A distributed state estimation method for multi-region power systems

Abstract

This application discloses a distributed state estimation method for a multi-regional power system. The method includes: constructing a mathematical model of a target multi-regional power system and obtaining real-time data of the target multi-regional 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 real-time data received by the relay node to a regional estimator through a transmission mechanism based on a preset number of bits; and controlling the regional estimator to evaluate the power system parameters of the target multi-regional power system based on the mathematical model and the real-time data received by the regional estimator through a preset distributed estimation algorithm. Relay nodes are introduced to optimize the communication topology, and the number of bits is preferentially processed based on energy-limited characteristics, effectively reducing communication energy consumption while ensuring estimation performance.

Description

Technical Field

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

[0002] With the increasing proportion of renewable energy generation and the widespread integration of power electronic equipment, modern power systems are gradually developing towards a highly interconnected, distributed, and collaborative approach. This transformation has altered the operating and control methods of power systems, placing higher demands on accurate estimation of system states. However, traditional centralized state estimation methods face challenges in multi-regional power systems, such as high communication overhead, heavy computational burden, and insufficient real-time performance. These challenges make it difficult to meet the demand for efficient and accurate state estimation in multi-regional power systems. Therefore, distributed state estimation methods have emerged, aiming to improve the accuracy of state estimation and the operational efficiency of multi-regional power systems through inter-regional information exchange.

[0003] In the framework of distributed state estimation, sensor nodes rely on wireless communication networks to transmit measurement data. However, since sensors are typically battery-powered, their energy constraints affect the frequency and quality of data transmission, and thus the accuracy of state estimation. Existing research has primarily focused on sensor transmission strategies from intra-regional sensors to estimators, while in-depth research on cross-regional data transmission mechanisms in multi-regional power systems is lacking. To overcome the energy constraints of sensors within each region and improve the estimation performance of the multi-regional power system, it is necessary not only to optimize intra-regional data transmission mechanisms but also to rationally design inter-regional data exchange transmission strategies to enhance the stability of the multi-regional power system. In state estimation of multi-regional power systems that rely on sensor communication, the number of bits of transmitted data directly affects communication energy consumption. A higher bit number increases the data volume and improves state estimation accuracy, but also increases energy consumption and shortens sensor life. However, a lower bit number, while reducing energy consumption, may lead to increased quantization error, affecting state estimation performance. Summary of the Invention

[0004] Based on this, a distributed state estimation method for multi-region power systems is proposed to address the technical problems in the existing technology, such as the failure to fully consider the energy-limited characteristics of battery-powered sensors, which leads to large errors in the estimation results.

[0005] In a first aspect, a method for distributed state estimation of a multi-region power system is provided, the method comprising:

[0006] 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;

[0007] 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 region estimator through a transmission mechanism based on a preset number of bits, wherein the preset number of bits is an optimal number of transmission bits determined with minimization of estimation error covariance as a goal and energy consumption as a constraint;

[0008] The regional estimator is controlled to evaluate power system parameters of a target multi-region power system according to the mathematical model and target data received by the regional estimator through a preset distributed estimation algorithm.

[0009] Optionally, the step of constructing a mathematical model of the target multi-region power system includes:

[0010] Constructing a first mathematical model of a generator subsystem in a target multi-regional power system, constructing a second mathematical model of an excitation subsystem in the target multi-regional power system, and constructing a third mathematical model of a transmission line subsystem in the target multi-regional power system;

[0011] A mathematical model of any regional power system in the target multi-region power system is constructed according to the first mathematical model, the second mathematical model, and the third mathematical model, wherein the mathematical representation of the mathematical model is:

[0012]

[0013] in, Representative The system parameter matrix of the region, is the input matrix of the region, is the adjacency matrix of the region. System state , by the rotor angular velocity deviation , power flow deviation from node i to node j , mechanical power deviation and turbine valve position deviation composition, Represents the connection line with the A collection of connected regions.

[0014] Optionally, the step of transmitting the real-time data to the relay node through a transmission mechanism based on a preset number of bits includes:

[0015] Mapping each dimension of the real-time data to a first preset quantization region to obtain first quantized data;

[0016] The first quantized data is transmitted to the relay node through a transmission mechanism based on a preset number of bits.

