Lightning protection system of offshore wind turbine generator

Abstract

The invention relates to a lightning protection system of an offshore wind turbine generator, which belongs to the technical field of wind power generation, and comprises a sensing unit used for collecting real-time resistance of a grounding loop in real time and collecting marine environment parameters in real time; the evaluation decision-making unit is used for generating a dynamic adjustment coefficient based on the grounding loop real-time resistance and the marine environment parameters, and generating a reference adjustment quantity based on the dynamic adjustment coefficient; a cooperative control unit for generating a specific adjustment amount allocated to the movable ground electrode on the basis of the reference adjustment amount; the adjustment execution unit is used for receiving the specific adjustment quantity so as to drive the movable grounding electrode to execute the physical adjustment action, the conversion from extensive adjustment to accurate and efficient execution is achieved, the adjustment execution unit avoids invalid or too frequent jitter caused by tiny fluctuation of the environment, and the adjustment precision is improved. The mechanical wear and energy consumption of the movable grounding electrode are significantly reduced, and the stability and economical efficiency of the system are improved.

Description

Technical Field

[0001] This invention relates to the field of wind power generation, specifically to a lightning protection system for offshore wind turbines. Background Technology

[0002] In the field of lightning protection for offshore wind turbines, current technical solutions mostly follow the static grounding theory of onshore wind power. These solutions adopt a passive protection strategy, and their inherent limitation is that they ignore the fact that the grounding status in the marine environment will fluctuate drastically due to factors such as wave height, tide level, and salinity. This static design cannot dynamically respond to environmental changes, and therefore it is difficult to achieve a balance between ensuring the stability of grounding resistance and the economy of the system structure.

[0003] The aforementioned shortcomings and deficiencies are mainly due to the limitations of the design concept. Traditional systems are constructed as static and passive structures, lacking the ability to accurately perceive and dynamically respond to complex operating conditions. They cannot incorporate key marine environmental parameters such as wave height, seawater salinity, and tidal position into the decision-making loop of the protection system. As a result, when the marine environment fluctuates drastically, traditional lightning protection systems cannot consistently maintain the grounding resistance below the safe threshold. Alarms can only be triggered or passive maintenance can only be performed after the resistance exceeds the standard. This is a reactive rather than preventative approach. This delayed response mechanism greatly affects the continuity and effectiveness of protective measures, thereby reducing the operational reliability and power generation efficiency of wind turbine units.

[0004] The information disclosed in the background section above is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a lightning protection system for offshore wind turbines to solve the problems mentioned in the background art.

[0006] The technical solution of the present invention includes:

[0007] The sensing unit is used to collect the real-time resistance of the grounding loop and the marine environmental parameters in real time.

[0008] The evaluation decision unit is used to generate dynamic adjustment coefficients based on the real-time resistance of the grounding loop and marine environmental parameters, and to generate a baseline adjustment amount based on the dynamic adjustment coefficients;

[0009] The cooperative control unit is used to generate specific adjustment amounts allocated to the movable grounding electrode based on the reference adjustment amount;

[0010] The adjustment execution unit is used to receive specific adjustment amounts to drive the movable grounding electrode to perform physical adjustment actions.

[0011] Preferably, the process by which the evaluation decision-making unit generates dynamic adjustment coefficients includes:

[0012] The wave height in the marine environmental parameters is obtained, and the wave height risk contribution value is determined based on the wave height and the preset safe wave height benchmark.

[0013] The salinity in marine environmental parameters is obtained, and the salinity risk contribution value is determined based on the salinity and a preset high salinity benchmark.

[0014] Obtain the tide level from marine environmental parameters, and determine the tide level risk contribution value based on the tide level and a preset excellent tide level benchmark;

[0015] A comprehensive environmental disturbance factor is generated based on the contribution values ​​of wave height risk, salinity risk, and tide level risk.

[0016] Preferably, the process of evaluating the decision-making unit to generate dynamic adjustment coefficients also includes:

[0017] The real-time resistance of the grounding loop and the preset target grounding resistance threshold are obtained to determine the resistance difference signal. The dynamic adjustment coefficient is generated by combining the resistance difference signal with the comprehensive environmental disturbance factor.

[0018] Preferably, the process by which the evaluation decision-making unit generates the baseline adjustment includes:

[0019] The dynamic adjustment coefficient is compared with the preset adjustment trigger threshold.

