2250 results about "Power load" patented technology

Disclosed in the present invention are a fuzzy-logic-control-based coordination method and system for a power grid requirement response and an energy storage system, the method comprising: S1, collecting real-time power grid data and prediction data, and constructing a corresponding real-time power grid data set and a corresponding prediction data set; S2, using a fuzzy algorithm to convert the real-time power grid data set, the prediction data set and multi-dimensional renewable energy information into a fuzzy set; S3, customizing a power grid requirement response measure and an operation strategy of an energy storage system; S4, executing the strategy customized in step S3; S5, monitoring in real time the execution effect of the strategy and collecting operation data such as a power grid load matching degree, energy storage device response speed and efficiency, and a requirement response participation degree; and S6, periodically updating a decision model of a fuzzy logic controller. In the present invention, the fuzzy logic controller is used to process and analyze power grid data in real time, such that the uncertainty and ambiguity during power grid operation can be effectively handled, especially for the production capacity fluctuation of renewable energy and the rapid changes of power loads.

The invention relates to a control method and system for a photovoltaic energy storage system, and the method comprises the steps: obtaining the real-time irradiance data of the photovoltaic energy storage system, carrying out the power generation prediction of an irradiance region, and obtaining a dynamic power generation prediction value; acquiring charge state parameters of a photovoltaic energy storage system and aggregated power load demand data of a target power utilization area, and performing energy storage scheduling analysis in combination with the dynamic power generation power prediction value to obtain a reference energy storage scheduling strategy; acquiring energy consumption equipment parameters of the target power utilization area, and performing equipment scheduling control with the reference energy storage scheduling strategy to obtain an equipment-level control instruction set; and performing grid-connected mode and off-grid mode collaborative analysis on the dynamic generation power prediction value, the reference energy storage scheduling strategy and the equipment-level control instruction set to obtain an energy storage global control strategy. According to the invention, multi-level collaborative optimization can be realized, and the economical efficiency and sustainability of the system are improved while stable power supply is guaranteed.

The invention relates to the technical field of power operation and maintenance, in particular to a power system operation and maintenance method and system of an intelligent power distribution cabinet for weak current control. The method comprises the following steps: acquiring power operation data and sensing data of a power distribution cabinet, and analyzing a topological structure of a power system to obtain a dynamic topological structure of the power system; performing potential load anomaly analysis on the sensing data of the power distribution cabinet to obtain load anomaly node data of the power system; performing load anomaly influence structure division based on the load anomaly node data of the power system to obtain load anomaly influence nodes; performing abnormal node classification on the load abnormal influence nodes according to the sensing data of the power distribution cabinet to obtain three-phase power load offset nodes and three-phase power improper wiring nodes; and performing power distribution reconstruction on the three-phase power load offset node and the three-phase power improper wiring node to obtain abnormal node power distribution data. According to the invention, the weak current control efficiency and the power distribution energy efficiency can be improved.

The invention relates to an electrical load prediction and optimization regulation and control method and system for high-energy-consumption equipment, and solves the problems of inaccurate load prediction, single regulation and control means and difficulty in dynamic adaptation of the high-energy-consumption equipment, and the method comprises the steps: collecting multi-source data of the high-energy-consumption equipment in real time, constructing a dynamic equipment collaborative causal graph after preprocessing, and extracting key constraints; inputting the data and the constraints into the dynamic digital sample model to obtain a system state simulation result; based on the result, a multi-objective optimization regulation and control strategy is generated and executed by using a meta-learning + reinforcement learning decision framework; and collecting actual data comparison deviation, starting hierarchical federated learning when a threshold value is exceeded, grouping and aggregating similar experiences according to a causal graph topology, and dynamically calibrating model parameters and a decision framework. The method has the following effects that accurate load prediction and multi-target cooperative regulation and control of the high-energy-consumption equipment are achieved, working condition changes are dynamically adapted, the cost is reduced, and continuous production and the service life of the equipment are guaranteed.