[0017] Optionally, the relay data received by the relay node is represented as , where the received data is specifically expressed as ,and represents the observed data with quantization error, is the transmission error. It is used to characterize the probability that the sensor successfully sends the quantitative data to the relay node. The mathematical formula is:

[0018]

[0019] in, 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 .

[0020] 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:

[0021] Mapping the data of each dimension of the relay data to a second preset quantization region to obtain second quantized data;

[0022] The second quantized data is transmitted to the region estimator through a transmission mechanism based on a preset number of bits.

[0023] Optionally, the target data received by the region estimator is represented 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 regional estimator successfully receives data, which is specifically expressed as:

[0024] ,

[0025] in, 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

[0026]

[0027] 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, the method further includes:

[0028] Under the bit-based transmission mechanism, the energy consumed by the sensor is , specifically expressed as:

[0029]

[0030] Among them, among them, is the transmission symbol rate, 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, Indicates the number of bits used in the quantization process of sensor data.

[0031] The energy consumed by the relay node is:

[0032]

[0033] The energy consumption relationship of building a coupling relationship between sensors and relay nodes is:

[0034]

[0035] Taking minimizing the estimation error covariance as the goal and energy consumption as the constraint, then:

[0036]

[0037] The solution is performed through a preset iterative method to obtain a preset number of bits.

[0038] Optionally, the step of solving by a preset iterative method to obtain a preset number of bits includes:

[0039] right Performing random initialization processing to obtain initialization data, and constructing a Lagrangian function based on the initialization data;

[0040] Calculating the gradient and the constrained gradient of the Lagrangian function, and updating the variables and Lagrangian multipliers in the Lagrangian function according to the gradient and the constrained gradient to obtain a target Lagrangian function;

[0041] Determine 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 number of iterations is not reached, continue the Perform random initialization processing to obtain initialization data, and construct a Lagrangian function based on the initialization data until the target Lagrangian function meets a preset condition or reaches an upper limit of the number of iterations, and obtain a preset number of bits based on the target Lagrangian function that meets the preset condition or reaches the upper limit of the number of iterations.

[0042] Optionally, the mathematical representation of the preset distributed estimation algorithm is:

[0043]

[0044] On the other hand, the present application provides a multi-region power system distributed state estimation device, the device comprising:

[0045] A data acquisition module is used 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;

[0046] 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 to 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, wherein the preset number of bits is an optimal number of transmission bits determined with the goal of minimizing the estimation error covariance and with energy consumption as a constraint;

[0047] A calculation module is used 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.

[0048] In a third aspect, a computer-readable storage medium is provided, wherein 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 multi-regional power system distributed state estimation method are implemented.

[0049] The present application constructs a mathematical model of a target multi-regional power system and obtains real-time data of the target multi-regional power system through sensors; transmits the real-time data to a relay node through a transmission mechanism based on a preset number of bits, and controls 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, wherein the preset number of bits is the optimal transmission bit number determined with the goal of minimizing the estimation error covariance and energy consumption as a constraint; controls the regional estimator to evaluate the power system parameters of the target multi-regional power system based on the mathematical model and the target data received by the regional estimator through a preset distributed estimation algorithm. By introducing relay nodes, a topology structure that is more in line with the actual power system communication process is constructed, the reliability and coverage of data transmission are improved, the accuracy and robustness of state estimation are improved, and the data transmission requirements within and between regions are comprehensively considered. The optimized bit number transmission mechanism is realized through the preset number of bits, and the energy utilization efficiency is improved, thereby enhancing the reliability and operation efficiency of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] in:

[0052] Figure 1 is a flow chart of a distributed state estimation method for a multi-region power system in one embodiment;

[0053] Figure 2 is a flow chart of a distributed state estimation method for a multi-region power system in another embodiment;

[0054] Figure 3 This is a diagram showing an application scenario of a distributed state estimation method for a multi-region power system in one embodiment;

[0055] Figure 4 A flowchart of solving a preset number of bits in a distributed state estimation method for a multi-region power system in one embodiment;

[0056] Figure 5 A block diagram of a distributed state estimation device for a multi-region power system according to an embodiment;

[0057] Figure 6 is a structural block diagram of a computer device in one embodiment;

[0058] Figure 7It is a structural block diagram of a computer device in another embodiment. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0060] The present invention is described in detail below through specific examples.

