CN115344991A - Automatic parameter optimization triggering method for digital twin of regional multi-energy system - Google Patents
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Abstract
The invention discloses an automatic parameter optimization triggering method for a digital twin body of a regional multi-energy system, which comprises the following steps: the digital twin system automatically monitors and collects the actual system operation data through the real-time communication interfaces of the metering system and the fault recording system; the threshold out-of-limit driving triggering module obtains simulation operation data according to the section data of the actual system, performs two-norm error calculation with historical operation data, and automatically corrects all key equipment parameters in the system according to the error; starting a transient process driving trigger module to monitor system operation data, automatically acquiring fault position nodes and voltage recording data, and correcting all key equipment parameters in a fault range; when the historical operating data of the metering system reaches an accumulated value, the periodic trigger module optimizes parameters of all key equipment, so that the self-updating and self-evolution effects of the digital twin are achieved, manual intervention is not needed, and the self-optimization of the digital twin is realized.
Description
Technical Field
The invention relates to the technical field of automatic parameter optimization, in particular to an automatic parameter optimization triggering method for a digital twin body of a regional multi-energy system.
Background
Multi-energy systems aim at improving energy utilization performance, i.e. safety, efficiency, sustainability, flexibility and self-healing capability of the energy supply system. The multi-energy system is a comprehensive system which closely connects an electric power system, a natural gas system, a cooling and heating system, distributed energy, energy storage, energy conversion and an intelligent information physical system with a terminal user.
At present, the operation control of the multi-energy system is limited by a specific control strategy and massive fragment information, and the safety and the economy of the multi-energy system are reduced. In addition, changes in topology and limited access to operational data can affect the effectiveness of traditional planning architectures.
The digital twin technology provides a new way for realizing panoramic perception and continuous control of the multi-energy system. The emerging technology can realize the visualization of a physical system, the simulation of an operation state and the verification of control performance by the real-time simulation of a digital mirror image of an actual system. Thereby improving the economy and reliability of the system and promoting the development of the multi-energy system industry.
The realization of the complex 'information-energy-environment' coupling dynamic accurate simulation of the multi-energy system is the premise of constructing the digital twin of the multi-energy system. Therefore, firstly, a digital twin model of the multi-energy system needs to be constructed to complete holographic replication and high-fidelity modeling of the multi-energy system, and an integrated virtual replica of an object, a model and a data set is established.
Different from the traditional knowledge-driven modeling method for describing the operation mechanism by adopting a multi-physics coupling kinetic equation, the idea of jointly driving by adopting knowledge and data is needed for constructing the digital twin. The digital twin model not only represents mathematical equations in various algebraic, differential or partial differential forms, but also comprises massive system measurement state data for realizing synchronization between the physical system and the virtual model, and a correlation model which is constructed by utilizing historical state data through statistics and machine learning and describes an operation rule. This results in a digital twin model that is not a constant set of mathematical equations, but rather an evolutionary model with time-varying, continuously updated parameters.
Therefore, in order to meet the requirement of accurately simulating an actual system, when the actual system parameters change, the change needs to be fed back to a digital space to reconstruct a digital twin model, and meanwhile, the accuracy of the reconstructed model needs to be checked to judge whether the model accuracy reaches the standard, so as to determine whether to perform subsequent model and parameter correction work.
Although researchers have conducted a lot of research on parameter correction of power systems at present, the basic steps are that firstly, according to the phase relation of the sensitivity of the parameters, the identifiability of the parameters can be determined; then, the difficulty degree of parameter identification can be determined according to the amplitude of the parameter sensitivity, so that key parameters can be determined; further, establishing an error index for measuring the accuracy of the model; and finally, optimizing key parameters by adopting a particle swarm algorithm to minimize the correction error index. However, a triggering mechanism of a parameter optimization algorithm is not introduced yet at the present stage, and the time for controlling parameter optimization is not good.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the technical problem solved by the invention is as follows: the existing parameter optimization method carries out precision verification on a traditional simulation model by comparing errors of a numerical simulation result and measured data, and is difficult to carry out real-time monitoring and continuous automatic verification on the precision of a nonlinear time-varying digital twin model by combining the real-time change condition of measured data.
