PMU-based power grid power flow real-time calculation and state estimation method and system

Abstract

The invention discloses a power grid power flow real-time calculation and state estimation method and system based on a PMU, and belongs to the technical field of electric measurement and electric fault positioning. The method comprises the following steps: synchronously measuring electrical variables, including a voltage phasor and a current phasor, of a power grid node through PMU equipment to obtain synchronous phasor data, and verifying time and topology consistency; the power grid observability grade is judged based on PMU configuration node distribution corresponding to the verified electric measurement data; directly generating a real-time state estimation result through linear calculation based on PMU electric measurement data if the whole domain is observable; and if a part is observable, fusing PMU and SCADA data, carrying out nonlinear state estimation through a weighted least square method, distributing a high weight for the PMU electrical measurement data, and dynamically adjusting the SCADA weight to suppress bad data. According to the method, through observability adaptive judgment, the precision and efficiency of state estimation are improved, a data basis is provided for power grid fault positioning, and the method is suitable for practical application scenes of various PMU coverage degrees.

Description

Technical Field

[0001] This application relates to the field of electrical measurement and electrical fault location technology, and in particular to a method and system for real-time power flow calculation and state estimation of power grid based on PMU. Background Technology

[0002] Power system state estimation is the foundation of real-time power grid analysis and control, and the accuracy of its results directly affects the reliability of power grid fault location, safety early warning, and operation optimization. Traditional state estimation methods mainly rely on measurement data provided by Supervisory Control and Data Acquisition (SCADA) systems. However, SCADA measurements have inherent defects such as asynchronous data, low refresh rate, and limited accuracy. This leads to the traditional state estimation process based on weighted least squares (WLS) requiring iterative calculations, resulting in slow convergence speed and susceptibility to interference from poor data, making it difficult to meet the real-time requirements of large power grids.

[0003] With the development of Wide Area Measurement Systems (WAMS), Synchronous Phasor Measurement Units (PMUs) can provide high-precision, highly synchronized voltage and current phasor measurements, offering a more ideal data source for state estimation. However, due to the high cost of PMU equipment, scenarios involving mixed PMU and SCADA measurements are common in actual power grids. Existing hybrid measurement state estimation methods have significant shortcomings. For example, CN101750562A discloses a dynamic process estimation method for non-PMU measurement points based on power flow equation sensitivity analysis. It uses PMU and SCADA data to estimate the dynamic process of non-PMU nodes through a sensitivity matrix. However, this method relies on real-time updates of network parameters and the Jacobian matrix, resulting in a large computational load, and it does not consider adaptive judgment of observability levels. CN103954874A proposes a substation operation state analysis method that combines whole-network and local models, focusing on the analysis of substation equipment after UHV capacity expansion, but it does not address the optimization problem of state estimation strategies under hybrid measurement. CN101661069A discloses a dynamic process estimation method for weakly observable non-PMU measurement points that does not rely on the state matrix, using recursive least squares to achieve dynamic estimation, but it also does not dynamically select the estimation strategy based on the observability level configured by the PMU.

[0004] The common drawbacks of existing technologies are: when the PMU configuration achieves full observability, nonlinear iterative calculations are still used, failing to fully utilize the potential of PMU data to support linear calculations, resulting in low computational efficiency; while when the PMU is only partially observable, simple data fusion strategies are insufficient to effectively suppress the impact of poor SCADA data, limiting estimation accuracy. Therefore, there is an urgent need in this field for a method that can intelligently select estimation strategies based on the actual coverage level of the PMU (i.e., observability level) to resolve the contradiction between computational efficiency, estimation accuracy, and robustness in state estimation under mixed measurement environments. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of this application provide a PMU-based real-time power flow calculation and state estimation method to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, this application provides a real-time power flow calculation and state estimation method for power grids based on a power management unit (PMU), comprising: Synchronous phasor measurement data is obtained by simultaneously measuring the voltage and current phasors of grid nodes using multiple PMU devices. The consistency of the synchronous phasor measurement data is verified to obtain the verified phasor data; Based on the verified phasor data, the observability level of the current power grid is determined, and the observability level is determined by the distribution of nodes equipped with PMUs in the network topology. When the level is determined to be observable across the entire domain, the real-time state estimation result of the power grid is generated through linear calculation based on the verified phasor data. When the level is determined to be partially observable, the verified phasor data is fused with the SCADA measurement data, and the real-time state estimation result of the power grid is generated by nonlinear state estimation calculation.

