CN116706939A - Oscillation suppression method with energy recovery function - Google Patents
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Abstract
The application discloses an oscillation suppression method with energy recovery, which comprises the steps of obtaining power grid voltage and current information and preprocessing data; establishing a distributed power grid management system according to the preprocessed data; according to the dynamic characteristics and change rules of the voltage and the current, the energy storage capacity of the power grid and the energy storage capacity distribution proportion are dynamically adjusted; establishing power grid equipment with an energy recovery function so as to realize the recovery and utilization of surplus energy of a power grid; and (3) periodically detecting and analyzing the energy storage capacity use condition of the power grid, and evaluating and diagnosing the health state of the power grid. The method can carry out parameter calibration and timely updating iteration of standard data through a comparison algorithm, and a series of problems caused by long debugging time and high cost due to difficult algorithm parameter adjustment and manual adjustment by professionals are avoided.
Description
Technical Field
The application relates to the technical field of oscillation suppression, in particular to an oscillation suppression method with energy recovery.
Background
Current oscillation suppression algorithms are mainly derived from the fields of control theory, signal processing and the like. The method has the core concept that on the premise of ensuring the stability and the performance of the system, a proper control strategy is designed to restrain oscillation by analyzing the dynamic characteristics of the system and modeling the dynamics of the system.
Current relatively common oscillation suppression algorithms include: PID controllers, model Predictive Control (MPC), filter compensation control, adaptive control, neural network control, and the like. However, these algorithms still have some technical drawbacks in practical applications, as follows: the influence of uncertainty of system parameters and external disturbance is large, and the method is difficult to adapt to complex industrial control scenes; the algorithm parameters are difficult to adjust, and professional personnel are required to manually adjust, so that the debugging time is long and the cost is high; the method is difficult to realize quick response and high-precision control, and has poor performance for high-speed and high-precision industrial control scenes; the algorithm has poor control effect on nonlinear, time-varying, multivariable and other complex systems.
Therefore, how to further optimize and improve the oscillation suppression algorithm and improve the application value and effect of the oscillation suppression algorithm is one of the important directions of current researches.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The present application has been made in view of the above-described problems occurring in the prior art.
Therefore, the application provides the oscillation suppression method with energy recovery, which can solve the problems that the traditional oscillation suppression method can not recover excessive energy, and the system has long response time, large power consumption and low efficiency.
In order to solve the technical problems, the application provides a vibration suppression method with energy recovery, which comprises the following steps:
acquiring power grid voltage and current information, and preprocessing data;
establishing a distributed power grid management system according to the preprocessed data;
according to the dynamic characteristics and change rules of the voltage and the current, the energy storage capacity of the power grid and the energy storage capacity distribution proportion are dynamically adjusted;
establishing power grid equipment with an energy recovery function so as to realize the recovery and utilization of surplus energy of a power grid;
and (3) periodically detecting and analyzing the energy storage capacity use condition of the power grid, and evaluating and diagnosing the health state of the power grid.
As a preferable embodiment of the oscillation suppression method with energy recovery according to the present application, wherein: the preprocessing comprises the steps of monitoring a power grid in real time, obtaining voltage and current information of the power grid, filtering and processing the voltage and the current to obtain voltage and current signals after filtering and processing, recording the processed voltage and current signals in a distributed energy storage system, and analyzing and diagnosing the voltage and the current in real time by utilizing a data processing technology.
As a preferable embodiment of the oscillation suppression method with energy recovery according to the present application, wherein: the distributed power grid management system is built by building a communication mechanism among multiple nodes, adopting a block chain technology to ensure the non-falsifiability and safety of power grid state information, building a corresponding power grid management account for each power grid node, and recording power grid historical state information.
As a preferable embodiment of the oscillation suppression method with energy recovery according to the present application, wherein: the dynamic adjustment of the energy storage capacity and the energy storage capacity distribution ratio of the power grid comprises the steps of collecting power system fault and normal data samples, respectively storing the power system fault and normal data samples as a fault data set and a normal data set, comparing and correcting data information through a k-nearest neighbor algorithm, and inhibiting system oscillation; the comparison comprises the steps of calculating Euclidean distances between a new data sample and all data samples in a fault data set for the new data sample to be compared, storing, sequencing the distances from small to large, taking out k data samples with the nearest distance to the new data sample, counting the number of fault data sets and normal data sets in the k data samples, judging the data sets to which the new data sample should be subordinate, and judging the new data sample as fault data if the number of the fault data sets is larger than that of the normal data sets; if the number of the fault data sets is smaller than or equal to the number of the normal data sets, otherwise, judging the new data sample as normal data.