In order to solve the above technical problems, the present invention provides the following technical solutions, including:
the digital twin system automatically monitors and collects the actual system operation data through the real-time communication interfaces of the metering system and the fault recording system;
the threshold out-of-limit driving triggering module obtains simulation operation data according to the section data of the actual system, performs two-norm error calculation with historical operation data, and automatically corrects all key equipment parameters in the system according to the error;
starting a transient process to drive a trigger module to monitor system operation data, automatically acquiring fault position nodes and voltage recording data, correcting all key equipment parameters in the fault range;
when the historical operating data of the metering system reaches an accumulated value, the periodic trigger module optimizes the parameters of all key equipment.
As a preferable scheme of the automatic parameter optimization triggering method for the regional multi-energy system digital twin, the method comprises the following steps:
the actual system operating data includes: profile data and transient events;
the metering system comprises: reading various data of the electric energy meter of the remote user, transmitting the data to the control center, storing and analyzing the data, generating a report and a curve, and supporting time-of-use electricity price and real-time data management of the user;
the fault recording system comprises: and recording the dynamic fault process of the power system.
As a preferable aspect of the method for triggering automatic parameter optimization of a regional multi-energy system digital twin according to the present invention, the method comprises: the profile data includes:
the digital twin body background acquires cross section data of a group of actual systems from the metering system every 15 minutes to obtain t 0 Time of day section data R (t) 0 )；
Section data R (t) 0 ) Expressed as:
R(t 0 )＝[x R (t 0 ) u R (t 0 ) y R (t 0 )]
wherein x is R (t 0 ) Is t 0 System actual state vector of time u R (t 0 ) Is t 0 System actual input vector of time, y R (t 0 ) Is t 0 The system at the time instance actually outputs the vector.
As a preferable aspect of the method for triggering automatic parameter optimization of a regional multi-energy system digital twin according to the present invention, the method comprises: at t 0 Starting an electromagnetic transient simulation program at the cross section of the moment, wherein the simulation result comprises the following steps:
simulation data, expressed as:
S(t 0 ,t S )＝[x S (t 0 ,t S ) u S (t 0 ,t S ) y S (t 0 ,t S )]
wherein x is s (t 0 ,t s ) Is from t 0 Time begins to t s Time of day systemSimulation state vector u s (t 0 ,t s ) Is from t 0 Time of day start to t s System simulation input vector of time, y s (t 0 ,t s ) Is from t 0 Time begins to t s The system at the moment simulates the output vector.
As a preferable aspect of the method for triggering automatic parameter optimization of a regional multi-energy system digital twin according to the present invention, the method comprises: performing two-norm error calculation on the simulation data and the historical operating data comprises:
the two-norm error, μ, is expressed as:
wherein R (t) 0 ,t s ) Is from t 0 Time begins to t s The system historical operating data matrix at time, S (t) 0 ,t s ) Is from t 0 Time begins to t s Time-of-day system simulation runs a data matrix, | | R (t) 0 ,t s )-S(t 0 ,t s )|| 2 The difference, two-norm, | | R (t) representing historical operating data and simulation operation 0 ,t s )|| 2 Representing the two-norm of historical operating data.
As a preferable aspect of the method for triggering automatic parameter optimization of a regional multi-energy system digital twin according to the present invention, the method comprises: automatically correcting all key equipment parameters in the system according to the error size comprises the following steps:
and when the two-norm error value mu of the simulation data and the historical operating data is greater than 10% of the threshold value, automatically selecting a specific type of parameter correction algorithm according to the type of the equipment to correct the parameters.
As a preferable scheme of the automatic parameter optimization triggering method for the regional multi-energy system digital twin, the method comprises the following steps: starting a transient process to drive a trigger module to monitor system operating data, comprising:
the fault or voltage fluctuation monitoring module monitors the transient event of the fault recording system in real time, and starts the fault position positioning module when the fault or voltage fluctuation event of the system is monitored.
As a preferable scheme of the automatic parameter optimization triggering method for the regional multi-energy system digital twin, the method comprises the following steps: automatically acquiring fault position node and voltage recording data, comprising:
and positioning to a specific fault occurrence position according to the fault position positioning module, automatically acquiring voltage recording data of a fault position node and all node buses electrically connected with the node in a recording system by acquiring a fault position voltage recording data module, and correcting trigger parameters of all key equipment in a fault range.
