CN117375527A - Photovoltaic power plant energy efficiency abnormality early warning system - Google Patents
Photovoltaic power plant energy efficiency abnormality early warning system Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S50/00—Monitoring or testing of PV systems, e.g. load balancing or fault identification
- H02S50/10—Testing of PV devices, e.g. of PV modules or single PV cells
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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
- H02J13/00001—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 the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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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
- H02J13/00002—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
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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
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S50/00—Monitoring or testing of PV systems, e.g. load balancing or fault identification
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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
- 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]
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Abstract
The invention belongs to the technical field of energy sources, and particularly relates to an energy efficiency abnormality early warning system of a photovoltaic power plant.
Description
Technical Field
The invention belongs to the technical field of energy sources, and particularly relates to an energy efficiency abnormality early warning system of a photovoltaic power plant.
Background
With the rise of industrial Internet, more and more industrial systems can realize on-line monitoring and fault early warning, and play a role in ensuring the safe operation of equipment. At present, a relatively large number of fault early warning algorithms of equipment are applied, namely early warning is sent out before the equipment breaks down, and related personnel are reminded to overhaul the equipment. However, if all the devices are not concerned or are about to fail, the normal operation of the photovoltaic power generation device also includes whether the photovoltaic panel is operated in a reasonable operation state, and when the operation device reaches a sub-health state, the efficiency is reduced, or the improper operation of operation and maintenance personnel can affect the overall operation of the system. Therefore, the energy efficiency of the system needs to be evaluated, so that operation and maintenance personnel can know the overall operation condition of the equipment systematically according to the evaluation result, and then find problems in time when the energy efficiency is reduced, and the possibility of equipment failure is reduced.
Disclosure of Invention
The invention aims to provide an energy efficiency abnormality early warning system of a photovoltaic power plant, which aims to solve the technical problems that the existing early warning system can early warn in time and is convenient for operation and maintenance personnel to find out the failure cause.
In order to solve the technical problems, the specific technical scheme of the invention is as follows:
in some embodiments of the present application, an energy efficiency anomaly early warning system of a photovoltaic power plant is provided, including:
acquiring and storing unit operation data to be detected;
based on the unit operation data, a preset machine learning algorithm is utilized to obtain the balance state of the unit energy efficiency, the balance condition of each unit energy efficiency is displayed on line in real time, and a unit energy efficiency dynamic curve is generated to realize continuous dynamic monitoring of the unit energy efficiency;
screening out state indexes of energy efficiency of the unit based on the collected unit operation data;
setting standard values of all state indexes, comparing all unit energy efficiency state indexes acquired in real time with corresponding standard values, judging unit energy efficiency according to the result, and determining whether the energy efficiency in the unit to be detected is compounded with preset conditions;
generating warning information for representing energy efficiency abnormality in response to determining that the state index of the energy efficiency of the unit does not accord with the preset standard;
and transmitting the warning information to target equipment, and controlling the target equipment to display the early warning information.
In some embodiments of the present application, obtaining unit operation data to be detected includes:
acquiring unit model data, coding the unit model and the type of the working condition to which the unit model belongs, and storing the unit model and the type of the working condition in sequence;
acquiring unit positioning data, and coding unit position information to form a unit subsection map;
acquiring unit productivity data, acquiring data of the energy produced by the unit on the same day or within a certain time, and coding the data to form the productivity data;
acquiring energy consumption data of the unit, acquiring the data of the energy consumed by the unit in the same day or within a certain time, and coding the data to form the energy consumption data;
and acquiring actual energy efficiency data of the unit, summarizing the capacity data and the energy consumption data of the unit, calculating a difference value, and acquiring the obtained difference value to form the actual energy efficiency data.
In some embodiments of the present application, the acquiring actual energy efficiency data of the unit is calculated according to the following formula:
N＝J 1 X (n)-J 2 X (n)，
n is used for representing actual energy efficiency data of the unit;
j1 represents unit productivity data of the current period;
j2 represents unit energy consumption data of the current period;
(n) represents the current model of the unit;
x represents the current working condition type of the unit.