[0007] To address the aforementioned problems, this application also provides a PMU-based real-time power flow calculation and state estimation system for power grids, the system comprising: The data acquisition module is used to synchronously measure the voltage and current phasors of the power grid nodes through multiple PMU devices to obtain synchronous phasor measurement data; The data verification module is used to perform consistency verification on the synchronous phasor measurement data to obtain verified phasor data. The observability judgment module is used to determine the observability level of the current power grid based on the verified phasor data. The observability level is determined by the distribution of nodes equipped with PMUs in the network topology. The linear estimation module is used to generate a real-time state estimation result of the power grid based on the verified phasor data when the level is determined to be globally observable. The hybrid estimation module is used to fuse the verified phasor data with SCADA measurement data when the level is determined to be partially observable, and to generate a real-time state estimation result of the power grid through nonlinear state estimation calculation.

[0008] Compared with the prior art, this application has the following beneficial effects: This invention effectively resolves the contradiction between accuracy and efficiency in state estimation under mixed measurement environments by introducing an observability adaptive judgment mechanism and through hierarchical optimization and intelligent adaptive mechanisms, providing a solid data foundation for rapid early warning and precise location of power grid faults.

[0009] At the data level, strict time and topology consistency checks effectively eliminate time scale deviations and topology correlation errors in PMU measurements, ensuring high quality and consistency of measurement data. This reduces the risk of misjudgment of faults caused by data asynchrony or topology errors from the source, providing a reliable data foundation for subsequent power grid fault location.

[0010] At the algorithm level, an innovative approach is taken to dynamically select the estimation strategy based on the PMU coverage (i.e., the level of observability): when the system achieves full observability, linear calculations are performed directly based on the PMU voltage phasors, avoiding the convergence process of traditional nonlinear iterations, resulting in an order-of-magnitude improvement in computational efficiency, which can meet the needs of real-time monitoring and rapid fault identification; when in a partially observable scenario, a hybrid measurement model of PMU and SCADA is constructed, and higher weights are assigned to high-precision PMU data. At the same time, standardized residual detection is used to dynamically suppress interference from bad SCADA data, which significantly enhances the robustness and accuracy of state estimation, and improves the accuracy of fault diagnosis in complex measurement environments.

[0011] At the system level, this invention achieves the organic integration of PMU and SCADA measurement systems, enabling flexible adaptation to the power grid application needs at different PMU deployment stages. This method not only fully leverages the potential of PMU measurements in improving power grid condition observability but also effectively extends the value of existing SCADA infrastructure, providing a smooth technical upgrade path for the power grid dispatching system and significantly improving the overall reliability of the system in fault early warning and location. Attached Figure Description

[0012] Figure 1 A flowchart illustrating a PMU-based real-time power flow calculation and state estimation method provided in an embodiment of this application; Figure 2 A functional block diagram of a PMU-based real-time power flow calculation and state estimation system provided in an embodiment of this application; The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0013] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0014] This application provides a method for real-time power flow calculation and state estimation based on a power management unit (PMU). The execution entity of this PMU-based method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the PMU-based method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms.

[0015] Reference Figure 1 The diagram shown is a flowchart illustrating a PMU-based real-time power flow calculation and state estimation method for power grids, provided in some embodiments of this application. In this embodiment, the PMU-based real-time power flow calculation and state estimation method includes: S1. Synchronously measure the voltage and current phasors of the power grid nodes using multiple PMU devices to obtain synchronous phasor measurement data; S2. Perform a consistency check on the synchronous phasor measurement data to obtain the checked phasor data; S3. Based on the verified phasor data, determine the observability level of the current power grid, wherein the observability level is determined by the distribution of nodes equipped with PMUs in the network topology; S4. When the level is determined to be observable across the entire region, a real-time state estimation result of the power grid is generated based on the verified phasor data through linear calculation; S5. When the level is determined to be partially observable, the verified phasor data is fused with the SCADA measurement data, and the real-time state estimation result of the power grid is generated by nonlinear state estimation calculation.