The invention discloses a power load prediction method based on heterogeneous ensemble learning and an attention mechanism. The method comprises the following steps: acquiring historical data of a power load, the historical data at least comprising time sequence data; preprocessing the historical data to obtain processed feature data; based on the feature data, an integrated learning model is constructed, and the integrated learning model at least comprises a combination of multiple base learners; predicting the feature data through the integrated learning model to obtain a preliminary prediction result; and introducing an attention mechanism to carry out weighted adjustment on the preliminary prediction result, and generating a final power load prediction value. According to the method, the nonlinear and complex modes of the power load are effectively captured, the prediction performance is remarkably improved, and accurate decision support is provided for intelligent operation of a power system.

The invention discloses a rural power grid user power load prediction method and system based on a liquid neural network. The method comprises the following steps: firstly, collecting rural power grid user power load historical data including multi-dimensional features such as weather and agricultural modes, and carrying out data preprocessing; then constructing liquid neurons based on biological neuron dynamics, and modeling the state of the liquid neurons through a differential equation; thirdly, constructing a liquid neural network based on liquid neurons, improving the characterization capability of multi-scale time sequence data through multi-level time constant setting and time gating residual connection, and supplementing network initial information in combination with a multi-layer perceptron architecture; completing model training by using the time sequence data set; the actual application performance of the model is tested based on the test data and the actual application scene; and finally, deploying the model to practical application, and carrying out power load prediction on rural power grid users. According to the method, the expression capability of the model for the multi-scale time sequence data is improved, and high-precision rural power grid user power load prediction is realized.

The invention relates to the technical field of power data processing, in particular to an energy storage configuration optimization method, which comprises the following steps: acquiring new energy output time sequence data and computing power load characteristic data; generating a space-time correlation coupling evaluation result of the new energy output volatility and the computing power load volatility; inputting a result into a hybrid power supply double-layer optimization model, recursively correcting parameters through a two-stage collaborative solution algorithm, and outputting a Pareto optimal solution set; a computing power task elastic regulation and control mechanism is embedded, and the task priority is dynamically adjusted according to the energy storage charge state and the new energy output level to generate a scheduling strategy; finally, an energy storage configuration scheme and a dynamic scheduling strategy are output, and collaborative optimization of cost effectiveness and power supply reliability is achieved. The method breaks through the coupling conflict of the economic target and the robust constraint in the traditional bilevel planning, remarkably reduces the energy storage configuration cost, and improves the system stability.

The invention relates to the technical field of data center energy management, and discloses a flywheel energy storage system control system for data center computing power load energy recovery, which comprises an energy storage unit, a load monitoring unit, a flywheel control unit and an energy distribution unit. The energy storage unit is provided with a plurality of groups of flywheel energy storage devices capable of charging / discharging; the load monitoring unit monitors load data by covering key nodes of computing power equipment through a power sensor; the flywheel control unit collects flywheel rotating speed and power grid frequency fluctuation data; and the energy distribution unit decides charging and discharging based on load data, establishes a load and frequency coordinated alternating control strategy, and dynamically adjusts and predicts the running state of the flywheel. The system adopts time sequence synchronous control, the energy distribution unit comprises multiple modules, the alternate controller supports dual-mode switching and parameter optimization, and the flywheel unit adopts an annular redundancy or star-shaped concentrated structure. The system realizes efficient recovery of computing power load energy and flywheel energy storage optimization scheduling, and improves the energy utilization rate of a data center and the stability of a power grid.

The invention provides an electricity stealing behavior detection method based on feature fusion and a CNN-LSTM hybrid model, and belongs to the technical field of electric power information technology and deep learning. According to the method, after power load data and user behavior characteristics are subjected to data processing and enhancement, a deep learning model is combined with a self-attention mechanism to process user electricity consumption data, user electricity stealing behavior anomaly detection is carried out, and the electricity stealing risk identification accuracy and calculation efficiency can be remarkably improved. According to the invention, power grid enterprises can be helped to efficiently deal with electricity stealing conditions, and comprehensive and accurate identification of electricity stealing behaviors is guaranteed. By learning user historical load data, integrating time sequence characteristics of user loads and optimizing a data abnormity diagnosis and judgment mechanism, the electricity stealing user detection accuracy is improved, and reliable support is provided for power grid enterprise decision making.