[0061] See also Figure 1 As shown, Figure 1 A schematic flow chart of a distributed state estimation method for a multi-region power system provided in an embodiment of the present invention includes the following steps:

[0062] S101, 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;

[0063] S102. 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 region estimator through a transmission mechanism based on a preset number of bits, where the preset number of bits is an optimal number of transmission bits determined with minimization of estimation error covariance as a goal and energy consumption as a constraint.

[0064] S103 : Control the regional estimator to evaluate power system parameters of the target multi-region power system according to the mathematical model and the target data received by the regional estimator using a preset distributed estimation algorithm.

[0065] For example, Figure 2As shown, a multi-regional power system composed of a generator, an excitation system and a transmission line is modeled, which is equivalent to constructing a mathematical model of a target multi-regional power system; the sensor collects data of the power system in real time, and transmits the data to the relay node using a transmission mechanism based on the number of bits, which is equivalent to obtaining real-time data of the target multi-regional power system through the sensor, and transmitting the real-time data to the relay node through a transmission mechanism based on a preset number of bits; the relay node receives the data from the sensor and performs preprocessing operations, and transmits data to the regional estimator using a transmission mechanism based on the number of bits, which is equivalent to controlling the relay node to transmit the data to the relay node through a transmission mechanism based on the preset number of bits. The relay data received by the point is transmitted to the regional estimator; the regional estimator receives the relay node data, uses the distributed estimation algorithm to perform state estimation, and calculates the estimation error covariance, which is equivalent to 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; constructing an optimization problem with minimizing the estimation error covariance as the goal and energy consumption as the constraint condition, and determining the optimal number of transmission bits, which is equivalent to minimizing the estimation error covariance as the goal and energy consumption as the constraint condition, solving through a preset iterative method, and obtaining a preset number of bits.

[0066] For example, Figure 3 As 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 to 4 are obtained through sensors, and then the real-time data are transmitted to the relay node through the sensors. Further, the relay node transmits the received data to the estimator.

[0067] In one possible implementation, the step of constructing a mathematical model of the target multi-region power system includes:

[0068] Constructing a first mathematical model of a generator subsystem in a target multi-regional power system, constructing a second mathematical model of an excitation subsystem in the target multi-regional power system, and constructing a third mathematical model of a transmission line subsystem in the target multi-regional power system;

[0069] A mathematical model of any regional power system in the target multi-region power system is constructed according to the first mathematical model, the second mathematical model, and the third mathematical model, wherein the mathematical representation of the mathematical model is:

[0070]

[0071] in, Representative The system parameter matrix of the 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 connection line with the A collection of connected regions.

[0072] For example, consider that the multi-region interconnected power system mainly consists of generators, excitation systems and transmission lines. The linearized generator dynamics in each region can be expressed as the following differential equation:

[0073]

[0074] in, Indicates the The generator rotor angle deviation in each region, For the The angular velocity deviation of the generator rotor in each area. For the The inertia coefficient of the area. For the The damping coefficient of the region. is the mechanical power deviation, is the load power deviation, From the area To area The current deviation on the tie line.

[0075] The excitation system mainly consists of an exciter, a regulator, and related control devices. Its function is to provide excitation current for the generator to regulate the voltage and enhance system stability. The corresponding differential dynamics of the excitation system satisfies:

[0076]

[0077] in, For the region The input control signal, For the Speed ​​adjustment for each zone. For non-reheat turbine The delay of a region. For the The governor time constant for each zone. It is The electrical power of the area.

[0078] In a multi-area power system, assuming that there is no active power loss in the tie line, the area and region The reactance between The power exchange relationship of the system can be described by the following power flow equation:

[0079] ,

[0080] in, For the region and region The output voltage, For the region and region The equivalent reactance between Respectively for regions and region The voltage phase angle of . Linearizing the initial operating point, we can get ,in, is the synchronous torque coefficient.

