436 results about "Power dispatch" patented technology

The invention discloses an intelligent power prediction method considering dynamic load change, and relates to the technical field of power grid load prediction, and the method comprises the steps: collecting original load data, carrying out the preprocessing, constructing a VMD constraint optimization model, carrying out the four-stage improvement of optimization parameters through employing an improved dung beetle optimization algorithm, so as to generate IMF components, reconstructing the IMF component by calculating a sample entropy to obtain a low-frequency component and a high-frequency component; establishing a Kalman filtering state space model based on the low-frequency component, and decomposing the low-frequency component into a residual component and a pseudo trend component through a Kalman filtering recursive algorithm; external features are obtained, the high-frequency component, the residual component and the pseudo trend component are aligned and spliced with the external features, multi-component collaborative prediction is carried out through a local-global interactive attention mechanism, and a final load prediction result is obtained; and generating a power demand visualization chart based on the final load prediction result. And reliable decision support is provided for power dispatching and energy management.

Embodiments of the present application provide a flexible resource partitioning method and system based on power flow distribution. The method comprises: using a flexible resource aggregation model partitioning method to perform topological aggregation on flexible resources to obtain aggregation units; using the aggregation units as the basic unit for aggregating the adjustable capacity of the flexible resources, using an adaptive robust optimization algorithm to construct an aggregated resource adjustable capacity calculation model considering network constraints, and performing aggregation calculation on a power adjustment range that can be provided by all the flexible resources within an aggregation area for power dispatch; and solving the aggregated resource adjustable capacity calculation model considering the network constraints, so that feasible upper and lower power bounds of the aggregated resource adjustable capacity calculation model can be decomposed and executed among the flexible resources, thereby implementing flexible resource partition aggregation in continuous time periods.

The invention discloses a power grid equipment intelligent numbering and anti-error checking method and system constructed based on a dispatching numbering agent, equipment and a medium, and belongs to the technical field of power systems and intelligent dispatching. The method comprises the steps that a knowledge graph is constructed through semantic analysis of a power dispatching standard document; extracting equipment attributes and topological contexts and organizing the equipment attributes and the topological contexts into structured prompts; the scheduling numbering agent generates a candidate number with an interpretable reasoning chain based on the knowledge graph; and verifying the number compliance through multi-dimensional introspection check and outputting a final number. According to the method, the intelligent workflow is constructed, the progress from rule matching to standard understanding is realized, and the traceability and logic consistency of the numbering process are ensured while the rule priority and the topology context are precisely processed by utilizing interpretable thinking chain reasoning and auto-reflection check closed loop.

The invention relates to the technical field of power dispatching, in particular to an energy storage combined frequency modulation control method and system in a source-network-load-storage integrated regional power grid, and the method comprises the steps: dynamically calculating the adjustable capacity of each resource, dynamically aggregating the total adjustable capacity of the system, sensing the multi-dimensional frequency situation and calculating the adjusting power, and selecting a frequency modulation mode and generating a power distribution strategy. And generating a splitting and isolated network frequency modulation stability control strategy and performing system grid-connected resynchronous operation. The method is suitable for guiding power supply, energy storage and load all-resource collaborative frequency modulation strategy generation in a regional power grid. For consistency of frequency deviation, frequency change rate and active power flow direction, multi-dimensional frequency situation awareness is constructed, power distribution strategies in different modes are generated based on a total adjustable capacity interval of a system, and a frequency stability control and grid-connected resynchronization strategy after splitting is given when the frequency and the adjustable interval exceed limits. Accurate matching of all-resource collaborative frequency modulation and strategies in a regional power grid is realized, and the frequency modulation response speed of the system is improved.

The invention discloses a power flow out-of-limit elimination method and device based on section sensitivity and source load characteristics, terminal equipment and a storage medium, and belongs to the technical field of power flow out-of-limit elimination. The method comprises the following steps: acquiring unit output data, bus load data, section sensitivity data and section power flow data of the power dispatching system in a preset full time period; dividing the section into a new energy output section, a load center feed-in section and a network loop section according to section sensitivity data, and respectively constructing a new energy output prediction adjustment optimization model, a bus load prediction adjustment optimization model, a non-new energy output adjustment optimization model and corresponding constraint conditions; and solving to obtain an optimal new energy output predicted value, a bus load predicted value and a non-new energy output value, and performing out-of-limit adjustment. By implementing the method and the device, the problem of low out-of-limit elimination efficiency caused by huge calculated amount when the scheduling system is subjected to overall modeling solution in the prior art can be solved.