As a preferable scheme of the automatic parameter optimization triggering method for the regional multi-energy system digital twin, the method comprises the following steps: the historical operating data of the metering system reaches an accumulated value, which comprises:
because the metering system acquires a group of actual system section data every 15 minutes, considering that 1 day is a period, when the metering system accumulates enough 96 groups of historical operating data, 96 groups of historical operating data within 1 day are imported (t) R ～t S ) The recording waveforms of all the state vectors, the input vector and the output vector are obtained to obtain a recording data matrix;
recording data matrix R (t) R ,t S ) Expressed as:
R(t R ,t S )＝[x R (t R ,t S ) u R (t R ,t S ) y R (t R ,t S )]
wherein x is s (t R ,t s ) Is from t R Time begins to t s The actual state vector of the system at time (within 1 day), u s (t R ,t s ) Is from t R Time begins to t s Time of day (within 1 day) system actual input vector, y s (t R ,t s ) Is from t R Time begins to t s The system actually outputs the vector at time (within 1 day).
As a preferable scheme of the automatic parameter optimization triggering method for the regional multi-energy system digital twin, the method comprises the following steps: the periodic trigger module optimizes parameters of all key devices, including: and periodically triggering and utilizing the historical running recording data, periodically triggering all key equipment leading parameter optimization in the system through a parameter correction algorithm of statistics and machine learning, and continuously correcting the parameters of the digital twin model.
The invention has the beneficial effects that: the automatic parameter optimization triggering method provided by the invention can periodically calculate the model precision of the digital twin body, when the model precision is found to be reduced, the parameter optimization algorithm can be automatically triggered without manual intervention, the leading parameters of key equipment are corrected, and the digital twin body can automatically approach the direction of an actual system, so that the self-optimization of the digital twin body is realized.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a parameter optimization algorithm triggering mode diagram of an automatic parameter optimization triggering method for a regional multi-energy system digital twin according to an embodiment of the present invention;
fig. 2 is a simulation waveform diagram of an automatic parameter optimization triggering method for a digital twin of a regional multi-energy system according to an embodiment of the present invention;
fig. 3 is a waveform diagram of a recording waveform of an automatic parameter optimization triggering method for a regional multi-energy system digital twin according to an embodiment of the present invention;
fig. 4 is a comparison graph of the effects before and after the parameter optimization of the photovoltaic device of the automatic parameter optimization triggering method for the digital twin of the regional multi-energy system according to an embodiment of the present invention;
fig. 5 is a comparison graph of the effects before and after the parameter optimization of the synchronous generator device according to the automatic parameter optimization triggering method for the digital twin of the regional multi-energy system in one embodiment of the present invention;
fig. 6 is a comparison diagram of effects before and after optimization of parameters of a transformer device in an automatic parameter optimization triggering method for a regional multi-energy system digital twin according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. 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.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. 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 "connected" in the present invention are to be construed broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in a specific case to those of ordinary skill in the art.
Example 1
Referring to fig. 1 to 3, a first embodiment of the present invention provides an automatic parameter optimization triggering method for a digital twin of an area multi-energy system, including:
s1, automatically monitoring and collecting actual system operation data by a digital twin system through real-time communication interfaces of a metering system and a fault recording system;
further, the actual system operating data includes: profile data and transient events;
the metering system comprises: reading various data of the electric energy meter of the remote user, transmitting the data to the control center, storing and analyzing the data, generating a report and a curve, and supporting time-of-use electricity price and real-time data management of the user;
the fault recording system comprises: and recording the dynamic fault process of the power system.
It should be noted that, because interaction exists between the digital twin model of the multi-energy system and the actual physical system, as a nonlinear time-varying system, system parameters will change constantly, and random variation of the system parameters will cause corresponding change of the coefficient matrix, firstly, a real-time communication interface between the digital twin system and the metering and wave recording system is developed, and the real-time communication interface can automatically monitor and collect actual system operation data.
S2, the threshold out-of-limit driving triggering module obtains simulation operation data according to the section data of the actual system, performs two-norm error calculation with historical operation data, and automatically corrects all key equipment parameters in the system according to the error;
further, the profile data includes:
the digital twin body background acquires a group of section data of an actual system from the metering system every 15 minutes to obtain t 0 Time of day section data R (t) 0 )；
Section data R (t) 0 ) Expressed as:
R(t 0 )＝[x R (t 0 ) u R (t 0 ) y R (t 0 )]
wherein x is R (t 0 ) Is t 0 System actual state vector of time u R (t 0 ) Is t 0 System actual input vector of time, y R (t 0 ) Is t 0 The system at the time instance actually outputs the vector.
