Novel power system fixed-time control method and device

Abstract

The present application relates to the technical field of power system control, and more particularly to a novel power system fixed-time control method and device, which is based on topology structure data to analyze fault propagation and construct a dynamic model of the power system; key control nodes are extracted to analyze fault influence and collect signals, and dynamic response data of the fault is obtained; state deviation is calculated and combined with propagation data to perform adaptive fault-tolerant analysis and sliding mode surface reconstruction to generate an adaptive sliding mode control law; finally, the control law is optimized for fixed-time convergence according to real-time operation data, a dynamic control path with minimized convergence time is obtained and parameters are adjusted, and the result is fed back to the control system to realize adaptive fixed-time sliding mode closed-loop control of the power system and stable risk early warning.

Description

Technical Field

[0001] This invention relates to the field of power system control technology, and in particular to a novel fixed-time control method and device for power systems. Background Technology

[0002] With the continuous expansion of modern power systems and the large-scale integration of new energy sources, the power grid structure is becoming increasingly complex, and the uncertainty of system operation is significantly increasing. Sensors and actuators, as key components in power system state perception and control execution, directly affect the safe and stable operation of the entire system. However, in actual operation, sensors and actuators often fail due to aging, environmental interference, or external attacks, leading to distorted measurement data or control command failures, which may result in serious consequences such as protection malfunctions, control failures, or even system instability. Traditional power system control strategies are mostly based on idealized assumptions and are difficult to effectively cope with the dynamic uncertainties caused by sudden sensor and actuator failures. Especially in scenarios with complex fault propagation paths and tight response times, existing control methods generally face problems such as weak fault tolerance, slow convergence speed, and insufficient robustness.

[0003] Sliding mode control, due to its strong robustness and insensitivity to external disturbances, has been widely used in power system control. However, traditional sliding mode control typically only guarantees asymptotic convergence of the system state, with the convergence time depending on the initial state, failing to meet the rapid response requirements under high real-time demands. In recent years, fixed-time sliding mode control has been proposed and applied to complex dynamic systems. Its advantage lies in the fact that regardless of the initial state, the system state can converge within a predetermined time, significantly improving the predictability and timeliness of control. However, existing fixed-time sliding mode control methods still lack the ability to dynamically perceive and adaptively adjust fault propagation characteristics when facing combined sensor and actuator faults, making it difficult to achieve synergistic optimization of fault-tolerant control and rapid convergence.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a novel fixed-time control method and device for power systems, aiming to solve the technical problem that existing power system control methods, when facing sensor and actuator failures, are unable to simultaneously consider the dynamic perception of fault propagation characteristics, rapid fault-tolerant response, and the coordinated optimization of fixed-time convergence, resulting in insufficient system robustness, real-time performance, and stability.

[0006] To achieve the above objectives, the present invention provides a novel fixed-time control method for power systems, the method comprising: Acquire power system topology data, perform fault propagation path analysis on the system based on the power system topology data, generate fault-related topology data, and use the fault-related topology data to perform virtual simulation modeling on the power system topology data to generate a dynamic model of the power system. Key control nodes of the power system dynamic model are extracted, and the fault impact range of the key control nodes is analyzed to generate fault propagation dynamic data. Fault characteristic signals are collected based on the key control nodes to obtain fault dynamic response data. Calculate the state deviation of the fault dynamic response data, perform adaptive fault-tolerant control analysis on the state deviation through fault propagation dynamic data, generate system fault-tolerant control parameters, and use the system fault-tolerant control parameters to reconstruct the sliding surface of the control strategy to generate an adaptive sliding mode control law. The system acquires real-time operating data of the power system, extracts the rate of change of state of the real-time operating data of the power system, and performs fixed-time convergence optimization on the adaptive sliding mode control law based on the rate of change of state to obtain a dynamic control path that minimizes the convergence time. Based on the dynamic control path that minimizes the convergence time, the control parameters are adjusted, and the adjustment results are fed back to the control system to realize the fault adaptive fixed-time sliding mode closed-loop control and stability risk early warning operation of the power system.

[0007] Optionally, the steps of acquiring power system topology data, performing fault propagation path analysis on the system based on the power system topology data to generate fault-related topology data, and using the fault-related topology data to perform virtual simulation modeling on the power system topology data to generate a power system dynamic model include: Acquire power system topology data, identify network nodes in the power system topology data to extract key control unit data; construct a fault propagation directed graph based on the key control unit data to generate the initial system topology graph; Network flow analysis is performed on the initial topology of the system to extract the fault correlation of nodes, generate fault correlation topology data, and use the fault correlation topology data to perform dynamic characteristic modeling of key control unit data to generate system dynamic mapping data. Virtual simulation modeling is performed based on the system dynamic mapping data to generate an initial dynamic model of the system; parameter identification and stability verification are performed on the initial dynamic model of the system to generate a dynamic model of the power system.

[0008] Optionally, the step of extracting key control nodes from the dynamic model of the power system, analyzing the fault impact range of the key control nodes, generating fault propagation dynamic data, and collecting fault characteristic signals based on the key control nodes to obtain fault dynamic response data includes: Sensitivity analysis was performed on the dynamic model of the power system to obtain the set of key control nodes; based on the set of key control nodes, a fault propagation adjacency matrix was constructed to obtain node fault correlation data. Perform topology calculations on the impact range of node fault correlation data to generate dynamic fault propagation data; Based on the set of key control nodes, fault signal acquisition points are deployed to generate signal acquisition layout data; multi-channel synchronous sampling control is performed on the signal acquisition layout data to obtain the original fault response data. Noise suppression and feature extraction are performed on the original fault response data to generate dynamic fault response data.

[0009] Optionally, the step of performing influence range topology calculations on the node fault correlation data to generate fault propagation dynamic data includes: The fault propagation probability is calculated from the node fault correlation data, high-risk propagation paths are extracted, and fault propagation network data is generated; the time delay characteristics of the fault propagation network data are analyzed, propagation time parameters are extracted, and fault propagation time delay characteristic data is generated. Spatiotemporal diffusion modeling is performed on the fault propagation time delay characteristic data, and the diffusion process is dynamically simulated to generate an initial fault propagation dynamic field. Calculate the fault propagation coefficient between nodes; adjust the weight of the initial fault propagation dynamic field using the fault propagation coefficient to generate a weighted fault propagation graph; perform propagation range integration and time-domain evolution analysis on the weighted fault propagation graph to calculate the fault arrival time of nodes and generate propagation time series matrix data. The failure impact is assessed using the propagation time series matrix data, generating dynamic data on failure propagation.

[0010] Optionally, the step of performing spatiotemporal diffusion modeling on the fault propagation time delay characteristic data and dynamically simulating the diffusion process to generate an initial fault propagation dynamic field includes: Spatiotemporal decoupling processing is performed on the fault propagation time delay characteristic data to separate the time delay and spatial distance parameters and generate a propagation spatiotemporal parameter set; based on the propagation spatiotemporal parameter set, a three-dimensional spatiotemporal grid is partitioned to divide the system space into discretized propagation units and generate a spatiotemporal grid unit set; Perform propagation probability weight allocation on the spatiotemporal grid cell set, calibrate the fault reception probability and propagation attenuation coefficient of each cell, and generate a propagation weight matrix; Based on the propagation weight matrix, the fault propagation direction vector decomposition is performed to extract the mainstream propagation direction and the side propagation components between units, and a local propagation direction map is generated. The local propagation pattern is spliced ​​and reconstructed at the system-wide scale to generate a unified initial fault propagation dynamic field.

[0011] Optionally, the calculation of the state deviation of the fault dynamic response data, the adaptive fault-tolerant control analysis of the state deviation using fault propagation dynamic data to generate system fault-tolerant control parameters, and the sliding surface reconstruction of the control strategy using the system fault-tolerant control parameters to generate an adaptive sliding mode control law, including: Perform state estimation and deviation calculation on the fault dynamic response data to generate system state deviation data; identify the fault type from the system state deviation data to determine the corresponding fault-tolerant control strategy. Fault mode classification is performed on the dynamic data of fault propagation to generate fault mode feature data; coupled analysis is performed on the system state deviation data and fault mode feature data to implement adaptive fault-tolerant control parameter tuning and generate system fault-tolerant control parameters. The robustness of the system's fault-tolerant control parameters is verified, and the verification results are used to optimize the parameters and generate optimized fault-tolerant control parameters. Based on the optimization of fault-tolerant control parameters, the traditional sliding surface is dynamically reconstructed, a fixed-time convergent sliding surface function is designed, and an adaptive sliding control law is generated.

[0012] Optionally, the step of dynamically reconstructing the traditional sliding surface based on optimized fault-tolerant control parameters, designing a fixed-time convergent sliding surface function, and generating an adaptive sliding mode control law includes: An adaptive adjustment mechanism for sliding surface parameters is constructed based on optimized fault-tolerant control parameters, and dynamic adjustment rules for the sliding surface are generated. A non-singular fast terminal sliding surface is designed based on the dynamic adjustment rules for the sliding surface, and an initial sliding surface function is generated. A fixed-time stability analysis is performed on the initial sliding surface function to calculate the upper bound of the convergence time and generate convergence performance index data. Multi-objective optimization is then performed on the convergence performance index data to select the optimal sliding surface parameters and generate the optimized sliding surface function. The control law for the optimized sliding mode surface function is derived, and an adaptive sliding mode control law is generated by combining it with the design of an adaptive disturbance estimator. The adaptive sliding mode control law is proven to have Lyapunov stability, ensuring that the system converges within a fixed time, and finally generating the adaptive sliding mode control law.

