3070 results about "Power flow" patented technology

The invention discloses an intelligent power distribution network line state real-time monitoring, analysis and evaluation system, and relates to the technical field of power system monitoring and control, the intelligent power distribution network line state real-time monitoring, analysis and evaluation system is constructed, data is collected in multiple dimensions, features are extracted through edge calculation, data is screened, a dynamic threshold model is constructed at a cloud end, and abnormity is identified. The method has the advantages that a three-dimensional digital twin network is constructed based on GIS data and an SCADA system through the power grid topology modeling module, and a unique'topology fingerprint 'containing electrical connection, physical position and logic control relation is generated for each piece of equipment; an upstream and downstream equipment list, a load influence range and a power flow transfer path are marked, and an association state evaluation module adopts a graph convolutional neural network and can accurately capture electrical coupling, physical proximity and historical fault association effects among equipment.

The invention relates to the technical field of power dispatching, in particular to a digital twin energy management method and system for source-network-load-storage cooperative dispatching, and the method comprises the steps: collecting source-network-load-storage multi-dimensional space-time operation data, and extracting space-time coupling features through a graph convolution-long and short-term memory network; establishing a simulation model of a digital twin environment, simulating an uncertain operation condition by using a Monte Carlo scene generator, and processing a power flow constraint by using a second-order cone relaxation technology; training an energy storage scheduling agent in a digital twin environment, and learning an energy storage charging and discharging strategy through a near-end strategy optimization algorithm; designing a source-network-load-storage hierarchical collaborative optimization framework, optimizing power output and load distribution by using an improved particle swarm optimization algorithm on the upper layer, and solving power flow distribution by using an alternating direction multiplier method on the lower layer; and establishing a self-adaptive feedback correction mechanism, and dynamically adjusting a cooperative scheduling strategy. According to the invention, intelligent collaborative scheduling of source network load storage is realized, and the operation efficiency and stability of a power system are improved.

The invention relates to the technical field of operation prediction, in particular to an optical storage flexible direct current operation prediction method based on deep learning, and the method achieves the precise evaluation of photovoltaic power fluctuation through the analysis of historical operation data and the combination of a power state conversion matrix, guarantees that the energy storage charging and discharging rate can be matched with the load demand distribution, and improves the prediction precision. Power scheduling is optimized, topology analysis and power flow path identification are carried out, topology weight calculation is carried out by using a graph convolutional network, power flow can be dynamically adjusted, the adaptability of an operation mode is improved, the influence of power fluctuation on system stability is reduced, power matching is carried out by adopting an adversarial generative network, photovoltaic output is optimized, and the system stability is improved. The energy storage release path is more reasonable, the power loss is reduced, the overall power distribution efficiency is improved, and the load compensation strategy is optimized through short-period fluctuation detection and long-period trend fitting. And variable correction is performed based on error calculation and anomaly detection, so that the reliability of regulation and control response is improved.

The invention relates to the technical field of power distribution network evaluation, and discloses a power distribution network bearing capacity evaluation system based on dynamic correction. The system comprises a dynamic data acquisition module, a multi-dimensional state space construction module, a security domain analysis module, a partition coupling degree calculation module and a bearing capacity evaluation engine module. The dynamic data acquisition module acquires a power injection quantity sequence, a voltage deviation ratio sequence and uncontrollable parameter fluctuation data of each partition node of the power distribution network; a multi-dimensional state space construction module performs dimension raising mapping on the sequence to generate a linearized power flow state space model containing a power-voltage Jacobian matrix; the security domain analysis module corrects the boundary of the model according to uncontrollable parameter fluctuation and generates a dynamic security operation constraint set; the partition coupling degree calculation module quantifies an electrical independence index by means of a spectrum radius; and the bearing capacity evaluation engine constructs a chance constraint optimization model, outputs the photovoltaic maximum accessible capacity of each partition and a safety guarantee supply control strategy set, and improves the evaluation accuracy and practicability.

A right-hand circularly-polarized patch antenna comprising a ground plane and a patch connected to each other with one or more wires for which the wire shape and location of the end points are selected such that they do not cause an antenna mismatch, and the electrical current carried in the wires produces an extra electromagnetic field subtracted from the patch field in the nadir direction.

