CN118071202A - Multi-source data fusion photovoltaic power station operation state reliability analysis method and system - Google Patents
Multi-source data fusion photovoltaic power station operation state reliability analysis method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for analyzing the reliability of the operation state of a photovoltaic power station by multi-source data fusion, and relates to the technical field of reliability analysis of photovoltaic power generation, wherein the method comprises the steps of collecting the operation data of the photovoltaic power station, inputting the data into a data fusion model to generate fusion data; dynamically adjusting a data fusion model according to data analysis requirements, and respectively inputting the fusion data into an operation stability model and a power generation efficiency model; and evaluating the reliability of the operation state of the photovoltaic power station by combining the operation stability and the power generation efficiency. According to the reliability analysis method for the operation state of the multi-source data fusion photovoltaic power station, provided by the invention, the reliability analysis is carried out according to the operation stability and the power generation benefit of the photovoltaic power station by analyzing the two data, and the total station efficiency is comprehensively considered, so that the reliability calculation is more accurate. And the fusion analysis is carried out by combining various data, so that the multidimensional understanding of the state of the photovoltaic power station is realized, and the accuracy and the depth of the analysis are increased.
Description
Technical Field
The invention relates to the technical field of reliability analysis of photovoltaic power generation, in particular to a method and a system for analyzing the reliability of the running state of a photovoltaic power station by multi-source data fusion.
Background
The operational state analysis and monitoring of photovoltaic power plants is an important component to ensure efficient and reliable operation thereof. In recent years, with rapid development of big data and cloud computing technology, the data processing capability of a photovoltaic power station is remarkably improved. These techniques allow valuable information to be extracted from a large amount of operational data to optimize the performance and operational management of the photovoltaic power plant. Current technology focuses primarily on a single data source or limited data types, e.g., relying solely on internal operating data or environmental data. These methods present challenges in processing large-scale, heterogeneous data sets, particularly when comprehensive multiple data types are required for in-depth analysis. In addition, with the rapid development of the renewable energy field, the data volume and complexity of the photovoltaic power station are continuously increased, and the conventional data processing method has difficulty in meeting the current requirements.
Despite the advances made in the state of the art in monitoring the operating condition of photovoltaic power plants, there are some key limitations. First, most existing methods lack the ability to comprehensively utilize multi-source data. Multisource data, such as internal operating data, environmental data and energy data, if effectively fused, will greatly enhance the accuracy and reliability of the operating state analysis. Secondly, the prior art still lacks in terms of flexibility and dynamic adjustment of the data fusion model. This limits a comprehensive understanding of the operating state of the photovoltaic power plant under different conditions. Finally, methods for integrating operational stability and power generation efficiency are rare in the prior art, which is critical to achieving accurate operational state analysis.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the existing photovoltaic power station operation state analysis method has the problems of single analysis data, insufficient flexibility and reliability evaluation by combining operation stability and power generation economic benefit.
In order to solve the technical problems, the invention provides the following technical scheme: a reliability analysis method for the operation state of a photovoltaic power station with multi-source data fusion comprises the steps of collecting the operation data of the photovoltaic power station, inputting the data into a data fusion model to generate fusion data; dynamically adjusting a data fusion model according to data analysis requirements, and respectively inputting the fusion data into an operation stability model and a power generation efficiency model; and evaluating the reliability of the operation state of the photovoltaic power station by combining the operation stability and the power generation efficiency.
As a preferable scheme of the photovoltaic power station operation state reliability analysis method for multi-source data fusion, the invention comprises the following steps: the collected photovoltaic power station operation data comprise internal data, external environment data and energy data of the photovoltaic power station; the internal data of the photovoltaic power station comprises current, voltage and power output of a photovoltaic panel, working states of an inverter, a transformer and a battery energy storage system, historical maintenance records, fault occurrence frequency and types; the external environment data comprises illumination intensity and shadow shielding rate; the energy data comprise the total power generation amount, the energy consumption of the power station and the electric energy transmitted to the power grid; after data acquisition is completed, dividing the internal data and the energy data of the photovoltaic power station into historical operation data and real-time operation data according to real-time performance, and constructing an environment data set E, a historical operation data set H and a real-time operation data set R, wherein the historical operation data set H and the real-time operation data set R are expressed as:
E＝{e1,e2,…,en}
H＝{h1,h2,…,hm}
R＝{r1,r2,…,rp}
Where e is environmental data, h represents historical operating data, and r represents real-time operating environmental data.
