299 results about "System topology" patented technology

The invention relates to the technical field of fault detection control, and discloses an automobile fault detection method and system fusing sensor data. The method comprises the following steps: modeling a multi-system sensor network through an automobile system topology mapping algorithm to obtain a sensor topology incidence matrix; performing synchronous processing on the sensor topological incidence matrix according to a vehicle dynamics constraint equation to obtain a synchronous multi-sensor data set; inputting the synchronous multi-sensor data set into a fault propagation path sensing network for fusion to obtain a vehicle system fault feature vector; detecting the fault feature vector based on an automobile physical model constraint optimizer to obtain a fault state recognition result; and tracing and controlling a fault state identification result according to the fault propagation atlas model to obtain a system adjustment control signal. According to the invention, the source identification accuracy of the complex coupling fault and the pertinence of control and adjustment are improved.

The invention relates to the technical field of automatic generation, in particular to a knowledge graph driven teaching material automatic generation method and system. The method comprises the following steps: receiving teaching target input, analyzing the teaching target input into a multi-layer structure based on a preset teaching intention meta-model, and mapping the multi-layer structure to a knowledge graph node; generating an optimal teaching path through a rule engine in combination with the node topology position and the teaching intention level; after the topological fingerprint of the target textbook system is recognized, compatibility analysis is executed, and if a structural gap exists, a transition module is automatically inserted to reconstruct a path; calling the bound multi-modal content unit according to the reconstruction path node, and completing modal coordination according to a cross-modal dependency graph; and finally, performing structured rendering on the integrated content, and outputting a textbook body adaptive to the terminal. The method has the advantages of teaching intention deep understanding, multi-modal content consistency control and cross-textbook system adaptation, and the intelligence level and generalization ability of textbook generation are improved.

The invention discloses an intelligent substation safety measure checking method and system, and the method comprises the steps: obtaining a secondary system topological structure, equipment information and historical safety measure ticket data of a substation, and constructing a quaternary knowledge graph; according to the quaternary knowledge graph, extracting multi-modal features of the maintenance task and identifying the type of a maintenance scene by combining a memory guide reflection decision reasoning mechanism, optimizing a rule reasoning process through a distributed guide local search algorithm, and generating an optimal safety measure operation set for a specific maintenance scene; automatically identifying a correlation loop and determining a minimum safety isolation range through a memory guide decision reasoning mechanism, dynamically generating a minimum safety operation set according to the real-time state of the equipment, and adaptively adjusting operation steps; and an operation dependency relationship model is constructed in combination with unwrapping variational multi-graph representation learning and a distributed guide local search algorithm, and operation sequence compliance verification, risk level assessment and dynamic visual early warning are realized. According to the invention, accurate formulation, dynamic adjustment and risk early warning of safety measures of the intelligent substation are realized.

An offshore wind power plant composite ring current collection system topology planning method based on an N-1 safety criterion comprises the following steps that S1, wind power plant basic data including wind turbine and transformer substation node coordinates, wind turbine rated power, cable electrical parameters and the like are obtained; s2, generating a candidate cable set and a cable cross avoidance constraint pair set based on the basic data; s3, a scene set containing a normal scene and all single cable fault scenes is constructed, an optimization model with the minimum total cost of the whole life cycle as the target is established, and cable construction / operation variables and direct current power flow power balance, node degrees and cable cross avoidance constraints are integrated; s4, solving the model to obtain an initial topology scheme; and S5, carrying out full fault set N-1 security verification on the initial scheme, and bringing a violation scene into a fault set to resolve until the scheme meets an N-1 criterion. According to the method, through non-predefined topological optimization and planning, reliability and economical efficiency are taken into consideration, calculation complexity is reduced, and the method can be expanded to a cross-substation scene.

