211 results about "Smart power" patented technology

The invention relates to the technical field of power plant management and control, and discloses an intelligent power plant management and control system based on the Internet of Things, and the system comprises the steps: when a plurality of labels related to the same equipment or parameter exist in different subsystems, according to a similarity index between the labels and an equipment association relationship; on the basis of the detected conflict label group, combining equipment historical operation and maintenance data and an upstream and downstream parameter flow relationship, constructing a label semantic evolution graph, and performing reasoning analysis on label equipment through a fusion rule engine and a graph neural network; through mapping knowledge domain fusion and semantic embedding comparison, matching and clustering among conflict labels are completed based on structural similarity and semantic relevancy, and a label alignment rule is constructed; according to a label coordination result, designing a mapping rule of a data field; and performing inter-system synchronous verification on a result after structure conversion and label standardization processing, and writing a standardized label into a unified semantic database. The method has the advantage of improving data semantic consistency.

The invention relates to the technical field of smart power grids, in particular to a hybrid game-based virtual power plant optimization operation method under multiple markets, and the method comprises the steps: building an electricity-certificate-carbon multi-market coupling transaction framework, deeply analyzing a master-slave game relationship between a virtual power plant operation platform and a VPP alliance, and building a price guidance-based double-layer interaction mechanism; according to the method, research is carried out aiming at the condition that a virtual power plant belongs to different interest subjects, a resource complementation-based alliance cooperation model is proposed, and a VPP alliance cooperation optimization framework considering demand side load elasticity is established by introducing a refined model of a flexible load; and establishing a virtual power plant operation platform and VPP alliance mixed game optimization model. Solving a master-slave game stage by adopting a double-layer iterative algorithm; and solving a cooperative game stage in combination with an alternating direction multiplier method. The virtual power plant is guided to participate in multi-market cooperative regulation and control through a mixed game mechanism, safe and stable operation of the power system is ensured, and the operation economy and environmental protection property are improved.

The invention discloses an intelligent power grid fault deduction method and device based on a knowledge graph and electronic equipment, and relates to the technical field of power grid fault diagnosis, and the method comprises the steps: receiving protection action alarm information and circuit breaker tripping alarm information when a target intelligent power grid breaks down; based on the protection action alarm information and the circuit breaker tripping alarm information, performing hypothesis reasoning by using a preset power grid topology knowledge graph to obtain a suspected fault equipment set; performing deductive reasoning on each suspected fault device in the suspected fault device set by using a preset protection configuration knowledge graph based on the protection action alarm information and the circuit breaker tripping alarm information to obtain observation event confidence corresponding to each suspected fault device; and evidence fusion reasoning is carried out on each suspected fault device based on each expected observation event confidence, and actual fault information of the smart power grid is obtained. The method can improve the accuracy of the fault diagnosis result of the smart power grid.

The invention relates to the technical field of smart power grids, in particular to a power distribution network energy storage optimization scheduling method based on dynamic load prediction, which comprises the following steps: acquiring power data, extracting a reversal node, calibrating a disturbance section, identifying a charge trend, screening a scheduling unit, constructing a scheduling link, identifying an intervention node by fusing voltage and current directions, and setting release limitation. And generating an energy storage release freezing instruction set. According to the method, the direction reversal point in the power data of the energy storage access node of the power distribution network is extracted, a trend sequence is constructed in combination with the change trend of the state of charge, a continuous descending unit is eliminated, synchronous matching of the energy storage trend and the disturbance behavior is realized in a time axis, and the power distribution network energy storage access node power data is obtained through combined screening of trend direction consistency and communication path conditions. An energy storage path map is constructed, the structural combination capability of energy storage resources is enhanced, the voltage fluctuation main vector and the current release auxiliary vector are fused, and the response capability of the energy storage resources of the power distribution network and the path regulation and control precision are remarkably improved.