An offshore wind power plant composite ring current collection system topology planning method based on an N-1 safety criterion comprises the following steps that S1, wind power plant basic data including wind turbine and transformer substation node coordinates, wind turbine rated power, cable electrical parameters and the like are obtained; s2, generating a candidate cable set and a cable cross avoidance constraint pair set based on the basic data; s3, a scene set containing a normal scene and all single cable fault scenes is constructed, an optimization model with the minimum total cost of the whole life cycle as the target is established, and cable construction / operation variables and direct current power flow power balance, node degrees and cable cross avoidance constraints are integrated; s4, solving the model to obtain an initial topology scheme; and S5, carrying out full fault set N-1 security verification on the initial scheme, and bringing a violation scene into a fault set to resolve until the scheme meets an N-1 criterion. According to the method, through non-predefined topological optimization and planning, reliability and economical efficiency are taken into consideration, calculation complexity is reduced, and the method can be expanded to a cross-substation scene.

The invention relates to the technical field of electrical engineering, and provides an electric energy storage system optimization scheduling method and system based on reinforcement learning, so as to solve the problems that it is difficult to effectively evaluate and control the battery aging risk in the frequent charging and discharging process and it is difficult to meet the real-time scheduling requirement in the edge side low computing power environment. The method comprises the steps of obtaining battery charging state and health state data in an electric energy storage system and power grid load and power grid electricity price data of an industrial and commercial park, and generating a state input data set; using the reinforcement learning agent to obtain a charging and discharging scheduling instruction; performing power distribution on the electric energy storage system, the multi-port converter power interface and the park load to generate multi-source cooperative power flow data; in combination with historical operation data, a reinforcement learning agent is utilized to optimize the charging and discharging scheduling instruction; and performing optimal scheduling of the electric power energy storage system according to the optimized instruction. According to the invention, efficient, intelligent and adaptive optimization scheduling of the power energy storage system in the industrial and commercial park is realized.

A new-power-system power grid restoration method and apparatus based on second-order cone transformation. The method comprises: acquiring power data of a new power system (101); on the basis of the power data and taking the output uncertainty of a new energy unit into consideration, constructing a mixed-integer nonlinear two-stage robust optimization model based on a multi-state uncertainty set (102), wherein the model comprises an alternating-current power flow constraint; using a second-order cone relaxation technique to simplify the alternating-current power flow constraint, and converting the mixed-integer nonlinear two-stage robust optimization model into a mixed-integer second-order cone two-stage robust optimization model (103); decomposing the mixed-integer second-order cone two-stage robust optimization model into a main question and a sub-question, and solving the main question and the sub-question, so as to obtain an optimization result (104); and on the basis of the optimization result, generating a power grid restoration scheme for the new power system (105).

The invention discloses an electric energy meter error real-time monitoring and intelligent calibration system and method, and relates to the technical field of electric energy meter calibration, and the system comprises a target power data calculation module which is used for collecting voltage and current signals in real time, and carrying out the calculation to obtain target power data; the reference power data calculation module is used for performing real-time calculation to obtain reference power data; the error vector acquisition module is used for acquiring an error vector; the error prediction result generation module is used for carrying out modeling analysis on the error vector and generating an error prediction result; and the target electric energy value output module is used for outputting the target electric energy value. The technical problem that the metering accuracy is insufficient due to the fact that the electric energy meter lacks an effective error analysis and prediction means and a dynamic calibration mechanism in a bidirectional power flow scene in the prior art is solved, and the purposes of real-time monitoring, error analysis and prediction and dynamic calibration of the forward and reverse power data of the electric energy meter are achieved. And the metering accuracy of the electric energy meter in a bidirectional power flow scene is improved.

The invention provides an active transformer area voltage out-of-limit early warning and photovoltaic bearing capacity evaluation method, which comprises the following steps: acquiring multi-source data of a power distribution transformer area, and generating a standardized multi-source time sequence data set through time alignment processing, data quality evaluation and feature fusion; extracting voltage out-of-limit features based on the time sequence similarity matrix and the node influence matrix; the periodicity, volatility and randomness characteristics of photovoltaic output are analyzed, a coupling relation matrix and a sensitivity vector are established, and an influence evaluation model is constructed; performing out-of-limit early warning based on an early warning index system and a deep learning model; and establishing a multi-dimensional evaluation index system for carrying out bearing capacity evaluation in combination with power flow analysis and the dynamic influence factors. Through multi-source data fusion and multi-dimensional feature analysis, the accuracy of voltage out-of-limit early warning is improved, accurate evaluation of the photovoltaic bearing capacity is realized, and effective technical support is provided for safe operation of the power distribution network.