[0020] If the dynamic adjustment coefficient is greater than the adjustment trigger threshold, the baseline adjustment amount is determined based on the dynamic adjustment coefficient and the preset maximum extendable length.

[0021] If the dynamic adjustment coefficient is not greater than the adjustment trigger threshold, the baseline adjustment amount is set to zero.

[0022] Preferably, the process for determining the wave height risk contribution value is as follows: subtract the safe wave height benchmark from the wave height, and divide the resulting difference by the safe wave height benchmark.

[0023] Preferably, the high salinity benchmark is subtracted from the salinity, and the difference is divided by the high salinity benchmark to determine the salinity risk contribution value; the excellent tide level benchmark is subtracted from the tide level, and the difference is divided by the excellent tide level benchmark to determine the tide level risk contribution value.

[0024] Preferably, the process by which the collaborative control unit generates a specific adjustment amount includes:

[0025] For each movable grounding electrode, determine the adjustment allocation coefficient, and multiply the reference adjustment amount by the adjustment allocation coefficient corresponding to each movable grounding electrode to determine the specific adjustment amount of the electrode.

[0026] Preferably, the adjustment distribution coefficient is dynamically determined by the collaborative control unit based on the cumulative working time of the movable grounding electrode or the local microenvironment data.

[0027] This invention provides an improved lightning protection system for offshore wind turbines, which has the following improvements and advantages compared to the prior art:

[0028] 1. This invention achieves a leap from passive response to proactive risk prediction. This achievement is rooted in the collaborative work of the sensing unit and the assessment and decision-making unit. The sensing unit not only acquires the real-time resistance of the grounding loop, which characterizes the current state, but more importantly, it can collect marine environmental parameters in real time, including wave height, salinity, and tide level. The assessment and decision-making unit receives this multi-dimensional data and quantifies the complex environmental impact through a built-in mathematical model optimized based on historical data and system identification methods. This is fundamentally different from the passive mode of existing technologies that can only trigger alarms or perform maintenance after the resistance exceeds the standard.

[0029] 2. This invention realizes the transformation from coarse adjustment to precise and efficient execution. The adjustment and execution unit avoids ineffective or excessively frequent jitter caused by minor environmental fluctuations, significantly reduces the mechanical wear and energy consumption of the movable grounding electrode, and improves the stability and economy of the system.

[0030] 3. This invention achieves an upgrade from isolated execution to distributed collaborative optimization, enabling the system to implement more advanced control strategies. For example, the collaborative control unit can proactively reduce the adjustment amplitude of a certain electrode that has been working for a long time, while increasing the adjustment amplitude of other electrodes, in order to balance the mechanical wear of the entire system and extend its overall service life. Furthermore, it can allocate a larger adjustment amount to electrodes that are in the trough of the wave and are submerged more deeply based on the data from local wave sensors, in order to achieve a more efficient adjustment effect. This is something that the static, non-cooperative grounding structure of the prior art cannot achieve. Attached Figure Description

[0031] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0032] Figure 1 This is a flowchart of a lightning protection system for offshore wind turbines according to the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0034] Example 1:

[0035] Please see Figure 1The present invention provides a lightning protection system for offshore wind turbines, comprising: a sensing unit for real-time acquisition of the real-time resistance of the grounding circuit and real-time acquisition of marine environmental parameters;

[0036] The evaluation decision unit is used to generate dynamic adjustment coefficients based on the real-time resistance of the grounding loop and marine environmental parameters, and to generate a baseline adjustment amount based on the dynamic adjustment coefficients;

[0037] The cooperative control unit is used to generate specific adjustment amounts allocated to the movable grounding electrode based on the reference adjustment amount;

[0038] The adjustment execution unit is used to receive specific adjustment amounts to drive the movable grounding electrode to perform physical adjustment actions;

[0039] To support subsequent collaborative control strategies, in addition to collecting macroscopic environmental parameters, the sensing unit also includes a distributed local sensing network. This network consists of multiple local sensors. For example, an independent ultrasonic or pressure-type wave height sensor is installed at or near each movable grounding electrode to measure the local peak or trough position of the electrode in real time and transmit this local micro-environment data to the evaluation decision unit and the collaborative control unit.

