4988 results about "Distribution grid" patented technology

The invention relates to the field of power distribution networks, in particular to a power distribution network voltage collaborative autonomous method and system based on dynamic partition. The method comprises the following steps: acquiring electrical measurement data and network topology parameters of distributed nodes of a power distribution network, and generating a characteristic state set representing the operation state of a system; performing dynamic subarea division based on node voltage coupling strength and power balance constraint to obtain a dynamic subarea set with an autonomous boundary; each partition control main body independently solves a voltage regulation objective function of the partition according to an autonomous boundary, and generates a partition autonomous control strategy; and boundary interactive iterative coordination is carried out between adjacent partitions, and a global optimal voltage cooperative control instruction is generated and executed. According to the method, the problems that partition division is not matched with the operation state, and partition collaboration is insufficient are solved, unification of partition autonomy and global optimization is achieved, and the real-time performance and accuracy of voltage regulation and control of the power distribution network are remarkably improved.

The invention relates to a regional power distribution network multi-time scale cooperative control method based on heterogeneous resource coupling, and the method comprises the steps: constructing a cloud-edge-end hierarchical autonomous operation framework which comprises a cloud layer, an edge layer and a terminal layer, the edge layer is used for rolling correction of a day-ahead scheduling plan and generation of a control instruction, and the terminal layer is used for real-time response control; and based on a source-network-load-storage interaction model, executing a multi-time scale collaborative optimization strategy in a cloud-edge-end hierarchical autonomous operation framework, and correspondingly controlling the working state of each device in the regional power distribution network. Compared with the prior art, the method can achieve the dynamic optimization matching of distributed energy, flexible load and energy storage, improves the flexibility and intelligent level of a regional power distribution network, improves the consumption rate of renewable energy sources, reduces the operation cost, and guarantees the power supply reliability and electric energy quality.

The embodiment of the invention provides a power distribution network dynamic reconstruction method based on knowledge-guided large model enhanced fine tuning and related equipment, and belongs to the technical field of smart power grids and artificial intelligence. The method comprises the following steps: constructing a power distribution network dynamic knowledge graph, and providing structured knowledge guidance for model training; subgraph sampling is carried out based on the timestamp and converted into a fine tuning sample, and a training data set is generated; utilizing the data set to supervise, finely adjust and preheat the large language model; designing a multi-dimensional reward function of fusion format accuracy, economy and security based on mechanism knowledge in the knowledge graph and expert experience; a group relative strategy optimization mechanism is adopted to carry out reinforced fine tuning on the large language model, and the large language model is guided to output a safe, reliable and economical dynamic reconstruction strategy in interaction with the environment. According to the method, the problems of lack of training data, lack of physical knowledge guidance and insufficient decision reliability of a large language model in the power grid field are solved, and the intelligent level and decision quality of dynamic reconstruction of the power distribution network are remarkably improved.

The invention relates to a power distribution network abnormal state intelligent identification method based on unmanned aerial vehicle routing inspection, and the method comprises the following steps: S1, obtaining power distribution network routing inspection related data, including asset main data, historical operation and maintenance data and environment constraints; s2, constructing the digital twinning of the power distribution network based on the power distribution network inspection related data, and obtaining the mapping from the equipment ID to the spatial pose and parameters; s3, generating a risk heat map based on historical defects and environmental risk scoring; s4, constructing an equipment-geography-working condition knowledge graph, and generating inspection targets and priorities according to line sections in combination with mapping from equipment IDs to spatial poses and parameters and a risk heat map to obtain a task package; and S5, performing multi-target route inspection according to the task package, the risk heat map and the mapping from the equipment ID to the spatial pose and the parameter, and intelligently identifying the abnormal state of the power distribution network. The operation reliability of the power distribution network is effectively improved, and the power supply quality is improved.