As a preferable embodiment of the oscillation suppression method with energy recovery according to the present application, wherein: the comparison further comprises the steps of if the new data sample is fault data, carrying out one-to-one mapping on the data in the sample and the standard data, defining the difference value between the confirmed positioned data and the standard data as a first fluctuation value, carrying out one-to-one mapping on the data in the sample and the data in the normal data set as a second fluctuation value, defining the corresponding difference value between the data which is positioned in the data set, carrying out point-to-point positioning on the standard data and the data in the normal data set, determining the difference value between the data in the normal data set and the corresponding standard data, defining the actual fluctuation tolerance value, respectively comparing the first fluctuation value and the second fluctuation value with the fluctuation tolerance value, carrying out analysis and judgment on the comparison result through the system logic unit, determining the interval range of the fault data needing approval and calibration, carrying out back transmission on the data and the data result of the confirmed approval interval range, and transmitting the data result back to the system logic unit to wait for the next operation instruction;
if the new data sample is normal data, searching the data quantity in the fault data set, confirming the data quantity, and determining that the data is smaller than or equal to the normal data set quantity;
if the data quantity of the definite fault data set is smaller than the normal data set quantity, defining a new data sample as a preferred data set, transcribing and retaining a data result, facilitating updating iterative computation of a subsequent normal data set and a standard data set, tracking and carding a workflow through a logic unit and a first analysis and judgment module of a system, analyzing and judging the data flows of the normal data and the fault data, and specially marking a flow node generating a difference and a corresponding acquisition unit;
if the data quantity of the definite fault data set is equal to the normal data set quantity, whether the new data sample is completely consistent with the original correct data set data is required to be confirmed, if the detection result is completely consistent, the comparison is carried out again, the comparison flow is reviewed, meanwhile, the process is monitored in real time through a logic unit, the calculation result is checked by using the Minkowski distance, when the verification result is not wrong, the correct data set checking time is updated, and the data result is subjected to the supplementary recording;
if the detection result is inconsistent with the original comparison result, confirming the problem node in the comparison process through the second analysis and judgment module, carrying out special marking on the problem node, importing the data into the comparison algorithm again for secondary comparison, stopping calculation work of the comparison algorithm if the problem node carrying out special marking is still abnormal, checking reliability of the comparison object data, and if the second analysis and judgment module confirms that the comparison process is not abnormal, returning the confirmation result to the first analysis and judgment module, tracking and tracing the data comparison process through the first analysis and judgment module, and uploading the tracing result to the logic unit for fault warning.
As a preferable embodiment of the oscillation suppression method with energy recovery according to the present application, wherein: the correction comprises the steps of retrieving data and data results of returned approval check interval ranges from a system logic unit, guiding the data and data results of the approval check interval ranges into an energy detection module, confirming whether energy overflow in oscillation is caused by data fluctuation, if the energy overflow occurs, immediately recovering the energy through an algorithm and a system in the module, transmitting signals to the logic unit, receiving signals by the logic unit, confirming whether special marks exist in a data output flow corresponding to the data results, if the special marks exist, calibrating the data flow, updating and iterating the algorithm appearing in the flow, guiding the original data into flow Cheng Kaiduan after updating is completed, and re-performing secondary calculation;
if no special label exists, the logic unit needs to analyze and judge the data information uploaded by the first analysis and judgment module, the problem node is defined as the original data or the normal data set, and after the problem main body is defined, the replaced correction data is imported into the original problem node to wait for the next operation instruction.
As a preferable embodiment of the oscillation suppression method with energy recovery according to the present application, wherein: the dynamic adjustment of the energy storage capacity of the power grid and the distribution proportion of the energy storage capacity further comprises the steps of increasing the energy storage capacity of the power grid when the voltage and the current fluctuate greatly, stabilizing the voltage and the current of the power grid, recovering surplus energy, setting charge and discharge control parameters of power grid nodes, dynamically adjusting according to the characteristics and the service condition of the nodes, monitoring the charge and discharge state, the temperature and the electric quantity parameters of the power grid nodes in real time by adopting a multi-dimensional data acquisition technology, recording the parameters in a distributed energy storage system, and diagnosing and processing the problems in the use process.