[0040] This embodiment provides a lightning protection system for offshore wind turbines, deployed on offshore wind turbines exposed to variable marine environments. The system is constructed as a closed-loop active protection system, with its operational logic starting from the precise perception of complex operating conditions by sensing units. These sensing units are configured as a distributed network, continuously measuring data in two dimensions: firstly, the real-time resistance of the grounding loop characterizing the system's current protection status; and secondly, marine environmental parameters indicating future risks, specifically wave height, seawater salinity, and tidal position. The data stream is transmitted to an evaluation and decision-making unit, which, as the system's computational center, is responsible for transforming the raw physical quantities into forward-looking data. Control commands; the collaborative control unit receives these macro-level commands and, based on an optimization strategy, decomposes them into precise fine-tuning commands for each independent movable grounding electrode; the adjustment execution unit faithfully executes these commands, driving the electrodes to adjust; this architecture transforms the entire lightning protection system from a static, passive structure into a dynamic collaborative adjustment system capable of predicting risks, responding dynamically, and coordinating operations. The effect is that, even when the marine environment fluctuates drastically, the grounding resistance can still be stably maintained below the safe threshold, thus resolving the inherent contradiction between economy and reliability in traditional static grounding methods, and greatly improving the operational reliability and power generation efficiency of wind turbine units;

[0041] Existing lightning protection systems for offshore wind turbines mostly follow the static grounding theory of onshore wind power. This passive protection strategy ignores the drastic fluctuations in grounding status in the marine environment and cannot balance grounding resistance stability with system structure economy. The solution provides a complete dynamic protection system, which achieves technological progress by introducing the coordinated work of four core units.

[0042] Example 2

[0043] The process by which the evaluation decision-making unit generates dynamic adjustment coefficients includes:

[0044] The wave height in the marine environmental parameters is obtained, and the wave height risk contribution value is determined based on the wave height and the preset safe wave height benchmark.

[0045] The salinity in marine environmental parameters is obtained, and the salinity risk contribution value is determined based on the salinity and a preset high salinity benchmark.

[0046] Obtain the tide level from marine environmental parameters, and determine the tide level risk contribution value based on the tide level and a preset excellent tide level benchmark;

[0047] Based on the wave height risk contribution value, salinity risk contribution value, and tide level risk contribution value, a comprehensive environmental disturbance factor is generated.

[0048] The process of evaluating the decision-making unit to generate dynamic adjustment coefficients also includes:

[0049] The real-time resistance of the grounding loop and the preset target grounding resistance threshold are obtained to determine the resistance difference signal. The dynamic adjustment coefficient is generated by combining the resistance difference signal and the comprehensive environmental disturbance factor.

[0050] To achieve the above functions, the evaluation decision unit is configured to perform a key mathematical transformation task, namely, to integrate multi-dimensional, heterogeneous sensor data into a single decision command with clear physical meaning; the underlying logic of this process lies in two sets of interrelated mathematical models.

[0051] The process involves constructing a forward-looking risk indicator. The complexity of the marine environment requires the system to rely on more than a single parameter for judgment; therefore, the concept of a comprehensive environmental disturbance factor is introduced, aiming to quantify the impact of multiple factors such as wave height, salinity, and tide level on grounding resistance. The technical motivation of this process is to provide a feedforward signal that can predict the trend of grounding state changes for subsequent control decisions. The process follows a linear superposition model.

[0052] Ψ(t)=k h h′(t)+k s s′(t)+k w w′(t)

[0053] In the formula, Ψ(t) is the dimensionless comprehensive environmental disturbance factor at time t, h′(t), s′(t), and w′(t) are the risk contribution values ​​of wave height, salinity, and tidal level after normalization, respectively, and k h ,k s ,k w The dimensionless weighting coefficients correspond to various risks; t is the time; these weighting coefficients are not arbitrarily set, but are determined by systematic identification or machine learning algorithm optimization of historical environmental data and wind turbine operation data of specific sea areas, ensuring the model's adaptability to specific application scenarios;

[0054] For example, the system identification method of multiple linear regression can be used to determine the weight coefficients k. h ,k s ,k w The steps are as follows: Over a relatively long period of time, such as one year, historical data is collected synchronously, including the normalized risk contribution values ​​h′(t), s′(t), and w′(t) as independent variables, and physical quantities that reflect the state of the grounding system as dependent variables, such as the rate of change of grounding resistance. Establish the following linear regression model:

[0055]

[0056] In the formula, the regression coefficient β h ,β s ,β w The constant term c0 has dimensions of resistance / time. c0: a constant term in the linear regression model. The regression coefficient β is obtained by fitting a large amount of data using the least squares method. h ,β s ,β w Since these regression coefficients are measured in units of resistance / time, they need to be normalized to obtain the dimensionless weighting coefficients k. h ,k s ,k w One possible normalization method is:

[0057]

[0058] In this way, regression analysis results with physical dimensions can be transformed into dimensionless weights suitable for calculating comprehensive environmental disturbance factors.

