capacitor discharges, and a storage battery pack discharges;
step four, predicting and controlling the SOC of the storage battery pack;
obtaining the current power of the accumulator batteryP pre-B If, ifP pre-B >P c-max-B Entering step 401; if it isP c-min-B <P pre-B <P c-max-B Go to step 402, if yesP f-min-B <P pre-B <P f-max-B Proceed to step 403, ifP pre-B <P f-min-B Step 404 is entered;
step 401, the computer sends a PWM pulse instruction to a PWM pulse control module through a first wireless communication module and a second wireless communication module, a converter is turned off, and a storage battery pack stops charging;
step 402, the computer sends a control instruction to the overcharge early warning circuit, and the overcharge early warning circuit gives an alarm;
step 403, the computer sends a control instruction to the over-discharge early warning circuit, and the over-discharge early warning circuit gives an alarm;
and step 404, the computer sends the PWM pulse instruction to the PWM pulse control module through the first wireless communication module and the second wireless communication module, the converter is turned off, and the storage battery pack stops discharging.
The above method is characterized in that: in step four, the current power of the storage battery packP pre-B The detection result is obtained by a storage battery electric energy detection module, the storage battery electric energy detection module is connected between a storage battery pack and a converter in series, and an enabling pin of the storage battery electric energy detection module is connected with a computer.
The above method is characterized in that: in a step 104 of the method, the step of the method,
Figure 436471DEST_PATH_IMAGE009
。
the above method is characterized in that: in step 105, the basic structure of the neural network prediction model is as follows: input layer, convolutional layer, pooling layer, full-connection layer, dropout, full-connection layer.
The above method is characterized in that: discount coefficient in step 101
Figure 298248DEST_PATH_IMAGE010
WhereinV t Representing the dc voltage of the dc grid at time t,V e represents the voltage rating of the dc grid,λrepresenting the percentage of the maximum voltage difference of the dc network.
Compared with the prior art, the invention has the following advantages:
1. the invention has simple structure, reasonable design and convenient realization, use and operation.
2. When the output power of the photovoltaic cell is calculated, the light intensity and temperature factors are considered at the same time, the light intensity function and the temperature function are fused, information loss and distortion are avoided, the light intensity factor and the temperature factor are adjusted in a following mode by adopting variable weight, the applicability is high, and the using effect is good.
3. The method adopts the neural network prediction model to predict the output power of the photovoltaic cell, the prediction result is clear and accurate, and the prediction precision is improved.
In conclusion, the photovoltaic power prediction method is simple in structure and reasonable in design, when the output power of the photovoltaic cell is calculated, the light intensity function and the temperature function are fused, the light intensity factor and the temperature factor are adjusted in a following mode by adopting variable weight, the output power of the photovoltaic cell is predicted by adopting the neural network prediction model, and the prediction result is clear and accurate.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
Fig. 1 is a schematic block diagram of a circuit of a distributed optical storage structure according to the present invention.
Fig. 2 is a schematic block circuit diagram of the control device of the present invention.
FIG. 3 is a flow chart of the method of the present invention.
Description of reference numerals:
Figure 932492DEST_PATH_IMAGE011
Detailed Description
The method of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments of the invention.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Spatially relative terms, such as "above … …," "above … …," "above … … surface," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial relationship to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is turned over, devices described as "above" or "on" other devices or configurations would then be oriented "below" or "under" the other devices or configurations. Thus, the exemplary term "above … …" can include both an orientation of "above … …" and "below … …". The device may be otherwise variously oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
As shown in fig. 1 to 3, the present invention includes a distributed optical storage structure and a control device, the distributed optical storage structure includes a storage battery 3 connected to a DC bus and a plurality of photovoltaic generator sets 1, a DC/DC converter 2 is connected between the photovoltaic generator sets 1 and the DC bus, a converter 4 is connected between the storage battery 3 and the DC bus, an inverter 5 is connected between the DC bus and an ac bus, and the ac bus is connected to a plurality of ac loads 6; the control device comprises a calculation end and a plurality of acquisition ends distributed on the peripheral side of the photovoltaic generator set 1, the calculation end comprises a computer 7 and a comparator 10 connected with the input end of the computer 7, the input end of the comparator 10 is connected with a load power acquisition module 8 for monitoring the sum of the load power values of a plurality of alternating current loads 6 and a photovoltaic power prediction module 9 for calculating the sum of the output power of the photovoltaic generator set 1, the output end of the computer 7 is also connected with an overcharge early warning circuit 17 and an overdischarge early warning circuit 18, the acquisition end comprises a microcontroller 19, the output end of the microcontroller 19 is connected with a PWM pulse control module 13 for controlling the on-off of the converter 4, the input end of the microcontroller 19 is connected with a light intensity sensor 20, a temperature sensor 21 and a wind speed sensor 22, a first wireless communication module 11 and a second wireless communication module 12 are connected between the computer 7 and the microcontroller 19 in sequence.
