CN114970635A - Dynamic load leading model parameter identification method and system based on noise-like signal - Google Patents
Dynamic load leading model parameter identification method and system based on noise-like signal Download PDFInfo
- Publication number
- CN114970635A CN114970635A CN202210645708.7A CN202210645708A CN114970635A CN 114970635 A CN114970635 A CN 114970635A CN 202210645708 A CN202210645708 A CN 202210645708A CN 114970635 A CN114970635 A CN 114970635A
- Authority
- CN
- China
- Prior art keywords
- data
- measurement data
- time
- parameters
- phasor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000005259 measurement Methods 0.000 claims abstract description 116
- 230000002159 abnormal effect Effects 0.000 claims abstract description 45
- 238000004364 calculation method Methods 0.000 claims abstract description 33
- 230000001360 synchronised effect Effects 0.000 claims abstract description 31
- 238000007781 pre-processing Methods 0.000 claims abstract description 27
- 238000006243 chemical reaction Methods 0.000 claims abstract description 14
- 230000006870 function Effects 0.000 claims description 13
- 238000012545 processing Methods 0.000 claims description 13
- 238000003860 storage Methods 0.000 claims description 11
- 230000003203 everyday effect Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 abstract description 10
- 238000004590 computer program Methods 0.000 description 12
- 238000010586 diagram Methods 0.000 description 9
- 238000004891 communication Methods 0.000 description 8
- 238000004458 analytical method Methods 0.000 description 4
- 238000004088 simulation Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 2
- 238000001308 synthesis method Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/22—Flexible AC transmission systems [FACTS] or power factor or reactive power compensating or correcting units
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Economics (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Signal Processing (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to a dynamic load leading model parameter identification method and a system based on a noise-like signal, comprising the following steps: acquiring load measurement data of a power grid running in real time, synchronously preprocessing the load measurement data, eliminating abnormal measurement data, and aligning the measurement data after the abnormal measurement data are eliminated; based on the measurement data aligned after synchronous preprocessing, carrying out conversion calculation of an instantaneous value and a synchronous phasor to obtain real-time phasor data; carrying out data noise signal state judgment on the real-time phasor data, extracting time window data meeting the noise signal state, and calculating to obtain effective parameters; and continuously calculating the effective parameters, and clustering the effective parameters to form time-sharing typical model parameters. The method can meet the load parameter identification application of the dynamic change characteristics of the power grid, track the dynamic change of the load and realize the online load model parameter calculation of the real-time measurement data of the power grid; the method can be applied to the technical field of power system load model parameter identification.
Description
Technical Field
The invention relates to the technical field of power system load model parameter identification, in particular to a dynamic load leading model parameter identification method and system based on a noise-like signal.
Background
The power system model is crucial to the operation simulation analysis and calculation of the power system, and the modeling of the load parameters plays a key role in the application of the power system model. Different load models and parameters are selected, so that differences exist in dynamic response results in simulation analysis of the power system, and even the judgment of the stability of the power system is influenced. Different power system stability problems are analyzed, requirements for load models are different, and different load models meeting conditions such as actual load positions, components and structures must be established according to application purposes of the load models and specific requirements of corresponding problems on the load models, so that accuracy of simulation analysis is guaranteed.
For the load identification method, a statistical synthesis method, a fault fitting method and a total measurement and identification method are mainly adopted, wherein the statistical synthesis method is to synthetically calculate load proportion parameters after load component information is manually summarized, and model parameter calculation cannot be carried out by tracking real-time load change characteristics; the fault fitting method performs model parameter fitting through actual fault data of the power grid, and calculation needs to be performed by relying on large-disturbance fault characteristic moment data.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a method and a system for identifying a dynamic load-dominated model parameter based on a noise-like signal, which can meet the load parameter identification application of dynamic change characteristics of a power grid, track the dynamic change of a load, and realize online load model parameter calculation of real-time measurement data of the power grid.
