CN116796633A - Power grid monitoring model correction method based on digital twin technology - Google Patents
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Abstract
The invention discloses a power grid monitoring model correction method based on a digital twin technology, which belongs to the field of system modeling and simulation and can effectively improve the accuracy and credibility of a digital twin model. Firstly, establishing a digital twin simulation-oriented intelligent power grid monitoring correction model which comprises a real power grid sensor, a sensor twin simulation model, a twin database and a power grid running state; then, a sensor measurement model based on a random finite set is established, and a sensor likelihood function is solved by considering omission and clutter; secondly, establishing a power grid monitoring digital twin simulation model based on a random finite set, and solving a Markov state transition density function of the sensor by considering dynamic environment changes of a power grid system; and finally, solving a posterior probability density function of the parameter vector based on a Bayesian estimation method, and calculating a correction value of the parameter vector through a maximum posterior estimation method.
Description
Technical Field
The invention belongs to the field of system modeling and simulation, and particularly relates to a digital twin simulation-oriented virtual-real combination correction method for a power grid monitoring model.
Background
The intelligent transformer station is the basis of the intelligent power grid, plays an extremely important role in the power system, the working condition of the intelligent transformer station directly influences the stability and safety of the whole power system, and the fault not only can influence the reliability of power supply, but also can cause serious economic loss. Therefore, potential faults can be found in time by monitoring the running condition, the real-time performance, the environmental parameters and the dynamic process data of sudden disturbance of the power grid, and the potential faults are maintained in early stage, so that the accidents are prevented, the equipment is enabled to run safely and stably, and the method has great significance in guaranteeing the reliability of power supply. Therefore, accurate judgment of the running state of the power grid is a key for ensuring stable and safe running of the power transformer.
Along with the development of the current power grid and the high-speed increase of the power demand, the requirements on the safety and the quality of the power grid are higher and higher, and the safety and the quality of the power grid are more important for effectively managing, overhauling and maintaining the established huge power network so as to ensure the normal operation of the power grid and ensure the safe transmission of the power. At present, management of a transformer substation mainly depends on manual analysis of operation and maintenance data generated in operation of the transformer substation by workers, and management of the transformer substation is performed according to analysis results. However, such a management method depends on the working experience of the staff, resulting in high labor repetition rate and large workload, and limiting the data processing efficiency of the staff. Therefore, a method is needed to improve the working efficiency of the staff and ensure the normal and healthy operation of the transformer substation.
In recent years, digital twinning (DigitalTwin) has been attracting attention from academia and enterprises, and especially digital twinning land-based applications have been focusing on hot spots. The digital twinning creates a virtual model of the physical entity in a digital mode, simulates the behavior of the physical entity in a real environment by means of data, and adds or expands new capability for the physical entity by means of virtual-real interaction feedback, data fusion analysis, decision iteration optimization and the like. The digital twinning fully utilizes the technology of models, data, intelligence and integration of multiple disciplines, plays the role of bridges and ties for connecting the physical world and the information world, and provides more real-time, efficient and intelligent service.
The model is the basis and the core of digital twinning, and as the digital twinning technology correspondingly lands in various application fields and industries, the creation of the digital twinning model meeting the technical development trend and requirements is the key of digital twinning landing application. The power grid is a complex system related to multiple links such as power generation, power transmission, power transformation, power distribution, power consumption and the like, and each link comprises multiple application scenes and a large amount of equipment, so that the establishment of a power grid twin model conforming to the power grid management operation maintenance service is particularly important, the foundation of the establishment of a power grid digital twin system is realized, and the improvement of the power grid operation and maintenance level and the intelligent level is facilitated.
Although the traditional sensor monitoring method of the power grid is simpler, the traditional sensor monitoring method has defects due to sensitivity and lazy nature to the environment, is extremely easy to be influenced by dust and other particles in the environment, and is required to be close to a detection target in order to improve the precision, and the traditional sensor monitoring method is difficult to meet the requirement of complex power grid environment detection in a large-space open area.