The invention relates to the field of laboratory environment control, and discloses a laboratory environment abnormity self-adaptive linkage control system based on multi-source heterogeneous perception, which comprises the following steps: when any environment parameter exceeds a set safety threshold value, judging whether comprehensive environment abnormity exists in combination with the numerical value change trend of other types of sensors; based on historical monitoring data and real-time sensing data, constructing a multi-dimensional environment characteristic spectrum, and identifying internal association and change paths between abnormal signals; whether linkage control measures are needed or not is evaluated by analyzing the current anomaly type and the influence area; dynamically generating a linkage control path and a priority sequence according to a system topological structure and an equipment operation state, and determining equipment objects needing to be linked; and according to the generated control path and parameters, the operation state of related equipment is automatically adjusted, active response control for abnormity is completed, and an environment feedback result after control is monitored. The method has the advantage of improving the intelligent control level.

The invention discloses a multi-modal attack identification method fusing BMama and difference to guide trans-attention, which comprises the following steps: simulating a false data injection attack, a denial of service attack, an address resolution protocol spoofing attack and a domain name system spoofing attack, collecting physical layer sensor data and network layer flow data, and preprocessing multi-modal data; bMama is constructed to perform dynamic time modeling on multi-modal data, a graph neural network is combined to adversariate a variational auto-encoder, features of a power grid system topology and a communication topology structure are fused, and robustness of potential representation is enhanced through adversarial training; the method comprises the following steps of: guiding feature complementary fusion by using modal difference through a difference guide iteration cross-attention fusion mechanism, improving the capability of distinguishing complex attacks, finally carrying out attack detection and classification on fused modals, and executing end-to-end optimization according to a weighted combination of loss of each part. The method can effectively detect and classify the multi-modal attack in the smart power grid, and enhances the safety and reliability of a complex system.

The invention discloses an optimal scheduling method for grid connection of a power distribution network and a distributed power supply, and particularly relates to the technical field of optimal scheduling, which comprises the following steps of: determining employment performance information during grid connection of the distributed photovoltaic power supply according to the difference between real-time and historical solar photovoltaic output models; based on charge state data of an energy storage system connected with the distributed photovoltaic power supply, determining energy storage performance information during grid connection of the distributed photovoltaic power supply, determining a grid connection strategy of the distributed photovoltaic power supply, and determining a grid connection strategy of the distributed photovoltaic power supply based on consideration of a node system topology connection relation of the power distribution network and related data of nodes and branches; possible risks existing in distributed photovoltaic power grid-connected nodes in the power distribution network are simulated, grid-connected risk information of the power distribution network is determined, the matching degree of output power and required power of the distributed photovoltaic power is considered, grid-connected load matching information of the power distribution network is determined, and the overall grid-connected performance of the power distribution network is quantified. According to the invention, stable operation of the power distribution network can be ensured.

The invention discloses a building group optimization scheduling method and system based on carbon emission responsibility of a high new energy permeability region, and the method comprises the steps: motivating a user side to carry out self-discipline adjustment through a dynamic carbon emission responsibility factor (Cr), constructing a multi-objective optimization model covering daily economy, carbon emission and user comfort, and solving through employing an improved particle swarm algorithm. The method specifically comprises the following steps: establishing an optical storage direct-flexible system topology model containing photovoltaic, energy storage and flexible loads; the dynamic Cr is calculated based on the responsibility allocation concept, and the principles of consistency, balance and the like are followed; a multi-time-scale optimization strategy is designed, and a scheduling instruction is adjusted in real time in combination with day-ahead prediction and intra-day rolling optimization; and the electric vehicle is used as energy storage equipment to participate in energy bidirectional interaction, so that the new energy consumption capability is improved. According to the invention, the daily carbon emission of the building group is reduced by more than 10%, the power grid voltage fluctuation is less than or equal to 5%, the user comfort is guaranteed, the power supply reliability and the energy utilization efficiency are improved, and technical support is provided for the low-carbon intelligent building group.