In order to achieve the purpose, the invention adopts the following technical scheme: a dynamic load dominated model parameter identification method based on noise-like signals comprises the following steps: acquiring load measurement data of a power grid running in real time, synchronously preprocessing the load measurement data, eliminating abnormal measurement data, and aligning the measurement data after the abnormal measurement data are eliminated; based on the measurement data aligned after synchronous preprocessing, carrying out conversion calculation of an instantaneous value and a synchronous phasor to obtain real-time phasor data; carrying out data noise signal state judgment on the real-time phasor data, extracting time window data meeting the noise signal state, and calculating to obtain effective parameters; and continuously calculating the effective parameters, and clustering the effective parameters to form time-sharing typical model parameters.
Further, the synchronously preprocessing the load measurement data, eliminating abnormal measurement data, and aligning the measurement data after the abnormal measurement data are eliminated includes:
and comparing the time scales of the load measurement data with the data time of the equal interval, and judging the measurement data with abnormal identification to obtain the synchronous equal interval alignment measurement data.
Further, the determining the abnormal measurement data includes:
removing abnormal measurement data with multiple continuous sections and unchanged, wherein the abnormal measurement data are removed if the abnormal measurement data do not meet preset calculation conditions;
and judging the number of the length settings of the measurement value fluctuation data segment and the window of the sliding interval, and regarding each data window, determining abnormal data if the number of the data windows which is not zero and is less than a preset threshold value is not zero according to different values or data variation of the defined data.
Further, the performing conversion calculation of the instantaneous value and the synchronous phasor based on the measurement data aligned after the synchronous preprocessing to obtain real-time phasor data includes:
obtaining a corresponding phasor expression according to the instantaneous value waveform of the cosine signal of the measured data;
every other T 0 Observing the cosine signal once in time, and then obtaining a series of phasors corresponding to the observation time as a phasor sequence; the phasor sequence serves as real-time phasor data.
Further, the distinguishing the real-time phasor data from the data noise signal state, extracting time window data meeting the noise signal state, and calculating to obtain effective parameters includes:
extracting time window data meeting the noise-like state as actually measured noise-like data;
calculating the state variables of the current moment and the next moment according to the determined model structure and the parameters to be calculated and the actually measured noise-like data and the parameters to be calculated;
respectively substituting the state variables at the current moment and the state variables at the next moment into an output equation of the current numerical value of the parameter to be identified, calculating a predicted value of the output variable, and calculating the deviation between the predicted value and the measured value at the next moment to obtain the numerical value of the target function;
and optimizing the objective function, and performing parameter fitting to obtain a numerical parameter of which the predicted value of the output variable has the same trend with the actually measured value or meets the preset fitting proportion, wherein the numerical parameter is used as an effective parameter.
Further, in the continuously calculating the effective parameters and clustering the effective parameters to form time-sharing typical model parameters, the clustering method includes:
and clustering according to the hour, taking a group of data similar to the average value of the load power in the current hour as the clustering parameter of the current hour, and so on to obtain the 24-hour clustering parameter every day.
Further, the method for clustering the effective parameters to form time-sharing typical model parameters comprises:
defining the 8 points in the morning to the 8 points in the evening as working periods, and clustering the other working periods to obtain clustering parameters of the working periods and the non-working periods respectively, thereby forming time-sharing typical model parameters.
A dynamic load dominated model parameter identification system based on noise-like signals, comprising: the first processing module is used for acquiring load measurement data of the real-time operation of the power grid, synchronously preprocessing the load measurement data, eliminating abnormal measurement data and aligning the measurement data after the abnormal measurement data are eliminated; the second processing module is used for carrying out conversion calculation on an instantaneous value and a synchronous phasor based on the measurement data aligned after synchronous preprocessing to obtain real-time phasor data; the third processing module is used for judging the state of a data noise-like signal of the real-time phasor data, extracting time window data meeting the noise-like state and calculating to obtain effective parameters; and the parameter forming module is used for continuously calculating the effective parameters and clustering the effective parameters to form time-sharing typical model parameters.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the above methods.
A computing device, comprising: one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the above-described methods.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the method can perform online calculation of the dynamic load leading model parameters through noise-like signal identification according to the load measurement data of the real-time operation of the power grid, avoids the condition that the traditional method performs model parameter technology through offline calculation or dependence on large disturbance fault data, and further improves the accuracy of the load model parameters through tracking the dynamic characteristics of the load and parameter calculation of fitting and clustering.