[0013] Optionally, the steps of acquiring real-time operating data of the power system, extracting the state change rate of the real-time operating data of the power system, and performing fixed-time convergence optimization on the adaptive sliding mode control law based on the state change rate to obtain a dynamic control path that minimizes the convergence time, adjusting control parameters based on the dynamic control path that minimizes the convergence time, and feeding the adjustment results back to the control system, to realize fault adaptive fixed-time sliding mode closed-loop control and stability risk early warning operation of the power system, include: Acquire real-time operating data of the power system, extract the derivatives and fluctuation amplitudes of the state variables from the real-time operating data of the power system to obtain dynamic data of state changes; perform spectrum analysis and abrupt point detection on the dynamic data of state changes to generate dynamic characteristic data of state. The dynamic characteristic data of the state are simulated in a closed loop with the adaptive sliding mode control law. The convergence performance under different control parameters is analyzed, and a graph of the relationship between control parameters and convergence time is generated. Based on the control parameter-convergence time relationship graph, a fixed-time convergence optimization objective function is constructed, the optimal combination of control parameters is solved, and a dynamic control path that minimizes the convergence time is generated. The dynamic control path that minimizes the convergence time is used to adjust the control parameters of the power system online and generate real-time control commands. Real-time control commands are input into the dynamic model of the power system for closed-loop control simulation verification, and the stability margin of the system response is evaluated to perform early warning of stability risks in the power system.

[0014] Optionally, the step of using a dynamic control path that minimizes convergence time to adjust the control parameters of the power system online and generate real-time control commands includes: A dynamic control path that minimizes convergence time is used to identify fault types in the power system in real time, thereby obtaining fault type data; Based on the fault type data, the control gain of the dynamic control path that minimizes the convergence time is adaptively adjusted to generate a gain adjustment coefficient; the control parameters are tuned online based on the gain adjustment coefficient, and the first control optimization is performed on the dynamic control path that minimizes the convergence time based on the tuning results to generate the first control optimization data; The power system is subjected to disturbance observation based on a dynamic control path that minimizes the convergence time, and the disturbance estimate is obtained. The disturbance estimate is then subjected to feedforward compensation control to generate second control optimization data. The first and second control optimization data are integrated into real-time control commands.

[0015] Furthermore, to achieve the above objectives, the present invention also provides a novel power system fixed-time control device, the device comprising: a memory, a processor, and a novel power system fixed-time control program stored in the memory and executable on the processor, the novel power system fixed-time control program being configured to implement the steps of the novel power system fixed-time control method as described above.

[0016] This invention provides a novel fixed-time control method for power systems. By integrating power system topology analysis and fault propagation path modeling, the method achieves precise location and impact range prediction of sensor and actuator faults, improving the foresight and accuracy of fault perception. Through key control node identification, fault dynamic response analysis, and adaptive fault-tolerant control parameter tuning, it enhances the system's fault tolerance and robustness under fault conditions. Through sliding mode surface dynamic reconstruction and fixed-time convergence optimization, it ensures rapid convergence of the system state within a predictable timeframe, unaffected by initial conditions, significantly improving the real-time performance and stability of the control response. Combined with real-time data-driven dynamic control path optimization and a closed-loop feedback mechanism, it enables online adaptive adjustment of control parameters and timely early warning of system stability risks. The overall method effectively solves the problems of slow convergence, poor robustness, and weak fault tolerance of traditional control strategies in complex fault scenarios, significantly improving the safe operation level and autonomous recovery capability of new power systems under uncertain environments. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an embodiment of the novel fixed-time control method for power systems according to the present invention. Figure 2 This is a schematic diagram illustrating the specific steps involved in obtaining dynamic fault response data in one embodiment of the novel fixed-time control method for power systems according to the present invention. Figure 3 This is a schematic diagram illustrating the specific steps involved in generating an adaptive sliding mode control law in one embodiment of the novel fixed-time control method for power systems according to the present invention.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0020] Reference Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the novel fixed-time control method for power systems according to the present invention.

[0021] In one embodiment, the novel power system fixed-time control method includes: Step S100: Obtain power system topology data, perform fault propagation path analysis on the system based on the power system topology data, generate fault-related topology data, and use the fault-related topology data to perform virtual simulation modeling on the power system topology data to generate a dynamic model of the power system.

[0022] The fault-related topology data can be structured data constructed based on the power system topology and fault propagation mechanism, characterizing the propagation paths of sensor and actuator faults in the system and their mutual influence relationships. It can provide structured prior information for fault location and impact range prediction, supporting the accurate deployment of subsequent fault-tolerant control strategies. In this embodiment, the fault-related topology data can be generated by analyzing the power system topology and combining it with a fault propagation dynamics model to simulate the fault injection and diffusion process in a virtual simulation environment. Furthermore, the fault-related topology data can include, but is not limited to, one or more of the following: inter-node fault coupling relationship data, branch-level fault propagation path data, and regional-level fault influence domain data.

[0023] Fault propagation path analysis based on power system topology data generates fault-related topology data. This can be achieved by using the power system topology as input to a graph model and combining it with fault propagation rules (such as electrical distance and control dependence) for path deduction. Furthermore, this operation can be implemented by injecting typical fault modes into a virtual simulation environment and tracking their propagation trajectories. This allows for structured modeling of the combined fault propagation mechanism of sensors and actuators, improving the foresight of fault perception.

[0024] Step S200: Extract the key control nodes of the power system dynamic model, analyze the fault impact range of the key control nodes, generate fault propagation dynamic data, collect fault characteristic signals based on the key control nodes, and obtain fault dynamic response data.

[0025] Key control nodes can be control devices or monitoring points that play a significant role in regulating system stability in the power system dynamic model and occupy a pivotal position in the fault propagation path. They can be used as core points for fault-tolerant control parameter tuning and sliding mode surface reconstruction, improving the efficiency of control resource utilization. In an exemplary embodiment, key control nodes can be identified using graph theory or control theory methods such as node degree centrality, betweenness centrality, or control energy efficiency indicators. For example, key control nodes may include voltage regulation key nodes, frequency response dominant nodes, and power flow control pivot nodes. Fault propagation dynamic data can be time-series evolution data describing the spread of the influence of key control nodes in the system over time under specific fault excitations. It can be used to quantify the expansion characteristics of faults in the spatial and temporal dimensions, providing dynamic boundary conditions for adaptive fault-tolerant control. Fault dynamic response data can be real-time signal sequences collected from key control nodes, reflecting the deviation of system state variables from their normal trajectories under fault disturbances. It can be used to calculate the state deviation degree, serving as a direct basis for fault-tolerant control parameter tuning.

[0026] Key control nodes in the dynamic model of a power system are extracted, and the fault impact range of these nodes is analyzed to generate dynamic fault propagation data. This can be achieved by identifying nodes sensitive to system stability in the dynamic model and simulating their state propagation process after a fault. Furthermore, this operation can be implemented by applying step or pulse-type fault excitations in the simulation environment and recording the system response, thereby quantifying the dynamic impact boundary of the fault in the system and providing spatiotemporal constraints for fault-tolerant control.

[0027] Step S300: Calculate the state deviation of the fault dynamic response data, perform adaptive fault-tolerant control analysis on the state deviation using the fault propagation dynamic data, generate system fault-tolerant control parameters, and use the system fault-tolerant control parameters to reconstruct the sliding surface of the control strategy to generate an adaptive sliding mode control law.

[0028] The system fault-tolerant control parameters can be an adjustable set of parameters that are adjusted online based on the dynamic response and propagation characteristics of faults to enhance the robustness of the control law. These parameters can be used to drive sliding surface reconstruction, enabling the control law to adapt to combined sensor and actuator faults. The adaptive sliding mode control law can be a nonlinear control law generated based on the reconstructed sliding surface, possessing fault-adaptive capabilities. It can be used to maintain system stability and improve robustness and timeliness even in the presence of measurement distortion or execution failure. In a specific embodiment, the adaptive sliding mode control law can dynamically adjust the traditional sliding surface using system fault-tolerant control parameters, combined with a fixed-time convergence design to generate the control input.

[0029] Adaptive fault-tolerant control analysis of state deviation using fault propagation dynamic data generates system fault-tolerant control parameters. These parameters are then used to reconstruct the sliding surface of the control strategy, generating an adaptive sliding mode control law. This can be achieved by fusing state deviation with fault propagation dynamic data to calculate control parameters adapted to the current fault scenario, and adjusting the sliding surface function accordingly. Furthermore, this operation can employ the Lyapunov function derivative constraint method to tune the fault-tolerant parameters and reconstruct the sliding surface, or use a reinforcement learning strategy to adjust the sliding surface coefficients online to match the current fault mode. This allows the sliding mode control law to possess adaptive capabilities for complex faults, overcoming the insufficient robustness of traditional methods.

[0030] Step S400: Obtain real-time operating data of the power system, extract the state change rate of the real-time operating data of the power system, and perform fixed-time convergence optimization on the adaptive sliding mode control law according to the state change rate to obtain the dynamic control path with the minimum convergence time. Adjust the control parameters based on the dynamic control path with the minimum convergence time, and feed the adjustment results back to the control system to realize the fault adaptive fixed-time sliding mode closed-loop control and stability risk early warning operation of the power system.

