666results about "Systems intergating technologies" patented technology

The invention discloses an integrated power distribution cabinet energy efficiency dynamic optimization method based on multi-modal data fusion, and relates to the technical field of intelligent power grids. The problems of failure of an energy efficiency optimization model and poor long-term operation stability caused by data time sequence misalignment and error accumulation of a multi-source sensor in the prior art are solved. According to the scheme, the time offset is dynamically corrected through the adaptive time sequence deviation prediction model; compensating missing data by adopting a non-uniform time step reconstruction algorithm and Kalman filtering; detecting an error drift trend through an exponentially weighted moving average model, updating a feature weight, and inhibiting long-term error accumulation; constructing a self-adaptive time sequence attention fusion network model, and fusing physical constraints and a data driving mechanism to generate an optimization decision; bayesian optimization is utilized to quantify parameter uncertainty, closed-loop feedback execution data is carried out, and model parameters are dynamically updated; according to the invention, the precision, long-term stability and equipment safety of energy efficiency optimization of the power distribution cabinet are remarkably improved, and efficient and reliable operation under multi-physics field coupling constraint is ensured.

Systems and methods for managing the distribution of electrical power are disclosed. The system can maintain a status of each account of a plurality of accounts. Each account can correspond to an electrical power generation device, an electrical power consumption device, and an electrical power transmission device. The system can generate a first record object in response to the generation of a unit of power. The system can identify a first path identifying a source device and a destination device to transmit the unit of power. The system can generate a control signal to route the unit of power from the source device to the destination device. The system can update an event tracker to indicate the routing of the unit of power. The system can generate a second record object corresponding to the destination device. The system can update a second account to include the second record object.

The invention discloses a disperse relaxation factor tidal current model based minimum electricity generating cost incremental quantity obtaining method. When a minimum electricity generating cost incremental quantity model only containing one balance node tidal current model is solved, the total demand unit change of a system is fixed on a balance node, and an obtained lambda, namely electricity generating cost minimum incremental quantity cannot reflect actual operation of the system in most cases during total demand unit change of a system. The disperse relaxation factor tidal current model based minimum electricity generating cost incremental quantity obtaining method adopts solves the provided model by means of an original antithesis interior point method, and the lambda of the system is controllable according to selected disperse relaxing factors.

The invention discloses a hybrid power flow calculation method and device based on a cross entropy algorithm, and belongs to the technical field of optimization algorithm and machine learning crossing. The method comprises the steps that system modeling and parameter initialization are carried out, and a state space, an action space and a target function are defined according to a specific application scene; setting and initializing row parameters; entropy algorithm-guided sample generation and elite screening: iteratively optimizing power grid parameters through probability distribution, and quickly approaching a global optimal solution; the strategy optimization target of the strategy gradient algorithm is that a dynamic strategy network is constructed based on optimized power grid parameters, and the running state of the power grid is adjusted in real time to deal with uncertainty; performing sample multiplexing and distribution fusion of importance sampling: setting a double-buffer mechanism and performing sample fusion and multiplexing; and parameter updating and convergence judgment. According to the method, global search is carried out through the cross entropy algorithm, local optimization is carried out through the strategy gradient algorithm, the sample utilization rate is improved through the importance sampling method, and cooperative processing of global optimization and local optimization is achieved.

The invention relates to the field of power distribution control, and particularly discloses a self-healing control system and method based on multi-agent collaboration, and the system comprises a regional agent and a local agent which are disposed in a power distribution network. Wherein the local intelligent agent is configured to obtain real-time operation data of a monitoring point in the power distribution network, and perform rapid fault detection on the real-time operation data; the regional intelligent agent is configured to fuse the fault analysis information of the plurality of local intelligent agents to determine fault position information when the power distribution network has a fault, generate a self-healing control strategy according to the fault position information, and send the self-healing control strategy to the local intelligent agents for execution; through cooperative work of the distributed intelligent agents, the problems of low response speed, low positioning precision, weak autonomous capability and the like in traditional power distribution network fault management are solved. In addition, the system does not need to depend on centralized control of a master station, can still operate autonomously when communication is interrupted, and improves the efficiency and reliability of fault processing through cooperation of multiple agents.