It should be noted that, in the present invention, the sensors are all prior art devices, and can be purchased directly and connected to the computer 7 or the microcontroller 19 for use.
During the in-service use, the number of photovoltaic generating set 1 is a plurality of, and a plurality of photovoltaic generating set 1 distribute in the position of difference, and the collection end is laid in photovoltaic generating set 1's week side along with photovoltaic generating set 1's position. The computer 7 is arranged in a remote monitoring center, and the first wireless communication module 11 and the second wireless communication module 12 are Bluetooth modules, wifi modules, 4G modules or ZigBee modules.
The light intensity sensor 20 employs a TSL2560T light intensity sensor.
The photovoltaic generator set 1 converts light energy into electric energy, the electric energy is merged into a direct current bus through the DC/DC converter 2, and the direct current bus merges the electric energy into an alternating current bus through the inverter 5 and supplies power to an alternating current load. The load power collection module 8 is used for monitoring the sum of the load power values of the multiple alternating current loads 6, the photovoltaic power prediction module 9 is used for calculating the sum of the output powers of the multiple photovoltaic generator sets 1, and the comparator 10 is used for comparing the sum of the load power values with the sum of the output powers of the photovoltaic generator sets 1.
If the sum of the load power values is larger than the sum of the output power of the photovoltaic generator set 1, the computer 7 sends a control instruction to the microcontroller 19 through the first wireless communication module 11 and the second wireless communication module 12, the microcontroller 19 sends a PWM pulse to the converter 4, and the storage battery 3 discharges to the alternating current load 6. If the sum of the load power values is smaller than the sum of the output power of the photovoltaic generator set 1, the computer 7 sends a control instruction to the microcontroller 19 through the first wireless communication module 11 and the second wireless communication module 12, the microcontroller 19 sends a PWM pulse to the converter 4, and the storage battery set 3 is charged.
In this embodiment, the system further includes a condenser and a condenser driving module 16, and the condenser driving module 16 is connected to an output end of the microcontroller 19. The condenser is connected with the photovoltaic module in a rotating way.
In practical use, when the storage battery 3 is overcharged, the microcontroller 19 controls the condenser driving module 16, and the condenser driving module 16 drives the condenser to rotate, so that the optical port of the condenser is far away from the photovoltaic module, and the charging efficiency is reduced.
In this embodiment, the converter 4 is a buck-boost converter. The BUCK-BOOST circuit is a commonly used DC/DC converter circuit, and its output voltage can be either lower or higher than the input voltage. When the power tube Q1 is closed, the inductor L1 is directly connected to the two ends of the power supply, and at this time, the inductor current gradually rises and the capacitor C discharges. When the power transistor Q1 is turned off, the input terminal VIN charges the input capacitor. Since the current of the inductor cannot change abruptly, the inductor L1 supplies power to the output capacitor COUT and the load RL through the freewheeling tube D1. After the system works stably, the inductance is conserved in volt-seconds.
Therefore, by controlling the power transistor Q1 to be closed and conducted, the charging and discharging of the battery pack 4 can be controlled.