[0059] For the weighting coefficient ω r With ω e The ratio of ω determines the relative strength of feedback control and feedforward control; this ratio can be optimized by performing step response tests in a simulation model; for example, by setting different ω values. r With ωe Combine the following methods: apply a simulated environmental disturbance, such as a sudden drop in salinity, to the system and observe the response curve of the grounding resistance R(t). Select the set of ω that results in the fastest system response, smallest overshoot, and smoothest regulation. r With ω e The value is used as the final engineering setting value;

[0060] The assessment decision-making unit applied this formula to successfully reduce the dimensionality of multiple independent environmental variables and output a unique risk quantification value Ψ(t). This quantification enables the system to perceive the risks that will arise under the combined effect of multiple factors, rather than passively responding only after the grounding resistance deteriorates.

[0061] Based on the feedforward signal and combined with real-time feedback, the final decision command is generated. The technical motivation for this step is to integrate the proportional-feedforward control concept, so that the adjustment command can not only respond to the current actual deviation, but also anticipate environmental disturbances. The evaluation decision unit uses the following model to calculate the dynamic adjustment coefficient:

[0062]

[0063] In the formula, α(t) is the dimensionless dynamic adjustment coefficient at time t, R(t) is the real-time resistance of the grounding loop collected by the sensing unit, R0 is the target grounding resistance threshold preset according to the safety regulations, Ψ(t) is the comprehensive environmental disturbance factor calculated by the previous model, and ω r With ω e These are dimensionless weighting coefficients that control the influence of the feedback and feedforward terms, respectively; the first part of this formula This forms a proportional feedback loop. The larger the deviation of the real-time resistance R(t) from the target R0, the larger the absolute value of this term, driving the system to make corrections; the second part ω e Ψ(t) constitutes a feedforward link, directly incorporating the predicted environmental risk Ψ(t) into the decision-making process. The assessment and decision-making unit generates a highly intelligent adjustment command by calculating α(t). The advantage of this command is that even if the current grounding resistance R(t) is still within a safe range, if the environmental factor Ψ(t) shows a deteriorating trend, α(t) will increase accordingly, thereby triggering the adjustment action in advance and eliminating potential risks. Its technical effect is to achieve a qualitative leap from post-event remediation to pre-event prevention, ensuring the continuity and effectiveness of lightning protection for wind turbine units under extreme sea conditions.

[0064] This invention achieves a leap from passive response to proactive risk prediction; this effect is rooted in the collaborative work of the sensing unit and the assessment and decision-making unit; the sensing unit not only acquires the real-time resistance of the grounding loop that characterizes the current state, but more importantly, it can collect marine environmental parameters in real time, including wave height, salinity and tide level; the assessment and decision-making unit receives this multi-dimensional data and quantifies the complex environmental impact through a built-in mathematical model optimized based on historical data and system identification methods;

[0065] The core of this model lies in two closely coupled formulas; first, the formula for calculating the comprehensive environmental disturbance factor Ψ(t)=k h h′(t)+k s s′(t)+k w w′(t) is used to combine multiple independent environmental variables, through their respective risk contribution values ​​h′(t), s′(t), w′(t), and weighting coefficient k. h ,k s ,k w Linear superposition is performed; the practical significance of this formula is that it transforms the originally discrete and heterogeneous environmental data into a unified dimensionless risk index Ψ(t) that can predict the trend of grounding resistance changes for the first time.