As a preferable embodiment of the oscillation suppression method with energy recovery according to the present application, wherein: the recovery and utilization of the surplus energy of the power grid comprise the steps of installing the surplus energy recovery device on a power grid node, realizing the recovery and utilization of the surplus energy, setting parameters of the surplus energy recovery device, enabling the system to recover the energy at proper time and storing the recovered energy in an energy storage system.
A computer device comprising a memory and a processor, said memory storing a computer program, characterized in that the processor, when executing said computer program, implements the steps of a method of oscillation suppression with energy recovery.
A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of a method of oscillation suppression with energy recovery.
The application has the beneficial effects that: the method is based on a comparison algorithm and an energy recovery strategy, and an improved and optimized oscillation suppression method with an energy recovery function is obtained, parameter calibration and timely updating iteration of standard data can be carried out through the comparison algorithm, a series of problems caused by long debugging time and high cost due to manual adjustment of algorithm parameters are avoided, meanwhile, the method can realize quick response and high-precision control, and has a good solution scheme for poor performance of high-speed and high-precision industrial control scenes, so that the control effect of the method can be remarkably improved for nonlinear, time-varying, multivariable and other complex systems, and the problem that the application technology and effect of the conventional oscillation suppression method cannot be improved is solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in 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 application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is a schematic flow chart of an oscillation suppression method with energy recovery according to an embodiment of the present application.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present application can be understood in detail, a more particular description of the application, 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 application without making any inventive effort, shall fall within the scope of the present application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the application. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present application have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the application. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present application, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present application and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present application. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1, a first embodiment of the present application provides an oscillation suppression method with energy recovery, including:
s1: acquiring power grid voltage and current information, and preprocessing data;
further, the preprocessing comprises the steps of monitoring the power grid in real time, obtaining voltage and current information of the power grid, filtering and processing the voltage and the current to obtain voltage and current signals after filtering and processing, recording the processed voltage and current signals in a distributed energy storage system, and analyzing and diagnosing the voltage and the current in real time by utilizing a data processing technology.
S2: establishing a distributed power grid management system according to the preprocessed data;
furthermore, the establishing a distributed power grid management system comprises the steps of establishing a communication mechanism among multiple nodes, adopting a block chain technology to ensure the non-falsifiability and the safety of power grid state information, establishing a corresponding power grid management account for each power grid node, and recording power grid historical state information.
S3: according to the dynamic characteristics and change rules of the voltage and the current, the energy storage capacity of the power grid and the energy storage capacity distribution proportion are dynamically adjusted;
further, the dynamic adjustment of the energy storage capacity and the energy storage capacity distribution ratio of the power grid comprises the steps of collecting power system fault and normal data samples, storing the fault and normal data samples as a fault data set and a normal data set respectively, comparing and correcting data information through a k-nearest neighbor algorithm, and inhibiting system oscillation;
it should be noted that, the comparison includes, for a new data sample to be compared, calculating euclidean distances between the new data sample and all data samples in the fault data set, storing, sorting the distances from small to large, taking out k data samples with the nearest distance to the new data sample, counting the number of fault data sets and normal data sets in the k data samples, and judging the data set to which the new data sample should be subordinate, if the number of fault data sets is greater than the number of normal data sets, judging the new data sample as fault data; if the number of the fault data sets is smaller than or equal to the number of the normal data sets, otherwise, judging the new data sample as normal data.
It should be noted that, the comparison further includes, if the new data sample is fault data, performing one-to-one mapping of data in the sample and standard data, defining a difference value between the data in the sample and the standard data as a first fluctuation value, performing one-to-one mapping of data in the sample and data in the normal data set as a second fluctuation value, performing point-to-point positioning of the standard data and the data in the normal data set, determining a difference value between the data in the normal data set and the corresponding standard data, defining an actual fluctuation tolerance value, comparing the first fluctuation value and the second fluctuation value with a fluctuation tolerance value, performing analysis and judgment on the comparison result through the system logic unit, determining a section range of the fault data requiring approval and verification, returning the data and the data result of the section range of approval and approval, and returning to the system logic unit to wait for a next operation instruction.
Further, if the new data sample is normal data, the number of data in the failure data set needs to be searched, and after the number of data is confirmed, the definite data is smaller than or equal to the number of normal data sets.