In some embodiments of the present application, based on the unit operation data, a preset machine learning algorithm is utilized to obtain a balance state of unit energy efficiency, and the steps of displaying the balance situation of each unit energy efficiency on line in real time and generating a unit energy efficiency dynamic curve specifically include:
analyzing the unit operation data, and identifying the model and the working condition type of unit equipment;
inquiring a device database according to the model of the unit, and determining real-time energy consumption parameters of the unit under different environments;
determining the energy consumption of the unit in each time period according to the time sequence, and generating a unit energy consumption curve;
the energy efficiency dynamic curve of the unit is generated by acquiring the capacity data and the energy consumption data in the unit operation data to respectively form a capacity curve and an energy consumption curve and acquiring the actual energy efficiency data of the unit.
In some embodiments of the present application, a corresponding calculation and analysis system is set in a preset machine learning algorithm;
the computational analysis system includes:
the data acquisition module is used for acquiring unit operation data, analyzing the unit operation data, acquiring model, positioning data and working condition information of the unit, and generating a corresponding unit energy efficiency dynamic curve;
the data acquisition module is used for acquiring capacity data, energy consumption data and actual energy efficiency data in the unit operation data, checking the capacity data, the energy consumption data and the actual energy efficiency data, comparing the capacity data, the energy consumption data and the actual energy efficiency data with a capacity curve, an energy consumption curve and a unit energy efficiency dynamic curve graph of a current period, judging whether deviation exists in the data of the current period, and acquiring data abnormal signals for target equipment if the deviation exists;
the energy efficiency evaluation module is used for comparing energy efficiency based on a unit energy efficiency dynamic curve, standard values of state indexes of all unit models are preset in the energy efficiency evaluation module, corresponding difference values are obtained through comparing and analyzing actual energy efficiency data in the data unit with the standard values of the state indexes of all unit models, whether the difference values in the current period are abnormal or not is judged, if so, an energy efficiency data abnormal signal is sent to target equipment, and meanwhile, marking is carried out in the unit energy efficiency dynamic curve.
In some embodiments of the present application, the calculation formula of the energy efficiency evaluation module difference value is as follows:
C＝B-N，
wherein C is a difference value in the energy efficiency evaluation module;
b is a standard value of a state index of each unit model preset in the energy efficiency evaluation module;
n is actual energy efficiency data of the unit in the current period;
if C is between-1 and-1, the method is normal;
if C is greater than 1 or less than-1, it is abnormal.
In some embodiments of the present application, the system further includes an abnormality determination module, where the abnormality determination module determines an abnormality type of the current unit by acquiring abnormality signal data in the energy efficiency evaluation module;
the abnormality determination module includes:
the external acquisition unit is used for acquiring state information of the unit in the current period;
the fault classification unit is used for classifying the state information of the current time unit acquired by the image acquisition unit;
the checking unit acquires state information of the unit in the current period of time in the image acquisition unit and fault classification information in the fault classification unit for comparison, and if the comparison is successful, the result is sent to the energy efficiency evaluation module; if the comparison fails, the fault classification unit reclassifies the current fault.
In some embodiments of the present application, the external acquisition unit acquires a temperature, a surface state, and a unit ambient environment state of the unit at a current period, including:
the temperature extraction unit is used for collecting temperatures of all parts of the unit in the current period and generating corresponding unit temperature characteristic data;
the surface extraction unit is used for collecting unit surface image information at the current time and generating corresponding unit surface characteristic data;
the surrounding environment extraction unit is used for collecting the surrounding environment state of the unit in the current period and generating corresponding surrounding environment characteristic data of the unit.
In some embodiments of the present application, fault image data of each unit is preset in the fault classification unit, and the temperature characteristic data, the unit surface characteristic data, and the unit surrounding environment characteristic data acquired in the external acquisition unit are received and compared with the database, and corresponding comparison results are generated and sent to the verification unit.