The invention discloses a power distribution area flexible interconnection group planning method for mutual energy assistance, and relates to the technical field of power distribution network planning, and the method comprises the steps: screening out area pairs meeting conditions based on the topological parameters of a power distribution network, area loads and new energy output data, and constructing an initial interconnection set; constructing an initial two-stage opportunity constraint optimization model comprising a planning stage and an operation stage based on the set; in the planning stage, an interconnection strategy and a new energy consumption rate are determined by taking minimization of investment cost and punishment cost as a target; the strategy feasibility is verified through a random scene in the operation stage; carrying out polyhedral approximate linearization processing on nonlinear constraints in the model, reconstructing a power flow model into a compact form, converting probabilistic opportunity constraints into deterministic bilinear constraints by using a sampling average approximation method, forming a deterministic approximation model, and carrying out iterative solution through a column constraint generation decomposition algorithm to obtain a target interconnection grouping scheme; and high-efficiency energy mutual aid and load balancing between power distribution stations are realized.

The invention discloses a power distribution network harmonic suppression and heat loss evaluation method and system, and relates to the technical field of electric energy quality optimization and heat loss evaluation, and the method comprises the steps: collecting a power distribution network signal, extracting harmonic features, and calculating the harmonic contribution degree of each load based on a harmonic power flow; cable additional heat loss caused by harmonic current is calculated, and cable temperature prediction is carried out based on a transient heat conduction equation; and dynamically adjusting parameters of the harmonic compensation device according to harmonic treatment and heat loss evaluation results, and optimizing load scheduling. According to the method, more accurate harmonic source identification and harmonic power flow calculation are provided, and the harmonic treatment accuracy is improved; in cable heat loss evaluation, eddy current loss modeling, heat conduction analysis and dynamic temperature prediction are introduced, so that the operation safety of the cable is enhanced; dynamic harmonic suppression optimization and load scheduling are combined, a closed-loop regulation mechanism is formed, the electric energy quality is improved, and meanwhile, the cable overheating risk is effectively avoided.

The invention provides a power grid cable loss analysis method and device and a storage medium, and the method comprises the steps: obtaining cable topology data, extracting parameter characteristics, and generating a parameter database; performing matrix conversion based on the parameter database to establish a network calculation model, executing load flow calculation, and outputting voltage power distribution data; establishing a temperature and loss coupling relation model according to the voltage power distribution data, calculating cable temperature distribution, and generating time-varying loss data; and performing multi-dimensional feature analysis on the time-varying loss data, calculating a loss risk index, identifying loss hotspots and generating an optimization scheme. According to the method, the dynamic relation model of the cable parameters and the loss is established, and the cable shielding layer characteristics and the temperature change factors are combined, so that accurate analysis of the power grid cable loss is realized, technical support is provided for identifying loss hotspots and formulating a targeted optimization scheme, and the power grid loss calculation deviation is effectively reduced.

A collaborative resource allocation method for a virtual power plant cluster and a power distribution network comprises the steps that a virtual power plant cluster-power distribution network joint operation framework is constructed, a double-layer optimization model is constructed, and an upper-layer optimization model formulates an electric energy and carbon quota P2P transaction plan through a cooperative game between virtual power plants; the lower-layer optimization model solves the optimal power flow of the power distribution network based on the transaction plan and feeds back the electricity-carbon comprehensive price; and a cooperative game model is constructed, and the double-layer optimization model is solved by adopting an alternating direction multiplier algorithm, so that cooperative resource allocation is realized. According to the method, a joint operation framework of the virtual power plant cluster and the power distribution network is constructed, and a double-layer optimization model and an asymmetric Nash bargaining theory are combined, so that P2P transaction of electric energy and carbon quota is realized, and the optimal power flow of the power distribution network is optimized.

The invention relates to an active splitting and isolated island operation method based on load importance under a disaster condition, and belongs to the technical field of power systems and automation thereof. On the basis of disaster types and power supply area characteristics, constructing a load importance dynamic grading system, optimizing the weight through an analytic hierarchy process-index weight optimization collaborative algorithm, and combining with real-time updating to generate a grading result; the method comprises the following steps: deploying wide-area and local monitoring devices to collect power grid data, setting a safety threshold, triggering a splitting decision when the safety threshold exceeds the limit and the trend deteriorates, calculating an instability rate by using a multi-parameter collaborative instability pre-judgment algorithm, and starting pre-splitting preparation; and the main control center calls a target splitting section optimization algorithm to determine a section in combination with the grading result and the power grid data, generates a splitting instruction after verifying the stability of the island through load flow calculation, and performs classified stability control on the split island. The method can realize accurate load grading, early instability pre-judgment, guarantee of important load power supply such as medical treatment and the like, reduces catastrophe loss, and is suitable for power grid management and control under various disasters.