The digital twin simulation system realizes the reciprocal symbiosis and the depth combination between the real physical test and the virtual simulation test, the real physical test can utilize the prediction analysis and evaluation functions of the virtual simulation test, the virtual simulation test utilizes the data of the real physical test to improve the accuracy and the credibility of the digital twin simulation system, and meanwhile, the digital twin simulation system provides the functional support of test design, state prediction, result analysis, efficiency evaluation, process control, auxiliary decision and the like for the real physical test. The primary task of digital twinning applications is to create a digital twinning model of the application object. The power grid state monitoring system is generally provided with a plurality of sensors, and how to use the sensors to sense the power grid running condition, real-time performance, environmental parameters and dynamic process data of sudden disturbance is an important basis for further completing the correction of the power grid state monitoring model based on the digital twin technology.
Disclosure of Invention
The invention aims at providing a virtual-real combined power grid sensor model correction method aiming at a digital twin simulation system of a smart power grid. In order to achieve the purpose, the invention discloses a power grid detection model correction method based on a digital twin technology, which comprises the following steps:
step 1: establishing a digital twin technology-oriented power grid monitoring virtual-real combination correction model; the model comprises a real power grid sensor, a sensor digital twin simulation model, a digital twin database and four modules of power grid running states and connection thereof; the power grid master control platform issues instructions to power grid equipment, and the power grid sensor acquires the running condition, the real-time performance, the environmental parameters and the dynamic process data of sudden disturbance in real time and transmits the dynamic process data back to the power grid master control platform; the power grid master control platform sends measurement data to the simulation engine through a datagram socket, and stores the measurement data in a digital twin database which is responsible for maintenance of the simulation engine to obtain a measurement space; the simulation engine operates the sensor digital twin simulation model and the virtual-real combination correction model, the sensor state space is obtained by operating the simulation model and is stored in the digital twin database, and then the measurement data in the measurement space and the state data in the state space are input into the virtual-real combination correction model;
in this model:
(1) Splitting a power grid into a plurality of functional units according to a single functional principle, wherein a state set of a sensor digital twin model of all the power grids at the k moment is X s.k ＝{x s (1),x s (2),...,x s (n k ) -wherein s represents the s-th sensor in the single grid functional unit sensor system, n k For the number of power grid functional units, x s (n k ) Represents the nth k State vector of each power grid functional unit and s-th sensor at k moment, n k And x s (n k ) All are random variables; building for same type of sensor in power grid functional unitModulo the set of states X of the sensor s s.k Abbreviated as X k ＝{x(1),x(2),...,x(n k )}＝{x(i)|i＝1,2,...,n k The state space is x= { X } k |k＝1,2,...}；
(2) Each measurement data is generated by at most one power grid functional unit, and the sensor of each power grid functional unit generates either one measurement data or no measurement data; considering a false alarm process, namely a clutter process, which is independent of a process of generating measurement data by a power grid sensor, and all measurement data are independent of the state of the power grid; the measurement set of the measurement data of the sensor at the moment k is Z k ＝{z(0),z(1),...,z(m k )}＝{z(j)|j＝0,1,2,...,m k }，m k To measure the number of elements of the data set at time k, m k When=0, the null observation is represented, m k And z (m) k ) All are random variables, and the measurement space Z= { Z k |k＝1,2,...}；
Step 2: establishing a digital twin measurement model of a power grid sensor, and considering the detection probability as P D Bernoulli measurement model of (x (i), θ) and independent homodistribution poisson clutter model C K Solving likelihood function g of sensor at k moment k (z (j) |x (i), θ, where θ is the parameter vector θ, X of the digital twin simulation sensor model k Is a state set, Z k For the measurement set, X (i) is X k The i-th element of (a);
step 3: establishing a digital twin simulation model of a power grid, and solving a Markov state transition density function f of a sensor at k moment by considering dynamic running conditions, real-time performance, environmental parameters and sudden disturbance of power grid equipment k|k-1 (X k |X k-1 ,θ)；