The invention discloses a virtual power plant capacity configuration and regulation operation optimization method, which comprises the following steps: constructing an aggregation model and physically consistent digital twinning, establishing a linearized power distribution network model containing voltage and power flow constraints, and depicting resource efficiency and time delay characteristics; forming a time-varying uncertainty set through quantile calibration and set drift constraint; establishing a capacity-operation joint double-layer optimization model, realizing capacity configuration under the constraint of the whole life cycle cost, and obtaining a rolling scheduling strategy through distributed robust optimization; before issuing, control shielding and formalized constraint are adopted to ensure the safety of the power grid; a multi-variety collaborative quotation is generated on the market side through opportunity constraint and risk measurement; hierarchical adaptive re-optimization is realized based on a trigger criterion, and a twin model is continuously calibrated by using a hardware-in-the-loop experiment; model updating is realized by adopting federated learning and differential privacy; executing degradation control under an abnormal condition, and performing smooth rollback after recovery; and finally, the capacity and operation parameters are evaluated and corrected through performance and service life linkage.

The present invention belongs to the field of smart power grids and power system automation control. Disclosed is a load emergency control method based on a dynamic event triggering mechanism, which method comprises: on the basis of an indicator of safe operation of a system, performing region division on a distribution network; proposing an adaptive load level division method based on user response willingness, and by taking the minimization of the system operation cost of the distribution network as an objective, establishing a precise load control model of the distribution network; and using a dynamic event triggering mechanism to reduce communication between substations and a master station of the distribution network, so as to realize effective exchange and precise execution of load control policy information. The method of the present invention can precisely realize precise control of a load, and can effectively alleviate communication transmission pressure of a distribution network, thereby ensuring the stability of a power grid and the continuous supply of critical loads.

The invention relates to the technical field of power systems, and provides a power distribution network multi-dimensional regulation and control decision method, system and device and a medium, and the method comprises the steps: inputting the preprocessed multi-source operation data into a preset state perception model, and obtaining a multi-dimensional state vector representing the operation state of a power distribution network; a multi-dimensional state vector is used as a state space, regulation and control operation is used as an action space, a composite reward function is established according to a power distribution network operation target, and modeling is carried out to obtain a Markov decision process framework; interacting with a power distribution network simulation environment by adopting a deep reinforcement learning algorithm, obtaining a current state from a state space, selecting and executing regulation and control operation in an action space according to a strategy network, updating strategy network parameters based on feedback of a composite reward function until an optimal regulation and control strategy network is obtained, and obtaining a deep reinforcement learning strategy model; and performing strategy rolling updating based on the real-time monitoring data to obtain a target regulation and control strategy. According to the invention, comprehensive optimal regulation and control of a complex operation scene can be realized.

Disclosed in the present invention are a power distribution network fault detection method and apparatus based on a traveling wave signal. The method comprises the following steps: acquiring a fault traveling wave signal of a power distribution network, and determining a fault location of the power distribution network; on the basis of an optimized decomposition algorithm, performing frequency decomposition on the fault traveling wave signal, so as to obtain an instantaneous frequency; on the basis of a pre-constructed optimized fault detection model, performing feature extraction on the instantaneous frequency, so as to provide fault characteristics of the power distribution network; and on the basis of the fault characteristics of the power distribution network, providing a fault category corresponding to the fault traveling wave signal. The present invention can accurately determine the specific location and category of a fault in a power distribution network, thereby improving the accuracy and efficiency of fault detection.

The invention discloses a power distribution network layered optimization method oriented to source-network-load interaction, and relates to the technical field of power system automation. The method comprises the following steps: constructing a dynamic adaptive collaborative architecture, and dynamically generating a three-layer logic structure of global coordination, regional autonomy and local execution according to the real-time state of the power distribution network; operating a three-layer mixed game mechanism, and determining an optimal strategy of each main body through master-slave, cooperation and bilateral interactive games; executing an evaluation-planning-operation three-level closed-loop optimization process, and taking a key performance index fed back by an operation layer as a basis for scheme correction of a planning layer; and a mixed intelligent strategy combining multi-agent deep reinforcement learning and an improved particle swarm optimization algorithm is adopted for solving. According to the method, the problems of hierarchical solidification, benefit coordination imbalance and planning operation splitting in the prior art are effectively solved, and the adaptive capacity, the economical efficiency and the flexibility of the power distribution network in the high-permeability source-network-load interaction scene are improved.

