An offshore wind power transformer benefit evaluation method based on a three-dimensional coupling model

Abstract

The application provides a kind of offshore wind power transformer benefit evaluation method based on three-dimensional coupling model, a kind of offshore wind power transformer benefit evaluation method based on three-dimensional coupling model, multiple sub-regions are divided in cable trench, multiple sensors are arranged in the top and bottom of each sub-region respectively, and the exhaust device in the sub-region is electrically connected;The sensor detects the concentration of harmful gas, and the sensor positions of the upper and lower parts of the cable trench are opposite, and multiple sensors form a rectangular distribution in the vertical plane;Each sensor separately detects whether the concentration of harmful gas reaches the warning threshold value.The beneficial effects of the application are: precise prevention and control are realized by spatial layering monitoring and dynamic risk algorithm.The system arranges gas sensors in pairs on the top and bottom of each cable trench sub-region, forming a rectangular monitoring network in the vertical direction.When the concentration of harmful gas is detected to break through the threshold value, the risk quantification model is started, the accumulated harmful gas is risk evaluated, and the risk response mechanism is started.

Description

Technical Field

[0001] This invention relates to the field of hazardous gas exhaust technology, specifically to a method for evaluating the profitability of offshore wind power transformers based on a three-dimensional coupled model. Background Technology

[0002] Offshore wind power, as a key pillar of global energy structure transformation and the achievement of "dual carbon" goals, is accelerating its development towards deep-sea and large-capacity applications. In this process, the investment, operation and maintenance costs, and reliability of offshore substation transformers—the core of power collection and transmission—directly determine the levelized cost of electricity (LCOE) and long-term economic benefits of the entire wind power project. Therefore, establishing a scientific and accurate transformer cost-benefit assessment model is crucial for guiding early-stage investment decisions, technology selection, and optimizing maintenance strategies during the operational phase. However, current widely adopted cost-benefit assessment methods reveal significant limitations and inadequacies in their theoretical frameworks and calculation models when facing the unique, complex, and harsh operating environment at sea, primarily in the following three aspects: The model has a single dimension: it often adopts a two-dimensional evaluation framework of "life cycle cost (LCC) + power generation revenue", ignoring the additional equipment loss costs caused by special marine environments (salt spray, wind waves, tides), resulting in an evaluation result deviation rate as high as 25%-30%.

[0003] Weak parameter correlation: Most cost and benefit parameters are statically assigned, without considering the dynamic coupling relationship between "environment-operating conditions-cost", such as the impact of wind and wave levels on operation and maintenance costs, and the nonlinear relationship between load factor and power generation gain, which cannot adapt to the complex scenario of offshore wind power.

[0004] Insufficient calculation accuracy: Traditional formulas mostly use linear coefficient corrections and do not introduce quantitative dynamic factors such as trigonometric functions and limit conditions. They are insufficient in characterizing cost fluctuations under extreme sea conditions (such as typhoons and storm surges), leading to increased investment decision-making risks.

[0005] In summary, existing cost-benefit assessment methods suffer from inherent deficiencies in dimensional systematicity, dynamic correlation, and computational accuracy, making it difficult to accurately and reliably reflect the actual economic profile of offshore wind power transformers throughout their entire lifecycle, especially in complex marine environments. Developing a comprehensive assessment model that deeply integrates marine environmental engineering, equipment reliability theory, and dynamic economic analysis has become a critical technical challenge that urgently needs to be addressed to improve the scientific nature of offshore wind power investment decisions, optimize asset management, and reduce overall project risks. Summary of the Invention