The invention discloses a broadband random power quality adaptive monitoring system, which relates to the field of power grid monitoring and comprises a broadband signal acquisition module, a synchronous phasor measurement module, an edge calculation module, a cloud edge collaborative analysis module and a local traceability module. The broadband signal acquisition module performs high-frequency sampling on a power grid signal and ensures integrity; the synchronous phasor measurement module is used for realizing time synchronization of multiple monitoring points and generating data with time marks; the edge calculation module is used for studying and judging disturbance and early warning through deep learning, and uploading data to the cloud; the cloud edge collaborative platform aggregates data, deduces regional power quality and issues an optimized AI model; the local traceability module quantifies harmonic responsibility and positions a broadband disturbance source. The system innovatively adopts an ensemble empirical mode decomposition and variational mode decomposition parallel strategy, combines a spectrum kurtosis maximization algorithm to determine the number of modes and accurately separate transient and steady components, solves the problem of feature confusion of a traditional method, and realizes the crossing from single analysis to multi-mode fusion research and judgment.

The invention relates to the technical field of new energy power system control, in particular to a distributed photovoltaic cluster intelligent control device, method, equipment and medium, and the device comprises a northbound communication unit which is configured to communicate with a power grid dispatching master station and is used for receiving and analyzing a dispatching instruction issued by the power grid dispatching master station; the southbound coordinated control unit is configured to communicate with photovoltaic cluster edge control equipment and is used for collecting operation data of each inverter in a photovoltaic cluster; the autonomous decision-making unit is used for collecting voltage, frequency and power flow data of the power grid in real time and generating a control strategy based on a dynamic priority algorithm; and the execution unit is used for executing the control strategy. The method has the beneficial effects that the intelligent control level of the distributed photovoltaic cluster can be remarkably improved, the stability of a power grid and the safety of equipment are enhanced, and the method has important significance in promoting large-scale application of distributed photovoltaic power generation.

A method of power management of a hybrid power plant includes at least one type of renewable power generation equipment and an energy storage system, including: before a first dispatching time range: using first forecasts of energy production capability and energy price, and in particular hybrid plant status and plant parameters, to derive a first power generation schedule defining power generation in the first dispatching time range; at a dispatch point in time within the first dispatching time range: using second forecasts of energy production capability and energy price, and in particular plant status and plant parameters, to derive a second power generation schedule defining power generation in a second dispatching time range; and calculating an optimal power injection value for the dispatch point in time based on the first power generation schedule and the second power generation schedule taking into account at least one constraint.

The invention relates to the technical field of safety and stability control of a power distribution network, in particular to a regional power distribution network collection point voltage reactive coupling characteristic evaluation method. The method comprises the following steps: deploying a synchronous phasor measurement unit for a plurality of transformer area convergent points, collecting electrical parameters and generating original state data of the convergent points; performing quality verification, feature extraction and time sequence reconstruction on the original state data to obtain a convergence point key feature parameter set; based on the key characteristic parameter set, an inverter group initial model is constructed through inverter operation characteristic clustering and control link characteristic analysis; performing electromagnetic transient-steady state hybrid simulation on the inverter group based on the model to generate a transient-steady state hybrid simulation scheme; on the basis of the scheme, through multi-working-condition interactive simulation and interactive gain calculation, a key coupling path of a collection point is mined. According to the method, the dynamic interaction influence between the inverter group and the power grid can be accurately captured, and the resonance path and the reactive coupling path can be effectively identified.

The invention relates to the technical field of industrial big data, in particular to a transformer consumption reduction intelligent control method and system based on a power generation end, and the method comprises the steps: building a source-load space-time correlated industrial big data set, determining an operation reference, and synchronously monitoring real-time electrical parameters; load component decoupling is executed, a basic load component is stripped, and a power fluctuation deviation parameter is extracted; reconstructing equivalent control power of a power generation end based on a nonlinear fluctuation compensation strategy; and constructing a power grid energy efficiency function to execute loss minimization optimization, determining a power supply voltage setting value and driving on-load voltage regulation. Through multi-dimensional data fusion and non-linear load reconstruction, the problem that a traditional method is difficult to deal with random fluctuation of the load is solved, collaborative optimization of transformer loss and line transmission loss is achieved, and operation energy consumption of a power supply loop of a power generation end is effectively reduced.