Step 4: based on Bayesian estimation method, a state set is solved according to a posterior probability density function of prediction and correction of the measurement set by a sensor measurement set and a state set and combining a measurement model and a digital twin simulation model, then the posterior probability density function of a parameter vector is solved, and a correction value of the parameter vector theta is calculated by a maximum posterior estimation method
Further, a digital twin technology-oriented power grid monitoring virtual-real combination correction model is established, and the specific method for solving the likelihood function of the sensor is as follows:
(1) A sensor measurement model corresponds to a likelihood function g k The sensor observes the running condition, real-time performance, environmental parameters and dynamic process state of sudden disturbance of the power grid, and the sensors of different power grids transmit and collect measurement data to a digital twin database through a power grid master control platform; θ represents a parameter vector including parameters of sensor bias and noise;
(2) For the state x (i) of the single grid functional unit, the detection probability of the sensor is P D (x (i), θ) to generate a measurement data; the missing detection probability is 1-P D (x (i), θ), i.e., no measurement data is generated;
(3) Clutter process C k Obeying the expected value lambda in time C Is spatially distributed subject to a probability density function c (z (j), θ); the time and space distribution represents the distribution of clutter quantity in a certain area in a period of time;
considering missed detection and clutter in the measuring process of the sensor, the random observation data set Z of the sensor k Watch (watch)
Shown as Z k ＝T k (X k )∪C k (1)
Wherein T is k (X k ) Is state X k Generated measurement data, T k (x (i)) is a measurement data set generated by a power grid functional unit sensor corresponding to the state x (i);
assuming that a network functional unit entity generates either a measurement data or no measurement data, T k (x (i)) has the following form:
T k (x(i))＝A∩{Z(x(i))}
wherein, the set A epsilon Z, Z (x (i)) is the output state of the sensor measurement model related to x (i);
uncertainty p of sensor measurement procedure b (a=g), A, G e Z, described by the bernoulli measurement model
Assuming that the clutter process Ck is { c1, …, cM }, m= |ck| is a random non-negative integer, if Ck is a poisson process, the probability distribution of M is
For state set X k It generates a measurement data set Z k Through likelihood function g k (Z k ∣X k θ) description; comprehensively considering influence of sensor output, clutter and noise, likelihood function g k (Z k ∣X k θ) is calculated as
Wherein the method comprises the steps ofIs the state set X k Element subscripts {1,2,., n. k Go to measurement set Z k The element subscripts {0,1,2, m k Correlation function }, of ∈>Representing a set of all associated functions;
further, a power grid detection model based on a digital twin technology is established, and the specific method for solving the Markov state transition density function of the sensor is as follows:
establishing a digital twin simulation model of a power grid, and solving a Markov state transition density function f of a sensor at k moment by considering dynamic running conditions, real-time performance, environmental parameters and sudden disturbance of power grid equipment k|k-1 (X k |X k-1 ,θ)；
(1) The digital twin simulation model of the single sensor adopts an additive model:
X k ＝H(X k-1 )+U k-1 +V k-1 ，U k-1 and V k-1 Respectively representing the sensor deviation and noise at the moment k-1, and the corresponding Markov state transition density function is f k|k-1 (x(n k )|x(n k-1 ) θ) represents that the state at time k-1 is x (n k-1 ) The sensor of (a) has a state x (n k ) Is to be used as a potential for a vehicle; θ represents a parameter vector including parameters of sensor bias and noise;
(2) The state at time k-1 is x (n k-1 ) The probability of the sensor of the power grid functional unit from normal operation to the moment k is simply marked as p s (x(n k ),θ)；
(3) At time k-1 the state is x (n k-1 ) The probability of deriving a new environmental state at time k of the power grid functional unit where the sensor of (2) is located is y k|k-1 (Y k|k-1 (X k-1 )|x(n k-1 ) θ), wherein Y k|k-1 (X k-1 ) A new set of environmental states for the grid sensor; the new set of environmental states at time k is equal to Y k Is y k (Y k ,θ)；
(4) The state set of the normal operation of the power grid functional unit at the moment k is S k|k-1 (X k-1 ) Wherein X is k-1 ＝{x'(1),x'(2),...,x'(n k-1 ) -a }; markov state transition density function f of digital twin simulation model of power grid sensor k∣k-1 (X k ∣X k-1 θ) can be expressed as
In the middle of
Wherein u is 0 (θ) is the initial stageThe expected number of grid functional units at the start time, y 0 (x (i), θ) is its state distribution, u i (X '(i), θ) is the expected number of new grid functional units derived from state X' (i), y (X (i) |x k-1 θ) is its state distribution; element subscripts {1,2, …, u, which are the k-moment state vector x' (i) k Element subscripts {1,2, …, u } to the state vector x' (i) at time-1 } -to k k The association function of } represents a set of all association functions.