[0006] To address the issues of limited dimensions and weak parameter correlation in existing assessment methods, a three-dimensional coupled model of "life cycle cost - environmental loss - power generation gain" is constructed. Dynamic coupling parameters and complex calculation methods are introduced to improve the accuracy and scenario adaptability of cost-benefit assessment for offshore wind power transformers. This invention provides a method for evaluating the revenue of offshore wind power transformers based on a three-dimensional coupled model, comprising the following steps: S1. Collect relevant data: Collect cost data, including the basic cost C for purchasing onshore standard transformers. p Onshore installation foundation cost C i The basic cost of a single onshore maintenance operation is C0, and the basic cost of onshore scrapping disposal is C. d , on-grid electricity price ρ, unit power fault loss F l ; Collect natural environmental data, including the annual average salt spray deposition angle θ. s Humidity exceedance rate φ h Wind field distance from shore D, seabed slope angle α', significant wave height H s , Seasonal wind angle γ and wind wave frequency ω for recovery operations _w Marine environmental periodic angular frequency ω sea ; Data collected from the equipment: One-way distance D of the maintenance vessel o , Total lifespan Y, Reference vibration power loss P vo Single maintenance time (T), annual average load factor (φ) l Transformer rated capacity P n Unit power failure loss F l Actual failure rate R l Baseline failure rate R0, annual equipment corrosion loss rate λ c ; S2. Calculate the Life Cycle Cost (LCC); S3. Calculate the environmental loss cost (ELC) for construction; S4. Calculate the power generation gain benefit (GEB); S5. Calculate the three-dimensional coupling evaluation index CEI = α × GEB - β × (LCC + ELC) × (1 + δ × sinω) sea ') α – Benefit weighting coefficient, 0.3 during construction period, 0.7 during operation period, and 0.2 during decommissioning period; β – Cost weighting coefficient, 0.8 during construction, 0.5 during operation, and 0.6 during decommissioning; δ—Environment-benefit coupling coefficient, with a value range of 0.05 to 0.15; ω sea—The angular frequency of the marine environment, ranging from π to 2π rad / year.

[0007] As a preferred option, the annual average salt spray settling angle θ directly reflects the impact of salt spray on equipment corrosion protection. s ', obtained through a salt spray monitor.

[0008] As a preferred approach, the impact of high humidity environments on insulation performance is characterized by statistical analysis using a humidity logger.

[0009] As a preferred option, the seabed slope angle α', which affects the difficulty and cost of laying submarine cables, is obtained through seabed surveying.

[0010] As a preferred option, LCC=C p ×(1+K m ×sinθ s '+K e ×cosφ h ')+Ci×(1+K v ×T i '+K d ×D×cosα')+Σ[C o ×(1+K s ×D o +K t ×T o +K h ×H s )]+C d ×(1+K r ×cosγ'): in: θ s '——Salt spray settling angle, with a value range of [0, π / 2]; φ h '——Humidity exceedance rate, value range [0,π / 2]; T i — Lifting efficiency correction factor, with a value range of 1.0 to 1.8; D – Distance of the wind farm from the shore, ranging from 5 to 50 kilometers; α' — Seabed slope angle, with a value range of [0, π / 6]; D o —The one-way distance of the maintenance vessel ranges from 5 to 55 kilometers; T o — Maintenance delay time, ranging from 0 to 15 days per instance; H s —Significant wave height, ranging from 0.5 to 6.0 meters; C pBasic cost of purchasing standard onshore transformers; K m Marine corrosion protection and sealing modification coefficient, with a value range of 0.15~0.25; K e : Marine weather-resistant electrical insulation upgrade factor, with a value range of 0.08~0.12; C i Onshore installation foundation costs; K y The influence coefficient of wind and waves on offshore hoisting, with a value range of 0.3 to 0.5; K d Correction factor for submarine cable laying distance; C o Basic cost of a single onshore maintenance operation; K s : The rental coefficient for offshore operation and maintenance platforms, with a value ranging from 0.05 to 0.08 per kilometer; K t Maintenance time loss coefficient; K h Additional factor for sea state classification, with a value range of 0.1 to 0.3; C d Basic costs of land-based scrapping and disposal; K r Marine environmental recycling coefficient, with a value range of 0.2 to 0.3; γ': Seasonal wind angle for standardized recycling operations, ranging from 0 to π / 2 rad.