The invention relates to the field of power dispatching control, in particular to a virtual power plant power supply optimization control method based on supply-demand balance. The method comprises the following steps: firstly, obtaining the generating capacity and the power consumption of different power supply areas in each historical period, obtaining the environmental data of different environmental indexes in each historical period, obtaining the power supply priority of the power supply areas according to the difference between the predicted generating capacity and the predicted power consumption of a target power supply area, and selecting the supply-demand imbalance time period of the power supply areas; analyzing the correlation between the generating capacity and the environmental data of the power supply area in the supply-demand imbalance time period, obtaining the environmental influence sensitivity of the supply-demand imbalance time period, and obtaining the schedulable potential value of the power supply area in combination with the environmental influence sensitivity and length of each supply-demand imbalance time period of the power supply area and the power supply priority; and the power supply of each power supply area is optimally controlled. The method can improve the power supply optimization control effect of the virtual power plant, and avoids the imbalance of power supply and demand.

Systems, devices and methods for optimising and managing distributed energy storage and flexibility resources on a localised and group aggregation basis, particularly around the determination, analysis and predictive learning of local data patterns, scoring availability for flexibility and risk profiles, to inform the optimisation of energy supply and behind the meter storage resources and local clusters of co-located or close resources within a community, low voltage network, feeder, neighbourhood or building. Said optimisation to involve scheduled, reactive and active management of data sources and local clusters of resources, for a range of goals such as price, energy supply, renewable leverage, asset value, constraint or risk management. Or where said optimisation achieves a local objective such as providing resources to off-set, aid local balancing or constraint management of larger local supplies and loads, or to aid active management of local energy demands and renewable supplies, storage resources, electric heat resources, electric vehicle charging resources or clusters of electric vehicle chargers, flexible loads in buildings.

The invention provides a high-efficiency energy-saving transformer structure design optimization method and system and a storage medium, and the method comprises the steps: determining optimization parameters based on a transformer design scheme, and determining constraint conditions based on a use standard of a transformer; based on the optimization parameters, constructing a target function with the minimization of the main material cost of the transformer as a target; under the action of the objective function, constructing an optimization mathematical model based on the optimization parameters; solving the optimized mathematical model by using a particle swarm optimization algorithm to obtain an optimal solution; under a constraint condition, screening the optimal solution by adopting a feasibility rule method to obtain an optimal electromagnetic optimization parameter combination; and optimizing transformer structure parameters based on the optimal electromagnetic optimization parameters. According to the method, the operation stability and energy-saving performance of the transformer are improved, the balance of economy, reliability and applicability is achieved, and the design efficiency and quality are greatly improved.

The invention discloses a power grid loss intelligent optimization system and method based on a heterogeneous computing architecture, and relates to the technical field of power grid loss optimization. A five-level closed-loop control process taking a disturbance density feature vector group Oflow as a perception basis, a resource mapping feature tensor Omap as scheduling input, a hot spot risk vector Orisk as a state constraint, a scheduling decision vector Odispatch as dynamic control execution and a global scheduling performance value Q as a feedback basis is constructed; a whole-process adaptive scheduling optimization mechanism aiming at heterogeneous task complexity and a power grid disturbance evolution trend in power grid operation is realized, and a local anomaly situation can be perceived in advance by constructing a disturbance density feature vector group Oflow before a clear anomaly is not formed in a task disturbance trend; cross-architecture granularity scheduling can be realized among multiple architectures through a resource mapping feature tensor Omap; risk diffusion constraints in spatial topology can be provided based on the hot spot risk vector Orisk.

The invention discloses a low-voltage distribution area topology identification method, system and device and a storage medium, and the method comprises the following steps: deploying a synchronous measurement unit in a low-voltage distribution area, and obtaining the outgoing line current phasor and voltage phasor data with a synchronous time scale of a whole network; constructing an incoming line and outgoing line current phasor set, and performing adaptive descending sorting according to amplitudes; determining that the nodes corresponding to the incoming line current phasors and the found nodes corresponding to the one or more outgoing line current phasors are in a father-son relationship, and iterating the process until all sets are empty, thereby completing the identification of the topological relationship of the power distribution side; and according to the equipment number contained in the obtained voltage phasor data, determining a meter box connected with the user electricity meters, and then clustering the user electricity meters connected to the same meter box according to the voltage phase angle to obtain the connection phase sequence of each user electricity meter. The problem of key information missing is solved from a data source, and accurate and efficient identification of topology is realized by fusing physical constraints.