[0031] The dynamic control path that minimizes the convergence time can be a control trajectory that, under a fixed-time convergence constraint, optimizes the control law parameters to bring the system state to equilibrium as quickly as possible. This can be used to ensure that the system state converges within a preset time limit, unaffected by the initial state, thus improving real-time control performance. In an exemplary embodiment, the dynamic control path that minimizes the convergence time can be solved by optimizing the convergence time of the adaptive sliding mode control law based on the real-time state change rate to obtain the optimal control parameter configuration.

[0032] Fixed-time convergence optimization of the adaptive sliding mode control law based on the rate of change of state yields a dynamic control path that minimizes the convergence time. This can be achieved by evaluating the current convergence speed using the real-time rate of change of state and adjusting the control law gain to minimize the theoretical upper limit of convergence time. Furthermore, this operation can be further refined by back-calculating the optimal control gain configuration based on the fixed-time stability theorem, or by using gradient descent to optimize the convergence time objective function online. This ensures that the system state converges within a preset time window, improving the determinism and real-time performance of the control response.

[0033] For example, in scenarios involving sensor-actuator composite fault handling in power grids with high penetration of new energy sources, the novel fixed-time control method for power systems in this embodiment can be applied to situations where, in a regional power grid with a high proportion of wind power integration, the voltage sensor of a key substation experiences output distortion due to electromagnetic interference, and simultaneously, some of its associated SVG actuators fail. The system first generates fault-related topology data based on the power grid topology, identifying the substation as a key control node. Then, it collects dynamic response data such as voltage and reactive power, and calculates the state deviation based on the fault propagation dynamic data. Based on this, it adjusts the system's fault-tolerant control parameters, reconstructs the sliding surface to generate an adaptive sliding mode control law, and finally optimizes the control law based on the real-time state change rate, generating a dynamic control path that minimizes the convergence time, enabling the system voltage to recover to a safe range within 2 seconds, and simultaneously triggering a stability risk warning.

[0034] In one embodiment, power system topology data is acquired, fault propagation path analysis is performed on the system based on the power system topology data to generate fault-related topology data, and virtual simulation modeling is performed on the power system topology data using the fault-related topology data to generate a dynamic power system model, including: Acquire power system topology data, identify network nodes in the power system topology data to extract key control unit data; construct a fault propagation directed graph based on the key control unit data to generate the initial system topology graph; The key control unit data can be a set of control devices or monitoring points identified from the power system topology that play a dominant role in fault propagation and system stability, along with their attribute information. This data can serve as the basic unit for constructing a directed fault propagation graph and dynamic modeling, improving the model's accuracy in depicting key areas. In this embodiment, the key control unit data can be generated based on power system topology data, filtered through node electrical centrality, control sensitivity, or observability and controllability indicators. For example, the key control unit data can include, but is not limited to, one or more of voltage support unit data, frequency regulation unit data, and reactive power compensation unit data. The directed fault propagation graph can be a directional graph structure established between key control units, used to describe the causal path of fault propagation from the source node to downstream nodes. It can provide directional constraints for network flow analysis, enabling causal interpretability of fault correlations. In an exemplary embodiment, the directed fault propagation graph can model the fault impact relationships between key control units as directed edges based on physical connection direction, control dependencies, or information flow direction.

[0035] The initial system topology can be a graph model representing the initial structure of the system, constructed with key control units as nodes and their electrical or control edges. This model can serve as the input basis for network flow analysis and support fault correlation extraction. Furthermore, the initial system topology can be generated by constructing a directed fault propagation graph based on key control unit data. This can be done by using key control units as graph nodes and establishing directed edges according to their physical connections, control command flows, or information interaction directions, forming a directed initial topology. In a specific embodiment, this operation can transform an undirected topology into a directed graph with fault propagation causal logic, thereby providing a directional basis for subsequent network flow analysis.

[0036] Network flow analysis is performed on the initial topology of the system to extract the fault correlation of nodes, generate fault correlation topology data, and use the fault correlation topology data to perform dynamic characteristic modeling of key control unit data to generate system dynamic mapping data. The system dynamic mapping data can be structured data that integrates fault-related topology data and the dynamic response characteristics of key control units. This data is used to characterize the time-varying behavior of each unit under fault excitation and can support the generation of a power system dynamic model with embedded fault propagation mechanisms, thus improving simulation realism. In an exemplary embodiment, the system dynamic mapping data can use fault-related topology data as coupling weights, embedding it into the differential equations or state-space models of key control units to form a coupled dynamic system description.

[0037] Network flow analysis is performed on the initial system topology to extract node fault correlations and generate fault-related topology data. This can be achieved by simulating virtual fault flows (such as maximum flow, minimum cut, or information diffusion models) on a directed fault propagation graph, quantifying the intensity of fault impact and path dependence between nodes. Furthermore, this operation can be implemented by calculating the fault propagation capacity between nodes using the maximum flow-minimum cut theorem, or by evaluating the impact of node faults based on PageRank or random walk models. This transforms the topology into fault-related data with quantified weights and directionality, enabling structured modeling of complex fault propagation paths. The fault-related topology data is then used to model the dynamic characteristics of key control unit data, generating dynamic system mapping data. This can be achieved by embedding the fault-related topology data as coupling coefficients into the state equations or transfer functions of key control units to construct interconnected dynamic system models. For example, this operation can be achieved by constructing a system of coupled differential-algebraic equations (where the coupling terms are weighted by fault-related topology data) or by using a graph neural network structure (with the fault-related topology as the adjacency matrix, learning the dynamic response mapping of nodes), so that the dynamic model not only reflects the static connection, but also embeds the dynamic interaction mechanism under fault disturbance, thereby improving the simulation's ability to reproduce the real fault response.

[0038] Virtual simulation modeling is performed based on the system dynamic mapping data to generate an initial dynamic model of the system; parameter identification and stability verification are performed on the initial dynamic model of the system to generate a dynamic model of the power system.

[0039] For example, in the scenario of regional power grid fault modeling with a multi-terminal flexible DC transmission system, the novel fixed-time control method of the power system in this embodiment can identify key control units such as converter stations and STATCOMs from the entire network topology; construct a directed fault propagation graph based on the flow of their control commands and electrical coupling relationship, and generate an initial system topology graph; then calculate the fault conduction intensity caused by DC voltage instability between each converter station in network flow analysis, and generate fault-related topology data; finally, use this data as coupling weights and embed it into the dynamic model of each converter station to form system dynamic mapping data, which is used to construct a power system dynamic model that can accurately reproduce the DC-side fault propagation and AC-side voltage collapse process.

[0040] In one embodiment, key control nodes of the power system dynamic model are extracted, and fault impact range analysis is performed on the key control nodes to generate fault propagation dynamic data. Based on the key control nodes, fault characteristic signals are collected to obtain fault dynamic response data, including: Sensitivity analysis was performed on the dynamic model of the power system to obtain the set of key control nodes; based on the set of key control nodes, a fault propagation adjacency matrix was constructed to obtain node fault correlation data. Perform topology calculations on the impact range of node fault correlation data to generate dynamic fault propagation data; Based on the set of key control nodes, fault signal acquisition points are deployed to generate signal acquisition layout data; multi-channel synchronous sampling control is performed on the signal acquisition layout data to obtain the original fault response data. Noise suppression and feature extraction are performed on the original fault response data to generate dynamic fault response data.

[0041] The key control node set can be a group of control nodes highly sensitive to system stability, selected from the power system dynamic model through sensitivity analysis. This set can be used to provide a focused target for fault impact analysis, signal acquisition and deployment, and fault-tolerant control, improving resource deployment efficiency. In this embodiment, the key control node set can be quantified based on the partial derivatives or energy function gradients of the system state variables with respect to the control input, quantifying the contribution of each node to the dynamic regulation of the system, and selecting nodes above a threshold to form the set. For example, the key control node set can include, but is not limited to, one or more of the following: voltage sensitivity-dominant node set, frequency response-highly sensitive node set, and damping torque key node set. Sensitivity analysis of the power system dynamic model yields the key control node set by calculating the sensitivity index of the system state to the input of each control node, sorting them by importance, and then selecting them to form the set. Furthermore, this operation can be achieved through full-system dynamic simulation combined with gradient evaluation, thereby enabling accurate identification of high-influence control nodes and avoiding computational redundancy caused by traversing the entire network.

[0042] The fault propagation adjacency matrix can be a directed weighted graph matrix constructed with the set of key control nodes as vertices and fault coupling strength as edge weights. It is used to characterize the topology of fault propagation between nodes and can be used to transform the fault propagation mechanism into a computable algebraic structure, supporting subsequent quantitative analysis of correlation and impact range. In an exemplary embodiment, the fault propagation adjacency matrix can be constructed based on state coupling terms or information flow dependencies in the power system dynamic model, calculating the fault propagation gain between node pairs and filling the matrix elements. Constructing the fault propagation adjacency matrix based on the set of key control nodes to obtain node fault correlation data can be achieved by using coupling terms in the dynamic model or simulation injection methods to quantify the fault propagation strength between nodes and construct the matrix, thereby calculating the correlation. Furthermore, this operation can be implemented by combining time-domain perturbation injection and frequency-domain response fitting, thereby transforming unstructured fault propagation relationships into structured graph data, supporting subsequent topology calculations.