[0016] Example 1 The background section points out that existing hybrid measurement methods, even when the PMU (Power Measure Unit) reaches full observability, still use traditional nonlinear iterative estimation algorithms, failing to fully utilize the potential of PMU data and resulting in low computational efficiency. This embodiment uses the classic IEEE 14-bus power grid test system as an example to demonstrate how this invention adaptively switches to a linear estimation algorithm when the system is determined to be at full observability level, thereby overcoming the aforementioned shortcomings.

[0017] In step S1, the synchronous measurement of voltage and current phasors of grid nodes through multiple PMU devices to obtain synchronous phasor measurement data includes: The PMU devices are deployed at pre-selected key nodes in the power grid to form a wide-area measurement architecture; The Global Positioning System is used to provide a unified time stamp signal for all deployed PMU devices, and each PMU device is controlled to synchronously perform the acquisition of voltage phasors and current phasors of its node based on the unified time stamp signal; The system aggregates time-stamped voltage and current phasors from various PMU devices to generate time-aligned synchronous phasor measurement data.

[0018] In this embodiment, based on network topology analysis, four key nodes—nodes 2, 6, 9, and 13 (the term "key nodes" refers to the set of nodes whose voltage phasors are known, allowing direct or indirect derivation of the voltages of all nodes in the network through linear relationships)—are selected for PMU deployment, forming a wide-area measurement architecture. All PMUs receive a unified time-stamped signal provided by GPS and synchronously measure the voltage phasors (amplitude and phase) and injected current phasors of their respective nodes at a rate of 30 frames per second. A data concentrator aggregates this time-stamped information to generate time-aligned synchronized phasor measurement data.

[0019] In step S2, the consistency verification of the synchronized phasor measurement data to obtain the verified phasor data includes: Perform a time axis alignment operation on the synchronized phasor measurement data to obtain a time-aligned phasor data sequence; The phasor data sequence is correlated with the network topology of the power grid to ensure that the electrical connection relationship of each node data in the network topology is consistent, and finally the verified phasor data is output.

[0020] In this embodiment, firstly, a time axis alignment operation is performed on the collected phasor data to ensure that all node data are cross-sectional data from the same sampling time (e.g., timestamp T0). Next, correlation verification is performed: based on the IEEE 14-node topology, it is verified whether the voltage difference and branch current measurements of adjacent nodes (e.g., node 2 with nodes 1, 3, 4, and 5) satisfy the approximate relationship of Kirchhoff's laws (KCL / KVL), thereby ensuring that the data are consistent in topological connectivity. Data that passes the verification is labeled as "verified phasor data".

[0021] In step S3, based on the verified phasor data, the observability level of the current power grid is determined. This observability level is determined by the distribution of nodes equipped with PMUs in the network topology, including: Based on the network topology of the power grid, all key nodes that maintain system connectivity and power flow distribution are identified, forming a set of key nodes. Analyze the PMU configuration node corresponding to the verified phasor data to determine the actual coverage of the PMU configuration node in the network topology; The set of key nodes is compared with the actual coverage area. If the set of key nodes is completely included in the actual coverage area, it is determined to be at the level of full observability; if there are key nodes that are not covered, it is determined to be at the level of partial observability.

[0022] In this embodiment, based on the IEEE 14-node topology, the set of key nodes maintaining system connectivity and power flow distribution is determined to be {2, 6, 9, 13} through offline analysis. The PMU configuration nodes in the verified phasor data obtained in analysis step S2 exactly cover this set. Therefore, the system determines that the current level is globally observable.

[0023] In the embodiments of this application, the set of key nodes is calculated offline using a minimum number of PMU configuration models in graph theory or a topology search algorithm based on system observability analysis, with the goal of achieving full network state observability using the fewest possible PMUs.

[0024] In step S4, when the level is determined to be globally observable, generating a real-time state estimation result of the power grid based on the verified phasor data through linear calculation includes: The voltage phasors of each PMU configuration node are extracted from the verified phasor data and used as node state quantities characterizing the grid operation status. Based on the branch parameters and topological connections of the power grid, a linear power flow calculation model is constructed with the node state variables as input. Substituting the node state variables into the linear power flow calculation model, the power flow distribution data of all branches in the entire network are calculated. The node state variables and the power flow distribution data are integrated to form the real-time state estimation result of the power grid.

