Renewable new energy power generation control system and method

Abstract

The invention discloses a renewable new energy power generation control system and method, and the method comprises the steps: introducing an initial model into a training module, taking the thermal conversion efficiency of each boiler, the conversion efficiency of each turbine, each cooling efficiency and each pump efficiency as the input, taking the corresponding geothermal power generation efficiency as the output, and retraining the initial model to obtain a power generation efficiency prediction model; the factor optimization unit takes each power generation control parameter as an ant individual, and generates an optimization pheromone volatilization factor and an optimization heuristic factor according to each environment parameter; the optimizing unit updates a state transition probability formula and a pheromone updating formula according to the optimized pheromone volatilization factor and the optimized heuristic factor to obtain an objective function value and a global optimal solution of each ant individual, and outputs each power generation control parameter contained in the global optimal solution as an optimal parameter set; and the terrestrial heat adjusting module dynamically adjusts the equipment parameters according to the optimal parameter set. The geothermal power generation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geothermal energy power generation, and in particular to a renewable energy power generation control system and method. Background Art

[0002] Geothermal energy is a renewable energy source that originates from the interior of the earth. It uses underground hot water or steam to generate electricity. The development and utilization of geothermal energy is mainly concentrated in areas with rich geothermal resources, because geothermal power generation usually requires higher geothermal temperatures, and only a sufficient temperature difference can be effectively converted into electricity. In order to develop and utilize geothermal resources, geothermal power stations are built in high-temperature geothermal areas. This type of area is mainly distributed along the edge of tectonic plates, where crustal activities are frequent and small and medium-sized earthquakes often occur. Therefore, geothermal power stations are in a high-temperature earthquake environment for a long time, and the internal equipment is easily affected by high temperature and vibration, which accelerates aging and affects the efficiency of geothermal power generation.

[0003] At present, the existing technology uses data acquisition sensors to collect the operating data of each device, and then conducts manual analysis to adjust the operating parameters of each device to improve the efficiency of geothermal power generation. However, the manual analysis process depends on the experience and analysis ability of the operator. At the same time, the manual analysis process usually takes a long time and cannot guarantee the adjustment speed and accuracy of the equipment parameter adjustment, which affects the power generation efficiency of geothermal power generation. Summary of the invention

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a renewable energy power generation control system and method for dynamically adjusting equipment parameters in a geothermal power generation system to improve geothermal power generation efficiency.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a renewable energy power generation control system, comprising:

[0006] A collection module is used to collect multiple waste heat boiler parameters, turbine parameters, condenser parameters, working fluid pump parameters and geothermal power generation parameters of a geothermal power station;

[0007] A calculation module, connected to the acquisition module, for calculating the corresponding boiler heat conversion efficiency, turbine conversion efficiency, cooling efficiency, pump efficiency and geothermal power generation efficiency according to the waste heat boiler parameters, the turbine parameters, the condenser parameters, the working fluid pump parameters and the geothermal power generation parameters;

[0008] a training module, connected to the acquisition module and the calculation module respectively, for introducing an initial model, taking the heat conversion efficiency of each boiler, the conversion efficiency of each turbine, the cooling efficiency and the pump efficiency as input, taking the corresponding geothermal power generation efficiency as output, retraining the initial model to obtain a power generation efficiency prediction model, wherein the input of the power generation efficiency prediction model is a plurality of power generation control parameters, and the output of the power generation efficiency prediction model is the geothermal predicted power generation efficiency;

[0009] An environmental detection module, used for detecting multiple environmental parameters outside the geothermal power station in real time;

[0010] The optimization module is connected to the training module and the environment detection module respectively, and includes:

[0011] A function establishment unit, used to establish an objective function according to the predicted geothermal power generation efficiency and a preset ideal power generation efficiency, wherein the output of the objective function is an objective function value;

[0012] A factor optimization unit, used for introducing a preset ant colony algorithm, treating each power generation control parameter as an ant individual, and generating an optimized pheromone volatilization factor and an optimized heuristic factor according to each environmental parameter;

[0013] an optimization unit, connected to the factor optimization unit and the function establishment unit, for updating the pheromone update formula and the state transition probability formula according to the optimized pheromone volatilization factor and the optimized heuristic factor respectively, and continuously iteratively updating the objective function value of each ant individual using the updated state transition probability formula and the pheromone update formula, and finally taking the ant individual with the minimum objective function value obtained by iterative updating as the global optimal solution, and outputting the power generation control parameters contained in the global optimal solution as the optimal parameter set;

[0014] The geothermal adjustment module is connected to the optimization module and is used to dynamically adjust the equipment parameters of the waste heat boiler, turbine, condenser and working fluid pump in the geothermal power plant according to the optimal parameter set.