The invention discloses a power distribution network topology state estimation method, electronic equipment, a medium and a product. The method comprises the steps of obtaining a topological structure and measurement data of a power system; constructing variable nodes and factor nodes according to the topological structure and the measurement data, and constructing a state-topological joint factor graph model according to the variable nodes, the factor nodes and the topological structure; based on the state-topology joint factor graph model, performing state estimation through a belief propagation algorithm to obtain an estimated value of state variable correction; if the estimated value of the state variable correction meets the convergence condition, correcting the topological state of the switch branch according to the active power and reactive power of the head end of the switch branch in the estimated value of the state variable correction; and outputting an estimation result of the topological state of the switch branch until the on-off state of the switch branch obtained according to the state variable is consistent with the original topological state of the switch branch. According to the method, asynchronous real-time updating of the topological state of the power system can be realized.

The invention discloses an equipment anomaly tracing method and system based on a digital twinborn and graph neural network, and belongs to the technical field of industrial intelligent operation and maintenance and fault diagnosis. The invention provides an innovative solution integrating digital twin high-fidelity simulation and a graph structure deep learning algorithm, aiming at the technical bottlenecks that the generalization performance of an existing data driving method is insufficient under the conditions of fault sample scarcity and category imbalance and the traceability accuracy of unknown and composite faults is poor. The method comprises the following steps: constructing a high-fidelity digital twin integrating multi-dimensional physical attributes and a system topology structure; based on a fault mode, influence and harmfulness analysis method system, constructing a fault mode library comprising a plurality of single fault modes and composite fault modes, and generating an enhanced training data set with accurate labels through an automatic fault injection mechanism; training a graph neural network model with a multi-level attention mechanism by using the data set so as to learn a propagation rule of a fault in a complex system topology; and finally deploying the model to carry out abnormity traceability analysis on real-time industrial Internet of Things monitoring data. According to the method, the fault diagnosis generalization ability and the positioning precision under the sample imbalance condition are remarkably improved.

The invention provides a collaborative suppression method for short-circuit current-subsynchronous oscillation of an offshore wind power flexible direct current system, and the method comprises the steps: 1, determining a common parameter for a broadband coupling problem: determining the common parameter through a system topological structure and theoretical analysis of offshore wind power which is directly connected to a receiving-end power grid through flexible direct current for the short-circuit current and subsynchronous oscillation problem; 3, an upper optimization layer based on the DDQN outputs an optimal adjustment instruction for the generality parameters according to the system state, a lower execution layer receives the adjustment instruction of the upper layer, a new generality parameter value is obtained and output to a current inner loop controller, and a current inner loop controller is constructed; the current inner loop controller generates a modulation signal to control the output of the converter. According to the invention, the dynamic damping controller is designed to optimize common parameters, so that collaborative suppression of short-circuit current and subsynchronous oscillation is realized.

The invention provides an intelligent fault diagnosis method and system for a photovoltaic system. The method comprises the following steps: collecting multi-source heterogeneous operation parameter data of the photovoltaic system based on an intelligent sensor network and carrying out standardized preprocessing; local feature extraction is carried out on edge nodes, a frequency moment is calculated, data summary aggregation is carried out through a frequency moment optimization algorithm, and a fusion key feature vector set is constructed by using a multi-modal data fusion model; performing anomaly detection based on the normal behavior mode baseline, and generating an abnormal state alarm signal and a preliminary anomaly type indication; constructing a weighted graph model based on a system topological structure, constructing a fault association sub-graph by using an approximate spanning tree algorithm, accurately positioning a fault unit and diagnosing a fault type; and generating and executing a self-healing operation scheme according to the fault diagnosis conclusion. Through the distributed multi-modal data fusion and approximate spanning tree fault positioning technology, rapid and accurate diagnosis and autonomous recovery of the photovoltaic system fault are realized, and the system reliability and the operation efficiency are improved.