[0043] Node fault correlation data can be a set of quantitative indicators derived from the fault propagation adjacency matrix, reflecting the intensity of mutual influence among key control nodes under fault excitation. This data can be used as input for influence range topology calculations to identify core paths and vulnerable points in fault propagation. Performing influence range topology calculations on the node fault correlation data to generate dynamic fault propagation data can be achieved by applying graph search or diffusion models to extrapolate the spatial expansion boundary of fault impact on the correlation graph. Furthermore, this operation can be implemented using the PageRank algorithm or random walk models, thereby achieving a mapping from local correlations to global impacts and improving fault prediction capabilities.

[0044] Signal acquisition layout data can be a sensor deployment scheme planned based on a set of key control nodes, including acquisition point locations, channel configurations, and synchronization triggering strategies. This data can be used to ensure the integrity of fault characteristic signals in terms of spatial coverage and temporal synchronization, providing hardware support for high-fidelity response data acquisition. In a specific embodiment, signal acquisition layout data can include, but is not limited to, centralized acquisition layouts, distributed edge acquisition layouts, and hybrid hierarchical acquisition layouts. Deploying fault signal acquisition points based on a set of key control nodes to generate signal acquisition layout data can involve mapping key control nodes to physical measurement and control points, and designing the acquisition topology in conjunction with communication latency and sampling accuracy constraints. Furthermore, this operation can optimize deployment locations by considering network bandwidth and node reachability, thereby optimizing sensor resource configuration and ensuring comprehensive signal coverage in key areas.

[0045] The raw fault response data can be unprocessed time-series measurement signals obtained from the signal acquisition layout through multi-channel synchronous sampling. This data can be used as the raw input for noise suppression and feature extraction, preserving dynamic details in the early stages of the fault. Multi-channel synchronous sampling control of the signal acquisition layout data to obtain the raw fault response data can be achieved by configuring a sampling clock synchronization mechanism based on the layout data, triggering simultaneous acquisition by multiple ADCs. Furthermore, this operation can achieve microsecond-level synchronization using the IEEE 1588 precision time protocol, or by uniformly triggering each acquisition unit based on a GPS timing module, thus ensuring the time alignment of multi-source signals and avoiding feature misjudgment caused by phase distortion. Noise suppression and feature extraction of the raw fault response data to generate dynamic fault response data can be performed by applying filtering and transform methods to remove interference components and retain dynamic features related to the fault mode. Furthermore, this operation can extract instantaneous features using wavelet threshold denoising combined with Hilbert-Huang transform, or by using adaptive notch filtering combined with principal component analysis to extract dominant modes, thereby improving the signal-to-noise ratio and discriminative power of the response data and providing a reliable basis for calculating state deviation.

[0046] Taking the composite fault monitoring of a multi-terminal flexible DC transmission system as an example, the novel fixed-time control method for power systems in this embodiment can be implemented in an AC / DC hybrid system containing a voltage source type HVDC transmission system. First, sensitivity analysis is performed on the dynamic model to identify the key control node set, including the AC side bus of the converter station and the DC voltage control point. Based on this, a fault propagation adjacency matrix is ​​constructed, and the fault correlation degree between each node is calculated. The topology diffusion algorithm is used to determine that if a converter valve sensor fails, its impact will affect the areas of two adjacent AC feeders. Simultaneously, synchronous acquisition terminals are deployed at key nodes to generate signal acquisition layout data. When the system experiences a disturbance, multi-channel synchronous sampling is used to acquire the original response data. After wavelet denoising and mode decomposition, high-fidelity fault dynamic response data is generated for subsequent fault-tolerant control parameter tuning.

[0047] In one embodiment, the influence range topology calculation is performed on the node fault correlation data to generate fault propagation dynamic data, including: Calculate the probability of fault propagation from the node fault correlation data, extract high-risk propagation paths, and generate fault propagation network data; The fault propagation network data can be a directed graph structure composed of high-risk propagation paths, representing the topological channels through which faults are most likely to spread between critical control nodes. This data can be used as a foundational input for time-delay analysis and dynamic field modeling, focusing on high-probability propagation paths to improve computational efficiency.

[0048] Time delay characteristics of fault propagation network data are analyzed to extract propagation time parameters and generate fault propagation time delay characteristic data; The fault propagation delay characteristic data can be a set of parameters describing the time delay required for a fault to propagate along the propagation path between different nodes, reflecting the system's dynamic response inertia and communication / control link delay. In an exemplary embodiment, the fault propagation delay characteristic data can be based on fault propagation network data, combined with system differential equations or measured response curves, to extract the propagation time constant or delay interval on each path. Furthermore, the fault propagation delay characteristic data can provide a time dimension constraint for spatiotemporal diffusion modeling, solving the prediction bias caused by traditional methods ignoring propagation delays. For example, the fault propagation delay characteristic data can include, but is not limited to, one or more of the following: electrical quantity propagation delay data, control command loop delay data, and state estimation update delay data.

[0049] Spatiotemporal diffusion modeling is performed on the fault propagation time delay characteristic data, and the diffusion process is dynamically simulated to generate the initial fault propagation dynamic field; The initial fault propagation dynamic field can be an initial spatiotemporal distribution model describing the continuous spatial diffusion process of fault disturbances, constructed based on fault propagation time delay characteristic data under unweighted correction conditions. The initial fault propagation dynamic field can be used as a reference field for weighted correction, reflecting the original dynamic form of fault propagation.

[0050] Calculate the fault propagation coefficient between nodes; adjust the weight of the initial fault propagation dynamic field using the fault propagation coefficient to generate a weighted fault propagation graph; The weighted fault propagation graph can be an enhanced propagation graph formed by weighting the initial fault propagation dynamic field by introducing fault transmission coefficients between nodes and adjusting their amplitude and direction. In a specific embodiment, the weighted fault propagation graph can use fault transmission coefficients (such as gain, damping ratio, or information flow intensity) as edge weights to modulate the influence intensity of each path in the dynamic field. The weighted fault propagation graph can be used to enable the propagation model to distinguish between strongly coupled and weakly coupled paths, improving the accuracy of fault influence range prediction. For example, the weighted fault propagation graph can be a gain-weighted propagation graph, a phase-sensitive weighted propagation graph, or an energy flow-weighted propagation graph.

[0051] The propagation range integral and time-domain evolution analysis are performed on the weighted fault propagation graph to calculate the fault arrival time of nodes and generate propagation time series matrix data. The propagation time series matrix data can be a two-dimensional time series structure that records the expected time sequence and arrival time of each node under fault disturbance. This data can be used to quantify the spatiotemporal evolution trajectory of a fault in the system, providing a temporal basis for impact assessment and control scheduling. For example, the propagation time series matrix data can include, but is not limited to, one or more of the following: earliest arrival time matrix, latest safe response time matrix, and multi-fault source superposition time series matrix.

[0052] The failure impact is assessed using the propagation time series matrix data, generating dynamic data on failure propagation.

[0053] Fault impact assessment of propagation time-series matrix data can generate dynamic fault propagation data. This can be achieved by combining node importance indicators with time-series arrival information to comprehensively evaluate the system-level impact of each node being disturbed. Furthermore, generating dynamic fault propagation data can be achieved by using a time-weighted entropy method to assess the severity of node fault consequences, or by constructing an impact function based on the product of arrival time and control margin. This transforms pure time-series information into dynamic impact data with engineering significance, supporting subsequent fault-tolerant control decisions.

[0054] For example, in the scenario of cascading fault early warning in a cross-regional interconnected power grid, the novel fixed-time control method of the power system in this embodiment can be as follows: after a voltage sensor drift fault occurs in a certain regional power grid, the system first identifies high-risk propagation paths to form fault propagation network data; then it analyzes the control loop delay and electrical propagation inertia on each path to generate fault propagation time delay characteristic data; based on this, it constructs an initial fault propagation dynamic field and uses the voltage-reactive power sensitivity between nodes as fault transmission coefficients for weighting to obtain a weighted fault propagation map; further, it calculates the arrival time of fault disturbances at each key substation to form propagation time sequence matrix data; finally, it combines the load level and time sequence information of each station to evaluate the impact, generates fault propagation dynamic data, and provides an early warning of possible voltage instability in the downstream area 3 seconds in advance, triggering adaptive sliding mode control law adjustment.

[0055] In one embodiment, spatiotemporal diffusion modeling is performed on the fault propagation time delay characteristic data, and dynamic simulation of the diffusion process is conducted to generate an initial fault propagation dynamic field, including: Spatiotemporal decoupling processing is performed on the fault propagation time delay characteristic data to separate the time delay and spatial distance parameters and generate a propagation spatiotemporal parameter set. The propagation spatiotemporal parameter set can be an independent set of parameters, representing time delay and spatial distance characteristics respectively, obtained from fault propagation delay feature data after spatiotemporal decoupling. This set can be used to eliminate spatiotemporal coupling interference, providing clear dimensional input for subsequent mesh generation and propagation modeling. In this embodiment, the propagation spatiotemporal parameter set can be obtained by separating the mixed time and space factors in the original time-delay data through methods such as singular value decomposition, principal component analysis, or physical model inversion. For example, the propagation spatiotemporal parameter set can include, but is not limited to, one or more subsets of time delay parameters, electrical distance parameters, and communication topology distance parameters. Performing spatiotemporal decoupling processing on the fault propagation delay feature data to separate time delay and spatial distance parameters and generate the propagation spatiotemporal parameter set can be achieved by using signal processing or system identification methods to decompose the mixed time-delay data into components that depend only on time or only on space. Furthermore, this operation can be achieved through the aforementioned mathematical or physical inversion methods, thereby eliminating spatiotemporal aliasing effects and improving the physical interpretability and computational accuracy of subsequent modeling.