It should be noted that the digital twin model of the multi-energy system is at t 0 The state quantity, input quantity, and output quantity at the time are expressed as:
x R (t 0 )＝[x 1 (t 0 )，x 2 (t 0 )，…] T
u R (t 0 )＝[u 1 (t 0 )，u 2 (t 0 )，…] T
y R (t 0 )＝[y 1 (t 0 )，y 2 (t 0 )，…] T
wherein x is R (t 0 ) Is t 0 System actual state vector of time, x 1 (t 0 )、x 2 (t 0 ) Each represents the state variable of each system at t 0 Actual measured value of time u R (t 0 ) Is t 0 The actual state vector of the system at the time of day,u 1 (t 0 )、u 2 (t 0 ) Respectively representing each system input variable at t 0 Actual measured value of time, y R (t 0 ) Is t 0 System actual state vector of time, y 1 (t 0 )、y 2 (t 0 ) Respectively representing each system output variable at t 0 Actual measured value of the time of day.
Further, at t 0 Starting an electromagnetic transient simulation program at the cross section of the moment, wherein the simulation result comprises the following steps:
simulation data, expressed as:
S(t 0 ,t S )＝[x S (t 0 ,t S ) u S (t 0 ,t S ) y S (t 0 ,t S )]
wherein x is s (t 0 ,t s ) Is from t 0 Time begins to t s System simulation state vector of time u s (t 0 ,t s ) Is from t 0 Time begins to t s System simulation input vector of time of day, y s (t 0 ,t s ) Is from t 0 Time begins to t s The system at the moment simulates the output vector.
It should be noted that, at t 0 A period of time (t) after the moment 0 ～t S ) Simulation data for all state vectors, input vectors, and output vectors, expressed as:
wherein, Δ t s Is the step size of the simulation,x s (t 0 ,t s ) Is from t 0 Time begins to t s System simulation state vector at time, x 1s (t 0 )、x 2s (t 0 ) Each represents the state variable of each system at t 0 Simulated value of time, x 1s (t 0 +Δt s )、x 2s (t 0 +Δt s ) Each represents the simulation value u of each system state variable at each simulation moment s (t 0 ,t s ) Is from t 0 Time of day start to t s System simulation input vector of time u 1s (t 0 )、u 2s (t 0 ) Each represents the input variable of each system at t 0 Simulated value of time u 1s (t 0 +Δt s )、u 2s (t 0 +Δt s ) Each represents the simulation value, y, of each system input variable at each simulation time s (t 0 ,t s ) Is from t 0 Time begins to t s System simulation output vector at time, y 1s (t 0 )、y 2s (t 0 ) Respectively represents each system output variable at t 0 Simulated value of time, y 1s (t 0 +Δt s )、y 2s (t 0 +Δt s ) And represents the simulation value of each system output variable at each simulation moment.
Further, performing a two-norm error calculation on the simulation data and the historical operating data comprises:
the two-norm error, μ, is expressed as:
wherein R (t) 0, t s ) Is from t 0 Time begins to t s The system historical operating data matrix at time, S (t) 0 ,t s ) Is from t 0 Time of day start to t s Time-of-day system simulation runs a data matrix, | | R (t) 0 ,t s )-S(t 0 ,t s )|| 2 The difference, two-norm, | | R (t) representing historical operating data and simulation operation 0 ,t s )|| 2 Representing the two-norm of historical operating data.
It should be noted that the distance between data can be represented by using the feature of vector, and the two-norm of a real matrix a is the square root value of the maximum feature root of the product of the transpose matrix of a and the matrix a, and is represented as:
where eig (X) is a function of eigenvalues of the square matrix X, returning vectors [ λ 1, λ 2, ·, λ n ·] T 。
And comparing the simulation data with historical operating data R accumulated by an actual system, eliminating noise, calculating the distance between the recording data and the simulation data, and verifying the precision of the model by adopting a difference matrix two-norm.
Further, automatically correcting all key equipment parameters in the system according to the error magnitude comprises:
and when the two-norm error value mu of the simulation data and the historical operating data is greater than 10% of the threshold value, automatically selecting a specific type of parameter correction algorithm according to the type of the equipment to correct the parameters.
It should be noted that the higher the threshold value is, the lower the trigger frequency is, and setting the trigger threshold value can ensure that the digital twin is always at a high precision level, so as to increase the trigger frequency.
S3, starting a transient process driving trigger module to monitor system operation data, automatically acquiring fault position nodes and voltage recording data, and correcting all key equipment parameters in a fault range;
furthermore, the starting of the transient process driving triggering module monitors the system operation data, which includes:
the fault or voltage fluctuation monitoring module monitors the transient event of the fault recording system in real time, and starts the fault position positioning module when the fault or voltage fluctuation event of the system is monitored.
