CN117691594B - Energy saving and consumption reduction judging method and system for transformer - Google Patents
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Abstract
The invention relates to the technical field of transformers, and discloses an energy-saving and consumption-reducing judging method and system for a transformer, wherein the method comprises the following steps of: collecting and processing real-time operation data, historical data and cost data of the transformer; analyzing the processed data; respectively distributing preset weights for each analysis result and grading; calculating a comprehensive score according to the score of each analysis result and the weight corresponding to the score; determining whether energy conservation and consumption reduction are needed according to whether the comprehensive score exceeds a preset comprehensive score threshold value; the invention also discloses an energy-saving consumption-reducing judging system for the transformer. The invention not only can realize multi-parameter monitoring and comprehensive analysis of the running state of the transformer, but also can objectively and accurately judge the energy-saving state and the energy-saving space of the transformer, realize the accurate judgment of the energy-saving state of the transformer, avoid blindness and more accurately realize the judgment of the energy-saving and consumption-reducing requirements.
Description
Technical Field
The invention relates to the technical field of transformers, in particular to an energy-saving consumption-reducing judging method and system for a transformer.
Background
Transformers are important devices in power systems, and their operating efficiency directly affects the energy saving and consumption reduction levels of the power grid. As a key device of the power system, the operation state and efficiency level of the transformer can have a direct influence on the energy saving condition of the whole power grid. A large number of transformer devices are often arranged in an electrical network system, which, when switching between different voltage levels, cause a certain energy loss if their own efficiency is low. This loss is avoided, and if the efficiency of the transformer itself can be improved, the overall loss of the power grid can be reduced, thereby achieving the purpose of saving energy. Therefore, the improvement of the working efficiency of the transformer is a very key aspect for the power department to realize energy conservation and consumption reduction.
However, the following problems still exist in the current energy-saving operation management of transformers:
1) The existing judging method lacks comprehensive monitoring analysis on various running states of the transformer, and cannot comprehensively judge the energy-saving optimization requirement. The existing energy-saving operation management of the transformer mostly adopts a single index analysis method, for example, only the load rate, the temperature rise condition and the like of the transformer are concerned, the actual operation state of the transformer can not be comprehensively reflected, and therefore, the energy-saving potential and the energy-saving measure requirement of the transformer are difficult to accurately judge.
2) The existing judging method is too dependent on a single index, and cannot reflect the overall operation condition of the transformer. The energy-saving space of the transformer is difficult to judge by means of a single index, the existing method cannot consider the mutual restriction relation among various factors such as the load rate, the power supply reliability, the running cost and the like of the transformer, so that certain blindness exists in energy-saving judgment, and the key position of the energy conservation of the transformer cannot be located.
For the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides an energy-saving and consumption-reducing judging method and system for a transformer, which are used for overcoming the technical problems existing in the prior related art.
For this purpose, the invention adopts the following specific technical scheme:
According to one aspect of the invention, there is provided a method for judging energy saving and consumption reduction of a transformer, the method comprising the steps of:
s1, collecting real-time operation data, historical data and cost data of a transformer, and performing data cleaning and standardization processing;
S2, analyzing the operation efficiency, the power consumption mode, the trend, the cost benefit and the wear evaluation of the transformer by using the processed real-time operation data, the history data and the cost data;
s3, respectively distributing preset weights for an operation efficiency analysis result, a power consumption mode and trend analysis result, a cost benefit analysis result and a wear evaluation analysis result;
S4, scoring the operation efficiency analysis result, the power consumption mode, the trend analysis result, the cost benefit analysis result and the wear evaluation analysis result according to a preset evaluation standard;
S5, calculating a comprehensive score according to the score of each analysis result and the weight corresponding to the score through a weighted summation method;
s6, analyzing whether the comprehensive score exceeds a preset comprehensive score threshold by using a comparison method, if so, saving energy and reducing consumption, and if not, saving energy and reducing consumption.
Preferably, the analyzing the operation efficiency, the power consumption mode and the trend, the cost effectiveness and the wear assessment of the transformer by using the processed real-time operation data, the history data and the cost data comprises the following steps:
S21, acquiring real-time operation data, historical data and cost data after data cleaning and standardization processing;
S22, calculating the operation efficiency of the transformer according to the real-time operation data of the transformer, and obtaining an operation efficiency analysis result;
s23, analyzing the power consumption mode and trend of the transformer by utilizing the historical data of the transformer to obtain a power consumption mode and trend analysis result;
S24, analyzing the cost benefit of the transformer through the cost data of the transformer to obtain a cost benefit analysis result;
s25, predicting the predicted loss of the transformer according to the real-time operation data and the historical data of the transformer by utilizing the quantum machine learning model, and obtaining a loss evaluation analysis result.