The invention discloses an identification method and device for sub-hyper-synchronous oscillation parameters of a power system, a terminal and a medium, and the method comprises the steps: firstly collecting the electric quantity sampling data of the power system through a broadband synchronous phasor measurement unit, and constructing a superposition model comprising a fundamental component and a plurality of oscillation modes; then, calculating a residual error between an actually measured signal and a model predicted value, constructing a nonlinear least square objective function, and generating a parameter updating vector in combination with a dynamic damping factor; in the iterative optimization process, the size of a damping factor is dynamically adjusted by comparing the change of an objective function value so as to adapt to the damping factor numerical value requirement of the current iterative stage, and finally, an optimized oscillation parameter is output as a final identification result. According to the scheme, through a dynamic damping factor adjustment mechanism, a large damping factor is adopted in the initial stage of iteration to inhibit matrix ill-conditioned influence, the damping factor is reduced when convergence is close to improve the convergence speed, and the stability and efficiency of the optimization process are effectively balanced.

A technique is disclosed to use time series phasor data to perform real-time dynamic line rating of electric power transmission lines. A variety of techniques are used to generate well-poised solutions to the determination of transmission line parameters from phasor data. Line health information can also be determined from changes to transmission line parameters, such as galloping, icing, vegetation encroachment, imperfect splicing, and conductor corrosion.

The invention relates to the technical field of disturbance identification of a power system, in particular to a disturbance identification method based on a Grubrum angle difference field and transfer learning, which comprises the following steps of: acquiring corresponding frequency and voltage amplitude time sequence data when a node of the power system IEEE39 is disturbed to obtain frequency and voltage amplitude data of a one-dimensional time sequence; the method comprises the following steps: respectively carrying out feature extraction and conversion into two-dimensional Grubrum angle field images, fusing feature extraction results based on an RGB image channel, inputting a feature two-dimensional image data set into a preset transfer learning-AlexNet network model for training, and constructing a disturbance recognition model based on an SE attention mechanism; and inputting the obtained current PMU disturbance data of the power system into the disturbance recognition model to obtain a disturbance recognition result output by the disturbance recognition model, so that the recognition capability and the anti-noise capability of the model can be improved, and the recognition precision of different disturbance types of the power system is further improved.

The invention discloses a power distribution network short-circuit parameter real-time measurement method, system and device based on PMU cooperation and a medium, and belongs to the technical field of power systems and automation, and the method comprises the steps: collecting PMU real-time measurement data, loading preset parameters, and constructing a PMU cooperation measurement network; carrying out a synchronous sampling algorithm, carrying out comprehensive preprocessing on real-time measurement data, and carrying out state reconstruction and inhibiting the influence of abnormal data according to a state estimator; establishing a short-circuit parameter identification model, and executing short-circuit parameter identification; performing multi-dimensional evaluation on the identification result, and evaluating the real-time performance of the algorithm; and based on the evaluation result, updating the key parameters, and executing adaptive adjustment of the system parameters. According to the invention, through high-precision synchronous acquisition, adaptive signal processing and closed-loop optimization mechanisms, comprehensive improvement of the real-time measurement precision, reliability and adaptability of the short-circuit parameters of the power distribution network is realized.

The invention relates to the technical field of power system stability control, and discloses a hybrid synchronous phase modifier quick response coordination control system with inertia support. According to the invention, wide-area data such as voltage phase, frequency deviation and the like are collected and verified through the data sensing module; the modal evaluation module calculates a global motion modal cooperation coefficient G to quantify the dynamic cooperation degree of the system; the coordination control module dynamically switches an emergency instability or preventive damping mode based on G, and generates a coordination control instruction of the phase modifier and the SVG; and the execution feedback module forms a closed loop and optimizes parameters, and realizes efficient response in combination with a layered distributed architecture. According to the invention, deep cooperative control of the two is realized, the disturbance resistance and stability maintenance capability of the power system are improved, and the method is suitable for a power grid with high-proportion renewable energy access.