[0025] In this embodiment of the application, since the system is observable across the entire domain, the linear calculation process is directly initiated, including the following steps: First, extract the node state variables by directly extracting the nodes from the verified phasor data. voltage phasors (such as ; Next, a linear power flow calculation model is constructed. Based on the branch parameters (resistance, reactance) and topology of the power grid, Kirchhoff's laws and the principle of linear superposition are used to establish a linear calculation relationship from the voltage of the PMU-configured node to the voltage of the non-PMU-configured node. For example, the voltage of node 1 can be calculated from the voltage of node 2 and the impedance and current of branch 2-1. . ,in, This is the voltage phasor (complex number, including voltage magnitude and voltage phase) of node 1, usually in per-unit (pu) or kilovolt (kV). For the voltage phasor (complex number, including voltage magnitude and voltage phase) of node 2. The current phasor (complex number, including current amplitude and current phase) of branch 2-1 flows from node 2 to node 1, and the unit is usually per unit (pu) or kiloampere (kA). The impedance of branch 2-1 is a complex number, including resistance and reactance, and is usually expressed in per-unit (pu) or ohms (Ω).

[0026] Secondly, the linear power flow calculation model is essentially a system of linear equations based on the KCL / KVL laws of circuit networks, which can be expressed as: , where the matrix and Determined by the branch parameters and topological connections of the power grid. and These are voltage and current phasors directly measured by the PMU. These are the non-PMU node voltage phasors to be determined.

[0027] Then, the power flow distribution is calculated by substituting all known node voltage phasors (including those obtained through direct measurement and linear calculation) into the branch power formula. Directly calculate the active and reactive power flow of all branches, where, The apparent power of branch ij includes active power and reactive power, and the unit is usually volt-ampere (VA) or per unit (pu). Let be the voltage phasor (complex number) at node i, including voltage magnitude and voltage phase, typically in kilovolts (kV) or per-unit (pu). In the formula, It is in phasor form; The voltage phasor (complex number) at node j includes voltage magnitude and voltage phase, with units of the same. ; The impedance (complex number) of branch ij includes resistance and reactance, and the unit is usually ohms (Ω) or per-unit (pu). Represents the conjugate operation for complex numbers. For example, This indicates taking the conjugate of the complex number A.

[0028] Finally, the voltage phasors of all network nodes and the power flow distribution data of branches are integrated to form a complete and consistent real-time state estimation result of the power grid under this time section. This process avoids the computational overhead of traditional iterative algorithms.

[0029] In this embodiment of the application, after generating the real-time state estimation results, the system further applies them to power grid operation risk early warning and fault location. Specifically, this includes: The calculated branch power flow distribution data is compared with the thermal stability limit of the line in real time. For example, if the active power of branch 1-2 is detected to exceed its limit, the system immediately issues an overload warning and accurately locates the risky line as "branch 1-2".

[0030] Based on the accurate node voltage phasors of the entire network, the voltage phase angle difference between adjacent nodes is calculated. When a drastic change in the voltage phase angle difference between the two ends of a branch (such as branch 2-3) is detected (far exceeding the normal fluctuation range), combined with the information from the protection device, the branch can be quickly identified as a suspected faulty branch, greatly narrowing the scope of fault investigation.

[0031] In the embodiments of this application, the linear estimation method of this application is adopted, which avoids the complex iterative process required by the traditional weighted least squares method, reduces the calculation time from the second level to the millisecond level, greatly improves the real-time performance of state estimation, and meets the requirements of real-time monitoring and rapid fault identification of the power grid.

[0032] The above Example 1 demonstrates the efficient operation of the system under a fully deployed PMU. However, in actual incremental deployments, the power grid may be in a partially observable state. Example 2 below will demonstrate how the system adaptively switches from a linear estimation mode to a hybrid estimation mode when the system detects a change in the PMU coverage area through the observability judgment module (such as a PMU exiting operation or a new PMU being added), ensuring that state estimation maintains optimal performance in different scenarios.