[0015] Furthermore, the environmental parameters include geothermal environment temperature and geothermal vibration, and the calculation formula for optimizing the pheromone volatilization factor is configured as:

[0016]

[0017] Among them, ρ optIt is used to represent the optimized pheromone volatilization factor, ρ is used to represent the pheromone volatilization factor in the ant colony algorithm, T is used to represent the geothermal environment temperature, V is used to represent the geothermal vibration amount, α is used to represent the influence coefficient of the geothermal environment temperature on the pheromone volatilization factor, β is used to represent the influence coefficient of the geothermal vibration amount on the pheromone volatilization factor, γ is used to represent the constant term in the denominator of the formula, and δ is used to represent the influence coefficient of the pheromone volatilization factor in the ant colony algorithm on the optimized pheromone volatilization factor.

[0018] Furthermore, the calculation formula of the optimization heuristic factor is configured as:

[0019]

[0020] Among them, η opt is used to represent the optimized heuristic factor, α′ is used to represent the influence coefficient of the geothermal environment temperature on the heuristic factor, β′ is used to represent the exponential influence factor of the geothermal environment temperature, is used to represent the influence coefficient of the geothermal vibration on the heuristic factor, δ′ is used to represent the constant term in the denominator, η is used to represent the initial heuristic factor in the ant colony algorithm, and ∈ is used to represent the influence coefficient of the initial heuristic factor in the ant colony algorithm on the optimized heuristic factor.

[0021] Furthermore, it also includes a device detection module, which is connected to the optimization module and includes:

[0022] A parameter detection unit, used for detecting the performance parameters of the turbine and the working fluid pump in real time;

[0023] An aging calculation unit, connected to the parameter detection unit, for inputting each of the performance parameters into a preset equipment aging calculation formula to calculate an aging potential risk score value;

[0024] A processing unit is connected to the aging calculation unit and is used to generate a parameter restriction range according to the aging hidden danger score value, and restrict each of the power generation control parameters according to the parameter restriction range.

[0025] Furthermore, the equipment aging calculation formula is configured as:

[0026]

[0027] Among them, A hs is used to represent the aging risk score value, P(w) is used to represent the power output of the turbine, V i is used to represent the flow rate of the working fluid pump, and θ, μ, σ, and τ are used to represent a preset first weight coefficient, a second weight coefficient, a third weight coefficient, and a fourth weight coefficient, respectively.

[0028] Furthermore, the parameter limit range is configured as:

[0029]

[0030] Among them, A hs ∈(A hmin , A hmax ), A, B are used to represent the turbine conversion efficiency and the pump efficiency, respectively. hmin and A hmax are used to represent the lower limit and upper limit of the aging potential risk score value, respectively. min and A max are used to represent the lower and upper limits of the turbine conversion efficiency, respectively, min and B max Respectively, for the lower and upper limits of the pump efficiency.

[0031] Further, the waste heat boiler parameters include waste heat boiler output heat and gas consumption, the turbine parameters include inlet steam pressure, inlet steam temperature, outlet steam pressure, outlet steam temperature, steam flow rate and turbine actual power, the working fluid pump parameters include pump head, pump fluid density, pump flow rate, pump torque, pump angular velocity and pump mechanical efficiency, the condenser parameters include actual heat dissipation and theoretical maximum heat dissipation, the geothermal power generation parameters include geothermal fluid temperature, geothermal fluid flow rate, geothermal fluid specific heat capacity and actual power generation;

[0032] The calculation module comprises:

[0033] A first calculation unit is used to calculate the heat conversion efficiency of the boiler according to the heat output of the waste heat boiler and the gas consumption;

[0034] a second calculation unit, configured to calculate an enthalpy drop according to the inlet steam pressure, the inlet steam temperature, the outlet steam pressure, and the outlet steam temperature, calculate a turbine theoretical power according to the enthalpy drop and the steam flow rate, and calculate a turbine conversion efficiency according to the turbine theoretical power and the turbine actual power;

[0035] a third calculation unit, configured to calculate the effective power of the pump according to the pump head, the pump fluid density, and the pump flow rate, calculate the shaft power according to the pump torque, the pump angular velocity, and the pump mechanical efficiency, and further calculate the pump efficiency according to the effective power of the pump and the shaft power;

[0036] a fourth calculation unit, configured to calculate the cooling efficiency according to the actual heat dissipation and the theoretical maximum heat dissipation;

[0037] The fifth calculation unit is used to calculate the thermal power according to the geothermal fluid temperature, the geothermal fluid flow rate and the geothermal fluid specific heat capacity, and calculate the geothermal power generation efficiency according to the thermal power and the actual power generation power.

