109 results about "Power vector" patented technology

The invention relates to the technical field of photovoltaic energy storage systems, and discloses a power regulation method, device and equipment for a photovoltaic energy storage system and a storage medium, and the method comprises the steps: carrying out the real-time collection and wavelet decomposition of voltage and current parameters of a photovoltaic array, and obtaining power fluctuation components and power trigger signals of different frequencies; executing power vector analysis to obtain an oscillation mode characteristic parameter set and an oscillation suppression strategy; constructing a photovoltaic side feedforward trigger channel and an energy storage side feedback trigger channel, and obtaining a coordination trigger instruction of the photovoltaic side and the energy storage side; and performing multi-objective optimization decomposition and slope limitation processing on the coordination triggering instruction to obtain a voltage reference value of the photovoltaic MPPT controller and a current reference value of the battery charging and discharging controller, thereby realizing accurate identification and prediction of different frequency power fluctuation components, realizing collaborative cooperation of photovoltaic side feed-forward and energy storage side feedback, and improving the reliability of the system. And the power oscillation risk caused by control mode switching is avoided.

The invention provides a power grid system control method and device, computer equipment and a storage medium in the field of power grid system control, and the method comprises the steps: firstly, determining a dynamic inertia coefficient through integrating node frequency deviation, an energy storage charge state and voltage deviation, calculating inertia compensation power through employing the frequency deviation, precisely capturing the dynamic characteristics of a system, and carrying out the calculation of inertia compensation power; and constructing a target function based on the equivalent impedance matrix and the power deviation, solving a target control sequence under constraint conditions, realizing system power optimization distribution, and further adjusting node power output from multiple aspects by determining composite inertia, synthesizing inertia power, regulating and controlling a power vector and an inertia matrix. And finally, based on energy function construction and partial derivative calculation, generating a control instruction of each node. The steps can effectively deal with system power disturbance, rapidly compensate insufficient inertia, optimize power distribution, maintain system frequency stability, improve safety, reliability and stability of operation of a power system, and enhance adaptability of the system to complex working conditions.

The invention relates to a computing power scheduling method and device, computer equipment, a readable storage medium and a program product. The method comprises the following steps: in response to a target computing power demand initiated by a user identifier, converting the target computing power demand into a first vector; performing vector retrieval on the computing power vector database based on the first vector, and determining a target computing power resource from the computing power vector database; and providing the computing power service of the target computing power resource to equipment corresponding to the user identifier. By adopting the method, the cost of computing power scheduling can be reduced.

A power vector analyzer to analyze power from a device under test (DUT) includes one or more channels to measure a reference voltage signal from a power line connected to the DUT, one or more channels to measure a reference current signal from the power line, a user interface comprising a display and one or more controls, and a quadrature synchronous detector (QSD) for each phase of apparent power being measured, the QSD configured to use a reference voltage signal from the one or more channels and a reference current signal from the one or more channels to determine the apparent power for each phase of power being measured by the DUT and display the apparent power for each phase on the display.

The invention discloses a power distribution area energy storage control and capacity configuration method, system, equipment and medium, and the method comprises the steps: firstly, setting a plurality of different energy storage control strategies according to a preset discharge lower limit and a preset charge upper limit of an energy storage device of a power distribution area in combination with the residual capacity condition of the energy storage device; then, on the basis of time-of-use electricity price data and load data of the power distribution area, according to an energy storage control strategy, an objective function composed of the minimum operation cost of the power distribution area, the maximum photovoltaic consumption rate of the power distribution area and the minimum net load variance of the power distribution area is constructed; solving the objective function through an optimization algorithm to obtain a charging and discharging power vector of the energy storage device; and finally, calculating the optimal configuration capacity of the energy storage device according to the charging and discharging power vector of the energy storage device. According to the invention, the problems of too low photovoltaic consumption rate and too high total cost are solved, and the stability of the power grid is improved.