In some embodiments of the present application, the verification unit is an external audit unit, and performs verification by receiving the comparison result in the fault classification unit, and if the verification is correct, sends the verification result to the energy efficiency evaluation module; if the verification fails, a reclassification signal is generated to the fault classification unit so that the fault classification unit reclassifies the current fault.
Compared with the prior art, the method has the beneficial effects that the operation data of the unit are obtained, the unit energy efficiency dynamic curve is generated through the operation data, the observation of operation and maintenance personnel is facilitated, the effect of real-time monitoring is achieved, whether the current energy efficiency data is abnormal or not is obtained in real time through comparing the actual energy efficiency data in the unit operation data with the preset standard value, the abnormal data is marked on the unit energy efficiency dynamic curve record, meanwhile, warning information is sent to target equipment, the operation and maintenance personnel is reminded, the current fault cause is analyzed through the abnormality judgment module, and the operation and maintenance personnel can conveniently and better remove the processing.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a schematic flow chart of the operation principle provided by the embodiment of the invention;
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
The present invention will be described in further detail with reference to the accompanying drawings for a better understanding of the objects, structures and functions of the present invention.
In some embodiments of the present application, the method comprises:
acquiring and storing unit operation data to be detected;
based on the unit operation data, a preset machine learning algorithm is utilized to obtain the balance state of the unit energy efficiency, the balance condition of each unit energy efficiency is displayed on line in real time, and a unit energy efficiency dynamic curve is generated to realize continuous dynamic monitoring of the unit energy efficiency;
the unit in the embodiment of the disclosure may be, for example, a photovoltaic generator set in a power plant, or may also be unit equipment in any other possible application scenario, which is not limited.
The unit operation data may be real-time operation data of the photovoltaic generator unit, that is, the unit energy efficiency abnormality diagnosis method provided by the embodiment may perform real-time diagnosis on the operation state of the photovoltaic generator unit, so as to ensure the energy efficiency of the unit.
The unit operation data may include a plurality of energy efficiency state indexes, which may also be referred to as energy efficiency operation parameters, where the energy efficiency state indexes may describe parameter conditions of each device in the unit operation process.
Screening out state indexes of energy efficiency of the unit based on the collected unit operation data;
the state index of the energy efficiency of the unit disclosed in the embodiment is an average value of the total energy efficiency of the unit in a certain time;
i.e.
S is t 1 To t 2 Sum of energy efficiency in time;
n is the actual energy efficiency of the unit;
after the unit operation data are determined, further, according to the preset standard values of all state indexes (the standard values are represented by B), all unit energy efficiency state indexes obtained in real time are compared with corresponding standard values, and according to the result, the unit energy efficiency is judged, and whether the energy efficiency in the unit to be detected is compounded with preset conditions is determined; i.e. an indicator or parameter of the occurrence of an anomaly during monitoring. The standard value interval is predetermined, and each energy efficiency state index can have a corresponding standard value interval. In this embodiment, each energy efficiency state index in the unit operation data may be compared with a corresponding standard value interval, and the energy efficiency state index exceeding the standard interval may be referred to as a target abnormal energy efficiency index, where the target abnormal energy efficiency index may be one or more, which is not limited.
In some embodiments, the standard value interval may be, for example, an upper limit range and a lower limit range of an energy efficiency state index during normal operation of the unit, or may also be a change rate range of the energy efficiency state index during normal operation of the unit, which is not limited, that is, an energy efficiency state index exceeding the upper limit range and the lower limit range of the energy efficiency state index or the change rate range during normal operation of the unit is a target abnormal energy efficiency index.
And generating warning information for representing energy efficiency abnormality in response to the fact that the state index of the energy efficiency of the unit does not accord with the preset standard, namely, generating a warning signal when the abnormal energy efficiency index is acquired, and enabling corresponding target equipment to perform warning operation, wherein the target equipment can be a voice player, an acousto-optic warning device and the like, the number of the target equipment can be one or more, and the warning information is not limited.
