29 results about "Power sector" patented technology

The invention discloses a source network load operation scene generation method, system and device under ice disaster disturbance based on transfer learning and a storage medium, and relates to the technical field of power system operation and analysis, and the method comprises the steps: firstly constructing a simulation sample library based on a power grid structure, a fault type and an icing condition, and then integrating and standardizing source domain samples, dividing risk levels, and constructing and pre-training a least square loss function condition generative adversarial network model; a final standardized target domain sample set is generated, a target domain ice disaster scene generator is obtained through transfer learning and model fine tuning, and then an ice disaster disturbance operation scene is generated; the ice disaster disturbance operation scene can be accurately generated, an electric power department can be helped to master risks in advance, equipment maintenance, resource allocation and emergency preparation can be made accordingly, the influence of ice disasters on an electric power system is reduced, power supply stability is guaranteed, power grid planning and transformation can be guided, and the overall disaster resistance and market risk management capacity can be improved.

The invention relates to the technical field of carbon tax analysis, and discloses a CGE model-based carbon tax income return method, system and device and a medium, and the method comprises the steps: constructing a dynamic CGE model which comprises four economic subjects, and dividing an economic population composed of the four economic subjects into 27 production departments; dividing each production department into an electric power department and a non-electric power department based on the production characteristics and the energy use types of each production department; based on a six-layer nested production function principle, respectively constructing production functions of a non-electric power department and an electric power department; performing parameter calibration on the dynamic CGE model in combination with related parameters to obtain a calibrated dynamic CGE model; and simulating the set reference scene and the main scene to obtain the influence of the electric power department and the non-electric power department under different carbon tax policy situations. According to the method, the dynamic CGE model is constructed to simulate the influence of a non-electric power department and an electric power department under different carbon tax policy situations, so that the influence of different carbon tax policies on an economic subject is analyzed.

The invention discloses a power electronic load-containing load equivalence and parameter identification method, which comprises the following steps of: preprocessing original power load data, and extracting data required by subsequent parameter identification; determining a load model structure, and performing aggregation equivalence on the power distribution network, the static load, the dynamic load and the power electronic load according to a principle that system response before and after aggregation remains unchanged; the method comprises the following steps: firstly, constructing a load model, then determining parameters, needing to be identified, of each type of load model, distinguishing dominant parameters and non-dominant parameters by adopting trajectory sensitivity, setting values of the non-dominant parameters as typical values, then identifying the dominant parameters through an improved quantum particle swarm algorithm, determining the values of the dominant parameters, and finally, performing accuracy evaluation on the established load model. According to the invention, a more accurate load model can be established for a modern power system containing a power electronic load, the simulation precision of the power system is improved, and the analysis and control of a power department on the power system are facilitated.

The invention is suitable for the technical field of electric energy metering, and relates to an electric energy metering method and device of an electric energy meter. Corresponding fundamental wave voltage current components, harmonic wave voltage current components, corresponding fundamental wave phase angles and harmonic wave phase angles are extracted, and based on the data, fundamental wave positive sequence active power, reactive power, non-fundamental wave equivalent apparent power, voltage distortion power, current distortion power, harmonic wave apparent power, unbalanced apparent power and the like are obtained through calculation; various electric parameters of the electric energy meter are metered so as to guide an electric power department to charge, make economic decisions, measure main harmonic pollution sources and the like, and the quality of electric power service is greatly improved.

This application discloses a reliable AI scheduling method, system, and computer equipment for microgrids. It includes: conditional independence testing of multi-source data; constructing a candidate causal graph among variables, constrained by prior knowledge in the power sector; calculating the differences in causal effects caused by fluctuations in the current scheduling strategy; performing counterfactual reasoning verification based on different differentiated scheduling strategies; and scoring the current scheduling strategy on causal interpretability based on causal effect differences, counterfactual verification results, and link integrity, followed by AI scheduling. This application reveals the causal interaction mechanism between variables, and the counterfactual reasoning that changes in variables lead to changes in scheduling results, improving the interpretability of the scheduling strategy. It can trace the causal driving factors of each scheduling instruction, significantly reducing the rate of human intervention. Prior knowledge in the power sector ensures safe and compliant scheduling, making the results more reliable. It solves the problem of dependence on human intervention in the application of AI scheduling in microgrids.