The invention provides a multiband dynamic optical network transmission optimization method and system, and the method comprises the steps: determining whether an optical fiber node is a compensation point or not based on a power spectrum of the optical fiber node in an unoptimized optical network; calculating a target gain curve corresponding to the compensation point based on the power spectrum corresponding to the compensation point; inputting the target gain curve into a pre-trained machine learning model, and constructing an initial pumping power vector based on an output value of the machine learning model; the method comprises the steps of performing iterative calculation on an initial pumping power vector through a plurality of iteration rounds, determining final applied pumping power, in each iteration round, determining a circulating pumping power vector based on a current iteration pumping power vector, calculating an environment difference based on the circulating pumping power vector and the initial pumping power vector, and determining the final applied pumping power according to the environment difference. A new round of iterative pump power vector is calculated based on the environmental difference, and it is determined whether the new round of iterative pump power vector corresponds to a final applied pump power.

The invention discloses an inertia and damping coefficient detection method, system and device of power electronic equipment, and relates to the technical field of power electronics, and the method comprises the steps: obtaining the AC voltage and AC current of to-be-detected power electronic equipment in real time; determining a frequency and a frequency change rate based on the AC voltage; determining active power based on the AC voltage and the AC current; generating a disturbance signal based on the frequency, the active power, the AC voltage or the AC current; when the disturbance signal is 0, no processing is carried out; when the disturbance signal is 1, obtaining a frequency vector, a frequency change rate vector and an active power vector during disturbance based on the frequency, the frequency change rate and the active power; and based on the frequency vector, the frequency change rate vector and the active power vector, determining an inertia coefficient and a damping coefficient of the to-be-tested power electronic equipment. The inertia and damping coefficient of the power electronic equipment can be accurately detected on line in real time, so that the dynamic behavior of the power electronic equipment can be reflected more comprehensively.

The invention discloses a fault identification and prediction method based on low-voltage distribution network topology modeling and an intelligent algorithm. Aiming at the problems of low fault positioning efficiency, insufficient intelligence and the like of the low-voltage power distribution network, the method realizes fault full-process processing through multi-module cooperation: firstly, collecting data such as voltage and current in real time through an Internet of Things terminal, and constructing a high-quality data set in combination with edge calculation and a four-step preprocessing technology; secondly, constructing a separated complex-valued neural network, and inputting a plurality of voltage and power vectors to realize accurate prediction of loop closing current and voltage deviation; reasoning fuzziness of equipment states and environmental factors through a fuzzy network, outputting TOP3 fault reasons, and tracking a fault propagation path for accurate positioning in combination with a shortest path algorithm and a D-S evidence theory; and finally, generating a switching operation sequence and a digital twinborn verification recovery scheme through an improved matrix algorithm. According to the method, the problems of dynamic modeling, uncertainty reasoning and the like are solved, and technical support is provided for intelligent operation and maintenance of the power distribution network.

The invention relates to the technical field of mathematical modeling, and discloses a modeling method for a power business decision model, and the method comprises the steps: constructing a power corpus block library and a power vector knowledge library, and determining a power business problem text; performing mixed retrieval on the power business problem text by means of a power corpus block library and a power vector knowledge library to obtain a first similarity score corresponding to a candidate corpus block and a second similarity score corresponding to a candidate document representation vector; screening the candidate corpus blocks and the candidate document representation vectors based on the first similarity score and the second similarity score, and determining a target reference text; inputting the power business problem text and the target reference text into a target large language model to obtain a target decision model; the target decision model comprises a description text of the power business problem, a target function and constraint conditions thereof. The method has the beneficial effects that the reliability and the accuracy of the target decision model are remarkably improved based on standard output of the hybrid retrieval and the large language model.

The invention discloses an underwater acoustic communication equalization method based on a leakage normalized least mean square algorithm, and belongs to the technical field of underwater acoustic communication. The method comprises the following steps: acquiring a baseband sampling signal; initializing a weight vector w = 0, an instantaneous power vector p = 0 and an error vector e = 0; and taking the baseband sampling signal x as an input signal, taking the training sequence as an expected signal d, and carrying out gradual leakage updating on the weight of a filter through iterative calculation based on a leakage factor control strategy so as to complete self-adaptive adjustment in a signal equalization process. According to the method, the leakage factor control strategy is introduced, and the adaptive step parameter design is adopted, so that the stability and the convergence speed of the weight of the filter are effectively balanced, finally, the received signal is accurately balanced, and the robustness and the signal quality of the underwater acoustic communication system are remarkably improved.