The invention discloses an electric power dispatching method and device fusing multi-modal data and dual verification, electronic equipment and a storage medium, and belongs to the field of intelligent dispatching of an electric power system.The method comprises the steps that multi-modal dynamic data, including equipment images, load time sequences, logs, dispatching instructions, meteorological early warning and electricity price data, of an electric power system are obtained; visual, time sequence and semantic features are extracted, cross-modal alignment is carried out, and a joint representation vector is generated; based on the vector and the real-time data, an initial scheduling strategy is generated through a reinforcement learning model; and through historical scheme matching and physical constraint verification, determining a target scheduling strategy and executing scheduling. Therefore, by implementing the method and the device, the problems of incomplete power grid state perception caused by insufficient multi-modal data fusion and low reliability caused by lack of automatic credibility verification of the AI scheduling strategy in the prior art can be solved.

The invention provides a wind power prediction method based on improved Diffusion-Q. According to the method, a diffusion model and a Q-learning reinforcement learning mechanism are deeply fused through a data preprocessing module, a diffusion model forward process, a Q-learning module, a diffusion model reverse process, an output prediction module and a feedback and optimization module; a generative prediction system with a dynamic strategy optimization capability is constructed, a future wind power sequence is generated through probability distribution of modeling time sequence data in a forward diffusion process and a reverse diffusion process, online dynamic adjustment of diffusion process parameters is realized, and dynamic prediction of the wind power sequence is realized by defining a state space, an action space and a reward function. The model can automatically select the optimal sampling path according to current input and historical performance, prediction precision and robustness are remarkably improved, and the method can be widely applied to wind power plant short-term power prediction, power dispatching system and intelligent power grid energy management and has good engineering application prospects.

The invention provides a power dispatching data asset full life cycle management method and system. The method comprises the following steps: firstly, collecting power grid real-time operation basic data, calculating a power grid risk grade by means of a power grid risk assessment model, formulating a matching collection strategy according to the grade, and collecting to obtain initial data assets; recognizing and processing abnormal data through a collaborative quality diagnosis algorithm in combination with the real-time state of the power grid, and screening effective data assets; and inputting into a power dispatching decision algorithm, and fusing risks and constraints to generate a power dispatching scheme. And then comprehensively evaluating the scheme by using the data asset utility index, analyzing and determining contribution data assets with remarkable scheduling contribution, and finally dynamically adjusting acquisition and decision algorithm parameters according to an evaluation result and the contribution data assets. According to the invention, full-process collaborative management and control of power dispatching data assets are realized, and the data utilization rate and the scientificity and reliability of dispatching decision are improved.

The invention belongs to the technical field of power dispatching, and particularly relates to a regional data center low-carbon dispatching method and system based on electricity-carbon calculation collaboration.The method comprises the steps that distributed data centers in a region are aggregated to construct a computing power cluster, and then the schedulable power range of the computing power cluster is calculated; then, an upper-layer optimization model of power grid and computing power cluster cooperative scheduling is constructed by taking maximization of scheduling income, electric quantity cost caused by computing power transfer and an error between total scheduling power of the computing power cluster and a power instruction of the power grid as targets, and the upper-layer optimization model of power grid and computing power cluster cooperative scheduling is constructed by taking minimization of regional total carbon emission penalty, task delay penalty and unbalanced load penalty as targets; and constructing a lower-layer optimization model of the internal power distribution of the computing power cluster, substituting the total scheduling power of the computing power cluster obtained by solving the upper-layer optimization model into the lower-layer optimization model, and obtaining the power adjustment amount of each distributed data center for realizing low-carbon scheduling. According to the method, the multi-dimensional information of the electric carbon calculation can be deeply fused, and economy, low-carbon property and reliability are achieved.