Furthermore, the automatic acquisition of fault location node and voltage recording data includes:
and positioning to a specific fault occurrence position according to the fault position positioning module, automatically acquiring voltage recording data of a fault position node and all node buses electrically connected with the node in a recording system by acquiring the fault position voltage recording data module, and correcting trigger parameters of all key equipment in a fault range.
S4, when the historical operation data of the metering system reaches an accumulated value, the regular trigger module optimizes parameters of all key equipment;
further, the historical operating data of the metering system reaches an accumulated value, including:
because the metering system acquires a group of actual system section data every 15 minutes, considering that 1 day is a period, when the metering system accumulates enough 96 groups of historical operating data, 96 groups of historical operating data within 1 day are imported (t) R ～t S ) The recording waveforms of all the state vectors, the input vector and the output vector are obtained to obtain a recording data matrix;
wave data matrix R (t) R ,t S ) Expressed as:
R(t R ,t S )＝[x R (t R ,t S ) u R (t R ,t S )y R (t R ,t S )]
wherein x is R (t R ,t S ) Is a state vector, y R (t R ,t S ) As an input vector, u R (t R ,t S ) Is the output vector.
It should be noted that 96 sets of historical operating data (t) are imported within 1 day R ～t S ) The recording data of all state vectors, input vectors and output vectors of (a) are expressed as:
wherein, Δ t R Sampling time intervals for recording, x R (t R ,t s ) Is from t R Time begins to t s Historical running state vector, x, of the system at time (in one day) 1R (t R )、x 2R (t R ) Each represents the state variable of each system at t R History value of time, x 1R (t R +Δt s )、x 2R (t R +Δt s ) Each represents the historical value of each system state variable at each recording moment, u s (t R ,t s ) Is from t R Time begins to t s System historical operational input vector of time u 1R (t R )、u 2R (t R ) Respectively representing each system input variable at t R Historical value of time, u 1R (t R +Δt R )、u 2R (t R +Δt R ) Respectively represent the historical value of each system input variable at each recording time, y R (t R ,t s ) Is from t R Time begins to t s Historical system running output vector at time, y 1R (t R )、y 2R (t R ) Respectively represents each system output variable at t R Historical value of time, y 1R (t R +Δt R )、y 2R (t R +Δt R ) And represents the historical value of each system output variable at each recording moment.
Further, the periodic trigger module optimizes parameters of all critical devices, including: and (3) regularly triggering and utilizing historical running recording data, regularly triggering all key equipment leading parameter optimization in the system through a parameter correction algorithm of statistics and machine learning, and continuously correcting the parameters of the digital twin model.
It should be noted that, the construction of the digital twin model of the multi-energy system must rely on a large number of measurement devices to perform complete measurement, transmission and storage on the state quantity, the input quantity and the output quantity of the multi-energy system, so that recording waveform files of all the state quantity, the input quantity and the output quantity of the multi-energy system can be obtained, and all key device leading parameters in the system are optimized periodically.
Example 2
Referring to fig. 4 to 6, a second embodiment of the present invention is shown, in order to verify the beneficial effects, scientific verification is performed through system simulation.
Taking a multi-energy system in a certain area in China as an example, the system is communicated with a metering system and a wave recording system of a main station of the system in real time through a specific communication protocol, and wave recording data and historical operating data are combined.
It can be seen from fig. 4,5,6 that the bus voltage waveform of the photovoltaic device, the generator device, and the transformer device after the automatic trigger parameter optimization by the method of the present invention is closer to the waveform of the measurement curve than the bus voltage waveform before the correction, thereby realizing the automatic optimization of the key equipment model dominant parameters in the multi-energy system in the area.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. An automatic parameter optimization triggering method for a regional multi-energy system digital twin is characterized by comprising the following steps:
the digital twin system automatically monitors and collects the actual system operation data through the real-time communication interfaces of the metering system and the fault recording system;
the threshold out-of-limit driving triggering module obtains simulation operation data according to the section data of the actual system, performs two-norm error calculation with historical operation data, and automatically corrects all key equipment parameters in the system according to the error;
starting a transient process driving trigger module to monitor system operation data, automatically acquiring fault position nodes and voltage recording data, and correcting all key equipment parameters in a fault range;
when the historical operation data of the metering system reaches an accumulated value, the periodic trigger module optimizes parameters of all key equipment.