The invention relates to the technical field of line fault analysis, in particular to a high-voltage line fault identification method based on phasor analysis, which comprises the following steps of: performing multi-time window nesting decomposition on voltage phasors and current phasors acquired in the operation process of a high-voltage line, extracting a phasor dynamic response sequence, and calculating the phasor dynamic response sequence; identifying a main disturbance mechanism characteristic triggered by a fault, and constructing a main disturbance mechanism type label; a driving response dominant phasor disturbance factor is identified, a phasor factor mapping graph is constructed, factor stripping operation is executed, and a dominant purification phasor set is generated; and performing phase evolution analysis on the dominant purification phasor set, and determining a fault initial phase transition boundary and a cross-phase offset trend. According to the method, the stability and the physical consistency of identification are ensured, and the finally output fault type and phase structured result not only has clear logic traceability, but also highly meets the requirements of dispatching automation and quick response of a protection system.

The invention relates to the technical field of a power distribution network of a power system, in particular to a fault prediction self-healing power distribution method, which comprises the following steps of: firstly, acquiring equivalent admittance and physical residual by controlled disturbance and synchronous phasor measurement, generating a spectrum-topology co-embedded coordinate and an innovation consistency index, and finishing topology check; fusing the space evidence, the time evidence and the physical evidence, and combining hazard rate evaluation to form a control admission intensity value; executing offset search under the constraint of feasible region prior, action shielding and control barrier, and performing power flow optimization by taking a condition in-danger value as a target to obtain a self-healing operation sequence; and finally, the operation threshold is shaped according to the anti-fact energy margin, complete sequence checking is completed according to the signal sequential logic and the satisfiability model theory, an execution packet is generated, low misoperation is achieved, and safety and rapid recovery can be proved.

The invention discloses a distributed power supply power coordinated optimization method and system based on edge calculation, and relates to the technical field of intelligent power grid optimization control, and the method comprises the steps: collecting operation parameters at an edge node, building a short-period power prediction model, calculating the power generation capability and load evolution trend, and combining the equipment working condition, load characteristics and historical operation rules, outputting a power output plan and a pre-distribution value; a lightweight communication mechanism is introduced, a short-period power prediction model is shared among edge nodes to predict an output value, an operation load and residual regulation capability, and local correction is performed on output power and load pressure; and after local correction, collecting current power grid fluctuation and economic signal data, outputting a final coordinated and optimized scheduling scheme, continuously monitoring an execution power output effect and a prediction deviation, and carrying out emergency compensation by adjusting the scheduling scheme. According to the method, real-time prediction, collaborative optimization and dynamic linkage of the power grid of the distributed power supply are realized, and the adaptability and energy efficiency optimization of the power grid are enhanced.

The invention discloses a power grid power flow real-time calculation and state estimation method and system based on a PMU, and belongs to the technical field of electric measurement and electric fault positioning. The method comprises the following steps: synchronously measuring electrical variables, including a voltage phasor and a current phasor, of a power grid node through PMU equipment to obtain synchronous phasor data, and verifying time and topology consistency; the power grid observability grade is judged based on PMU configuration node distribution corresponding to the verified electric measurement data; directly generating a real-time state estimation result through linear calculation based on PMU electric measurement data if the whole domain is observable; and if a part is observable, fusing PMU and SCADA data, carrying out nonlinear state estimation through a weighted least square method, distributing a high weight for the PMU electrical measurement data, and dynamically adjusting the SCADA weight to suppress bad data. According to the method, through observability adaptive judgment, the precision and efficiency of state estimation are improved, a data basis is provided for power grid fault positioning, and the method is suitable for practical application scenes of various PMU coverage degrees.

A method for managing an energy system having one or more renewable energy sources, one or more energy storage devices, one or more loads, and a grid connection for connecting at least temporarily to an external energy distribution grid is disclosed. The method generates a prediction of energy demand of the loads using historical energy demand data, and a prediction of renewable energy availability from the renewable energy sources using weather forecast data. An amount of energy to be obtained from the distribution grid is determined in dependence on the prediction of renewable energy availability and the prediction of energy demand. An energy conservation strategy is generated using the predictions and determined energy amount, and energy supplied to one or more of the energy storage devices and / or one or more of the loads is adjusted automatically according to the energy conservation strategy.