[0056] Based on the propagation spatiotemporal parameter set, a three-dimensional spatiotemporal grid is partitioned, dividing the system space into discretized propagation units and generating a spatiotemporal grid unit set; The spatiotemporal grid unit set can be a set of discretized basic propagation units divided in a three-dimensional spatiotemporal domain (two-dimensional space + one-dimensional time) based on the propagation spatiotemporal parameter set. It can be used to discretize the continuous propagation process, facilitating numerical simulation and probability weight allocation. In an exemplary embodiment, the spatiotemporal grid unit set can be spatially divided by region or equipment granularity, and temporally layered by sampling period or dynamic response scale, based on the system topology node distribution and typical propagation velocity. Exemplarily, the spatiotemporal grid unit set can include, but is not limited to, substation-level spatiotemporal units, feeder section-level spatiotemporal units, and bus-line coupled spatiotemporal units.

[0057] Based on the propagation spatiotemporal parameter set, a three-dimensional spatiotemporal mesh is generated, dividing the system space into discretized propagation units and producing a spatiotemporal mesh unit set. This can be achieved by constructing a non-uniform or adaptive mesh based on spatial topology density and time response scale, covering key control node regions. Furthermore, this operation can be implemented using a structured mesh generation algorithm, thereby enabling a structured discrete representation of the propagation space of complex power grids and supporting refined simulation.

[0058] Perform propagation probability weight allocation on the spatiotemporal grid cell set, calibrate the fault reception probability and propagation attenuation coefficient of each cell, and generate a propagation weight matrix; The propagation weight matrix can be a two-dimensional weight structure, assigned to each spatiotemporal grid cell, containing the fault reception probability and propagation attenuation coefficient. It can be used to quantify the sensitivity and energy attenuation characteristics of each cell to fault disturbances, supporting direction vector decomposition and dynamic field construction. Propagation probability weight assignment is performed on the spatiotemporal grid cell set, calibrating the fault reception probability and propagation attenuation coefficient of each cell to generate the propagation weight matrix. This can be achieved by combining historical fault data, system damping characteristics, or Monte Carlo simulations to assign probability and attenuation parameters to each cell. Furthermore, this operation can be implemented through statistical modeling or stochastic process simulation, thereby enabling the model to have differentiated expressions of propagation capabilities in different regions, enhancing physical realism.

[0059] Based on the propagation weight matrix, the fault propagation direction vector decomposition is performed to extract the mainstream propagation direction and the side propagation components between units, and a local propagation direction map is generated. The local propagation pattern can be a vector representation of the dominant fault propagation direction and its side components, decomposed from the propagation weight matrix within a single or adjacent spatiotemporal grid cell. It can be used to characterize the propagation trend of the fault in a local region, providing a basis for directional consistency in the overall system assembly. In a specific embodiment, the local propagation pattern may include, but is not limited to, a radial dominant propagation pattern, annular diffusion component patterns, and multi-source interferometric patterns.

[0060] The local propagation pattern is spliced ​​and reconstructed at the system-wide scale to generate a unified initial fault propagation dynamic field.

[0061] The initial fault propagation dynamic field can be an unweighted, continuous field model reconstructed at the system-wide scale, describing the diffusion pattern of fault disturbances in the spatiotemporal grid. This model can be used to completely preserve the original propagation dynamic characteristics. Reconstructing the local propagation direction map at the system-wide scale generates a unified initial fault propagation dynamic field. This can be achieved through direction field interpolation, vector field fusion, or graph neural network concatenation of local direction information to form a globally consistent dynamic field. Furthermore, this operation can be implemented using a field reconstruction algorithm, thereby completely restoring the diffusion pattern and dominant path of the fault throughout the network, providing a high-fidelity foundation for subsequent weighted correction.

[0062] For example, in the scenario of voltage collapse early warning in a high-density power distribution network in the core urban area, the novel fixed-time control method of the power system in this embodiment can be as follows: When a photovoltaic inverter causes abnormal reactive power output due to communication interruption, the system first performs spatiotemporal decoupling on the existing fault propagation time delay characteristic data, separating the communication delay (time parameter) and electrical distance (spatial parameter) to form a propagation spatiotemporal parameter set; based on this, a spatiotemporal grid unit set centered on the substation is divided in the three-dimensional space of the urban power distribution network; each unit is assigned an attenuation coefficient determined by the reception probability and line impedance based on historical voltage fluctuation statistics to generate a propagation weight matrix; then, the mainstream direction of radiation from the fault point to the surrounding feeders and the side components flowing along the ring network are decomposed to form a local propagation direction map; finally, the entire network direction map is stitched together to generate an initial fault propagation dynamic field, accurately predicting that a voltage drop will occur at the ends of two adjacent feeders 300 milliseconds later, triggering the fault-tolerant control to take early action.

[0063] In one embodiment, the state deviation of the fault dynamic response data is calculated, and adaptive fault-tolerant control analysis is performed on the state deviation using fault propagation dynamic data to generate system fault-tolerant control parameters. The system fault-tolerant control parameters are then used to reconstruct the sliding surface of the control strategy to generate an adaptive sliding mode control law, including: Perform state estimation and deviation calculation on the fault dynamic response data to generate system state deviation data; identify the fault type from the system state deviation data to determine the corresponding fault-tolerant control strategy. Among them, the system state deviation data can be quantitative data that characterizes the degree of deviation between the actual state and the expected state of the system, obtained by state estimation and deviation calculation from the fault dynamic response data. It can be used as the basic input for fault type identification and fault-tolerant control parameter tuning, reflecting the actual impact of the current fault on the system operating state.

[0064] Fault mode classification is performed on the dynamic data of fault propagation to generate fault mode feature data; coupled analysis is performed on the system state deviation data and fault mode feature data to implement adaptive fault-tolerant control parameter tuning and generate system fault-tolerant control parameters. The fault mode feature data can be a structured feature set extracted from fault mode classification based on fault propagation dynamic data. This set distinguishes different fault mechanisms and propagation characteristics and provides fault semantic information for fault-tolerant control strategy selection and parameter tuning, supporting differentiated responses of control laws to different fault types. Coupled analysis of system state deviation data and fault mode feature data enables adaptive fault-tolerant control parameter tuning. This involves jointly modeling deviation data reflecting the degree of state deviation with mode features characterizing the fault mechanism, dynamically generating control parameters adapted to the current fault scenario based on their mapping relationship. Furthermore, this operation can be achieved by constructing a fuzzy rule-based coupled inference engine to match parameter adjustment strategies according to deviation levels and fault categories, or by using a neural network model to learn the nonlinear mapping relationship between deviation, mode, and parameters, thus enabling end-to-end parameter tuning. This allows for dynamic adaptation of fault-tolerant control parameters to complex faults, overcoming the adaptability bottleneck of traditional fixed-parameter strategies in variable fault scenarios.

[0065] The robustness of the system's fault-tolerant control parameters is verified, and the verification results are used to optimize the parameters and generate optimized fault-tolerant control parameters. Among them, the optimized fault-tolerant control parameters can be an improved set of control parameters obtained by robustness verification and feedback optimization based on the initial system fault-tolerant control parameters. It can be used to improve the actual robust performance of the control law under uncertain disturbances and model errors, and ensure the effectiveness and safety of fault-tolerant control.

[0066] Based on the optimization of fault-tolerant control parameters, the traditional sliding surface is dynamically reconstructed, a fixed-time convergent sliding surface function is designed, and an adaptive sliding control law is generated.

[0067] The fixed-time convergent sliding surface function can be a mathematical expression of a sliding surface with fixed-time convergence characteristics, designed by integrating optimized fault-tolerant control parameters. It can be used as a core component of the adaptive sliding mode control law, ensuring that the system state converges within a preset time limit, and that the convergence time is independent of the initial state. In this embodiment, the fixed-time convergent sliding surface function can introduce nonlinear power function terms and adaptive gains into the traditional sliding surface structure to satisfy the fixed-time stability condition. Based on the optimized fault-tolerant control parameters, the traditional sliding surface is dynamically reconstructed to design a fixed-time convergent sliding surface function. This can be achieved by using the optimized fault-tolerant control parameters as coefficients to reconstruct the sliding surface function structure and embedding a fixed-time convergence mechanism. For example, this operation can be achieved by introducing a combination of sign functions and fractional power terms into the sliding surface, adjusting the convergence rate with parameters, or by using a piecewise defined sliding surface to apply different convergence dynamics in different state regions, coordinated by optimized parameters. This allows the system state to achieve deterministic convergence within a preset time while retaining the strong robustness of sliding mode control, improving control timeliness and predictability.

[0068] For example, in the scenario of handling voltage over-limit faults in distribution networks with distributed photovoltaic access, the novel fixed-time control method of the power system in this embodiment can be as follows: When a feeder experiences a sudden drop in photovoltaic output due to a change in cloud cover, and its local voltage sensor experiences a drift fault, the system first calculates the system state deviation data based on dynamic response data to identify the coexistence of voltage drop and measurement distortion; simultaneously, it extracts the "source-load-measurement" coupled disturbance mode from the fault propagation dynamic data to generate fault mode characteristic data; after coupling analysis of the two, the initial system fault-tolerant control parameters are tuned, and optimized into optimized fault-tolerant control parameters after robustness verification; finally, these parameters are used to reconstruct the sliding mode surface to generate an adaptive sliding mode control law with fixed-time convergence characteristics, driving the SVG to restore the voltage to the qualified range within 1.8 seconds, and the control process is not affected by the initial deviation magnitude.

