Distributed state estimation method for multi-region power system

Abstract

The invention discloses a distributed state estimation method for a multi-region power system, and the method comprises the steps: constructing a mathematical model of a target multi-region power system, and obtaining the real-time data of the target multi-region power system through a sensor; transmitting the real-time data to a relay node through a transmission mechanism based on a preset bit number, and controlling the relay node to transmit the real-time data received by the relay node to an area estimator through the transmission mechanism based on the preset bit number; and controlling the region estimator to evaluate the power system parameters of the target multi-region power system according to the mathematical model and the real-time data received by the region estimator through a preset distributed estimation algorithm. Relay node optimization communication topology is introduced, based on the energy limitation characteristic, the bit number is preferentially processed, and the communication energy consumption is effectively reduced while the estimation performance is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power system estimation, and particularly to a distributed state estimation method for a multi - area power system. Background Art

[0002] With the increase in the proportion of new - energy power generation and the large - scale access of power - electronic devices, modern power systems are gradually developing towards a highly interconnected and distributed collaborative direction. This transformation has changed the operation mode and control method of power systems, and put forward higher requirements for the accurate estimation of system states. However, traditional centralized state - estimation methods face problems such as large communication overhead, heavy computational burden, and insufficient real - time performance in multi - area power systems, and it is difficult to meet the requirements of multi - area power systems for efficient and accurate state estimation. Therefore, distributed state - estimation methods have emerged, which improve the accuracy of state estimation and the operation efficiency of the system through information interaction between regions.

[0003] In the framework of distributed state estimation, sensor nodes rely on wireless communication networks to transmit measurement data. However, since sensors are usually powered by batteries, their energy - limited characteristics will affect the data - transmission frequency and quality, and thus affect the accuracy of state estimation. Existing research mainly focuses on the sensor - transmission strategy from sensors to estimators within a region, and lacks in - depth research on the data - transmission mechanism of cross - region data in multi - area power systems. In multi - area power systems, to overcome the energy - limited problems of sensors within each region and improve the estimation performance of multi - area power systems, it is necessary not only to optimize the data - transmission mechanism within the region, but also to reasonably design the data - interaction and transmission strategy between regions to improve the stability of multi - area power systems. In the state estimation of multi - area power systems that rely on sensor communication, the number of bits of transmitted data directly affects communication energy consumption. The higher the number of bits, the larger the data volume, the higher the state - estimation accuracy, but the energy consumption increases, shortening the sensor life. However, the lower the number of bits, although the energy consumption is reduced, it may lead to an increase in quantization error, affecting the state - estimation performance. Summary of the Invention

[0004] Based on this, in view of the technical problems such as large errors in estimation results caused by the failure to fully consider the energy - limited characteristics of battery - powered sensors in the prior art, a distributed state - estimation method for a multi - area power system is proposed.

[0005] In a first aspect, a distributed state - estimation method for a multi - area power system is provided, and the method includes: Construct a mathematical model of the target multi - area power system, and obtain real - time data of the target multi - area power system through sensors; Transmit the real-time data to the relay node through a transmission mechanism based on a preset number of bits, and control the relay node to transmit the relay data received by the relay node to the area estimator through a transmission mechanism based on a preset number of bits, where the preset number of bits is the optimal transmission bit number determined with the goal of minimizing the estimation error covariance and with energy consumption as a constraint condition; Control the area estimator to evaluate the power system parameters of the target multi-area power system according to the mathematical model and the target data received by the area estimator through a preset distributed estimation algorithm.

[0006] Optionally, the steps of constructing the mathematical model of the target multi-area power system include: Construct a first mathematical model of the generator subsystem in the target multi-area power system, construct a second mathematical model of the excitation subsystem in the target multi-area power system, and construct a third mathematical model of the transmission line subsystem in the target multi-area power system; Construct the mathematical model of any area power system in the target multi-area power system according to the first mathematical model, the second mathematical model, and the third mathematical model, where the mathematical representation of the mathematical model is:

[0007] where, represents the system parameter matrix of the th area, is the input matrix of the area, is the adjacency matrix of the area. The system state , consists of the rotor angular velocity deviation , the power flow deviation from node i to node j , the mechanical power deviation , and the turbine valve position deviation , represents the set of areas connected to the th area through the connection line.