The invention belongs to the field of power grid energy storage configuration, and discloses a power grid energy storage optimal configuration method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring peak-valley period data of a power grid; constructing an effective arbitrage time length calculation formula based on the power grid peak and valley time period data; acquiring related data of an extreme risk event in the power grid; constructing a multi-dimensional risk influence factor based on risk event related data in the power grid; constructing an internal yield implicit equation by integrating an effective arbitrage duration calculation formula and the multi-dimensional risk influence factors; converting the internal yield implicit equation into a linear equation to obtain an internal yield probability function; converting the internal yield probability function into a constraint condition, solving a pre-constructed optimal configuration model which comprehensively considers economy and power grid stability, and obtaining a power grid energy storage optimal configuration scale; the economical efficiency and the power grid stability are comprehensively considered, and the energy storage configuration scale meeting the requirements of the economical efficiency and the power grid stability can be obtained.

The invention belongs to the technical field of energy storage regulation and control, and particularly relates to an energy storage cluster multi-modal optimization regulation and control method and system based on wide-area measurement data, and the method comprises the steps: collecting the power data of each measurement point, unifying the data of each measurement point to a common time reference system, and then carrying out the preprocessing of the data of each measurement point; aiming at the energy storage system, constructing a ramp rate model, an efficiency characteristic model and a capacity attenuation model, and establishing an energy storage state index system; establishing a multi-mode regulation and control strategy for an energy storage operation state, and dynamically selecting an optimal control mode according to different operation scenes and energy storage states; according to the method, a scheduling framework with day-ahead-intra-day time layering and superior-subordinate space grading is constructed, a day-ahead scheduling model and an intra-day scheduling model are established accordingly, the day-ahead scheduling model and the intra-day scheduling model are rapidly solved based on fuzzy decision, and global optimization of cluster resources is achieved. According to the method, global collaborative optimization of energy storage resources is realized by fusing high-precision wide-area measurement data processing and a multi-mode regulation and control strategy.

The invention relates to a disturbance source positioning method based on dynamic mode decomposition, which belongs to the technical field of power system operation and analysis and comprises the following steps of: accurately extracting oscillation mode characteristics of a system by performing matrix decomposition on actually measured data; furthermore, a participation factor analysis model fusing time-space characteristics is constructed, and the spatial distribution characteristics and the time evolution characteristics of the modal are comprehensively considered, so that accurate positioning of the oscillation source is realized. Compared with a traditional method, the method has the advantages of no dependence on a detailed system model, high positioning precision and the like, is realized completely based on measurement data calculation, avoids complex system modeling, and has relatively high practical application value and popularization potential.

A mobile solar power unit control system providing power to an associated equipment item comprising: at least one mobile solar power unit comprising an assembly of inter-connected solar collector panels; an energy storage module connected to receive power from the assembly of inter-connected solar panels; and a control system for controlling operation of both the energy storage module and associated equipment item. The control system comprises a local controller onboard or proximate the at least one mobile solar power unit and a remote controller, communicable with the local controller, located remotely from said at least one mobile solar power unit. The mobile solar power unit conveniently provides power for an associated equipment item and any selected auxiliary loads located in an off-grid location.

The embodiment of the invention discloses a power grid optimization method and device based on an improved fast marching algorithm. The method comprises the steps of obtaining topological structure data and operation data of a power grid; predicting the dynamic load change and operation state of the power grid by using a pre-trained artificial intelligence model, and generating an optimization initial condition set and a dynamic adaptive speed function; initializing an improved fast marching algorithm based on the optimization initial condition set, constructing a non-uniform propagation model by loading the dynamic adaptive speed function, and calculating an energy transmission optimal path meeting multi-target constraints, wherein the multi-target constraint comprises the steps of minimizing the active loss of the system, maximizing the static stability margin and optimizing the transient response time; and generating an energy distribution scheme including a generator output instruction and a topology switching strategy according to the optimal path, and optimizing the power grid according to the energy distribution scheme. According to the invention, efficient real-time optimization control of the power grid is realized.