[0038] Furthermore, the power generation efficiency prediction model is a BP neural network model, and the mathematical expression of the power generation efficiency prediction model is configured as:

[0039] E predicted =BPNN(η boiler , η turbine , η cooler , η pump , T e , P, M, t)

[0040] Among them, E predicted is used to represent the predicted geothermal power generation efficiency, BPNN is used to represent the BP neural network model, η boiler Used to represent the boiler heat conversion efficiency, η turbine Used to express the turbine conversion efficiency, η cooler Used to express the cooling efficiency, η pump Used to express the pump efficiency, T e It is used to indicate the temperature of geothermal fluid, P is used to indicate the pressure of geothermal fluid, M is used to indicate the mass of geothermal fluid, and t is used to indicate the time variable.

[0041] A renewable energy power generation control method, applied to the above renewable energy power generation control system, comprises:

[0042] Step S1, the collection module collects multiple waste heat boiler parameters, turbine parameters, condenser parameters, working fluid pump parameters and geothermal power generation parameters of the geothermal power station;

[0043] Step S2, the calculation module calculates the corresponding boiler heat conversion efficiency, turbine conversion efficiency, cooling efficiency, pump efficiency and geothermal power generation efficiency according to the waste heat boiler parameters, the turbine parameters, the condenser parameters, the working fluid pump parameters and the geothermal power generation parameters;

[0044] Step S3, the training module introduces an initial model, and takes the heat conversion efficiency of each boiler, the conversion efficiency of each turbine, the cooling efficiency and the pump efficiency as input, takes the corresponding geothermal power generation efficiency as output, retrains the initial model to obtain a power generation efficiency prediction model, the input of the power generation efficiency prediction model is a plurality of power generation control parameters, and the output of the power generation efficiency prediction model is the geothermal predicted power generation efficiency;

[0045] Step S4, the environment detection module detects multiple environmental parameters outside the geothermal power station in real time;

[0046] Step S5, the function establishment unit establishes an objective function according to the predicted geothermal power generation efficiency and the preset ideal power generation efficiency, the output of the objective function is the objective function value, the factor optimization unit introduces a preset ant colony algorithm, takes each of the power generation control parameters as an ant individual, and generates an optimized pheromone volatilization factor and an optimized heuristic factor according to each of the environmental parameters;

[0047] Step S6, the optimization unit updates the pheromone update formula and the state transition probability formula according to the optimized pheromone volatilization factor and the optimized heuristic factor respectively, and continuously iterates and updates the objective function value of each ant individual using the updated state transition probability formula and the pheromone update formula, and finally takes the ant individual with the minimum objective function value obtained by iterative update as the global optimal solution, and outputs the power generation control parameters contained in the global optimal solution as the optimal parameter set;

[0048] Step S7: The geothermal adjustment module dynamically adjusts the equipment parameters of the waste heat boiler, turbine, condenser and working fluid pump in the geothermal power plant according to the optimal parameter set.

[0049] Beneficial effects of the present invention:

[0050] The invention uses the heat conversion efficiency of each boiler, the conversion efficiency of each turbine, the cooling efficiency and the efficiency of each pump as input, and uses the corresponding geothermal power generation efficiency as output to train a power generation efficiency prediction model, establishes an objective function according to the geothermal predicted power generation efficiency and the ideal power generation efficiency, uses multiple power generation control parameters input into the power generation efficiency prediction model as ant individuals, and introduces environmental parameters to generate optimized pheromone volatilization factors and optimized heuristic factors, and uses the optimized pheromone volatilization factors and optimized heuristic factors to update the pheromone update formula and state transition probability formula in the ant colony algorithm, and filters out external loops through updating and improvement. The influence of the environment on each power generation control parameter is taken into consideration, and the global convergence of the algorithm is ensured while improving the solution efficiency and quality. Then, the improved ant colony algorithm is used to continuously iterate and update for optimization, and the ant individual with the minimum objective function is found as the global optimal solution, and the power generation control parameters in the global optimal solution are taken as the optimal parameter set. Finally, according to the optimal parameter set, the equipment parameters of the waste heat boiler, turbine, condenser and working fluid pump in the geothermal power station are dynamically adjusted, so that the waste heat boiler, turbine, condenser and working fluid pump in the geothermal power station operate under the optimal power generation efficiency condition, thereby significantly improving the geothermal power generation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1It is a structural schematic diagram of the renewable energy power generation control system in the present invention;

[0052] Figure 2 It is a structural schematic diagram of the device detection module in the present invention;

[0053] Figure 3 It is a structural diagram of the calculation module in the present invention;

[0054] Figure 4 It is a flow chart of the steps of the renewable energy power generation control method in the present invention.