[0066] Based on this risk indicator, the formula for generating the dynamic adjustment coefficient is as follows: It further integrates the concepts of feedback control and feedforward control; its left-hand term It is a proportional feedback based on the deviation between the real-time resistance R(t) of the grounding loop and the target threshold R0, while the right-hand term ω e Ψ(t) introduces the aforementioned predictive environmental risk Ψ(t) as a feedforward signal. The practical significance and advancement of this formula lies in the fact that the dynamic adjustment coefficient α(t) it generates not only responds to the resistance deviation that has already occurred, but also anticipates and responds to upcoming environmental disturbances. For example, even if the current grounding resistance is still within the safe range, if the system detects a rapid drop in tide level or a decrease in salinity due to heavy rain, the value of Ψ(t) will increase, thereby increasing the value of α(t) and triggering adjustment in advance. This is fundamentally different from the passive mode of existing technologies that can only trigger alarms or perform maintenance after the resistance exceeds the standard.

[0067] Example 3

[0068] The process for determining the wave height risk contribution value is as follows: subtract the safe wave height benchmark from the wave height, and divide the resulting difference by the safe wave height benchmark;

[0069] The salinity risk contribution value is determined by subtracting the salinity from the high salinity benchmark and dividing the difference by the high salinity benchmark; the tidal level risk contribution value is determined by subtracting the tidal level from the excellent tidal level benchmark and dividing the difference by the excellent tidal level benchmark.

[0070] Within the assessment and decision-making unit, to ensure that the calculation of the comprehensive environmental disturbance factor Ψ(t) has a consistent and correct physical meaning, the normalization process of the raw environmental parameters collected by the sensing unit is precisely designed. The technical motivation is that all normalized risk contribution values ​​must follow the same monotonicity, that is, the larger the value, the higher the threat to the stability of the grounding system.

[0071] The process for determining the contribution value of high-risk waves is defined as follows:

[0072]

[0073] In the formula, h′(t) is the normalized dimensionless wave height risk contribution value, h(t) is the real-time wave height collected by the sensing unit, and h ref It is a preset safe wave height benchmark; this definition ensures that when the real-time wave height h(t) exceeds the safe benchmark, h′(t) is positive and increases with the increase of wave height, which intuitively reflects the risk brought about by the enhanced wave impact. The above benchmark value is an engineering benchmark set based on long-term historical marine environmental data of a specific sea area and the safe operation requirements of wind turbine units.

[0074] The logic behind determining the risk contribution values ​​of salinity and tidal level reflects a profound understanding of physical laws; the salinity risk contribution value is determined as follows:

[0075]

[0076] In the formula, s′(t) is the normalized dimensionless salinity risk contribution value, s(t) is the real-time seawater salinity, and s ref It is a high salinity benchmark representing excellent electrical conductivity; the molecules here are designed to be s ref -s(t) is motivated by the negative correlation between salinity and resistance, that is, a decrease in salinity will lead to a decrease in the conductivity of seawater and an increase in grounding resistance; this definition ensures that when salinity s(t) decreases, s′(t) is an increasing positive value, which correctly maps the physical phenomenon to an increase in risk value.

[0077] Similarly, the contribution value of tidal level risk is determined as follows:

[0078]

[0079] In the formula, w′(t) is the normalized dimensionless tidal level risk contribution value, w(t) is the real-time tidal level, and w refIt is an excellent tide level benchmark to ensure that the grounding electrode obtains the ideal submersion depth; low tide will reduce the contact area between the grounding electrode and the seawater, thereby increasing the grounding resistance; therefore, this definition ensures that when the tide level w(t) drops, the value of w′(t) will increase, accurately quantifying this risk; through the above precise normalization process, the assessment decision unit can transform environmental parameters with different physical properties into risk inputs of a uniform scale, laying a solid foundation for the accuracy of subsequent comprehensive decision-making.

[0080] Example 4

[0081] The process by which the evaluation decision-making unit generates the baseline adjustment includes:

[0082] The dynamic adjustment coefficient is compared with the preset adjustment trigger threshold.

[0083] If the dynamic adjustment coefficient is greater than the adjustment trigger threshold, the baseline adjustment amount is determined based on the dynamic adjustment coefficient and the preset maximum extendable length.

[0084] If the dynamic adjustment coefficient is not greater than the adjustment trigger threshold, the baseline adjustment amount is set to zero.