[0008] Optionally, the steps of transmitting the real-time data to the relay node through a transmission mechanism based on a preset number of bits include: Map each dimension data of the real-time data to a first preset quantization area to obtain first quantization data; Transmit the first quantization data to the relay node through a transmission mechanism based on a preset number of bits.

[0009] Optionally, the relay data received by the relay node is expressed as , where the received data is specifically expressed as , and represents the observed data with quantization error, is the transmission error. is the probability used to characterize that the sensor successfully sends the quantization data to the relay node, and its mathematical formula is expressed as:

[0010] wherein, is a parameter related to the channel state, is the energy consumption of the sensor, is the noise power spectral density, is the communication channel bandwidth, and satisfies .

[0011] Optionally, the step of controlling the relay node to transmit the relay data received by the relay node to the area estimator through a transmission mechanism based on a preset number of bits includes: Mapping each dimension data of the relay data to a second preset quantization area to obtain second quantization data; Transmitting the second quantization data to the area estimator through a transmission mechanism based on a preset number of bits.

[0012] Optionally, the target data received by the area estimator is expressed as , and its specific expression is , and represents the transmission data of the relay node with quantization error, expressed as . is the probability that the area estimator successfully receives data, and the specific expression is: , wherein, is a parameter related to the channel state, is the energy required by the relay node, is the noise power spectral density, is the communication channel bandwidth, and satisfies

[0013] Optionally, before the step of transmitting the real-time data to the relay node through a transmission mechanism based on a preset number of bits and controlling the relay node to transmit the relay data received by the relay node to the area estimator through a transmission mechanism based on a preset number of bits, it further includes: Under the transmission mechanism based on the number of bits, the energy consumed by the sensor is , and the specific expression is:

[0014] wherein, wherein, is the symbol rate of transmission, is the path loss, is the path loss exponent, is a constant determined by the receiver noise figure and the surface thermal noise spectral density, is the flipping probability of the transmitted data, represents the number of bits used by the sensor data in the quantization process.

[0015] The energy consumed by the relay node is:

[0016] The energy consumption relationship with the coupling relationship between the sensor and the relay node is constructed as:

[0017] With the goal of minimizing the estimation error covariance and the energy consumption as the constraint condition, then:

[0018] Solve through a preset iterative method to obtain the preset number of bits.

[0019] Optionally, the step of solving through a preset iterative method to obtain the preset number of bits includes: Perform random initialization processing on to obtain initialization data, and construct a Lagrangian function based on the initialization data; Calculate the gradient and constraint gradient of the Lagrangian function, and update the variables and Lagrange multipliers in the Lagrangian function according to the gradient and the constraint gradient to obtain the target Lagrangian function; Judge whether the target Lagrangian function meets the preset conditions. If the target Lagrangian function does not meet the preset conditions and the upper limit of the iteration times is not reached, continue the step of performing random initialization processing on to obtain initialization data and construct a Lagrangian function based on the initialization data until the target Lagrangian function meets the preset conditions or reaches the upper limit of the iteration times, and obtain the preset number of bits based on the target Lagrangian function that meets the preset conditions or reaches the upper limit of the iteration times.