[0055] Figure numerals: 1. acquisition module; 2. calculation module; 21. first calculation unit; 22. second calculation unit; 23. third calculation unit; 24. fourth calculation unit; 25. fifth calculation unit; 3. training module; 4. environment detection module; 5. optimization module; 51. function establishment unit; 52. factor optimization unit; 53. optimization unit; 6. geothermal adjustment module; 7. equipment detection module; 71. parameter detection unit; 72. aging calculation unit; 73. processing unit. DETAILED DESCRIPTION

[0056] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. The same parts are represented by the same reference numerals. It should be noted that the words "front", "rear", "left", "right", "upper" and "lower" used in the following description refer to directions in the accompanying drawings, and the words "bottom surface" and "top surface", "inner" and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0057] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides a renewable energy power generation control system, which can significantly improve the efficiency of geothermal power generation, including:

[0058] The acquisition module 1 is used to acquire multiple waste heat boiler parameters, turbine parameters, condenser parameters, working fluid pump parameters and geothermal power generation parameters of the geothermal power station;

[0059] The calculation module 2 is connected to the acquisition module 1 and is used to calculate the corresponding boiler heat conversion efficiency, turbine conversion efficiency, cooling efficiency, pump efficiency and geothermal power generation efficiency according to the waste heat boiler parameters, turbine parameters, condenser parameters, working fluid pump parameters and geothermal power generation parameters;

[0060] The training module 3 is connected to the acquisition module 1 and the calculation module 2 respectively, and is used to introduce an initial model, and take the heat conversion efficiency of each boiler, the conversion efficiency of each turbine, the cooling efficiency and the efficiency of each pump as input, and take the corresponding geothermal power generation efficiency as output, and retrain the initial model to obtain a power generation efficiency prediction model. The input of the power generation efficiency prediction model is a plurality of power generation control parameters, and the output of the power generation efficiency prediction model is the geothermal predicted power generation efficiency;

[0061] The environmental detection module 4 is used to detect multiple environmental parameters outside the geothermal power station in real time;

[0062] The optimization module 5 is connected to the training module 3 and the environment detection module 4 respectively, and includes:

[0063] A function establishment unit 51 is used to establish an objective function according to the predicted geothermal power generation efficiency and the preset ideal power generation efficiency, and the output of the objective function is the objective function value;

[0064] The factor optimization unit 52 is used to introduce a preset ant colony algorithm, take each power generation control parameter as an ant individual, and generate an optimized pheromone volatilization factor and an optimized heuristic factor according to each environmental parameter;

[0065] The optimization unit 53, the connection factor optimization unit 52 and the function establishment unit 51, is used to update the pheromone update formula and the state transition probability formula according to the optimized pheromone volatilization factor and the optimized heuristic factor respectively, and continuously iterate and update the objective function value of each ant individual using the updated state transition probability formula and the pheromone update formula, and finally take the ant individual with the minimum objective function value obtained by iterative update as the global optimal solution, and output the power generation control parameters contained in the global optimal solution as the optimal parameter set;

[0066] The geothermal adjustment module 6 is connected to the optimization module 5 and is used to dynamically adjust the equipment parameters of the waste heat boiler, turbine, condenser and working fluid pump in the geothermal power station according to the optimal parameter set.

[0067] Working principle of embodiment 1:

[0068] The acquisition module 1 includes multiple data acquisition sensors, which respectively collect parameters of the waste heat boiler, turbine, condenser, working fluid pump and geothermal well in the geothermal power station to obtain multiple waste heat boiler parameters, multiple turbine parameters, multiple condenser parameters, multiple working fluid pump parameters and multiple geothermal power generation parameters. The calculation module 2 can be a calculation program configured on the intelligent processing terminal, which is used to input each waste heat boiler parameter, each turbine parameter, each condenser parameter, each working fluid pump parameter and each geothermal power generation parameter into their respective pre-configured calculation formulas, and respectively calculate the corresponding boiler heat conversion efficiency, turbine conversion efficiency, cooling efficiency and geothermal power generation efficiency. The training module 3 can be a model training program pre-configured on the intelligent processing terminal. The model training program pre-stores an initial model, which is a BP neural network model. The power generation efficiency prediction model is retrained based on the initial model. The mathematical expression of the power generation efficiency prediction model is configured as:

[0069] E predicted =BPNN(η boiler , η turbine , η cooler , η pump , T e , P, M, t)

[0070] Among them, E predicted It is used to represent the geothermal power generation efficiency prediction, BPNN is used to represent the BP neural network model, η boiler Used to express boiler heat conversion efficiency, η turbine Used to express turbine conversion efficiency, η cooler Used to express cooling efficiency, η pump Used to express pump efficiency, T e It is used to indicate the temperature of geothermal fluid, P is used to indicate the pressure of geothermal fluid, M is used to indicate the mass of geothermal fluid, and t is used to indicate the time variable.

[0071] The environment detection module 4 may include multiple environment sensors for real-time detection of multiple environment parameters outside the geothermal power plant. The geothermal adjustment module 6 may include multiple equipment controllers for adjusting equipment parameters of the waste heat boiler, turbine, condenser and working fluid pump.

