466results about "Systems intergating technologies" patented technology

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 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 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.

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 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 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.

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 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.

The invention relates to the technical field of power grid monitoring, in particular to a fault monitoring method, system and equipment for a small power generation and distribution system. According to the method, fluctuation characteristics of monitoring signals are analyzed, fluctuation time periods are determined, fluctuation trends are obtained through the lengths of the fluctuation time periods and voltage instability, and short-term fluctuation time periods and long-term fluctuation time periods are obtained through classification. And analyzing each time point one by one, and further screening out a superposed short-term fluctuation time period in the long-term fluctuation time periods through the voltage value difference between the continuous time points. And for the superposed short-term fluctuation time period, the continuation range is adjusted through the fluctuation trend characteristics, and a monitoring signal with accurate characteristic information is obtained. According to the method, the superposed short-term fluctuation time period is effectively extracted, wavelet transform is used for reconstruction, the monitoring signal with accurate feature information is obtained, the power grid monitoring effect can be improved, and subsequent power control over the micro-grid is facilitated.

Disclosed are a method for distribution of PHASE to monitor the operating state of a power system by using heterogeneous data obtained from measurement of SCADA and a time synchronized PMU and a method for processing defect data in mixed DSE by using same. The method for distribution of PHASE to monitor the operating state of a large scale power system includes the steps of: defining an extended state variable and an extended state variable set for each region; performing a SCADA-based DSE by using a SCADA measurement value for each region and a covariance matrix thereof, and parallelly performing a PMU-based DSE by using a PMU measurement value for each region and a covariance matrix thereof; and mixing the estimation results of the SCADA-based and the PMU-based DSE algorithms so as to perform a phasor-aided normalized residual test and a general normalized residual test.

The invention discloses a low-cost precise time keeping method, system and device based on a gating circulation unit, and relates to the technical field of power grid measurement. The method is executed when the synchronous phasor measurement device cannot receive external signals, and comprises the following steps: monitoring operation parameters which influence the frequency stability of an internal crystal oscillator of the phasor measurement unit in real time; calculating a current frequency error of the crystal oscillator in real time by using a crystal oscillator frequency drift prediction model based on a gated cycle unit network based on the operation parameters; generating a time correction amount according to the current frequency error; and applying the time correction to a local clock of the phasor measurement unit to compensate the frequency drift of the crystal oscillator so as to realize accurate time keeping. According to the invention, through accurate prediction of crystal oscillator errors and lightweight software compensation, the synchronous phasor measurement device adopting a low-cost crystal oscillator can also realize long-time high-precision time keeping, the hardware cost is obviously reduced, and the reliability of power grid monitoring during the Beidou / GPS lock losing period is improved.

A power system transient voltage stability evaluation method based on a gain-swin transformer comprises the following steps: step 1, collecting historical online data and analogue simulation data of a power system, and constructing an initial sample set; converting the multivariate time series data into a GAF transient state integrated image with reserved time characteristics; generating an unstable or stable label for each sample image, and randomly dividing a training set and a test set according to a certain proportion; 2, a feature extraction module of the Swin Transform model is improved, and multi-scale spatial-temporal features are extracted through a shift window attention mechanism and a cross-window attention mechanism; and in the offline training process, an optimal transient voltage stability evaluation model is obtained. And step 3, deploying the optimal model in an online evaluation system, monitoring transient voltage data in real time and performing stability evaluation. When the power system is unstable, the model can also position a key bus. Compared with a traditional method, the method can achieve efficient and accurate voltage stability evaluation in a large-scale power system.

The invention relates to the technical field of intelligent power distribution network safety detection and fault processing, in particular to a multi-source coupling leakage current accurate traceability and cooperative suppression method and system for a power distribution network, and the method comprises the steps: collecting leakage current, voltage phase and power fluctuation data through a synchronous phasor measurement device; based on the collected data, constructing a four-dimensional feature library, carrying out decoupling processing on leakage current signals, and then realizing accurate traceability positioning of the leakage current according to a matching result of the leakage current signals and the four-dimensional feature library and a time difference between sensors; data are uploaded to a distribution network dispatching center, leakage current is evaluated based on a dynamically adjusted safety threshold value, and then operation states of a photovoltaic inverter, an energy storage converter and a charging pile are cooperatively controlled to realize active suppression of the leakage current; and performing preliminary diagnosis on the leakage current at the edge side, uploading a result to a cloud end, and determining a final diagnosis conclusion. According to the invention, the accuracy of multi-source coupling leakage current tracing and the accuracy of cooperative suppression of the power distribution network are improved.

The invention relates to the technical field of power system transient stability assessment, and discloses a power system transient stability assessment method based on FTF-ACGAN and space-time attention. According to the method, an original data set reflecting different power flow operation modes and fault conditions is generated, and transient electrical quantities are extracted as input characteristics; generating an adversarial network through a fuzzy transfer field-auxiliary classifier to extract modals with different characteristics of a sample, and revealing transient characteristics of the electrical signal; and establishing a mapping relation between original high-dimensional input and a system stability or instability label according to the processed transient measurement data of the power system by considering respective capturing capabilities of a spatial feature extraction module and a time feature extraction module in the dual-module multi-head space-time attention network on space-time local association and global dependency features. The method can consider the time-frequency domain information of the electric power system sample, construct a data set with moderate and abundant sample characteristics, and accurately judge the actual operation condition of the electric power system.