[0081] Based on the above generator system, excitation system and transmission line model, The continuous-time state space model of a region can be expressed as

[0082]

[0083] in, Representative The system state matrix of the 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 connection line with the A set of connected regions. Specifically, the system parameter matrix is ​​expressed as

[0084] , ,

[0085] By choosing an appropriate sampling time , and using the first-order hold discretization method, the region The power system model is transformed into a regularized discrete-time system model:

[0086]

[0087] in , , . The mean is 0 and the covariance is Gaussian noise vector.

[0088] At the moment , each area Deploy sensors to measure status , the measurement equation can be written as

[0089] ,

[0090] in For deployment in The measurement matrix of area sensors, The mean is 0 and the covariance is Gaussian noise vector.

[0091] In a possible implementation, the step of transmitting the real-time data to the relay node through a transmission mechanism based on a preset number of bits includes:

[0092] Mapping each dimension of the real-time data to a first preset quantization region to obtain first quantized data;

[0093] The first quantized data is transmitted to the relay node through a transmission mechanism based on a preset number of bits.

[0094] 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:

[0095] ,

[0096] in, 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

[0097] .

[0098] For example, for the region Sensor measurements (real-time data) , each dimension of data will be mapped to a preset quantization area (the first preset quantization area), and the first quantization area is specifically represented as

[0099] .

[0100] 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 number of bits is

[0101] ,

[0102] Each bit is

[0103]

[0104] is the quantitative level, and is the number of bits used by the sensor to transmit data (preset number of bits). Since wireless channels are affected by factors such as signal attenuation, multipath effect, and noise, packet loss is inevitable, which may cause the data sent by the sensor to fail to be successfully transmitted to the relay node. Therefore, under the transmission mechanism based on the number of bits, the data received by the relay node is ,in , is the probability that the sensor successfully sends data to the relay node, which is mathematically described as:

[0105] ,

[0106] in, 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

[0107]

[0108] In a possible implementation, 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:

[0109] Mapping the data of each dimension of the relay data to a second preset quantization region to obtain second quantized data;

[0110] The second quantized data is transmitted to the region estimator through a transmission mechanism based on a preset number of bits.

[0111] In one possible implementation, the target data received by the region estimator is represented as ,

[0112] Its specific expression is ,and Represents the transmission data of the relay node with quantization error, expressed as . is the probability that the regional estimator successfully receives data, which is specifically expressed as:

[0113] ,

[0114] in, 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

[0115]

[0116] For example, similar to the transmission mechanism of sensors, relay nodes will receive data (relay data) Each dimension of the measurement value is quantized in the region (the second preset quantization region), and the second preset quantization region is

[0117]

[0118] Specifically, yes No. dimensional data, and is a given scalar. Relay data by The data is sent to the region estimator in the form of . Specifically,

[0119] ,

[0120] 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 .

[0121] In a possible implementation, 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 the preset number of bits, the method further includes:

[0122] Under the bit-based transmission mechanism, the energy consumed by the sensor is , specifically expressed as:

[0123]

[0124] 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, Indicates the number of bits used in the quantization process of sensor data.

[0125] Furthermore, the energy consumed by the relay node is:

[0126]

[0127] in Indicates the number of bits used by the relay node.

[0128] The energy consumption relationship of building a coupling relationship between sensors and relay nodes is:

[0129]

[0130] and It is the upper limit of energy consumption.

[0131] Taking minimizing the estimation error covariance as the goal and energy consumption as the constraint, then:

[0132]

[0133] The solution is performed through a preset iterative method to obtain a preset number of bits.

[0134] For example, under the bit-based transmission mechanism, the energy consumed by the sensor is , expressed as:

[0135]

[0136] Among them, among them, is the transmission symbol rate, 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, Indicates the number of bits used in the quantization process of sensor data.

[0137] The energy consumed by the relay node is:

[0138]

[0139] in Indicates the number of bits used by the relay node.

[0140] The energy consumption relationship between the sensor and the relay node with a coupling relationship is constructed as follows:

[0141]

[0142] and It is the upper limit of energy consumption.