The invention discloses a power dispatching monitoring data anomaly detection method based on artificial intelligence, and relates to the field of data anomaly detection, and the method comprises the steps: firstly carrying out the multi-modal alignment and preprocessing of high-frequency time sequence monitoring data and sparse log text of power dispatching, and constructing the association mapping of heterogeneous data under a time window; a retrieval enhancement generation technology is utilized to deeply parse a log text to construct a global semantic context, and time series data is synchronously subjected to double-flow coding to extract inherent fluctuation features. Based on this, a cross-modal semantic gating feature modulation mechanism is established, and a dynamic reweighting sequential sequence is guided by using log semantics, so that when a semantic background of a specific scheduling operation is perceived, the attention on compliance data mutation is automatically reduced. And finally, performing signal reconstruction and adaptive anomaly judgment based on the feature sequence under semantic guidance. In this way, false alarms can be significantly reduced while the real fault sensing capability is guaranteed, and the intelligence and robustness of the power monitoring system are improved.

The invention discloses an AGC information physical system reliability verification method and system, and relates to the technical field of power dispatching automation. The problems that an existing AGC system only adopts a static operation mode and an ideal communication condition in a simulation test, and coupling response of continuous operation and sudden disturbance of a power grid under a long time scale is difficult to reproduce truly are solved. The state evolution under event driving is realized by constructing three types of simulation event sources of a time sequence operation sample, a master station control instruction and external disturbance and setting a priority mechanism of external disturbance gt, master station instruction gt and time sequence propulsion; an uplink channel fault model is introduced to simulate on-off, delay and errors of measurement data, and a downlink channel fault model is introduced to simulate transmission abnormity of a control instruction; closed-loop simulation of frequency response and electrical quantity updating is realized by combining promotion of a rotor motion equation and dynamic load flow calculation; and finally, by analyzing indexes such as frequency deviation, tie line deviation and control response delay, comprehensive reliability evaluation of the AGC system in a complex information physical environment is completed.

The invention discloses an intelligent micro-grid energy management system and method, relates to the technical field of power grid adjustment and distribution, and solves the technical problem that a single and independent power load prediction method is adopted in existing micro-grid management, the accuracy of a prediction result is difficult to guarantee, and the rationality of micro-grid management is reduced. According to the power utilization data and the relationship network diagram, constructing an association relationship of the power utilization units; obtaining time period environment data corresponding to the power utilization unit, generating an initial time sequence prediction sequence according to the time period environment data, and inputting the initial time sequence prediction sequence into the power utilization load prediction model to obtain a preliminary estimated load; generating a time sequence prediction sequence for rechecking according to the association relationship and the preliminary estimated load, and inputting the time sequence prediction sequence into the electrical load prediction model to obtain a final estimated load; and finally, performing power dispatching according to the estimated generating capacity and the estimated load. Therefore, the accuracy of the prediction result is ensured, and the reasonability of subsequent power dispatching according to the prediction result is ensured.

The invention relates to the technical field of power dispatching, and discloses an energy-carbon platform multi-source early warning information generation method, system and device based on a large language model, and a medium, and the method comprises the steps: collecting multi-source energy data of a monitoring region, and carrying out the data preprocessing; performing data fusion through a multi-modal data fusion method, and performing anomaly identification through an anomaly prediction model to obtain a predicted value and a composite anomaly score; and constructing a structured context based on the predicted value and abnormal associated data obtained by composite abnormal score analysis, and generating natural language early warning information by constructing a large language model. According to the method, by fusing multi-source data and combining the natural language generation capability and the numerical value verification mechanism of the large language model, intelligent identification and accurate description of abnormal conditions and automatic generation of executable suggestions are realized.