The invention relates to the technical field of power system operation management, and particularly discloses a main power distribution network planned maintenance optimization method and system based on deep learning. The method comprises the following steps: constructing an energy state graph based on a power grid topological structure, equipment parameters and a main-distribution hierarchical relationship; establishing a mapping relation between the maintenance task and the power grid topology, and defining a task attribute, a time window, a resource demand and an edge available state variable; calculating energy state propagation and cross-layer influence in a task execution state, detecting consistency of capacity, redundancy and power failure influence, and generating a conflict set; and adjusting a task feasible region through a constraint propagation mechanism, selecting a replacement starting point according to a minimum disturbance principle, and outputting a final maintenance plan by combining the combinatorial optimization objective function and the interactive arrangement layer. According to the method, the scientificity, the safety and the economical efficiency of the collaborative maintenance plan of the main power distribution network can be effectively improved, and the defects of a traditional method in the aspects of cross-layer collaboration, dynamic response and conflict processing are overcome.

The invention discloses a main station big data-based low-voltage transformer area topology hierarchy identification method and system, and the method comprises the steps: constructing a multi-dimensional voltage feature vector through the voltage time sequence data, collected by a main station side, of a transformer area general meter and a user ammeter, and forming an electrical feature matrix through feature extraction; and on the basis, judging the station area affiliation and the phase line affiliation of the user electric meter through similarity calculation. Then, generating a stable consensus cluster based on multi-time window clustering and the copolymerization matrix, and mapping the cluster into a virtual electric meter box node; constructing a minimum spanning tree and carrying out directionalization to obtain an initial radial topological structure; further introducing an electrical consistency criterion based on correlation of voltage drop and subtree current, performing multi-candidate structure optimization on the preliminary topology, and finally obtaining an optimal transformer area topology hierarchical structure consistent with a physical rule; high-precision automatic identification of the low-voltage transformer area topology can be realized without manual participation, and effective support is provided for digital operation and maintenance of a power distribution network.

The invention relates to the technical field of power system power distribution network fault detection, in particular to an arc fault diagnosis and analysis method based on an artificial intelligence algorithm, and the method comprises the four steps: synchronous data collection and topological excitation, deployment of terminals at transformer area nodes, injection of characteristic current, and synchronous collection of response waveforms; signal preprocessing: separating a key frequency band through double-digital band-pass filtering, and calculating energy ratio and other enhanced fault features; performing multi-source feature fusion and intelligent diagnosis, constructing a vector containing statistical features, time domain distortion features and topology identification, and inputting a gradient boosting decision tree and deep neural network hybrid model to obtain fault confidence and type; and based on fault positioning and verification of topology, scheduling multi-node cooperative monitoring, and determining a fault point in combination with a topological relation. The method improves the data reliability and diagnosis precision, achieves the precise positioning of a fault, and guarantees the safe operation of a power distribution network.

The invention relates to the technical field of power system fault diagnosis, and discloses a flexible DC power distribution network fault diagnosis method and system. The method comprises the steps of collecting a fault current time sequence signal of a direct current side of the flexible direct current power distribution network, inputting the fault current time sequence signal into a pre-trained fault diagnosis model, and performing adaptive time frequency feature extraction on the fault current time sequence signal by using an interpretable complex time frequency convolution layer to obtain a time-frequency-amplitude tensor, a time-amplitude trajectory, a time-frequency trajectory and a frequency-amplitude trajectory are constructed, the trajectories are subjected to Gramer difference angular field coding, a three-channel time-frequency feature matrix is generated, the three-channel time-frequency feature matrix is input to a parallel double-branch feature extraction module, and global features and local features are extracted; and carrying out adaptive weighted fusion on the extracted global features and local features, classifying the fused features, and outputting a diagnosis result of a fault category. According to the invention, the accuracy of fault diagnosis of the flexible DC power distribution network is improved.