[0020] Optionally, the mathematical representation of the preset distributed estimation algorithm is:

[0021] On the other hand, the present application provides a multi-region power system distributed state estimation device, and the device includes: A data acquisition module, configured to construct a mathematical model of a target multi - area power system and obtain real - time data of the target multi - area power system through sensors; A data transmission module, configured to transmit the real - time data to a relay node through a transmission mechanism based on a preset number of bits, and control the relay node to transmit the relay data received by the relay node to a regional estimator through a transmission mechanism based on a preset number of bits. The preset number of bits is the optimal transmission bit number determined with the goal of minimizing the estimation error covariance and with energy consumption as a constraint condition; A calculation module, configured to control the regional estimator to evaluate the power system parameters of the target multi - area power system according to the mathematical model and the target data received by the regional estimator through a preset distributed estimation algorithm.

[0022] In a third aspect, a computer - readable storage medium is provided. The computer - readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above - mentioned distributed state estimation method for a multi - area power system are implemented.

[0023] In this application, a mathematical model of a target multi - area power system is constructed, and real - time data of the target multi - area power system is obtained through sensors; the real - time data is transmitted to a relay node through a transmission mechanism based on a preset number of bits, and the relay node is controlled to transmit the relay data received by the relay node to a regional estimator through a transmission mechanism based on a preset number of bits. The preset number of bits is the optimal transmission bit number determined with the goal of minimizing the estimation error covariance and with energy consumption as a constraint condition; the regional estimator is controlled to evaluate the power system parameters of the target multi - area power system according to the mathematical model and the target data received by the regional estimator through a preset distributed estimation algorithm. By introducing relay nodes, a topological structure that better conforms to the actual power system communication process is constructed, the reliability and coverage of data transmission are improved, the state estimation accuracy and robustness are enhanced, the data transmission requirements within and between regions are comprehensively considered, and an optimized bit - number transmission mechanism is realized through the preset number of bits, improving the energy utilization efficiency, thereby enhancing the reliability and operation efficiency of the power system. Description of the Drawings

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0025] Among them: Figure 1Flow chart of the distributed state estimation method for a multi - area power system in one embodiment; Figure 2 Flow chart of the distributed state estimation method for a multi - area power system in another embodiment; Figure 3 Application scenario diagram of the distributed state estimation method for a multi - area power system in one embodiment; Figure 4 Flow chart for solving the preset number of bits in the distributed state estimation method for a multi - area power system in one embodiment; Figure 5 Structural block diagram of the distributed state estimation device for a multi - area power system in one embodiment; Figure 6 Structural block diagram of a computer device in one embodiment; Figure 7 Structural block diagram of a computer device in another embodiment. Detailed implementation manners

[0026] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0027] The present invention will be described in detail through specific embodiments below.

[0028] Please refer to Figure 1 as shown Figure 1 A flow schematic diagram of the distributed state estimation method for a multi - area power system provided by an embodiment of the present invention includes the following steps: S101. Construct a mathematical model of the target multi - area power system and obtain real - time data of the target multi - area power system through sensors; S102. Transmit the real - time data to a relay node through a transmission mechanism based on a preset number of bits, and control the relay node to transmit the relay data received by the relay node to a regional estimator through a transmission mechanism based on a preset number of bits. The preset number of bits is the optimal transmission number of bits determined with the goal of minimizing the estimation error covariance and with energy consumption as a constraint condition; S103. Control the regional estimator to evaluate the power system parameters of the target multi - area power system according to the mathematical model and the target data received by the regional estimator through a preset distributed estimation algorithm.

[0029] Exemplarily, as Figure 2As shown in the figure, modeling a multi - area power system composed of a generator, an excitation system, and a transmission line is equivalent to constructing a mathematical model of the target multi - area power system; sensors collect data of the power system in real - time and transmit the data to relay nodes using a transmission mechanism based on the number of bits, which is equivalent to obtaining real - time data of the target multi - area power system through sensors and transmitting the real - time data to relay nodes through a transmission mechanism based on a preset number of bits; relay nodes receive data from sensors and perform pre - processing operations, and transmit data to the regional estimator using a transmission mechanism based on the number of bits, which is equivalent to controlling the relay nodes to transmit the relay data received by the relay nodes to the regional estimator through a transmission mechanism based on a preset number of bits; the regional estimator receives data from relay nodes, performs state estimation using a distributed estimation algorithm, and calculates the estimation error covariance, which is equivalent to controlling the regional estimator to evaluate the power system parameters of the target multi - area power system according to the mathematical model and the target data received by the regional estimator through a preset distributed estimation algorithm; constructing an optimization problem with minimizing the estimation error covariance as the objective and energy consumption as the constraint condition, and determining the optimal number of transmission bits, which is equivalent to solving through a preset iterative method with minimizing the estimation error covariance as the objective and energy consumption as the constraint condition to obtain the preset number of bits.