The invention relates to a high-order analysis power flow and carbon flow tracking method based on full-pure embedding, and the method comprises the specific steps: introducing a complex embedding parameter s, constructing a node voltage vector, an injection power vector and a tracking matrix in a power grid power flow equation into an analysis power series and an analysis power series of the complex embedding parameter s based on the analysis continuation characteristic of full-pure embedding, solving a node voltage initial value when the power grid is in a basic operation state by adopting a Newton-Raphson method; calculating a zero-order line active power vector based on the node voltage initial value, and constructing a zero-order distribution matrix representing a power distribution relation in a basic operation state of the power grid; solving a zero-order power flow tracking matrix based on the zero-order line active power vector and the zero-order power distribution matrix; calculating node indirect total carbon emission based on the zero-order power flow tracking matrix; and replacing the power distribution matrix, the node outflow power and the power flow direction matrix in the zero-order power distribution matrix with power series expansion of corresponding variables.

To analyze power signals of a motor from a new perspective.SOLUTION: A power vector analyzer 130 for analyzing power from a DUT motor 120 comprises: one or more channels for measuring a reference voltage signal from a power line connected to the DUT 120; one or more channels for measuring a reference current signal from the power line; a user interface having a display and one or more operating units; and a quadrature synchronous detector (QSD) 46 for each phase of apparent power to be measured. The QSD 46 is configured to use the reference voltage signal and the reference current signal to determine the apparent power for each phase of the power to be measured by the DUT 120 and display the apparent power for each phase on the display 144.SELECTED DRAWING: Figure 14

The invention relates to a method and a system for acquiring dissipated power of a radio frequency device and related equipment. The method comprises the following steps: acquiring respective scattering parameters of all circuit elements in a circuit of the radio frequency device; connecting the respective scattering parameters of all the circuit elements to obtain an overall scattering parameter of the radio frequency device; acquiring an incident wave vector of each circuit element; according to the overall scattering parameter and the incident wave vector of each circuit element, acquiring an emergent power vector corresponding to each circuit element; and constructing a transmission matrix based on the corresponding emergent power vector according to the connection relationship among all the circuit elements, and calculating and obtaining the dissipation power of the radio frequency device through the transmission matrix. According to the method, the dissipation power is obtained by taking the scattering parameters as the data source, so that automatic processing is conveniently realized, and the design efficiency of the radio frequency device is favorably improved.

The invention provides a photovoltaic panel movement control optimization method and a photovoltaic power generation container, and belongs to the technical field of photovoltaic panels, and the method comprises the steps: obtaining the preliminary required electric quantity according to a user activity scene, capturing the future weather information of the user activity scene, constructing a weather matrix, analyzing the weather matrix, and obtaining the optimal electric quantity of the user activity scene; determining a plurality of variables influencing illumination and the influence weight of each variable; analyzing the required electric quantity according to the plurality of variables and the influence weight in combination with the electric quantity conversion efficiency of the single model of the photovoltaic panel, and obtaining the initial number of the required photovoltaic panels; according to an equivalent modeling mode and a minimum cluster number principle, constructing a photovoltaic mobile cluster based on a single block model; each photovoltaic panel in each photovoltaic mobile cluster is controlled to move to an optimal illumination receiving position to construct a power vector, and all the power vectors are analyzed to optimize the corresponding photovoltaic mobile cluster, so that the application efficiency of the photovoltaic panels is improved under the condition that the power demand is met as far as possible.

The invention relates to the technical field of digital ring main unit power distribution operation regulation and control, and discloses an intelligent power distribution operation regulation and control system of a digital ring main unit, which comprises a state sensing module, a logic judgment unit and an execution regulation and control module, the logic discrimination unit identifies a feeder power distribution state according to the power flow distribution data and generates a topology reconstruction scheme; the execution regulation and control module drives a switch to act, migrates the feeder loop to a target bus section, forms transition impedance by using closing pre-breakdown current during migration, inhibits transient inrush current, drives an impedance compensation branch to access according to a zero-sequence current vector of the feeder loop, and counteracts neutral point potential drift by using potential bias generated by reactive power injection; according to the method, the power distribution topology is converted from static hard connection to logic remapping, circuit transient state physical process reconstruction and stock hardware redundancy mining are utilized, node power vector balanced distribution is achieved under the condition that the feeder line continuous power receiving state is maintained, and power flow randomness conflicts are eliminated.