Preferably, the calculating the operation efficiency of the transformer according to the real-time operation data of the transformer, and the obtaining the operation efficiency analysis result includes:
S221, obtaining an input end voltage U 1, an input end current I 1, an input end effective power P 1, an output end voltage U 2, an output end current I 2, an output end effective power P 2, an unloaded current I 0 and an unloaded loss P 0 of the transformer;
s222, combining the power factor of the input end of the transformer Calculating the input effective power P in of the transformer, wherein the calculation formula is/>；
S223, combining power factor of output end of transformerCalculating the output effective power P out of the transformer, wherein the calculation formula is/>；
224. Calculating the operation efficiency eta of the transformer, wherein the calculation formula is as follows；
S225, calculating the ratio between the operation efficiency eta of the transformer and the standard efficiency eta 0 of the transformer to obtain an operation efficiency analysis result;
analyzing the power consumption mode and trend of the transformer by using the historical data of the transformer, and obtaining the power consumption mode and trend analysis result comprises the following steps:
S231, acquiring power consumption data of a transformer in a preset time period, including effective power, voltage and current, sorting the power consumption data, drawing a power consumption trend graph, and observing a change mode of the power consumption trend graph;
S232, analyzing the power consumption difference in different time periods, judging whether a large fluctuation range exists, calculating average power consumption, maximum consumption value and minimum consumption value in a statistics mode, and judging the fluctuation range of the power consumption;
s233, acquiring a historical maintenance record of the transformer, analyzing whether the condition of abnormal operation caused by long-time maintenance is existed, comparing the power consumption data of the similar transformers, and judging whether the transformer is abnormal;
S234, analyzing the power consumption change mode, the fluctuation range of the power consumption and the abnormality judgment result of the transformer to obtain a power consumption mode and a trend analysis result.
Preferably, the analyzing the cost benefit of the transformer by the cost data of the transformer, and obtaining the cost benefit analysis result includes:
S241, acquiring the purchase and installation budget cost of energy-saving equipment, predicting the annual electric power saving amount after implementing energy-saving measures, and estimating the annual electric charge saving cost according to the query electric power unit price;
S242, calculating an investment recovery period, and obtaining a cost benefit analysis result, wherein the investment recovery period=total investment cost/annual electricity fee saving;
the predicting the predicted loss of the transformer according to the real-time operation data and the historical data of the transformer by utilizing the quantum machine learning model, and the obtaining of the loss evaluation analysis result comprises the following steps:
S251, acquiring real-time operation data and historical data of the transformer and carrying out missing value and normalization processing;
s252, constructing and training an improved quantum neural network model by using the processed historical data of the transformer to obtain a trained improved quantum neural network model;
S253, outputting predicted loss of the transformer corresponding to the real-time operation data by using the trained improved quantum neural network model;
S254, obtaining a loss evaluation analysis result by comparing the difference between the predicted loss of the transformer and the actual loss of the transformer.
Preferably, the method for constructing and training the improved quantum neural network model by using the processed historical data of the transformer, and obtaining the trained improved quantum neural network model comprises the following steps:
S2521, constructing a quantum neural network model, wherein an input layer of the quantum neural network model is a transformer characteristic vector, an hidden layer is a turnstile adjusting quantum state, and an output layer is the predicted loss of the transformer;
S2522, adjusting the quantum state of the transformer characteristic vector by utilizing a quantum revolving door, extracting nonlinear characteristics of data, and optimizing the rotation angle by utilizing a quantum evolution algorithm;
S2523, inputting the preprocessed historical data of the transformer into the quantum neural network as a training sample, and adjusting the network connection weight through repeated iterative training until the training is finished when the network output result is consistent with the actual label, so as to obtain the trained improved quantum neural network model.
Preferably, the adjusting the quantum state of the transformer eigenvector by using the quantum rotation gate, extracting the nonlinear characteristic of the data, and optimizing the rotation angle by using the quantum evolution algorithm comprises the following steps:
Defining a characteristic vector of the transformer, wherein the characteristic vector comprises a load rate characteristic, a temperature characteristic and a voltage characteristic;
mapping each feature to the superposition state of the quantum bit by using a Hadamard gate to obtain a quantum feature vector;
setting an initial rotation angle of the rotating gate, and adjusting quantum superposition states of the feature vectors one by one through the rotating gate by quantum feature vectors;
the method comprises the steps of utilizing a revolving door to introduce an initial rotation angle phase to realize nonlinear mapping of characteristics, and defining quantum codes of the initial rotation angle as quantum individual genes;
and designing a quantum individual fitness function to evaluate the influence of different initial rotation angles on the training effect, executing the iterative search of the quantum evolutionary algorithm to the optimal rotation angle, and replacing the rotation gate angle to be the optimal rotation angle obtained by the quantum evolutionary algorithm.
Preferably, the assigning the preset weights to the operation efficiency analysis result, the power consumption pattern and trend analysis result, the cost benefit analysis result, and the wear-assessment analysis result respectively includes:
configuring a first weight for the operation efficiency analysis result;
configuring a second weight for the power consumption mode and the trend analysis result;
configuring a third weight for the cost-benefit analysis result;
configuring a fourth weight for the wear-leveling analysis result;
wherein, the first weight > the second weight=the fourth weight > the third weight, and the first weight+the second weight+the third weight+the fourth weight=1.