The invention provides a microgrid power load prediction and dynamic control method based on CNN-LSTM-AM, and relates to the technical field of intelligent control of a power system. According to the method, multi-source data is collected, data preprocessing is carried out, a CNN-LSTM-AM hybrid prediction model is constructed, and a CNN layer comprises a double-branch multi-scale one-dimensional convolution kernel; the output of the input layer and the output of the LSTM layer are connected to the DSTCW module, the DSTCW module outputs weighted load characteristics and photovoltaic / wind power characteristics, the charging and discharging priority is optimized based on the energy storage SOC and the real-time electricity price, a multi-stage cooperative stability control strategy is triggered through a closed-loop control link, and dynamic control is achieved. Wide-area spatial features of distributed photovoltaic / wind power are extracted through a multi-scale one-dimensional convolution kernel, and the control response speed is increased by combining the spatial-temporal relevance between a dynamic attention mechanism focusing load and renewable energy sources; and the load power and the photovoltaic / wind power output are synchronously predicted by adopting dual-task output, so that the power grid stability and the control real-time performance in a high-proportion renewable energy scene are improved.

The invention is suitable for the technical field of short-term power load prediction and intelligent scheduling, and provides a short-term power load prediction method based on improved sparrow search algorithm (ISSA) optimization. The method comprises the following steps: constructing a multi-scene prediction task according to the time resolution and regional seasonal characteristics of a load; local features are extracted in combination with a convolutional neural network (CNN), time sequence dependence is modeled by a long short-term memory (LSTM) network, and an attention mechanism is introduced to strengthen key features; meanwhile, an ISSA is adopted to optimize a model network structure and hyper-parameters, the number of layers, the learning rate and the batch size of the CNN and the LSTM are adjusted in a self-adaptive mode, and the search efficiency and convergence performance of the ISSA are improved through Latin hypercube sampling, cosine annealing, dynamic spiral search and a Levy flight strategy. Simulation results show that the method can maintain high prediction precision under different time resolutions and regional and seasonal conditions, the generalization ability and cross-scene adaptability of the model are enhanced, and a stable and efficient load prediction scheme is provided for power grid dispatching optimization.

The invention discloses a power grid power load intelligent prediction method, system and device and a medium, and relates to the technical field of power distribution network transmission optimization. Power load data is decomposed into a trend component, a periodic component and a random fluctuation component, and then the importance of different components is evaluated by using a feature channel attention layer; the time sequence attention layer captures key moments in each component time sequence and extracts features at the key moments, and then the features extracted based on the importance and the features extracted at the key moments are fused, so that the multi-scale features of the power load are captured; and then inputting the fused features into the dynamic gating residual connection LSTM network for prediction, and in the prediction process, improving the attention degrees of long-term sequences, short-term sequences and fluctuation sequences in different features through a residual modulation function, thereby more accurately capturing the multi-scale features of the power load and obtaining a prediction value of the power load.

The invention relates to the technical field of industrial equipment temperature control, and discloses a temperature control system, which comprises a multi-source state sensing module for acquiring a power load, temperature, humidity and feedback temperature; the feed-forward prediction and analysis module is used for generating a feed-forward signal based on the load and the temperature; the closed-loop feedback and safety guarantee module is used for calculating a feedback signal according to the temperature deviation; the cooperative control decision module combines the feed-forward signal and the feedback signal to generate a fan instruction, and generates a baffle instruction for balancing heat dissipation and regeneration according to the feed-forward signal and humidity; the self-adaptive heat dissipation execution module is used for executing a fan instruction to generate cooling airflow; and the dynamic energy regeneration module executes a baffle instruction to distribute the waste gas flow direction. According to the method, the power load is introduced to serve as a prospective index, correction is conducted in combination with the environment temperature, a predictive heat dissipation control model is constructed, the system can start heat dissipation adjustment before a large amount of heat is generated, and therefore smooth and stable control over the equipment temperature is achieved.