The invention discloses a power demand response collaborative optimization method and device based on flexibility sharing, equipment and a medium, and relates to the technical field of power system demand response, and the method comprises the steps: obtaining load flexibility demands and load flexibility supply cost information reported by all target users participating in power demand response collaborative operation; the load flexibility demand is a power vector difference between an actual load curve and a standard load curve after each target user independently adjusts the load, and the load flexibility demand and the load flexibility supply cost information are input into a preset power demand response collaborative optimization model; determining a corresponding power demand response collaborative scheme through a preset power demand response collaborative optimization model; and executing the power demand response coordination scheme to perform flexible settlement and flexible transaction surplus distribution according to the execution condition of the target user. The execution of a demand response cooperation scheme based on flexibility sharing is realized, and the economical efficiency and reliability of load regulation are improved.

The invention discloses a multi-target operation optimization method and system for an optical storage micro-grid, and relates to the technical field of new energy power system optimization, and the method comprises the steps: carrying out the collection and digital twinning mapping to form a health semantic graph as a state base; calculating an equilibrium priority index on the health semantic graph in a self-adaptive clustering manner to generate a health weight; dynamic weights are fused, charge and discharge power proportions are distributed according to clusters, and health constraint, aging suppression and load response collaboration are achieved; inputting the updated electric quantity vector and the predicted aging trajectory into a multi-target scheduling solver, and synchronously optimizing energy consumption cost, carbon emission estimation and frequency deviation penalty to form a planned power sequence; when the sudden change of the electricity price or the load is detected, the solver agent model is called to quickly recalculate the plan and write the plan back to the health semantic graph to ensure that the control strategy evolves along with the scene. Through mutual feedback and control interlocking of each layer of information, the overall gain of power grid frequency difference convergence and comprehensive operation cost reduction is realized.

A power-imbalanced multi-level decoding method for sparse code multiple access includes: encoded bits of all users are mapped to multi-dimensional sparse codewords through predetermined codebooks; a factor graph matrix is constructed using the predetermined codebooks, all users are classified according to the factor graph matrix under predetermined constraints to determine Z levels; based on a predetermined total transmission power, a progressive multi-level power optimization algorithm is employed to perform power-imbalanced allocation for all users according to the Z levels, thereby determining a locally optimal power vector; transmission signals corresponding to the multi-dimensional sparse codewords are transmitted according to the locally optimal power vector; SCMA detection and power-oriented decoding are sequentially performed for users each level, and outputs decoded bit sequences for the L levels.

The invention relates to the technical field of unmanned aerial vehicle communication, in particular to an unmanned aerial vehicle group collaborative awareness method and system, and the method comprises the steps: obtaining the distance between APs and a routing table updating period in the unmanned aerial vehicle group collaborative awareness process; the method comprises the following steps: acquiring a metric value weight of connection establishment between each AP and a next hop AP in a communication connection path, selecting an optimal routing path between unmanned aerial vehicles, calculating a power adjustment characteristic value between each AP and other APs which establish communication connection with each AP, acquiring a subcarrier interference characteristic value of each power vector, acquiring an optimal power vector of each AP by using a particle swarm optimization algorithm, and determining the optimal power vector of each AP according to the optimal power vector. And the sub-carrier transmission power is used as the sub-carrier transmission power of each AP in communication connection with the AP, and communication scheduling of the APs in the unmanned aerial vehicle group collaborative awareness process is completed. The communication performance in the cooperative sensing operation of the unmanned aerial vehicle group can be improved.

The invention discloses an identity authentication method and system for a power wireless local area network based on a block chain. The identity authentication method is characterized in that a terminal sends an authentication request containing a unique identifier to an access point; the AP responds to generate a challenge random number and a hash target value calculated based on the terminal historical time delay and the current load, and issues the challenge random number and the hash target value; the terminal collects real-time electric parameters of an electric power line within a limited time to generate a quantized electric power state vector, combines a challenge number and an identifier of the terminal, performs cascade Hash operation by traversing Nonce until a result is not greater than a target value, forms an activity proof containing the Nonce and the electric power vector, and submits the activity proof to the AP; after the AP verifies and proves the validity, randomly selecting a verification committee from a preset node pool by taking the hash value as a VRF seed; each node of the committee calculates a signature weight according to own equity pledge and a network topology distance between the node and the terminal, signs an authentication decision, attaches the weight and returns; and the AP aggregates all signatures with weights, and determines that the terminal authentication is passed when the total weight exceeds a preset threshold value.