[0072] The optimization module 5 can be a parameter optimization program configured on the intelligent processing terminal. The environmental parameters in the environment will affect the performance of each device in the geothermal power station, and then affect the accuracy of the power generation control parameters. Therefore, the ant individuals of the ant colony algorithm are affected, which affects the global search ability and convergence speed of the ant colony algorithm. In order to filter out the influence of the external environment on the ant colony algorithm, this embodiment introduces multiple environmental parameters to generate optimized pheromone volatility factors and optimized heuristic factors, and uses the optimized pheromone volatility factors and optimized heuristic factors to update the pheromone update formula and state transition probability formula in the ant colony algorithm. The update content of the pheromone update formula is: the original pheromone volatility factor in the formula is replaced by the optimized pheromone volatility factor, and the rest of the formula content remains unchanged. The update content of the state transition probability formula is: the original heuristic factor in the formula is replaced by the optimized heuristic factor, and the rest of the formula content remains unchanged.

[0073] Through the two formulas, the influence of the external environment on each power generation control parameter is filtered out, ensuring the global convergence of the algorithm while improving the efficiency and quality of the solution. Then, the improved ant colony algorithm is used to continuously iterate and update to find the optimal solution, and the ant individual with the minimum objective function is found as the global optimal solution, and the power generation control parameters in the global optimal solution are used as the optimal parameter set. Finally, according to the optimal parameter set, the equipment parameters of the waste heat boiler, turbine, condenser and working fluid pump in the geothermal power station are dynamically adjusted, so that the waste heat boiler, turbine, condenser and working fluid pump in the geothermal power station operate under the best power generation efficiency conditions, thereby significantly improving the efficiency of geothermal power generation.

[0074] Preferably, the environmental parameters include geothermal environment temperature and geothermal vibration, and the calculation formula for optimizing the pheromone volatilization factor is configured as:

[0075]

[0076] Among them, ρ opt It is used to represent the optimized pheromone volatilization factor, ρ is used to represent the pheromone volatilization factor in the ant colony algorithm, T is used to represent the geothermal environment temperature, V is used to represent the geothermal vibration, α is used to represent the influence coefficient of the geothermal environment temperature on the pheromone volatilization factor, β is used to represent the influence coefficient of the geothermal vibration on the pheromone volatilization factor, γ is used to represent the constant term in the denominator of the formula, δ is used to represent the influence coefficient of the pheromone volatilization factor in the ant colony algorithm on the optimized pheromone volatilization factor, t is used to represent the time variable, and i is used to represent the integral index.

[0077] Specifically, in this embodiment, the pheromone volatility factor determines the speed at which pheromones disappear over time, and its size directly affects the global search capability and convergence speed of the ant colony algorithm. If the pheromone volatility factor is too large, although it can speed up the convergence speed, it is easy to fall into the local optimum; conversely, if the pheromone volatility factor is too small, it may cause the algorithm's global search capability to decline;

[0078] The influence coefficient of geothermal environment temperature on pheromone volatilization factor indicates the degree of amplification or reduction of pheromone volatilization factor by temperature change, the influence coefficient of geothermal vibration on pheromone volatilization factor indicates the degree of amplification or reduction of pheromone volatilization factor by vibration change, and the influence coefficient of pheromone volatilization factor on optimized pheromone volatilization factor indicates the importance of original pheromone volatilization factor in the optimization process. The value range of optimized pheromone volatilization factor depends on the selection of various parameters and the actual input geothermal environment temperature, geothermal vibration and pheromone volatilization factor in ant colony algorithm. In practical application, the value range of optimized pheromone volatilization factor is between [0,1] to ensure the practical significance and application effect of pheromone volatilization factor.

[0079] Preferably, the calculation formula for optimizing the heuristic factor is configured as:

[0080]

[0081] Among them, η opt is used to represent the optimization heuristic factor, α′ is used to represent the influence coefficient of geothermal environment temperature on the heuristic factor, β′ is used to represent the exponential influence factor of geothermal environment temperature, is used to represent the influence coefficient of geothermal vibration on the heuristic factor, δ′ is used to represent the constant term in the denominator, η is used to represent the initial heuristic factor in the ant colony algorithm, and ∈ is used to represent the influence coefficient of the initial heuristic factor in the ant colony algorithm on the optimized heuristic factor.

[0082] Specifically, in this embodiment, the influence coefficient of geothermal environment temperature on the heuristic factor indicates the degree of amplification or reduction of the heuristic factor by temperature change, the exponential influence factor of geothermal environment temperature is used to adjust the nonlinear influence of temperature on the heuristic factor, the influence coefficient of geothermal vibration on the heuristic factor indicates the degree of amplification or reduction of the heuristic factor by vibration change, δ′ is used to adjust the proportional relationship of the overall formula to prevent the denominator from being zero, and the influence coefficient of the initial heuristic factor in the ant colony algorithm on the optimized heuristic factor indicates the importance of the original heuristic factor in the optimization process. The value range of the optimized heuristic factor depends on the selection of each parameter and the actual input geothermal environment temperature, geothermal vibration and heuristic factor in the ant colony algorithm. In practical applications, the value range of the optimized heuristic factor is between [0,1] to ensure the practical significance and application effect of the heuristic factor.