Further, the specific method for solving the posterior probability density function of the parameter vector and calculating the correction value of the parameter vector is as follows:
let Z 1:k ＝{Z 1 ,Z 2 ,...,Z k Time series of sensor measurement data is represented by l k|k (X k |Z 1:k θ) represents a posterior probability density function of the state of each power grid functional unit at the k moment; let a posterior probability density function l at time k-1 k-1|k-1 (X k-1 |Z 1:k-1 θ) is known and the measurement data Z accumulated at time k is obtained 1:k The posterior probability density functions of prediction and correction are obtained according to the Bayesian estimation method
l k∣k-1 (X k ∣Z tk-1 ,θ)＝∫f k∣k-1 (X k ∣X k-1 ,θ)l k-1∣k-1 (X k-1 ∣Z 1:k-1 ,θ)dX k-1
The purpose of digital twin simulation-oriented grid sensor correction is to obtain a posterior probability density function p (theta|Z) 1:k ) In possession of a priori information p 0 On the premise of (theta), a Bayesian reasoning method is applied to obtain
Wherein the probability density function p (Z 1:k |θ) is calculated as
Finally, the average value of the posterior probability density function p (theta|Z1: k) is used, and the correction value of the parameter vector theta is calculated by a maximum posterior estimation method to be
Drawings
Fig. 1 is a frame diagram of a digital twin technology-oriented grid monitoring virtual-real combination correction model;
FIG. 2 is a schematic diagram of a digital twin measurement model of a grid sensor according to the present invention;
Detailed Description
The invention is described in further detail below with reference to the drawings and examples.
The invention provides a digital twin simulation-oriented power grid monitoring model correction method, which comprises the following specific implementation steps:
step 1: and establishing a digital twin technology-oriented power grid monitoring virtual-real combination correction model.
As shown in figure 1, the model comprises a real power grid sensor, a sensor digital twin simulation model, a digital twin database, four modules of the power grid running state and connection thereof; the power grid master control platform issues instructions to power grid equipment, and the power grid sensor acquires the running condition, the real-time performance, the environmental parameters and the dynamic process data of sudden disturbance in real time and transmits the dynamic process data back to the power grid master control platform; the power grid master control platform sends measurement data to the simulation engine through a datagram socket, and stores the measurement data in a digital twin database which is responsible for maintenance of the simulation engine to obtain a measurement space Z; the simulation engine operates the sensor digital twin simulation model and the virtual-real combination correction model, the sensor state space X is obtained by operating the simulation model, the sensor state space X is stored in the digital twin database, and then the measurement data in the measurement space and the state data in the state space are input into the virtual-real combination correction model;
in this model:
splitting a power grid into a plurality of functional units according to a single functional principle, wherein a state set of a sensor digital twin model of all the power grids at the k moment is X s.k ＝{x s (1),x s (2),...,x s (n k ) -wherein s represents the s-th sensor in the single grid functional unit sensor system, n k For the number of power grid functional units, x s (n k ) Represents the nth k State vector of each power grid functional unit and s-th sensor at k moment, n k And x s (n k ) All are random variables; state x s (n k ) According to the specific meaning of the sensor s, the dynamic running condition, the real-time performance, the environmental parameter and the sudden disturbance of the power grid equipment are analyzed by the data of the sensor.