Preferably, the scoring the operation efficiency analysis result, the power consumption pattern and trend analysis result, the cost benefit analysis result, and the wear-leveling analysis result according to the preset evaluation criteria includes:
Determining the score of an operation efficiency analysis result according to the ratio between the operation efficiency eta of the transformer and the standard efficiency eta 0 of the transformer, wherein if the ratio is more than or equal to 0.95, the score is 10 minutes, the ratio is less than or equal to 0.90 and less than or equal to 0.95, the score is 8 minutes, the ratio is less than or equal to 0.85 and less than or equal to 0.90, the score is 6 minutes, the ratio is less than or equal to 0.80 and less than or equal to 0.85, the score is 4 minutes, and the ratio is less than or equal to 0.80 and the score is 2 minutes;
Determining the power consumption mode and the score of a trend analysis result according to the power consumption change mode of the transformer, the fluctuation range of the power consumption and the abnormality judgment result, wherein if the power consumption change mode is stable and abnormal, the score is 10 points, the score is 8 points if periodic fluctuation exists, the score is 6 points if the fluctuation range exceeds a threshold value, and the score is 4 points if abnormal records exist;
Determining the score of the cost benefit analysis result according to the investment recovery period, wherein if the investment recovery period is within 2 years, the score is 10 points, the score is 8 points, the score is 6-8 years, the score is 4 points, and the score is 2 points when the investment recovery period is over 2 years;
Determining the score of a loss evaluation prediction result according to the difference between the predicted loss of the transformer and the actual loss of the transformer, wherein if the difference is less than 5%, the score is 10 points, the difference is less than or equal to 5% and less than 10%, the score is 8 points, the difference is less than or equal to 10% and less than 15%, the score is 6 points, the difference is less than or equal to 15% and less than 20%, the score is 4 points, and the difference is more than or equal to 20%, and the score is 2 points.
Preferably, the calculation formula of the composite score is: Where w 1 denotes a first weight, s 1 denotes a score of an operation efficiency analysis result, w 2 denotes a second weight, s 2 denotes a score of a power consumption pattern and a trend analysis result, w 3 denotes a third weight, s 3 denotes a score of a cost benefit analysis result, w 4 denotes a fourth weight, and s 4 denotes a score of a wear-assessment prediction result.
According to another aspect of the invention, an energy-saving and consumption-reducing judging system for a transformer is provided, and comprises a data acquisition module, a data analysis module, a weight configuration module, a result scoring module, a comprehensive calculation module and an analysis and comparison module;
The data acquisition module is used for acquiring real-time operation data, historical data and cost data of the transformer and performing data cleaning and standardization processing;
The data analysis module is used for analyzing the operation efficiency, the power consumption mode and trend, the cost benefit and the wear evaluation of the transformer by using the processed real-time operation data, the history data and the cost data;
the weight configuration module is used for respectively distributing preset weights to the operation efficiency analysis result, the power consumption mode, the trend analysis result, the cost benefit analysis result and the wear evaluation analysis result;
The result scoring module is used for scoring the operation efficiency analysis result, the power consumption mode, the trend analysis result, the cost benefit analysis result and the consumption evaluation analysis result according to preset evaluation standards;
The comprehensive calculation module is used for calculating a comprehensive score according to the score of each analysis result and the weight corresponding to the score through a weighted summation method;
The analysis and comparison module is used for analyzing whether the comprehensive score exceeds a preset comprehensive score threshold by using a comparison method, if so, energy saving and consumption reduction are performed, and if not, energy saving and consumption reduction are not performed.
Compared with the prior art, the invention provides the energy-saving and consumption-reducing judging method and system for the transformer, and the method has the following beneficial effects:
(1) According to the invention, the real-time operation data, the historical data and the cost data of the transformer are collected, and comprehensive monitoring analysis is carried out on multiple aspects of the operation efficiency, the power consumption mode, the cost efficiency, the wear assessment and the like of the transformer based on the collected data, so that the multi-parameter monitoring and comprehensive analysis on the operation state of the transformer can be realized, and the problem that the traditional judging method is too dependent on a single index is effectively solved.
(2) According to the invention, by comprehensively evaluating various indexes by combining weight distribution with a scoring mechanism, the energy-saving state and the energy-saving space of the transformer can be objectively and accurately judged, the accurate judgment of the energy-saving state of the transformer is realized, and blindness is avoided; in addition, the invention can also adopt an improved quantum machine learning algorithm to predict the loss of the transformer, thereby greatly improving the prediction accuracy of the complex running state of the transformer and more accurately realizing the judgment of the energy saving and consumption reduction requirements.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
Fig. 1 is a flowchart of a method for determining energy saving and consumption reduction of a transformer according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
According to the embodiment of the invention, an energy saving and consumption reduction judging method and system for a transformer are provided.
The invention will now be further described with reference to the accompanying drawings and detailed description, as shown in fig. 1, according to an embodiment of the invention, there is provided a method for determining energy saving and consumption reduction of a transformer, the method comprising the steps of:
s1, collecting real-time operation data, historical data and cost data of a transformer, and performing data cleaning and standardization processing;
wherein, the operation data comprises input/output voltage, current, frequency, load level, environmental temperature and other conditions; historical data includes past power consumption, maintenance records, load conditions, operating environments, equipment status, and the like; the cost data includes purchase and installation costs of the energy saving device, expected power cost savings, and the like.