It should be noted that, if the number of data in the definite fault data set is smaller than the number of normal data sets, the new data sample is defined as the preferred data set, and the data result is transcribed and kept, so that the subsequent updating iterative computation of the normal data set and the standard data set is facilitated, meanwhile, the working flow is tracked and combed by the logic unit and the first analysis and judgment module of the system, the data flows of the normal data and the fault data are analyzed and judged, and the flow nodes generating the difference and the corresponding acquisition units are specially marked.
Furthermore, if the data quantity of the definite fault data set is equal to the normal data set quantity, whether the new data sample is completely consistent with the original correct data set data is required to be confirmed, if the detection result is completely consistent, the comparison is carried out again, the comparison flow is rechecked, meanwhile, the process is monitored in real time through the logic unit, the calculated result is checked by using the Minkowski distance, when the verification result is not wrong, the correct data set checking time is updated, and the data result is subjected to the supplementary recording.
It should be noted that if the detection result is inconsistent with the original comparison result, confirming the problem node in the comparison process by the second analysis and judgment module, performing special marking on the problem node, importing the data into the comparison algorithm again for secondary comparison, stopping the calculation work of the comparison algorithm if the problem node with special marking is still abnormal, checking the reliability of the comparison object data, if the second analysis and judgment module confirms that the comparison process is not abnormal, returning the confirmation result to the first analysis and judgment module, tracking and tracing the data comparison process by the first analysis and judgment module, and uploading the tracing result to the logic unit for fault warning.
Further, the correction includes that data and data results of the returned approval correction interval range are called from the system logic unit, the data and data results of the approval correction interval range are led into the energy detection module, whether energy overflow in oscillation is caused by data fluctuation is confirmed, if the energy overflow occurs, energy recovery is needed to be carried out immediately through an algorithm and a system in the module, signals are conducted to the logic unit, the logic unit receives the signals, whether special labels exist in a data output flow corresponding to the data results is confirmed, if the special labels exist, the data flow is calibrated, updating iterative calculation is carried out on the algorithm appearing in the flow, after updating is completed, the original data is led into the flow Cheng Kaiduan, and secondary calculation is carried out again.
It should be noted that if no special label exists, the logic unit needs to analyze and judge the data information uploaded by the first analysis and judgment module, and determines that the problem node is the original data or the normal data set, and after the problem main body is determined, the replaced correction data is imported into the original problem node to wait for the next operation instruction.
Furthermore, the dynamic adjustment of the energy storage capacity and the energy storage capacity distribution ratio of the power grid further comprises the steps of increasing the energy storage capacity of the power grid when the voltage and the current fluctuate greatly, stabilizing the voltage and the current of the power grid, recovering the surplus energy, setting the charge and discharge control parameters of the power grid nodes, dynamically adjusting according to the characteristics and the service condition of the nodes, monitoring the charge and discharge state, the temperature and the electric quantity parameters of the power grid nodes in real time by adopting a multi-dimensional data acquisition technology, recording the parameters in a distributed energy storage system, and diagnosing and processing the problems in the use process.
S4: and establishing power grid equipment with an energy recovery function so as to realize the recovery and utilization of excess energy of the power grid.
Furthermore, the recovery and utilization of the surplus energy of the power grid comprises the steps of installing the surplus energy recovery device on a power grid node, realizing the recovery and utilization of the surplus energy, setting parameters of the surplus energy recovery device, enabling the system to recover the energy at proper time and storing the recovered energy in the energy storage system.
It should be noted that the components of the energy recovery device, such as the electronic components and the sensors, are maintained and serviced to ensure that the components are functioning properly.
S5: and (3) periodically detecting and analyzing the energy storage capacity use condition of the power grid, and evaluating and diagnosing the health state of the power grid.
Furthermore, the energy storage capacity of the power grid is periodically detected and analyzed in a charge-discharge state, and the health state of the power grid is estimated and diagnosed.
It should be noted that the application range is various distributed power grids, including different types of distributed power grids such as renewable energy power systems, micro-grids, direct-current power distribution networks and the like, so as to solve the problems that the system surplus energy consumption and the system oscillation cannot be managed.
Example 2
In another embodiment of the present application, an oscillation suppression method with energy recovery is provided, and scientific demonstration is performed through experiments in order to verify the beneficial effects of the present application.
Assume a set of power system data samples, including a fault data 12 set and a normal data 8 set, each data containing three attributes: current, voltage and power. The current of the existing group of electric power system data samples to be compared is 10A, the voltage is 220V, and the power is 2000W. And adopting a k-nearest neighbor algorithm, taking k=5, and calculating whether the data sample is fault data or normal data.