Modeling for the same type of sensor in the power grid functional unit, and collecting the state set X of the sensor s s.k Abbreviated as X k ＝{x(1),x(2),...,x(n k )}＝{x(i)|i＝1,2,...,n k The state space is x= { X } k |k＝1,2,...}；
Each measurement data is generated by at most one power grid functional unit, and the sensor of each power grid functional unit generates either one measurement data or no measurement data; considering a false alarm process, namely a clutter process, which is independent of a process of generating measurement data by a power grid sensor, and all measurement data are independent of the state of the power grid; the measurement set of the measurement data of the sensor at the moment k is Z k ＝{z(0),z(1),...,z(m k )}＝{z(j)|j＝0,1,2,...,m k }，m k To measure the number of elements of the data set at time k, m k When=0, the null observation is represented, m k And z (m) k ) All are random variables, and the measurement space Z= { Z k |k＝1,2,...}；
Step 2: establishing a digital twin measurement model of a power grid sensor, and considering the detection probability as P D Bernoulli measurement model of (x (i), θ) and independent homodistribution poisson clutter model C K Solving the sensor at the time kLikelihood function g of (2) k (z (j) |x (i), θ, where θ is the parameter vector θ, X of the digital twin simulation sensor model k Is a state set, Z k For the measurement set, X (i) is X k The i-th element of (a);
a sensor measurement model corresponds to a likelihood function g k The sensor observes the running condition, real-time performance, environmental parameters and dynamic process state of sudden disturbance of the power grid, and the sensors of different power grids transmit and collect measurement data to a digital twin database through a power grid master control platform; θ represents a parameter vector including parameters of sensor bias and noise;
for the state x (i) of the single grid functional unit, the detection probability of the sensor is P D (x (i), θ) to generate a measurement data; the missing detection probability is 1-P D (x (i), θ), i.e., no measurement data is generated;
clutter process C k Obeying the expected value lambda in time C Is spatially distributed subject to a probability density function c (z (j), θ); the time and space distribution represents the distribution of clutter quantity in a certain area in a period of time;
and (4) taking omission and clutter in the measuring process of the sensor into consideration, and establishing a digital twin measuring model of the power grid sensor as shown in figure 2. The model integrates a noise model of the sensor, and can also describe the detection probability, clutter and data transmission noise of the sensor, then the random observation data set Z of the sensor k Represented as
Z k ＝T k (X k )∪C k (1)
Wherein T is k (X k ) Is state X k Generated measurement data, T k (x (i)) is a measurement data set generated by a power grid functional unit sensor corresponding to the state x (i);
assuming that a network functional unit entity generates either a measurement data or no measurement data, T k (x (i)) has the following form:
T k (x(i))＝A∩{Z(x(i))}
wherein, the set A epsilon Z, Z (x (i)) is the output state of the sensor measurement model related to x (i);
uncertainty p of sensor measurement procedure b (a=g), A, G e Z, described by the bernoulli measurement model
Assume clutter process C k Is { c } 1 ,...,c M }，M＝|c k I is a random non-negative integer, if C k For poisson process, the probability distribution of M is