[0033] Example 2 As mentioned in the background section of this application, in scenarios where the PMU is partially observable, simple data fusion strategies are insufficient to effectively suppress adverse data in SCADA measurements, resulting in limited improvement in state estimation accuracy. This embodiment simulates a real-world regional power grid where the PMU does not fully cover all critical nodes, and the system classifies it as partially observable. This application adaptively initiates hybrid measurement nonlinear estimation and introduces a dynamic weight fine-tuning mechanism specifically designed to address the problem of interference from adverse SCADA data.

[0034] In this embodiment of the application, PMUs are deployed at 500kV hub stations A and B in the regional power grid. Their data acquisition and consistency verification processes are the same as steps S1 and S2 in Embodiment 1. The verified PMU data provides high-precision voltage phasors for stations A and B.

[0035] In step S3, offline topology analysis shows that the set of critical nodes in the power grid includes {500kV station A, 500kV station B, and 220kV station C}. The actual PMU configuration only covers stations A and B, failing to cover 220kV station C. Therefore, the system determines the current level to be partially observable.

[0036] In step S5, when the level is determined to be partially observable, the verified phasor data is fused with the SCADA measurement data, and a real-time state estimation result of the power grid is generated through nonlinear state estimation calculation, including: The verified phasor data and SCADA measurement data together form a hybrid measurement set, wherein the verified phasor data is used as a high-precision voltage phasor measurement, and the SCADA measurement data is used as a node injection power and branch power measurement. A nonlinear measurement equation is established with the voltage amplitude and phase of all network nodes as state variables. The nonlinear measurement equation describes the physical relationship between all measurement types and state variables in the hybrid measurement set. The nonlinear measurement equation is solved by applying a weighted least squares estimation algorithm, and the verified phasor data is assigned a weight higher than that of the SCADA measurement data in the weighted least squares estimation algorithm. The optimal state quantity estimate is obtained by iterative calculation. Based on the optimal state quantity estimates, the complete power flow distribution of the power grid is calculated, and the real-time state estimation results of the power grid are generated.

[0037] In some embodiments, assigning a higher weight to the verified phasor data than to the SCADA measurement data in the weighted least squares estimation algorithm includes: Based on the known measurement accuracy level of the PMU device corresponding to the verified phasor data, determine its basic weight value; Analyze the historical error statistics of each measurement in the SCADA measurement data to determine its basic weight value; The base weight value of the verified phasor data is set to be greater than the base weight value of the SCADA measurement data to form an initial weight allocation scheme.

[0038] In some embodiments, the iterative solution process of the weighted least squares estimation algorithm further includes a weight dynamic fine-tuning step: calculating the standardized residual of each SCADA measurement data in each iteration; when the absolute value of the standardized residual is greater than a preset threshold, the SCADA measurement data is determined to be bad data, and the weight of the SCADA measurement data is reduced in subsequent iterations.

[0039] In some embodiments, calculating the complete power flow distribution based on the optimal state quantity estimate and generating the real-time state estimation result of the power grid includes: Based on the optimal state variable estimates, the power flow distribution data of all branches of the power grid are calculated, and the power flow distribution data includes the active power and reactive power of each branch. Based on the optimal state quantity estimates and the power flow distribution data, the injected power data of all nodes in the power grid are calculated. The optimal state quantity estimates, the power flow distribution data, and the injected power data are integrated to form the real-time state estimation result of the power grid.

[0040] In this embodiment, the high-precision state estimation result provides a reliable data foundation for addressing the challenge of fault location under interference from poor data. The system performs the following operations: SCADA malfunctions that have been flagged and weighted down during the condition estimation process (such as erroneous power measurements at a substation) will be directly masked during the fault analysis phase to prevent them from misleading diagnostic conclusions.

[0041] By utilizing the high-precision voltage and current phasors of stations A and B provided by the PMU, combined with the estimated overall network status, more accurate fault location can be achieved. For example, for a transmission line originating from station A, the double-ended traveling wave ranging principle can be used. Due to the high synchronization accuracy of the current phasors at both ends, the fault location error can be reduced from hundreds of meters to within tens of meters.

[0042] By comparing the state estimation results with the original SCADA measurements, systematic deviations (i.e., latent faults) in certain measuring devices can be identified and reported for calibration, thereby eliminating potential faults in advance.