[0143] In energy-constrained situations, sensors and relay nodes may use low-power transmission to reduce energy consumption, which will undoubtedly lead to signal fading or packet loss. By allocating a reasonable number of bits to sensors and relay nodes through energy optimization strategies, not only energy consumption is reduced, but also the probability of packet loss is reduced to a certain extent, thereby ensuring the stability and accuracy of the estimator. Therefore, with the goal of minimizing the estimation error covariance and energy consumption as the constraint, the following optimization problem is established:

[0144]

[0145] The constraints are further expanded to:

[0146]

[0147] The constraint problem is further transformed into:

[0148]

[0149] In a possible implementation, the step of solving by a preset iterative method to obtain a preset number of bits includes:

[0150] right Performing random initialization processing to obtain initialization data, and constructing a Lagrangian function based on the initialization data;

[0151] Calculating the gradient and the constrained gradient of the Lagrangian function, and updating the variables and Lagrangian multipliers in the Lagrangian function according to the gradient and the constrained gradient to obtain a target Lagrangian function;

[0152] Determine 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 number of iterations is not reached, continue the , Perform random initialization processing to obtain initialization data, and construct a Lagrangian function based on the initialization data until the target Lagrangian function meets a preset condition or reaches an upper limit of the number of iterations, and obtain a preset number of bits based on the target Lagrangian function that meets the preset condition or reaches the upper limit of the number of iterations.

[0153] For example, due to the combination of matrix inversion and inverse operation, the entire objective function is non-convex. Therefore, it is not possible to directly obtain the global optimal solution by analytically derivation and setting the gradient to zero. Moreover, the constraints involve all variables The nonlinear summation does not satisfy the standard convex optimization problem form, which makes the classical Lagrange multiplier method unable to solve it directly. Figure 4 The preset iterative method shown finds the appropriate .

[0154] Specifically, through the formula:

[0155]

[0156] The gradient calculation formula is:

[0157]

[0158] make

[0159]

[0160]

[0161] in: , is a unit diagonal matrix, in The first position is set to 1, and the rest are set to 0.

[0162]

[0163] Through the above algorithm, the appropriate number of bits (preset number of bits) is determined for the sensor and relay nodes.

[0164] In a possible implementation, the mathematical representation of the preset distributed estimation algorithm is:

[0165] .

[0166] For example, for each region , the designed distributed estimator (preset distributed estimation algorithm) is

[0167]

[0168] Build Area The estimated error The evolution equation of

[0169]

[0170] Will from arrive Stacking to construct estimation errors of multi-regional power systems

[0171] ,

[0172] The specific symbols are:

[0173]

[0174] Deriving the expected estimation error covariance ,

[0175]

[0176] in:

[0177]

[0178] To minimize the estimation error covariance, the estimator gain can be determined by the following formula

[0179]

[0180] Based on the estimator gain determined above, the minimum estimation error covariance can be expressed as

[0181]

[0182] Set zero initial conditions , using mathematical induction, for , assuming Established. Through recursive calculation, we can deduce that:

[0183]

[0184] because , so we can get From the above analysis, we can see that the estimated error covariance is monotonically increasing under zero initial conditions and satisfies the relationship: .

[0185] Furthermore, under any initial conditions, that is, , it is derived that

[0186]

[0187] because ,when , .

[0188] In summary, if there exists an estimator gain such that is mean square stable. Then the estimation error covariance is bounded.

[0189] On the other hand, Figure 5 As shown, the present application provides a multi-region power system distributed state estimation device, the device comprising:

[0190] The data acquisition module 201 is used 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;

[0191] 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 to 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, wherein the preset number of bits is an optimal number of transmission bits determined with the goal of minimizing the estimation error covariance and with energy consumption as a constraint;

[0192] The calculation module 203 is 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.

[0193] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. 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 via a network connection. When the computer program is executed by the processor, it implements the functions or steps on the service side of a multi-regional power system distributed state estimation method.

[0194] In one embodiment, a computer device is provided. The computer device may be a client, and its internal structure diagram may be as follows: Figure 7As shown. The computer device includes a processor, memory, network interface, display screen and input device connected via a system bus. 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 computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps on the client side of a multi-regional power system distributed state estimation method.