The invention relates to a power system scheduling method and device based on multi-modal data fusion. The method comprises the following steps: acquiring multi-modal data of a power system, wherein the multi-modal data comprises power grid topology data, meter information, wind and light energy output prediction data, maintenance plan text data and energy comprehensive data; performing feature extraction, space-time alignment processing and hierarchical attention learning on each modal data to obtain a fusion feature vector; training the scheduling model by using the fusion feature vector until a total loss function meets a preset condition, the total loss function comprising a power flow equation constraint term; the method comprises the following steps: acquiring real-time multi-modal data of a power system, and obtaining a real-time fusion feature vector through feature extraction, space-time alignment processing and hierarchical attention learning; and inputting the real-time fusion feature vector into the trained scheduling model to obtain a real-time scheduling decision, and scheduling the power system according to the real-time scheduling decision. The method can improve the dispatching precision of the power system.

The invention provides a power distribution network operation risk intelligent identification and early warning method, which comprises the steps of identifying an equipment contact failure probability through an actual wear state, and when the equipment contact failure probability exceeds a safe operation requirement, determining an equipment fault early warning signal through historical fault statistical data, identifying potential equipment failure risk points and extracting risk distribution characteristics; identifying a high-risk equipment node through the equipment fault early warning signal, evaluating whether a cascading fault of adjacent equipment overload is caused after power flow redistribution of a power grid according to the identified high-risk node, extracting a fault propagation path, and determining a system risk level distribution diagram; and carrying out risk area division on the system risk level distribution diagram, identifying key equipment nodes in a high-risk area, extracting a load transfer scheme of the high-risk area, and determining a load distribution path and a power transmission direction.

The invention discloses an island micro-grid operation scheduling method and device. According to the scheme, the method comprises the steps that power generation characteristic modeling is conducted on a distributed power source and an energy storage system of a diesel storage island micro-grid; constructing a function taking the minimum daily total operation cost as a target, wherein the function covers the operation cost of a diesel generator and an energy storage system and the pollutant treatment cost; establishing a power impact type load mathematical model including power characteristics, system dynamic frequency response characteristics, micro-grid dynamic frequency safety constraints and diesel generator frequency control constraints; establishing a micro-grid operation constraint model which has constraints such as power balance, Distflow power flow, energy storage, charging, discharging and capacity, diesel generator output and climbing rate; and solving the model by using a particle swarm optimization algorithm, generating a diesel generator output, energy storage system charging and discharging and frequency modulation reserve capacity distribution strategy, and obtaining an island microgrid optimal operation scheduling decision.

The invention discloses a power network parameter-based power flow system ill-conditioned evaluation method and system, and mainly relates to the technical field of data calculation of a power system. Comprising the following steps: generating a multi-dimensional evaluation index set comprising a network topology structure index, an admittance matrix characteristic index and branch impedance parameter statistics according to basic network parameters of a power system; through comprehensive analysis of the generated multi-dimensional evaluation index set, traditional Jacobian matrix singular value calculation is replaced, and the ill-conditioned condition number of power flow calculation of the power system is evaluated; according to the power grid type and the operation scene, dynamically adjusting the judgment threshold value of each index, and outputting the ill-conditioned risk level and the optimization suggestion. The method has the advantages that calculation independent of Jacobian matrix singular values is achieved, dynamic quantitative evaluation is carried out on bad operation conditions of the system, and meanwhile the load flow calculation stability evaluation precision of a large-scale power grid is remarkably 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 invention discloses a new energy access lower harbor area transformer insulation performance and service life evaluation method, which comprises the following steps: firstly, establishing a wind and light storage AC / DC hybrid micro-grid equivalent simulation model, and quantifying a composite action mechanism of new energy fluctuation and harmonic characteristics on dynamic loss and hot-spot temperature rise of a transformer; analyzing a harmonic loss mechanism of the transformer in different operation modes; secondly, constructing a quay crane and field crane operation simulation model, and analyzing the harmonic characteristics of bridge crane equipment and the influence of the harmonic characteristics on the harmonic distortion rate of the power distribution network; then, on the basis of the data, a transformer hot-spot temperature dynamic calculation model is established in combination with bidirectional power flow characteristics; and finally, calculating transient temperature distribution of the transformer through a flow field-temperature field coupling method, and constructing a multi-physical field coupled insulation aging rate model to realize evaluation of the residual life of the transformer. The method solves the problem that the traditional method cannot quantify the bidirectional power flow and harmonic wave composite aging effect.