The invention also comprises a distributed optical storage-based remote safe operation and maintenance method for the power distribution network, which comprises the following steps:
step one, predicting the output power of a photovoltaic cellP pre-p ：
Step 101, constructing a light intensity function, wherein
Figure 701865DEST_PATH_IMAGE012
，m(s t ) The output power of the photovoltaic generator set 1 at the time t under the influence of light intensity is shown,s t representing the light intensity collected by the light intensity sensor 20 at time t,A r indicates the area of the r-th photovoltaic power generation cell,η r represents the photoelectric conversion efficiency of the r-th photovoltaic power generation cell,ϕ T which represents the light intensity correction coefficient, is,βrepresenting the discount coefficient.
Since the light intensity is affected by temperature, a light intensity function is constructedm(s t ) When considering the influence of temperature on the light intensity, in the light intensity functionm(s t ) Adding light intensity correction coefficientϕ T Correction coefficient of light intensityϕ T Based on temperatureT t Taking values such that the light intensity functionm(s t ) More closely to reality.
According toFormula (II)
Figure 472375DEST_PATH_IMAGE013
It can be seen that the temperature is not lower thanT t Greater than a set point temperatureT set When the temperature of the water is higher than the set temperature,
Figure 629686DEST_PATH_IMAGE014
less than 1, when the temperature isT t Less than a temperature set pointT set When the utility model is used, the water is discharged,
Figure 659216DEST_PATH_IMAGE015
greater than 1, in accordance with the experimental theory, i.e. the higher the temperature, the lower the photovoltaic efficiency. In the formula, the temperature is less than or equal to-15 DEG CT t ≤40℃，T set =26℃。
Adding the discount coefficient to the light intensity correction coefficientϕ T Making corrections of the discount coefficients
Figure 560176DEST_PATH_IMAGE016
WhereinV t Representing the dc voltage of the dc grid at time t,V e represents the voltage rating of the dc grid,λrepresenting the percentage of the maximum voltage difference of the dc grid,λ=10%。
step 102, constructing a temperature function m (m:)T t ) Wherein
Figure 388454DEST_PATH_IMAGE017
，m(T t ) The output power of the photovoltaic generator set 1 at the time t influenced by the plate temperature of the photovoltaic component is shown,T t the temperature of the photovoltaic module at the moment t is shown,T R representing the ambient temperature collected via the temperature sensor 21,T N the panel temperature of the photovoltaic module under the NOCT condition is shown,W t representing the wind speed collected via the wind speed sensor at time t,φ 1 、φ 2 、φ 3 、φ 4 both represent fitting coefficients.
In the conventional formula for calculation, the formula of the calculation,
Figure 716668DEST_PATH_IMAGE018
since the temperature is closely related to the wind speed, the temperature of the photovoltaic module plate is calculated by the methodT t When considering the influence of wind speed on temperature. As can be seen from the formula, when the wind speed is equal to 1m/s, this application describesT t The calculation formula of (a) is the same as the conventional formula.
Step 103, calculating a variable weight: the computer 7 is based on the formula
Figure 59924DEST_PATH_IMAGE019
Calculating a variable weight, wherein
Figure 374362DEST_PATH_IMAGE004
，
Figure 181781DEST_PATH_IMAGE005
Representing the variable weight of the light intensity function,
Figure 556262DEST_PATH_IMAGE006
the initial weight of the light intensity function is represented,
Figure 386814DEST_PATH_IMAGE020
represents the variable weight of the temperature function,
Figure 629577DEST_PATH_IMAGE008
representing the initial weight of the temperature function.
When the utility model is used in practice,
Figure 665404DEST_PATH_IMAGE021
and
Figure 600999DEST_PATH_IMAGE022
there are two methods for the value of (A), one is
Figure 325372DEST_PATH_IMAGE023
=0.5。
Figure 371826DEST_PATH_IMAGE024
= 0.5. In the other way, the water-saving agent is used,
Figure 91520DEST_PATH_IMAGE025
，
Figure 807803DEST_PATH_IMAGE026
。
since the light intensity and the temperature change in real time, as can be seen from the calculation formula,
Figure 409686DEST_PATH_IMAGE005
and
Figure 869617DEST_PATH_IMAGE007
the light intensity and the temperature are changed along with the magnitude of the light intensity and the temperature, and the adaptability is good.