The invention discloses an intelligent peak regulation method with optimized combination of energy storage and conventional peak regulation means, and the method comprises the following steps: building a holographic situation awareness layer: capturing the voltage, current phase angle and frequency deviation of a power grid, and monitoring the pressure fluctuation in a diesel engine cylinder; data transmission and preprocessing: encrypting data of the miniature phasor measurement unit and the quantum sensor by using a quantum key distribution technology; the self-adaptive decision engine is used for performing Nash equilibrium calculation by utilizing a federal reinforcement learning architecture, through a local Agent based on a power generation / energy storage unit state autonomous optimization control strategy and in combination with a digital twin platform, solving multi-target conflicts of economy, reliability and carbon emission, and generating a global optimal scheduling instruction; the execution control layer is used for tracking resonance points and dispersing harmonic energy spectrums; dynamic topology reconstruction: a harmonic source is positioned by using a digital twin platform, and the topology of the energy storage system is automatically switched according to the harmonic frequency and position; continuously optimizing the digital twin platform; and full-process closed-loop feedback and self-learning are carried out.

The invention discloses a transformer substation switching operation risk pre-control method and system based on deep reinforcement learning, and relates to the technical field of power system automation, and the method comprises the steps: collecting transformer substation equipment data in real time, processing the collected data, generating a standardized time series data set, building a system topology structure represented by multiple graphs based on the processed data, and carrying out the pre-control of the transformer substation switching operation risk. Integrating dynamic characteristics, based on a system topological structure, generating a switching operation decision sequence, dynamically adjusting a risk boundary threshold, based on a real-time system state, evaluating the security of the decision sequence, intercepting operations which do not conform to a security boundary, performing reward attribution on contributions of operation sequence steps, and constructing a digital twin environment; the model performance is improved through interactive calibration of real data and simulation data. According to the substation switching operation risk pre-control method based on deep reinforcement learning, high-precision risk prediction is realized, the early warning time is sufficient, and the operation sequence is optimized, so that the safety of substation switching operation is remarkably improved.

The invention relates to the technical field of liquid cooling flow distribution, and discloses a multi-GPU server cluster liquid cooling flow distribution method based on reinforcement learning, and the method comprises the steps: S1, collecting the operation state parameters of each GPU in a multi-GPU server, enabling the operation state parameters to comprise core temperature, voltage, current and load utilization rate, carrying out the thermal behavior modeling through a sliding time window, generating a thermal dynamic feature vector representing the GPU short-time thermal trend; and S2, collecting the flow velocity, the temperature difference of inlet and outlet water and the thermal resistance of the cold plate of each channel of the liquid cooling system in real time, and representing the liquid cooling assembly as a graph structure. According to the method, the technical scheme of joint modeling based on the graph neural network and reinforcement learning is adopted, fusion coding is performed on the GPU thermal dynamic characteristics and the topological state of the liquid cooling system, and an Actor-Critic architecture is introduced to realize self-adaptive regulation and control of the liquid cooling flow, so that the technical effect of improving the heat dissipation efficiency and the energy consumption balance control capability of the multi-GPU server cluster is achieved.

The invention provides an offshore wind plant current collection network topology optimization method based on an improved grey wolf optimization algorithm, and the method comprises the steps: S1, obtaining basic data of an offshore wind plant, initializing the grey wolf algorithm, and generating an initial solution set; s2, adopting polar coordinates and a minimum spanning tree to optimize and expand the initial solution set; s3, optimizing a connection mode in each group; s4, executing the optimal connection of each group of fan combination; s5, performing optimal solution set exploration on the grey wolf algorithm by adopting an operator; s6, repeatedly executing until a preset maximum number of iterations is reached, and generating a topology model of the current collection system of the offshore wind plant; the method can improve the initial solution quality and the convergence efficiency, gives consideration to the global search capability and the local optimization precision, and achieves the quick and effective processing of geometric constraints.

The invention discloses a power system optimization method based on topology compression, and the method comprises the following steps: 1) constructing a power system topological graph: abstracting a power system into a weighted graph, representing power equipment by nodes, representing power transmission paths by edges, distributing electrical attributes and weights for the nodes and the edges, and providing a basis for subsequent topology compression and optimization; 2) analysis and implementation of topological compression: identifying redundant nodes and edges through cut point and bridge edge algorithms in a graph theory, and simplifying a topological structure by merging or removing redundant parts; 3) power flow analysis and optimal scheduling: performing power flow analysis on the compressed topological structure, and realizing power scheduling and load distribution by using an optimization algorithm to improve the operation efficiency of the system; and 4) dynamic adjustment and fault recovery: updating a topological structure and node attributes based on real-time data, and quickly responding to load fluctuation and equipment faults of the power system through an optimization algorithm. The method has the beneficial effects that the calculation complexity can be remarkably reduced, and the optimization effect and the scheduling efficiency are improved.