Method for continuous anisotropy testing of an electrical machine (2), in particular an electrical synchronous machine, comprising the following steps: a) Provision of an electric machine (2) with a stationary rotor (1), b) Performing a first application to determine the angle w_d_est of a longer semi-axis d of an ellipse using a first injection signal; c) Running a second application to determine the angle w_q_est of a shorter semi-axis q of the ellipse using a second injection signal; d) Determination of the angle difference of the magnitudes of the longer semi-axis d and the shorter semi-axis q according to delta_w=|w_d_est-w_q_est|, e) If the condition is met that the angular difference of the magnitudes of the longer semi-axis d and the shorter semi-axis q lies within a defined interval between and a defined error tolerance value FTW, according to |delta_w-90|≤FTW, energize the electrical machine (2) and terminate the procedure, f) If the condition is met that the difference in the magnitudes of the lengths between the first AC vector and the second AC vector is |delta_w-90|>FTW, issue a warning message and / or shut down the system. g) Repeat steps b) to f).

The present invention discloses a multi-user power allocation method based on a one-dimensional convolutional neural network, comprising: 1. Initializing a single-cell multi-user system model; 2. Using an improved multi-proportional fractional programming algorithm based on quadratic transformation to collect a data set for neural network training; 3. Designing a specific one-dimensional convolutional neural network structure; 4. Training the one-dimensional convolutional neural network using a mean square error loss function; 5. Saving the one-dimensional convolutional neural network structure. The present invention improves the multi-proportional fractional programming method based on quadratic transformation, and uses a one-dimensional convolutional neural network for learning. The present invention uses the channel state information of each user in the cell as input, and uses the power vector of each user under the multi-proportional fractional programming algorithm as output, to perform supervised learning training on the one-dimensional convolutional neural network. The present invention can reduce the computational complexity while achieving a near fractional programming algorithm.

The present invention discloses an echo cancellation method based on adaptive decorrelation and variable step-size proportional M estimation, comprising the following steps: A. signal acquisition; B. calculating the decorrelation coefficient vector #imgabs0# according to the speech signal sampling and the expected signal sampling results; C. calculating the decorrelation input vector x D (n) and the expected signal d after decorrelation D (n); D. Calculate the output y(n) of the adaptive filter and the output y of the adaptive filter after decorrelation D (n); E, calculate the error signal e(n) and the error signal e after decorrelation D (n); F, calculate the proportional matrix G(n); G, calculate the error signal #imgabs1# after robust decorrelation for iteration in M ​​estimation; H, calculate the step size parameter μ(n); I, update the tap weight vector of the adaptive filter and enter the processing at the next moment. In addition to having good robustness to impulse noise, the present invention also has the advantages of fast convergence speed and good steady-state error.

This application relates to the field of mathematical modeling technology and discloses a modeling method for a power business decision-making model. The method includes: constructing a power corpus segmentation library and a power vector knowledge base to determine the power business problem text; performing a hybrid retrieval of the power business problem text using the power corpus segmentation library and the power vector knowledge base to obtain a first similarity score corresponding to candidate corpus segments and a second similarity score corresponding to candidate document representation vectors; filtering the candidate corpus segments and candidate document representation vectors based on the first and second similarity scores to determine the target reference text; and inputting the power business problem text and the target reference text into a target large language model to obtain the target decision model. The target decision model includes a description of the power business problem, an objective function, and its constraints. Its beneficial effect is that, based on hybrid retrieval and the standardized output of the large language model, the reliability and accuracy of the target decision model are significantly improved.