Step 104, for m (m) of the multiple history timess t ) And m: (T t ) Information fusion is carried out to obtain a fusion characteristic setM，M={m 1 ,…m t ,…,m n Will fuse feature setsMIs divided into a training set and a testing set,m t m (m) at time ts t ) And time t m: (T t ) Fusion value of 1. ltoreqt≤n。
Due to m: (s t ) And m: (T t ) In order to avoid information loss and distortion, m: (s t ) And m: (a)T t ) The weighted fusion is carried out, and the weight of the fusion is calculated,
Figure 443818DEST_PATH_IMAGE027
and the use effect is good.
105, constructing a neural network prediction model in the photovoltaic power prediction module 9; the basic structure of the neural network prediction model is as follows: input layer, convolutional layer, pooling layer, full-connection layer, dropout, full-connection layer.
The neural network prediction model can be an AlexNet network, a VGG series network or a ResNet series network.
Step 106, training a prediction model through a training set: training a neural network by using training set data, and adjusting weight parameters of the convolutional neural network according to a method of minimizing errors;
step 107, model evaluation is performed through the test set: until the prediction accuracy of the test set meets the precision requirement, the precision requirement is more than 95%;
step 108, obtaining the light intensity at the current moment in real times t’ And temperatureT t’ Calculating the fusion value at the current time t' according to the step 101-104m t’ ；
Step 109, fusing the current t' timem t’ Inputting the power into a neural network prediction model, and outputting the photovoltaic cell output power predicted value at the current moment by the neural network prediction modelP pre-p ；
Step two, load power detection: the load power collecting module 8 is used for monitoring the sum of load power values of a plurality of alternating current loads 6 at the current momentP tes-p And the sum of the monitored current load power valuesP tes-p To the comparator 10;
step three, remotely controlling the charging and discharging of the storage battery:
the comparator 10 predicts the output power of the photovoltaic cell at the current momentP pre-p And the sum of the current moment load power valuesP tes-p Make a comparison ifP pre-p >P tes-p If yes, the storage battery pack 3 is charged, and the step 301 is executed; if it isP pre-p <P tes-p If yes, discharging the storage battery pack 3 and entering step 302;
step 301, the computer 7 sends a PWM pulse instruction to the PWM pulse control module 13 through the first wireless communication module 11 and the second wireless communication module 12, the converter 4 adopts a buck-boost converter, the switch of the converter 4 is turned off, the inductor generates reverse electromotive force, the diode is turned on from the cut-off state, and the storage battery 3 is charged;
step 302, the computer 7 sends the PWM pulse instruction to the PWM pulse control module 13 through the first wireless communication module 11 and the second wireless communication module 12, the switch of the converter 4 is turned on, the inductor stores energy, the capacitor discharges, and the storage battery 3 discharges;
step four, predicting the SOC of the storage battery pack;
current power of accumulator group 3P pre-B The detection result is obtained by a storage battery electric energy detection module, the storage battery electric energy detection module is connected between the storage battery pack 3 and the converter 4 in series, and an enabling pin of the storage battery electric energy detection module is connected with the computer 7. The storage battery electric energy detection module acquires the current power of the storage battery pack 3P pre-B If, ifP pre-B >P c-max-B Entering step 401; if it isP c-min-B <P pre-B <P c-max-B Go to step 402, if yesP f-min-B <P pre-B <P f-max-B Proceed to step 403, ifP pre-B <P f-min-B Step 404 is entered.
Current power of the accumulator battery 3 in actual useP pre-B It can also be calculated by multiplying (detection voltage-charging voltage-discharging voltage) by the current.
Step 401, the computer 7 sends the PWM pulse instruction to the PWM pulse control module 13 through the first wireless communication module 11 and the second wireless communication module 12, the converter 4 is turned off, and the storage battery 3 stops charging;
step 402, the computer 7 sends a control instruction to the overcharge warning circuit 17, and the overcharge warning circuit 17 gives an alarm;
step 403, the computer 7 sends a control instruction to the overdischarge early warning circuit 18, and the overdischarge early warning circuit 18 gives an alarm;
step 404, the computer 7 sends the PWM pulse command to the PWM pulse control module 13 through the first wireless communication module 11 and the second wireless communication module 12, the converter 4 is turned off, and the battery pack 3 stops discharging.