The invention relates to the technical field of smart power grids, and discloses a hierarchical safety early warning method based on smart power grid analog simulation, and the method comprises the following steps: S1, collecting the operation data of a smart power grid, including the real-time monitoring data of voltage, current, power factor and temperature, and processing the collected operation data; s2, optimizing estimation of a power grid state by using an extended Kalman filtering algorithm; s3, establishing a nonlinear dynamic model of the smart power grid, and predicting the health state of the power grid according to the dynamic behavior of the power grid; and S4, constructing a Bayesian network model, evaluating the risk of the power grid through probabilistic reasoning, and judging the health condition of the power grid. According to the invention, through combination of intelligent power grid analog simulation, deep reinforcement learning and adaptive optimal control, intelligence, adaptive adjustment and real-time response capability of the power grid early warning system are realized, and stability and emergency response efficiency of the power grid in a complex environment are improved.

The invention, which relates to the technical field of power grid electrical equipment monitoring, discloses an electrical equipment abnormal power state monitoring system comprising a data acquisition module, a data processing module, a monitoring analysis module and a management module. The monitoring analysis module comprises a power reference offset sub-module, a power fluctuation sub-module and a comprehensive abnormal power risk sub-module, the abnormal power state monitoring effect of the electrical equipment can be remarkably improved, the influence of multiple factors on power reference offset is considered by the system, the power reference can be accurately corrected, and power grid interaction factors can be effectively improved. Therefore, the abnormal power risk can be comprehensively and accurately assessed based on the power reference offset, the power fluctuation stability and multiple factors, so that the safe and stable operation of a smart power grid and a large-scale power grid can be powerfully ensured.

The invention discloses an electricity consumption anomaly detection method, system and device based on an environmental perception graph convolutional network and a medium, and belongs to the technical field of smart power grids. The method comprises the steps that historical electricity consumption of a power grid area unit and external environmental factors are acquired to construct a multi-dimensional space-time association graph; a periodic trend component and environment-related residual fluctuation are separated through the multi-dimensional space-time correlation diagram, and a two-channel diagram convolution feature is obtained; in combination with the convolution features of the two-channel graph, feature space decoupling of a periodic channel and a residual channel is realized, and decoupling features are obtained; and extracting a historical residual fluctuation sequence based on the decoupling features, and constructing an environmental state sensitive probability density function. According to the method, the historical electricity consumption and the external environment factors such as temperature, humidity or weather events are closely fused by constructing the multi-dimensional space-time association diagram, so that the space-time dependence of the electricity consumption behavior is effectively captured, and misjudgment caused by ignoring environment dynamics in a traditional method is avoided.

The invention relates to the technical field of smart power grids, and discloses a smart power grid real-time electricity price adjustment method and system, and the method comprises the steps: collecting user electricity consumption data in real time through a sensor, and carrying out the preprocessing; clustering the power utilization data of the users, dividing the users into different groups, and designing a personalized electricity price strategy; and dynamically adjusting the electricity price by adopting a preset rule according to the real-time load state of the power grid. The electricity utilization data of the users are collected in real time, personalized clustering is carried out, the electricity price is dynamically adjusted in combination with the load state of the power grid, and a more flexible and accurate electricity price strategy is achieved. The method not only can improve the supply and demand balance of the power grid, but also encourages the user to adjust the power consumption behavior in the peak period through a differential pricing strategy, supports the demands of renewable energy sources and electric vehicle users, facilitates the optimization of the use of power grid resources, and improves the overall efficiency and sustainability of a power system.

The invention provides an environment abnormity monitoring and warning method and system for a smart power plant, and relates to the technical field of abnormity monitoring. Determining a power plant environment anomaly region and a region environment anomaly recognition result; obtaining a first coefficient of the environmental anomaly risk; obtaining a second coefficient of the environmental anomaly risk; based on the first weight of the environmental anomaly risk and the second weight of the environmental anomaly risk, performing weighted calculation on the first coefficient of the environmental anomaly risk and the second coefficient of the environmental anomaly risk to obtain an environmental anomaly risk detection coefficient; and generating an environment abnormity early warning signal. According to the invention, the technical problem in the prior art that a power plant cannot respond in time when an abnormal condition occurs due to the fact that the potential influence on other regions is difficult to evaluate according to the abnormality of one region due to the lack of an effective abnormality propagation prediction mechanism can be solved; the technical effects of improving the response speed and the early warning accuracy of the power plant to the environment abnormity and reducing the accident risk are achieved.