The invention relates to a multi-scheduling-instruction cooperative control method and device, computer equipment, a storage medium and a program product. The method comprises the steps of receiving current dispatching instructions issued by at least two power dispatching systems for a power plant, and obtaining current power grid state information of the power plant; if the current power grid state information satisfies a preset power grid state condition, determining a current operation mode of the power plant according to a current demand overlapping degree between current scheduling demand information in the at least two current scheduling instructions; when an updated scheduling instruction issued for the power plant is received, performing demand overlapping degree analysis on updated scheduling demand information carried in the updated scheduling instruction to obtain an updated demand overlapping degree; and updating the current operation mode according to the current demand overlapping degree and the updated demand overlapping degree to obtain an updated operation mode of the power plant, and obtaining a power dispatching result of the power plant based on power dispatching processing of the updated operation mode. By adopting the method, the switching efficiency of the power plant operation mode can be improved.

The invention discloses an electric power system vulnerability dynamic risk assessment method and system. The method comprises the steps of obtaining power grid assets, business functions borne by the power grid assets and vulnerability information; calculating asset physical importance based on the real-time data of the power grid; for each vulnerability, respectively calculating a physical risk evaluation value and a business influence evaluation value of the vulnerability; and finally, carrying out weighted fusion on the two to obtain dynamic comprehensive risk scores and sorting. By fusing vulnerability static attributes, asset dynamic physical importance and business criticality, a scene-adaptive multi-dimensional risk assessment model is constructed, and the transformation of the vulnerability risk of the power system from'static pervasive assessment 'to'dynamic accurate portrait' is realized. According to the method, the integrating degree and decision guidance value of the risk assessment result and the actual security situation of the power grid are improved, the problem that the assessment result is one-sided and rigid due to separation from a business scene and a physical operation state in the prior art is solved, and the initiative and accuracy of network security protection of the power dispatching system are remarkably improved.

The invention discloses an electric power and carbon management collaborative optimization method and system for a multi-industry symbiotic zero-carbon park, and the method comprises the steps: constructing a mixed integer programming model which comprises an electric power dispatching sub-model, a carbon collection sub-model and a source-sink matching sub-model, and defining a coupling relation between an electric power dispatching variable and a carbon management variable; designing a multi-objective optimization function, and bringing coal consumption cost, start-stop cost, carbon transaction cost, wind curtailment penalty cost, deep peak regulation cost, peak regulation compensation benefit and transportation cost into a unified optimization framework; and setting time-space coordination constraint conditions, including power balance constraint, output upper and lower limit constraint, climbing rate constraint, capture-transport volume balance constraint and carbon sink capacity limitation constraint, to realize coordination optimization of power production and carbon logistics in time and space dimensions. According to the method, the total cost of the system is effectively reduced, the utilization efficiency of carbon sink resources is improved, real-time controllability of carbon emission intensity is ensured, and the problem of global optimum deficiency caused by a traditional two-stage optimization method is solved.

The invention relates to a multi-mode multi-agent RAG-based power dispatching method and system, and the method comprises the steps: generating a dispatching plan through the cooperative work of a plurality of heterogeneous agents with different division of labor, and receiving query input containing a power dispatching task; the planner agent decomposes the power dispatching task into a plurality of sub-tasks which are mutually dependent and generates a corresponding execution plan; according to the execution plan, the searcher agent sequentially executes two stages of fine-grained multi-modal retrieval on each sub-task from the multi-modal power knowledge base, coarse-grained whole-page retrieval is carried out in the first stage, fine-grained area fine reading and modal expansion are carried out in the second stage, and evidence related to the current sub-task is obtained and added into an evidence pool; and according to all evidences in the evidence pool, the responder agent answers the initially input query to generate a scheduling plan. According to the method, the problems of timeliness and multi-scene adaptability in super-large-scale power dispatching are effectively solved, and an innovative path is provided for guaranteeing dynamic safety and stability of a power grid.

The invention provides a software quality evaluation method and device for a power grid dispatching automation system. Belongs to the technical field of power dispatching automation. The method comprises the following steps: acquiring running state data of power grid dispatching automation system software, and extracting a third-level index from the running state data; determining subjective and objective fusion weights of the third-level indexes; aggregating the third-level indexes related to the same quality feature according to the subjective and objective fusion weights of the third-level indexes to obtain corresponding second-level indexes; an index vector composed of the second-level indexes is input into the dynamic fuzzy neural network model, and the first-level indexes of the corresponding subsystems are output to serve as comprehensive quality evaluation scores; and determining the software quality grade of the business subsystem according to the comprehensive quality evaluation score and a preset grade threshold value. The method is suitable for power grid dispatching automation system software quality evaluation oriented to complex operation scenes, and comprehensive evaluation and self-adaptive optimization of dispatching software under multi-dimensional and dynamic conditions can be achieved.