2. According to the invention, load model data are identified on line through power grid actual measurement data to form model parameters of classified loads, and the typical model parameters of the classified loads are constructed by clustering the typical model parameters through the model parameters of multiple time scales. The classification load model parameters calculated by the online application of the measured data meet the load parameter identification application of the dynamic change characteristics of the power grid, the dynamic change of the load is tracked, and the online load model parameter calculation of the real-time measurement data of the power grid is realized.
Drawings
FIG. 1 is a flowchart illustrating an overall method for identifying parameters of a dynamic load dominated model based on noise-like signals according to an embodiment of the invention;
FIG. 2 is a detailed flowchart of a method for identifying parameters of a dynamic load dominated model based on noise-like signals according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The invention provides a dynamic load leading model parameter identification method and system based on a noise-like signal, belongs to a mode of a general measurement and identification method, and is a technical application of model parameter identification through measured data. The use objects of the invention comprise power industry scheduling management users, and aim at the application requirements of stable operation analysis, mode calculation and power grid operation simulation inversion of a power system. The hardware adopted by the load model parameter identification device adopted by the invention is relatively mature in technology. The method comprises the steps of carrying out parameter calculation on the basis of a dynamic load leading model of a noise-like signal through actual measurement data of a power grid, and realizing typical parameter clustering calculation of classified loads through periodic data clustering.
The invention comprises the following steps: acquiring load measurement data of the power grid running in real time, synchronously preprocessing the load measurement data, eliminating abnormal measurement data, and aligning the measurement data after the abnormal measurement data are eliminated; based on the aligned measurement data after synchronous preprocessing, carrying out conversion calculation of an instantaneous value and a synchronous phasor to obtain real-time phasor data; carrying out data noise signal state discrimination on the real-time phasor data, extracting time window data meeting the noise signal state, and calculating to obtain effective parameters; and continuously calculating effective parameters, and clustering the effective parameters to form time-sharing typical model parameters. The method can meet the load parameter identification application of the dynamic change characteristics of the power grid, track the dynamic change of the load and realize the online load model parameter calculation of the real-time measurement data of the power grid.
In an embodiment of the present invention, as shown in fig. 1, a method for identifying parameters of a dynamic load-dominated model based on a noise-like signal is provided, and this embodiment is illustrated by applying the method to a terminal, it is to be understood that the method may also be applied to a server, and may also be applied to a system including the terminal and the server, and is implemented by interaction between the terminal and the server. In this embodiment, the method includes the steps of:
1) acquiring load measurement data of the real-time operation of the power grid, synchronously preprocessing the load measurement data, eliminating abnormal measurement data, and aligning the measurement data after the abnormal measurement data are eliminated;
2) based on the aligned measurement data after synchronous preprocessing, carrying out conversion calculation of an instantaneous value and a synchronous phasor to obtain real-time phasor data;
3) carrying out data noise signal state discrimination on the real-time phasor data, extracting time window data meeting the noise signal state, and calculating to obtain effective parameters;
4) and continuously calculating effective parameters, and clustering the effective parameters to form time-sharing typical model parameters.
In the step 1), the load measurement data of the real-time operation of the power grid comprises real-time measurement data of voltage and current.
In the step 1), the load measurement data is synchronously preprocessed to remove abnormal measurement data, and the measurement data from which the abnormal measurement data is removed is aligned, specifically: and comparing the time scales of the load measurement data with the data time of the equal interval, and judging the measurement data with abnormal identification to obtain the synchronous equal-interval alignment measurement data.
The method for judging the abnormal measurement data comprises the following steps:
1.1) removing abnormal measurement data with multiple continuous sections and no change if the abnormal measurement data does not meet preset calculation conditions;
1.2) judging the number of the length settings of the measurement value fluctuation data segment and the window of the sliding interval, and for each data window, according to different value numbers of the definition data or data variation (subtracting t-1 time data from t time data), if the number of the data windows which is not zero and is less than a preset threshold value, determining abnormal data.