S2, analyzing the operation efficiency, the power consumption mode, the trend, the cost benefit and the wear evaluation of the transformer by using the processed real-time operation data, the history data and the cost data;
In this embodiment, the reasons for the comprehensive judgment by combining the operation efficiency calculation, the historical data analysis, the cost benefit analysis and the quantum computation model prediction result are as follows:
The operation efficiency calculation can judge the current operation state of the transformer, but can not reflect the history condition; historical data analysis can judge the operation trend of the transformer through long-term statistics, but the situation that the data is incomplete possibly exists; cost-effectiveness analysis focuses on economic considerations, but does not take into account actual operating parameters of the transformer; quantum computing models can predict results quickly, but depending on the model quality, errors may exist. Therefore, each analysis has the limitation that a single analysis cannot comprehensively judge the energy-saving requirement of the transformer, a plurality of analysis methods can complement each other, and comprehensive judgment can be carried out by combining respective advantages, namely operation efficiency calculation focuses on the current state, historical data analysis focuses on long-term trend, energy-saving power can be judged by combining the two methods, cost-effective analysis ensures economical feasibility of energy-saving measures, the combination of the two methods with the technical analysis method is more comprehensive, quantum calculation provides a new modern analysis means, the judgment accuracy is improved, and comprehensive utilization of a plurality of analysis results can enable energy-saving evaluation to be more systematic and comprehensive, and the judgment result is more accurate.
Wherein the analyzing the operation efficiency, the power consumption mode and the trend of the transformer, the cost benefit and the wear evaluation by using the processed real-time operation data, the history data and the cost data comprises the following steps:
S21, acquiring real-time operation data, historical data and cost data after data cleaning and standardization processing;
S22, calculating the operation efficiency of the transformer according to the real-time operation data of the transformer, and obtaining an operation efficiency analysis result;
specifically, the calculating the operation efficiency of the transformer according to the real-time operation data of the transformer, and obtaining the operation efficiency analysis result includes:
S221, obtaining an input end voltage U 1, an input end current I 1, an input end effective power P 1, an output end voltage U 2, an output end current I 2, an output end effective power P 2, an unloaded current I 0 and an unloaded loss P 0 of the transformer;
s222, combining the power factor of the input end of the transformer Calculating the input effective power P in of the transformer, wherein the calculation formula is/>Input terminal power factor/>；
S223, combining power factor of output end of transformerCalculating the output effective power P out of the transformer, wherein the calculation formula is/>Output power factor/>；
S224, calculating the operation efficiency eta of the transformer, wherein a calculation formula is as follows；
S225, calculating the ratio between the operation efficiency eta of the transformer and the standard efficiency eta 0 of the transformer to obtain an operation efficiency analysis result;
s23, analyzing the power consumption mode and trend of the transformer by utilizing the historical data of the transformer to obtain a power consumption mode and trend analysis result;
Specifically, the analyzing the power consumption mode and the trend of the transformer by using the historical data of the transformer, and obtaining the power consumption mode and the trend analysis result includes:
S231, acquiring power consumption data of a transformer in a preset time period, including effective power, voltage and current, sorting the power consumption data, drawing a power consumption trend graph, and observing a change mode of the power consumption trend graph;
S232, analyzing the power consumption difference of different time periods (such as seasons, weekends and the like), judging whether a large fluctuation range exists, calculating average power consumption, maximum consumption values and minimum consumption values in a statistics mode, and judging the fluctuation range of the power consumption;
s233, acquiring a historical maintenance record of the transformer, analyzing whether the condition of abnormal operation caused by long-time maintenance is existed, comparing the power consumption data of the similar transformers, and judging whether the transformer is abnormal;
S234, analyzing the power consumption change mode, the fluctuation range of the power consumption and the abnormality judgment result of the transformer to obtain a power consumption mode and a trend analysis result.
S24, analyzing the cost benefit of the transformer through the cost data of the transformer to obtain a cost benefit analysis result;
Specifically, the analyzing the cost benefit of the transformer by the cost data of the transformer, and obtaining the cost benefit analysis result includes:
S241, acquiring the purchase and installation budget cost of energy-saving equipment, predicting the annual electric power saving amount after implementing energy-saving measures, and estimating the annual electric charge saving cost according to the query electric power unit price;
S242, calculating an investment recovery period, and obtaining a cost benefit analysis result, wherein the investment recovery period=total investment cost/annual electricity fee saving;
s25, predicting the predicted loss of the transformer according to the real-time operation data and the historical data of the transformer by utilizing the quantum machine learning model, and obtaining a loss evaluation analysis result.