2. The method for triggering the automatic parameter optimization of the regional multi-energy system digital twin according to claim 1, wherein:
the actual system operating data includes: profile data and transient events;
the metering system comprises: reading various data of the remote user electric energy meter, transmitting the data to the control center, storing and analyzing the data, generating a report and a curve, and supporting time-of-use electricity price and user real-time data management;
the fault recording system comprises: and recording the dynamic fault process of the power system.
3. The method as claimed in claim 2, wherein the profile data includes:
the method comprises the steps that a digital twin background acquires section data of a group of actual systems from a metering system every 15 minutes to obtain section data R (t 0) at the time of t 0;
section data R (t 0), expressed as:
R(t 0 )＝[x R (t 0 ) u R (t 0 ) y R (t 0 )]
wherein x is R (t 0 ) Is t 0 System actual state vector of time of day, u R (t 0 ) Is t 0 System actual input vector of time, y R (t 0 ) Is t 0 System actual output vector of time of day。
4. The method as claimed in claim 3, wherein at t, the triggering method for automatic parameter optimization of the regional multi-energy system digital twin is 0 Starting an electromagnetic transient simulation program at the cross section of the moment, wherein the simulation result comprises the following steps:
simulation data, expressed as:
S(t 0 ,t S )＝[x S (t 0 ,t S ) u S (t 0 ,t S ) y S (t 0 ,t S )]
wherein x is S (t 0 ,t S ) Is a state vector, u S (t 0 ,t S ) As an input vector, y S (t 0 ,t S ) Is the output vector.
5. The method as claimed in claim 4, wherein the performing of the two-norm error calculation on the simulation data and the historical operating data includes:
the two-norm error, μ, is expressed as:
wherein R (t) 0 ,t s ) Is from t 0 Time of day start to t s Time of day system historical operating data matrix, S (t) 0 ,t s ) Is from t 0 Time begins to t s Time-of-day system simulation runs a data matrix, | | R (t) 0 ,t s )-S(t 0 ,t s )|| 2 Two-norm of difference, R (t), representing historical operating data and simulation operations 0 ,t s )|| 2 Representing the two-norm of historical operating data.
6. The method as claimed in claim 5, wherein automatically modifying all critical device parameters in the system according to error magnitude comprises:
and when the two-norm error value mu of the simulation data and the historical operating data is greater than 10% of the threshold value, automatically selecting a specific type of parameter correction algorithm according to the type of the equipment to correct the parameters.
7. The method as claimed in claim 6, wherein the step of activating the transient process driving triggering module to monitor the system operation data includes:
the fault or voltage fluctuation monitoring module monitors the transient event of the fault recording system in real time, and starts the fault position positioning module when the fault or voltage fluctuation event of the system is monitored.
8. The method for triggering automatic parameter optimization of a regional multi-energy system digital twin according to claim 7, wherein automatically acquiring fault location node and voltage recording data comprises:
and positioning to a specific fault occurrence position according to the fault position positioning module, automatically acquiring voltage recording data of a fault position node and all node buses electrically connected with the node in a recording system by acquiring a fault position voltage recording data module, and correcting trigger parameters of all key equipment in a fault range.
9. The method as claimed in claim 8, wherein the step of measuring historical operating data of the system to reach an accumulated value comprises:
because the metering system acquires a group of actual system section data every 15 minutes, considering that 1 day is a period, when the metering system accumulates enough 96 groups of historical operating data, 96 groups of historical operating data within 1 day are imported (t) R ～t S ) The recording waveforms of the input vector and the output vector are obtained to obtain a recording data matrix;
wave dataMatrix R (t) R ,t S ) Expressed as:
R(t R ,t S )＝[x R (t R ,t S )u R (t R ,t S )y R (t R ,t S )]
wherein x is R (t R ,t S ) Is a state vector, y R (t R ,t S ) As an input vector, u R (t R ,t S ) Is the output vector.
10. The method as claimed in claim 9, wherein the periodic triggering module performs parameter optimization on all critical devices, and includes: and regularly triggering and utilizing the historical running recording data, regularly triggering all key equipment leading parameter optimization in the system through means of statistics and machine learning, and continuously correcting the parameters of the digital twin model.
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| CN117891644B (en) * | 2024-03-11 | 2024-06-04 | 南京市计量监督检测院 | Data acquisition system and method based on digital twin technology |
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