The application discloses an evaluation device and method for evaluating current harmonic feed-in content of a power grid of a charging station, the evaluation device comprises a central controller CCU, a grid-side collector PQA, charging pile-side collectors Ax and charging piles Changx, the central controller CCU is the core of the evaluation device, the grid-side current collector PQA is installed at the output end of a transformer, and the charging pile-side collectors Ax are installed at the front end of each charging pile; the evaluation method establishes a functional relationship between each harmonic and the output power of the charging pile, evaluates the content of the charging station harmonic feed-in side of the distribution network, solves the quantitative evaluation problem of the current harmonic feed-in side of the power grid generated when the electric vehicle charging station operates, proposes an evaluation device and method for the current harmonic feed-in side of the power grid of the charging station, effectively evaluates the content of the charging station harmonic feed-in side of the distribution network, provides the user with a suggestion on whether it is necessary to install a filtering device, and guarantees the power supply quality of the power department.

The present invention relates to the technical field of lightning protection for power systems. Disclosed are a lightning activity analysis method and system for power transmission lines, and a device and a storage medium. The method comprises: acquiring characteristic parameters of towers of a power transmission line, and discretizing the characteristic parameters to obtain a tower attribute table; performing data mining on data items in the tower attribute table on the basis of an FP-Growth algorithm, so as to obtain frequent 1-itemsets and a frequent pattern tree, establishing a hash linked list on the basis of the frequent 1-itemsets and the frequent pattern tree, and performing association rule mining on the basis of the hash linked list, so as to obtain frequent multi-itemsets; selecting, from the frequent multi-itemsets, association rules using lightning trips as rule consequents, and analyzing the association rules to obtain a lightning activity analysis result; and on the basis of the lightning activity analysis result, establishing a lightning protection strategy for the power transmission line. The present invention improves the applicability of correlations from data analysis and the efficiency of pattern mining, thereby providing data support for lightning protection work on power transmission lines in electric power departments.

The invention relates to the related technical field of carbon transactions, in particular to a modeling method for introducing CCER transactions and a cancellation mechanism into a national carbon market, which comprises the following steps: S1, constructing a CGE model, S2, establishing a data basis of the CGE model, S3, constructing the CCER transactions and the cancellation mechanism, and S4, carrying out analogue simulation on a CCER scene. By constructing a modeling framework formed by CCER supply, offset and price, the traditional limitation is broken through, and a carbon transaction mechanism modeling method is perfected; the accuracy and the reliability of the model are improved by finely dividing electric power departments and reasonably distributing the cost; by designing various policy scenario simulation, the policy influence is deeply analyzed, and scientific basis and comprehensive support are provided for policy making. The modeling method is wide in application prospect, facilitates optimization of carbon market policies and promotes green, efficient and intelligent development of the energy internet.

The application discloses a kind of wind turbine power characteristics evaluation method based on cloud model and optimal combination weighting based on survival analysis intelligent electric energy meter reliability evaluation method, step S101, electric energy meter original data acquisition and category characteristic numerical processing;Step S102, category characteristics are reduced dimension processing;Step S103, evaluate batch intelligent electric energy meter reliability;Step S104, evaluate the reliability of individual intelligent electric energy meter;The method can realize the reliability analysis of batch intelligent electric energy meter, and the survival rate of each individual intelligent electric energy meter is evaluated, which helps electric power department to find the abnormal condition of intelligent electric energy meter in time, provides theoretical support for the transformation of intelligent electric energy meter from "expiration rotation" to "state replacement", and improves the lean operation and maintenance management level of intelligent electric energy meter.