The invention discloses a distribution network-oriented traveling wave fault location centralized analysis system, which relates to the technical field of power system fault location and comprises a centralized master station and a plurality of monitoring terminals. The monitoring terminals are deployed at different node positions of a power distribution network, and synchronously collect fault traveling wave signals and upload the fault traveling wave signals to the centralized master station. The centralized master station comprises a topology modeling module, a data preprocessing module, a wave head identification module, a candidate branch screening module, a matching evaluation module and an accurate positioning module. According to the invention, through establishing a full-process quality tracking and adaptive weighting mechanism from preprocessing to accurate positioning, a system can adjust a positioning strategy according to a distribution network topology structure and a signal state based on a two-dimensional comprehensive evaluation system of topology sensitivity and signal quality; high fault positioning accuracy and reliability are kept in a complex environment of the distribution network, and the robustness and practicability of the system are improved.

The invention relates to the technical field of power systems, and discloses a power distribution network fault positioning method, device and equipment and a medium, and the method comprises the steps: synchronously collecting fault traveling wave data through a transformer area intelligent terminal and a feeder terminal disposed in a power distribution network; dynamically calculating traveling wave transmission parameters based on the topological information and the real-time operation parameters of the power distribution network; performing feature extraction and fusion processing on fault traveling wave data acquired by the transformer area intelligent terminal and the feeder terminal to generate a joint feature matrix; based on the joint feature matrix and the traveling wave transmission parameters, cooperative verification is carried out through the single-end positioning information and the double-end positioning information, and a fault section is determined; and performing fault type identification on the fault section by using the intelligent classification model, and performing consistency verification according to an obtained fault type identification result and the fault section to generate a fault diagnosis result. According to the invention, accurate positioning of the power distribution network fault can be realized, and the problems of positioning blind areas, high cost and easy misjudgment in the prior art are solved.

The invention discloses a multi-source cooperative power distribution network fault self-recovery and recovery optimization method, system and device and a storage medium, and relates to the technical field of power system power distribution networks, and the method comprises the steps: collecting the electrical quantity and parameters of each node in real time, and obtaining system state information through data processing; detecting faults by using a multi-criterion algorithm, and determining types and severity; determining a fault section according to a result and minimizing isolation; a collaborative recovery strategy is formulated by integrating multiple sources, and network reconstruction and switching operation optimization are carried out; and finally evaluating a recovery effect, and combining with a historical data feedback adjustment strategy to improve the recovery efficiency. The fault recovery capability of the power distribution network can be remarkably improved, the fault isolation is fast, the load recovery rate is high, the recovery time is short, the economic loss is reduced, the self-healing of different fault scenes is strong, and the unification of technology and economic benefits is realized.

The invention discloses a distribution-micro collaborative operation simulation optimization method, which belongs to the technical field of simulation optimization, and comprises the steps of preprocessing grid-connected point voltage data, tie line power data and communication time delay data, constructing a power distribution network power flow physical network following a Kirchhoff's law, outputting a source load power prediction curve by using a long short-term memory network algorithm, and calculating the distribution-micro collaborative operation according to the source load power prediction curve. And a distribution-micro collaborative simulation optimization model is obtained based on residual error rolling correction tie line impedance parameters, a delay penalty term is set in a target function in combination with the preprocessed communication delay data, a power regulation instruction is obtained by using a particle swarm optimization algorithm, and a dynamic simulation video stream is generated. According to the invention, through rolling correction of the tie line impedance parameters and setting of the delay penalty term positively correlated with the time delay, the problem of control failure caused by physical deviation caused by model parameter solidification and communication time delay accumulation is solved, and the defects of voltage deviation calculation distortion and inaccurate network loss evaluation are eliminated. And the simulation precision and the operation stability of distribution-micro cooperation are improved.