The invention discloses an urban-level source website plant sewage integrated real-time regulation and control system, computer equipment and a storage medium, and the system comprises a topological structure construction module which firstly obtains the geographic position information of a sewage source, a pump station, a pipe network and a sewage treatment plant, and constructs a system topological structure; the real-time data acquisition and transmission module deploys sensors at an inlet and an outlet of a water pump to acquire original parameters based on the topological structure, and stores the original parameters as real-time data associated with space coordinates of the water pump; the water pump regulation and control parameter generation module calls real-time data and historical data, extracts features to construct a data set, generates a rotating speed instruction through algorithm analysis, and feeds back water pump operation state data after the instruction is executed; and the partition regulation and control execution module analyzes the overall discharge trend based on the topological hierarchical relationship, performs space-time correlation matching on the water pump operation state data and the regional real-time data, and determines the regional regulation and control priority. And the urban sewage transmission and treatment efficiency, safety and resource utilization rate are effectively improved.

The invention relates to an MMC-CLCC novel DC power transmission system coordination control method, and belongs to the technical field of hybrid DC power transmission. The method comprises the following steps: establishing an MMC-CLCC hybrid DC power transmission system topology architecture, determining an optimal proportion of full-bridge and half-bridge sub-modules according to requirements of system DC voltage and the like, and determining a control strategy during steady-state operation; analyzing the dynamic process of the system under the alternating current fault of the receiving end, and clarifying the power operation characteristics of the CLCC; designing a calculation process of direct current, active power and reactive power operation limits of the inverter station under multi-condition constraints, and constructing a safe operation domain of the inverter station; and proposing a CLCC turn-off angle adjustment strategy based on reactive sensitivity constraint and an MMC coordination strategy based on maximum power transmission. The method can provide support for the receiving-end AC system during the fault period of the receiving-end power grid, guarantees the DC power transmission capability to the greatest extent, and is suitable for a large-scale new energy cross-regional power transmission scene.

The invention provides a well-station-oil depot coupling simulation-based crude oil coagulation risk optimization method, which comprises the following steps of: firstly, designing a well-station-oil depot pipe network system topological structure, analyzing a pipe network structure, and numbering wells, stations, oil depots and pipelines to obtain the numbers of an upstream pipeline and a downstream pipeline of each pipeline; then, a well-station-oil depot pipe network system is disassembled into a node + pipeline form, each pipeline is dispersed into a plurality of calculation units, and the pipe section length, diameter and dip angle of each calculation unit are obtained; according to the fluid temperature of each wellhead, the fluid entering pressure of each station and a PVT physical property table, assuming the temperature and pressure of all nodes to obtain the average temperature and pressure of each pipe section; and calculating crude oil physical property parameters, crude oil flow parameters, pressure drop and temperature drop parameters of each pipe section by adopting an iterative method to obtain pressure and temperature of all nodes, and finally judging the pipe condensation risk according to the physical property parameters and the pipeline pressure and temperature. The pipe condensation risk can be automatically judged, and the production efficiency and safety are improved.

The invention provides a method for automatically generating a vertical power distribution system diagram based on BIM in forward design, and relates to the technical field of building electrical, comprising the following steps: performing intelligent analysis on a building BIM three-dimensional model of forward design, and accurately identifying electrical professional components; key data of the distribution boxes are extracted, and a three-level electrical system topology network of a total distribution cabinet, layer distribution boxes and terminal distribution boxes is constructed; standardized data are generated through multiple verifications of data formats, logic relations and parameter thresholds; automatic drawing output is achieved in a tiled mode and an aligned mode, and a bidirectional linkage mechanism of a BIM model and a system drawing is established. The method aims at solving the problems that traditional AutoCAD two-dimensional manual drawing is low in efficiency, disordered in version management, lack of three-dimensional space expression and insufficient in design accuracy.