As a preferable scheme of the photovoltaic power station operation state reliability analysis method for multi-source data fusion, the invention comprises the following steps: the step of generating fusion data by the data input data fusion model comprises the steps of inputting an environment data set, a historical operation data set and a real-time operation data set into the data fusion model, and performing data processing to be expressed as:
wherein, Weight coefficient representing dynamic environment data adjusted with time, δ represents time attenuation coefficient, t j represents time parameter, and θ k represents weight coefficient of real-time data.
As a preferable scheme of the photovoltaic power station operation state reliability analysis method for multi-source data fusion, the invention comprises the following steps: generating the fusion data from the data input data fusion model further includes inputting the data fusion model based on the processed data, expressed as:
Wherein f α (T, H, R) represents a time dependency of environmental data, f β (T, E, R) represents a time dependency of history data, f γ (T, E, H) represents a time dependency of real-time data, T represents a time period, and κ α、ωβ and ω γ represent adjustment parameters of the time dependency.
As a preferable scheme of the photovoltaic power station operation state reliability analysis method for multi-source data fusion, the invention comprises the following steps: the step of respectively inputting the fusion data into the operation stability model and the power generation efficiency model comprises the step of carrying out data abnormal fluctuation detection according to the fusion data to carry out stability assessment, wherein the data abnormal fluctuation detection is expressed as follows:
Wherein D k represents the fusion data of different periods in one period, lambda k represents the weight coefficient of the kth fusion data in the state change rate evaluation, the weight coefficient of the kth fusion data in the anomaly detection score of theta k, Representing the historical variance of the fused data, Z s represents the normalization factor.
As a preferable scheme of the photovoltaic power station operation state reliability analysis method for multi-source data fusion, the invention comprises the following steps: the step of inputting the fusion data into the operation stability model and the power generation efficiency model respectively further comprises the step of analyzing the power generation efficiency in a single period, wherein the power generation efficiency is expressed as follows:
Where E rate denotes a power generation efficiency change rate score, ζ i denotes a power generation change influence coefficient, ζ i denotes a power generation reference threshold, and P p denotes a historical average power generation efficiency score.
As a preferable scheme of the photovoltaic power station operation state reliability analysis method for multi-source data fusion, the invention comprises the following steps: the step of evaluating the reliability of the operation state of the photovoltaic power station by combining the operation stability and the power generation efficiency comprises the step of performing operation stability score and power generation efficiency score output for one period, and then performing operation state reliability analysis of the photovoltaic power station, wherein the operation state reliability analysis is expressed as follows:
wherein, Representing the stability adjustment factor and β representing the nonlinear adjustment factor.
The invention also aims to provide a reliability analysis system for the operation state of the photovoltaic power station with multi-source data fusion, which can analyze the operation stability and the power generation benefit of the photovoltaic power station according to the two data, and solve the problems that the reliability calculation and judgment basis of the operation reliability analysis method of the existing photovoltaic power station is too simple to rely on manpower or calculation, and only the stability is considered without considering the power generation benefit.
As a preferable scheme of the photovoltaic power station operation state reliability analysis system with multi-source data fusion, the invention comprises the following steps: the system comprises a data fusion module, a data analysis module and a reliability analysis module; the data fusion module is used for collecting the operation data of the photovoltaic power station and inputting the data into the data fusion model to generate fusion data; the data analysis module is used for dynamically adjusting the data fusion model according to the data analysis requirement and respectively inputting the fusion data into the operation stability model and the power generation efficiency model; the reliability analysis module is used for evaluating the reliability of the operation state of the photovoltaic power station by combining the operation stability and the power generation efficiency.
