4886 results about "Power usage" patented technology

The invention relates to the technical field of power system dispatching, in particular to an emerging load power supply potential improvement method based on dynamic prediction, which comprises the following steps of: acquiring emerging load power consumption, meteorological environment and power grid schedulable resource data in a target area through an Internet of Things sensing terminal, and performing two-channel modeling to obtain a new load power supply potential improvement model; a deep space-time network is used to predict a load curve, a model is established to quantify resource regulation potential, a scheduling priority list and a capacity allocation strategy are generated by means of a matching rule base according to load fluctuation and resource evaluation results, an actual scheduling effect is fed back to the prediction model, parameters are corrected through error back propagation, a closed-loop optimization link is formed, and the scheduling efficiency is improved. The method improves load prediction accuracy and resource scheduling adaptability, is suitable for emerging load power supply optimization in a novel power system, and guarantees stable and efficient operation of a power grid.

The present disclosure relates to the technical field of intelligent monitoring and management of power systems, and specifically relates to an intelligent collaborative power consumption regulation and control method, apparatus and system for source-grid-load-storage, an electronic device and a storage medium. Said system comprises an energy regulation and control center and energy regulation and control units provided in microgrids; the energy regulation and control units use a temporal attention mechanism-based LRCN dual-layer network combined model to predict power consumption amounts, so as to generate power consumption surpluses and shortages within a future preset time; and on the basis of the power consumption surpluses and shortages and latest current electricity prices of the microgrids, the energy regulation and control center uses a fusion multi-objective algorithm based on a Pareto front curve and a fuzzy algorithm to generate a microgrid collaborative power consumption regulation and control solution, and sends the regulation and control solution to the energy regulation and control units for execution, so as to ensure the balance of energy supply and demand of the microgrids. Therefore, the present disclosure achieves efficient, intelligent and refined energy management for microgrid clusters, reducing energy consumption and costs, and providing solid support for sustainable development of microgrids.

The invention relates to an electrical load prediction and optimization regulation and control method and system for high-energy-consumption equipment, and solves the problems of inaccurate load prediction, single regulation and control means and difficulty in dynamic adaptation of the high-energy-consumption equipment, and the method comprises the steps: collecting multi-source data of the high-energy-consumption equipment in real time, constructing a dynamic equipment collaborative causal graph after preprocessing, and extracting key constraints; inputting the data and the constraints into the dynamic digital sample model to obtain a system state simulation result; based on the result, a multi-objective optimization regulation and control strategy is generated and executed by using a meta-learning + reinforcement learning decision framework; and collecting actual data comparison deviation, starting hierarchical federated learning when a threshold value is exceeded, grouping and aggregating similar experiences according to a causal graph topology, and dynamically calibrating model parameters and a decision framework. The method has the following effects that accurate load prediction and multi-target cooperative regulation and control of the high-energy-consumption equipment are achieved, working condition changes are dynamically adapted, the cost is reduced, and continuous production and the service life of the equipment are guaranteed.

The invention relates to the technical field of electric energy metering and electric power system safety, and discloses an electricity larceny prevention method and system for multi-source data fusion of an electric energy meter, and the method comprises the steps: obtaining and preprocessing current and voltage waveform data and user electricity consumption behavior data; generating multi-dimensional power consumption characteristic parameters, performing screening to form a characteristic vector set, and constructing an abnormal power consumption detection reference set; dynamically verifying power utilization data based on the reference set, and analyzing a phase relation and a mismatch state of current and voltage signals; and generating an electricity consumption behavior deviation index to judge an electricity stealing behavior. The system comprises a multi-source data acquisition module, a feature modeling analysis module, an electrical relation verification module and an electricity stealing judgment module. According to the invention, through multi-source data fusion and multi-dimensional feature analysis, accurate detection of electricity larceny behaviors is realized, accuracy and reliability of electricity larceny prevention are improved, and the method is suitable for power utilization safety management of a smart power grid.