The invention relates to the technical field of power dispatching, and discloses a power supply system dispatching method and a power supply system thereof, and the power supply system comprises a monitoring module, a dispatching module, a protection module, a data prediction module, an energy storage module, a remote control and communication module, an artificial intelligence and adaptive dispatching module, a power quality monitoring module and an intelligent load management module. The power quality monitoring module collects harmonic wave and voltage fluctuation data in real time, gives an alarm immediately when the harmonic wave and voltage fluctuation data exceed the standard, and pushes governance suggestions; and the scheduling module is matched to adjust the non-linear load operation time, so that quality problems are reduced from the source. Scene features are identified through deep learning, and an emergency strategy is automatically generated; and the scheduling module receives feedback in real time and corrects the strategy. The protection module accurately locates a fault point through multi-sensor cross validation and automatically cuts off a fault loop; the remote control module pushes fault information to the operation and maintenance terminal in real time, remote diagnosis is supported, and the on-site inspection frequency is reduced.

The invention discloses a power grid dispatching fault intelligent processing system based on intelligent agent adaptive arrangement, and the system comprises an alarm clustering module which is used for carrying out the clustering processing of an alarm sample of a power dispatching system through employing a clustering algorithm, and obtaining an alarm cluster set; the fault event judgment module is used for inputting the alarm clusters in the alarm cluster set into the fault detection model and outputting a judgment result about whether the alarm events corresponding to the alarm clusters are fault events or not; the root cause positioning module is used for positioning equipment where a root cause of the fault event is located; and the fault processing module is used for adopting a guide type reward strategy optimization type algorithm in all fault processing strategies obtained through intelligent agent arrangement based on the attribute of the equipment where the root cause of the fault event is located and the running state of the equipment. And finding out the fault processing strategy with the highest reward score in the algorithm of the guide type reward strategy optimization type. According to the method, the manual intervention requirement is reduced, the dependence on advanced operation and maintenance personnel is reduced, and the labor cost is remarkably saved.

The invention relates to the technical field of photovoltaic power supply management, and discloses a photovoltaic parking building power supply management method, device and system, and a storage medium. The method comprises the steps of determining real-time irradiation and component temperature data of a parking building ceiling photovoltaic component matrix, calculating an actual light receiving area according to component surface shadow distribution, and generating a photovoltaic output power correction parameter in combination with a historical attenuation coefficient; constructing a photovoltaic power generation capacity fluctuation sequence in unit time based on the parameters, identifying a power generation capacity decline time period and a power output gap, and marking the power generation capacity decline time period and the power output gap as a power supply unstable interval; according to the interval time sequence distribution, matching real-time load demand data of the charging piles in the building, calculating the power difference between the load demand and the power generation capacity, and generating a priority power supply sequence table; and dynamically distributing photovoltaic power to charging loads with different priorities according to the sequence table, adjusting charging and discharging threshold values of the building energy storage system, and generating a photovoltaic power dispatching instruction. The method can accurately evaluate the power generation capacity, respond to power generation fluctuation and optimize power distribution.

The invention relates to the technical field of resource matching, in particular to an intelligent power dispatching data network resource matching method, system, equipment and medium, comprising the following steps: extracting network node state measurement consumption and collecting service requirements, splicing feature vectors for service attributes and calculating distances to screen a matching resource pool; the states are recombined into a node matrix, depth features are extracted through a graph network, a resource pool feature mapping flow table is combined to deduce a path and a distribution strategy, and a logic is constructed for device configuration according to the strategy to generate a scheduling result. According to the method, distance evaluation and neighborhood aggregation are performed on a multi-source service request through fusion of dynamic topology, global state constraints are extracted, interference terms are eliminated, optimal distribution is explored in an enhanced deduction space in combination with a multi-dimensional observation sequence, and a mapping relation between a service boundary and equipment configuration is directly established based on decision evolution. Dependence of passive fixed threshold and artificial log research and judgment is abandoned, and the risk of flow cutoff and congestion caused by intervention delay is weakened so as to guarantee communication of a communication link.