It should be noted that, the obtaining the unit operation data to be detected includes:
the machine set model data is obtained, and the machine set model and the belonging working condition type are encoded and sequenced, namely, the model and the working condition type of each machine set are encoded, so that the machine set classification is achieved, and the operation and maintenance personnel can find conveniently;
if (n) represents the current unit model and x represents the current unit working condition type; representing the model and working condition information of the current unit through X (n);
the method comprises the steps of obtaining unit positioning data, encoding unit position information to form a unit distribution map, namely generating a corresponding topographic map through the unit positioning data, wherein the current topographic map can be added into the topographic map, so that an operation and maintenance person can more clearly and definitely find the corresponding unit position;
if Dn is used for representing the position information, the position of the current unit is represented by X (n) -Dy.
Acquiring unit capacity data, acquiring data of the energy produced by the unit on the same day or within a certain time, and encoding to form capacity data, wherein the capacity data is specifically an average value of the unit capacity sum in the current period, and the maximum value and/or the minimum value can be omitted;
acquiring energy consumption data of the unit, acquiring the energy consumption data of the unit on the same day or within a certain time, and coding the energy consumption data to form energy consumption data, wherein the energy consumption data is an average value of the energy consumption sum of the unit in the current period, and the maximum value and/or the minimum value can be omitted;
and acquiring actual energy efficiency data of the unit, summarizing the capacity data and the energy consumption data of the unit, calculating a difference value, and acquiring the obtained difference value to form the actual energy efficiency data.
The actual energy efficiency data of the acquisition unit is calculated according to the following formula:
N＝J 1 X (n)-J 2 X (n)，
n is used for representing actual energy efficiency data of the unit;
j1 represents unit productivity data of the current period;
j2 represents unit energy consumption data of the current period;
(n) represents the current model of the unit;
x represents the current working condition type of the unit.
Based on the unit operation data, a preset machine learning algorithm is utilized to obtain the balance state of the unit energy efficiency, the balance condition of each unit energy efficiency is displayed on line in real time, and a unit energy efficiency dynamic curve is generated, and the method specifically comprises the following steps:
analyzing the unit operation data, and identifying the model and the working condition type of unit equipment;
inquiring a device database according to the model of the unit, and determining real-time energy consumption parameters of the unit under different environments;
determining the energy consumption of the unit in each time period according to the time sequence, and generating a unit energy consumption curve;
the energy efficiency dynamic curve of the unit is generated by acquiring the capacity data and the energy consumption data in the unit operation data, respectively forming a capacity curve and an energy consumption curve, and by acquiring the actual energy efficiency data of the unit, namely, a three-line diagram is generated in a coordinate system, an X-axis represents a time value, and a Y-axis represents an energy value.
It should be noted that a corresponding calculation and analysis system is arranged in the preset machine learning algorithm;
the computational analysis system includes:
the data acquisition module is used for acquiring the running data of the unit, analyzing the running data, acquiring the model, positioning data and working condition information of the unit, and generating a corresponding unit energy efficiency dynamic curve, namely generating a corresponding capacity curve, an energy consumption curve and a unit energy efficiency dynamic curve schematic diagram by acquiring the running data of the unit;
the data acquisition module is used for acquiring productivity data, energy consumption data and actual energy efficiency data in the unit operation data, checking the productivity data, the energy consumption data and the actual energy efficiency data (checking whether the actual energy efficiency data has calculation errors or not), comparing the productivity data, the energy consumption curve and the unit energy efficiency dynamic curve in the current period, judging whether deviation exists in the data in the current period, if so, acquiring data abnormal signals to target equipment, and enabling the unit operation data to be acquired again;
the energy efficiency evaluation module is used for comparing energy efficiency based on unit energy efficiency dynamic curves, standard values of state indexes of all unit models are preset in the energy efficiency evaluation module, namely standard curve intervals of all units are preset in the energy efficiency evaluation module, and the standard curve intervals are compared with the unit energy efficiency dynamic curves; in other words, the actual energy efficiency data in the data unit is obtained and compared with the standard value of each unit model state index to obtain a corresponding difference value, whether the difference value in the current period is in the standard curve interval is judged, if the difference value is not in the standard curve interval, an energy efficiency data abnormal signal is sent to the target equipment, and meanwhile, the unit energy efficiency dynamic curve is marked.