[0043] In this embodiment of the application, a hybrid measurement set is constructed, wherein the PMU measures the voltage phasors (amplitude and phase) of station A and station B from step S2; the SCADA measurement refers to the voltage amplitude, injected active power and reactive power of station C collected at the same time, as well as the branch power of multiple key lines.

[0044] In this embodiment of the application, the state variables are the voltage amplitude and phase of all nodes in the entire network. Establishing nonlinear measurement equations based on state variables ,in For a mixed measurement set, For measurement error, A function that describes the nonlinear relationship between measurements and state variables, such as the power equation. ,in, Let be the active power of branch ij. Let be the conductance (real part) of branch ij. Let be the susceptance (imaginary part) of branch ij. This represents the voltage phase difference between nodes i and j, typically expressed in radians (rad).

[0045] Furthermore, mixed measurement sets It includes all PMU and SCADA measurement values. PMU measurements include voltage amplitude, voltage phase, current amplitude, and current phase; SCADA measurements include node injected power (active and reactive), branch power (active and reactive), and voltage amplitude. It is a vector whose dimension equals the total number of measurements.

[0046] Furthermore, A nonlinear function vector, where each element describes a measurement and a state variable. The physical relationships between them (such as power equations). For example, for power measurement, Calculate the power value based on state variables.

[0047] Furthermore, the state vector It includes the voltage amplitude and voltage phase of all nodes in the entire network. If the power grid has n nodes, then... The dimension is 2n (each node corresponds to a voltage amplitude and a voltage phase).

[0048] Furthermore, the measurement error vector Each element corresponds to a random error in the measurement, with the mean assumed to be zero and the variance determined by the measurement accuracy.

[0049] In this embodiment of the application, PMU phasor measurement accuracy is much higher than SCADA, so it is assigned a high weight.

[0050] Specifically, the weight of the PMU voltage amplitude measurement is set to Phase measurement weights are set to (Based on its) (Accuracy level), the weight of SCADA power measurement is set according to its historical error standard deviation. .

[0051] In the embodiments of this application, the weight is typically set as a certain proportion of the reciprocal of the measurement error variance, i.e. Assume the standard deviation of the PMU voltage amplitude measurement error. (per unit value), then its weight is approximately SCADA power measurement error standard deviation (per unit value), then its weight is approximately The examples given and It is the actual value based on this type of calculation.

[0052] In this embodiment, the weighted least squares method is used to iteratively solve the state variables. ,include: After each iteration, the standardized residual of each SCADA measurement is calculated (the residual is divided by its estimated standard deviation), and a threshold of 3.0 is set. If the standardized residual of power measurement for a certain line is found to be greater than 3.0 for an extended period, it is considered defective data. In subsequent iterations, the weight of this poor data measurement is reduced to 1 / 100 of its initial value to suppress its impact on the estimation results.

[0053] Specifically, the preset threshold is usually set based on the characteristics of normal distribution, such as 3.0, which means that data points with an absolute residual value greater than 3 times the measurement standard deviation are regarded as low-probability events, i.e. bad data.

[0054] Furthermore, it is assumed that the algorithm converges after 3 iterations, yielding the optimal state quantity estimate.

[0055] In this embodiment, based on the optimal estimate, the power flow distribution data of all branches and the injected power data of all nodes are calculated; these are then integrated to obtain a complete and high-precision real-time state estimation result that includes the voltage of all network nodes, branch power flow, and node injected power. This process effectively utilizes the high precision of PMU data to correct and supplement SCADA data and eliminates the influence of poor data.

[0056] In the embodiments of this application, by assigning high weights to PMU data and dynamically suppressing defective SCADA data, the estimation results of this application have higher accuracy and robustness compared to traditional hybrid measurement methods (whose SCADA weights are fixed or have simple adjustment strategies). For example, the estimation error of a critical node voltage is reduced from 1% in the traditional method to 0.2%, significantly improving the reliability of subsequent fault location.

[0057] The above embodiments demonstrate that this invention achieves adaptive selection of the state estimation method through intelligent judgment of the observability level. Embodiment 1 shows the extremely high computational efficiency brought by the linear method under ideal PMU coverage. Embodiment 2 proves that high-precision estimation results can still be obtained during the PMU deployment transition period through intelligent data fusion and robust algorithms. Seamless switching between the two modes ensures the reliability, accuracy, and real-time performance of power grid state estimation across all scenarios.