A computer device comprising a memory storing a computer program and a processor executing the steps of the method for analyzing the reliability of the operation state of a photovoltaic power plant, wherein the method is used for realizing multi-source data fusion.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of a photovoltaic power plant operational state reliability analysis method for multi-source data fusion.
The invention has the beneficial effects that: according to the reliability analysis method for the operation state of the multi-source data fusion photovoltaic power station, provided by the invention, the reliability analysis is carried out according to the operation stability and the power generation benefit of the photovoltaic power station by analyzing the two data, so that the problem that the reliability analysis only depends on the operation stability to neglect the power generation benefit is avoided, and the total station efficiency is comprehensively considered, so that the reliability calculation is more accurate. And the fusion analysis is carried out by combining various data, so that the multidimensional understanding of the state of the photovoltaic power station is realized, and the accuracy and the depth of the analysis are increased. The invention has better analysis accuracy and comprehensiveness.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an overall flowchart of a method for analyzing reliability of operation states of a photovoltaic power station with multi-source data fusion according to a first embodiment of the present invention.
Fig. 2 is an overall flowchart of a photovoltaic power station operation state reliability analysis system with multi-source data fusion according to a third embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
Referring to fig. 1, for one embodiment of the present invention, a method for analyzing reliability of operation state of a photovoltaic power station with multi-source data fusion is provided, including:
S1: and collecting the operation data of the photovoltaic power station, and inputting the data into a data fusion model to generate fusion data.
Further, the collection of the photovoltaic power plant operation data includes photovoltaic power plant internal data, external environment data, and energy data.
The internal data of the photovoltaic power station comprises current, voltage and power output of the photovoltaic panel, and working states of the inverter, the transformer and the battery energy storage system, historical maintenance records, fault occurrence frequency and types.
The external environment data includes illumination intensity and shading rate.
The energy data comprises the total power generation amount, the energy consumption of the power station and the electric energy transmitted to the power grid.
After data acquisition is completed, dividing the internal data and the energy data of the photovoltaic power station into historical operation data and real-time operation data according to real-time performance, and constructing an environment data set E, a historical operation data set H and a real-time operation data set R, wherein the historical operation data set H and the real-time operation data set R are expressed as:
E＝{e1,e2,…,en}
H＝{h1,h2,…,hm}
R＝{r1,r2,…,rp}
Where e is environmental data, h represents historical operating data, and r represents real-time operating environmental data.
When the data is considered to be time-efficient, the data is classified as real-time running data, the data is not time-efficient, or the data which has been calculated is considered to be historical data.
It should be noted that, generating the fusion data by using the data input data fusion model includes inputting the environment data set, the history operation data set and the real-time operation data set into the data fusion model, and performing data processing is represented as:
wherein, Weight coefficient representing dynamic environment data adjusted with time, δ represents time attenuation coefficient, t j represents time parameter, and θ k represents weight coefficient of real-time data.
It should be further noted that generating the fusion data from the data input data fusion model further includes generating the fusion data from the processed data input data fusion model, expressed as:
Wherein f α (T, H, R) represents a time dependency of environmental data, f β (T, E, R) represents a time dependency of history data, f γ (T, E, H) represents a time dependency of real-time data, T represents a time period, and κ α、ωβ and ω γ represent adjustment parameters of the time dependency.
S2: and dynamically adjusting the data fusion model according to the data analysis requirement, and respectively inputting the fusion data into the operation stability model and the power generation efficiency model.
Furthermore, each weight is dynamically adjusted, the adjusting direction is mainly influenced by weather and equipment using time, the fused data is respectively input into the operation stability model and the power generation efficiency model, and the stability evaluation is carried out by carrying out data abnormal fluctuation detection according to the fused data, which is expressed as:
Wherein D k represents the fusion data of different periods in one period, lambda k represents the weight coefficient of the kth fusion data in the state change rate evaluation, the weight coefficient of the kth fusion data in the anomaly detection score of theta k, Representing the historical variance of the fused data, Z s represents the normalization factor.
It should be noted that, inputting the fusion data into the operation stability model and the power generation efficiency model respectively further includes analyzing the power generation efficiency in a single cycle, expressed as:
Where E rate denotes a power generation efficiency change rate score, ζ i denotes a power generation change influence coefficient, ζ i denotes a power generation reference threshold, and P p denotes a historical average power generation efficiency score.