The invention relates to the technical field of power system online monitoring, and particularly discloses an icing galloping monitoring device evaluation method based on fuzzy comprehensive evaluation. In order to solve the problems that an existing evaluation method depends on experience judgment, standards are not uniform, evaluation dimensions are single, and a scientific basis cannot be provided for device type selection and arrangement, the method comprises the steps that a three-level evaluation index system of a tree structure is constructed, and weights of indexes of all levels are determined in a grading mode; and then dynamically adjusting the weight by adopting multiple strategies according to the actual configuration condition of the device, and finally calculating a comprehensive evaluation value and grade by applying a multi-level fuzzy comprehensive evaluation method. According to the method, comprehensive and accurate quantitative evaluation of the icing galloping monitoring device from technical parameters to overall performance is realized, the accuracy and comparability of an evaluation result are remarkably improved, and a scientific and reliable decision basis is provided for an electric power department to perform device type selection, purchase and optimized layout.

The invention relates to the technical field of data processing, in particular to a CGE modeling method for analyzing and canceling electricity price subsidy, which comprises the following steps: S1, constructing a CGE model; s2, a data basis of the CGE model; s3, establishing an electricity price subsidy mechanism; s4, the analogue simulation of the electricity price subsidy scene is canceled; by constructing a CGE model subdivided by an electric power department, the economic influence of canceling electricity price subsidy is accurately simulated, and a scientific basis is provided for policy making.

The application discloses an electric energy meter and a meter terminal clock field detection method and system, relates to the technical field of timing and synchronization, and comprises a clock calibrator GPS-7A system which is constructed and intercepts high-voltage signals for overload protection during data acquisition; converts the optocoupler on-off signal output by the electric energy meter into an identifiable electrical signal, and acquires standard time by using a GPS & Beidou receiver; measures the daily timing error of the electric energy meter and the overtime, and forcibly synchronizes the electric energy meter with the local time for timing by using high-precision local time. The application guarantees the interests of the power department and the power consumer, acquires more accurate matching parameters by matching the crystal oscillator and the load capacitor through the measurement of the daily timing error, simultaneously accesses two-way electric energy meters to improve the test efficiency, more quickly screens qualified products, effectively prevents the problem that the user mistakenly connects the 220V power supply to the test port, fully protects the core circuit of the equipment, effectively reduces the equipment failure rate, and provides a good use experience for the user.

The application provides a kind of multi-type hybrid charging station safe operation control method and system, belongs to charging station safety control technical field, including the following steps: install sensor in the area where charging pile, energy storage equipment and power distribution equipment are located, collect operating parameter data, data is transmitted to station central control system;Data is preprocessed and different dimension data is normalized, mark abnormal point when out of limit and record time, position and equipment information;Determine normal, potential failure or failure state of equipment;Risk is divided into low, medium and high three levels;Start warning when medium risk, risk is prompted through broadcast, SMS, monitoring pop-up window;When high risk, automatically cut off power supply of faulty equipment, and send alarm information containing location and fault type to fire department and power department.The multi-type hybrid charging station safe operation control method provided by the application can monitor all risks in real time, accurately warn and quickly dispose, and improve the safety and stability of charging station.

This invention relates to a method for extracting multimodal entity relationships in the power industry based on transfer learning, comprising the following steps: collecting and preprocessing power industry text and image data; constructing a cross-modal relationship feature extraction model to obtain high-level features r of multimodal data in the power industry. t High-level features of open-domain multimodal corpora s Constructing a power multimodal entity relationship extraction model based on transfer learning: This involves extracting high-level features r... s High-level features r corresponding to the power sector t Adaptation enables higher-level features r t With high-level features r s The high-level feature r with the smallest difference value is selected when the difference value is less than a preset threshold. t With high-level features r s The result is input to the next layer; the classification relationship output layer outputs the relationship recognition result. The beneficial effects of this invention are: it utilizes rich corpora from other fields to extract entity relationships from multimodal power data, thus alleviating the problem of limited labeled corpus resources in the power field.

The application discloses a power grid reconstruction site selection method, system, device and medium based on quantitative scoring, and the method comprises the following steps: acquiring feeder topology structure data, automatic power grid nodes and medium and low voltage user data of a region to be reconstructed in a distribution network; segmenting the feeder with the automatic power grid nodes as endpoints to obtain segmented regions, and then performing power supply safety analysis to obtain qualified sites; calculating the scrap net value and investment cost of power equipment in all qualified sites, and using z-score standardization sigmod conversion scoring to obtain scrap net value scoring and investment cost scoring; performing weighted summation based on the scrap net value scoring and the investment cost scoring, acquiring quantitative scoring of the qualified sites, sorting, and selecting the qualified site with the highest quantitative scoring as the best power grid reconstruction site. The application comprehensively considers the influencing factors when selecting the automatic site, simply and effectively realizes the automatic selection of the power grid reconstruction site, and improves the efficiency of power grid analysis and power grid planning of the power department.