The invention discloses an electricity utilization information acquisition intelligent configuration method based on a pattern recognition algorithm, and relates to the technical field of intelligent power grids, and the method comprises the steps: collecting a directional data stream, inputting a federal map neural network to construct a power distribution network physical connection relation, and generating a spatio-temporal topological feature vector; extracting a current effective value component of the directional data flow as an electrical load sequence, extracting an equipment state code to identify a voltage sag event, injecting voltage sag event associated disturbance into the electrical load sequence in combination with an event propagation path weight of a spatio-temporal topological feature vector, and generating an anti-fact sample set; and compressing the new configuration strategy through a knowledge distillation engine, and outputting an event response logic and a parameter adjustment instruction to form an executable configuration strategy. According to the method, through anti-fact sample generation and reinforcement learning optimization under spatial-temporal topological feature vector constraint, a physical rule deep embedding decision is realized.

The invention relates to the technical field of power load decomposition, and discloses a non-intrusive power load decomposition method and system based on multi-modal feature learning. The method comprises the following steps: synchronously acquiring electric power parameter data of an intelligent electric meter, environmental parameter data of an environmental sensor and use behavior data of user equipment to obtain multi-modal load monitoring data; performing cross-modal feature extraction through a non-negative matrix factorization layer of the first equipment state recognition model to obtain a multi-modal fusion feature vector; carrying out load mode recognition through a first decomposition layer of the first equipment state recognition model to obtain a first decomposition load matrix; performing clustering optimization through a second decomposition layer of the first equipment state recognition model to obtain a second load decomposition matrix; and executing a dynamic fuzzy decision based on the second load decomposition matrix to obtain an equipment operation state identification result. According to the method, the limitation that a traditional method only depends on a single power signal is broken through, and high-precision and high-robustness non-intrusive power load decomposition is achieved.

The invention provides a factory power load monitoring and early warning method based on digital twinning, and belongs to the technical field of digital twinning and power load prediction, WTConv and i Transform are combined for power load prediction, and the challenge that a traditional power load prediction method is difficult to deal with in a complex dynamic factory environment is solved; in the actual factory operation process, the power load is influenced by multiple factors such as the production rhythm, the equipment operation state and the external environment, so that load data have high time-varying characteristics and nonlinear characteristics; through the combination of WTConv and iTransform, the method can better capture a complex space-time mode and multi-scale features, thereby remarkably improving the accuracy and robustness of power load prediction, providing more reliable load prediction support for a power system, helping enterprises to optimize production scheduling, reducing energy loss, and effectively preventing the load risk of the power system.

The invention discloses an intelligent computing center automatic operation and maintenance management method based on computing power resource allocation, and relates to the technical field of resource management, and the method comprises the steps: obtaining real-time computing power load data and a to-be-processed task queue of an intelligent computing center; constructing a resource state matrix according to the real-time computing power load data, performing historical data backtracking analysis on the resource state matrix by using a sliding window algorithm, and calculating a resource load predicted value and a resource availability score of each computing node; based on the resource availability score, an improved genetic algorithm is adopted to carry out optimal allocation solution on the constructed task-resource matching matrix, and an optimal task allocation scheme is generated; and converting the optimal task allocation scheme into a resource allocation instruction set, sending the resource allocation instruction set to each target computing node to execute task scheduling, and updating the resource occupation state of the corresponding node in the task-resource matching matrix. According to the invention, the technical jump from passive response type management to active prediction type management is realized, and the resource configuration efficiency of an intelligent computing center is improved.