The application discloses a power demand response collaborative optimization method and device based on flexibility sharing, equipment and medium, relates to the technical field of power system demand response, and comprises the following steps: acquiring the load flexibility demand and load flexibility supply cost information reported by each target user participating in power demand response collaborative operation; the load flexibility demand is the power vector difference between the actual load curve of each target user after independently adjusting the load and the standard load curve; the load flexibility demand and load flexibility supply cost information are input into a preset power demand response collaborative optimization model, so that the corresponding power demand response collaborative scheme is determined through the preset power demand response collaborative optimization model; and the power demand response collaborative scheme is executed to perform flexibility settlement and flexibility transaction surplus distribution according to the target user execution situation. The execution of the demand response collaborative scheme based on flexibility sharing is realized, and the economy and reliability of load adjustment are improved.

The invention discloses an integrated sensing and backscatter communication method and device based on phase-free extension Rytov approximation, and relates to the technical field of communication, and the method comprises the steps: generating a modulation signal through a transmitting antenna based on the load impedance of a dynamic modulation tag, and backscattering the modulation signal to a receiving antenna to measure an RSSI vector, determining an environment object tag structure power vector and a tag antenna power vector, and after sensing and identifying a tag according to the determined combined xRA relative dielectric constant comparison vector and the tag antenna xRA relative dielectric constant comparison vector, activating the tag through a transmitting antenna and transmitting communication data to a receiving antenna by adopting ASK (Amplitude Shift Keying) modulation; and demodulating by combining the environment object tag structure power vector, the tag antenna power vector and the RSSI vector corresponding to the communication data, and determining information bits. Sensing and backscatter communication are realized by adopting non-phase backscatter based on extended Rytov approximation, and backscatter communication which keeps stable is assisted on the whole.

The present application relates to the field of drone communication technology, and specifically to a drone swarm collaborative perception method and system. The method includes: obtaining the distance between each AP and the routing table update period during the drone swarm collaborative perception process; obtaining the metric weight of each AP in the communication connection path to establish a connection with the next-hop AP, selecting the optimal routing path between drones, calculating the power adjustment characteristic value between each AP and each other AP with which it establishes a communication connection, obtaining the subcarrier interference characteristic value of each power vector, and using a particle swarm algorithm to obtain the optimal power vector of each AP as the subcarrier transmission power of each AP with which each AP establishes a communication connection, thereby completing the communication scheduling of the AP during the drone swarm collaborative perception process. The present application can improve the communication performance in drone swarm collaborative perception operations.

The invention discloses a complex valued neural network dynamic modeling method for power grid fault identification and prediction, and relates to the field of smart power grids. According to the method, a separated complex valued neural network is constructed, voltage phasors and power vectors in a complex number form are input, and power grid characteristics are extracted through not less than 3 complex number convolution layers and not less than 2 full connection layers. And training the model by using historical loop closing operation data not less than 100,000 times, wherein the training period does not exceed 4 hours. The model can accurately predict loop closing current amplitude and output voltage deviation, and a continuous prediction value is converted into a switch state decision signal through an embedded binarization module. According to the invention, the precision and efficiency of power grid loop closing operation and fault prediction are improved, and core model support is provided for intelligent operation and maintenance of the power grid.

An example system includes processing circuitry including n transmission paths; and a memory to store data specifying m of the n transmission paths that are operable for a first operation, m≤n. In operation, the processing circuitry starts performing the first operation on the m transmission paths; receives a signal for a second operation to be performed on p transmission paths, p≠m, and in response, preserves the data stored in the memory and stops performance of the first operation. Thereafter, the second operation is performed on the p transmission paths to completion, after which the data is restored to the memory, and performance of the first operation on the m of transmission paths is resumed.

The invention belongs to the technical field of radar signal processing, and discloses a multi-input-output radar target direction estimation method and system based on tail optimization. The method comprises the following steps: performing matched filtering and vectorization processing on a received signal to realize the expansion and structured expression of an MIMO signal aperture; re-parameterization of a guiding matrix is achieved by conducting structure rearrangement on a vectorization result of a received signal covariance matrix, and unknown amplitude-phase error factors in the guiding matrix are extracted from a guiding structure and fused into an equivalent signal power vector. According to the method, high-resolution and low-complexity target direction estimation is realized through a structure reconstruction and tail optimization mechanism under the condition that complex amplitude-phase error correction or priori error information acquisition is not needed, and the method is suitable for an MIMO radar system in various actual complex scenes.