[0011] As a preferred option, ELC=C p ×λ c ×Y×sinθ s '+P vo ×K _wind ×λ v ×ρ×Y×T a ×φ1×sinω _w '; in: Y – Equipment lifecycle, ranging from 20 to 25 years; K _wind — Wave intensity correction factor, with a value range of 0.1 to 0.3; λ v —Vibration loss amplification factor, ranging from 1.2 to 1.4; ρ—Grid connection price, ranging from 0.3 to 0.8 yuan / kWh; T a —Equivalent annual maintenance time, ranging from 20 to 100 hours per year; ω _w —Standardized wind and wave angular frequency, ranging from π to 2π rad / year; P vo —Reference vibration power loss, with a value range of 5~8kW.

[0012] As a preferred option, GEB=P n ×T a ×ρ×[μ o -μ l ×φ l π / 200]+F l ×Y×(R l -R o ×K pn ); P n —Transformer rated capacity: Unit (MVA), value range 50~200MVA, is the core design parameter of the equipment, and determines the upper limit of power generation gain; μ o — Rated capacity utilization rate, with a value range of 0.8 to 0.9; μ l —Load rate correction factor, with a value range of 0.6 to 0.8; φ1—Average load factor; F l —Unit power failure loss, ranging from 50,000 to 150,000 yuan / MVA; K pn —Full load influence coefficient, with a value range of 1.0~1.2.

[0013] The beneficial effects of this invention are as follows: 1. The four-level evaluation framework constructed in this invention is the first to systematically integrate and quantitatively couple the three core dimensions of finance, environment, and performance. It not only calculates the full-cycle economic cost (LCC) of equipment procurement, construction, operation and maintenance, and decommissioning, but also considers the potential damage to the marine ecological environment caused by project operation, such as biological impacts and water quality changes, as environmental costs (ELC), while accurately quantifying its power generation output and energy substitution benefits (GEB). Finally, a multi-dimensional normalized comprehensive evaluation is conducted through the CEI index, transforming project evaluation from the traditional "single-threaded economic comparison" to a "multi-objective optimization of cost-loss-benefit."

[0014] 2. This invention deeply embeds a series of dynamic environmental parameters, such as salt spray corrosion rate, the effect of humidity on electrical insulation, fatigue damage caused by wind and wave loads, and the constraints of sea conditions on operation and maintenance accessibility, into the evaluation models at all levels. This makes the evaluation results no longer static estimates under ideal laboratory conditions, but dynamic anti-interference evaluations that can truly reflect the project's ability to "survive" and "produce" in a specific sea area, greatly improving the reliability of the evaluation results. Detailed Implementation

[0015] To illustrate the features of the present invention, the present invention will be further described below with reference to embodiments.

[0016] Example 1: This invention is deployed in a cable trench system of a 220kV substation. The cable trench has a total length of 150.4m × width of 1.8m × depth of 2.2m, and is divided longitudinally into 24 standard sub-areas of 6m each and one end area of ​​6.4m. A monitoring matrix is ​​constructed for each sub-area: GDS60 CO / H2S composite sensors (range 0-2000ppm / resolution 1ppm) based on the NDIR principle are arranged at 0.44m below the trench top cover and at 1.54m below the trench top cover, respectively, in a 2×3 matrix, forming 6 monitoring nodes in the vertical direction (vertical spacing 1.1m / horizontal spacing 2m). The detection data from each node is connected to a local PLC control box via armored shielded cables. Each sub-area is equipped with two GD-5K explosion-proof axial flow exhaust fans as ventilation devices. The fan inlet is located 0.6m above the monitoring surface at the bottom of the trench, and the outlet extends to the ground pressure relief tower. The CO safety threshold C... 11 =40ppm, H2S safety threshold C 22 =8ppm; the safety threshold for the rate of change of gas concentration is set at V according to OSHA standards. 11 =2ppm / min (CO), V 12 =1ppm / min (H2S); Spatial weighting coefficient characteristic assigned to W 下 =0.6、W_up=0.4.