[0058] like Figure 2 The diagram shown is a functional block diagram of a PMU-based real-time power flow calculation and state estimation system provided in an embodiment of this application.

[0059] The PMU-based real-time power flow calculation and state estimation system 100 described in this application can be installed in an electronic device. Depending on the functions implemented, the PMU-based real-time power flow calculation and state estimation system 100 may include a data acquisition module 101, a data verification module 102, an observation judgment module 103, a linear estimation module 104, and a hybrid estimation module 105. The module described in this application can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.

[0060] In this embodiment, the functions of each module / unit are as follows: Data acquisition module 101 is used to synchronously measure the voltage phasor and current phasor of the power grid node through multiple PMU devices to obtain synchronous phasor measurement data; The data verification module 102 is used to perform consistency verification on the synchronous phasor measurement data to obtain verified phasor data. The observability judgment module 103 is used to determine the observability level of the current power grid based on the verified phasor data. The observability level is determined by the distribution of nodes configured with PMUs in the network topology. The linear estimation module 104 is used to generate a real-time state estimation result of the power grid based on the verified phasor data through linear calculation when the level is determined to be globally observable. The hybrid estimation module 105 is used to fuse the verified phasor data with the SCADA measurement data when the level is determined to be partially observable, and to generate a real-time state estimation result of the power grid through nonlinear state estimation calculation.

[0061] In the several embodiments provided in this application, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0062] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0063] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0064] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application.

[0065] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. A method for real-time power flow calculation and state estimation of a power grid based on a power management unit (PMU), characterized in that, The method includes: Synchronous phasor measurement data is obtained by simultaneously measuring the voltage and current phasors of grid nodes using multiple PMU devices. The consistency of the synchronous phasor measurement data is verified to obtain the verified phasor data; Based on the verified phasor data, the observability level of the current power grid is determined, and the observability level is determined by the distribution of nodes equipped with PMUs in the network topology. When the level is determined to be observable across the entire domain, the real-time state estimation result of the power grid is generated through linear calculation based on the verified phasor data. When the level is determined to be partially observable, the verified phasor data is fused with the SCADA measurement data, and the real-time state estimation result of the power grid is generated by nonlinear state estimation calculation.

2. The method for real-time power flow calculation and state estimation based on PMU as described in claim 1, characterized in that, The method of synchronously measuring the voltage and current phasors of grid nodes through multiple PMU devices to obtain synchronous phasor measurement data includes: The PMU devices are deployed at pre-selected key nodes in the power grid to form a wide-area measurement architecture; The Global Positioning System is used to provide a unified time stamp signal for all deployed PMU devices, and each PMU device is controlled to synchronously perform the acquisition of voltage phasors and current phasors of its node based on the unified time stamp signal; The system aggregates time-stamped voltage and current phasors from various PMU devices to generate time-aligned synchronous phasor measurement data.

3. The method for real-time power flow calculation and state estimation based on PMU as described in claim 1, characterized in that, The process of performing a consistency check on the synchronized phasor measurement data to obtain the checked phasor data includes: Perform a time axis alignment operation on the synchronized phasor measurement data to obtain a time-aligned phasor data sequence; The phasor data sequence is correlated with the network topology of the power grid to ensure that the electrical connection relationship of each node data in the network topology is consistent, and finally the verified phasor data is output.

4. The method for real-time power flow calculation and state estimation based on PMU as described in claim 1, characterized in that, The observability level of the current power grid is determined based on the verified phasor data. This observability level is determined by the distribution of nodes equipped with PMUs in the network topology, including: Based on the network topology of the power grid, all key nodes that maintain system connectivity and power flow distribution are identified, forming a set of key nodes. Analyze the PMU configuration node corresponding to the verified phasor data to determine the actual coverage of the PMU configuration node in the network topology; The set of key nodes is compared with the actual coverage area. If the set of key nodes is completely included in the actual coverage area, it is determined to be at the level of full observability; if there are key nodes that are not covered, it is determined to be at the level of partial observability.