The invention discloses a large-scale power dispatching optimization method and system based on agent collaboration, and the method comprises the steps: collecting parameters such as power generation side unit output, power transmission side line transmission, power utilization side load, market side electricity price and the like through an energy Internet intelligent decision platform, and carrying out the dynamic processing of the parameters, and obtaining a power utilization main body market response coefficient; combining the coefficient and a multi-side parameter construction model to determine a multi-agent initial bargaining scheme, inputting the scheme into a reinforcement learning model, and setting a state, action and reward related element training optimization game strategy; and adjusting parameters according to the optimization strategy to generate scheduling plans such as unit start-stop, output distribution and line power flow control, feeding back the scheduling plans to a platform for simulation, outputting indexes such as a network loss rate and a load satisfaction rate, and performing iterative adjustment if a threshold value is not met. According to the system construction method, corresponding functional units operate cooperatively, multi-side parameters can be fully integrated, a closed-loop optimization process is formed, market adaptability and scheduling accuracy are improved, and safe and stable operation of a large-scale power system is guaranteed.

The invention belongs to the technical field of new energy power plant output, and particularly relates to a new energy power plant output and power grid bearing capacity matching method and device, equipment and a medium, and the method comprises the following specific steps: obtaining real-time operation data and meteorological prediction information of a new energy power plant to carry out power plant output prediction, and obtaining a new energy output prediction result; power grid operation parameters are obtained, load flow calculation and stability analysis are carried out, and power grid bearing capacity sequences in different time periods are determined; inputting the new energy output prediction result and a power grid bearing capacity sequence into a pre-established matching optimization model, and outputting a matching optimization result; and generating a power scheduling instruction of the new energy power station based on the matching optimization result. And by fusing output prediction and bearing capacity evaluation, taking a new energy output prediction result and a power grid bearing capacity sequence as input, performing multi-objective optimization calculation, generating a power scheduling instruction and performing real-time adjustment, thereby realizing dynamic matching of the new energy output and the power grid bearing capacity.

The invention discloses a digital asset value evaluation method and system for power dispatching. The method comprises the steps of firstly obtaining a digital asset set in a target scheduling scene, then determining internal attributes of the digital asset set based on a power system physical topology and an operation rule, and determining external value characteristics of the digital asset set based on a power market environment and a scheduling decision demand. Thirdly, value calculation is carried out on various digital assets by utilizing the model in combination with real-time and historical scheduling operation data, and a basic value score is output; and generating a comprehensive value evaluation result and a value label according to the score in combination with a preset rule and an adjustment factor. Finally, the evaluation result and the label are applied to a power dispatching auxiliary decision support system, and a quantitative basis is provided for dispatching plan optimization, data asset internal pricing and external transaction. According to the method, accurate, dynamic and business value evaluation of the power dispatching digital assets is realized, and value release and efficient utilization of data elements are effectively supported.

The invention belongs to the technical field of power dispatching, and discloses a data center multi-target dispatching system considering new energy uncertainty, which comprises a data center model unit, a new energy supply model unit and a multi-target optimization unit, the data center model unit is used for establishing a characterization model for energy consumption equipment, computing power load, an energy storage system and power grid power supply of a data center; the new energy supply model unit is used for establishing a representation model for new energy output power generation, and obtaining a new energy typical output scene by using an improved k-means method; and the multi-objective optimization unit is used for establishing an electric power-computing power collaborative multi-objective optimization model according to the representation model established by the data center model unit and the new energy typical output scene, and solving by using an NSGA-II algorithm to obtain an optimal scheduling strategy so as to realize optimized operation of the data center.