[0030] Exemplarily, as Figure 3 shown, the application scenarios of the distributed state estimation method for multi - area power systems include Area 1, Area 2, Area 3, and Area 4. Real - time data of Areas 1 - 4 are obtained through sensors, and then the real - time data are transmitted to relay nodes through sensors. Further, the relay nodes transmit the received data to the estimator.

[0031] In a possible implementation manner, the step of constructing the mathematical model of the target multi - area power system includes: Constructing a first mathematical model of the generator subsystem in the target multi - area power system, constructing a second mathematical model of the excitation subsystem in the target multi - area power system, and constructing a third mathematical model of the transmission line subsystem in the target multi - area power system; According to the first mathematical model, the second mathematical model, and the third mathematical model, constructing the mathematical model of any regional power system in the target multi - area power system, where the mathematical representation of the mathematical model is:

[0032] Among them, represents the system parameter matrix of the th region, is the input matrix of the region, is the adjacency matrix of the region. is the state of the i - th region, is the control matrix, represents the set of areas connected to the area through the connection line.

[0033] Exemplarily, consider that a multi-area interconnected power system mainly consists of generators, excitation systems, and transmission lines. Specifically, the linearized generator dynamics of the th area can be expressed by the following differential equation:

[0034] where, represents the generator rotor angle deviation of the th area, is the angular velocity deviation of the generator rotor in the th area. is the inertia coefficient of the th area. is the damping coefficient of the th area. is the mechanical power deviation, is the load power deviation, is the power flow deviation on the tie line from area to area .

[0035] The excitation system mainly consists of an exciter, a regulator, and related control devices, and its function is to provide excitation current for the generator to regulate the voltage and enhance the system stability. Then, the corresponding differential dynamics of the excitation system satisfy:

[0036] where, is the input control signal of area , is the speed regulation of the th area. is the time delay of the non-reheat steam turbine in the th area. is the governor time constant of the th area. is the electric power of the th area.

[0037] In a multi-area power system, assuming that there is no active power loss on the tie line, area and area are connected by a tie line with reactance . The power exchange relationship of the system can be described by the following power flow equation , where, is the area and the area output voltage, is the area and the area equivalent reactance between, are respectively the area and the area voltage phase angle. Linearizing the initial operating point, we can obtain , where is the synchronous torque coefficient.

[0038] Based on the above generator system, excitation system and transmission line model, the continuous-time state-space model of the th area can be expressed as

[0039] where represents the system state matrix of the th area, is the input matrix of the area, is the adjacency matrix of the area. is the state of the th area, is the control matrix, represents the set of areas connected to the , ,

[0040] By selecting an appropriate sampling time , and using a first-order hold discretization method, the power system model of the th area can be transformed into a normalized discrete-time system model:

[0041] where , , . is a Gaussian noise vector with a mean of 0 and a covariance of .

[0042] At time , sensors are deployed in each th area to measure the state , and the measurement equation can be written as , where is the measurement matrix of the sensor deployed in the th area, The mean is 0 and the covariance is Gaussian noise vector.

[0043] In a possible implementation manner, the step of transmitting the real-time data to the relay node through a transmission mechanism based on a preset number of bits includes: Mapping each dimension of the real-time data to a first preset quantization region to obtain first quantized data; The first quantized data is transmitted to the relay node through a transmission mechanism based on a preset number of bits.