The invention relates to the technical field of electric energy meter metering, and discloses an electric energy meter metering anomaly analysis method and system, and the method comprises the steps: collecting data, such as voltage waveforms, to generate a metering feature vector set, and constructing an anomaly detection rule base; setting a scene parameter type set, and establishing an abnormal association judgment model; dynamically correcting the threshold value by combining the model, and generating an optimized error threshold value set and an abnormal triggering condition set; and updating a metering analysis strategy to generate an abnormal judgment scheme, and correcting data verification sequential logic. The system comprises a data acquisition module, an abnormal rule base construction module, a scene parameter configuration module, a correlation model training module, a dynamic threshold optimization module, a strategy updating module and a time sequence correction module. Through multi-dimensional data modeling, scene-based threshold configuration and dynamic time sequence calibration, the accuracy and adaptability of electric energy meter measurement anomaly detection are improved, and the method is suitable for measurement anomaly analysis of diversified power consumption scenes in a smart power grid.

The invention discloses an intelligent electric equipment monitoring and optimizing method, and relates to the technical field of electric power, and the method comprises the steps: collecting high-dimensional voltage-current time sequence data and transient event marks, calculating topological invariant features, inputting the topological invariant features to a lightweight neural network model, and recognizing the features of all electric equipment; performing abnormal attribution and anti-fact energy efficiency prediction by using causal reasoning and dynamic regularization regression based on the identified characteristics of each electric device, and constructing a multi-objective optimization function through the abnormal attribution and anti-fact energy efficiency prediction; and based on the multi-objective optimization function, generating an equipment operation scheduling strategy through a deep reinforcement learning agent, based on the operation scheduling strategy, sending a control instruction to the electric equipment, and collecting an operation result feedback in real time for optimization and updating. According to the method, through fusion of topological features, causal reasoning and safety reinforcement learning, the precision, robustness and safety of monitoring and optimization of the intelligent electric equipment are improved.

The invention relates to the technical field of user power utilization chain analysis, and discloses a special transformer user power utilization abnormal chain construction method fusing deep learning, which comprises the following steps: collecting time sequence power utilization data and power grid topological data of special transformer users, taking each special transformer user as a node, constructing a graph structure comprising a physical connection edge and a behavior association edge, and constructing a graph structure comprising a physical connection edge and a behavior association edge; and inputting the constructed graph structure into a space-time graph neural network model, introducing a power grid physical constraint condition into an optimization target, taking a power grid operation rule as a constraint embedding model, and outputting node-level, edge-level and sub-graph-level multi-level anomaly detection results. Constructing a heterogeneous graph containing user nodes, anomaly type nodes and time slice nodes through the multi-level anomaly detection result, performing path search in the heterogeneous graph through a predefined association mode template, generating a candidate anomaly chain, performing causal strength verification on the candidate anomaly chain by adopting a time sequence causal relationship verification model, and obtaining a multi-level anomaly detection result of the user nodes, the anomaly type nodes and the time slice nodes. And intelligent analysis of the user electricity consumption abnormity chain is realized.

The invention relates to the field of load monitoring, in particular to an electric energy meter time-sharing load monitoring method and system based on multi-dimensional electricity utilization characteristics, and the method comprises the steps: collecting and preprocessing the electrical time sequence data of a user electric energy meter; extracting multi-dimensional statistical characteristics of current and voltage of the users under a plurality of analysis windows, and clustering to obtain a cluster and membership degree of each user; for the target user, respectively calculating a first anomaly degree of the target user and a historical sequence thereof and a second anomaly degree of the target user and a sequence of other users in the same period in the cluster to which the target user belongs; according to the membership degree of the user under each analysis window, carrying out weighted fusion on the second anomaly degree to obtain a third anomaly degree; and calculating a comprehensive abnormality based on the first abnormality and the third abnormality, and determining whether the load is abnormal. According to the invention, through dual reference of individual history and a dynamic group and multi-scale credible fusion, the accuracy, the adaptive ability and the reliability of load anomaly monitoring are significantly improved.