For state set X k It generates a measurement data set Z k Through likelihood function g k (Z k ∣X k θ) description; comprehensively considering influence of sensor output, clutter and noise, likelihood function g k (Z k ∣X k θ) is calculated as
Wherein the method comprises the steps ofIs the state set X k Element subscripts {1,2,., n. k Go to measurement set Z k The element subscripts {0,1,2, m k Correlation function }, of ∈>Representing a set of all associated functions;
step 3: establishing a digital twin simulation model of a power grid, and solving a Markov state transition density function of a sensor at k moment by considering dynamic running conditions, real-time performance, environmental parameters and sudden disturbance of power grid equipmentNumber f k|k-1 (X k |X k-1 ,θ)；
The digital twin simulation model of the single sensor adopts an additive model: x is X k ＝H(X k-1 )+U k-1 +V k-1 ，U k-1 And V k-1 Respectively representing the sensor deviation and noise at the moment k-1, and the corresponding Markov state transition density function is f k|k-1 (x(n k )|x(n k-1 ) θ) represents that the state at time k-1 is x (n k-1 ) The sensor of (a) has a state x (n k ) Is to be used as a potential for a vehicle; θ represents a parameter vector including parameters of sensor bias and noise;
the state at time k-1 is x (n k-1 ) The probability of the sensor of the power grid functional unit from normal operation to the moment k is simply marked as p s (x(n k ),θ)；
At time k-1 the state is x (n k-1 ) The probability of deriving a new environmental state at time k of the power grid functional unit where the sensor of (2) is located is y k|k-1 (Y k|k-1 (X k-1 )|x(n k-1 ) θ), wherein Y k|k-1 (X k-1 ) A new set of environmental states for the grid sensor; the new set of environmental states at time k is equal to Y k Is y k (Y k ,θ)；
The state set of the normal operation of the power grid functional unit at the moment k is S k|k-1 (X k-1 ) Wherein X is k-1 ＝{x'(1),x'(2),...,x'(n k-1 )}；
Markov state transition density function f of digital twin simulation model of power grid sensor k∣k-1 (X k ∣X k-1 θ) can be expressed as
In the middle of
Step 4:based on Bayesian estimation method, a state set is solved according to a posterior probability density function of prediction and correction of the measurement set by a sensor measurement set and a state set and combining a measurement model and a digital twin simulation model, then the posterior probability density function of a parameter vector is solved, and a correction value of the parameter vector theta is calculated by a maximum posterior estimation method
Let Z 1:k ＝{Z 1 ,Z 2 ,...,Z k Time series of sensor measurement data is represented by l k|k (X k |Z 1:k θ) represents a posterior probability density function of the state of each power grid functional unit at the k moment; let a posterior probability density function l at time k-1 k-1|k-1 (X k-1 |Z 1:k-1 θ) is known and the measurement data Z accumulated at time k is obtained 1:k The posterior probability density functions of prediction and correction are obtained according to the Bayesian estimation method
l k∣k-1 (X k ∣Z tk-1 ,θ)＝∫f k∣k-1 (X k ∣X k-1 ,θ)l k-1∣k-1 (X k-1 ∣Z 1:k-1 ,θ)dX k-1
The purpose of digital twin simulation-oriented grid monitoring correction is to obtain a posterior probability density function p (theta|Z) 1:k ) In possession of a priori information p 0 On the premise of (theta), a Bayesian reasoning method is applied to obtain
Finally, a posterior probability density function p (θ|Z is used 1:k ) And calculating the correction value of the parameter vector theta by a maximum a posteriori estimation methodIs that
What is not described in detail in the present invention belongs to the prior art known to those skilled in the art.