The invention discloses a new energy station management system and method based on an intelligent power grid, and the system comprises a real-time monitoring module which is used for predicting the charging load fluctuation of an electric vehicle based on real-time monitoring data, a scheduling module which is used for adjusting the output of new energy power generation equipment according to the predicted load fluctuation, and an optimization module. The power distribution network management module is used for optimizing charging scheduling in a charging station and avoiding overload caused by peak charging at the same time, the dynamic adjustment module is used for dynamically adjusting the voltage and frequency of a power distribution network, and the real-time monitoring module is used for monitoring the state of the power distribution network in real time and dynamically updating a management strategy; according to the new energy station management system and method based on the smart power grid, the problems that in the prior art, due to instability and rapid change characteristics of electric vehicle charging loads, severe fluctuation of the loads can bring extra pressure to a power distribution network, the overload problem of the power distribution network is caused, and then the reliability and safety of the power grid are affected are solved.

The invention relates to the technical field of power grid power dispatching. The new energy power cooperative scheduling method and system based on the smart power grid are provided, and the method comprises the steps: generating an original data set; based on the original data set, constructing a digital twinborn model of the wind turbine generator, and generating a real-time fatigue damage distribution prediction result of the gearbox and the bearing; the blade tip speed ratio and the variable pitch angle are dynamically optimized, and dynamic control parameters are generated; the dynamic control parameters are issued to a wind turbine generator controller for execution, the power grid dispatchable capacity is calculated based on a real-time fatigue damage distribution prediction result, an adjustable capacity upper limit is generated, a cross-energy compensation strategy is triggered, closed-loop optimization is achieved, the problem that accumulated damage of high-frequency power adjustment to equipment cannot be accurately quantified is solved, and the power grid dispatching capacity is optimized. The efficiency of a multi-objective optimization algorithm is improved, and the phenomena of frequent shutdown, wind curtailment rate rise and peak regulation resource waste are reduced, so that the new energy consumption capability and the power grid economy are improved.

The invention provides an integrated optimal scheduling method and system for a smart power plant, and belongs to the technical field of unit scheduling, and the method specifically comprises the steps: taking weather data as a basis, and when determining that the matching coefficient of the frequency modulation response data of the smart power plant in the remaining time period of the current date and the electric energy output of a new energy output device meets the requirement, determining that the new energy output device outputs the new energy output device; and obtaining frequency modulation response data of the smart power plant in the current time period of the current date and historical matching data of electric energy output of the new energy output device, and dynamically adjusting an energy storage utilization form of the new energy output device of the smart power plant in the current time period of the current date in combination with the residual electric quantity of an electric energy storage device of the smart power plant. Therefore, the flexibility of the unit is improved.

The invention provides a spare power automatic switching risk prediction method and system for a smart power grid, and relates to the technical field of smart power grids, and the method comprises the steps: collecting and screening an initial feature set of the smart power grid, and generating a dynamic feature set; outputting a conventional risk probability based on a time sequence feature through a random forest model, identifying a sudden abnormal mode based on a frequency domain feature through an XGBoost model, and synchronously generating a feature importance matrix; determining a power grid state by combining the time sequence characteristics and the frequency domain characteristics, dynamically fusing output results of the models according to fusion weights of the models determined by the power grid state, and generating a comprehensive risk level; inputting the comprehensive risk level and the dynamic feature set into a reinforcement learning environment model, and solving a multi-objective function generated by the model to obtain a spare power automatic switching execution strategy; and executing the spare power automatic switching execution strategy, and dynamically adjusting the sliding window for feature acquisition until the execution result meets the preset condition, so that the accuracy of spare power automatic switching risk prediction and the adaptability of the strategy are remarkably improved.