The present application relates to the technical field of electric power, and discloses a kind of resident short-term electric power load automation prediction method, comprising the following steps: S1: model establishment: establish resident short-term electric power load automation prediction model;S2: regional division: the geographical position of each resident home in a large area is divided in space using K-means algorithm, and is divided into several characteristic related regions;S3: data collection: collect the total power consumption and temperature data of S2 region in the past two years;S4: data division: the data collected and counted in S3 is processed and divided by sliding window method;S5: model training: the data divided in S4 is input into the model established in S1 for data training;S6: result prediction: the future day or week dimension electric power load size of local is automatically predicted by the trained network;The present application can reduce the workload of personnel, and can make reasonable electricity price for the power department, to provide a guidance scheme for ensuring the normal work of power plant.

The invention relates to the technical field of artificial intelligence, and discloses a power field-oriented RAG and preference alignment collaborative optimization method and system. The method at least comprises the following steps: carrying out text slicing on a power business decision knowledge base, and carrying out vectorization representation and retrieval enhancement generation on each obtained text block to obtain a power business adaptive text; generating a plurality of power business decision-making problems and expert decision-making texts and common decision-making texts corresponding to each power business decision-making problem based on the power business adaptive text in combination with a preset first large language model and retrieval enhancement; and based on the preference data set, performing fine tuning training on a pre-trained second large language model by adopting a direct preference optimization and probability ratio preference optimization weighted fusion mode to obtain a target large language model. According to the method, a reliable intelligent modeling tool is provided for complex decision making of a power system, and a reusable optimization normal form is also provided for modeling of reinforcement learning in the professional field.

The utility model relates to the technical field of power distribution signal detection of an electric power department, in particular to a universal signal converter capable of setting various electric quantity parameter detection, which comprises a shell formed by splicing a front panel and a rear cover shell, an input wiring terminal strip, an eight-bit nixie tube, a key module and an output wiring terminal strip are sequentially embedded in the front panel from top to bottom, an upper layer circuit board and a lower layer circuit board are arranged in the rear cover shell, and the lower layer circuit board is electrically connected with the output wiring terminal strip. The upper layer circuit board is electrically connected with the lower layer circuit board through a first plug-in flat cable, the eight-bit nixie tube and the key module are electrically connected with the lower layer circuit board through a second plug-in flat cable, and the lower layer circuit board is provided with a control circuit used for adapting to multi-electric-quantity parameter detection. According to the utility model, the control circuit with the microprocessor as the core is adopted, and test modes and output modes of different parameters can be selected through operation setting, so that convenience is provided for general tests of various electric quantities.

The invention discloses a generalized comprehensive load modeling and parameter identification method for a distribution network containing an energy storage battery, and the method comprises the steps: building a load model of the energy storage battery, adding a correction expression, and obtaining a more precise improved load model of the energy storage battery; connecting the improved energy storage battery load model with the static and dynamic load models in parallel to obtain a low-voltage side generalized comprehensive load model; adding the line impedance of the power distribution network structure and the transformer structure into the low-voltage side generalized comprehensive load model so as to construct a high-voltage side generalized comprehensive load model; determining important model parameters, needing to be identified, of each load model; and determining the optimal value of each key parameter to be identified by adopting a grey wolf optimization algorithm according to the actually measured data. By means of the load equivalence and parameter identification method containing the energy storage battery, a more accurate load model can be established for a modern electric power system containing the energy storage battery, the simulation precision of the electric power system is improved, and an electric power department can conveniently analyze and control the electric power system.