The invention discloses an electricity larceny identification method based on graph calculation, and particularly relates to the technical field of electricity utilization anomaly detection of an electric power system. Historical power consumption data and a power supply topological relation of power consumers are collected, and a multi-dimensional behavior graph model fusing behavior characteristics and structural information is constructed; performing structure disturbance analysis on each node in the graph, calculating information entropy change before and after node removal, performing attention fusion on a time sequence behavior feature of the node and a structure disturbance vector, constructing a joint feature vector, and enhancing feature expression through spectral clustering and linear reconstruction; a behavior propagation field and a disturbance adjustment mechanism are introduced into the graph to form a disturbance response graph, and an abnormal gathering area is identified through path energy analysis and focusing area fitting; calculating confidence scores of the nodes and outputting a suspicious user list; the method can realize efficient identification of electricity stealing behaviors with strong concealment and complex transmissibility, and has the advantages of high precision, strong interpretability and wide application scene adaptability.

The invention provides an electricity load equipment identification method based on spatial-temporal feature fusion and an attention mechanism, and the method comprises the steps: carrying out the dual preprocessing of the data through the real-time collection of the electrical time sequence data, operation state labels, spatial layout information and environment parameters of target equipment, comprising the steps of dynamically aligning a time sequence curve by time warping and adaptively adjusting data distribution in a standardized manner. A spatial correlation graph is constructed by using a graph neural network, and space-time proximity is calculated to generate a weighted adjacency matrix. A multi-head attention mechanism is adopted to fuse electrical time sequence features and spatial topology features, and bidirectional asymmetric attention modulation network optimization feature fusion is constructed. Time and space dimension features are extracted through a time flow module and a space flow module respectively, the features are dynamically weighted and fused through a gating attention unit, and the identification model is input to output the equipment type and the working state. According to the method, the accuracy and robustness of electricity load equipment identification can be improved, and the method has good adaptability and expansibility.

The invention discloses a load decomposition method based on fusion feature data enhancement, and the method comprises the steps: synchronously collecting the low-frequency power data of a bus end of an electrical loop of a building and the low-frequency power data of all electric equipment ends, and generating a confusion power sequence of similar equipment through Beta distribution mixing, so as to enhance the recognition capability of a model for power overlapping features; based on the power time sequence data, extracting a mutation feature, an equipment state feature and a time coding feature to construct a multi-dimensional feature vector; a CNN-BiLSTM double-branch neural network is adopted, spatial-temporal characteristics are fused through a dynamic weight attention mechanism, and equipment state classification and power decomposition tasks are jointly optimized. In practical application, bus end power data is input, and the operation state and power distribution of each electric device are obtained. According to the method, an adversarial training strategy and a gating feature fusion mechanism are innovatively introduced, the load decomposition performance in a complex power utilization scene is remarkably improved, and the method is particularly suitable for identification and power prediction of equipment with similar rated power.

The invention discloses a metering box abnormal power consumption behavior diagnosis method and system based on edge calculation, and belongs to the technical field of power monitoring, and the method comprises the steps: collecting the voltage, current and time mark data of a metering box in real time through an edge end, synchronously obtaining the physical state information of a box door state and the like, generating a space-time feature matrix through an electric parameter space-time coupling analysis method, and carrying out the calculation of the time-space feature matrix; inputting an electric parameter feature mask pruning algorithm to obtain an optimized feature set, triggering an edge-sensing sleep wake-up linkage device when current abnormity is monitored, and collecting magnetic field data; the edge end calculates a total-branch electric energy difference value, combines the magnetic field data and a box door state, and constructs an electricity consumption abnormity classification determination tree through a metering error-electricity stealing behavior coupling diagnosis method to distinguish electricity stealing behaviors; an intermittent abnormal trajectory splicing algorithm is adopted to process fragmented abnormal data to generate an electricity stealing trajectory, and the electricity stealing trajectory is fed back to an electric parameter space-time coupling analysis method to dynamically update the space-time feature weight; according to the invention, the abnormity diagnosis precision and real-time performance are improved, and the energy consumption is reduced.