The invention discloses a smart park electric energy monitoring and management system, and relates to the technical field of smart power grids and Internet of Things, and the system comprises the following components: a data collection layer, a big data processing and analysis layer, an equipment health degree evaluation layer, a fault early warning and diagnosis layer, and an operation and maintenance decision support layer. The operation data of the equipment is continuously collected, the equipment health degree evaluation layer calculates and updates the equipment health index in real time, the equipment performance reduction or potential fault risk is found in time, and once the equipment health index is lower than the preset threshold value, the system automatically triggers an early warning mechanism to inform related personnel to pay attention to or take corresponding measures, so that the safety of the equipment is improved. Through the real-time monitoring and early warning functions, operation and maintenance personnel can quickly respond to equipment problems, and fault expansion or influence on the whole electric energy supply of the park is avoided, so that the operation and maintenance efficiency and stability of the park are improved.

The invention discloses a power demand response system which comprises the following steps: S1, data acquisition and monitoring: the system acquires detailed power consumption data such as power consumption, power consumption time, power consumption power and the like of a user in real time through widely distributed intelligent electric meters, and uses sensors installed at key nodes of a power grid to determine the power consumption of the user; operation parameters such as voltage, frequency and load of a power grid are monitored in an omnibearing manner; and S2, data analysis and load prediction: fusing and sorting various types of data from a user side and a power grid side, and eliminating data format differences and repeated data. According to the power demand response system, a power consumption mode is remodeled through two-way interaction, the operation efficiency and economical efficiency of a power grid are improved, key support is provided for energy transformation and carbon neutralization targets, and the power demand response system is an important tool for achieving an intelligent power grid and a sustainable energy system and has wide application prospects. The successful implementation of the method depends on policy support, technical innovation and collaborative optimization of a user participation mechanism.

The invention relates to the technical field of data early warning, in particular to an intelligent power plant safety early warning method based on multi-data coupling. The method comprises the following steps: acquiring multi-source coupling data including power plant production operation data, key equipment state monitoring data, environment monitoring data and historical accident records, and calculating acquisition time intervals and synchronous delays between data sources; defining the ratio of the acquisition time interval to the topological distance between the corresponding nodes of the key equipment as a coupling propagation rate; constructing a time sequence-topology association window based on the coupling propagation rate, and judging the state type of the key equipment according to the time sequence-topology association window; a key equipment risk parameter is obtained by quantifying the state type; according to the method, key equipment risk parameter quantification and potential risk marking are realized through multi-source coupling data and time sequence-topology correlation analysis, so that dynamic grading early warning is supported, and the operation safety and early warning real-time performance of a power plant are improved.

The invention discloses an electric vehicle charging method, device and equipment based on time-of-use electricity price, and a medium, and relates to the field of smart power grids, and the method comprises the steps: generating a target power data set according to the load data of a power grid side, the charging data of a target charging pile, and a preset time-of-use electricity price parameter; performing power grid load curve prediction based on the target power data set through a preset prediction model, and performing peak-valley period division on a predicted target prediction power grid load curve to obtain divided periods; generating a time-of-use electricity price signal according to the divided time periods, and constructing a charging strategy function according to the target power data set and the time-of-use electricity price signal; and solving the charging strategy function to obtain a corresponding target solution set, scoring a plurality of solutions in the target solution set through a preset weight, and generating a charging strategy based on the target solution with the highest score, so that the electric vehicle can be charged based on the charging strategy. Therefore, the electric vehicle charging cost of the user can be reduced while the intra-day load peak-valley difference is reduced.