The calculation formula of the energy efficiency evaluation module difference value is as follows:
C＝B-N，
wherein C is a difference value in the energy efficiency evaluation module;
b is a standard value of a state index of each unit model preset in the energy efficiency evaluation module;
n is actual energy efficiency data of the unit in the current period;
for example, a section of curve interval is taken as-1-1, if C is between-1 and-1, the curve interval is normal; if C is greater than 1 or less than-1, it is abnormal.
Through the technical scheme, the technical effects generated in the embodiment of the application are as follows:
the model, the working condition type and the position information of the unit are encoded, so that operation and maintenance personnel can find and maintain conveniently, the actual energy efficiency data of the unit in a corresponding time period is obtained through the capacity data and the energy consumption data in the unit operation data, whether the energy efficiency state of the current unit is abnormal or not is judged through comparing the actual energy efficiency data with a standard curve interval, the state of the current unit can be accurately judged, and a basis is provided for timely early warning of the operation and maintenance personnel and rapid finding of fault reasons.
In some embodiments of the present application, the solution in the foregoing embodiments is further included in the present application, where the abnormality determination module determines an abnormality type of the current unit by acquiring abnormality signal data in the energy efficiency evaluation module;
the abnormality determination module includes:
the external acquisition unit is used for acquiring state information of the unit in the current period; the external acquisition unit mainly acquires the temperature, the surface state and the surrounding environment state of the unit in the current period, and the external acquisition unit comprises:
the temperature extraction unit is used for collecting the temperatures of all parts of the unit in the current period and generating corresponding unit temperature characteristic data, is arranged on the unit, and the number and the positions of the unit temperature characteristic data are selected according to actual requirements, but the unit temperature characteristic data are not limited to the unit temperature characteristic data;
the surface extraction unit is used for collecting unit surface image information at the current time and generating corresponding unit surface characteristic data, and the number and the arrangement mode of the unit surface characteristic data are selected and arranged according to actual requirements;
the surrounding environment extraction unit is used for collecting the surrounding environment states of the unit in the current period, generating corresponding characteristic data of the surrounding environment of the unit, and selecting and arranging the quantity and the arrangement modes according to actual demands.
The temperature extraction unit is a temperature detection sensor, and the surface extraction unit and the surrounding environment extraction unit are imaging devices, but the invention is not limited thereto.
The fault classification unit is used for classifying the state information of the current time unit acquired by the image acquisition unit; the fault classification unit is preset with fault image data of each unit, and the temperature characteristic data, the unit surface characteristic data and the unit surrounding environment characteristic data acquired by the external acquisition unit are received to be compared with the database, and corresponding comparison results are generated and sent to the checking unit.
It should be noted that, a database is preset in the fault classification unit, and a plurality of fault image information data are stored in the database, and the unit state information acquired in the external acquisition unit is compared with a plurality of fault image information data pre-stored in the database; and selecting a certain number of most similar samples, recording the type of each sample data, and comparing the type of the sample data with the unit state information acquired in an external acquisition unit, wherein the type of the sample data with the most characteristic information is the type of the current fault.