The invention relates to the technical field of power grid data analysis, in particular to a power consumer anomaly detection and risk assessment method, system and device based on spatial-temporal feature fusion and a storage medium. The method comprises the following steps: acquiring multi-source power consumption data of various power consumers, constructing a space-time correlation feature extraction model, and establishing a user behavior space-time coupling relationship through time sequence analysis and spatial distribution characteristic analysis; constructing a multi-dimensional risk mapping model based on a long short term memory network and a convolutional neural network, establishing a nonlinear mapping relation from feature information to a risk assessment result, and identifying an abnormal power consumption behavior; establishing a distributed node credibility evaluation system, and performing credibility quantitative analysis on the data nodes in combination with a fuzzy comprehensive evaluation method; and based on an abnormal detection result and credibility evaluation, intelligent analysis and dynamic early warning of the electric charge management and control risk are realized. The time-space coupling characteristic of power consumption data can be fully mined, and high-precision identification of abnormal power consumption behaviors and accurate detection of non-technical power loss are realized.

The invention relates to the technical field of electricity metering anomaly monitoring, and provides a distributed resource access-oriented lightweight electricity metering anomaly monitoring method, which comprises the following steps of: acquiring electrical data and environment associated data of distributed access points at an edge side, and constructing a fluctuation fingerprint feature vector; when it is detected that the fluctuation fingerprint feature vector meets a preset fluctuation triggering condition, based on Pearson correlation analysis between the electrical data and the environment associated data, performing decoupling and elimination on false anomalies caused by environmental factors to obtain a fluctuation fingerprint feature vector of non-environmental suspicious anomalies; based on historical normal operation data, constructing a normal working condition mode library by adopting a clustering analysis method, and determining a feature center and a dynamic safety radius of each normal working condition mode; and calculating the feature distance between the non-environmental suspicious abnormal fluctuation fingerprint feature vector and each normal working condition feature center at the edge side, and judging whether the electricity consumption metering is abnormal or not according to the relationship between the feature distance and the dynamic safety radius.

The invention belongs to the technical field of intelligent electric meters, and discloses an intelligent electric meter system with an abnormal power consumption behavior recognition function. The system is composed of a data acquisition module, an electric energy quality monitoring module, a data preprocessing module, a power utilization behavior feature extraction module, a short-time behavior monitoring module, an anomaly detection and diagnosis module, a behavior trend analysis module, an alarm and visualization module and a remote collaborative management module. Through deep mining and learning of historical power utilization data of a user, the system can accurately grasp power utilization habits, time period change rules and load characteristics of the user, along with dynamic evolution of power utilization conditions, the system can automatically optimize an anomaly detection threshold value, limitation brought by a traditional fixed threshold value is abandoned, and through the personalized and adaptive design, the power utilization efficiency of the user is improved. The system can exert the optimal efficiency in different regions and different types of users, and the application range and the practicability of the system are remarkably improved.

The invention relates to a virtual power plant optimization operation method and system based on data center shared energy storage and load space-time migration, and the method comprises the steps: quantifying the electric energy utilization efficiency of a data center according to the power consumption of IT equipment, the power consumption of refrigeration equipment and the power consumption of other auxiliary equipment in a data center power consumption model; in the load space-time migration model, delay processing time calculation is carried out on batch processing loads, and the load migration amount across the data centers is calculated among the data centers; dynamically distributing the energy storage capacity of each data center in the shared energy storage model, and sharing the energy storage investment cost by adopting a Shapley value; in the double-layer optimization model, the upper-layer model generates an electricity price signal and a demand response instruction according to the wind and light output prediction data, the real-time electricity price of the power grid and the initial load demand of each data center, and transmits the electricity price signal and the demand response instruction to the lower-layer model; and the lower-layer model feeds back the obtained data center response and the electrical load to the upper-layer model. Compared with the prior art, the method has the advantages of high collaboration, high efficiency, high consumption and the like.