According to the above calculation method, the distances between the data sample to be compared and all the data samples in the failure data set (assuming that euclidean distance is used) are calculated first, and then the 5 data samples closest to the data sample are sorted and taken out. Suppose that the five data samples are: [11A,230V, 630W ], [11A,210V,2000W ], [13A,238V, 160W ], [9A,220V, 210W ], [12A,225V, 190W ].
Next, the number of the five data samples belonging to the faulty data set and the normal data set was counted, and it was found that 3 belong to the faulty data set and 2 belong to the normal data set. Therefore, the data sample to be compared is judged as the fault data.
The formula character specifically represents what meaning in the calculation method:
-k: parameters in the k-nearest neighbor algorithm represent how many nearest neighbor data samples to consider;
-D: a data set comprising a plurality of data samples;
-x: new data samples to be compared;
dist (D, x): calculating the distances between all data samples in the data set D and the data samples x to be compared, and returning a list or matrix of the distances;
sortedDistIndex: a distance value index sequence obtained after sequencing the distances from small to large in dist (D, x);
-k_labels: a tag sequence of the data set (fault or normal) to which the first k nearest data samples belong;
count_labels: the number of the failed and normal data sets of the first k data samples is calculated.
The application relates to an oscillation suppression method technology with energy recovery, which is mainly used for suppressing system oscillation. According to the method, firstly, power grid voltage and current information is obtained, data are preprocessed, a distributed power grid management system is built according to the preprocessed data, then the power grid energy storage capacity and the energy storage capacity distribution proportion are dynamically adjusted according to the dynamic characteristics and change rules of the voltage and the current, power grid equipment with an energy recovery function is built, recycling of surplus energy of the power grid is achieved, and then the energy storage capacity use condition of the power grid is regularly detected and analyzed to evaluate and diagnose the health state of the power grid.
It should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application 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 application may be modified or substituted without departing from the spirit and scope of the technical solution of the present application, which is intended to be covered in the scope of the claims of the present application.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be realized by adopting various computer languages, such as object-oriented programming language Java, an transliteration script language JavaScript and the like.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. A method of oscillation suppression with energy recovery, characterized by: comprising the steps of (a) a step of,
acquiring power grid voltage and current information, and preprocessing data;
establishing a distributed power grid management system according to the preprocessed data;
according to the dynamic characteristics and change rules of the voltage and the current, the energy storage capacity of the power grid and the energy storage capacity distribution proportion are dynamically adjusted;
establishing power grid equipment with an energy recovery function so as to realize the recovery and utilization of surplus energy of a power grid;
and (3) periodically detecting and analyzing the energy storage capacity use condition of the power grid, and evaluating and diagnosing the health state of the power grid.
2. A method of oscillation suppression with energy recovery as defined in claim 1, wherein: the preprocessing comprises the steps of monitoring a power grid in real time, obtaining voltage and current information of the power grid, filtering and processing the voltage and the current to obtain voltage and current signals after filtering and processing, recording the processed voltage and current signals in a distributed energy storage system, and analyzing and diagnosing the voltage and the current in real time by utilizing a data processing technology.
3. A method of oscillation suppression with energy recovery as defined in claim 2, wherein: the distributed power grid management system is built by building a communication mechanism among multiple nodes, adopting a block chain technology to ensure the non-falsifiability and safety of power grid state information, building a corresponding power grid management account for each power grid node, and recording power grid historical state information.
4. A method of oscillation suppression with energy recovery as defined in claim 3, wherein: the dynamic adjustment of the energy storage capacity and the energy storage capacity distribution ratio of the power grid comprises the steps of collecting power system fault and normal data samples, respectively storing the power system fault and normal data samples as a fault data set and a normal data set, comparing and correcting data information through a k-nearest neighbor algorithm, and inhibiting system oscillation;
the comparison comprises the steps of calculating Euclidean distances between a new data sample and all data samples in a fault data set for the new data sample to be compared, storing, sequencing the distances from small to large, taking out k data samples with the nearest distance to the new data sample, counting the number of fault data sets and normal data sets in the k data samples, judging the data sets to which the new data sample should be subordinate, and judging the new data sample as fault data if the number of the fault data sets is larger than that of the normal data sets; if the number of the fault data sets is smaller than or equal to the number of the normal data sets, otherwise, judging the new data sample as normal data.