[0044] In a possible implementation manner, the relay data received by the relay node is represented as ,in It is used to characterize the probability that the sensor successfully sends the quantitative data to the relay node. The mathematical representation of is: , in, is a parameter related to the channel status. is the energy consumption of the sensor, is the noise power spectral density, is the communication channel bandwidth, is the transmission error and satisfies .

[0045] For example, for the area Sensor measurements (real-time data) , each dimension of data will be mapped to a preset quantization region (the first preset quantization region), and the first quantization region is specifically represented as .

[0046] Specifically, yes No. dimensional data, and is a given scalar. During the transmission process, the measured value In the form of bits to the area The specific bit number is in the form of , Each bit is

[0047] is the quantitative level, and is the number of bits used by the sensor to transmit data (predetermined number of bits). Due to the influence of factors such as signal attenuation, multipath effect, and noise in the wireless channel, packet loss is inevitable, resulting in the data sent by the sensor may not be successfully transmitted to the relay node. Therefore, in the transmission mechanism based on the number of bits, the data received by the relay node is , where , is the probability that the sensor successfully sends the data to the relay node, and its mathematical description is: , where, is a parameter related to the channel state, is the energy consumption of the sensor, is the noise power spectral density, is the communication channel bandwidth. is the transmission error, and satisfies

[0048] In a possible implementation manner, the step of controlling the relay node to transmit the relay data received by the relay node to the area estimator through the transmission mechanism based on the preset number of bits includes: Mapping each dimension data of the relay data to a second preset quantization region to obtain second quantization data; Transmitting the second quantization data to the area estimator through the transmission mechanism based on the preset number of bits.

[0049] In a possible implementation manner, the target data received by the area estimator is represented as , and its specific representation is , and represents the transmission data of the relay node with quantization error, which is represented as . is the probability that the area estimator successfully receives the data, and the specific representation is: , where, is a parameter related to the channel state, is the energy required by the relay node, is the noise power spectral density, is the communication channel bandwidth, and satisfies

[0050] Exemplarily, similar to the transmission mechanism of the sensor, the relay node will receive data (relay data) Each dimensional measurement value is quantized in a region (the second preset quantization region), and the second preset quantization region is

[0051] Specifically, is the -dimensional data, and is a given scalar. The relay data sends data to the region estimator in the form of . Specifically, , is the quantizer level, and is the number of bits used by the relay node to transmit data. Under the relay transmission mechanism based on the number of bits, the data received by the remote estimator is .

[0052] In a possible implementation manner, before the step of transmitting the real-time data to the relay node through the transmission mechanism based on the preset number of bits and controlling the relay node to transmit the relay data received by the relay node to the region estimator through the transmission mechanism based on the preset number of bits, it further includes: Under the transmission mechanism based on the number of bits, the energy consumed by the sensor is , specifically expressed as:

[0053] wherein, wherein, is the transmission symbol rate, is the path loss, is the path loss exponent, is a constant determined by the receiver noise figure and the surface thermal noise spectral density, is the flipping probability of the transmitted data, represents the number of bits used by the sensor data in the quantization process.

[0054] Furthermore, the energy consumed by the relay node is:

[0055] where represents the number of bits used by the relay node.

[0056] Construct the energy consumption relationship in which the sensor and the relay node have a coupling relationship as:

[0057] and is the upper limit of energy consumption.

[0058] With the goal of minimizing the estimation error covariance and the energy consumption as the constraint, then:

[0059] Solve it through the preset iterative method to obtain the preset number of bits.

[0060] Exemplarily, in the transmission mechanism based on the number of bits, the energy consumed by the sensor is , expressed as:

[0061] Among them, among them, is the transmission symbol rate, is the path loss, is the path loss exponent, is a constant determined by the receiver noise figure and the surface thermal noise spectral density, is the flip probability of the transmitted data, represents the number of bits used by the sensor data in the quantization process.