The invention provides a power system voltage control method and device based on dynamic steady state collaborative correction, and the method comprises the steps: building an electromagnetic transient model for a preset voltage node in electromagnetic transient simulation software PSCAD, simplifying a power grid with the electrical distance to a target region exceeding a preset distance into a dynamic Thevenin equivalent model, initializing load flow calculation in the BPA to obtain a dynamic-steady state hybrid model; updating dynamic Thevenin equivalent parameters of the power system in real time according to the data of the phasor measurement unit of the power system, and synchronizing the dynamic Thevenin equivalent parameters to the BPA; based on the dynamic-steady state hybrid model, the transient overvoltage of the power system is suppressed within the time shorter than the first preset duration, and the steady state voltage of the power system is optimized within the time shorter than the second preset duration in combination with wide-area coordination control, so that the parameter bidirectional correction of the dynamic and steady state models is realized, and the head end voltage prediction error is reduced. And the voltage control robustness is improved.

The invention relates to a power distribution network voltage partition control method and system based on a hierarchical K-means clustering algorithm, and belongs to the technical field of power distribution system voltage partition optimization. According to the technical scheme, power distribution network node parameters are collected in a self-adaptive partition mode, a node sensitivity coefficient and an eigenvector of an electrical distance are constructed, and then a K-means algorithm is used for fine region division; selecting a dominant node: solving a Jacobi matrix through load flow calculation, extracting a voltage sensitivity coefficient, and selecting a node with the maximum sensitivity as a dominant node in each partition; multi-objective optimization: constructing an optimization model with minimum network loss, minimum voltage deviation and highest voltage stability as objectives; and partition cooperative control: accessing wind power and photovoltaic power to the dominant node, adjusting reactive power output in real time according to an optimization result, and realizing partition autonomy and global cooperation. According to the invention, voltage fluctuation and out-of-limit are inhibited, system network loss is reduced, control efficiency and economy are improved, and the method is suitable for complex topology and high permeability scenes.

The invention belongs to the technical field of power dispatching control, discloses an intelligent dispatching accident plan optimization method based on deep learning, and aims to solve the problems that traditional power accident dispatching depends on artificial experience, the plan generation efficiency is low, and the current situation of wide application of distributed photovoltaic and energy storage is difficult to adapt. According to the core technical path, on the basis of massive historical dispatching accident data, accident processing key features are automatically mined through a deep learning model, distributed photovoltaic output and energy storage charging and discharging features are fused, and a multi-source feature learning framework considering power flow balance in the accident state is constructed; and generating a dynamically optimized structured plan. By applying the method, the generation efficiency and accuracy of the accident plan can be remarkably improved, the accident handling speed is increased, the power failure duration and the economic loss are effectively reduced, meanwhile, the power flow stability of the power system after distributed energy access is guaranteed, and finally the overall reliability and the safe operation level of the power system are improved.

The invention discloses a power grid maintenance plan reliability post-evaluation method based on Monte Carlo simulation and data driving, and belongs to the technical field of power system operation and reliability analysis. The method comprises the following steps: firstly, collecting multi-source data such as historical load, renewable energy output, equipment operation state and maintenance record of a power grid, constructing a time sequence database through cleaning, time alignment and feature extraction, and establishing a load and renewable energy probability model; constructing a maintenance plan model containing a state variable, a constraint condition and a peak clipping weight mechanism, and establishing a continuous time Markov chain state model for the key equipment to generate an availability sequence; generating a large-scale random operation scene set through a Monte Carlo method based on multiple models, and carrying out supply-demand balance and power flow analysis on each scene; multi-dimensional indexes of reliability, economy and safety are calculated and subjected to weighted fusion, a comprehensive post-evaluation report is generated after results are counted, and finally the maintenance plan is optimized according to the report. According to the method, the uncertainty of the power system can be comprehensively considered, multi-dimensional quantitative evaluation and closed-loop optimization of the maintenance plan are realized, intelligent support is provided for power grid maintenance decision making, and the method is suitable for a power transmission network, a power distribution network and a micro-grid.