The invention discloses a morning orbit computing power satellite, and belongs to the field of aerospace technology and space infrastructure. The satellite comprises a satellite platform and a computing force load arranged on the satellite platform. The satellite platform is provided with a condensation type energy system and a space pump drive fluid loop system. The condensation type energy system adopts an expandable condensation type solar cell array to generate power, switches a power supply mode according to an illumination period and an earth shadow period of a morning and night orbit, and supplies power to computing force loads and platform equipment. The space pump drive fluid loop system adopts an expandable flexible radiation cooling plate for heat dissipation, heat dissipation is conducted on computing force loads through a fluid loop, and a heating assembly is arranged to prevent a working medium from being frozen under the low-temperature working condition. Through the expandable structure and the orbit environment adaptability design, continuous energy supply and effective heat management are provided for the computing power load in the morning and night orbit environment, and high-power-consumption computing power load in-orbit stable operation is supported.

The invention provides a power load prediction method and device, and belongs to the technical field of power load prediction.The method comprises the steps that current waveform data are obtained, and fundamental wave and harmonic components in the current waveform data are extracted; carrying out waveform spatial form geometric analysis to obtain a real-time load characteristic sequence, and then carrying out segmentation processing; the current effective value sequence of each time window is converted into a time-frequency domain energy distribution vector, and then a three-level feature library is constructed; constructing a three-dimensional tensor model through equipment start-stop event identification, inputting the three-dimensional tensor model into a multi-target optimizer to evolve feature weights, and filtering abnormal samples to obtain a feature cluster; performing random masking processing on the time sequence data of the feature cluster to generate a mask sequence, inputting the mask sequence into an encoder to reconstruct masking data, comparing, learning and judging abnormal output correction data, and inputting the corrected data into a prediction network to generate a feedback signal flow; and analyzing the feedback signal flow to update the prediction network weight. Based on the method, the invention also provides power load prediction equipment. According to the invention, the precision of power load prediction is obviously improved.

The invention provides a new energy station intelligent scheduling method based on multi-source data analysis and transfer learning, and relates to the technical field of new energy power station intelligent scheduling, and the method comprises the steps: collecting meteorological data, equipment state data and power load data to generate a multi-source fusion data set; encoding the power generation equipment to form an equipment identification group; generating an operation characteristic spectrum through space-time correlation analysis; generating an original scheduling strategy in combination with the transfer learning model; obtaining equipment model information to perform constraint injection on the original strategy to obtain a target scheduling strategy; after the control station executes scheduling, actual operation data are collected for strategy verification, and if the deviation exceeds a threshold value, strategy correction is triggered. According to the invention, adaptive optimization of the scheduling strategy of the new energy station can be realized, the scheduling precision of the power generation equipment and the operation efficiency of the station are improved, and the continuous reliability of the strategy is ensured through a closed-loop verification mechanism.

The invention provides an energy storage power station optimization operation mode decision-making method and system, and relates to the technical field of energy storage power station optimizing.A hybrid prediction model is constructed to realize high-precision decomposition prediction of power load, and meanwhile, the internal resistance of a battery is estimated in real time by adopting a recursive least square method; and the battery capacity and internal resistance parameters are dynamically corrected in combination with a temperature compensation mechanism. Through health state multi-index fusion evaluation, self-adaptive distribution of charging and discharging power is achieved, and compared with the prior art, the problem that a traditional static model cannot adapt to complex environment changes is solved. The battery capacity fading risk can be predicted in advance by introducing a double-compensation mechanism of an environmental influence index and an electric power influence index. According to the scheme, the response speed and economical efficiency of energy storage in a high fluctuation load scene are remarkably improved, and a reliable dynamic optimization decision support system is provided.

The invention provides a consumption reduction method and device for a wind and light energy storage complementary thermal power plant system, and the method comprises the steps: obtaining the wind speed, illumination intensity, energy storage charge state, auxiliary power load curve, power grid dynamic electricity price signal and thermal power generating unit auxiliary machine operation parameters of the thermal power plant system, and taking the parameters as a data set; based on a weather prediction model, a load prediction model and an electricity price fluctuation model, generating a wind power generation output prediction value, a photovoltaic power generation output prediction value, a load demand prediction value and an electricity price interval prediction value in a future preset time period according to the data set, and taking the prediction values as prediction results; by taking minimization of plant power cost and maximization of renewable energy consumption as targets, generating an energy storage charging and discharging instruction, a wind power generation and photovoltaic power generation grid-connected priority sequence and a thermal power auxiliary engine regulation and control strategy according to the data set and the prediction result to serve as control instructions; control instructions are executed through control equipment in the wind-solar energy storage complementary thermal power plant system, and energy conservation and consumption reduction are achieved by integrating wind energy, solar energy and an energy storage system.

The invention discloses an electric vehicle charging power aggregation distributed regulation and control method, and relates to the technical field of AC charging pile charging power regulation and control, and the method specifically comprises the following steps: carrying out the cleaning and stabilization processing of power load and new energy data, employing an ARIMA model to predict the load, and employing an LSTM-Transform model to predict the new energy generation power; the upper layer aggregates the total state of the charging station, including the electricity price, the load, the total vehicle energy, the total docking power of the lower-layer single vehicle model and the battery demand; based on an MCTS enhanced DDPG algorithm, the total charging power is optimized through cost discount rewards and energy penalty; an improved ADMM algorithm distributes total power to each pile, and battery stress and heat loss are minimized; and comparing the actual battery energy with the virtual battery energy, and updating the data and re-deciding when the energy is inconsistent. According to the invention, three industry bottlenecks of poor real-time response of electric vehicle charging, slow large-scale scheduling and difficulty in consideration of economy and battery health are overcome, and power grid dynamic response acceleration, decision-making efficiency jump and comprehensive benefit breakthrough are realized.

The invention discloses an electrical load risk early warning method based on residual analysis of an ARIMA model, and aims to realize efficient and accurate load risk early warning through residual analysis of electrical load data. The method comprises the following steps: firstly, constructing an ARIMA model by using historical electrical load data, and predicting a load change trend; and then, detecting an unexpected or uncontrollable abnormal fluctuation condition by combining statistical analysis of a dynamic disturbance value on a model residual error. And based on residual feature extraction and threshold setting, carrying out graded early warning on potential electrical load risks. According to the method, abnormal load change can be recognized in time, and the safety and stability of power grid dispatching and operation are improved. Compared with the prior art, the method can effectively make up for the problem of insufficient load anomaly detection accuracy in a traditional method, has the characteristics of high calculation efficiency and strong applicability, and is suitable for the intelligent risk management requirements of power grid companies and large electricity users.

The invention relates to the technical field of photovoltaic inverter regulation and control, in particular to a photovoltaic inverter intelligent regulation and control method and system based on the Internet of Things, and the method comprises the following steps: setting a time window based on an illumination prediction value of a photovoltaic inverter and a real-time voltage measurement value of a grid-connected point, and carrying out the serialized calculation of the voltage in the window, and establishing an out-of-limit risk early warning. According to the invention, the time window is set based on the illumination prediction value and the grid-connected point voltage measurement value, serialized calculation is carried out on the voltage in the window, and point-by-point comparison with the voltage threshold is carried out, so that an out-of-limit risk time point can be identified in advance, and the pre-warning capability of local voltage abnormity is improved. On the basis, a voltage out-of-limit amplitude value is extracted by combining indoor and outdoor temperature information of a building and is converted into a voltage regulation requirement, and a quantifiable voltage consumption index is further formed, so that power load regulation is changed from traditional static setting to dynamic quantifiable regulation, and the accuracy of regulation response is effectively improved.

The invention belongs to the technical field of intelligent energy and power system automatic control, and particularly relates to a cross-level collaborative regulation and control system integrating meteorological prediction, power market and equipment state, which comprises a central collaborative controller, a data acquisition unit, a communication network and an execution terminal, the data acquisition unit is used for acquiring meteorological data, power load data, electric energy consumption cost data and equipment operation state data in real time; the execution terminal at least comprises a photovoltaic inverter, an energy storage converter and a charging pile controller; the central cooperative controller is configured to execute three cooperative optimization closed loops, namely, a data fusion and prediction closed loop, a multi-target dynamic optimization closed loop and a multi-time scale control closed loop. According to the system and the method provided by the invention, the problems of real-time performance and accuracy of multi-source heterogeneous data fusion are solved; the balance optimization bottleneck of multiple targets such as economy, stability and environmental protection in the dynamic process is broken through; full-time-scale seamless cooperative control from second-level emergency response to hour-level economic dispatching is realized; and the self-adaptive capability and the overall performance of the system in different application scenes are improved.

The invention discloses a power grid photovoltaic output and load sequence modeling method, system and device and a storage medium, and the method comprises the steps: comprehensively utilizing the multi-scale feature extraction capability of a time-frequency decomposition technology, the time sequence dependence modeling capability of a long and short-term memory network, and the global hyper-parameter optimization capability of a Bayesian optimization algorithm; and carrying out collaborative modeling and prediction on the photovoltaic output and the power load under a unified framework. By introducing a source load time-delay correlation analysis and probability interval construction mechanism, point prediction results and uncertainty intervals of photovoltaic, load and net load can be output at the same time, and a set of source load integrated prediction system with high prediction precision, strong robustness and reliable interval characterization capability is constructed. The method can improve the precision and reliability of photovoltaic power and load prediction, also can reduce the risk in power system scheduling, optimizes the energy storage configuration strategy, and especially has wide popularization potential and application prospects in the scenes of new energy grid-connected operation, intelligent micro-grid and virtual power plant management and the like.

The invention discloses a server energy-saving system and method based on a lag compensation type dynamic thermal impedance model, and relates to the field of server energy saving.By collecting operation data of a server hardware bottom layer in real time, a multi-dimensional thermal-computing force coupling time sequence atlas is constructed, and based on the multi-dimensional thermal-computing force coupling time sequence atlas, a dynamic thermal impedance model is established. Calculating a heat conduction time constant of hardware in a current environment; establishing a lag compensation type dynamic thermal impedance model based on the heat conduction time constant; constructing a thermodynamic phase space according to operation data; analyzing a noise reduction evolution trajectory in combination with the lag compensation type dynamic thermal impedance model; the method comprises the steps of generating a weighted thermal resonance state vector, activating an adaptive gain MPC predictive damping scheduling strategy based on the weighted thermal resonance state vector, constructing and solving a dynamic weighted multi-objective optimization function, generating an optimal damping scheduling instruction, and thoroughly solving the fan asthma phenomenon that the fan PWM duty ratio fluctuates substantially in a sine mode under the constant computing power load.

The invention discloses a multi-dimensional data collaborative analysis early warning method based on intelligent operation and maintenance of a power transmission network, and relates to the technical field of power transmission network early warning, and the method comprises the steps: collecting target data of target equipment, and obtaining sudden change data of a power load in a system; performing correlation analysis on the instant characteristics of the electrical quantity and the dynamic change of the power grid structure in combination with the sudden change data of the power load to obtain correlation factors capable of predicting a fault; according to the power transmission network intelligent operation and maintenance early warning method, the problem that fault early warning is inaccurate and not timely due to insufficient data collaboration and early warning response lag in power transmission network operation and maintenance is solved, and the effect of improving the early warning precision and response speed of power transmission network intelligent operation and maintenance is achieved.

The invention provides a data center cooling system performance evaluation method and system. The method comprises the following steps: acquiring operation data of data center cooling equipment and power load data of a server, and combining service time and maintenance records of the equipment to determine a performance baseline; calculating resonance frequency points caused by harmonic waves under different working conditions based on the vibration characteristics of the base line and the current harmonic characteristics in the electric energy quality in combination with equipment structure parameters and a working condition empirical formula; frequency sweeping is performed by actively adjusting the rotating speed of equipment, the dominant harmonic frequency and the contribution proportion thereof are verified, and a proportional relation model of harmonic and vibration characteristics is established; adjusting equipment parameters according to the model, and calculating the deviation degree of a harmonic component and a performance baseline; and finally, combining harmonic change characteristics with deviation degree correlation, and constructing energy efficiency and health state evaluation indexes of the cooling system. According to the invention, collaborative evaluation of the energy efficiency and the health state of the data center cooling system is realized.