The invention relates to the technical field of power distribution network monitoring, in particular to an intelligent operation and maintenance management platform for a power distribution network. Obtaining suspected abnormal points according to data discrete features and local data fluctuation features of the feature data sequence; according to the number characteristics of the suspected abnormal points in all the operation and maintenance characteristics and the data distribution characteristics of the suspected abnormal points, risk statistical characteristic values of the power distribution network equipment are obtained; clustering suspected abnormal points in the operation and maintenance features, and obtaining risk distribution feature values of the power distribution network equipment according to time interval features between the data point clusters and interval features of the suspected abnormal points in the data point clusters; and obtaining the risk degree of the power distribution network equipment according to the risk statistical characteristic value and the risk distribution characteristic value. According to the invention, the inspection sequence of all power distribution network equipment is sorted according to the risk degree, and inspection is carried out in sequence, so that the inspection efficiency and the power utilization stability are improved.

The invention discloses an electric power utilization anomaly detection and early warning method based on the Internet of Things, which relates to the technical field of electric power utilization and comprises the following steps of: performing fitting comparison on identified inductive current fluctuation and a behavior residual error template generated based on a real load behavior; analyzing a response time window, a fluctuation amplitude track and a behavior continuity characteristic, and judging whether the inductive current fluctuation generated by a non-target loop belongs to a real load change behavior or not under the condition that a plurality of loops are grounded together according to the response time window, the fluctuation amplitude track and the behavior continuity characteristic; and combining the behavior residual error fitting score, the phase consistency value and the frequency response stability value to generate a signal credibility judgment sequence, and carrying out credibility grade division on the inductive current fluctuation. According to the method, the problem of false fluctuation misjudgment caused by inductive signal interference under the condition that multiple loops share the grounding condition is solved, accurate identification and dynamic regulation and control on the authenticity of inductive current fluctuation are achieved, and therefore the accuracy and reliability of power utilization anomaly detection and early warning are improved.

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 embodiment of the invention provides a model training method, a carbon emission prediction method, a device and equipment. The method comprises the following steps: firstly, obtaining carbon emission sample sequence data; then, preprocessing the carbon emission sample data sequence to obtain preprocessed carbon emission sample sequence data; further, according to the preprocessed carbon emission analysis sample sequence data, determining a time sample feature and an interaction feature sample sequence; and then. Processing the sample time feature and the preprocessed power consumption sample sequence data to obtain a sample time feature component and a sample power consumption feature component; and finally, inputting the sample time characteristic component, the sample power consumption characteristic component, the interaction characteristic sample sequence and the preprocessed carbon emission sample sequence data into an initial Transform model for optimization training, and obtaining an improved target Transform model. In this way, the prediction precision of the prediction model and the generalization ability of the model are improved, and therefore accurate prediction of carbon emission data is achieved.

The invention discloses an electricity consumption data anomaly detection method and device based on a space-time dual-hybrid expert model, and belongs to the technical field of electric energy metering. The method comprises the following steps: acquiring original power consumption data, preprocessing the original power consumption data, generating a time sequence set, and building a time dimension hybrid expert model and a channel dimension hybrid expert model based on the time sequence set; for a multivariable time sequence anomaly detection task, obtaining target power consumption data, taking the target power consumption data as input of a time dimension hybrid expert model and a channel dimension hybrid expert model, and performing time and space dependency relationship fusion on an output result to obtain a fusion result; and on the basis of a preset comprehensive anomaly score, according to a fusion result, performing anomaly detection, and judging whether the target power consumption data is abnormal or not. According to the implementation of the method, the extraction capability of the model on the normal mode in the high-dimensional complex time series data and the robustness of anomaly judgment are remarkably improved.