The invention discloses a power distribution-micro cooperative fault recovery method considering network dynamic reconstruction, belongs to the technical field of power distribution networks, and completes power distribution-micro cooperative fault recovery through a power distribution-micro cooperative fault recovery model. The method has the following beneficial effects: the optimization model taking the electricity purchase cost, the voltage deviation, the network loss, the load reduction and the microgrid operation cost as the target is constructed, and the power flow constraint, the safe operation constraint and the network reconstruction constraint of the power distribution network are comprehensively considered. For privacy protection requirements of distributed energy in the micro-grid, a k-order approximation method is adopted to establish a micro-grid operation feasible region model, and operation characteristics of the micro-grid are represented through power and energy boundary constraints. In order to further realize distribution-microgrid collaborative optimization, a non-iterative solution method based on equivalent projection is provided, an original problem is decomposed into distribution network layer optimization and microgrid layer sub-problems, internal variables are eliminated through an equivalent projection technology, and collaborative optimization is realized on the premise of protecting microgrid privacy.

The invention relates to the technical field, and discloses a power distribution network multi-time scale fault scene deduction method comprising the following steps: obtaining meteorological and power grid operation data, establishing a time sequence fault tree model, and analyzing the trigger probability of multi-line disconnection and rainstorm short circuit faults in a target time period; extracting cross-level fault propagation features, analyzing a dynamic coupling relationship among multi-line disconnection, transformer substation flooding and cascading trip, forming a fault feature mode set, and identifying a single fault and a cascading fault in combination with time sequence analysis; historical fault data are analyzed, time sequence features and topological features of single and cascading faults are extracted, if a single fault propagation path is in a single level, a support vector machine is used for being combined with the features to judge fault types, and a classification result is output; and performing anomaly detection and confidence evaluation on a fault classification result, outputting a fault evolution path and risk evaluation, and analyzing the contribution degree of cascading trip to a large-area power failure risk in combination with historical blackout data to obtain power failure risk probability distribution.

The invention provides a low-voltage power distribution network load change trend intelligent prediction and management method, belongs to the field of low-voltage power distribution network load prediction, and is used for solving the problems of large load fluctuation, insufficient prediction precision and high cloud deployment delay of a hybrid industry transformer area in related technologies. The method is deployed at an edge node of a transformer area, high-quality data is output through multi-modal data anomaly detection and scene completion, a four-dimensional dynamic load portrait is constructed based on the high-quality data, model super-parameter self-adaptive parameter adjustment is realized by combining transfer learning and Bayesian optimization, and accurate load data is output through three-dimensional linkage resource scheduling and dynamic fusion residual error correction. The method improves the load prediction precision and efficiency, reduces the response delay, and can effectively support the real-time scheduling of the power distribution network.

The invention discloses a graph contrast learning identification method for the low-voltage transformer area topology of a power distribution network. The method comprises the following steps: data acquisition and standardization; constructing a feature extraction module; constructing a time sequence encoder, a voltage correlation module, a time alignment module and a power mode module; semi-supervision and measurement combined training: carrying out graph contrast learning based on attributes, and carrying out supervision training on transformer attribution and meter box attribution by adopting cross entropy loss; and topology identification and reasoning output: inputting time sequence data of a user and a transformer under the condition of zero sample of a new transformer area, and outputting a user-transformer attribution probability and a meter box attribution probability through a feature extraction and classifier. According to the method, physical interpretable features such as voltage correlation and time alignment are introduced and are combined with time sequence coding and metric learning, so that the accuracy and stability of topology identification of the low-voltage transformer area are remarkably improved under the condition of small samples.

The invention relates to the technical field of power distribution network fault positioning, in particular to a power distribution network fault traveling wave positioning method and system, and the method comprises the steps: synchronously collecting a transient signal through a distributed traveling wave sensing unit, and extracting an initial traveling wave arrival time; constructing an adaptive wave velocity model based on line physical parameters to calculate an equivalent propagation velocity; dynamically generating a traveling wave propagation tree according to the real-time switch state to determine an effective monitoring path; based on the propagation path and the actual arrival time, an optimization algorithm is adopted to determine a preliminary fault point; and calculating a consistency index of the polarity of the traveling waves of the adjacent nodes, starting a multi-hypothesis tracking mechanism in a preliminary point neighborhood when the consistency index is lower than a threshold value, performing confidence coefficient weighted evaluation by combining a traveling wave attenuation model, and outputting an accurate positioning result. According to the method, through cooperation of dynamic topology mapping, adaptive wave velocity correction and multi-source closed-loop verification, the precision, reliability and adaptive capability of fault positioning in a complex power distribution network environment are remarkably improved, and the operation and maintenance requirements of rapid self-healing are met.