Claims (5)
1. A power grid monitoring model correction method based on a digital twin technology is characterized by comprising the following steps:
step 1: establishing a digital twin technology-oriented power grid monitoring virtual-real combination correction model; the model comprises a real power grid sensor, a sensor digital twin simulation model, a digital twin database and four modules of power grid running states and connection thereof; the power grid master control platform issues instructions to power grid equipment, and the power grid sensor acquires the running condition, the real-time performance, the environmental parameters and the dynamic process data of sudden disturbance in real time and transmits the dynamic process data back to the power grid master control platform; the power grid master control platform sends measurement data to the simulation engine through a datagram socket, and stores the measurement data in a digital twin database which is responsible for maintenance of the simulation engine to obtain a measurement space Z; the simulation engine operates the sensor digital twin simulation model and the virtual-real combination correction model, the sensor state space X is obtained by operating the simulation model, the sensor state space X is stored in the digital twin database, and then the measurement data in the measurement space and the state data in the state space are input into the virtual-real combination correction model;
step 2: establishing a digital twin measurement model of a power grid sensor, and considering the detection probability as P D Bernoulli measurement model of (x (i), θ) and independent homodistribution poisson clutter model C K Solving likelihood function g of sensor at k moment k (z (j) |x (i), θ, where θ is the parameter vector θ, X of the digital twin simulation sensor model k Is a state set, Z k For the measurement set, X (i) is X k The i-th element of (a);
step 3: establishing a digital twin simulation model of a power grid, and solving a Markov state transition density function f of a sensor at k moment by considering dynamic running conditions, real-time performance, environmental parameters and sudden disturbance of power grid equipment k|k-1 (X k |X k-1 ,θ)；
Step 4: based on Bayesian estimation method, a state set is solved according to a posterior probability density function of prediction and correction of the measurement set by a sensor measurement set and a state set and combining a measurement model and a digital twin simulation model, then the posterior probability density function of a parameter vector is solved, and a correction value of the parameter vector theta is calculated by a maximum posterior estimation method。
2. The digital twin technology-oriented grid monitoring virtual-real combination correction model according to claim 1, comprising the following specific steps:
step 1-1: splitting a power grid into a plurality of functional units according to a single functional principle, wherein a state set of a sensor digital twin model of all the power grids at the k moment is X s.k ＝{x s (1),x s (2),...,x s (n k ) -wherein s represents the s-th sensor in the single grid functional unit sensor system, n k For the number of power grid functional units, x s (n k ) Represents the nth k State vector of each power grid functional unit and s-th sensor at k moment, n k And x s (n k ) All are random variables; modeling for the same type of sensor in the power grid functional unit, and collecting the state set X of the sensor s s.k Abbreviated as X k ＝{x(1),x(2),...,x(n k )}＝{x(i)|i＝1,2,...,n k The state space is x= { X } k |k＝1,2,...}；
Step 1-2: each measurement data is generated by at most one power grid function unit, and the sensor of each power grid function unit is eitherGenerating a measurement data or not generating the measurement data; considering a false alarm process, namely a clutter process, which is independent of a process of generating measurement data by a power grid sensor, and all measurement data are independent of the state of the power grid; the measurement set of the measurement data of the sensor at the moment k is Z k ＝{z(0),z(1),...,z(m k )}＝{z(j)|j＝0,1,2,...,m k }，m k To measure the number of elements of the data set at time k, m k When=0, the null observation is represented, m k And z (m) k ) All are random variables, and the measurement space Z= { Z k |k＝1,2,...}。
3. The method for establishing a digital twin measurement model of a power grid sensor according to claim 1, solving a likelihood function of the sensor, comprising the following specific steps:
step 2-1: a sensor measurement model corresponds to a likelihood function g k The sensor observes the running condition, real-time performance, environmental parameters and dynamic process state of sudden disturbance of the power grid, and the sensors of different power grids transmit and collect measurement data to a digital twin database through a power grid master control platform; θ represents a parameter vector including parameters of sensor bias and noise;