The invention discloses a power grid dispatching scheme generation method and system based on load optimization, and the method comprises the steps: predicting a reference load change curve and a confidence interval of each node in a future set time period according to the load data and meteorological data of each node in a power grid topological graph in a corresponding time period; continuously determining a risk area based on the risk assessment model and determining a predicted load offset and a standard deviation so as to correspondingly optimize and broaden a reference load change curve and a confidence interval of each node in the risk area; and constructing an uncertain scene set based on the reference load change curve and the confidence interval, then constructing an objective function, and solving the objective function based on the uncertain scene set to generate an elastic scheduling scheme with the minimum power generation cost and the minimum load vacancy in the worst load scene. According to the invention, through supplementing the prediction load offset and the standard deviation, the prediction precision of the short-term load change under the condition of sudden power consumption peak or extreme weather is obviously enhanced, and the power grid dispatching level is improved.

The invention discloses a non-intrusive household load identification method based on CUSUM event detection and a convolutional neural network, which is applied to total load data output by an intelligent electric meter installed at a user total incoming line, and comprises the following steps: data preprocessing; carrying out load event detection based on a sliding window bilateral CUSUM algorithm so as to determine an occurrence moment and a continuous interval of a load event; in a continuous interval corresponding to each load event, calculating an active power variation delta P, a reactive power variation delta Q, a current harmonic distortion variation delta Ihd and a current span Itc in steady-state data segments before and after the load event, and combining to form a multi-dimensional event feature vector; the method comprises the following steps: sequentially executing the steps on operation data of each electric device in a family in different operation modes to obtain a multi-dimensional event feature vector marked with a device type and an operation state label, and constructing a standardized device load feature database; and training a convolutional neural network classification model and realizing load identification.

The invention discloses a household electrical load optimization scheduling method and system based on an MAPPO algorithm. The method comprises the following steps: (1) collecting multi-source heterogeneous data from a user side and an external environment and carrying out preprocessing; (2) estimating the future equipment use probability of the user by collecting historical equipment use data of the user, and outputting an equipment behavior prediction vector; (3) acquiring real-time electricity price information, performing short-term electricity price trend prediction by using the time sequence prediction model based on a historical electricity price sequence, and outputting an electricity price prediction sequence; (4) generating a scheduling strategy by the MAPPO network based on the equipment behavior prediction vector and the electricity price prediction sequence, and outputting a corresponding scheduling result; and (5) sending a scheduling result to the home gateway, controlling the equipment, collecting an execution feedback result in real time, updating internal parameters of the MAPPO network, and realizing strategy iteration and adaptive adjustment of the MAPPO network.

The invention provides a photovoltaic access power distribution network voltage stability evaluation method, which comprises the following steps: acquiring ship docking time, staying duration and crane working frequency in a port area in real time, fusing obtained meteorological information, identifying a photovoltaic power generation output power change trend, and obtaining a daily power load peak-valley time period and a load fluctuation amplitude; according to the adjusted voltage value of each node, generating a switch operation instruction and a power adjustment instruction of the shore power supply equipment, identifying load abrupt change by executing the instructions, and determining a real-time power dispatching scheme by combining the power adjustment instruction and the ship arrival time; and according to the target voltage control value, determining the power utilization safety state of the port power distribution network, and evaluating the voltage stability of the photovoltaic access power distribution network through the operation data of the photovoltaic power generation access point.

The invention relates to the technical field of power system optimization scheduling, and provides a multi-time-scale new energy-stored energy joint output optimization method and system based on spot price, so as to solve the problems of poor system operation stability and low new energy regulation and control precision in the prior art. The method comprises the following steps: detecting a temperature change rate of a composite phase change material in an energy charging and discharging process to determine a heat storage capability state of an energy storage unit, and generating a day-ahead joint scheduling plan considering operation cost and a new energy consumption rate in a coordinated optimization framework by combining day-ahead electricity price data; constructing an elastic matrix based on historical electricity consumption data, correcting the elastic matrix by using latest market information and a system state, and determining a load adjustment amount in combination with an intraday electricity price; and based on the real-time electricity price and the load adjustment amount, a model prediction control algorithm is adopted to calculate real-time output adjustment instructions of the new energy unit and the energy storage unit in a rolling manner, and the operation states of the two units are synchronously adjusted to realize collaborative optimization. The system operation stability and the new energy regulation and control precision are improved.