Specifically, the predicting the predicted loss of the transformer according to the real-time operation data and the historical data of the transformer by using the quantum machine learning model, and obtaining the loss evaluation analysis result includes:
S251, acquiring real-time operation data and historical data of the transformer and carrying out missing value and normalization processing;
S252, constructing and training an improved quantum neural network model by using the processed historical data of the transformer to obtain the trained improved quantum neural network model, and specifically comprising the following steps of:
S2521, constructing a quantum neural network model, wherein an input layer of the quantum neural network model is a transformer characteristic vector, an hidden layer is a turnstile adjusting quantum state, and an output layer is the predicted loss of the transformer;
S2522, adjusting the quantum state of the transformer characteristic vector by utilizing a quantum revolving door, extracting nonlinear characteristics of data, and optimizing the rotation angle by utilizing a quantum evolution algorithm;
The method for adjusting the quantum state of the transformer feature vector by utilizing the quantum revolving door, extracting the nonlinear feature of data and optimizing the rotation angle by utilizing the quantum evolutionary algorithm comprises the following steps:
Defining a characteristic vector of the transformer, wherein the characteristic vector comprises a load rate characteristic, a temperature characteristic and a voltage characteristic;
mapping each feature to the superposition state of the quantum bit by using a Hadamard gate to obtain a quantum feature vector;
setting an initial rotation angle of the rotating gate, and adjusting quantum superposition states of the feature vectors one by one through the rotating gate by quantum feature vectors;
the method comprises the steps of utilizing a revolving door to introduce an initial rotation angle phase to realize nonlinear mapping of characteristics, and defining quantum codes of the initial rotation angle as quantum individual genes;
Designing a quantum individual fitness function to evaluate the influence of different initial rotation angles on a training effect, executing a quantum evolutionary algorithm to search an optimal rotation angle in an iterative manner, and replacing a rotation gate angle to be the optimal rotation angle obtained by the quantum evolutionary algorithm, wherein the method comprises the following specific steps of:
The genes defining the quantum individuals are expressed as codes of the rotation angle θ;
Designing fitness functions, training a network and calculating loss functions on a training set ；
Setting the fitness function as the inverse of the loss function；
Initializing quantum population, wherein the genes of quantum individuals are different theta;
For each quantum individual in the population, its fitness is evaluated ；
Executing a quantum differential evolution algorithm, and performing iterative search to adaptThe largest quantum unit;
Obtaining the final quantum evolution result as the optimal rotation angle And replacing the angle of the revolving door with/>。
S2523, inputting the preprocessed historical data of the transformer into the quantum neural network as a training sample, and adjusting the network connection weight through repeated iterative training until the training is finished when the network output result is consistent with the actual label, so as to obtain the trained improved quantum neural network model.
S253, outputting predicted loss of the transformer corresponding to the real-time operation data by using the trained improved quantum neural network model;
S254, obtaining a loss evaluation analysis result by comparing the difference between the predicted loss of the transformer and the actual loss of the transformer.
S3, respectively distributing preset weights for an operation efficiency analysis result, a power consumption mode and trend analysis result, a cost benefit analysis result and a wear evaluation analysis result;
wherein the assigning the preset weights to the operation efficiency analysis result, the power consumption pattern and trend analysis result, the cost benefit analysis result, and the wear-leveling analysis result respectively includes:
configuring a first weight for the operation efficiency analysis result;
configuring a second weight for the power consumption mode and the trend analysis result;
configuring a third weight for the cost-benefit analysis result;
configuring a fourth weight for the wear-leveling analysis result;
wherein, the first weight > the second weight=the fourth weight > the third weight, and the first weight+the second weight+the third weight+the fourth weight=1.
Specifically, the operating efficiency directly reflects the transformer operating efficiency, so a higher weight, for example, set to 0.35, may be given; the power consumption pattern and trend reflect the long-term operation of the transformer, so that a medium weight, for example set to 0.25, can be given; the cost-effectiveness is related to the economic effectiveness of the energy-saving measures, and is therefore given a lower weight, for example set to 0.15; wear assessment reflects the wear condition of the transformer and is therefore given a medium weight, e.g. set to 0.25.
S4, scoring the operation efficiency analysis result, the power consumption mode, the trend analysis result, the cost benefit analysis result and the wear evaluation analysis result according to a preset evaluation standard;
Wherein scoring the operation efficiency analysis result, the power consumption mode, the trend analysis result, the cost benefit analysis result and the wear evaluation analysis result according to the preset evaluation standard comprises:
Determining the score of an operation efficiency analysis result according to the ratio between the operation efficiency eta of the transformer and the standard efficiency eta 0 of the transformer, wherein if the ratio is more than or equal to 0.95, the score is 10 minutes, the ratio is less than or equal to 0.90 and less than or equal to 0.95, the score is 8 minutes, the ratio is less than or equal to 0.85 and less than or equal to 0.90, the score is 6 minutes, the ratio is less than or equal to 0.80 and less than or equal to 0.85, the score is 4 minutes, and the ratio is less than or equal to 0.80 and the score is 2 minutes;
Determining the power consumption mode and the score of a trend analysis result according to the power consumption change mode of the transformer, the fluctuation range of the power consumption and the abnormality judgment result, wherein if the power consumption change mode is stable and abnormal, the score is 10 points, the score is 8 points if periodic fluctuation exists, the score is 6 points if the fluctuation range exceeds a threshold value, and the score is 4 points if abnormal records exist;
Determining the score of the cost benefit analysis result according to the investment recovery period, wherein if the investment recovery period is within 2 years, the score is 10 points, the score is 8 points, the score is 6-8 years, the score is 4 points, and the score is 2 points when the investment recovery period is over 2 years;
Determining the score of a loss evaluation prediction result according to the difference between the predicted loss of the transformer and the actual loss of the transformer, wherein if the difference is less than 5%, the score is 10 points, the difference is less than or equal to 5% and less than 10%, the score is 8 points, the difference is less than or equal to 10% and less than 15%, the score is 6 points, the difference is less than or equal to 15% and less than 20%, the score is 4 points, and the difference is more than or equal to 20%, and the score is 2 points.