5. A method of oscillation suppression with energy recovery as defined in claim 4, wherein: the comparison further comprises the steps of if the new data sample is fault data, carrying out one-to-one mapping on the data in the sample and the standard data, defining the difference value between the confirmed positioned data and the standard data as a first fluctuation value, carrying out one-to-one mapping on the data in the sample and the data in the normal data set as a second fluctuation value, defining the corresponding difference value between the data which is positioned in the data set, carrying out point-to-point positioning on the standard data and the data in the normal data set, determining the difference value between the data in the normal data set and the corresponding standard data, defining the actual fluctuation tolerance value, respectively comparing the first fluctuation value and the second fluctuation value with the fluctuation tolerance value, carrying out analysis and judgment on the comparison result through the system logic unit, determining the interval range of the fault data needing approval and calibration, carrying out back transmission on the data and the data result of the confirmed approval interval range, and transmitting the data result back to the system logic unit to wait for the next operation instruction;
if the new data sample is normal data, searching the data quantity in the fault data set, confirming the data quantity, and determining that the data is smaller than or equal to the normal data set quantity;
if the data quantity of the definite fault data set is smaller than the normal data set quantity, defining a new data sample as a preferred data set, transcribing and retaining a data result, facilitating updating iterative computation of a subsequent normal data set and a standard data set, tracking and carding a workflow through a logic unit and a first analysis and judgment module of a system, analyzing and judging the data flows of the normal data and the fault data, and specially marking a flow node generating a difference and a corresponding acquisition unit;
if the data quantity of the definite fault data set is equal to the normal data set quantity, whether the new data sample is completely consistent with the original correct data set data is required to be confirmed, if the detection result is completely consistent, the comparison is carried out again, the comparison flow is reviewed, meanwhile, the process is monitored in real time through a logic unit, the calculation result is checked by using the Minkowski distance, when the verification result is not wrong, the correct data set checking time is updated, and the data result is subjected to the supplementary recording;
if the detection result is inconsistent with the original comparison result, confirming the problem node in the comparison process through the second analysis and judgment module, carrying out special marking on the problem node, importing the data into the comparison algorithm again for secondary comparison, stopping calculation work of the comparison algorithm if the problem node carrying out special marking is still abnormal, checking reliability of the comparison object data, and if the second analysis and judgment module confirms that the comparison process is not abnormal, returning the confirmation result to the first analysis and judgment module, tracking and tracing the data comparison process through the first analysis and judgment module, and uploading the tracing result to the logic unit for fault warning.
6. A method of oscillation suppression with energy recovery as defined in claim 5, wherein: the correction comprises the steps of retrieving data and data results of returned approval check interval ranges from a system logic unit, guiding the data and data results of the approval check interval ranges into an energy detection module, confirming whether energy overflow in oscillation is caused by data fluctuation, if the energy overflow occurs, immediately recovering the energy through an algorithm and a system in the module, transmitting signals to the logic unit, receiving signals by the logic unit, confirming whether special marks exist in a data output flow corresponding to the data results, if the special marks exist, calibrating the data flow, updating and iterating the algorithm appearing in the flow, guiding the original data into flow Cheng Kaiduan after updating is completed, and re-performing secondary calculation;
if no special label exists, the logic unit needs to analyze and judge the data information uploaded by the first analysis and judgment module, the problem node is defined as the original data or the normal data set, and after the problem main body is defined, the replaced correction data is imported into the original problem node to wait for the next operation instruction.
7. A method of oscillation suppression with energy recovery as defined in claim 6, wherein: the dynamic adjustment of the energy storage capacity of the power grid and the distribution proportion of the energy storage capacity further comprises the steps of increasing the energy storage capacity of the power grid when the voltage and the current fluctuate greatly, stabilizing the voltage and the current of the power grid, recovering surplus energy, setting charge and discharge control parameters of power grid nodes, dynamically adjusting according to the characteristics and the service condition of the nodes, monitoring the charge and discharge state, the temperature and the electric quantity parameters of the power grid nodes in real time by adopting a multi-dimensional data acquisition technology, recording the parameters in a distributed energy storage system, and diagnosing and processing the problems in the use process.
8. A method of oscillation suppression with energy recovery as defined in claim 7, wherein: the recovery and utilization of the surplus energy of the power grid comprise the steps of installing the surplus energy recovery device on a power grid node, realizing the recovery and utilization of the surplus energy, setting parameters of the surplus energy recovery device, enabling the system to recover the energy at proper time and storing the recovered energy in an energy storage system.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 8 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 8.
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