[0062] The energy consumed by the relay node is:

[0063] Among them represents the number of bits used by the relay node.

[0064] Construct the energy consumption relationship in which the sensor and the relay node have a coupling relationship, as follows:

[0065] And is the upper limit of energy consumption.

[0066] In the case of energy constraints, the sensor and the relay node may use low-power transmission signals to reduce energy consumption, which will undoubtedly lead to signal fading or packet loss. Through the energy optimization strategy, reasonable numbers of bits are allocated to the sensor and the relay node, which not only reduces the energy consumption, but also reduces the packet loss probability to a certain extent, thus ensuring the stability and accuracy of the estimator. Therefore, with the goal of minimizing the estimation error covariance and the energy consumption as the constraint, the following optimization problem is established:

[0067] The constraint conditions are further expanded as:

[0068] The constraint problem is further transformed into:

[0069] In a possible implementation, the step of obtaining a preset number of bits by solving through a preset iterative method includes: Perform Random initialization processing to obtain initialization data, and construct a Lagrangian function based on the initialization data; Calculate the gradient and constraint gradient of the Lagrangian function, and update the variables and Lagrange multipliers in the Lagrangian function according to the gradient and the constraint gradient to obtain a target Lagrangian function; Judge whether the target Lagrangian function meets the preset conditions. If the target Lagrangian function does not meet the preset conditions and the upper limit of the iteration times is not reached, then continue to perform , Random initialization processing to obtain initialization data, and the step of constructing a Lagrangian function based on the initialization data until the target Lagrangian function meets the preset conditions or reaches the upper limit of the iteration times, and obtain the preset number of bits based on the target Lagrangian function that meets the preset conditions or reaches the upper limit of the iteration times.

[0070] Exemplarily, due to the combination of matrix inversion and inverse operation, the entire objective function is non-convex. Therefore, the global optimal solution cannot be directly obtained by analytical derivation and setting the gradient to zero. Moreover, the constraint involves the non-linear summation of all variables and does not meet the form of a standard convex optimization problem, making it impossible to directly solve using the classical Lagrange multiplier method. Furthermore, find a suitable Figure 4 through the preset iterative method as shown in .

[0071] Specifically, through the formula:

[0072] The gradient calculation formula is:

[0073] Let

[0074]

[0075]

[0076] Where: , is the unit diagonal matrix, taking 1 at the th position and 0 at the remaining positions.

[0077]

[0078] Through the above algorithm, appropriate numbers of bits (predetermined numbers of bits) are determined for the sensors and relay nodes.

[0079] In one possible implementation, the mathematical representation of the predetermined distributed estimation algorithm is:

[0080] Exemplarily, for each area , the designed distributed estimator (predetermined distributed estimation algorithm) is

[0081] Construct the evolution equation of the estimation error of the constructed area ,

[0082] Stack from to to construct the estimation error

[0083] of the multi-area power system, Specific symbol representation is:

[0084] Derive the expected estimation error covariance ,

[0085] where:

[0086] To minimize the estimation error covariance, the estimator gain can be determined by the following formula

[0087] Based on the determined estimator gain above, the minimum estimation error covariance can be expressed as

[0088] Set zero initial conditions , and using mathematical induction, for , assume holds. Through recursive calculation, it is derived that:

[0089] Since , therefore we can obtain ​From the above analysis, it can be seen that the estimation error covariance is monotonically increasing under zero initial conditions and satisfies the relationship: .

[0090] Furthermore, under arbitrary initial conditions, that is , it is derived that

[0091] Since , when , .

[0092] In summary, it can be known that: if there exists an estimator gain such that is mean-square stable. Then the estimation error covariance is bounded.