The utility model provides an embedded 5G edge computing controller, which comprises a central processing unit CPU, a 5G module, an ADC module, a DAC module, a WIFI module and a DDR4 memory, and is characterized in that the central processing unit CPU is connected with the 5G module through a miniPCIE interface, the central processing unit CPU is connected with the ADC module and the DAC module through SPI interfaces, the central processing unit CPU is connected with the WIFI module through an SDIO interface, and the central processing unit CPU is connected with the DDR4 memory through a DDR interface. A core chip and an operating system of the equipment are all localized, domestic replacement and autonomous controllability are really achieved, meanwhile, the safety of data transmission is considered, the bandwidth is improved, delay is reduced, multiple interfaces are expanded, convenient secondary development is provided, and the requirements of multiple aspects such as data collection, data calculation and data secure transmission in the power field can be met.

The invention is suitable for the technical field of information extraction, and provides an intelligent enterprise production and operation information extraction method and system based on cloud computing, and the method comprises the following steps: uploading a production and operation knowledge graph and a data source directory, each part of source data in the data source directory corresponding to a data label and a timestamp; association between the source data and nodes in the production and operation knowledge graph is established through semantic analysis, so that each piece of source data corresponds to a node number; receiving keyword information, inputting the keyword information into a pre-trained power field large model, and processing to determine a structured query blueprint; matching the query blueprint with a production and operation knowledge graph, determining a matched node, and calling source data corresponding to the node; and carrying out fusion analysis and information extraction on all the called source data based on the business logic relationship in the production and operation knowledge graph. By using the method, key information can be more accurately understood and extracted, and the quality and effect of data processing are improved.

This invention provides a method and system for detecting abnormal contact in metering devices. The method includes: extracting current and voltage data from the metering device within a preset time period; extracting data points from the current and voltage data according to preset extraction criteria; determining whether judgment conditions are met based on the number of extracted data points and the number of days in which the data points are located; if the judgment conditions are met, determining whether the voltage of the metering device within the preset time period meets a preset voltage state; if the preset voltage state is met, determining that the metering device has normal contact; if the preset voltage state is not met and the device is not a low-voltage load switch, determining that the metering device has abnormal contact. This invention can promptly detect low voltage and poor contact from hundreds of millions of metering devices. If applied to a system, it can achieve real-time online monitoring of low voltage, saving manpower and resources, reducing economic losses for the power sector, and preventing power fire accidents.

The invention discloses a field knowledge graph construction method and system for power market clearing decision, and belongs to the technical field of power market clearing, and the field knowledge graph construction method for power market clearing decision comprises the steps: obtaining multi-source data; constructing a professional large model in the electric power field based on field adaptive pre-training and task specific fine tuning; on the basis of a preset entity type and a relationship type related to the power market clearing decision, constructing cue words and extracting domain knowledge; knowledge fusion is realized based on entity alignment and conflict resolution optimization, and graph construction is carried out; and dynamically updating the atlas based on an update trigger mechanism and a feedback loop mechanism. According to the method, deep knowledge of the power market can be automatically extracted and fused from multi-source heterogeneous data in the power field, the deep knowledge can be closely integrated with clearing decisions to construct a specialized field knowledge graph, the method has a continuous evolution capability, and the scientificity, robustness and intelligent level of clearing of the power market are effectively improved.

The invention discloses a method, a device and equipment for identifying power failure of medium and low voltage users and a storage medium, belongs to the technical field of power monitoring, and solves the problems that real power failure and metering equipment faults cannot be distinguished and power failure duration cannot be accurately calculated in the prior art. The invention discloses a method for identifying power failure of medium and low voltage users. The method comprises the following steps: S1, acquiring data of the medium and low voltage users and preprocessing the data; s2, establishing a checking model, inputting the preprocessed data into the checking model, and judging whether an abnormal mode exists or not, if yes, determining that the abnormal mode is a suspected power failure event, and entering the step S3, and if not, determining that the power failure event does not occur; and S3, analyzing and verifying the suspected power failure event, and calculating the power failure duration of the suspected power failure event. According to the method, the power failure event of the transformer area is accurately judged, the power failure duration is accurately calculated, and the response speed and the processing efficiency of an electric power department to the power failure event are effectively improved.