The invention relates to the technical field of smart power grids, in particular to a state grid smart energy unit detection method based on correlation analysis, which comprises the following steps: S1, collecting multi-dimensional operation data in real time; s2, carrying out preprocessing on the acquired multi-dimensional operation data; s3, extracting related features from the preprocessed data, wherein the related features comprise a time domain feature, a frequency domain feature and a trend feature; s4, identifying a potential association relationship in the smart energy unit; s5, monitoring the running state of the system in real time, and identifying abnormal characteristics deviating from a normal running state; and S6, judging whether the smart energy unit has a fault and the specific type of the fault. According to the invention, through a multi-dimensional data processing method based on association analysis, related features are accurately extracted, the association relationship between the features is identified, and by combining multi-dimensional anomaly detection and a decision tree algorithm, the fault diagnosis precision and real-time performance of the smart energy unit are effectively improved, and false alarm and missing alarm are significantly reduced.

The invention discloses a power grid regulation and control scene generation method and device, electronic equipment and a storage medium, and relates to the field of smart power grids or other related technical fields, and the method comprises the steps: collecting power grid load data, and carrying out the preprocessing of the power grid load data, and obtaining the preprocessed power grid load data; performing feature extraction and coding on the preprocessed power grid load data to obtain a load feature vector; inputting the load feature vector into an unsupervised generation model, and outputting a power grid regulation and control scene through the unsupervised generation model; and calculating the similarity between the power grid regulation and control scene and a pre-constructed reference regulation and control scene, configuring a type label for the generated power grid regulation and control scene based on the similarity, and generating a target power grid regulation and control scene based on the power grid regulation and control scene and the type label. According to the invention, the technical problem of low efficiency of a power grid regulation and control scene generation method based on historical data and an expert strategy in related technologies is solved.

The invention discloses a power distribution cabinet for electric power engineering construction based on a smart power grid, and relates to the technical field of smart power grids, the power distribution cabinet comprises a base, the top of the base is fixedly connected with a power distribution cabinet outer frame, and the top of the power distribution cabinet outer frame is fixedly connected with a control mechanism; the left side and the right side of the inner wall of the power distribution cabinet outer frame of the top of the base are movably connected with element assembly cabinets respectively, and the top of the power distribution cabinet outer frame is movably connected with a power distribution cabinet top cover. Specifically, if cooperation of multiple persons is needed, two hydraulic push rods and a push plate are activated to drive an outer sliding rod and a push-pull rod to move inwards, a rotating rod and a two-way hinge rod rotate along with the outer sliding rod and the push-pull rod, so that a top cover of the power distribution cabinet moves upwards, the two sides of an outer frame are opened, the L-shaped push-pull rod moves outwards, and an assembly cabinet body moves out of the two sides of the outer wall.

The invention discloses a new energy station primary frequency modulation control method considering power grid fault characteristics, and relates to the technical field of smart power grids, comprising the following steps: collecting operation data of a power grid, and preprocessing the operation data; constructing a fault prediction model based on historical power grid operation data and fault data, inputting the preprocessed data into the fault prediction model, and generating a prediction result; adjusting the charge state of the energy storage equipment according to the prediction result; combining the adjusted charge state of the energy storage equipment with power grid operation data collected in real time, and analyzing the real-time monitoring data to extract fault feature information; based on the fault feature information and the adjusted charge state of the energy storage device, dynamically distributing the frequency modulation response priority of the energy storage device, and generating an instant frequency modulation power instruction of the energy storage device; and dynamically adjusting the power output of the energy storage equipment according to the instant frequency modulation power instruction. The instant frequency modulation power instruction is generated according to the calculated frequency modulation response priority of the energy storage equipment, and frequency modulation is accurately carried out.

The present invention relates to the technical field of power supply. Disclosed is a smart power supply device suitable for a smart construction site computer service gateway, said device comprising a bottom plate, the top end of the bottom plate being fixedly connected with a top plate by means of columns. When a mounting position is inclined due to condition limitations, the power supply can be leveled, ensuring that the power supply is used in a horizontal state. A water addition amount below a housing can be automatically adjusted according to the weight of the power supply, to achieve adjustment of a lower counterweight, thereby ensuring that the whole power supply device is not top-heavy, and ensuring the stability of the power supply device after leveling. The required counterweight water addition amount can be clearly known, thereby avoiding time wastage due to adding more water. A ball can be dried during leveling, so as to prevent slipping caused by a wet surface, thereby ensuring the stability of the positioning and fixing of the ball, and thus ensuring the stability of the whole device after leveling.