The invention discloses a power distribution equipment health assessment method and system based on multi-source data, and the method comprises the steps: mapping each modal feature into a comparable measure, constructing a learnable cost containing power flow and heat consistency, outputting a modal weight through scene gating, and forming a fusion representation of physical consistency; driving a neural differential equation by fusing the stress force obtained through representation decoding, adopting monotone weight parameterization and introducing equipment-level damage budget, and obtaining damage and health indexes which are irreversible along with time; under the constraint of physical baseline life, combining a working condition input time-scene gating danger rate model, performing causal consistency correction through virtual intervention, and outputting an interval failure probability and residual life; and calibrating a dynamic threshold value in the working condition cluster, and triggering routing inspection, sampling and load shedding based on the risk sensitivity and the topological linkage risk priority. According to the method, multi-source physical consistent fusion, individualized health modeling and dynamic closed-loop optimization can be realized, and the accuracy and interpretability of health assessment of the power distribution equipment are improved.

A system includes control circuitry for identifying, during operation in a first operating mode wherein electrical power is limited by protection devices, an indicator to enter a second operating mode. The indicator corresponds to power consumption of the electrical system exceeding a power capacity of the electrical system, nearing, achieving, or exceeding a limit. In response to the indicator, the system operates in the second operating mode by retrieving reference information comprising load preferences and limits, and managing one or more loads, sources, or both to modify power consumption, power capacity, or both based on the indicator and based on the reference information. The system communicates with loads or sources, and manages branch circuit using controllable elements to prevent power consumption from exceeding power capacity. The system modifies setpoints, turns devices on or off, schedules device operation or otherwise manages the electrical system based on predetermined preferences.

The invention relates to the technical field of large-scale power grid safety guarantee, in particular to an optimal decentralized coordination control method for a micro-grid in a new energy power system, which comprises the following steps of: performing virtual disconnection verification on key power transmission lines one by one based on online acquired power grid topology and real-time line power flow data, and establishing a whole-grid cascade risk quantitative index. According to the method, power grid topology and line power flow data are acquired in real time, virtual disconnection verification of key power transmission lines is implemented one by one, simulation and analysis of a line fault scene are realized, whole-network cascade risk quantitative indexes are established on the basis, and an initial fault line with the maximum threat to system safety is identified by comparing the indexes with a safety threshold value in real time, so that the safety of the system is improved. A fragile line risk contribution list is generated, and high-risk lines are positioned and prevented in advance; and further directly calculating the power adjustment amount required by the micro-grid group according to the risk contribution list, and forming a forward-looking tide dredging instruction set.

The invention belongs to the technical field of transformer evaluation, and particularly relates to a harbor area transformer bearing capacity evaluation method, which comprises the steps of S1, analyzing voltage and current distribution characteristics and a harmonic component propagation rule of a harbor area microgrid under high-proportion new energy access; analyzing a change rule of a winding leakage magnetic field, an iron core magnetic flux density and winding and iron core loss; s2, calculating the hot spot temperature of the transformer in each historical time period; s3, calculating the predicted residual service life Tb and the predicted actual total service life Tins of the transformer; s4, calculating an aging coefficient B and a primary health index H1 of the transformer; s5, calculating a comprehensive health index Hcom of the transformer; s6, calculating a final health index H of the transformer; s7, if H reaches a preset threshold value, equipment is replaced; and if not, reversely deducing and predicting the residual life Tb1. According to the method, the operation state of the transformer can be accurately evaluated under the working condition of frequent power flow switching.

The invention discloses a wind power generation energy storage load intelligent prediction and power distribution management method, and relates to the technical field of new energy power generation, and the method comprises the steps: generating a clock synchronization signal when a phase gradient quantity exceeds a stable threshold value, correcting the node voltage phase deviation of a power distribution network according to the clock synchronization signal, and outputting a whole network synchronization voltage waveform; inputting an energy storage charging and discharging control instruction into the power flow optimization model, and generating a voltage suppression control vector through a dynamic power deviation compensation algorithm; and extracting stability parameters in the wind power operation data, carrying out weight distribution and state matching on the energy storage charging and discharging control instruction and the voltage suppression control vector, and outputting a cooperative control instruction. According to the method, heterogeneous data such as wind speed, power and voltage are fused into multi-dimensional sequence parameters through a space-time dynamic coupling method, the phase gradient quantity is generated through phase field gradient extraction, a quantitative correlation model of wind speed fluctuation and power grid response is established, and the load prediction accuracy is improved.