[0093] On the other hand, as Figure 5 shown, the present application provides a multi-region power system distributed state estimation device, and the device includes: A data acquisition module 201, configured to construct a mathematical model of the target multi-region power system and obtain real-time data of the target multi-region power system through sensors; A data transmission module 202, configured to transmit the real-time data to a relay node through a transmission mechanism based on a preset number of bits, and control the relay node to transmit the relay data received by the relay node to a regional estimator through a transmission mechanism based on a preset number of bits, where the preset number of bits is an optimal transmission bit number determined with the goal of minimizing the estimation error covariance and with energy consumption as a constraint condition; A calculation module 203, configured to control the regional estimator to evaluate the power system parameters of the target multi-region power system according to the mathematical model and the target data received by the regional estimator through a preset distributed estimation algorithm.

[0094] In one embodiment, a computer device is provided. This computer device can be a server, and its internal structure diagram can be as Figure 6 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client through a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the server side of a multi-region power system distributed state estimation method.

[0095] In one embodiment, a computer device is provided. The computer device can be a client, and its internal structural diagram can be as follows Figure 7 shown. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server through a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the client side of a multi-region power system distributed state estimation method.

[0096] In one embodiment, a computer device is proposed, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented: constructing a mathematical model of a target multi-region power system, and acquiring real-time data of the target multi-region power system through sensors; transmitting the real-time data to a relay node through a transmission mechanism based on a preset number of bits, and controlling the relay node to transmit the relay data received by the relay node to a regional estimator through a transmission mechanism based on a preset number of bits. The preset number of bits is the optimal transmission bit number determined with the goal of minimizing the estimation error covariance and with energy consumption as a constraint condition; controlling the regional estimator to evaluate the power system parameters of the target multi-region power system according to the mathematical model and the target data received by the regional estimator through a preset distributed estimation algorithm.

[0097] In one embodiment, a computer-readable storage medium is proposed. The computer-readable storage medium stores a computer program. When the computer program is executed by the processor, the following steps are implemented: constructing a mathematical model of a target multi-region power system, and acquiring real-time data of the target multi-region power system through sensors; transmitting the real-time data to a relay node through a transmission mechanism based on a preset number of bits, and controlling the relay node to transmit the relay data received by the relay node to a regional estimator through a transmission mechanism based on a preset number of bits. The preset number of bits is the optimal transmission bit number determined with the goal of minimizing the estimation error covariance and with energy consumption as a constraint condition; controlling the regional estimator to evaluate the power system parameters of the target multi-region power system according to the mathematical model and the target data received by the regional estimator through a preset distributed estimation algorithm.

[0098] It should be noted that for the functions or steps that can be achieved by the above computer-readable storage medium or computer device, reference can be made to the relevant descriptions on the server side and the client side in the foregoing method embodiments. To avoid repetition, they will not be described in detail here.

[0099] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0100] Those skilled in the art can clearly understand that for the sake of convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0101] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A distributed state estimation method for a multi-region power system, characterized in that, The method includes: Constructing a mathematical model of the target multi - area power system and obtaining real - time data of the target multi - area power system through sensors; Transmitting the real - time data to a relay node through a transmission mechanism based on a preset number of bits, and controlling the relay node to transmit the relay data received by the relay node to a regional estimator through a transmission mechanism based on a preset number of bits, where the preset number of bits is the optimal transmission bit number determined with the goal of minimizing the estimation error covariance and with energy consumption as a constraint condition; Controlling the regional estimator to evaluate the power system parameters of the target multi - area power system according to the mathematical model and the target data received by the regional estimator through a preset distributed estimation algorithm.

2. The distributed state estimation method for a multi-region power system according to claim 1, wherein The step of constructing the mathematical model of the target multi - area power system includes: Constructing a first mathematical model of the generator subsystem in the target multi - area power system, constructing a second mathematical model of the excitation subsystem in the target multi - area power system, and constructing a third mathematical model of the transmission line subsystem in the target multi - area power system; Constructing a mathematical model of any regional power system in the target multi - area power system according to the first mathematical model, the second mathematical model, and the third mathematical model, where the mathematical representation of the mathematical model is: Among them, represents the system state matrix of the area numbered , is the input matrix of the area, is the adjacency matrix of the area, is the state of the i-th area, is the control matrix, represents the set of areas connected to the area through connection lines.