In the step 2), based on the aligned measurement data after the synchronous preprocessing, the conversion calculation of the instantaneous value and the synchronous phasor is performed to obtain the real-time phasor data, which includes the following steps:
2.1) obtaining a corresponding phasor expression according to the instantaneous value waveform of the cosine signal of the measured data;
2.2) every T 0 Observing the cosine signal once in time, and then obtaining a series of phasors corresponding to the observation time as a phasor sequence; the phasor sequence serves as real-time phasor data.
In this embodiment, the instantaneous waveforms of the voltage and current signals measured in real time and the corresponding standard cosine signals x (t) are:
x(t)＝X m cos(ωt+φ)，
wherein, X m Is the root mean square of the cosine signal, omega is the angular frequency of the cosine signal,
Is the initial phase of the cosine signal;
the expression for phasor X is:
wherein, X r Is the real part of the phasor, X i Is the imaginary part of the phasor. The phasor X is expressed independently of the frequency of the signal, and its phase is determined by the start time (t ═ 0) of the cosine signal.
If every T 0 The cosine signal is observed once in time, namely the observation time is {0, T 0 ，2T 0 ，3T 0 ，…，nT 0 …, a series of phasors { X } is obtained 0 ，X 1 ，X 2 ，X 3 … }. This phasor sequence is equivalent to the phasor sequence obtained starting from each observation instant. If T is 0 Exactly equal to cosineThe phasors obtained each time are constant, for an integral multiple of the signal period. On the contrary, the phasor amplitude obtained each time is not changed, and the phase is different.
In the step 3), the real-time phasor data is subjected to data noise signal state discrimination, time window data meeting the noise-like state is extracted, and effective parameters are calculated and obtained, wherein a calculation method combining prediction error and target optimization is adopted in the embodiment, and the method comprises the following steps:
3.1) extracting time window data meeting the noise-like state to be actually measured noise-like data; wherein the time window data includes voltage amplitude and phase angle, active power and reactive power.
3.2) calculating the state variables of the current moment and the next moment according to the known and determined model structure and the parameters to be calculated and through actually measured noise-like data and the parameters to be calculated;
3.3) respectively substituting the state variables at the current moment and the state variables at the next moment into an output equation of the current numerical value of the parameter to be identified, calculating the predicted value of the output variable, and obtaining the numerical value of the target function by calculating the deviation between the predicted value and the measured value at the next moment;
the method comprises the following specific implementation steps:
firstly, a mechanism model and a parameter to be identified are determined.
And secondly, calculating the state variable of each moment according to the system measurement and the numerical value of the parameter to be identified.
And thirdly, calculating the state variable at the next moment according to the calculated state variable and the differential equation substituted into the current value of the parameter to be identified, and calculating the predicted value of the output variable according to the output equation substituted into the current value of the parameter to be identified.
And fourthly, calculating the deviation between the predicted value and the measured value at the next moment to obtain the numerical value of the target function.
And fifthly, optimizing the objective function, and then repeating the second step to the fourth step until the minimum value of the objective function is obtained.
And 3.4) optimizing the objective function, and performing parameter fitting to obtain a numerical parameter of which the predicted value of the output variable has the same trend with the actually measured value or meets the preset fitting proportion, wherein the numerical parameter is used as an effective parameter.
In this embodiment, the real-time phasor data is subjected to data-based noise signal state discrimination, and discrimination is performed according to a preset criterion condition. Taking the voltage phasor noise-like state judgment as an example, selecting a data 100ms time window to calculate whether noise-like phasors meeting mutation conditions exist, if so, extracting data to perform next calculation, and if not, continuing the calculation of the next group of data windows.
In the step 4), the effective parameters are continuously calculated, and the effective parameters are clustered to form time-sharing typical model parameters, wherein the clustering method comprises the following steps:
and clustering according to the hour, taking a group of data similar to the average value of the load power in the current hour as the clustering parameter of the current hour, and so on to obtain the 24-hour clustering parameter every day.