The invention relates to the technical field of data analysis, in particular to a multi-source heterogeneous system data collaborative power distribution network graph-real consistency checking system, which comprises a topological feature extraction module, a cross-system topological alignment module and an anomaly traceability tracking module, wherein the topological feature extraction module is used for extracting multi-dimensional attribute features and topological connection relations of equipment nodes from a multi-source heterogeneous system and generating dynamic fingerprint vectors; the cross-system topology alignment module receives the dynamic fingerprint vector, constructs an intermediate semantic layer and outputs a standard fingerprint vector; the method comprises the following steps: generating a unified topology model by analyzing a differential topology rule of a multi-source heterogeneous system, and calculating a comparison difference value of standard fingerprint vectors among different systems and an equivalent connection relationship of electrical connectivity characteristics; the anomaly traceability tracking module receives the comparison difference value, constructs an anomaly propagation tree and generates an anomaly propagation path; and an abnormal root node is positioned by analyzing and comparing the coupling weight of the equivalent connection relation and the comparison difference value.

The invention relates to a power system topological structure optimization method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: determining an optimization index of a power grid architecture according to the power grid architecture of a power system and distributed new energy power supply access data; according to the optimization index of the power grid architecture, establishing an optimization planning model of a power system topological structure; improving the ant colony algorithm by using a pheromone updating mode; and calculating and generating an optimization result of the topological structure of the power system according to the optimization planning model by using an improved ant colony algorithm. By adopting the method, the optimization result of the topological structure of the power system is calculated and generated based on the improved ant colony algorithm and the optimization planning model; the result meets the power grid operation constraint, the electric energy quality, the power supply reliability, the economical efficiency and the flexibility are remarkably improved, and meanwhile, the method adapts to distributed new energy power supply access proportion change and load dynamic change.

The invention provides a power collection system topology optimization method and device based on a variable weight minimum spanning tree, and relates to the technical field of offshore wind power topology optimization. The method comprises the following steps: grouping a plurality of fans based on the angle difference among the plurality of fans according to the position information of the offshore booster station to obtain a plurality of fan sets; a Delaunay triangulation method is adopted to construct a reference grid connecting line of each fan set; and based on the reference grid connection line of each fan set, generating the optimal topology of the to-be-processed offshore current collection system based on a variable weight minimum spanning tree algorithm. According to the method, the grouping clustering method, the Delaunay triangulation method and the variable weight minimum spanning tree algorithm are combined, and the topology design of the offshore wind plant current collection system is efficiently, economically and rapidly achieved. According to the invention, topological connection and cable specification type selection can be optimized synchronously, and a global optimal solution is sought between the cable path length and the type cost difference caused by the change of the current-carrying capacity, so that the fine control of the overall cost of the current collection system is realized.

The invention relates to the technical field of data processing, and discloses a fault analysis method and system based on a label propagation algorithm. The method comprises the following steps: acquiring operation state data through a monitoring equipment network, extracting the data to obtain a system topology data set, performing label propagation fault analysis on the system topology data set to obtain node fault information, and performing fault influence weight analysis on each node based on the node fault information to obtain a maintenance priority, and inputting the influence weight data into the graph neural network for coping strategy analysis to obtain a target processing strategy. According to the invention, the efficiency of fault propagation analysis and the accuracy of decision making are improved.