S5, calculating a comprehensive score according to the score of each analysis result and the weight corresponding to the score through a weighted summation method;
Wherein, the calculation formula of the comprehensive score is as follows: Where w 1 denotes a first weight, s 1 denotes a score of an operation efficiency analysis result, w 2 denotes a second weight, s 2 denotes a score of a power consumption pattern and a trend analysis result, w 3 denotes a third weight, s 3 denotes a score of a cost benefit analysis result, w 4 denotes a fourth weight, and s 4 denotes a score of a wear-assessment prediction result.
S6, judging whether the comprehensive score exceeds a preset comprehensive score threshold value by a comparison method, determining whether energy conservation and emission reduction are implemented on the transformer according to a judgment result, and determining whether energy conservation and emission reduction are implemented on the transformer according to the judgment result comprises the following steps: if yes, energy saving and consumption reduction are carried out, and if not, energy saving and consumption reduction are not carried out.
Specifically, in this embodiment, the threshold value of the preset composite score may be set to 8 points, if the composite score is greater than or equal to the threshold value, it is determined that energy saving and consumption reduction are required, and if the composite score is less than the threshold value, it is determined that energy saving and consumption reduction are not required temporarily.
According to the comparison result, whether to save energy can be directly judged, a plurality of threshold intervals can be set, the energy saving requirement is subdivided, the sensitivity of the energy saving judgment can be controlled by adjusting the height of the threshold, the higher the comprehensive score is, the more urgent the energy saving requirement is, the comparison method can be used for judging whether to save energy or not simply and directly through the threshold, and in addition, a reasonable threshold can be set according to the actual situation in the embodiment.
According to another aspect of the invention, an energy-saving and consumption-reducing judging system for a transformer is provided, and comprises a data acquisition module, a data analysis module, a weight configuration module, a result scoring module, a comprehensive calculation module and an analysis and comparison module;
The data acquisition module is used for acquiring real-time operation data, historical data and cost data of the transformer and performing data cleaning and standardization processing;
The data analysis module is used for analyzing the operation efficiency, the power consumption mode and trend, the cost benefit and the wear evaluation of the transformer by using the processed real-time operation data, the history data and the cost data;
the weight configuration module is used for respectively distributing preset weights to the operation efficiency analysis result, the power consumption mode, the trend analysis result, the cost benefit analysis result and the wear evaluation analysis result;
The result scoring module is used for scoring the operation efficiency analysis result, the power consumption mode, the trend analysis result, the cost benefit analysis result and the consumption evaluation analysis result according to preset evaluation standards;
The comprehensive calculation module is used for calculating a comprehensive score according to the score of each analysis result and the weight corresponding to the score through a weighted summation method;
The analysis and comparison module is used for analyzing whether the comprehensive score exceeds a preset comprehensive score threshold by using a comparison method, if so, energy saving and consumption reduction are performed, and if not, energy saving and consumption reduction are not performed.
In summary, by means of the technical scheme, the real-time operation data, the historical data and the cost data of the transformer are collected, and comprehensive monitoring analysis is performed on multiple aspects of operation efficiency, power consumption mode, cost benefit, wear evaluation and the like of the transformer based on the collected data, so that multi-parameter monitoring and comprehensive analysis on the operation state of the transformer can be realized, and the problem that the traditional judging method is too dependent on a single index is effectively solved.
Meanwhile, the invention carries out comprehensive evaluation on various indexes by combining weight distribution with a scoring mechanism, can objectively and accurately judge the energy-saving state and the energy-saving space of the transformer, realizes the accurate judgment of the energy-saving state of the transformer and avoids blindness; in addition, the invention can also adopt an improved quantum machine learning algorithm to predict the loss of the transformer, thereby greatly improving the prediction accuracy of the complex running state of the transformer and more accurately realizing the judgment of the energy saving and consumption reduction requirements.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description. Those of ordinary skill in the art will appreciate that all or some of the steps in implementing the methods of the above embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, where the program when executed includes the steps described in the above methods, where the storage medium includes: Magnetic disks, optical disks, etc.
The above examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. The energy saving and consumption reduction judging method for the transformer is characterized by comprising the following steps of:
s1, collecting real-time operation data, historical data and cost data of a transformer, and performing data cleaning and standardization processing;
S2, analyzing the operation efficiency, the power consumption mode, the trend, the cost benefit and the wear evaluation of the transformer by using the processed real-time operation data, the history data and the cost data;
s3, respectively distributing preset weights for an operation efficiency analysis result, a power consumption mode and trend analysis result, a cost benefit analysis result and a wear evaluation analysis result;
S4, scoring the operation efficiency analysis result, the power consumption mode, the trend analysis result, the cost benefit analysis result and the wear evaluation analysis result according to a preset evaluation standard;
S5, calculating a comprehensive score according to the score of each analysis result and the weight corresponding to the score through a weighted summation method;
s6, judging whether the comprehensive score exceeds a preset comprehensive score threshold value by a comparison method, and determining whether energy conservation and emission reduction are implemented on the transformer according to a judgment result.