[0083] Embodiment 2 is the second embodiment of the present invention. Different from the previous embodiment, this embodiment provides an equipment detection module 7, which can reduce the potential safety hazards caused by equipment aging and improve the safety of the geothermal power generation system. The equipment detection module 7 is connected to the optimization module 5, such as Figure 2 As shown, the device detection module 7 includes:

[0084] A parameter detection unit 71, used for real-time detection of performance parameters of the turbine and the working fluid pump;

[0085] The aging calculation unit 72 is connected to the parameter detection unit 71 and is used to input various performance parameters into a preset equipment aging calculation formula to calculate an aging potential risk score value;

[0086] The processing unit 73 is connected to the aging calculation unit 72 and is used to generate a parameter limit range according to the aging hidden danger score value, and limit each power generation control parameter according to the parameter limit range.

[0087] Working principle of embodiment 2:

[0088] The turbine and the working fluid pump will age faster under the influence of high temperature and vibration. The performance of the aged equipment will decline. As the performance of the equipment declines, if the power generation control parameters of the turbine and the working fluid pump are not restricted, it may cause equipment damage. In order to extend the service life of the equipment, it is necessary to restrict the power generation control parameters according to the aging degree of the turbine and the working fluid pump. After restriction, the safety hazards caused by the aging of the turbine and the working fluid pump will be greatly reduced, and the safety of the geothermal power generation system will be significantly improved. The parameter detection unit 71 includes a plurality of detection sensors for detecting the power output of the turbine and the flow rate of the working fluid pump. Preferably, the equipment aging calculation formula is configured as:

[0089] Among them, A hs It is used to indicate the aging risk score value, P(w) is used to indicate the power output of the turbine, V i It is used to represent the flow rate of the working fluid pump, and θ, μ, σ, and τ are used to represent a preset first weight coefficient, a second weight coefficient, a third weight coefficient, and a fourth weight coefficient, respectively.

[0090] Specifically, in this embodiment, the first weight coefficient, the second weight coefficient, the third weight coefficient and the fourth weight coefficient are used to adjust the contribution of each item to the aging potential risk score value. In the above formula, the influence of turbine power output performance, working fluid pump flow, external geothermal environment temperature and geothermal vibration on aging potential risks is taken into account, and the overall aging situation over a period of time is comprehensively evaluated through integration.

[0091] Preferably, the parameter limit range is configured as:

[0092]

[0093] Among them, A hs ∈(A hmin , A hmax ), A, B are used to represent turbine conversion efficiency and pump efficiency respectively, A hmin and A hmax They are used to represent the lower and upper limits of the aging risk score, respectively. min and A hmax They are used to represent the lower and upper limits of turbine conversion efficiency, respectively. min and B max Used for lower and upper values ​​of pump efficiency respectively.

[0094] Specifically, in this embodiment, by limiting the turbine conversion efficiency between the lower limit value and the upper limit value of the turbine conversion efficiency, the turbine can be placed in a relatively safe working range, thereby avoiding damage to the turbine; similarly, by limiting the pump efficiency between the lower limit value and the upper limit value of the pump efficiency, the working fluid pump can be placed in a safe working range, thereby avoiding damage to the working fluid pump.

[0095] Preferably, the waste heat boiler parameters include waste heat boiler output heat and gas consumption, the turbine parameters include inlet steam pressure, inlet steam temperature, outlet steam pressure, outlet steam temperature, steam flow rate and turbine actual power, the working fluid pump parameters include pump head, pump fluid density, pump flow rate, pump torque, pump angular velocity and pump mechanical efficiency, the condenser parameters include actual heat dissipation and theoretical maximum heat dissipation, and the geothermal power generation parameters include geothermal fluid temperature, geothermal fluid flow rate, geothermal fluid specific heat capacity and actual power generation;

[0096] like Figure 3 As shown, the calculation module 2 includes:

[0097] The first calculation unit 21 is used to calculate the boiler heat conversion efficiency according to the heat output of the waste heat boiler and the gas consumption;

[0098] The second calculation unit 22 is used to calculate the enthalpy drop according to the inlet steam pressure, the inlet steam temperature, the outlet steam pressure and the outlet steam temperature, calculate the turbine theoretical power according to the enthalpy drop and the steam flow rate, and calculate the turbine conversion efficiency according to the turbine theoretical power and the turbine actual power;

[0099] The third calculation unit 23 is used to calculate the effective power of the pump according to the pump head, the pump fluid density and the pump flow rate, calculate the shaft power according to the pump torque, the pump angular velocity and the pump mechanical efficiency, and then calculate the pump efficiency according to the effective power of the pump and the shaft power;

[0100] A fourth calculation unit 24, used for calculating the cooling efficiency according to the actual heat dissipation and the theoretical maximum heat dissipation;

[0101] The fifth calculation unit 25 is used to calculate the thermal power according to the geothermal fluid temperature, the geothermal fluid flow rate and the geothermal fluid specific heat capacity, and calculate the geothermal power generation efficiency according to the thermal power and the actual power generation power.