[0085] After generating the core dynamic adjustment coefficient α(t), the evaluation decision unit then converts it into a specific, executable physical command, namely the baseline adjustment amount. The technical motivation for this process is the need for a mechanism to determine the necessity of adjustment and, within a linear range, map the dimensionless decision intensity to the execution magnitude in the physical world. To prevent the system from frequently responding to small, meaningless disturbances and to avoid mechanical wear and energy waste, a linear mapping function with a dead-zone setting is introduced. This function is constructed as follows:

[0086]

[0087] In the formula, θ extend It is a preset positive number to trigger the extension threshold, while θ retract It is a preset negative retraction trigger threshold; when α(t) falls within [θ retract ,θ extend Within the specified interval, the system remains inactive, creating a control dead zone. This avoids frequent adjustments for minor fluctuations. ΔL(t) represents the adjustment displacement, with positive values ​​indicating extension and negative values ​​indicating retraction. max It is the maximum physical limit length that a single movable grounding electrode can extend, γ is a dimensionless system response gain coefficient used to scale the adjustment amplitude, and these thresholds, θ extend and θ retract The setting is based on a trade-off analysis of the system noise level and acceptable response delay to ensure that the system only initiates physical regulation when the risk accumulates to a certain level.

[0088] The determination of the adjustment trigger threshold θ can follow the following quantitative analysis steps: By long-term monitoring of the dynamic adjustment coefficient α(t) of the system under no significant environmental changes, its noise fluctuation range is statistically analyzed, for example, its standard deviation σ is calculated. noise To prevent the system from responding to pure signal-noise, a threshold θ is extended. extend The lower limit should be set to several times the noise level, for example, θ. extend ≥3σ noise The shrinkage threshold θ retract It can be symmetrically set as θ retract ≤-3σ noise Secondly, assess the mechanical wear cost of the regulating unit and the energy consumption E of a single regulation. single Taking into account both the acceptable risk of grounding resistance deviation and operational economy, a tolerance deviation is set. Only when α(t) exceeds the adjustment demand intensity represented by the threshold θ will the resulting safety benefits outweigh the implementation costs. extend and θ retract The final value is the optimal value determined through simulation analysis or field tests, in this trade-off between safety and economy.

[0089] When evaluating the decision-making unit, the calculated α(t) will be compared with the threshold θ. extend and θ retract The comparison is performed; only when α(t) exceeds this threshold, indicating that the adjustment demand is clear and necessary, will a non-zero baseline adjustment amount ΔL(t) be calculated; this technique has two effects: on the one hand, it ensures the decisiveness and effectiveness of the system response, using adjustment resources where they are most needed; on the other hand, by setting a control dead zone, it greatly improves the system's operational stability and economy, and extends the mechanical life of the adjustment execution unit;

[0090] This invention achieves a transformation from extensive adjustment to precise and efficient execution; when generating the baseline adjustment amount ΔL(t), the evaluation decision unit uses a linear mapping function ΔL(t) = γ·α(t)·L with a dead zone setting. max and adjust the trigger threshold θ according to the preset settings. extend and θ retract As a starting condition, these thresholds are set based on a trade-off analysis of system noise and mechanical response costs, ensuring the necessity and efficiency of physical actions. The beneficial effect is that the regulating actuator avoids ineffective or excessively frequent jitter caused by minor environmental fluctuations, significantly reduces the mechanical wear and energy consumption of the movable grounding electrode, and improves the stability and economy of the system.

[0091] Example 5

[0092] The process by which the collaborative control unit generates specific adjustment values ​​includes:

[0093] For each movable grounding electrode, determine the adjustment allocation coefficient, multiply the reference adjustment amount by the adjustment allocation coefficient corresponding to each movable grounding electrode, and determine the specific adjustment amount of the electrode.

[0094] The adjustment distribution coefficient is dynamically determined by the collaborative control unit based on the cumulative working time of the movable grounding electrode or the local microenvironment data.

[0095] After receiving the macroscopic baseline adjustment amount ΔL(t) from the evaluation and decision-making unit, the collaborative control unit allocates this macroscopic adjustment task to the network of N movable grounding electrodes constituting the adjustment execution unit according to the preset optimization objective. The technical motivation for this process is that a simple average allocation strategy cannot achieve the optimization of the overall system performance. A more advanced control strategy needs to consider the individual state and local environment of each electrode to achieve global objectives such as balancing losses and minimizing energy consumption. In this model, the baseline adjustment amount ΔL(t) is defined as a benchmark value characterizing the average adjustment amplitude, rather than the sum of the adjustment amounts of all electrodes. The collaborative control unit calculates a dynamic adjustment allocation coefficient for each electrode and generates the final execution command accordingly.