[0017] 1) Calculate the bottom risk component: When the sensor at the bottom of the trench in the sub-region detects H2S = 10 ppm (ΔH2S / Δt = 2 ppm / min) and CO = 45 ppm (ΔCO / Δt = 3 ppm / min) at time t0... R 下_H2S =0.6×[0.6×(10 / 8)+0.4×((2) / 1)]=0.93 R 下_CO =0.6×[0.6×(45 / 40)+0.4×((3) / 2)]=0.765 2) Calculate the top risk component: At time t0, the sensor at the top of the trench in the sub-region detects H2S = 8 ppm (ΔH2S / Δt = 1 ppm / min) and CO = 40 ppm (ΔCO / Δt = 2 ppm / min). R 上_H2S =0.4×[0.6×(8 / 8)+0.4×((1) / 1)]=0.4 R 上_CO =0.4×[0.6×(40 / 40)+0.4×((2) / 2)]=0.4 3) Calculate the evaluation values ​​of H2S and CO (top trigger 18 seconds after bottom trigger, K is 1.5; 3 out of 6 nodes exceed the threshold, P=3 / 6=0.5): H _H2S =(0.93+0.4)×1.5×0.5=0.9975 H _CO =(0.765+0.4)×1.5×0.5=0.87375 4) Based on H_ H2S =0.9975, H_ CO =0.87375 triggers a level 4 response.

[0018] This embodiment achieves precise prevention and control through spatial layered monitoring and dynamic risk algorithms. The system deploys gas sensors in pairs at the top and bottom of each cable trench area, forming a vertical rectangular monitoring network. When the concentration of harmful gas exceeds a threshold, a risk quantification model is activated to assess the risk of the accumulated harmful gas and initiate a corresponding risk response mechanism. A six-level progressive strategy is employed, escalating from passive ventilation to regional coordinated defense.

[0019] Furthermore, the system employs a multi-gas classification approach, using differentiated risk algorithm parameters for CO and H2S. A more stringent rate-of-change threshold is set for highly toxic H2S, increasing its sensitivity by 50% compared to CO, ensuring a rapid response to highly toxic substances. When multiple harmful gases are triggered simultaneously in the trench, the system automatically selects the execution strategy with the higher risk level to avoid underestimating the combined toxicity effects.

[0020] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. The present invention has been described in detail with reference to preferred embodiments. Those skilled in the art should understand that any changes, modifications, additions, or substitutions made by those skilled in the art within the scope of the present invention do not depart from the spirit of the present invention and should also fall within the protection scope of the claims of the present invention. Other related technical structures not disclosed in detail in the present invention are existing technologies in the field.

Claims

1. A method for evaluating the revenue of offshore wind power transformers based on a three-dimensional coupled model, characterized in that, Includes the following steps: S1. Collect relevant data: Collect cost data, including the basic cost C for purchasing onshore standard transformers. p Onshore installation foundation cost C i The basic cost of a single onshore maintenance operation is C0, and the basic cost of onshore scrapping disposal is C. d , Grid connection price ρ, Unit power fault loss F l ; Collect natural environmental data, including the annual average salt spray deposition angle θ. s Humidity exceedance rate φ h Wind field distance from shore D, seabed slope angle α', significant wave height H s , Seasonal wind angle γ and wind wave frequency ω for recovery operations _w Marine environmental periodic angular frequency ω sea ; Data collected from the equipment: One-way distance D of the maintenance vessel o , Total lifespan Y, Reference vibration power loss P vo Single maintenance time (T), annual average load factor (φ) l Transformer rated capacity P n Unit power failure loss F l Actual failure rate R l Baseline failure rate R0, annual equipment corrosion loss rate λ c ; S2. Calculate the Life Cycle Cost (LCC); S3. Calculate the environmental loss cost (ELC) for construction; S4. Calculate the power generation gain benefit (GEB); S5. Calculate the three-dimensional coupling evaluation index CEI = α × GEB - β × (LCC + ELC) × (1 + δ × sinω) sea ') α – Benefit weighting coefficient, 0.3 during construction period, 0.7 during operation period, and 0.2 during decommissioning period; β – Cost weighting coefficient, 0.8 during construction, 0.5 during operation, and 0.6 during decommissioning; δ—Environment-benefit coupling coefficient, with a value range of 0.05 to 0.15; ω sea —The angular frequency of the marine environment, ranging from π to 2π rad / year.