The above embodiments are only examples of the present invention, and are not intended to limit the present invention, and all simple modifications, changes and equivalent structural changes made to the above embodiments according to the technical spirit of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims (8)

1. A distribution network system based on distributed light stores up which characterized in that: the distributed optical storage structure comprises a storage battery pack (3) connected with a direct current bus and a plurality of photovoltaic generator sets (1), wherein a DC/DC converter (2) is connected between each photovoltaic generator set (1) and the direct current bus, a converter (4) is connected between each storage battery pack (3) and the direct current bus, an inverter (5) is connected between each direct current bus and the alternating current bus, and the alternating current bus is connected with a plurality of alternating current loads (6);
the control device is including the collection end of calculating end and a plurality of distribution photovoltaic generating set (1) week side, the calculation end include computer (7) and with comparator (10) that computer (7) input meets, the input termination of comparator (10) has the load power collection module (8) that are used for monitoring a plurality of alternating current load (6) load power value sum and is used for calculating photovoltaic power prediction module (9) that photovoltaic generating set (1) output power summed, and the output of computer (7) has still been connect overcharge warning circuit (17) and have been crossed and have been put warning circuit (18), the collection end includes microcontroller (19), the output termination of microcontroller (19) has PWM pulse control module (13) that are used for controlling converter (4), the input termination of microcontroller (19) has light intensity sensor (20), The temperature sensor (21) and the wind speed sensor (22), connect gradually first wireless communication module (11) and second wireless communication module (12) between computer (7) and microcontroller (19).
2. A distributed optical storage based power distribution network system according to claim 1, wherein: the device also comprises a condenser and a condenser driving module (16), wherein the condenser driving module (16) is connected with the output end of the microcontroller (19).
3. A distributed optical storage based power distribution network system according to claim 1, wherein: the converter (4) is a buck-boost converter.
4. A remote safe operation and maintenance method for a power distribution network based on distributed optical storage is characterized by comprising the following steps: the method comprises the following steps:
step one, predicting the output power of a photovoltaic cellP pre-p ：
Step 101, constructing a light intensity functionm(s t ) Wherein
Figure 745701DEST_PATH_IMAGE001
，m(s t ) The output power of the photovoltaic generator set (1) at the time t under the influence of light intensity is shown,s t representing the light intensity collected by the light intensity sensor (20) at time t,A r indicates the area of the r-th photovoltaic power generation cell,η r represents the photoelectric conversion efficiency of the r-th photovoltaic power generation cell,ϕ T which represents the light intensity correction coefficient, is,βrepresenting a discount coefficient;
step 102, constructing a temperature function m (m:)T t ) Wherein:
Figure 951554DEST_PATH_IMAGE002
；
m(T t ) The output power of the photovoltaic generator set (1) at the time t is influenced by the plate temperature of the photovoltaic component,T t the temperature of the photovoltaic module at the moment t is shown,T R representing the ambient temperature collected by the temperature sensor (21),T N the temperature of the photovoltaic module plate under the NOCT condition is shown,W t representing the wind speed collected via the wind speed sensor at time t,φ 1 、φ 2 、φ 3 、φ 4 all represent fitting coefficients;
step 103, calculating a variable weight: computer (7)According to the formula
Figure 678202DEST_PATH_IMAGE003
Calculating a variable weight, wherein
Figure 560576DEST_PATH_IMAGE004
，
Figure 5464DEST_PATH_IMAGE005
Representing the variable weight of the light intensity function,
Figure 458749DEST_PATH_IMAGE006
the initial weight of the light intensity function is represented,
Figure 39903DEST_PATH_IMAGE007
represents the variable weight of the temperature function,
Figure 843911DEST_PATH_IMAGE008
an initial weight representing a function of temperature;
step 104, for m (m) of the multiple history timess t ) And m: (a)T t ) Information fusion is carried out to obtain a fusion characteristic setM，M={m 1 ,…m t ,…,m n Will fuse feature setsMIs divided into a training set and a testing set,m t m (m) at time ts t ) And time t m: (T t ) Fusion value of 1. ltoreqt≤n；