The invention relates to the technical field of thermal power plant abnormal data processing, in particular to a thermal power plant production monitoring system network security data set abnormal data identification and cleaning method, which comprises the following steps: mapping a spatial-temporal feature vector to a preset thermal power plant multi-level causal graph to obtain a data causal graph; performing information propagation and node updating on the data causal graph, and calculating an abnormal score of each node in a preset thermal power plant multilevel causal graph; identifying abnormal data according to the abnormal score and the network security data set; and cleaning the abnormal data according to the abnormal type and a preset data cleaning strategy. According to the method, correlation modeling of data on time and a system topological structure is realized through a multi-level cause and effect graph; an abnormal score is calculated through information spreading and node updating, and an abnormal event in the network security data can be accurately identified; the abnormal data is cleaned based on the preset data cleaning strategy, redundant, wrong and abnormal data can be effectively removed, and meanwhile key risk information is reserved.

The invention provides an energy storage system, a control method, a storage medium, a program and a vehicle, and relates to the field of electric power. Comprising a lithium battery energy storage cluster and a sodium battery energy storage cluster; and the lithium battery energy storage cluster and the sodium battery energy storage cluster are bridged between the positive direct current bus and the negative direct current bus. According to the invention, the full-range coverage of the energy storage system from an energy type to a power type is realized, and the network construction capability of the energy storage system based on the MMC system topology is improved. The superimposed current flowing through the energy storage cluster is reduced, the cycle life of the energy storage medium in the energy storage cluster is prolonged, and the service life of the whole system is indirectly prolonged. The voltage stress of a single sub energy storage module in the energy storage cluster is reduced, the network construction capability of the whole system is guaranteed, and the reliability and stability of the whole system are improved.

The invention discloses an underwater wireless transmission system multi-parameter online identification energy efficiency improvement method, which comprises the following steps: firstly, constructing a topological graph of an underwater wireless power transmission system, when the system works normally, acquiring voltage U1 and current I1 of a transmitting coil, and calculating input impedance; determining an equivalent circuit, and establishing an equation of a mutual inductor M, a bridging capacitor Ce and an equivalent load RLe through the voltage U1 and the current I1; and solving the mutual inductance M, the bridging capacitor Ce and the equivalent load RLe to realize multi-parameter identification. According to the technology, the driving frequency of a system does not need to be changed, a complex circuit does not need to be added, and the identification requirements of mutual inductance, bridging capacitance and load can be rapidly and accurately met. Afterwards, the working state of the system is judged according to the obtained bridging capacitance value, the method can accurately control the power and efficiency of the system, and the stability and reliability of the charging process are improved.

The invention belongs to the field of short-circuit current calculation, and discloses a fault analysis method based on a power network equation and a related system.Unified modeling of the overall structure of a power system is achieved by obtaining bus information of the power system and constructing a system topological relation model, and the fault analysis accuracy is improved. The adaptation difficulty caused by different system scales and different wiring modes is avoided, and the universality and the expandability of the method are improved; according to the method, an automatic processing mechanism is introduced into the calculation and decomposition of the admittance matrix, so that the positive sequence admittance matrix, the negative sequence admittance matrix and the zero sequence admittance matrix of the power system can be quickly obtained, manual derivation and gradual simplification processes are reduced, the probability of analysis errors is reduced, and the stability of a calculation result is improved; a three-sequence network equation is uniformly adopted in fault calculation to solve a framework, and a direct coupling relation between an admittance matrix and a short circuit operation condition is utilized, so that key to-be-solved quantities such as fault current and voltage can be quickly and accurately obtained, the calculation process is remarkably shortened, and the analysis efficiency is improved.

The invention discloses a local monitoring control method suitable for a variable air volume air conditioner, and relates to the technical field of variable air volume air conditioners. The method comprises the following steps: acquiring an adjusting air volume share corresponding to each game node, adjusting the air volume of each game node, acquiring operation state data and system topology information of each VAV box of the variable air volume air conditioning system through comprehensive data acquisition, and constructing a connection structure chart to visually display a system structure. Then, the importance degree of each node in the system and the influence relation on the downstream are quantified by calculating the connectivity and the downstream dependency coefficient, adjustment priority values are calculated in combination with parameters such as the real-time room temperature deviation value and the turbulence index, and the adjustment priority values of the nodes are determined; and finally, the air volume of each VAV box is precisely adjusted according to the adjustment priority value and the air volume share, and local monitoring control of the system is achieved.