S3: and evaluating the reliability of the operation state of the photovoltaic power station by combining the operation stability and the power generation efficiency.
Further, the step of evaluating the reliability of the operation state of the photovoltaic power station by combining the operation stability and the power generation efficiency includes the step of performing the reliability analysis of the operation state of the photovoltaic power station after completing the output of the operation stability fraction and the power generation efficiency fraction for one period, which is expressed as:
wherein, Representing the stability adjustment factor and β representing the nonlinear adjustment factor.
Example 2
In order to verify the beneficial effects of the invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.
First, in this example, four different photovoltaic power plants were selected as test subjects, named power plant a, power plant B, power plant C, and power plant D, respectively. First, data including photovoltaic panel output power, historical failure frequency, and ambient light intensity are collected from these power stations. These data represent photovoltaic plant internal data, external environmental data, and energy data.
After the data are collected, the data are input into a data fusion model to generate fusion data. Considering timeliness of the data, the internal data and the energy data of the photovoltaic power station are divided into historical operation data and real-time operation data. And then, dynamically adjusting a data fusion model according to the data analysis requirement, and respectively inputting the fusion data into the operation stability model and the power generation efficiency model for analysis.
Through model analysis, an operational stability score and a power generation efficiency score are generated for each power station. These scores are calculated based on the abnormal fluctuation detection of the data and the rate of change of the power generation efficiency, and can effectively reflect the operation condition and the power generation efficiency of the power station.
TABLE 1A, B, C, D Power station data sheet
The stability score reflects fluctuations and anomalies in the data in the operation of the plant. A higher stability score means that the plant maintains better performance consistency under various environmental changes and operating conditions.
In contrast, power stations a and C exhibit higher stability scores, and may suffer from insufficient power generation efficiency or more comprehensive maintenance, and reliability analysis requires continued aggregation of subsequent power generation efficiency scores.
The power generation efficiency score reflects the power generation efficiency of the plant in actual operation relative to its potential capacity.
The higher power generation efficiency scores of plants B and D indicate that they are superior in the efficiency of converting solar energy into electrical energy. This may be related to their technology being updated, maintenance being more in place or there being more sunlight shining in the geographic location, but there is also the possibility of high power generation and low stability.
Example 3
Referring to fig. 2, for one embodiment of the present invention, a system for analyzing reliability of an operating state of a photovoltaic power station with multi-source data fusion is provided, which includes a data fusion module, a data analysis module, and a reliability analysis module.
The data fusion module is used for collecting the operation data of the photovoltaic power station and inputting the data into the data fusion model to generate fusion data. The data analysis module is used for dynamically adjusting the data fusion model according to the data analysis requirement, and respectively inputting the fusion data into the operation stability model and the power generation efficiency model. The reliability analysis module is used for evaluating the reliability of the operation state of the photovoltaic power station by combining the operation stability and the power generation efficiency.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like. It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. The method for analyzing the reliability of the operation state of the photovoltaic power station by multi-source data fusion is characterized by comprising the following steps of:
collecting operation data of the photovoltaic power station, and inputting the data into a data fusion model to generate fusion data;
Dynamically adjusting a data fusion model according to data analysis requirements, and respectively inputting the fusion data into an operation stability model and a power generation efficiency model;
and evaluating the reliability of the operation state of the photovoltaic power station by combining the operation stability and the power generation efficiency.
2. The method for analyzing the reliability of the operation state of the multi-source data fusion photovoltaic power station according to claim 1, wherein the method comprises the following steps: the collected photovoltaic power station operation data comprise internal data, external environment data and energy data of the photovoltaic power station;
The internal data of the photovoltaic power station comprises current, voltage and power output of a photovoltaic panel, working states of an inverter, a transformer and a battery energy storage system, historical maintenance records, fault occurrence frequency and types;
the external environment data comprises illumination intensity and shadow shielding rate;
the energy data comprise the total power generation amount, the energy consumption of the power station and the electric energy transmitted to the power grid;
After data acquisition is completed, dividing the internal data and the energy data of the photovoltaic power station into historical operation data and real-time operation data according to real-time performance, and constructing an environment data set E, a historical operation data set H and a real-time operation data set R, wherein the historical operation data set H and the real-time operation data set R are expressed as:
E＝{e1,e2,…,en}
H＝{h1,h2,…,hm}
R＝{r1,r2,…,rp}
Where e is environmental data, h represents historical operating data, and r represents real-time operating environmental data.