step 2-2: for the state x (i) of the single grid functional unit, the detection probability of the sensor is P D (x (i), θ) to generate a measurement data; the missing detection probability is 1-P D (x (i), θ), i.e., no measurement data is generated;
step 2-3: clutter process C k Obeying the expected value lambda in time C Is spatially distributed subject to a probability density function c (z (j), θ); the time and space distribution represents the distribution of clutter quantity in a certain area in a period of time;
considering missed detection and clutter in the measuring process of the sensor, the random observation data set Z of the sensor k Represented as
Z k ＝T k (X k )∪C k (1)
Wherein T is k (X k ) Is state X k Generated measurement data, T k (x (i)) is a measurement data set generated by a power grid functional unit sensor corresponding to the state x (i);
assuming that a network functional unit entity generates either a measurement data or no measurement data, T k (x (i)) has the following form:
T k (x(i))＝A∩{Z(x(i))}
wherein, the set A epsilon Z, Z (x (i)) is the output state of the sensor measurement model related to x (i);
uncertainty p of sensor measurement procedure b (a=g), A, G e Z, described by the bernoulli measurement model
Assume clutter process C k Is { c } 1 ,...,c M }，M＝|c k I is a random non-negative integer, if C k For poisson process, the probability distribution of M is
For state set X k It generates a measurement data set Z k Through likelihood function g k (Z k ∣X k θ) description; comprehensively considering influence of sensor output, clutter and noise, likelihood function g k (Z k ∣X k θ) is calculated as
Wherein the method comprises the steps ofIs the state set X k Element subscripts {1,2,., n. k Go to measurement set Z k The element subscripts {0,1,2, m k Correlation function }, of ∈>Representing a set of all associated functions.
4. The method for establishing a digital twin simulation model of a power grid, which solves a markov state transition density function of a sensor according to claim 1, comprises the following specific steps:
step 3-1: the digital twin simulation model of the single sensor adopts an additive model: x is X k ＝H(X k-1 )+U k-1 +V k-1 ，U k-1 And V k-1 Respectively representing the sensor deviation and noise at the moment k-1, and the corresponding Markov state transition density function is f k|k-1 (x(n k )|x(n k-1 ) θ) represents that the state at time k-1 is x (n k-1 ) The sensor of (a) has a state x (n k ) Is to be used as a potential for a vehicle; θ represents a parameter vector including parameters of sensor bias and noise;
step 3-2: the state at time k-1 is x (n k-1 ) The probability of the sensor of the power grid functional unit from normal operation to the moment k is simply marked as p s (x(n k ),θ)；
Step 3-3: at time k-1 the state is x (n k-1 ) The probability of deriving a new environmental state at time k of the power grid functional unit where the sensor of (2) is located is y k|k-1 (Y k|k-1 (X k-1 )|x(n k-1 ) θ), wherein Y k|k-1 (X k-1 ) A new set of environmental states for the grid sensor; the new set of environmental states at time k is equal to Y k Is y k (Y k ,θ)；
Step 3-4: the state set of the normal operation of the power grid functional unit at the moment k is S k|k-1 (X k-1 ) Wherein
X k-1 ＝{x'(1),x'(2),...,x'(n k-1 )}；
Markov state transition density function f of digital twin simulation model of power grid sensor k∣k-1 (X k ∣X k-1 θ) can be expressed as
In the middle of
5. The method for solving the posterior probability density function of the parameter vector according to claim 1, and calculating the correction value of the parameter vector, comprising the steps of:
let Z 1:k ＝{Z 1 ,Z 2 ,...,Z k Time series of sensor measurement data is represented by l k|k (X k |Z 1:k θ) represents a posterior probability density function of the state of each power grid functional unit at the k moment; let a posterior probability density function l at time k-1 k-1|k-1 (X k-1 |Z 1:k-1 θ) is known and the measurement data Z accumulated at time k is obtained 1:k The posterior probability density functions of prediction and correction are obtained according to the Bayesian estimation method
The purpose of digital twin simulation-oriented grid monitoring correction is to obtain a posterior probability density function p (theta|Z) 1:k ) In possession of a priori information p 0 On the premise of (theta), a Bayesian reasoning method is applied to obtain
Finally, a posterior probability density function p (θ|Z is used 1:k ) And calculating the correction value of the parameter vector theta by a maximum a posteriori estimation methodIs that
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