The method for clustering the effective parameters to form time-sharing typical model parameters comprises the following steps: defining the 8 points in the morning to the 8 points in the evening as working periods, and clustering the other working periods to obtain clustering parameters of the working periods and the non-working periods respectively, thereby forming typical model parameters of time sharing.
In one embodiment of the present invention, a dynamic load dominated model parameter identification system based on noise-like signals is provided, comprising:
the first processing module is used for acquiring load measurement data of the real-time operation of the power grid, synchronously preprocessing the load measurement data, eliminating abnormal measurement data and aligning the measurement data after the abnormal measurement data are eliminated;
the second processing module is used for carrying out conversion calculation on an instantaneous value and a synchronous phasor based on the aligned measurement data after synchronous preprocessing to obtain real-time phasor data;
the third processing module is used for judging the state of a data noise-like signal of the real-time phasor data, extracting time window data meeting the noise-like state, and calculating to obtain effective parameters;
and the parameter forming module is used for continuously calculating the effective parameters and clustering the effective parameters to form time-sharing typical model parameters.
The system provided in this embodiment is used for executing the above method embodiments, and for details of the process and the details, reference is made to the above embodiments, which are not described herein again.
In an embodiment of the present invention, a schematic structural diagram of a computing device is provided, where the computing device may be a terminal, and the computing device may include: a processor (processor), a communication Interface (Communications Interface), a memory (memory), a display screen, and an input device. The processor, the communication interface and the memory are communicated with each other through a communication bus. The processor is used to provide computing and control capabilities. The memory comprises a nonvolatile storage medium and an internal memory, wherein the nonvolatile storage medium stores an operating system and a computer program, and the computer program is executed by a processor to realize a dynamic load dominant model parameter identification method based on a noise-like signal; the internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a manager network, NFC (near field communication) or other technologies. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the computing equipment, an external keyboard, a touch pad or a mouse and the like. The processor may call logic instructions in the memory to perform the following method: acquiring load measurement data of the real-time operation of the power grid, synchronously preprocessing the load measurement data, eliminating abnormal measurement data, and aligning the measurement data after the abnormal measurement data are eliminated; based on the aligned measurement data after synchronous preprocessing, carrying out conversion calculation of an instantaneous value and a synchronous phasor to obtain real-time phasor data; carrying out data noise signal state discrimination on the real-time phasor data, extracting time window data meeting the noise signal state, and calculating to obtain effective parameters; and continuously calculating effective parameters, and clustering the effective parameters to form time-sharing typical model parameters.
In addition, the logic instructions in the memory may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that the above-described configurations of computing devices are merely some of the configurations associated with the present application and do not constitute limitations on the computing devices to which the present application may be applied, as a particular computing device may include more or fewer components, or some components in combination, or have a different arrangement of components.
In one embodiment of the invention, a computer program product is provided, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions that, when executed by a computer, enable the computer to perform the methods provided by the above-described method embodiments, for example, comprising: acquiring load measurement data of the real-time operation of the power grid, synchronously preprocessing the load measurement data, eliminating abnormal measurement data, and aligning the measurement data after the abnormal measurement data are eliminated; based on the aligned measurement data after synchronous preprocessing, carrying out conversion calculation of an instantaneous value and a synchronous phasor to obtain real-time phasor data; carrying out data noise signal state discrimination on the real-time phasor data, extracting time window data meeting the noise signal state, and calculating to obtain effective parameters; and continuously calculating effective parameters, and clustering the effective parameters to form time-sharing typical model parameters.
In one embodiment of the invention, a non-transitory computer-readable storage medium is provided, which stores server instructions that cause a computer to perform the methods provided by the above embodiments, for example, including: acquiring load measurement data of the power grid running in real time, synchronously preprocessing the load measurement data, eliminating abnormal measurement data, and aligning the measurement data after the abnormal measurement data are eliminated; based on the aligned measurement data after synchronous preprocessing, carrying out conversion calculation of an instantaneous value and a synchronous phasor to obtain real-time phasor data; carrying out data noise signal state discrimination on the real-time phasor data, extracting time window data meeting the noise signal state, and calculating to obtain effective parameters; and continuously calculating effective parameters, and clustering the effective parameters to form time-sharing typical model parameters.