[0102] A renewable energy power generation control method is applied to the above renewable energy power generation control system, such as Figure 4 As shown, including:

[0103] Step S1, the acquisition module 1 acquires multiple waste heat boiler parameters, turbine parameters, condenser parameters, working fluid pump parameters and geothermal power generation parameters of the geothermal power station;

[0104] Step S2, the calculation module 2 calculates the corresponding boiler heat conversion efficiency, turbine conversion efficiency, cooling efficiency, pump efficiency and geothermal power generation efficiency according to the waste heat boiler parameters, turbine parameters, condenser parameters, working fluid pump parameters and geothermal power generation parameters;

[0105] Step S3, the training module 3 introduces an initial model, and takes the heat conversion efficiency of each boiler, the conversion efficiency of each turbine, the cooling efficiency and the pump efficiency as input, takes the corresponding geothermal power generation efficiency as output, and retrains the initial model to obtain a power generation efficiency prediction model, the input of the power generation efficiency prediction model is a plurality of power generation control parameters, and the output of the power generation efficiency prediction model is the geothermal predicted power generation efficiency;

[0106] Step S4, the environment detection module 4 detects multiple environmental parameters outside the geothermal power station in real time;

[0107] Step S5, the function establishment unit 51 establishes an objective function according to the predicted geothermal power generation efficiency and the preset ideal power generation efficiency, the output of the objective function is the objective function value, the factor optimization unit 52 introduces the preset ant colony algorithm, takes each power generation control parameter as an ant individual, and generates an optimized pheromone volatilization factor and an optimized heuristic factor according to each environmental parameter;

[0108] Step S6, the optimization unit 53 updates the pheromone update formula and the state transition probability formula according to the optimized pheromone volatilization factor and the optimized heuristic factor, and continuously iterates and updates the objective function value of each ant individual using the updated state transition probability formula and the pheromone update formula, and finally takes the ant individual with the minimum objective function value obtained by iterative update as the global optimal solution, and outputs the power generation control parameters contained in the global optimal solution as the optimal parameter set;

[0109] Step S7, the geothermal adjustment module 6 dynamically adjusts the equipment parameters of the waste heat boiler, turbine, condenser and working fluid pump in the geothermal power station according to the optimal parameter set.

[0110] The above are only preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A renewable energy power generation control system, characterized in that: include: A collection module (1) is used to collect multiple waste heat boiler parameters, turbine parameters, condenser parameters, working fluid pump parameters and geothermal power generation parameters of a geothermal power station; A calculation module (2), connected to the acquisition module (1), for calculating the corresponding boiler heat conversion efficiency, turbine conversion efficiency, cooling efficiency, pump efficiency and geothermal power generation efficiency according to the waste heat boiler parameters, the turbine parameters, the condenser parameters, the working fluid pump parameters and the geothermal power generation parameters; A training module (3) is connected to the acquisition module (1) and the calculation module (2) respectively, and is used to introduce an initial model, and take the heat conversion efficiency of each boiler, the conversion efficiency of each turbine, the cooling efficiency and the pump efficiency as input, and take the corresponding geothermal power generation efficiency as output, and retrain the initial model to obtain a power generation efficiency prediction model, wherein the input of the power generation efficiency prediction model is a plurality of power generation control parameters, and the output of the power generation efficiency prediction model is the geothermal predicted power generation efficiency; An environmental detection module (4), used for detecting multiple environmental parameters outside the geothermal power station in real time; The optimization module (5) is connected to the training module (3) and the environment detection module (4) respectively, and comprises: A function establishment unit (51) is used to establish an objective function according to the predicted geothermal power generation efficiency and a preset ideal power generation efficiency, wherein the output of the objective function is an objective function value; A factor optimization unit (52) is used to introduce a preset ant colony algorithm, take each power generation control parameter as an ant individual, and generate an optimized pheromone volatilization factor and an optimized heuristic factor according to each environmental parameter; An optimization unit (53) is connected to the factor optimization unit (52) and the function establishment unit (51), and is used to update the pheromone update formula and the state transition probability formula according to the optimized pheromone volatilization factor and the optimized heuristic factor respectively, and continuously iterate and update the objective function value of each ant individual using the updated state transition probability formula and the pheromone update formula, and finally take the ant individual with the minimum objective function value obtained by iterative update as the global optimal solution, and output each of the power generation control parameters contained in the global optimal solution as the optimal parameter set; The geothermal adjustment module (6) is connected to the optimization module (5) and is used to dynamically adjust the equipment parameters of the waste heat boiler, turbine, condenser and working fluid pump in the geothermal power station according to the optimal parameter set.