The checking unit acquires state information of the unit in the current period of time in the image acquisition unit and fault classification information in the fault classification unit for comparison, and if the comparison is successful, the result is sent to the energy efficiency evaluation module; if the comparison fails, the fault classification unit reclassifies the current fault. The checking unit can be of an external checking type or a secondary checking type, checking is carried out manually when the checking unit is of the external checking type, and when the checking unit is of the secondary checking type, a second database pre-stored with various fault image information data is preset in the checking unit, and the unit state information acquired by the external acquisition unit and the fault type information of the fault classification unit are received and are secondarily compared with the database. If the verification is correct, the verification is sent to an energy efficiency evaluation module, an operation and maintenance person is notified, and the corresponding unit energy efficiency dynamic curve is marked, so that the operation and maintenance person can conveniently and timely learn the current fault cause; if the verification fails, a reclassification signal is generated to the fault classification unit, so that the fault classification unit reclassifies the current fault, and when reclassifies, the fault classification unit reclassifies the unit state information acquired by the external acquisition unit, selects two samples with the largest number of similarity, records the type of each sample data, transmits the type to the verification unit, and the verification unit further verifies the type.
Through the technical scheme, the technical effects generated in the embodiment of the application are as follows:
through addding the unusual decision module, the state that is located current unit through unusual decision module to obtain the trouble type of current unit, send to the energy efficiency evaluation module in, and mark in unit energy efficiency dynamic curve, the operation and maintenance personnel of being convenient for in time know the trouble reason of unit, in order to reach the characteristics that can in time handle.
In the description of the present application, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate description of the present application and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present application.
The terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art in a specific context.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. An energy efficiency anomaly early warning system of a photovoltaic power plant, comprising:
acquiring and storing unit operation data to be detected;
based on the unit operation data, a preset machine learning algorithm is utilized to obtain the balance state of the unit energy efficiency, the balance condition of each unit energy efficiency is displayed on line in real time, and a unit energy efficiency dynamic curve is generated to realize continuous dynamic monitoring of the unit energy efficiency;
screening out state indexes of energy efficiency of the unit based on the collected unit operation data;
setting standard values of all state indexes, comparing all unit energy efficiency state indexes acquired in real time with corresponding standard values, judging unit energy efficiency according to the result, and determining whether the energy efficiency in the unit to be detected is compounded with preset conditions;
generating warning information for representing energy efficiency abnormality in response to determining that the state index of the energy efficiency of the unit does not accord with the preset standard;
and transmitting the warning information to target equipment, and controlling the target equipment to display the early warning information.
2. The photovoltaic power plant energy efficiency anomaly early warning system of claim 1, wherein the obtaining unit operation data to be detected comprises:
acquiring unit model data, coding the unit model and the type of the working condition to which the unit model belongs, and storing the unit model and the type of the working condition in sequence;
acquiring unit positioning data, and coding unit position information to form a unit subsection map;
acquiring unit productivity data, acquiring data of the energy produced by the unit on the same day or within a certain time, and coding the data to form the productivity data;
acquiring energy consumption data of the unit, acquiring the data of the energy consumed by the unit in the same day or within a certain time, and coding the data to form the energy consumption data;
and acquiring actual energy efficiency data of the unit, summarizing the capacity data and the energy consumption data of the unit, calculating a difference value, and acquiring the obtained difference value to form the actual energy efficiency data.
3. The photovoltaic power plant energy efficiency abnormality early warning system according to claim 2, wherein the acquiring actual energy efficiency data of the acquiring unit is calculated according to the following formula:
N＝J 1 X (n)-J 2 X (n)，
n is used for representing actual energy efficiency data of the unit;
j1 represents unit productivity data of the current period;
j2 represents unit energy consumption data of the current period;
(n) represents the current model of the unit;
x represents the current working condition type of the unit.
4. The photovoltaic power plant energy efficiency abnormality early warning system according to claim 1, wherein the steps of obtaining a balance state of the energy efficiency of the unit based on the unit operation data by using a preset machine learning algorithm, displaying the balance condition of each unit energy efficiency on line in real time, and generating a unit energy efficiency dynamic curve specifically comprise:
analyzing the unit operation data, and identifying the model and the working condition type of unit equipment;
inquiring a device database according to the model of the unit, and determining real-time energy consumption parameters of the unit under different environments;
determining the energy consumption of the unit in each time period according to the time sequence, and generating a unit energy consumption curve;
the energy efficiency dynamic curve of the unit is generated by acquiring the capacity data and the energy consumption data in the unit operation data to respectively form a capacity curve and an energy consumption curve and acquiring the actual energy efficiency data of the unit.