The invention relates to the technical field of power distribution network automation, and discloses an electrical variable monitoring method for ring main unit operation, which comprises the following steps: acquiring three-phase electrical variable data in response to a condition that a load current change rate exceeds a preset micro-motion excitation threshold value; calculating a reference voltage based on the circuit state equation containing the inter-phase mutual inductance coupling term; the method comprises the following steps: constructing a rotating coordinate system taking a current vector as a reference axis, performing vector decomposition on a voltage deviation vector, separating a resistive temperature drift voltage component parallel to the reference axis, and taking a nonlinear impedance voltage component perpendicular to the reference axis as a monitoring parameter to judge a contact state. According to the method, linear resistance drift caused by conductor temperature rise is effectively decoupled, baseline interference is eliminated, weak nonlinear impedance distortion characteristics caused by mechanical contact degradation can be captured, and the recognition sensitivity and accuracy of early latent faults of the ring main unit are improved.

The invention discloses a power failure event cooperative processing method, system and device based on geographic information and a medium, and relates to the technical field of power system power distribution network fault processing and informatization. The method comprises the following steps: when a power failure event occurs, acquiring multi-source heterogeneous data from an associated system; performing fault analysis on the multi-source heterogeneous data through a fault diagnosis model, and outputting fault positioning information and a fault type; obtaining associated maintenance resource state data and real-time traffic road condition data according to the fault positioning information; the maintenance resource state data comprises a rush repair team position, a skill level and a vehicle state; and based on the fault positioning information, the fault type, the user attribute, the historical work order data, the maintenance resource state data and the real-time traffic road condition data, generating an optimal maintenance scheduling scheme through a reinforcement learning scheduling model. The cross-department and cross-business cooperative processing is realized, and the first-aid repair efficiency and the resource utilization rate are remarkably improved.

The invention discloses a self-adaptive filtering method and system for an intelligent electric energy meter, and relates to the technical field of power grid signal processing, and the method comprises the steps: obtaining a power grid voltage and current signal set collected by a target intelligent electric energy meter historically, and obtaining a signal noise spectrum data set and a noise level evaluation result through spectrum analysis and noise level evaluation; if the result exceeds a preset noise threshold value, identifying an interference type and building a noise adaptive filtering multi-channel, integrating and deploying the noise adaptive filtering multi-channel into a target intelligent electric meter to complete signal filtering test and effect evaluation, and dynamically optimizing and generating an electric energy meter adaptive filtering multi-channel based on the obtained multi-channel filtering effect index parameter; and finally, signal filtering closed-loop control is realized. The technical problem that a traditional electric energy meter is weak in filtering pertinence and cannot adapt to dynamic noise of a power grid in electric power related scenes with complex dynamic interference is solved, and the technical effects that the electric variable measurement accuracy of the electric energy meter is improved, and the adaptive capacity of the electric energy meter to different interference scenes is enhanced are achieved.

The invention relates to the technical field of power distribution network bidirectional power flow coordination control, and discloses a power distribution network bidirectional power flow coordination control method based on V2G aggregation, and the method comprises the following steps: S1, constructing a logic energy storage unit composed of a plurality of V2G charging piles and vehicles supporting reverse discharge; s2, aggregating the logic energy storage units into virtual power plant nodes which can be dispatched by a power grid through an information communication technology (ICT); and S3, bidirectional power flow sensing and real-time state estimation: utilizing an intelligent electric meter, a sensor and a topology identification algorithm. According to the method, a logic energy storage unit composed of a plurality of V2G charging piles and vehicles supporting reverse discharging is constructed, the logic energy storage unit is aggregated into a virtual power plant node capable of being dispatched by a power grid through an information communication technology (ICT), and specific charging and discharging actions are decomposed and executed, so that dispersed V2G resources can be effectively integrated, and the V2G resources can be effectively integrated. And collaborative optimization with operation states such as voltage and power flow of the power distribution network is carried out.