[0195] In one embodiment, a computer device is proposed, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: constructing a mathematical model of a target multi-regional power system, and acquiring real-time data of the target multi-regional 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, wherein the preset number of bits is an optimal transmission bit number determined with the goal of minimizing the estimation error covariance and energy consumption as a constraint; and controlling the regional estimator to evaluate the power system parameters of the target multi-regional power system according to the mathematical model and the target data received by the regional estimator through a preset distributed estimation algorithm.

[0196] In one embodiment, a computer-readable storage medium is proposed, which stores a computer program, and when the computer program is executed by a processor, it implements the following steps: constructing a mathematical model of a target multi-regional power system, and obtaining real-time data of the target multi-regional 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, wherein the preset number of bits is the optimal transmission bit number determined with the goal of minimizing the estimation error covariance and energy consumption as a constraint condition; controlling the regional estimator to evaluate the power system parameters of the target multi-regional power system according to the mathematical model and the target data received by the regional estimator through a preset distributed estimation algorithm.

[0197] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can be found in the relevant descriptions of the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0198] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may 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 (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0199] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, 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.

[0200] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A distributed state estimation method for a multi-region power system, characterized in that: The method comprises: 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; Under the bit-based transmission mechanism, the energy consumed by the sensor is , specifically expressed as: in, is the transmission symbol rate, 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, Indicates the number of bits used in the quantization process of sensor data; The energy consumed by the relay node can be expressed as: Indicates the number of bits to which the data of the relay node is quantized; The energy consumption relationship of building a coupling relationship between sensors and relay nodes is: Indicates the upper limit of total energy consumption; Taking minimizing the estimation error covariance as the goal and energy consumption as the constraint, then: Solving by a preset iterative method to obtain a preset number of bits; 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 region estimator through a transmission mechanism based on a preset number of bits, wherein the preset number of bits is an optimal number of transmission bits determined with minimization of estimation error covariance as a goal and energy consumption as a constraint; The regional estimator is controlled to evaluate power system parameters of a target multi-region power system according to the mathematical model and target data received by the regional estimator through a preset distributed estimation algorithm.

2. A distributed state estimation method for a multi-region power system according to claim 1, characterized in that: The step of constructing a mathematical model of the target multi-region power system includes: Constructing a first mathematical model of a generator subsystem in a target multi-regional power system, constructing a second mathematical model of an excitation subsystem in the target multi-regional power system, and constructing a third mathematical model of a transmission line subsystem in the target multi-regional power system; A mathematical model of any regional power system in the target multi-region power system is constructed according to the first mathematical model, the second mathematical model, and the third mathematical model, wherein the mathematical representation of the mathematical model is: in, Representative The system state matrix of the 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 connection line with the A collection of connected regions.

3. The method for distributed state estimation of 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 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.

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 expressed as ,and represents the observed data with quantization error, is the transmission error, It is used to characterize the probability that the sensor successfully sends quantitative data to the relay node. The mathematical formula is: in, 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. The method for distributed state estimation of 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 area estimator through a transmission mechanism based on a preset number of bits includes: Mapping the data of each dimension of the relay data to a second preset quantization region to obtain second quantized data; The second quantized data is transmitted to the region 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 represented 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 regional estimator successfully receives data, which is specifically expressed as: ， in, 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 。 7. A distributed state estimation method for a multi-region power system according to claim 1, characterized in that: The step of solving the problem by a preset iterative method to obtain a preset number of bits includes: right Performing random initialization processing to obtain initialization data, and constructing a Lagrangian function based on the initialization data; Calculating the gradient and the constrained gradient of the Lagrangian function, and updating the variables and Lagrangian multipliers in the Lagrangian function according to the gradient and the constrained gradient to obtain a target Lagrangian function; Determine 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 number of iterations is not reached, continue the Perform random initialization processing to obtain initialization data, and construct a Lagrangian function based on the initialization data until the target Lagrangian function meets a preset condition or reaches an upper limit of the number of iterations, and obtain a preset number of bits based on the target Lagrangian function that meets the preset condition or reaches the upper limit of the number of iterations.

8. The method for distributed state estimation of 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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