5. The photovoltaic power plant energy efficiency abnormality early warning system according to claim 4, wherein a corresponding calculation and analysis system is arranged in the preset machine learning algorithm;
the computational analysis system includes:
the data acquisition module is used for acquiring unit operation data, analyzing the unit operation data, acquiring model, positioning data and working condition information of the unit, and generating a corresponding unit energy efficiency dynamic curve;
the data acquisition module is used for acquiring capacity data, energy consumption data and actual energy efficiency data in the unit operation data, checking the capacity data, the energy consumption data and the actual energy efficiency data, comparing the capacity data, the energy consumption data and the actual energy efficiency data with a capacity curve, an energy consumption curve and a unit energy efficiency dynamic curve graph of a current period, judging whether deviation exists in the data of the current period, and acquiring data abnormal signals for target equipment if the deviation exists;
the energy efficiency evaluation module is used for comparing energy efficiency based on a unit energy efficiency dynamic curve, standard values of state indexes of all unit models are preset in the energy efficiency evaluation module, corresponding difference values are obtained through comparing and analyzing actual energy efficiency data in the data unit with the standard values of the state indexes of all unit models, whether the difference values in the current period are abnormal or not is judged, if so, an energy efficiency data abnormal signal is sent to target equipment, and meanwhile, marking is carried out in the unit energy efficiency dynamic curve.
6. The photovoltaic power plant energy efficiency abnormality early warning system according to claim 5, wherein the calculation formula of the energy efficiency evaluation module difference value is as follows:
C＝B-N，
wherein C is a difference value in the energy efficiency evaluation module;
b is a standard value of a state index of each unit model preset in the energy efficiency evaluation module;
n is actual energy efficiency data of the unit in the current period;
if C is between-1 and-1, the method is normal;
if C is greater than 1 or less than-1, it is abnormal.
7. The photovoltaic power plant energy efficiency abnormality early warning system according to claim 1, further comprising an abnormality determination module, wherein the abnormality determination module determines an abnormality type of the current unit by acquiring abnormality signal data in the energy efficiency evaluation module;
the abnormality determination module includes:
the external acquisition unit is used for acquiring state information of the unit in the current period;
the fault classification unit is used for classifying the state information of the current time unit acquired by the image acquisition unit;
the checking unit acquires state information of the unit in the current period of time in the image acquisition unit and fault classification information in the fault classification unit for comparison, and if the comparison is successful, the result is sent to the energy efficiency evaluation module; if the comparison fails, the fault classification unit reclassifies the current fault.
8. The photovoltaic power plant energy efficiency anomaly early warning system of claim 7, wherein the external acquisition unit acquires the temperature, the surface state and the ambient state of the unit at the current time interval, and comprises:
the temperature extraction unit is used for collecting temperatures of all parts of the unit in the current period and generating corresponding unit temperature characteristic data;
the surface extraction unit is used for collecting unit surface image information at the current time and generating corresponding unit surface characteristic data;
the surrounding environment extraction unit is used for collecting the surrounding environment state of the unit in the current period and generating corresponding surrounding environment characteristic data of the unit.
9. The photovoltaic power plant energy efficiency abnormality early warning system according to claim 7, wherein each unit fault image data is preset in the fault classification unit, the temperature characteristic data, the unit surface characteristic data and the unit surrounding environment characteristic data acquired in the external acquisition unit are received and compared with the database, corresponding comparison results are generated, and the comparison results are sent to the checking unit.
10. The photovoltaic power plant energy efficiency abnormality early warning system according to claim 7, wherein the verification unit is an external verification unit, and performs verification by receiving the comparison result in the fault classification unit, and if the verification is correct, the verification is sent to the energy efficiency evaluation module; if the verification fails, a reclassification signal is generated to the fault classification unit so that the fault classification unit reclassifies the current fault.
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