[0096] To clarify the adjustment allocation coefficient κ i The present invention provides a calculation method based on multi-objective optimization to dynamically determine the loss factor and environmental gain factor of each electrode.

[0097] Loss factor C loss,i (t) Based on cumulative working hours T acc,i To determine the optimal balance of strain, the calculation formula can be designed as follows:

[0098]

[0099] In the formula, T acc,i (t) represents the cumulative working time of the i-th electrode up to time t. The average cumulative working time of all electrodes is ∈, which is a very small positive constant with the dimension of time. For example, it can be set to 1 second to prevent the denominator from being zero. This formula ensures that the longer the working time of an electrode, the smaller its loss factor.

[0100] Environmental gain factor C env,i (t) Based on local microenvironment data, the aim is to improve regulation efficiency. Taking local wave height as an example, if the sensing unit can obtain the local relative wave height h near each electrode... local,i (t), relative to mean sea level, environmental gain factor C env,i(t) Based on local microenvironment data, this method aims to improve regulation efficiency by prioritizing regulation of electrodes that are submerged deeper, i.e., located in the trough. It is defined as follows:

[0101]

[0102] This definition ensures that h is at a trough. local,i Electrodes with negative (t) obtain a gain factor greater than 1, while at the peak, h local,i Electrodes with a positive (t) gain factor of less than 1 are preferentially driven to drive electrodes that are submerged deeper.

[0103] Finally, the weighted multiplication of each factor yields the preliminary allocation weights W. i (t):

[0104] W i (t)=w loss ·C loss,i (t)+w env ·C env,i (t)

[0105] In the formula, W i (t): Initial weighting of the i-th electrode, w loss and w env These are preset weights that satisfy w loss +w env =1;

[0106] To satisfy the constraints For weight W i (t) is normalized to obtain the final adjustment allocation coefficient κ. i (t):

[0107]

[0108] Through the specific mathematical model described above, the collaborative control unit can transform the two fuzzy principles of cumulative working time and local micro-environment into precise and executable allocation coefficient values, thereby achieving refined collaborative management of the electrode network.

[0109] The calculation model for the adjustment amount is defined as follows:

[0110] ΔL i (t)=κ i (t)·ΔL(t)

[0111] Where, ΔL i ΔL(t) is the actual extension length adjustment allocated to the i-th electrode at time t, ΔL(t) is the reference adjustment amount transmitted from upstream, and κ is the actual extension length adjustment allocated to the i-th electrode at time t. i(t) represents the dimensionless adjustment allocation coefficient dynamically determined by the collaborative control unit for the i-th electrode at time t; i is the electrode number; all allocation coefficients are designed to satisfy the constraints. This constraint ensures that under the equal distribution strategy, all κ i When (t) is constant at 1, the adjustment amount of each electrode is the reference adjustment amount ΔL(t); when unbalanced distribution is performed, this constraint allows the system to dynamically adjust the contribution of each independent electrode while maintaining the overall adjustment strength; κ i The dynamism of (t) stems from its dynamic determination based on the health status of each electrode provided by the sensing unit or the system's own state monitoring module, such as the cumulative working time or local microenvironment data, such as the peak and trough positions sensed by a local wave sensor; accordingly, the collaborative control unit can allocate a larger κ to electrodes located at trough positions. i The value of (t) is assigned to the electrode at the peak position, while a smaller value is assigned to the electrode at the peak position. The application of this cooperative strategy makes the lightning protection system no longer a simple command executor, but a complex system capable of internal resource optimization and scheduling. The technical effect is that while achieving the overall grounding resistance adjustment target, it also realizes the health management and energy efficiency optimization of the execution unit network, which significantly improves the long-term operational reliability and economy of the entire system.

[0112] This invention achieves an upgrade from isolated execution to distributed collaborative optimization; after the evaluation and decision-making unit determines the macroscopic baseline adjustment amount, the collaborative control unit does not perform a simple average allocation, but dynamically determines an adjustment allocation coefficient κ for each movable grounding electrode. i (t) is used to achieve optimized management of the entire electrode network; its core formula is ΔL i (t)=κ i (t)·ΔL(t), where κ i The determination of (t) is based on the cumulative working time of each electrode or the local microenvironment data in which it is located. The beneficial effect of this mechanism is that the system can achieve a more advanced control strategy. For example, the cooperative control unit can actively reduce the adjustment range of a certain electrode that has been working for a long time, while increasing the adjustment range of other electrodes, so as to balance the mechanical wear of the entire system and extend the overall service life. Furthermore, it can allocate a larger adjustment amount to the electrode that is in the trough of the wave and is submerged more deeply based on the data of the local wave sensor, so as to achieve a more efficient adjustment effect. This is something that the static, non-cooperative grounding structure of the existing technology cannot achieve at all.