[0069] In one embodiment, the traditional sliding surface is dynamically reconstructed based on optimized fault-tolerant control parameters, a fixed-time convergent sliding surface function is designed, and an adaptive sliding control law is generated, including: An adaptive adjustment mechanism for sliding surface parameters is constructed based on optimized fault-tolerant control parameters, and dynamic adjustment rules for the sliding surface are generated. The dynamic adjustment rule for the sliding surface can be a mapping rule or functional relationship generated based on optimized fault-tolerant control parameters, used to adjust the sliding surface structure parameters in real time. This allows the sliding surface structure to dynamically adapt to the current fault condition, avoiding control performance degradation caused by a fixed structure. In this embodiment, the dynamic adjustment rule can use optimized fault-tolerant control parameters as input variables to establish a mapping relationship between them and the sliding surface structure parameters (such as gain, power exponent, etc.). For example, the dynamic adjustment rule can use piecewise linear functions or lookup tables to establish parameter adjustment rules, or it can design an adaptive law based on Lyapunov function derivative constraints to achieve online parameter updates. This allows the sliding surface structure to dynamically evolve with the severity and type of fault, enhancing the scenario adaptability of the control strategy.

[0070] Design a non-singular fast terminal sliding surface based on the sliding surface dynamic adjustment rule, and generate an initial sliding surface function; Among them, the non-singular fast terminal sliding surface can be an improved terminal sliding surface structure. By introducing non-singular terms, it eliminates the control singularity of traditional terminal sliding near the origin, while retaining the fast convergence characteristics. It can be used to provide a basic dynamic structure for fixed-time convergence and avoid the control law from diverging or oscillating when the state approaches the equilibrium point.

[0071] A fixed-time stability analysis is performed on the initial sliding surface function, the upper bound of the convergence time is calculated, and convergence performance index data is generated. The convergence performance index data can be a set of quantitative indicators characterizing the system's convergence speed and time upper bound, obtained through fixed-time stability analysis of the initial sliding surface function. This can be used as an evaluation basis for multi-objective optimization, supporting the scientific selection and performance trade-offs of sliding surface parameters. Furthermore, performing fixed-time stability analysis on the initial sliding surface function and calculating the upper bound of the convergence time to generate convergence performance index data can be achieved by deriving the maximum time for the system state to converge to the equilibrium point using fixed-time stability theory (such as homogeneity theory or the Lyapunov function construction method). In an exemplary embodiment, this operation can be performed by calculating the upper bound of the convergence time based on homogeneous system theory, or by constructing a composite Lyapunov function and solving for its derivative upper bound to inversely deduce the convergence time, thereby explicitly quantifying the convergence performance and providing a comparable performance benchmark for subsequent multi-objective optimization.

[0072] Multi-objective optimization is performed on the convergence performance index data to select the optimal sliding surface parameters and generate the optimized sliding surface function. Among them, the optimized sliding surface function can be the mathematical expression of the sliding surface that is determined after multi-objective optimization and is optimal in terms of convergence speed, robustness and control energy consumption. It can be used as the basis for the final control law design to ensure that the system has both speed and stability under compound faults.

[0073] The control law for the optimized sliding mode surface function is derived, and an adaptive sliding mode control law is generated by combining it with the design of an adaptive disturbance estimator. The adaptive disturbance estimator can be a dynamic observer used to estimate the total uncertainty term caused by sensor / actuator failures and external disturbances online. It can provide disturbance compensation signals for the adaptive sliding mode control law, improving control accuracy and robustness. In one specific embodiment, the adaptive disturbance estimator can be constructed based on system state and output information using methods such as high-order sliding mode observers, extended state observers, or neural network approximators. Further, the control law is derived from the optimized sliding surface function, and combined with the adaptive disturbance estimator design to generate the adaptive sliding mode control law. This can be achieved by differentiating the optimized sliding surface function and substituting it into the system dynamics equations, then designing equivalent control and switching control terms based on the disturbance estimate. For example, this operation can employ a combined structure of equivalent control + disturbance compensation + boundary layer smoothing, or design a second-order sliding mode control law based on the superspiral algorithm and integrate the disturbance estimation results. This effectively suppresses model uncertainties and external disturbances caused by faults while ensuring fixed-time convergence, thereby improving actual control performance.

[0074] The adaptive sliding mode control law is proven to have Lyapunov stability, ensuring that the system converges within a fixed time, and finally generating the adaptive sliding mode control law.

[0075] For example, in a scenario of frequency instability in a regional power grid involving energy storage for frequency regulation, the novel fixed-time control method of this embodiment can be implemented when a region experiences a rapid frequency drop due to a sudden decrease in wind power and a communication interruption with the energy storage converter, and frequency measurement is subject to jump interference. The system first uses optimized fault-tolerant control parameters to generate a sliding surface dynamic adjustment rule, constructing a non-singular fast terminal sliding surface. Then, through fixed-time stability analysis, the upper bound of the convergence time is obtained as 2.1 seconds, forming convergence performance index data. An optimized sliding surface function that balances control energy consumption and response speed is selected through multi-objective optimization. Simultaneously, an adaptive disturbance estimator estimates the total disturbance caused by actuator failure and measurement noise in real time. Finally, the control law integrates the optimized sliding surface and disturbance compensation term, restoring the frequency to 50±0.05Hz within 1.9 seconds. Lyapunov proofs ensure that this performance holds true under any initial deviation.

[0076] In one embodiment, the steps of acquiring real-time operating data of the power system, extracting the state change rate of the real-time operating data of the power system, and performing fixed-time convergence optimization on the adaptive sliding mode control law based on the state change rate to obtain a dynamic control path that minimizes the convergence time, adjusting control parameters based on the dynamic control path that minimizes the convergence time, and feeding the adjustment results back to the control system, so as to realize the fault adaptive fixed-time sliding mode closed-loop control and stability risk early warning operation of the power system, include: Acquire real-time operating data of the power system, extract the derivatives and fluctuation amplitudes of the state variables from the real-time operating data of the power system to obtain dynamic data of state changes; perform spectrum analysis and abrupt point detection on the dynamic data of state changes to generate dynamic characteristic data of state. The dynamic state change data can be a time-series dataset reflecting the transient evolution characteristics of the system, composed of the derivatives of state variables and fluctuation amplitudes extracted from real-time power system operation data. This dataset can be used to accurately characterize the dynamic response characteristics of the system under fault disturbances, providing raw input for subsequent feature extraction and control optimization. In this embodiment, the dynamic state change data can be obtained by performing differential operations and amplitude statistical processing on real-time acquired state variables such as voltage, frequency, and power. For example, the dynamic state change data can include, but is not limited to, one or more of the following: first-order derivative sequences of state variables, second-order derivative sequences of state variables, and fluctuation envelope data of state variables. Extracting the derivatives of state variables and fluctuation amplitudes from real-time power system operation data to obtain the dynamic state change data can be achieved by performing numerical differentiation and sliding window amplitude calculation on the real-time acquired state variable signals. Furthermore, this operation can be achieved by applying the central difference method to the high-sampling-rate state variable sequence and combining it with sliding window peak-valley statistics, thereby quantifying the system's dynamic change rate and disturbance intensity, providing high-resolution input for fault evolution perception.

[0077] State dynamic feature data can be a set of high-dimensional features extracted from dynamic state change data through spectral analysis and abrupt change detection, characterizing precursors to system instability or dynamic anomalies. This data can be used to identify key dynamic patterns nearing system instability, improving the sensitivity and foresight of control parameter adjustments. In an exemplary embodiment, state dynamic feature data can be obtained by performing spectral analysis using short-time Fourier transform or wavelet transform, combined with CUSUM or sliding window variance method for abrupt change detection. Furthermore, generating state dynamic feature data through spectral analysis and abrupt change detection of dynamic state change data can involve segmenting the dynamic state change data and performing frequency domain feature extraction and time domain abrupt change identification separately. For example, this operation can be achieved by using wavelet packet decomposition to extract multi-scale spectral energy features and combining this with a Bayesian online change point detection algorithm to identify abrupt change times, or by using short-time Fourier transform to construct a time-spectrum graph and combining it with sliding window variance ratio to detect abrupt change points. This allows for the separation of system instability precursor features from noise interference, improving early fault identification capabilities.

[0078] The dynamic characteristic data of the state are simulated in a closed loop with the adaptive sliding mode control law. The convergence performance under different control parameters is analyzed, and a graph of the relationship between control parameters and convergence time is generated. The control parameter-convergence time relationship graph can be a mapping database established through closed-loop simulation, describing the system's fixed-time convergence performance (i.e., the upper limit of convergence time) under different control parameter configurations. It can be used to provide queryable performance priors for online optimization, realizing the shift from experience-based parameter tuning to data-driven optimization. In a specific embodiment, the control parameter-convergence time relationship graph can be constructed by traversing multiple sets of control parameter combinations and recording the corresponding theoretical or simulated convergence times in a power system dynamic model based on an adaptive sliding mode control law. For example, the control parameter-convergence time relationship graph may include, but is not limited to, a gain-convergence time mapping table, a switching function coefficient-convergence time surface, and a boundary layer thickness-convergence time relationship matrix.