3. A distributed state estimation method for a multi-region power system according to claim 1, characterized in that, The step of transmitting the real - time data to the relay node through a transmission mechanism based on a preset number of bits includes: Mapping each dimension data of the real - time data to a first preset quantization region to obtain first - quantized data; Transmitting the first - quantized data to the relay node through a transmission mechanism based on a preset number of bits.

4. A distributed state estimation method for a multi-region power system according to claim 1, characterized in that The relay data received by the relay node is represented as , where the received data is specifically represented as , and represents the observed data with quantization error, is the transmission error, is the probability used to characterize the successful transmission of the quantization data from the sensor to the relay node, The mathematical formula of is expressed as: Among them, is a parameter related to the channel state, is the energy consumption of the sensor, is the noise power spectral density, is the communication channel bandwidth, and satisfies .

5. A distributed state estimation method for a multi-region power system according to claim 1, characterized in that, The step of controlling the relay node to transmit the relay data received by the relay node to the regional estimator through a transmission mechanism based on a preset number of bits includes: Mapping each dimension data of the relay data to a second preset quantization region to obtain second - quantized data; Transmitting the second - quantized data to the regional estimator through a transmission mechanism based on a preset number of bits.

6. A distributed state estimation method for a multi-region power system according to claim 1, characterized in that The target data received by the region estimator is expressed as , which is specifically expressed as , and represents the transmission data of the relay node with quantization error, expressed as , is the probability that the region estimator successfully receives the data, specifically expressed as: ， Among them, is a parameter related to the channel state, is the energy required by the relay node, is the noise power spectral density, is the communication channel bandwidth, and it satisfies 。 7. A distributed state estimation method for a multi-region power system according to claim 1, characterized in that Before the step of transmitting the real - time data to the relay node through a transmission mechanism based on a preset number of bits and controlling the relay node to transmit the relay data received by the relay node to the regional estimator through a transmission mechanism based on a preset number of bits, it further includes: Under the bit - based transmission mechanism, the energy consumed by the sensor is , which is specifically expressed as: Among them, is the transmission symbol rate, is the path loss, is the path loss exponent, is a constant determined by the receiver noise figure and the surface thermal noise spectral density, is the flip probability of the transmitted data, represents the number of bits used by the sensor data in the quantization process; The energy consumed by the relay node can be expressed as: Indicates the number of bits by which the data of the relay node is quantized; Constructing an energy consumption relationship in which the sensor and the relay node have a coupling relationship as: Indicates the upper limit of the total energy consumption; With the goal of minimizing the estimation error covariance and with energy consumption as a constraint condition, then: Solving through a preset iterative method to obtain the preset number of bits.

8. A distributed state estimation method for a multi-region power system according to claim 7, characterized in that, The step of solving through a preset iterative method to obtain the preset number of bits includes: Pair Perform random initialization processing to obtain initialization data, and construct a Lagrangian function based on the initialization data; Calculating the gradient of the Lagrangian function and the constraint gradient, and updating the variables and Lagrange multipliers in the Lagrangian function according to the gradient and the constraint gradient to obtain the target Lagrangian function; Determine whether the target Lagrangian function satisfies a preset condition. If the target Lagrangian function does not satisfy the preset condition and the upper limit of the iteration number is not reached, continue the step of performing random initialization processing to obtain initialization data, and constructing a Lagrangian function based on the initialization data until the target Lagrangian function satisfies the preset condition or reaches the upper limit of the iteration number, and obtaining a preset number of bits based on the target Lagrangian function that satisfies the preset condition or reaches the upper limit of the iteration number.

9. A distributed state estimation method for a multi-region power system according to claim 1, characterized in that, The mathematical representation of the preset distributed estimation algorithm is: 。

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