The invention discloses a wide-area oscillation source positioning method and system based on physical feature embedding and a graph attention network, and the method comprises the steps: obtaining synchronous phasor data of a power system, carrying out the modal parameter identification of subsynchronous oscillation and / or super-synchronous oscillation based on the synchronous phasor data, constructing a topological graph of the power system, and carrying out the positioning of a wide-area oscillation source. And constructing the oscillation amplitude, the attenuation factor and the phase of each node obtained by identification into a feature vector of each node in a topological graph, inputting the topological graph and the feature vector into a graph attention network, and outputting an oscillation source positioning result, so that high-frequency recording data after an accident does not need to be waited, and the positioning accuracy of the oscillation source is improved only on the basis of synchronous phasor data. The identified modal parameters are used as physical characteristics to be embedded into the graph attention network, and the interpretability of a physical mechanism and the graph structure mining capability of deep learning are fused, so that subsynchronous oscillation and super-synchronous oscillation are accurately positioned.

Disclosed is a system and method for automated outage and workflow planning system in a distributed electrical power grid with planned de-energization of electrical equipment as opposed to responding to an unplanned power outage. An automated system provides core conflict resolution and power outage planning. The present invention automatically notifies requestors of upcoming work orders. In addition, the present invention reduces scheduling inefficiencies by requiring requestors to confirm, decline, or edit on the fly their pending work order(s). The system also determines if all the orders are not completed and places the order back on the calendar for another day.

The invention provides a power distribution network fault processing system and method based on a capacitive energy-taking power supply, and the system comprises a data unit which is provided with a multi-physical-quantity fusion sensor and is used for collecting multi-dimensional power grid data; the analysis unit is used for preprocessing the power grid data to obtain fusion data and extracting fault features; the positioning unit is used for obtaining a fault result according to the fault features on the basis of a pre-constructed inference engine; and the power supply unit is used for providing electric energy for the system through the combination of the multiple types of energy storage capacitors under the corresponding electric energy requirements according to the electric energy requirements of the multi-physical quantity fusion sensor in the data unit and the electric energy requirements of the analysis unit and the positioning unit when the power grid fails. The multi-physical-quantity fusion sensor with the microsecond-level sampling interval is adopted to collect multi-dimensional power grid data, the power supply unit is matched to supply energy through the multi-type energy storage capacitor combination according to the electric energy requirement of each unit, and the fault detection precision, the reliability and the overall performance of the system are effectively improved.

The invention relates to a new energy power station configuration optimization method based on an agent model and multi-objective optimization. The method comprises the steps of obtaining historical power system configuration parameters and corresponding historical stability characteristic values, and performing preprocessing; inputting the preprocessed historical power system configuration parameters into a neural network agent model based on a multi-layer perceptron architecture, performing offline training in combination with corresponding historical stability characteristic values and physical prior constraints, and outputting predicted stability characteristic values; taking the prediction stability characteristic value as an optimization target, and constructing a target function of multi-target Bayesian optimization; and iteratively solving a Pareto optimal solution set based on the objective function, and determining an optimal new energy power station configuration scheme. According to the method, multi-objective optimization and Bayesian optimization are combined, a multi-objective acquisition function is adopted to approach the Pareto frontier between stability characteristic values, precision and multi-objective tradeoff can be considered, and a more flexible and scientific configuration scheme is provided for new energy station construction.

The invention provides a power distribution network early fault section identification method and system based on multi-dimensional feature probability mapping, and belongs to the technical field of power system power distribution network fault detection. The method comprises the following steps: collecting zero-sequence voltage of a power distribution network bus and zero-sequence current signals of head ends of all feeder lines, and obtaining derivative signals of the zero-sequence voltage and the zero-sequence current signals; calculating the third harmonic energy of the zero-sequence current of each feeder line, and when the third harmonic energy exceeds a set threshold value, judging that the feeder line has an early fault; processing the zero-sequence current signal, and extracting an IMF1 component; calculating a correlation characteristic quantity and a dot product characteristic quantity for each section formed by the current difference values of the adjacent measuring devices based on the IMF1 component; and respectively carrying out standardized transformation on the correlation characteristic quantity and the dot product characteristic quantity, substituting the correlation characteristic quantity and the dot product characteristic quantity into a Laplacian cumulative distribution function, calculating a probability value of a fault in each section, and judging a fault section according to a set probability threshold value. The method can effectively and reliably identify the early fault section of the power distribution network.

A device and method for fault management for an electric power distribution network incorporating intermittent generation sources. The method involves configuring multiple hyperparameters for a series of Convolutional Neural Networks (CNNs). A first CNN is trained using current signal imagery from phasor measuring units (PMUs) during fault conditions to classify faults. A second CNN is trained with signal images from PMUs captured during pre-fault and fault cycles for identifying fault sections. Similarly, a third CNN is trained using these images to determine the exact fault location. Once trained, the CNNs are employed sequentially. The first CNN classifies the fault, the second detects the fault section, and the third ascertains the fault location. Subsequently, a comprehensive fault management strategy is deployed.

A demand and supply planning method generates a supply-demand plan including information that specifies operation of an energy generation unit for each unit time in a microgrid having energy generation units being connected to an external energy supply system and generating energy while consuming fuel, an energy storage unit, and a load unit; by calculating an optimization problem related to an objective function including terms related to energy transfer from the external energy supply system in the microgrid and terms related to fuel consumption in the microgrid by using a constraint condition related to a relationship between information related to an operating duration time in each of the one or more energy generation units at a start time for generating the supply-demand plan and a minimum operation time constraint of the energy generation unit; and outputting the supply-demand plan based on a calculation result of the optimization problem.