2. The method for evaluating the revenue of offshore wind power transformers based on a three-dimensional coupled model according to claim 1, characterized in that: The annual average salt spray settling angle θ directly reflects the impact of salt spray on equipment corrosion protection. s ', obtained through a salt spray monitor.

3. The method for evaluating the revenue of offshore wind power transformers based on a three-dimensional coupled model according to claim 1, characterized in that: The impact of high humidity on insulation performance was characterized by statistical analysis using a humidity recorder.

4. The method for evaluating the revenue of offshore wind power transformers based on a three-dimensional coupled model according to claim 1, characterized in that: The seabed slope angle α', which affects the difficulty and cost of laying submarine cables, is obtained through seabed surveying.

5. The method for evaluating the revenue of offshore wind power transformers based on a three-dimensional coupled model according to claim 1, characterized in that: LCC=C p ×(1+K m ×sinθ s '+K e ×cosφ h ')+C i ×(1+K v ×Ti'+K d ×D×cosα')+Σ[C o ×(1+K s ×D o +K t ×T o +K h ×H s )]+C d ×(1+K r ×cosγ'): in: θ s '——Salt spray settling angle, with a value range of [0, π / 2]; φ h '——Humidity exceedance rate, value range [0,π / 2]; T i — Lifting efficiency correction factor, with a value ranging from 1.0 to 1.8; D – Distance of the wind farm from the shore, ranging from 5 to 50 kilometers; α' — Seabed slope angle, with a value range of [0, π / 6]; D o —The one-way distance of the maintenance vessel ranges from 5 to 55 kilometers; T o — Maintenance delay time, ranging from 0 to 15 days per instance; H s —Significant wave height, ranging from 0.5 to 6.0 meters; C p Basic cost of purchasing standard onshore transformers; K m Marine corrosion protection and sealing modification coefficient, with a value range of 0.15~0.25; K e : Marine weather-resistant electrical insulation upgrade factor, with a value range of 0.08~0.12; C i Onshore installation foundation costs; K y The influence coefficient of wind and waves on offshore hoisting, with a value range of 0.3 to 0.5; K d Correction factor for submarine cable laying distance; C o Basic cost of a single onshore maintenance operation; K s : The rental coefficient for offshore operation and maintenance platforms, with a value ranging from 0.05 to 0.08 per kilometer; K t Maintenance time loss coefficient; K h Additional factor for sea state classification, with a value range of 0.1 to 0.3; C d Basic costs of land-based scrapping and disposal; K r Marine environmental recycling coefficient, with a value range of 0.2 to 0.3; γ': Seasonal wind angle for standardized recycling operations, ranging from 0 to π / 2 rad.

6. The method for evaluating the revenue of offshore wind power transformers based on a three-dimensional coupled model according to claim 1, characterized in that: ELC=C p ×λ c ×Y×sinθ s '+P vo ×K _wind ×λ v ×ρ×Y×T a ×φ1×sinω _w '; in: Y – Equipment lifecycle, ranging from 20 to 25 years; K_wind — Wind and wave intensity correction factor, with a value range of 0.1 to 0.3; λ v —Vibration loss amplification factor, ranging from 1.2 to 1.4; ρ—Grid connection price, ranging from 0.3 to 0.8 yuan / kWh; T a —Equivalent annual maintenance time, ranging from 20 to 100 hours per year; ω _w' —Standardized wind and wave angular frequency, ranging from π to 2π rad / year; P vo —Reference vibration power loss, with a value range of 5~8kW.

7. The method for evaluating the revenue of offshore wind power transformers based on a three-dimensional coupled model according to claim 1, characterized in that: GEB=P n ×T a ×ρ×[μ o -m l ×φ l π / 200]+F l ×Y×(R l -R o ×K pn ); P n —Transformer rated capacity: Unit (MVA), value range 50~200MVA, is the core design parameter of the equipment, and determines the upper limit of power generation gain; μ o — Rated capacity utilization rate, with a value range of 0.8 to 0.9; μ l —Load rate correction factor, with a value range of 0.6 to 0.8; φ1—Average load factor; F l —Unit power failure loss, ranging from 50,000 to 150,000 yuan / MVA; K pn —Full load influence coefficient, with a value range of 1.0~1.2.