The implementation principle and technical effect of the computer-readable storage medium provided by the above embodiments are similar to those of the above method embodiments, and are not described herein again.
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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A dynamic load-dominated model parameter identification method based on noise-like signals is characterized by comprising the following steps:
acquiring load measurement data of a power grid running in real time, synchronously preprocessing the load measurement data, eliminating abnormal measurement data, and aligning the measurement data after the abnormal measurement data are eliminated;
based on the measurement data aligned after synchronous preprocessing, carrying out conversion calculation of an instantaneous value and a synchronous phasor to obtain real-time phasor data;
carrying out data noise signal state judgment on the real-time phasor data, extracting time window data meeting the noise signal state, and calculating to obtain effective parameters;
and continuously calculating the effective parameters, and clustering the effective parameters to form time-sharing typical model parameters.
2. The method according to claim 1, wherein the synchronously preprocessing the load measurement data, eliminating abnormal measurement data, and aligning the measurement data after the abnormal measurement data is eliminated comprises:
and comparing the time scales of the load measurement data with the data time of the equal interval, and judging the measurement data with abnormal identification to obtain the synchronous equal interval alignment measurement data.
3. The method as claimed in claim 2, wherein the determining the abnormal measurement data includes:
removing abnormal measurement data with multiple continuous sections and unchanged, wherein the abnormal measurement data are removed if the abnormal measurement data do not meet preset calculation conditions;
and judging the number of the length settings of the measurement value fluctuation data segment and the window of the sliding interval, and regarding each data window, determining abnormal data if the number of the data windows which is not zero and is less than a preset threshold value is not zero according to different values or data variation of the defined data.
4. The method according to claim 1, wherein the performing a conversion calculation between an instantaneous value and a synchronous phasor to obtain real-time phasor data based on the measurement data aligned after the synchronous preprocessing comprises:
obtaining a corresponding phasor expression according to the instantaneous value waveform of the cosine signal of the measured data;
every other T 0 Observing the cosine signal once in time, and then obtaining a series of phasors corresponding to the observation time as a phasor sequence; the phasor sequence serves as real-time phasor data.
5. The method for identifying parameters of a dynamic load-dominated model based on noise-like signals as claimed in claim 1, wherein the step of discriminating the state of the data noise-like signals from the real-time phasor data, extracting time window data satisfying the noise-like state, and calculating to obtain effective parameters comprises the steps of:
extracting time window data meeting the noise-like state as actually measured noise-like data;
calculating the state variables of the current moment and the next moment according to the determined model structure and the parameters to be calculated and the actually measured noise-like data and the parameters to be calculated;
respectively substituting the state variables at the current moment and the state variables at the next moment into an output equation of the current numerical value of the parameter to be identified, calculating a predicted value of the output variable, and calculating the deviation between the predicted value and the measured value at the next moment to obtain the numerical value of the target function;
and optimizing the objective function, and performing parameter fitting to obtain a numerical parameter of which the predicted value of the output variable has the same trend with the actually measured value or meets the preset fitting proportion, wherein the numerical parameter is used as an effective parameter.
6. The method according to claim 1, wherein the effective parameters are continuously calculated and clustered to form time-sharing typical model parameters, and the clustering method comprises:
and clustering according to the hour, taking a group of data similar to the average value of the load power in the current hour as the clustering parameter of the current hour, and so on to obtain the 24-hour clustering parameter every day.
7. The method according to claim 6, wherein the method for clustering the effective parameters to form time-sharing typical model parameters comprises:
defining the 8 points in the morning to the 8 points in the evening as working periods, and clustering the other working periods to obtain clustering parameters of the working periods and the non-working periods respectively, thereby forming time-sharing typical model parameters.