[0079] By performing closed-loop simulations of dynamic state characteristics with an adaptive sliding mode control law, the convergence performance under different control parameters is analyzed, and a control parameter-convergence time relationship graph is generated. This can be achieved by injecting the current state dynamic characteristics as disturbances into the power system dynamic model, traversing the control parameter combinations, and recording the convergence time. Furthermore, this operation can be implemented by synchronously injecting measured disturbance characteristics into a digital twin environment and executing multiple rounds of sliding mode control simulations, thereby establishing an explicit mapping between control parameters and fixed-time convergence performance, supporting subsequent optimization decisions.

[0080] Based on the control parameter-convergence time relationship graph, a fixed-time convergence optimization objective function is constructed, the optimal combination of control parameters is solved, and a dynamic control path that minimizes the convergence time is generated. The dynamic control path that minimizes the convergence time is used to adjust the control parameters of the power system online and generate real-time control commands. The dynamic control path that minimizes the convergence time can be a control trajectory that, under fixed-time convergence constraints, optimizes the control law parameters to bring the system state to equilibrium as quickly as possible. This ensures that the system state converges within a preset time limit, unaffected by the initial state, thus improving real-time control performance. Based on the control parameter-convergence time relationship graph, a fixed-time convergence optimization objective function is constructed, and the optimal control parameter combination is solved to generate the dynamic control path that minimizes the convergence time. This can be achieved by minimizing the theoretical upper limit of convergence time, combined with the current state's dynamic characteristic constraints, to solve for the optimal control parameters. Furthermore, this operation can be performed by using nonlinear programming to solve the constrained convergence time minimization problem, or by using a joint strategy of graph interpolation and gradient descent to approximate the optimal parameter combination online. This allows for online adaptive tuning of the control parameters, ensuring the system achieves stable recovery in the shortest possible time.

[0081] Real-time control commands are input into the dynamic model of the power system for closed-loop control simulation verification, and the stability margin of the system response is evaluated to perform early warning of stability risks in the power system.

[0082] Taking the handling of voltage drop faults in distribution networks with a high proportion of photovoltaic power as an example, the novel fixed-time control method of this embodiment can be used when a feeder experiences a sudden drop in photovoltaic output due to cloud cover, causing rapid voltage fluctuations. The system collects bus voltage data in real time, extracts its derivative and fluctuation amplitude to obtain dynamic data of state changes; through wavelet spectrum analysis and CUSUM mutation detection, high-frequency oscillations and voltage slope mutation characteristics are identified to form dynamic state characteristic data; subsequently, in the digital twin model, this characteristic is injected into the closed-loop simulation of the adaptive sliding mode control law to generate convergence time spectra under different sliding mode gains; based on this spectra, an optimization objective function is constructed, the optimal gain combination is solved, and a dynamic control path that minimizes the convergence time is generated; accordingly, the reactive power output command of the SVG is adjusted to suppress voltage fluctuations within 1.2 seconds, and the system damping ratio is evaluated simultaneously to trigger a low-risk warning rather than an emergency trip, avoiding over-control.

[0083] In one embodiment, a dynamic control path that minimizes convergence time is used to adjust the control parameters of the power system online, generating real-time control commands, including: A dynamic control path that minimizes convergence time is used to identify fault types in the power system in real time, thereby obtaining fault type data; The fault type data can be sensor or actuator fault category information identified by analyzing the dynamic response characteristics of the power system. This data can provide a semantic basis for adaptive adjustment of the control gain, achieving precise alignment between the control strategy and the fault mode. In this embodiment, the fault type data can be matched and identified based on the state evolution pattern implicit in the dynamic control path that minimizes the convergence time, combined with a fault dictionary or classification model. For example, the fault type data may include, but is not limited to, one or more of the following: sensor bias fault identifier, actuator jamming fault identifier, and communication delay fault identifier.

[0084] Real-time fault type identification in power systems is achieved using dynamic control paths that minimize convergence time, thereby obtaining fault type data. This can be accomplished by inputting features such as state trajectories and control input changes from the dynamic control path into a lightweight fault classifier or rule engine. Furthermore, this operation can be implemented by deploying embedded classification models or rule matching engines, enabling online identification of sensor / actuator fault categories and providing semantic support for subsequent gain adaptation.

[0085] Based on the fault type data, the control gain of the dynamic control path that minimizes the convergence time is adaptively adjusted to generate a gain adjustment coefficient; the control parameters are tuned online based on the gain adjustment coefficient, and the first control optimization is performed on the dynamic control path that minimizes the convergence time based on the tuning results to generate the first control optimization data; The gain adjustment coefficient can be a set of proportional factors that dynamically adjust the gain term in the sliding mode control law based on fault type data. This allows the control law to maintain sufficient switching strength and robustness boundaries under different fault modes, preventing increased chattering or convergence failure due to gain mismatch. In an exemplary embodiment, the gain adjustment coefficient can map fault type data to a preset gain adjustment rule base or online learning model, outputting the corresponding adjustment coefficient. The first control optimization data can be a control law correction amount adapted to the current fault type, generated after online tuning of control parameters based on the gain adjustment coefficient. This can be used to achieve structural adaptation of the control law to specific fault modes, improving fault tolerance.

[0086] Based on fault type data, the control gain of the dynamic control path minimizing convergence time is adaptively adjusted to generate gain adjustment coefficients. This can be achieved by querying a fault-gain mapping table or calling a parameter tuning model, outputting a gain scaling factor that matches the current fault type. Furthermore, this operation can retrieve corresponding coefficients from a pre-trained fault-gain knowledge base using a lookup table, or generate continuous gain adjustment coefficients through regression based on fault type data using an online neural network model. This ensures that the sliding mode control gain matches the severity and type of the fault, avoiding over- or under-control. Control parameters are then tuned online based on the gain adjustment coefficients, and the tuning results are used to perform first control optimization on the dynamic control path minimizing convergence time, generating first control optimization data. This can be achieved by applying the gain adjustment coefficients to the switching term or equivalent control term of the sliding mode control law, updating the control parameters, and correcting the control path. This allows for structural adaptation of the control law to the fault mode, improving fault tolerance accuracy.

[0087] The power system is subjected to disturbance observation based on a dynamic control path that minimizes the convergence time, and the disturbance estimate is obtained. The disturbance estimate is then subjected to feedforward compensation control to generate second control optimization data. The disturbance estimate can be a real-time observation estimate of the coupling effects of unmodeled dynamics, external disturbances, and faults in the power system. It can be used to provide accurate disturbance information for feedforward compensation, mitigating the negative impact of uncertainty on fixed-time convergence performance. In one specific embodiment, the disturbance estimate can be used to construct a disturbance observer (such as an extended state observer or a sliding mode observer) based on a dynamic control path that minimizes the convergence time, inverting the total disturbance from the system output deviation. For example, the disturbance estimate can include, but is not limited to, the total equivalent disturbance estimate, high-frequency measurement noise components, and slow time-varying parameter drift components. The second control optimization data can be a control increment generated after feedforward compensation based on the disturbance estimate, used to offset the effects of external disturbances. It can be used to enhance the system's ability to suppress external disturbances and model uncertainties, ensuring the predictability of the convergence time. Observing disturbances in the power system based on a dynamic control path that minimizes the convergence time to obtain the disturbance estimate can be achieved by deploying a disturbance observer within the dynamic control path framework. The total disturbance can be reconstructed in real time using the system state and control input, thereby separating and quantifying the composite uncertainties in the system, providing a basis for feedforward compensation.

[0088] Feedforward compensation control is applied to the disturbance estimate to generate second control optimization data. This can be achieved by negativeing ​​the disturbance estimate and adding it to the control law output to form a compensation term. Furthermore, this operation can be performed by directly adding the negative value of the disturbance estimate as a feedforward term to the control input, or by processing the disturbance estimate through low-pass filtering before feedforward, suppressing high-frequency noise amplification. This allows the system to actively cancel external disturbances and model errors, ensuring robust achievement of fixed-time convergence performance.

[0089] The first and second control optimization data are integrated into real-time control commands.

[0090] Taking a microgrid with energy storage system as an example, in a scenario involving a combination of actuator failure and sudden load changes, the novel fixed-time control method for power systems in this embodiment can be as follows: In a microgrid, the output of a PCS actuator is limited due to IGBT aging, while industrial loads are suddenly switched on and off. Based on a dynamic control path that minimizes convergence time, the system analyzes frequency and SOC trajectory characteristics, identifies a composite fault type of "actuator output saturation + step load disturbance," and generates corresponding fault type data. Based on this, a preset gain adjustment coefficient is retrieved to reduce the sliding mode switching gain to avoid chattering deterioration, completing the first control optimization. Simultaneously, an extended state observer estimates the total disturbance, which includes a 0.8 pu load step and a 0.2 pu actuator nonlinear deviation, generating a disturbance estimate. This estimate is then fed forward to form the second control optimization data. Finally, the two optimization data are fused to generate a real-time control command, coordinating the energy storage and diesel engine to work together, restoring the frequency to within ±0.1 Hz within 1.5 seconds, and triggering a medium-risk warning indicating that the actuator needs maintenance.

[0091] Furthermore, to achieve the above objectives, the present invention also provides a novel power system fixed-time control device, the device comprising: a memory, a processor, and a novel power system fixed-time control program stored in the memory and executable on the processor, the novel power system fixed-time control program being configured to implement the steps of the novel power system fixed-time control method as described above.