5. The method for real-time power flow calculation and state estimation based on PMU as described in claim 1, characterized in that, When the system is determined to be at the level of full observability, the real-time state estimation result of the power grid is generated through linear calculation based on the verified phasor data, including: The voltage phasors of each PMU configuration node are extracted from the verified phasor data and used as node state quantities characterizing the grid operation status. Based on the branch parameters and topological connections of the power grid, a linear power flow calculation model is constructed with the node state variables as input. Substituting the node state variables into the linear power flow calculation model, the power flow distribution data of all branches in the entire network are calculated. The node state variables and the power flow distribution data are integrated to form the real-time state estimation result of the power grid.

6. The method for real-time power flow calculation and state estimation based on PMU as described in claim 1, characterized in that, When the level is determined to be partially observable, the verified phasor data is fused with the SCADA measurement data, and a real-time state estimation result of the power grid is generated through nonlinear state estimation calculation, including: The verified phasor data and SCADA measurement data together form a hybrid measurement set, wherein the verified phasor data is used as a high-precision voltage phasor measurement, and the SCADA measurement data is used as a node injection power and branch power measurement. A nonlinear measurement equation is established with the voltage amplitude and phase of all network nodes as state variables. The nonlinear measurement equation describes the physical relationship between all measurement types and state variables in the hybrid measurement set. The nonlinear measurement equation is solved by applying a weighted least squares estimation algorithm, and the verified phasor data is assigned a weight higher than that of the SCADA measurement data in the weighted least squares estimation algorithm. The optimal state quantity estimate is obtained by iterative calculation. Based on the optimal state quantity estimates, the complete power flow distribution of the power grid is calculated, and the real-time state estimation results of the power grid are generated.

7. The method for real-time power flow calculation and state estimation based on PMU as described in claim 6, characterized in that, The step of assigning a higher weight to the verified phasor data than to the SCADA measurement data in the weighted least squares estimation algorithm includes: Based on the known measurement accuracy level of the PMU device corresponding to the verified phasor data, determine its basic weight value; Analyze the historical error statistics of each measurement in the SCADA measurement data to determine its basic weight value; The base weight value of the verified phasor data is set to be greater than the base weight value of the SCADA measurement data to form an initial weight allocation scheme.

8. The method for real-time power flow calculation and state estimation based on PMU as described in claim 6 or 7, characterized in that, The iterative solution process of the weighted least squares estimation algorithm also includes a dynamic weight fine-tuning step: calculating the standardized residual of each SCADA measurement data in each iteration; when the absolute value of the standardized residual is greater than a preset threshold, the SCADA measurement data is determined to be bad data, and the weight of the SCADA measurement data is reduced in subsequent iterations.

9. The method for real-time power flow calculation and state estimation based on PMU as described in claim 6, characterized in that, The process of calculating the complete power flow distribution based on the optimal state variable estimates and generating the real-time state estimation results of the power grid includes: Based on the optimal state variable estimates, the power flow distribution data of all branches of the power grid are calculated, and the power flow distribution data includes the active power and reactive power of each branch. Based on the optimal state quantity estimates and the power flow distribution data, the injected power data of all nodes in the power grid are calculated. The optimal state quantity estimates, the power flow distribution data, and the injected power data are integrated to form the real-time state estimation result of the power grid.

10. A PMU-based real-time power flow calculation and state estimation system for power grids, used to implement the PMU-based real-time power flow calculation and state estimation method according to any one of claims 1-9, characterized in that, The system includes: The data acquisition module is used to synchronously measure the voltage and current phasors of the power grid nodes through multiple PMU devices to obtain synchronous phasor measurement data; The data verification module is used to perform consistency verification on the synchronous phasor measurement data to obtain verified phasor data. The observability judgment module is used to determine the observability level of the current power grid based on the verified phasor data. The observability level is determined by the distribution of nodes configured with PMUs in the network topology. The linear estimation module is used to generate a real-time state estimation result of the power grid based on the verified phasor data when the level is determined to be globally observable. The hybrid estimation module is used to fuse the verified phasor data with SCADA measurement data when the level is determined to be partially observable, and to generate a real-time state estimation result of the power grid through nonlinear state estimation calculation.

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