The invention belongs to the technical field of electric heating joint optimization scheduling, and discloses a cold region novel power system flexibility improvement method and system based on heat supply network inertia, and the method comprises the steps: fusing the inertia characteristics of a heat supply network, building an electric heating cooperation model, and achieving the whole-cycle cost minimization solving and flexibility quantitative evaluation through the combination of multiple constraints, and a heat storage adjusting space is provided for the system according to the heat delay and heat loss characteristics of heat supply network inertia, the CHP unit is allowed to preferentially respond to power dispatching on the premise that heat supply stability is guaranteed, the straight condensing thermal power unit is allowed to adjust power supply and demand through climbing, a heat pump supplements a heat supply gap, and the flexible adjusting capacity of the system is greatly improved. The whole-cycle cost optimization target reduces unit operation and wind and light abandoning losses, multi-dimensional flexibility index quantification verifies the remarkable improvement effect of heat supply network inertia on the flexibility of a power grid, the stable operation capacity of a power system under high-proportion new energy access is enhanced, and the winter heat supply reliability of the cold region is guaranteed.

The invention discloses a main transformer oil temperature abnormity real-time monitoring and statistical analysis method, system, equipment and medium, and belongs to the technical field of power equipment state monitoring and fault diagnosis, and the method comprises the steps: collecting temperature data and voiceprint data, carrying out the preprocessing, splicing the data into a combined oil temperature value with voiceprint, calculating the historical distribution of the current temperature, and carrying out the statistical analysis of the current temperature; outputting a dynamic threshold median, calculating a residual error duration, judging whether an oil temperature abnormal mark is triggered or not, when the oil temperature abnormal mark is triggered and an aging index is obtained, generating a root cause event list, performing efficiency evaluation and cooler regulation and control based on the root cause event list, outputting a regulation and control instruction and a current comprehensive energy efficiency ratio, and performing power regulation and control through a power dispatching data network. And requesting to obtain comprehensive efficiency ratios and oil temperature data of other main transformers in the same partition, generating a regional efficiency thermodynamic table, and carrying out oil temperature anomaly statistics. According to the invention, a closed-loop intelligent monitoring system from accurate sensing to co-processing is constructed, and the initiative and accuracy of main transformer oil temperature abnormity management and the overall safety of a power grid are improved.

The invention relates to an anti-misoperation intelligent inspection method and system for secondary equipment of a transformer substation and a medium. The method comprises the following steps: acquiring operation state data of a secondary soft pressing plate and primary equipment; establishing an equipment relation model based on a power dispatching procedure, a relay protection procedure and a substation field operation procedure; constructing an anti-error rule base; when a switching operation instruction of the transformer substation is received, simulation rehearsal is carried out on the operation sequence based on the anti-error rule base and the operation state data of the secondary soft pressing plate and the primary equipment; the primary equipment and the secondary equipment are operated based on the operation sequence of the simulation rehearsal, and an inspection report is generated; model decomposition, graph-model matching, rule calling and the like of the transformer substation are achieved through the topology technology, misjudgment prevention of primary and secondary equipment operation is further achieved, and it is effectively avoided that a protection pressing plate is missed in the primary equipment operation process; the method has the advantages that the equipment relation model and the anti-error rule base are established, the substation model decomposition and anti-error judgment are realized, and the protection pressing plate is effectively prevented from being missed to be put and withdrawn.

The invention relates to the technical field of power dispatching, in particular to a new energy active automatic power generation control parameter setting method and system, and the method comprises the steps: synchronizing the external conditions of a power grid, and completing AGC modeling and power grid modeling; setting a control parameter range, a setting step length and test verification time which need to be set to form a control parameter combination to be set, and starting a control closed-loop simulation test from the first group of control parameter combination; acquiring initial power flow and frequency, calculating regional control deviation, and decomposing the regional control deviation to form a control strategy; executing the control strategy, calculating the power flow and frequency of the power grid at the next moment when the strategy is executed, and carrying out alternate iteration until the test verification time is reached; calculating a control performance index in the verification time, and recording the performance of the current control parameter combination; selecting a next group of control parameter combinations, and repeating the closed-loop simulation test process until all the control parameter combinations are traversed; and selecting a control parameter combination meeting a set condition to generate a recommendation report.