[0092] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A fixed-time control method for a power system, characterized in that, The method includes: Acquire power system topology data, perform fault propagation path analysis on the system based on the power system topology data, generate fault-related topology data, and use the fault-related topology data to perform virtual simulation modeling on the power system topology data to generate a dynamic model of the power system. Key control nodes of the power system dynamic model are extracted, and the fault impact range of the key control nodes is analyzed to generate fault propagation dynamic data. Fault characteristic signals are collected based on the key control nodes to obtain fault dynamic response data. Calculate the state deviation of the fault dynamic response data, perform adaptive fault-tolerant control analysis on the state deviation through fault propagation dynamic data, generate system fault-tolerant control parameters, and use the system fault-tolerant control parameters to reconstruct the sliding surface of the control strategy to generate an adaptive sliding mode control law. The system acquires real-time operating data of the power system, extracts the state change rate of the real-time operating data of the power system, and performs fixed-time convergence optimization on the adaptive sliding mode control law based on the state change rate to obtain a dynamic control path that minimizes the convergence time. Based on the dynamic control path that minimizes the convergence time, the control parameters are adjusted, and the adjustment results are fed back to the control system to realize the fault adaptive fixed-time sliding mode closed-loop control and stability risk early warning operation of the power system. The process involves extracting key control nodes from the dynamic model of the power system, analyzing the fault impact range of these key control nodes, generating dynamic fault propagation data, and collecting fault characteristic signals based on these key control nodes to obtain dynamic fault response data, including: Sensitivity analysis was performed on the dynamic model of the power system to obtain the set of key control nodes; based on the set of key control nodes, a fault propagation adjacency matrix was constructed to obtain node fault correlation data. Perform topology calculations on the impact range of node fault correlation data to generate dynamic fault propagation data; Based on the set of key control nodes, fault signal acquisition points are deployed to generate signal acquisition layout data; multi-channel synchronous sampling control is performed on the signal acquisition layout data to obtain the original fault response data. Noise suppression and feature extraction are performed on the raw fault response data to generate dynamic fault response data; The calculation of the state deviation of the fault dynamic response data, the adaptive fault-tolerant control analysis of the state deviation using fault propagation dynamic data, the generation of system fault-tolerant control parameters, the use of system fault-tolerant control parameters to reconstruct the sliding surface of the control strategy, and the generation of an adaptive sliding mode control law, including: Perform state estimation and deviation calculation on the fault dynamic response data to generate system state deviation data; identify the fault type from the system state deviation data to determine the corresponding fault-tolerant control strategy. Fault mode classification is performed on the dynamic data of fault propagation to generate fault mode feature data; coupled analysis is performed on the system state deviation data and fault mode feature data to implement adaptive fault-tolerant control parameter tuning and generate system fault-tolerant control parameters. The robustness of the system's fault-tolerant control parameters is verified, and the verification results are used to optimize the parameters and generate optimized fault-tolerant control parameters. Based on the optimization of fault-tolerant control parameters, the traditional sliding surface is dynamically reconstructed, a fixed-time convergent sliding surface function is designed, and an adaptive sliding control law is generated. The method of dynamically reconstructing the traditional sliding surface based on optimized fault-tolerant control parameters, designing a fixed-time convergent sliding surface function, and generating an adaptive sliding mode control law includes: An adaptive adjustment mechanism for sliding surface parameters is constructed based on optimized fault-tolerant control parameters, and dynamic adjustment rules for the sliding surface are generated. A non-singular fast terminal sliding surface is designed based on the dynamic adjustment rules for the sliding surface, and an initial sliding surface function is generated. A fixed-time stability analysis is performed on the initial sliding surface function to calculate the upper bound of the convergence time and generate convergence performance index data. Multi-objective optimization is then performed on the convergence performance index data to select the optimal sliding surface parameters and generate the optimized sliding surface function. The control law for the optimized sliding mode surface function is derived, and an adaptive sliding mode control law is generated by combining it with the design of an adaptive disturbance estimator. The adaptive sliding mode control law is proven to have Lyapunov stability, ensuring that the system converges within a fixed time, and finally generating the adaptive sliding mode control law.

2. The fixed-time control method for power systems as described in claim 1, characterized in that, The process of acquiring power system topology data, performing fault propagation path analysis based on the power system topology data to generate fault-related topology data, and using the fault-related topology data to perform virtual simulation modeling of the power system topology data to generate a dynamic power system model includes: Acquire power system topology data, identify network nodes in the power system topology data to extract key control unit data; construct a fault propagation directed graph based on the key control unit data to generate the initial system topology graph; Network flow analysis is performed on the initial topology of the system to extract the fault correlation of nodes, generate fault correlation topology data, and use the fault correlation topology data to perform dynamic characteristic modeling of key control unit data to generate system dynamic mapping data. Virtual simulation modeling is performed based on the system dynamic mapping data to generate an initial dynamic model of the system; parameter identification and stability verification are performed on the initial dynamic model of the system to generate a dynamic model of the power system.

3. The fixed-time control method for power systems as described in claim 1, characterized in that, The step of performing influence range topology calculations on node fault correlation data to generate dynamic fault propagation data includes: The fault propagation probability is calculated from the node fault correlation data, high-risk propagation paths are extracted, and fault propagation network data is generated; the time delay characteristics of the fault propagation network data are analyzed, propagation time parameters are extracted, and fault propagation time delay characteristic data is generated. Spatiotemporal diffusion modeling is performed on the fault propagation time delay characteristic data, and the diffusion process is dynamically simulated to generate an initial fault propagation dynamic field. Calculate the fault propagation coefficient between nodes; adjust the weight of the initial fault propagation dynamic field using the fault propagation coefficient to generate a weighted fault propagation graph; perform propagation range integration and time-domain evolution analysis on the weighted fault propagation graph to calculate the fault arrival time of nodes and generate propagation time series matrix data. The failure impact is assessed using the propagation time series matrix data, generating dynamic data on failure propagation.

4. The fixed-time control method for power systems as described in claim 3, characterized in that, The process of performing spatiotemporal diffusion modeling on fault propagation time-delay characteristic data and dynamically simulating the diffusion process to generate an initial fault propagation dynamic field includes: Spatiotemporal decoupling processing is performed on the fault propagation time delay characteristic data to separate the time delay and spatial distance parameters and generate a propagation spatiotemporal parameter set; based on the propagation spatiotemporal parameter set, a three-dimensional spatiotemporal grid is partitioned to divide the system space into discretized propagation units and generate a spatiotemporal grid unit set; Perform propagation probability weight allocation on the spatiotemporal grid cell set, calibrate the fault reception probability and propagation attenuation coefficient of each cell, and generate a propagation weight matrix; Based on the propagation weight matrix, the fault propagation direction vector decomposition is performed to extract the mainstream propagation direction and the side propagation components between units, and a local propagation direction map is generated. The local propagation pattern is spliced ​​and reconstructed at the system-wide scale to generate a unified initial fault propagation dynamic field.

5. The fixed-time control method for power systems as described in claim 1, characterized in that, The process of acquiring real-time operating data of the power system, extracting the state change rate of the real-time operating data of the power system, and performing fixed-time convergence optimization on the adaptive sliding mode control law based on the state change rate to obtain a dynamic control path that minimizes the convergence time, adjusting control parameters based on the dynamic control path that minimizes the convergence time, and feeding the adjustment results back to the control system, in order to realize the fault adaptive fixed-time sliding mode closed-loop control and stability risk early warning operation of the power system, includes: Acquire real-time operating data of the power system, extract the derivatives and fluctuation amplitudes of the state variables from the real-time operating data of the power system to obtain dynamic data of state changes; perform spectrum analysis and abrupt point detection on the dynamic data of state changes to generate dynamic characteristic data of state. The dynamic characteristic data of the state are simulated in a closed loop with the adaptive sliding mode control law. The convergence performance under different control parameters is analyzed, and a graph of the relationship between control parameters and convergence time is generated. Based on the control parameter-convergence time relationship graph, a fixed-time convergence optimization objective function is constructed, the optimal combination of control parameters is solved, and a dynamic control path that minimizes the convergence time is generated. The dynamic control path that minimizes the convergence time is used to adjust the control parameters of the power system online and generate real-time control commands. Real-time control commands are input into the dynamic model of the power system for closed-loop control simulation verification, and the stability margin of the system response is evaluated to perform early warning of stability risks in the power system.

6. The fixed-time control method for power systems as described in claim 5, characterized in that, The method of using a dynamic control path that minimizes convergence time to adjust the control parameters of the power system online and generate real-time control commands includes: A dynamic control path that minimizes convergence time is used to identify fault types in the power system in real time, thereby obtaining fault type data; Based on the fault type data, the control gain of the dynamic control path that minimizes the convergence time is adaptively adjusted to generate a gain adjustment coefficient; the control parameters are tuned online based on the gain adjustment coefficient, and the first control optimization is performed on the dynamic control path that minimizes the convergence time based on the tuning results to generate the first control optimization data; The power system is subjected to disturbance observation based on a dynamic control path that minimizes the convergence time, and the disturbance estimate is obtained. The disturbance estimate is then subjected to feedforward compensation control to generate second control optimization data. The first and second control optimization data are integrated into real-time control commands.

7. A power system fixed-time control device, characterized in that, The device includes: a memory, a processor, and a power system fixed-time control program stored in the memory and executable on the processor, the power system fixed-time control program being configured to implement the steps of the power system fixed-time control method as described in any one of claims 1 to 6.

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