The invention relates to the technical field of power dispatching, in particular to a photovoltaic power generation and energy storage electric cabinet power optimization distribution method based on artificial intelligence. Carrying out meteorological prediction to obtain a future meteorological data sequence in a future time window, and collecting a current operation moment; performing photovoltaic power generation prediction and power consumption demand prediction according to the future meteorological data sequence and the operation time to obtain a future power generation data sequence and a future power consumption data sequence; according to the operation time, analyzing the confidence of the future meteorological data sequence and the future power consumption data sequence, and obtaining a meteorological confidence sequence and a power consumption confidence sequence; according to the future power generation data sequence, the future power utilization data sequence, the meteorological confidence coefficient sequence and the power utilization confidence coefficient sequence, power distribution parameter configuration and optimization are carried out, optimized power distribution parameters are obtained, and power distribution power supply and power storage of the energy storage electric cabinet are carried out in a future time window. And the stability and the economical efficiency of the power system are obviously improved.

The invention provides a global adaptive power supply adjustment method and system under the condition of local load increase, and relates to the technical field of power system dispatching. The method comprises the following steps: acquiring regional power grid topology, equipment parameters and real-time operation data; historical power consumption data of classified users in recent three years are collected and preprocessed; generating a reference load curve based on real-time data and a historical rule, calculating a deviation ratio, predicting a load in future 2 hours, and outputting a risk level; the regional energy storage equipment is called to dynamically adjust the discharge power when the risk is medium or low, and the adjacent regional resources are evaluated and cross-regional line switching and power supply support are executed when the risk is high or the energy storage is insufficient. Through layered adjustment and a dynamic response mechanism, rapid identification and accurate control of sudden load increase are realized, power supply stability and resource utilization efficiency are improved, and the method is suitable for local load fluctuation response of scenes such as residential areas and industrial parks.

The invention provides a communication master control system based on intelligent power supply, and relates to the technical field of communication, and the system comprises a power supply monitoring module which is used for monitoring the commercial power supply state of a communication base station, and automatically controlling a power switching circuit to switch a power supply mode to a standby battery power supply mode when the commercial power interruption is detected; the state acquisition module is used for acquiring the charge state, the discharge efficiency and the health state data of the standby battery in real time in a standby battery power supply mode, and acquiring the residual capacity, the actual discharge capacity and the life index of the battery; and the load receiving module is used for uploading the residual capacity, the actual discharge capacity and the life index of the battery to the communication main control unit, and receiving load data in the current communication network at the same time. According to the invention, intelligent scheduling of power utilization is realized, and the power supply reliability and stability of the communication system are improved.

The invention discloses a distribution box operation state monitoring method and system, and relates to the field of distribution boxes, and the system comprises an acquisition module which is used for collecting current and voltage signals of each loop in a distribution box, surface temperature distribution of a box body and opening and closing state signals of a cabinet door, and converting the collected analog signals into a digital signal sequence; the extraction module is used for receiving the digital signal sequence and extracting a signal fluctuation feature, a temperature field distribution feature and a state signal change feature to generate a three-dimensional feature set; the system can obtain loop electrical parameters, box body temperature and cabinet door state in real time, accurately analyze operation characteristics to deduce state trend, quickly position defective loop components and influence range in case of abnormity, scientifically evaluate health level, timely give an early warning, synchronously disconnect power supply of associated equipment in case of severity, effectively reduce fault shutdown, and improve work efficiency. The power utilization safety and the maintenance efficiency are improved, and stable operation of the distribution box is guaranteed.