2. The method for determining energy saving and consumption reduction of a transformer according to claim 1, wherein the analyzing the operation efficiency, the power consumption pattern and trend, the cost effectiveness and the wear assessment of the transformer by using the processed real-time operation data, the history data and the cost data comprises the steps of:
S21, acquiring real-time operation data, historical data and cost data after data cleaning and standardization processing;
S22, calculating the operation efficiency of the transformer according to the real-time operation data of the transformer, and obtaining an operation efficiency analysis result;
s23, analyzing the power consumption mode and trend of the transformer by utilizing the historical data of the transformer to obtain a power consumption mode and trend analysis result;
S24, analyzing the cost benefit of the transformer through the cost data of the transformer to obtain a cost benefit analysis result;
s25, predicting the predicted loss of the transformer according to the real-time operation data and the historical data of the transformer by utilizing the quantum machine learning model, and obtaining a loss evaluation analysis result.
3. The method for determining energy saving and consumption reduction of a transformer according to claim 2, wherein the calculating the operation efficiency of the transformer according to the real-time operation data of the transformer, and obtaining the operation efficiency analysis result comprises:
S221, obtaining an input end voltage U 1, an input end current I 1, an input end effective power P 1, an output end voltage U 2, an output end current I 2, an output end effective power P 2, an unloaded current I 0 and an unloaded loss P 0 of the transformer;
s222, combining the power factor of the input end of the transformer The input effective power P in of the transformer is calculated, and the calculation formula is as follows；
S223, combining power factor of output end of transformerCalculating the output effective power P out of the transformer, wherein the calculation formula is as follows；
S224, calculating the operation efficiency of the transformerThe calculation formula is/>；
S225, calculating the ratio between the operation efficiency eta of the transformer and the standard efficiency eta 0 of the transformer to obtain an operation efficiency analysis result;
analyzing the power consumption mode and trend of the transformer by using the historical data of the transformer, and obtaining the power consumption mode and trend analysis result comprises the following steps:
S231, acquiring power consumption data of a transformer in a preset time period, including effective power, voltage and current, sorting the power consumption data, drawing a power consumption trend graph, and observing a change mode of the power consumption trend graph;
S232, analyzing the power consumption difference in different time periods, judging whether a large fluctuation range exists, calculating average power consumption, maximum consumption value and minimum consumption value in a statistics mode, and judging the fluctuation range of the power consumption;
s233, acquiring a historical maintenance record of the transformer, analyzing whether the condition of abnormal operation caused by long-time maintenance is existed, comparing the power consumption data of the similar transformers, and judging whether the transformer is abnormal;
S234, analyzing the power consumption change mode, the fluctuation range of the power consumption and the abnormality judgment result of the transformer to obtain a power consumption mode and a trend analysis result;
The cost benefit of the transformer is analyzed through the cost data of the transformer, and the cost benefit analysis result comprises the following steps:
S241, acquiring the purchase and installation budget cost of energy-saving equipment, predicting the annual electric power saving amount after implementing energy-saving measures, and estimating the annual electric charge saving cost according to the query electric power unit price;
S242, calculating an investment recovery period, and obtaining a cost benefit analysis result, wherein the investment recovery period=total investment cost/annual electricity fee saving;
the predicting the predicted loss of the transformer according to the real-time operation data and the historical data of the transformer by utilizing the quantum machine learning model, and the obtaining of the loss evaluation analysis result comprises the following steps:
S251, acquiring real-time operation data and historical data of the transformer and carrying out missing value and normalization processing;
s252, constructing and training an improved quantum neural network model by using the processed historical data of the transformer to obtain a trained improved quantum neural network model;
S253, outputting predicted loss of the transformer corresponding to the real-time operation data by using the trained improved quantum neural network model;
S254, obtaining a loss evaluation analysis result by comparing the difference between the predicted loss of the transformer and the actual loss of the transformer.
4. A method for determining whether to implement energy saving and emission reduction for a transformer according to the determination result as claimed in claim 3, comprising the steps of: if yes, energy saving and consumption reduction are carried out, and if not, energy saving and consumption reduction are not carried out.
5. The method for determining energy saving and consumption reduction of a transformer according to claim 4, wherein the step of constructing and training the improved quantum neural network model using the processed historical data of the transformer to obtain the trained improved quantum neural network model comprises the steps of:
S2521, constructing a quantum neural network model, wherein an input layer of the quantum neural network model is a transformer characteristic vector, an hidden layer is a turnstile adjusting quantum state, and an output layer is the predicted loss of the transformer;
S2522, adjusting the quantum state of the transformer characteristic vector by utilizing a quantum revolving door, extracting nonlinear characteristics of data, and optimizing the rotation angle by utilizing a quantum evolution algorithm;
S2523, inputting the preprocessed historical data of the transformer into the quantum neural network as a training sample, and adjusting the network connection weight through repeated iterative training until the training is finished when the network output result is consistent with the actual label, so as to obtain the trained improved quantum neural network model.
6. The method for judging energy saving and consumption reduction of a transformer according to claim 5, wherein the steps of adjusting the quantum state of the transformer eigenvector by using a quantum rotation gate, extracting the nonlinear characteristic of the data, and optimizing the rotation angle by using a quantum evolution algorithm comprise the following steps:
Defining a characteristic vector of the transformer, wherein the characteristic vector comprises a load rate characteristic, a temperature characteristic and a voltage characteristic;
mapping each feature to the superposition state of the quantum bit by using a Hadamard gate to obtain a quantum feature vector;
setting an initial rotation angle of the rotating gate, and adjusting quantum superposition states of the feature vectors one by one through the rotating gate by quantum feature vectors;
the method comprises the steps of utilizing a revolving door to introduce an initial rotation angle phase to realize nonlinear mapping of characteristics, and defining quantum codes of the initial rotation angle as quantum individual genes;
and designing a quantum individual fitness function to evaluate the influence of different initial rotation angles on the training effect, executing the iterative search of the quantum evolutionary algorithm to the optimal rotation angle, and replacing the rotation gate angle to be the optimal rotation angle obtained by the quantum evolutionary algorithm.