The invention belongs to the field of power distribution network monitoring, and provides a power distribution box damage behavior monitoring method and system based on human body posture estimation, and the method comprises the steps: obtaining a monitoring video of a power distribution box terminal for preprocessing, and obtaining a preprocessed video frame; based on the preprocessed video frame, performing posture recognition by using a pre-trained human body and distribution box posture recognition model to obtain a human body and distribution box posture recognition result; key point tracking and identity association are performed by using a human body and distribution box posture recognition result, a space-time skeleton diagram is constructed, and the space-time skeleton diagram is screened to generate a skeleton space-time sequence; on the basis of the skeleton space-time sequence, a pre-trained space-time diagram convolutional network model is utilized to identify a damage behavior, and a damage behavior identification result is obtained; when the confidence coefficient in the damage behavior recognition result exceeds an alarm threshold value, alarm information is generated, and an alarm process and safety linkage control are triggered. According to the invention, accurate monitoring and real-time early warning of the damage behavior of the distribution box are realized.

The invention relates to the technical field of low-carbon dispatching of a power system, in particular to a carbon footprint optimization dispatching method and system of a hydrogen-electricity collaborative power distribution network, electronic equipment and a medium. The method comprises the steps of firstly obtaining multi-source operation parameters and market data; constructing a carbon footprint tracking model for realizing dynamic allocation of node carbon emission according to the multi-source operation parameters and the market data; constructing a representative scene set according to set historical data; integrating the carbon footprint tracking model and the representative scene set to construct a multi-period optimization scheduling model for collaborative optimization of economic cost and carbon emission cost; and solving the multi-period optimization scheduling model by adopting a decomposition coordination algorithm, and outputting an optimal scheduling scheme. Through the mode, the technical problem that multi-target collaboration is difficult in the low-carbon transformation process of the hydrogen-electricity collaborative power distribution network is solved, and the carbon emission control precision, the operation robustness and the comprehensive decision-making capability of the system are improved.

The invention discloses a power distribution network line loss predictive detection system under a dynamic load, and relates to the technical field of power distribution network monitoring, and the system comprises a data collection module, a line loss prediction module, an abnormity traceability diagnosis module, a line loss pre-regulation module and a data interaction module. The line loss prediction module is combined with dynamic load characteristics and multi-source operation data to construct a prediction model, the accuracy of line loss prediction is effectively improved, the abnormality traceability diagnosis module is linked with a topology identification result and sensitivity matrix analysis, rapid positioning of an abnormal line loss root is realized, and the accuracy of line loss prediction is improved. A targeted regulation and control strategy is output in advance through the line loss pre-regulation and control module, a traditional post-event accounting mode is converted into a mode of combining pre-event prevention and in-event regulation and control, a complete closed loop of abnormal prediction-root positioning-active regulation and control is formed, line loss increment caused by dynamic loads is effectively reduced, and the system performance is improved. And the operation efficiency and the energy utilization economy of the power distribution network are further improved.

The invention belongs to the technical field of fault positioning, and discloses a Beidou satellite time service-based power distribution network fault section positioning method, which comprises the following steps of: acquiring three-phase current, and performing phase synchronization alignment on the three-phase current by using a Beidou satellite to obtain a three-phase current synchronization data sequence; performing modulus transformation on the three-phase current synchronous data sequence to obtain a zero-mode current component and a line-mode current component; performing adaptive waveform decomposition on the zero mode current component and the line mode current component to obtain an intrinsic mode component, and performing signal reconstruction on a target component to obtain an enhanced fault traveling wave signal; performing waveform curvature sudden change detection on the enhanced fault traveling wave signal to obtain a wave head arrival time and a time difference observation sequence; performing geometric analysis on the topological structure based on the time difference observation sequence and the traveling wave propagation speed to obtain a suspected fault section; and comprehensively studying and judging the suspected fault section to obtain an actual fault section. The power distribution network fault positioning efficiency can be improved.