3. The method for analyzing the reliability of the operation state of the multi-source data fusion photovoltaic power station according to claim 2, wherein the method comprises the following steps: the step of generating fusion data by the data input data fusion model comprises the steps of inputting an environment data set, a historical operation data set and a real-time operation data set into the data fusion model, and performing data processing to be expressed as:
wherein, Weight coefficient representing dynamic environment data adjusted with time, δ represents time attenuation coefficient, t j represents time parameter, and θ k represents weight coefficient of real-time data.
4. A method for analyzing the reliability of the operation state of a multi-source data fusion photovoltaic power station according to claim 3, wherein: generating the fusion data from the data input data fusion model further includes inputting the data fusion model based on the processed data, expressed as:
Wherein f α (T, H, R) represents a time dependency of environmental data, f β (T, E, R) represents a time dependency of history data, f γ (T, E, H) represents a time dependency of real-time data, T represents a time period, and κ α、ωβ and ω γ represent adjustment parameters of the time dependency.
5. The method for analyzing the reliability of the operation state of the multi-source data fusion photovoltaic power station according to claim 4, wherein the method comprises the following steps: the step of respectively inputting the fusion data into the operation stability model and the power generation efficiency model comprises the step of carrying out data abnormal fluctuation detection according to the fusion data to carry out stability assessment, wherein the data abnormal fluctuation detection is expressed as follows:
Wherein D k represents the fusion data of different periods in one period, lambda k represents the weight coefficient of the kth fusion data in the state change rate evaluation, the weight coefficient of the kth fusion data in the anomaly detection score of theta k, Representing the historical variance of the fused data, Z s represents the normalization factor.
6. The method for analyzing the reliability of the operation state of the multi-source data fusion photovoltaic power station according to claim 5, wherein the method comprises the following steps: the step of inputting the fusion data into the operation stability model and the power generation efficiency model respectively further comprises the step of analyzing the power generation efficiency in a single period, wherein the power generation efficiency is expressed as follows:
Where E rate denotes a power generation efficiency change rate score, ζ i denotes a power generation change influence coefficient, ζ i denotes a power generation reference threshold, and P p denotes a historical average power generation efficiency score.
7. The method for analyzing the reliability of the operation state of the multi-source data fusion photovoltaic power station according to claim 6, wherein the method comprises the following steps: the step of evaluating the reliability of the operation state of the photovoltaic power station by combining the operation stability and the power generation efficiency comprises the step of performing operation stability score and power generation efficiency score output for one period, and then performing operation state reliability analysis of the photovoltaic power station, wherein the operation state reliability analysis is expressed as follows:
wherein, Representing the stability adjustment factor and β representing the nonlinear adjustment factor.
8. A system employing the method for analyzing reliability of operation state of a photovoltaic power station by using multi-source data fusion according to any one of claims 1 to 7, wherein: the system comprises a data fusion module, a data analysis module and a reliability analysis module;
the data fusion module is used for collecting the operation data of the photovoltaic power station and inputting the data into the data fusion model to generate fusion data;
The data analysis module is used for dynamically adjusting the data fusion model according to the data analysis requirement and respectively inputting the fusion data into the operation stability model and the power generation efficiency model;
The reliability analysis module is used for evaluating the reliability of the operation state of the photovoltaic power station by combining the operation stability and the power generation efficiency.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, performs the steps of the method for analyzing the reliability of the operation state of a photovoltaic power plant for multi-source data fusion according to any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method for analyzing the reliability of the operation state of a photovoltaic power plant for multi-source data fusion according to any one of claims 1 to 7.
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