7. The method of claim 1, wherein assigning the predetermined weights to the operation efficiency analysis result, the power consumption pattern and trend analysis result, the cost benefit analysis result, and the wear-leveling analysis result, respectively, comprises:
configuring a first weight for the operation efficiency analysis result;
configuring a second weight for the power consumption mode and the trend analysis result;
configuring a third weight for the cost-benefit analysis result;
configuring a fourth weight for the wear-leveling analysis result;
wherein, the first weight > the second weight=the fourth weight > the third weight, and the first weight+the second weight+the third weight+the fourth weight=1.
8. The method according to claim 4, wherein scoring the operation efficiency analysis result, the power consumption pattern and trend analysis result, the cost benefit analysis result and the wear-out evaluation analysis result according to the predetermined evaluation criteria comprises:
Determining the score of an operation efficiency analysis result according to the ratio between the operation efficiency eta of the transformer and the standard efficiency eta 0 of the transformer, wherein if the ratio is more than or equal to 0.95, the score is 10 minutes, the ratio is less than or equal to 0.90 and less than or equal to 0.95, the score is 8 minutes, the ratio is less than or equal to 0.85 and less than or equal to 0.90, the score is 6 minutes, the ratio is less than or equal to 0.80 and less than or equal to 0.85, the score is 4 minutes, and the ratio is less than or equal to 0.80 and the score is 2 minutes;
Determining the power consumption mode and the score of a trend analysis result according to the power consumption change mode of the transformer, the fluctuation range of the power consumption and the abnormality judgment result, wherein if the power consumption change mode is stable and abnormal, the score is 10 points, the score is 8 points if periodic fluctuation exists, the score is 6 points if the fluctuation range exceeds a threshold value, and the score is 4 points if abnormal records exist;
Determining the score of the cost benefit analysis result according to the investment recovery period, wherein if the investment recovery period is within 2 years, the score is 10 points, the score is 8 points, the score is 6-8 years, the score is 4 points, and the score is 2 points when the investment recovery period is over 2 years;
Determining the score of a loss evaluation prediction result according to the difference between the predicted loss of the transformer and the actual loss of the transformer, wherein if the difference is less than 5%, the score is 10 points, the difference is less than or equal to 5% and less than 10%, the score is 8 points, the difference is less than or equal to 10% and less than 15%, the score is 6 points, the difference is less than or equal to 15% and less than 20%, the score is 4 points, and the difference is more than or equal to 20%, and the score is 2 points.
9. The method for determining energy saving and consumption reduction of a transformer according to claim 7, wherein the calculation formula of the composite score is: Where w 1 denotes a first weight, s 1 denotes a score of an operation efficiency analysis result, w 2 denotes a second weight, s 2 denotes a score of a power consumption pattern and a trend analysis result, w 3 denotes a third weight, s 3 denotes a score of a cost benefit analysis result, w 4 denotes a fourth weight, and s 4 denotes a score of a wear-assessment prediction result.
10. The energy saving and consumption reduction judging system for the transformer is used for realizing the steps of the energy saving and consumption reduction judging method for the transformer according to any one of claims 1-9, and is characterized by comprising a data acquisition module, a data analysis module, a weight configuration module, a result scoring module, a comprehensive calculation module and an analysis and comparison module;
The data acquisition module is used for acquiring real-time operation data, historical data and cost data of the transformer and performing data cleaning and standardization processing;
The data analysis module is used for analyzing the operation efficiency, the power consumption mode and trend, the cost benefit and the wear evaluation of the transformer by using the processed real-time operation data, the history data and the cost data;
the weight configuration module is used for respectively distributing preset weights to the operation efficiency analysis result, the power consumption mode, the trend analysis result, the cost benefit analysis result and the wear evaluation analysis result;
The result scoring module is used for scoring the operation efficiency analysis result, the power consumption mode, the trend analysis result, the cost benefit analysis result and the consumption evaluation analysis result according to preset evaluation standards;
The comprehensive calculation module is used for calculating a comprehensive score according to the score of each analysis result and the weight corresponding to the score through a weighted summation method;
The analysis and comparison module is used for analyzing whether the comprehensive score exceeds a preset comprehensive score threshold by using a comparison method, if so, energy saving and consumption reduction are performed, and if not, energy saving and consumption reduction are not performed.
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-
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Non-Patent Citations (2)
| Title |
|---|
| 一种可变权重的变压器运行状态综合评估方法;毛志强;胡云;胡旭冉;胡文峰;;山东电力技术;20150125(第01期);第25-29, 34页 * |
| 基于多目标遗传算法的大功率高频变压器优化设计;曹小鹏;陈武;宁光富;乔光尧;王琛;;中国电机工程学报;20180305(第05期);第1348-1355页 * |
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