The invention provides an equipment layout optimization method and system in power transmission and transformation project design, and relates to the technical field of smart power grids, and the method comprises the steps: carrying out the region division of design drawing information, and obtaining N power transmission and transformation project regions; calling a power transmission and transformation project layout data space, performing effect evaluation fitting, and constructing a power transmission and transformation project layout simulation module; carrying out equipment layout optimization on the N power transmission and transformation project areas in sequence, and determining equipment layout schemes of the N power transmission and transformation project areas; carrying out three-dimensional modeling, generating an initial engineering equipment layout model, and carrying out anomaly identification marking; and performing global optimization analysis on the initial engineering equipment layout model based on the equipment layout abnormal feature information, and determining a power transmission and transformation engineering equipment layout scheme. According to the method, the technical problem of low power transmission and transformation engineering equipment layout efficiency caused by unreasonable space utilization is solved, and the power transmission and transformation engineering equipment layout efficiency is improved through region division and global optimization in combination with anomaly recognition.

The invention discloses a comprehensive energy demand response scheduling method and platform for a smart power grid, and relates to the technical field of energy management, and the method comprises the steps: predicting a user load demand through a future time zone window, and building a comprehensive energy demand scheduling space based on multi-energy cooperative scheduling; k energy supply units are modeled, and initial optimization and variation expansion optimization are performed in combination with a power grid energy scheduling evaluation channel, so that a better scheduling space is formed; and introducing an energy scheduling optimization operator, carrying out deep optimization on the scheduling space, obtaining a comprehensive energy demand scheduling strategy, and carrying out dynamic response scheduling. According to the invention, the technical problems of low energy utilization efficiency and insufficient scheduling flexibility caused by lack of efficient cooperative scheduling of multiple energy types and accurate adaptation capability of user load dynamic demands in the prior art are solved, and the purpose of improving the scheduling efficiency through multi-energy cooperative optimization and dynamic response scheduling is achieved. And the energy utilization efficiency and the scheduling flexibility are improved.

The invention relates to the technical field of smart power grids, and discloses a power distribution network reactive voltage control system and method based on multi-region end self-discipline collaboration, and the method comprises the steps: monitoring the operation state of a system through accessed online monitoring data; monitoring and state warning are carried out on operation of the reactive voltage equipment, and the treatment effect of the reactive voltage equipment is analyzed; performing global decision through the reinforcement learning network model, outputting an optimal voltage regulation decision of the equipment intelligent agent, and issuing an instruction to a lower-level intelligent agent; the system display platform obtains equipment data from the MQTT, and displays a system operation state, an equipment operation state, equipment fault alarm and control effect analysis; setting business rules of equipment state monitoring, recording and displaying alarm logs and action logs, providing screening and paging functions, performing effect analysis on the action logs, calculating an optimization rate, and evaluating a control effect. Through wide application of the system, the problem of high voltage of users can be effectively suppressed, and safe, reliable and high-quality power supply of a power grid is realized.

The invention relates to the technical field of intelligent power distribution network safety detection and fault processing, in particular to a multi-source coupling leakage current accurate traceability and cooperative suppression method and system for a power distribution network, and the method comprises the steps: collecting leakage current, voltage phase and power fluctuation data through a synchronous phasor measurement device; based on the collected data, constructing a four-dimensional feature library, carrying out decoupling processing on leakage current signals, and then realizing accurate traceability positioning of the leakage current according to a matching result of the leakage current signals and the four-dimensional feature library and a time difference between sensors; data are uploaded to a distribution network dispatching center, leakage current is evaluated based on a dynamically adjusted safety threshold value, and then operation states of a photovoltaic inverter, an energy storage converter and a charging pile are cooperatively controlled to realize active suppression of the leakage current; and performing preliminary diagnosis on the leakage current at the edge side, uploading a result to a cloud end, and determining a final diagnosis conclusion. According to the invention, the accuracy of multi-source coupling leakage current tracing and the accuracy of cooperative suppression of the power distribution network are improved.