8. A system for identifying parameters of a dynamic load dominated model based on noise-like signals, comprising:
the first processing module is used for acquiring load measurement data of the real-time operation of the power grid, synchronously preprocessing the load measurement data, eliminating abnormal measurement data and aligning the measurement data after the abnormal measurement data are eliminated;
the second processing module is used for carrying out conversion calculation on an instantaneous value and a synchronous phasor based on the measurement data aligned after synchronous preprocessing to obtain real-time phasor data;
the third processing module is used for judging the state of a data noise-like signal of the real-time phasor data, extracting time window data meeting the noise-like state and calculating to obtain effective parameters;
and the parameter forming module is used for continuously calculating the effective parameters and clustering the effective parameters to form time-sharing typical model parameters.
9. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-7.
10. A computing device, comprising: one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210645708.7A CN114970635A (en) | 2022-06-09 | 2022-06-09 | Dynamic load leading model parameter identification method and system based on noise-like signal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210645708.7A CN114970635A (en) | 2022-06-09 | 2022-06-09 | Dynamic load leading model parameter identification method and system based on noise-like signal |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114970635A true CN114970635A (en) | 2022-08-30 |
Family
ID=82962646
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210645708.7A Pending CN114970635A (en) | 2022-06-09 | 2022-06-09 | Dynamic load leading model parameter identification method and system based on noise-like signal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114970635A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116908524A (en) * | 2023-09-13 | 2023-10-20 | 中国建筑科学研究院有限公司 | Abnormal sensing monitoring system of building electrical system based on artificial intelligence |
-
2022
- 2022-06-09 CN CN202210645708.7A patent/CN114970635A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116908524A (en) * | 2023-09-13 | 2023-10-20 | 中国建筑科学研究院有限公司 | Abnormal sensing monitoring system of building electrical system based on artificial intelligence |
| CN116908524B (en) * | 2023-09-13 | 2023-12-01 | 中国建筑科学研究院有限公司 | Abnormal sensing monitoring system of building electrical system based on artificial intelligence |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Bian et al. | Study on power consumption load forecast based on K-means clustering and FCM–BP model | |
| JP2019528032A (en) | Battery management system | |
| CN106600463B (en) | Local shape similarity ultra-short term load prediction method and device | |
| Deng et al. | Modified kernel principal component analysis using double-weighted local outlier factor and its application to nonlinear process monitoring | |
| CN108053110B (en) | PMU data-based transformer state online diagnosis method | |
| Cheng et al. | Enhanced state estimation and bad data identification in active power distribution networks using photovoltaic power forecasting | |
| CN109376953A (en) | A kind of Middle and long term electricity consumption forecasting method and system | |
| KR20190013038A (en) | System and method for trend predicting based on Multi-Sequences data Using multi feature extract technique | |
| CN109615124B (en) | SCADA master station load prediction method based on deep learning | |
| CN104407688A (en) | Virtualized cloud platform energy consumption measurement method and system based on tree regression | |
| KR20200128232A (en) | Apparatus for predicting electricity demand and method thereof | |
| WO2019161589A1 (en) | Real-time tracking method for structural modal parameter | |
| CN110766236A (en) | Power equipment state trend prediction method based on statistical analysis and deep learning | |
| CN116595394A (en) | Training method of wind speed correction model, wind speed prediction method, wind speed prediction equipment and medium | |
| CN111680841A (en) | Short-term load prediction method and system based on principal component analysis and terminal equipment | |
| CN114970635A (en) | Dynamic load leading model parameter identification method and system based on noise-like signal | |
| CN112307677A (en) | Power grid oscillation mode evaluation and safety active early warning method based on deep learning | |
| CN113705929A (en) | Spring festival holiday load prediction method based on load characteristic curve and typical characteristic value fusion | |
| CN115685045A (en) | Online evaluation method for voltage transformer | |
| CN103514488A (en) | Electrical power system short-term load forecasting device and method based on combination forecasting model | |
| CN114819385A (en) | Wind power prediction method and device, electronic equipment and storage medium | |
| CN114184870A (en) | Non-invasive load identification method and equipment | |
| Sun et al. | Short-term power load prediction based on VMD-SG-LSTM | |
| Jiang et al. | Medium-long term load forecasting method considering industry correlation for power management | |
| Li et al. | Research on the prediction method of stock price based on rbf neural network optimization algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |