805 results about "Electricity system" patented technology

The invention provides a carbon ceramic resistor and a preparation method thereof, and relates to the technical field of special material production of power systems and electrical equipment. The preparation method comprises the steps that carbon-containing electric conductor raw materials are mixed to obtain a first mixed raw material, and the carbon-containing electric conductor raw materials comprise, by mass, 15 parts of gluconic acid, 10 parts of aluminum oxide and 55 parts of deionized water; performing hydrothermal reaction on the first mixed raw material to obtain a carbon-containing conductor precursor; the carbon-containing conductor precursor is carbonized in a protective atmosphere, a carbon-containing conductor of a core-shell structure is obtained, and the carbon-containing conductor takes aluminum oxide as an inner core and takes a conductive carbon material formed by gluconic acid as a conductive outer shell; mixing the carbon-containing electric conductor with the core-shell structure with a carbon ceramic matrix raw material to obtain a resistor blank; and sintering the resistor blank to obtain the carbon ceramic resistor. The uniformity, the stability and the reliability of the carbon ceramic resistor are improved, so that the carbon ceramic resistor can better meet the application requirements of ultrahigh-voltage and extra-high-voltage alternating-current power transmission systems.

The invention belongs to the technical field of novel power system photovoltaic and energy storage system optimization configuration, and discloses an optical storage system optimization method based on carbon emission and line loss rate collaborative modeling, which comprises the following steps: constructing a double-layer planning structure of an upper layer planning model and a lower layer operation model, a carbon emission calculation model and an improved line loss rate calculation model are introduced into the lower layer, joint optimization of configuration and operation is realized through parameter coupling, and the charge and discharge efficiency loss power consumption of the energy storage device is introduced as an independent parameter in line loss rate calculation, so that the line loss calculation precision is improved; and solving by adopting an improved particle swarm optimization algorithm, and objectively sorting candidate schemes in combination with an information entropy method and a TOPSIS comprehensive evaluation method. A simulation result based on an IEEE33 node power distribution system shows that the model can effectively reduce carbon emission and line loss rate, improves node voltage level, and has good convergence and engineering applicability.

The invention belongs to the technical field of power system operation control, and discloses a virtual power plant aggregation operation optimization method considering flexible and adjustable resource hierarchical and partitioned regulation, which is characterized in that dynamic clustering and clustering are performed on a distributed power supply based on an improved K-medoids algorithm, and response characteristic analysis is performed on an electric vehicle, an energy storage load and a temperature control load; by establishing a self-adaptive layering and partitioning mechanism, dynamic layer region boundary adjustment is realized. An optimization model with the lowest cost as the target is constructed, the optimal resource scheduling scheme is solved by comprehensively considering various cost factors and operation constraint conditions, and efficient configuration of resources and effective control of the cost are achieved; and the adjustable load and the energy storage resource are guided to participate in system regulation and control, so that the peak-valley difference of the system is reduced with relatively low regulation and control cost. According to the method, the resource scheduling efficiency is effectively improved, high-quality peak regulation auxiliary service is provided for the power grid, and efficient utilization of distributed energy is assisted.

The invention discloses an electric vehicle charging load data virtual acquisition method and system based on a conditional diffusion model, and a storage medium, and relates to the technical field of power system operation optimization and artificial intelligence, and the method comprises the steps: constructing a dual-channel feature extraction network, and outputting conditional information; injecting conditional information into the conditional diffusion model by using a cross attention mechanism for de-noising; introducing a self-supervised alignment loss function, and constructing a self-supervised regularization mechanism with a stable interpolation result; and training the conditional diffusion model according to the historical charging load data set, and performing reverse denoising on the target missing data fragment. The method has the advantages that data features are comprehensively captured, accurate guidance of condition information is achieved, output stability is effectively improved, and a high-fidelity interpolation result highly consistent with real data distribution is generated through reverse denoising.

A microgrid island division method based on a cumulative foreground theory and a transfer learning thought comprises the following steps: step 1, aiming at a day-ahead decision-making scene, taking income maximization in a scheduling period as a core target, integrating short-term renewable energy output prediction and electricity price fluctuation data, and constructing a capacity optimization configuration model; 2, constructing a double-layer model, constructing a transaction decision model by the upper-layer model by taking energy storage power station revenue maximization as a target, and determining a power station quotation strategy; the lower layer model takes the sum of the electric energy cost and the frequency modulation service cost as the target to construct a combined clearing model of the electric energy and the frequency modulation market; and 3, solving the double-layer model constructed in the step 2. According to the method, energy storage can participate in an electric energy and frequency modulation market through strategic quotation under a reasonable market mechanism, and an energy storage power station is stimulated to optimize resource allocation, so that the frequency modulation pressure of a power system is reduced, the energy storage economic benefit is improved, and the stability and resource utilization efficiency of the power market are further improved.

The invention relates to the technical field of power system assessment, and discloses a power system vulnerability assessment method and system considering extreme weather influence, and the method comprises the steps: building a line fault probability model of a power system in extreme weather to determine the line time-varying fault probability under dynamic weather influence; generating a typical scene through a time sequence relativity matching generative adversarial network model with gradient penalty and Monte Carlo simulation; taking the generated typical scene as input, simulating cascading failure evolution through an alternating current cascading failure model, and outputting a cascading failure evolution path; and based on a fault evolution result, through a spectrogram theory and a Bayesian network, in combination with severity analysis, structural vulnerability evaluation and causal-probability pre-judgment, multi-level vulnerability quantification of the power system is realized, and the problems that single-level evaluation is insufficient and causal association is ignored in the prior art are solved. And the risk assessment precision and the disaster prevention capability of the power system in extreme weather are improved.

This document describes, among other things, a vehicle power system that includes a traction battery, a six-phase inverter divided into two groups of six switches, a six-phase motor, and a pair of switches. In drive mode, the switches configure the inverter to connect the traction battery and the motor. In plug-in mode, the system reconfigures so that one group of switches connects an AC source to the motor, while the other group connects the motor to the traction battery.

The invention belongs to the field of power system operation and control, and particularly relates to an operation control method for a wind-light hydrogen storage multi-energy complementary system, which comprises the following steps: performing empirical mode decomposition on an original output sequence to obtain an IMF component, training parameters by combining a hidden Markov chain model and a forward-backward algorithm and solving an optimal state sequence, and fusing a predicted value by using Bayesian estimation to obtain an optimal state sequence; outputting a wind and light output and load demand global prediction value; by taking the minimum comprehensive operation cost, the minimum carbon emission intensity and the minimum power supply shortage rate as targets, obtaining equipment, power balance and hydrogen storage capacity constraints through a constraint verification algorithm, and constructing a multi-target optimization model; a scene trigger function identifies four types of typical scenes, a chaos particle swarm optimization algorithm is improved to solve a model, and density peak clustering is carried out to obtain a global scheduling scheme containing output of an electrolytic cell and a fuel cell and energy storage charge and discharge power. According to the invention, through scene adaptive optimization and intelligent clustering screening, the randomness and volatility of wind and light output are dealt with.

The invention discloses a carbon emission factor calculation method adaptive to a gateway meter and a functional module, and belongs to the technical field of power system carbon emission, and the method comprises the following steps: collecting real-time carbon emission power flow data and upstream and downstream position information of a power system by using a carbon emission measurement function of the gateway meter; filling missing data; aiming at a source side carbon emission factor calculation function module, establishing a thermal power generating unit carbon emission model of a thermal power generating unit carbon emission quantification algorithm based on time sequence feature enhancement; calculating grid-side and load-side carbon emission factors; according to different positions of a gateway meter, a carbon emission factor calculation function module with corresponding carbon metering factor calculation and communication functions is designed, calculated carbon emission factors are uploaded to a cloud electric carbon platform, and regional node hour-level carbon emission factors are displayed on a local carbon emission factor calculation function module. According to the method, the accuracy, the real-time performance and the reliability of the regional carbon emission data are remarkably improved by constructing the MS-CBAM-CNN cascade architecture.

The invention belongs to the technical field of multi-energy micro-grid system optimization scheduling, and particularly relates to a multi-energy micro-grid distribution robust low-carbon economic scheduling method based on deep learning, electronic equipment and a medium. According to the method, a mathematical model of a multi-energy micro-grid system is established according to coupling characteristics of various energy sources among power systems. In order to improve the economical efficiency and the low-carbon property of the system, a load demand response mechanism and a carbon transaction mechanism are adopted, and an electric heating load demand response model and a reward and punishment type stepped carbon transaction model are constructed. In order to solve the wind and light uncertainty of the integrated energy system and improve the robustness of the system, a scene set of uncertain variables is generated by using a conditional generative adversarial network in deep learning, and the generated scenes are clustered by using a K-means clustering method to obtain typical scenes. In order to obtain more real probability distribution, a fluctuation range of a typical scene is constrained by using a comprehensive norm, and a probability distribution fuzzy set of uncertain variables is obtained. And based on the constructed fuzzy set, the demand response model and the reward and punishment type stepped carbon transaction model, a two-stage distribution robust low-carbon economic optimization model of the multi-energy microgrid is established, in the first stage, an energy storage equipment start-stop plan of the system is determined, and in the second stage, an initial plan is adjusted and supplemented after uncertainties are revealed. And finally, carrying out iterative solution on the established model by utilizing a column and constraint generation method to obtain an optimal scheduling scheme, thereby ensuring the low-carbon property, the economical efficiency and the robustness of the system.

The invention relates to the technical field of wind power prediction, in particular to a wind power cluster short-term power prediction method fusing multi-source heterogeneous data, and the method comprises the steps: cleaning the historical abnormal power of a wind power cluster sub-station, and preferably selecting seven types of discrete NWP heterogeneous meteorological sensitive factors, such as wind speed and temperature, based on an mRMR principle; reconstructing missing power data, performing error correction on heterogeneous meteorological data, and constructing a meteorological network; and compressing and aligning two learning features output by the space attention block and the space-time convolution block, integrating the two learning features into the same semantic space for time coding, and mapping the fused complex space-time features to cluster power. According to the method, the original data quality is improved, the regional or whole-province wind power prediction precision is effectively improved, the peak regulation and frequency modulation pressure of a power system is relieved, the large-scale cluster type wind power consumption problem is solved in an assisted mode, and power supply guaranteeing and consumption guaranteeing of wind power energy under the background of a high-proportion new energy power system are promoted.

The invention provides an intelligent lightning arrester for a line. The intelligent lightning arrester comprises a lightning arrester body and an intelligent control box, the lightning arrester body is used for providing a low-impedance grounding channel for overcurrent when overvoltage caused by lightning or switching operation occurs on a line, so that the overvoltage is limited below a safety level, subsequent electrical equipment is protected from being damaged, the lightning arrester body recovers to an insulation state after the overvoltage disappears, and normal operation of a power system is guaranteed. The intelligent control box comprises an embedded intelligent sensing module and a diagnosis module, and the embedded intelligent sensing module is used for collecting operation state parameters of the lightning arrester body in real time; and the diagnosis module is used for evaluating the insulation state of the lightning arrester body by adopting a partial discharge mode recognition model according to the operation state parameters of the lightning arrester body, and predicting the aging trend of a valve plate unit of the lightning arrester body by adopting a valve plate aging trend prediction model. The lightning arrester disclosed by the invention has the dual advantages of compact structure and high-performance protection.

The invention belongs to the technical field of energy management and power system optimization, and particularly discloses a port microgrid optimization scheduling method, device and equipment, a storage medium and a product. According to the application, the microgrid operation optimization model is constructed according to the predicted load demand, the predicted wind power generation power and the predicted solar power generation power; solving a target scheduling strategy according to the power grid operation optimization model and the multi-dimensional microgrid operation constraint conditions; and performing optimal scheduling on the port microgrid according to the target scheduling strategy. Through the above mode, comprehensive optimization of microgrid operation is realized by using the microgrid operation optimization model on the basis of considering the operation cost and the environment cost, the operation stability is ensured according to the multi-dimensional microgrid operation constraint condition, and then optimization scheduling is performed on the port microgrid according to the target scheduling strategy. Therefore, the accuracy of dispatching the port micro-grid can be effectively improved, the operation economy and the energy utilization efficiency of the micro-grid are improved, and carbon emission is reduced.

The invention discloses a multi-energy complementary optimal configuration method and system for a new energy base, and belongs to the technical field of power system planning. The method comprises the following steps: acquiring energy base system operation parameters, power generation equipment parameters, delivery channel capacity and power market electricity price data; constructing an optimal configuration model taking a receiving end time-of-use electricity price signal as input and aiming at stabilizing new energy output fluctuation and meeting delivery channel constraint; solving the model to obtain a thermal power, wind power, photovoltaic and energy storage capacity configuration scheme and a time sequence output plan; and determining a final optimal configuration scheme by analyzing the operation stability and delivery power volatility of the configuration scheme in different pricing modes. According to the method, the technical problem that the influence of the receiving end electricity price market is not fully considered during optimal configuration of the existing new energy base is solved, an optimal configuration scheme which meets the technical constraint and adapts to the market environment can be generated, and the operation economy and stability of the new energy base are remarkably improved.

The invention discloses a novel electric power-oriented energy storage double-layer optimization configuration method and system, and the method comprises the steps: S1, constructing an energy storage system model, defining the static parameters of energy storage, including rated capacity, rated power, charge and discharge efficiency, initial charge state and the like, and further quantifying the supporting capability of energy storage for the dynamic stability of the system, therefore, the enhancement effect of energy storage on frequency stability and small interference stability is directly reflected. According to the method, the system dynamic stability index, the green energy consumption index and the market economic index are uniformly incorporated into an energy storage optimization configuration framework, so that the limitation of single target of a traditional method is overcome, and a key technical support is provided for constructing a safe, green and economic novel power system; secondly, market price and risk constraints based on probability prediction are introduced into a lower-layer operation model, so that an energy storage operation strategy obtained through optimization can effectively cope with market fluctuation, the stability of earnings is guaranteed while high earnings are pursued, and the anti-risk capability of investment decision making is enhanced.

The invention provides an electric power emergency resource cooperative scheduling method and device, and relates to the technical field of electric power system emergency management and intelligent optimization scheduling, and the method comprises the steps: firstly, fusing the multi-source data of meteorology, hydrology, electric power and the like, predicting disaster evolution, and quantifying the power failure influence; secondly, constructing an urban integrated network integrating electric power facilities and roads, and dividing emergency service subareas based on node importance; thirdly, establishing a pre-stationing optimization model with minimization of response time, power failure loss and operation cost as targets before a disaster, and solving the optimal positions, the number and the inventory ratio of the storage points and the mobile power supply gathering points; and finally, updating information in real time during a disaster period, solving the scheduling model considering multiple constraints, and generating an optimal scheduling scheme. According to the method and the device, through cooperation of pre-distribution before the disaster and intelligent scheduling in the disaster, fine management of emergency resources is realized, power failure loss can be effectively reduced, and emergency response efficiency is improved.

The invention relates to the technical field of power system optimization, and discloses a charging load estimation and charging mode optimization method based on an electric vehicle. According to the method, a source load storage-medium voltage distribution network collaborative planning framework is initialized, and charging station geographical distribution and user charging behavior modes are integrated; collecting charging load sequence data in real time, and judging a charging load control state through a volatility analysis algorithm; when the charging load control is in an unstable state, synchronously analyzing voltage fluctuation characteristics and charging power change characteristics of the power distribution network, and generating a charging efficiency evaluation value; dividing the charging effect into three levels of high efficiency, conventional and low efficiency based on the evaluation value; for a conventional grade charging effect, a time sequence decomposition technology is adopted to extract a load trend component, and an overall load stability index is calculated; and fusing the charging efficiency evaluation value and the overall load stability index, and outputting a charging power adjustment instruction. The method effectively improves the operation stability and the charging efficiency of the power distribution network.

The invention provides an energy storage charging and discharging time sequence optimization scheduling method and system based on revenue maximization, and relates to the technical field of power system optimization scheduling, and the method comprises the steps: obtaining price data and system parameters, recognizing a price local extreme point, constructing an envelope line, and stripping an oscillation component to obtain a price trend component; and establishing a corresponding relation between a charge state target value and a time period identifier, enabling the charge state track to converge to the target value step by step, and dynamically correcting the target value and adjusting a price peak value identification method according to real-time feedback data. According to the invention, price fluctuation can be effectively coped, the economic benefit of the energy storage system is improved, and self-adaptive optimization control of the charging and discharging process is realized.

The invention discloses a dispatching method and system based on a distributed diesel generator and a virtual power plant, and relates to the technical field of power systems and energy internet. Comprising the steps that a local control unit establishes an operation constraint model containing a nonlinear fuel efficiency and carbon deposition risk model, and generates a health recommendation domain; the cloud end generates a triple scheduling instruction packet containing reference power, offset bandwidth and weight vectors in combination with power grid constraints; identifying the impedance of a grid-connected point on line at the edge side, and carrying out asymmetric compression correction on the bandwidth based on the voltage sensitivity under a weak grid; and the local unit performs rolling optimization solution under the double constraints of the corrected instruction and the local model, and switches to the anti-carbon deposition autonomous mode containing dummy load input when the communication is interrupted and the carbon deposition risk is high. According to the invention, through cloud-side-end cooperation and hierarchical closed-loop correction, the conflict between the weak network voltage out-of-limit risk and the low-load carbon deposition of the unit is effectively solved, and the safety and the performability of scheduling are improved.

The invention relates to a method for calculating equivalent parameters of a data-driven permanent magnet direct drive wind power plant, which belongs to the technical field of analog computation of a power system and comprises the following steps of: identifying a wind turbine generator group with similar dynamic behaviors by carrying out clustering analysis on a transient current time sequence of an outlet of a wind turbine generator; a neural network model is constructed, key electrical parameters such as operation condition parameters and voltage drop serve as input, a clustering result serves as output, and automatic classification and dynamic response feature extraction of the wind turbine generator are achieved by training a neural network; calculating unit equivalent parameters and current collection line parameters to obtain an equivalent model; and multi-operation scene simulation verification of the large-scale permanent magnet direct-driven wind power plant is passed. According to the method, equivalent modeling of the large-scale permanent-magnet direct-drive wind power plant is realized, and the modeling efficiency and the adaptability to complex operation scenes are remarkably improved.

The invention relates to the technical field of new energy grid-connected scheduling optimization, and discloses a photo-thermal-photovoltaic combined power station optimization scheduling method based on deep reinforcement learning, which aims at minimizing the total cost of a system, and uses an improved LSTM-PER-TD3 algorithm to research a photo-thermal-photovoltaic combined power station optimization scheduling strategy. Establishing a refined scheduling model of the photo-thermal-photovoltaic combined power station, considering the coupling effect of an electric heater, constructing a generalized inertia calculation model, and embedding RoCoF and frequency deviation safety constraints; reinforcement learning is introduced, a scheduling problem is converted into a Markov decision process, and an improved TD3 algorithm fusing LSTM and PER is provided. According to the method, the defects of insufficient utilization of historical information and low key sample learning efficiency in a strong time sequence coupling scheduling problem are overcome, the solving speed of an optimal scheduling strategy is greatly increased, and an efficient intelligent scheduling tool is provided for a high-proportion new energy power system.

The invention discloses an extreme weather electrical control method based on multi-source sensing fusion, and the method comprises the following steps: S1, collecting the data of a multi-source sensor disposed in a power system, and constructing an original sensing data set; s2, performing time synchronization and frequency unification processing on the original sensing data set to generate a multi-source alignment data sequence; s3, extracting a fusion feature vector; s4, inputting the fusion feature vector into an Autoformer model, capturing long-term time sequence dependence among multi-source features, and outputting an extreme weather grade evaluation result and an electrical operation risk scoring vector; s5, inputting the risk score into an improved Gumbel-Softmax strategy selection network, and outputting a control action sequence and parameter configuration; and S6, finally converting the control strategy into an equipment control instruction and issuing the equipment control instruction in real time. And risk prediction and adaptive control of the electrical system under extreme conditions are realized.

The invention discloses a new energy and pumped storage combined power generation system full life cycle carbon emission evaluation method, and relates to the technical field of power system carbon footprints. Comprising the following steps: firstly, obtaining and preprocessing carbon emission data of a new energy power station including a wind power station, a photovoltaic station and a pumped storage power station, and generating a training sample data set; then, a neural network model is established based on the data set, and training is carried out by taking power station construction feature data including the material consumption, the transportation distance and the equipment number as input and taking the carbon emission as a label; and then, inputting construction characteristic data and environmental parameters of the power station to be evaluated, and correcting a carbon emission predicted value. And finally, in combination with the dynamic growth index of the load demand of the power grid, calculating the full-life-cycle carbon emission, and realizing accurate evaluation. The method has the characteristics of systematicness, intellectualization and data driving, and is suitable for carbon emission management and optimization of a new energy and pumped storage combined power generation system.

The invention relates to a power system scheduling method based on a wind and light output scene and a virtual energy storage model, and the method comprises the steps: sampling and generating day-ahead wind and light data, and carrying out the scene reduction to obtain a typical wind and light output scene; constructing a virtual energy storage model based on the electric vehicle, and introducing the deviation electric quantity to correct the virtual energy storage model; constructing a day-ahead stage multi-agent collaborative optimization model taking the minimum operation cost of an energy storage operator and the minimum energy consumption cost of the park integrated energy system as optimization objectives; and solving the day-ahead stage multi-subject collaborative optimization model by adopting an improved sparrow search optimization algorithm to obtain an optimal solution, namely an optimal scheduling scheme. A typical wind and light output scene set is constructed, the influence of long-time scale prediction deviation on a scheduling plan is reduced, and the robustness of a power system is enhanced; an electric vehicle cluster is modeled as virtual energy storage, the flexible adjustment potential of the electric vehicle cluster is mined through load-storage dual characteristics, a space-time complementary mechanism is formed with physical energy storage, and the overall energy efficiency of the system is improved.

The invention discloses a method and a system for evaluating internal dynamic complementary characteristics of a multi-energy complementary power system. The method comprises the following steps: S1, acquiring an actual output time sequence of each unit; defining a fluctuation energy unit total output sequence, an adjustment energy unit total output sequence and a system total output sequence; s2, basic quantitative indexes of the random fluctuation dimension, the climbing trend dimension and the energy time shift dimension are calculated respectively; s3, calculating a random fluctuation compensation index, a climbing compensation index and an energy time shift index; s4, a comprehensive evaluation index MIRCI is calculated; s5, calculating an adjustment capability utilization factor RUF of each adjustment unit; and S6, based on MIRCI and RUF, performing complementary effect diagnosis. According to the method, the overall stabilizing effect of the adjusting resources on the fluctuating energy is effectively quantified, diagnosis of the complementary effect and limiting factor positioning are achieved, and the absorption capacity and the operation efficiency of the high-proportion renewable energy system are remarkably improved.

Systems and methods are described for powering a load, such as a data center, with renewable energy from a renewable energy source. When the renewable energy is greater than a demand of the load, excess renewable energy is used to power a hydrogen production device or charge a battery depending on whether or not the charge level of the battery satisfies an upper threshold charge level, respectively. When the renewable energy is less than the demand of the load and the charge level of the battery satisfies a lower threshold charge level, the load is powered with energy from the battery. When the renewable energy is less than the demand of the load and the charge level of the battery does not satisfy the lower threshold charge level, the load is powered and the battery is charged with energy generated by the hydrogen-based energy generator.

The invention discloses a cross-modal plant station main wiring blueprint primitive feature extraction and representation design method, system and device based on natural language description and a medium, and belongs to the technical field of electrical engineering digitization and power system automation, and the method comprises the steps: respectively extracting multi-scale visual features of a blueprint image and semantic features of text prompt; cross-modal feature interaction fusion is realized through a feature enhancement layer, and language-guided target query is generated based on feature similarity; visual positioning and semantic information are fused in query through iterative optimization of a cross-modal decoder; and outputting a primitive bounding box and category prediction which are completely matched with the text description. The end-to-end open vocabulary detection method is constructed by successfully adapting a target detection model DINO detection framework based on Transform to the field of a plant station electrical main wiring blueprint and introducing a multi-level cross-modal fusion mechanism and text representation.

The invention relates to the technical field of electric power measurement, in particular to a novel intelligent electric meter box electricity larceny prevention system and an application method. The electricity larceny prevention system comprises a microcontroller, a data transmission module, a data storage module, a camera, a box door detection switch, various sensors, an electricity larceny detection and remote control alarm module, an identity recognition module, a circuit breaker module and a strong magnetic and strong electric shock detection module. The application method comprises the following steps: starting and self-checking the system; judging and processing a self-checking result; real-time monitoring and event monitoring are carried out; an abnormal event response process; a remote instruction response process; recovering monitoring; the electric energy metering box is economical, practical, safe and reliable, real-time monitoring, electricity stealing behavior detection and alarm can be carried out on the electric energy metering box, remote control over the electric energy metering box can be further achieved, the identity of a box opening person can be effectively recognized, and protection over the metering box is achieved.

The invention discloses a water-electricity coupling system toughness evaluation method considering drought risk evolution, and relates to the technical field of water-electricity coupling systems. Comprising the following steps: S1, modeling a water-electricity coupling system network; s2, modeling based on seepage water-electricity coupling network drought risk propagation; and S3, carrying out system simulation and toughness evaluation on the water-electricity coupling system network on the basis of the two models. According to the method, a heterogeneous weighted double-layer network model is constructed, a cross-scale dynamic modeling method is developed, a seepage theory and physical flow are dynamically combined, a two-way seepage equation of disaster cascade propagation is established, a toughness evaluation framework based on the seepage theory is provided, and laws of different coupling effects of a water-electricity system are disclosed; and a scientific basis can be provided for risk assessment, toughness improvement and emergency management strategy formulation of the key infrastructure.

The present invention relates to a wind turbine park (100), comprising at least a first wind turbine (110) and a second wind turbine (120) for generating a power product, and a main line (101) connecting the first wind turbine (110) and the second wind turbine (120) for transporting the power product. The wind turbine park (100) further comprises an auxiliary power line (102) being connected to the first wind turbine (110) and the second wind turbine (120), wherein the auxiliary power line (102) is configured for transporting auxiliary power for a wind turbine maintenance or standby operation to at least one of the first wind turbine (110) and the second wind turbine (120). Furthermore, the wind turbine park (100) comprises an auxiliary power unit (103) for generating the auxiliary power, wherein the auxiliary power unit (103) is connected to the auxiliary power line (102).