105, constructing a neural network prediction model in a photovoltaic power prediction module (9);
step 106, training a prediction model through a training set: training a neural network by using training set data, and adjusting weight parameters of the convolutional neural network according to a method of minimizing errors;
step 107, model evaluation is performed through the test set: until the prediction accuracy of the test set meets the precision requirement, the precision requirement is more than 95%;
step 108, obtaining the light intensity at the current moment in real times t’ And temperatureT t’ Calculating the fusion value at the current time t' according to the step 101-104m t’ ；
Step 109, merging the fusion value of the current t' momentm t’ Inputting the power into a neural network prediction model, and outputting the photovoltaic cell output power predicted value at the current moment by the neural network prediction modelP pre-p ；
Step two, load power detection: the load power acquisition module (8) is used for monitoring the sum of load power values of a plurality of alternating current loads (6) at the current momentP tes-p And the sum of the monitored current load power valuesP tes-p To a comparator (10);
step three, remotely controlling the charging and discharging of the storage battery:
the comparator (10) predicts the output power of the photovoltaic cell at the current momentP pre-p And the sum of the current moment load power valuesP tes-p Make a comparison ifP pre-p >P tes-p If yes, charging the storage battery pack (3) and entering step 301; if it isP pre-p <P tes-p If yes, discharging the storage battery pack (3) and entering step 302;
step 301, the computer (7) sends a PWM pulse instruction to the PWM pulse control module (13) through the first wireless communication module (11) and the second wireless communication module (12), a switch of the converter (4) is turned off, reverse electromotive force is generated on an inductor, a diode is turned on from cut-off, and the storage battery pack (3) is charged;
step 302, the computer (7) sends a PWM pulse instruction to a PWM pulse control module (13) through a first wireless communication module (11) and a second wireless communication module (12), a switch of a converter (4) is conducted, inductance energy storage is realized, a capacitor is discharged, and a storage battery pack (3) is discharged;
step four, predicting and controlling the SOC of the storage battery pack;
obtaining the current power of the accumulator battery (3)P pre-B If, ifP pre-B >P c-max-B Entering step 401; if it isP c-min-B <P pre-B <P c-max-B Go to step 402, if yesP f-min-B <P pre-B <P f-max-B Proceed to step 403, ifP pre-B <P f-min-B Step 404 is entered;
step 401, the computer (7) sends a PWM pulse instruction to the PWM pulse control module (13) through the first wireless communication module (11) and the second wireless communication module (12), the converter (4) is turned off, and the storage battery pack (3) stops charging;
step 402, the computer (7) sends a control instruction to the overcharge early warning circuit (17), and the overcharge early warning circuit (17) gives an alarm;
step 403, the computer (7) sends a control instruction to the over-discharge early warning circuit (18), and the over-discharge early warning circuit (18) gives an alarm;
and step 404, the computer (7) sends the PWM pulse instruction to the PWM pulse control module (13) through the first wireless communication module (11) and the second wireless communication module (12), the converter (4) is turned off, and the storage battery pack (3) stops discharging.
5. The method of claim 4, wherein: in the fourth step, the current power of the storage battery pack (3)P pre-B The detection is carried out by a storage battery electric energy detection module, the storage battery electric energy detection module is connected between a storage battery pack (3) and a converter (4) in series, and an enabling pin of the storage battery electric energy detection module is connected with a computer (7).
6. The method of claim 4, wherein: in a step 104 of the method, the step of the method,
Figure 759783DEST_PATH_IMAGE009
。
7. the method of claim 4, wherein: in step 105, the basic structure of the neural network prediction model is as follows: input layer, convolutional layer, pooling layer, full-connection layer, dropout, full-connection layer.
8. The method of claim 4, wherein: discount coefficient in step 101
Figure 41860DEST_PATH_IMAGE010
WhereinV t Representing the dc voltage of the dc grid at time t,V e represents the voltage rating of the dc grid,λrepresenting the percentage of the maximum voltage difference of the dc network.
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