The invention relates to the technical field of intelligent power plants, in particular to a robot inspection control method and system for an intelligent power plant. Comprising the steps that an inspection equipment library and a power plant monitoring model are established, and the power plant monitoring model is used for generating a risk thermodynamic diagram; generating an inspection demand package according to the risk thermodynamic diagram, and generating a primary inspection strategy according to a preset inspection model and the inspection demand package; obtaining a feedback data packet of each inspection sub-device according to a preset feedback time node, and judging whether to generate an operation correction instruction or not according to the feedback data packet; the method comprises the following steps: periodically predicting power plant equipment, constructing a corresponding risk thermodynamic diagram, screening first-level risk points according to the risk thermodynamic diagram, performing path optimization based on position parameters of each first-level risk point according to an inspection model, generating corresponding inspection sub-paths, and completing an inspection task by matching the optimal inspection sub-equipment. The inspection efficiency and fault early warning efficiency of the power plant are improved, and safe operation of the power plant is ensured.

The invention discloses a block chain enabling smart power grid transaction system based on a game theory, and relates to the technical field of regional chains, and the system comprises a strategy management module which is used for making a buying and selling strategy based on the optimal utility through the game theory according to the electric meter storage space of a user, power utilization historical information and transaction record information, the electric equipment of the whole network is dispatched through the block chain; the transaction contract module is used for creating and updating a transaction contract based on the operation strategy formulated by the strategy management module; and the user management module is used for managing the information of the power users and the historical electricity utilization records. According to the invention, an efficient, fair and transparent power transaction mode is constructed by using the security of the block chain technology and the real-time response characteristic of the smart power grid, and the method aims at fully utilizing the distributed power storage units to achieve power utilization peak regulation so as to reduce the energy loss.

The invention discloses an intelligent power utilization data acquisition control method and system based on edge calculation. The method comprises the following steps: acquiring acquisition task information of a master station; the acquisition task information of the master station is divided into a plurality of sub-acquisition tasks according to regions; sending the sub-collection tasks to collectors and concentrators in the corresponding areas for storage; based on the sub-collection task, the collector collects user-side electric energy meter data and verifies the data to obtain first collection data; based on the concurrent reading command, the concentrator reads the first aggregated data in the collector to obtain second aggregated data; and based on the set requirement of the master station, the master station extracts the corresponding electric energy meter data in the collector. According to the invention, local collection of a user side is completed by reading meter data storage through the collector, minute-level high-frequency collection and power failure active reporting of data such as voltage, current and electric energy of the electric energy meter can be realized, and a data basis is provided for services such as power supply line aging trend analysis, power grid voltage quality monitoring and load monitoring analysis.

The invention relates to an automatic pre-warning system after equipment failure for a smart power plant. The automatic pre-warning system comprises a data acquisition unit, a fault point judgment unit, a fault propagation relationship network establishment unit, a shutdown risk assessment unit and a pre-warning unit. The method has the beneficial effects that through cooperative work of data acquisition, fault point judgment, fault propagation relationship network modeling, shutdown risk assessment and an early warning unit, fault propagation chain reaction is dynamically quantified, and the shutdown risk probability of the whole plant is generated, so that the defects of incomplete path analysis and lagging risk calculation in a multi-equipment fault scene in a traditional method are overcome; the accuracy of fault early warning is improved, so that an effective emergency response basis is provided for operation and maintenance personnel of an intelligent power plant; the propagation probability matrix generation module corrects the final propagation probability according to the actual fault propagation mark value, and the final propagation probability is corrected in each fault propagation event, so that prediction becomes more and more accurate along with system operation.