The invention discloses a power utilization evaluation method, system and device based on ELCTRE-I and a storage medium, and relates to the technical field of power system evaluation and decision making, and the method comprises the steps: obtaining the multi-dimensional data related to the power utilization of an enterprise according to the characteristics of an industrial chain, and constructing an initial evaluation matrix; weighting different indexes by applying an entropy weight method, and calculating index weights to obtain an index weight result; constructing a multi-attribute decision evaluation method based on an index weight result, and determining the advantage and disadvantage relationship between the enterprises through harmony test and non-disharmony test; evaluating the power consumption of the enterprises by using the constructed multi-attribute decision evaluation method, and obtaining a sorting relationship between the enterprises; the method can accurately evaluate the power consumption condition of an enterprise, and fits the characteristics of an industrial chain. And eliminating data dimension influence and objectively empowerment. The method is advantaged in that advantages and disadvantages of enterprises are strictly determined, reasonable resource distribution of an electric power department is facilitated, enterprises are helped to discover power utilization problems, cost reduction and efficiency improvement are realized, and energy saving and emission reduction and sustainable development are promoted.

This invention discloses a method and apparatus for monitoring, analyzing, and modeling carbon emissions in the energy and power sector, specifically relating to the field of carbon emissions in the power industry. It addresses the problem that existing carbon emission analyses typically focus on specific regions, resulting in lengthy analyses and an inability to directly predict overall carbon emission trends. The method involves dividing the carbon emission regulatory area into several smaller regulatory regions, calculating the carbon emission intensity of each region, determining whether the intensity meets standards, and marking and issuing warnings for regions that do not meet the standards. This invention calculates the carbon emission intensity of each region, filters out regions that do not meet the standards, performs cluster analysis on different regions to filter out regions outside the standard range and issue alarms, and finally combines the status of all regions to predict and evaluate the overall region, facilitating the formulation of relevant optimization policies.

The application provides a method and system for detecting abnormality of a load switch of a low-voltage meter box, comprising: obtaining target parameter information of a target low-voltage meter box, and screening meter information in the target parameter information; determining the number of meters in the screened target parameter information, and if the number of meters in the screened target parameter information is greater than a preset first quantity threshold, then performing phase-by-phase investigation on phase lines of the meters according to a preset investigation standard to obtain corresponding investigation results; determining whether the meters have abnormality in the load switch according to the investigation results, and if so, outputting corresponding alarm signals. The application can timely find low-voltage load switches with poor contact, avoid loss of state-owned assets caused by electricity loss, avoid electrical fire accidents, save manpower and material resources, reduce economic losses of the power department, and avoid the occurrence of electrical fire safety accidents.

This disclosure relates to the power sector and discloses an adaptive grid-connected control method, system, device, and medium for adapting to phase angle abrupt changes. The method includes: establishing a full-state oscillator model to generate the internal potential of the photovoltaic-storage grid-connected power source; establishing a phase angle adaptive model to correct the internal potential of the photovoltaic-storage grid-connected power source; and establishing a current inner-loop control model to control the stable operation of the photovoltaic-storage grid-connected power source based on the corrected internal potential. This disclosure enables photovoltaic energy storage systems to provide stable synchronous grid-connected characteristics under large phase angle abrupt changes, and allows microgrids containing a very high proportion of new energy sources to achieve flexible and reliable grid-connected / off-grid stable operation.

This invention discloses a large-model-driven multi-objective photovoltaic-storage distribution network control method under extreme weather conditions, belonging to the field of distribution network control technology. The method includes: collecting real-time electrical data and extreme weather data from the distribution network; identifying topology change events and generating structured natural language scene descriptions; generating a dynamic reward function with four objectives—voltage stability, frequency recovery, etc.—adapted to the scenario using a lightweight large-language model fine-tuned with power sector corpora; extracting global high-dimensional features using a topology and feature dual-attention graph convolutional network; using a vector concatenated from features and reward weights as state input, generating multi-device collaborative control instructions through a near-end policy optimization algorithm, and outputting the instructions after verification. This invention achieves dynamic perception of the distribution network topology, dynamic adaptation of control objectives, and collaborative optimization control of multiple devices under extreme weather conditions, comprehensively improving the operational resilience and renewable energy utilization efficiency of high-proportion renewable energy distribution networks under extreme weather conditions.