[0113] In summary, this solution, through the interconnected sensing unit, evaluation and decision-making unit, collaborative control unit, and regulation and execution unit, transforms the control of grounding resistance from a passive, static problem into an active, dynamic optimization process. It not only corrects existing resistance deviations as in existing technologies, but also achieves risk prediction and early intervention by introducing quantitative assessments of marine environmental parameters. Furthermore, the collaborative control strategy optimizes the economy and reliability of regulation actions, fundamentally improving the performance of offshore wind turbine lightning protection systems, ensuring equipment safety, and reducing operation and maintenance costs.

[0114] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A lightning protection system for offshore wind turbines, characterized in that, include: The sensing unit is used to collect the real-time resistance of the grounding loop and the marine environmental parameters in real time. The evaluation decision unit is used to generate dynamic adjustment coefficients based on the real-time resistance of the grounding loop and marine environmental parameters, and to generate a baseline adjustment amount based on the dynamic adjustment coefficients; The cooperative control unit is used to generate specific adjustment amounts allocated to the movable grounding electrode based on the reference adjustment amount; The adjustment execution unit is used to receive specific adjustment amounts to drive the movable grounding electrode to perform physical adjustment actions.

2. The lightning protection system for offshore wind turbines according to claim 1, characterized in that, The process by which the evaluation decision-making unit generates dynamic adjustment coefficients includes: The wave height in the marine environmental parameters is obtained, and the wave height risk contribution value is determined based on the wave height and the preset safe wave height benchmark. The salinity in marine environmental parameters is obtained, and the salinity risk contribution value is determined based on the salinity and a preset high salinity benchmark. Obtain the tide level from marine environmental parameters, and determine the tide level risk contribution value based on the tide level and a preset excellent tide level benchmark; A comprehensive environmental disturbance factor is generated based on the contribution values ​​of wave height risk, salinity risk, and tide level risk.

3. The lightning protection system for offshore wind turbines according to claim 2, characterized in that, The process of evaluating the decision-making unit to generate dynamic adjustment coefficients also includes: The real-time resistance of the grounding loop and the preset target grounding resistance threshold are obtained to determine the resistance difference signal. The dynamic adjustment coefficient is generated by combining the resistance difference signal with the comprehensive environmental disturbance factor.

4. The lightning protection system for offshore wind turbines according to claim 1, characterized in that, The process by which the evaluation decision-making unit generates the baseline adjustment includes: The dynamic adjustment coefficient is compared with the preset adjustment trigger threshold. If the dynamic adjustment coefficient is greater than the adjustment trigger threshold, the baseline adjustment amount is determined based on the dynamic adjustment coefficient and the preset maximum extendable length. If the dynamic adjustment coefficient is not greater than the adjustment trigger threshold, the baseline adjustment amount is set to zero.

5. A lightning protection system for offshore wind turbines according to claim 2, characterized in that, The process for determining the wave height risk contribution value is as follows: subtract the safe wave height benchmark from the wave height, and then divide the difference by the safe wave height benchmark.

6. A lightning protection system for offshore wind turbines according to claim 2, characterized in that, The salinity risk contribution value is determined by subtracting the salinity from the high salinity benchmark and dividing the difference by the high salinity benchmark; the tidal level risk contribution value is determined by subtracting the tidal level from the excellent tidal level benchmark and dividing the difference by the excellent tidal level benchmark.

7. A lightning protection system for offshore wind turbines according to claim 1, characterized in that, The process by which the collaborative control unit generates specific adjustment values ​​includes: For each movable grounding electrode, determine the adjustment allocation coefficient, and multiply the reference adjustment amount by the adjustment allocation coefficient corresponding to each movable grounding electrode to determine the specific adjustment amount of the electrode.

8. A lightning protection system for offshore wind turbines according to claim 7, characterized in that, The adjustment distribution coefficient is dynamically determined by the collaborative control unit based on the cumulative working time of the movable grounding electrode or the local microenvironment data.

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