2. The renewable energy power generation control system according to claim 1, characterized in that: The environmental parameters include geothermal environment temperature and geothermal vibration, and the calculation formula for optimizing the pheromone volatilization factor is configured as: Among them, ρ opt It is used to represent the optimized pheromone volatilization factor, ρ is used to represent the pheromone volatilization factor in the ant colony algorithm, T is used to represent the geothermal environment temperature, V is used to represent the geothermal vibration amount, α is used to represent the influence coefficient of the geothermal environment temperature on the pheromone volatilization factor, β is used to represent the influence coefficient of the geothermal vibration amount on the pheromone volatilization factor, γ is used to represent the constant term in the denominator of the formula, and δ is used to represent the influence coefficient of the pheromone volatilization factor in the ant colony algorithm on the optimized pheromone volatilization factor.

3. The renewable energy power generation control system according to claim 2 is characterized in that: The calculation formula of the optimization heuristic factor is configured as: Among them, η opt is used to represent the optimized heuristic factor, α′ is used to represent the influence coefficient of the geothermal environment temperature on the heuristic factor, β′ is used to represent the exponential influence factor of the geothermal environment temperature, is used to represent the influence coefficient of the geothermal vibration on the heuristic factor, δ′ is used to represent the constant term in the denominator, η is used to represent the initial heuristic factor in the ant colony algorithm, and ∈ is used to represent the influence coefficient of the initial heuristic factor in the ant colony algorithm on the optimized heuristic factor.

4. The renewable energy power generation control system according to claim 2, characterized in that: It also includes a device detection module (7), which is connected to the optimization module (5) and includes: A parameter detection unit (71) for detecting performance parameters of the turbine and the working fluid pump in real time; An aging calculation unit (72), connected to the parameter detection unit (71), is used to input each of the performance parameters into a preset equipment aging calculation formula to calculate an aging potential risk score value; A processing unit (73) is connected to the aging calculation unit (72) and is used to generate a parameter restriction range according to the aging hidden danger score value, and to restrict each of the power generation control parameters according to the parameter restriction range.

5. The renewable energy power generation control system according to claim 4, characterized in that: The equipment aging calculation formula is configured as: Among them, A hs is used to represent the aging risk score value, P(w) is used to represent the power output of the turbine, V i is used to represent the flow rate of the working fluid pump, and θ, μ, σ, and τ are used to represent a preset first weight coefficient, a second weight coefficient, a third weight coefficient, and a fourth weight coefficient, respectively.

6. The renewable energy power generation control system according to claim 5, characterized in that: The parameter limit range is configured as: Among them, A hs ∈(A hmin , A hmax ), A, B are used to represent the turbine conversion efficiency and the pump efficiency, respectively. hmin and A hmax are used to represent the lower limit and upper limit of the aging potential risk score value, respectively. min and A max are used to represent the lower and upper limits of the turbine conversion efficiency, respectively, min and B max Respectively, for the lower and upper limits of the pump efficiency.

7. The renewable energy power generation control system according to claim 1, characterized in that: The waste heat boiler parameters include the waste heat boiler output heat and gas consumption, the turbine parameters include inlet steam pressure, inlet steam temperature, outlet steam pressure, outlet steam temperature, steam flow rate and turbine actual power, the working fluid pump parameters include pump head, pump fluid density, pump flow rate, pump torque, pump angular velocity and pump mechanical efficiency, the condenser parameters include actual heat dissipation and theoretical maximum heat dissipation, the geothermal power generation parameters include geothermal fluid temperature, geothermal fluid flow rate, geothermal fluid specific heat capacity and actual power generation; The calculation module (2) comprises: A first calculation unit (21) is used to calculate the heat conversion efficiency of the boiler according to the heat output of the waste heat boiler and the gas consumption; a second calculation unit (22), configured to calculate an enthalpy drop according to the inlet steam pressure, the inlet steam temperature, the outlet steam pressure, and the outlet steam temperature, calculate a turbine theoretical power according to the enthalpy drop and the steam flow rate, and calculate a turbine conversion efficiency according to the turbine theoretical power and the turbine actual power; a third calculation unit (23), configured to calculate the effective power of the pump according to the pump head, the pump fluid density, and the pump flow rate, calculate the shaft power according to the pump torque, the pump angular velocity, and the pump mechanical efficiency, and further calculate the pump efficiency according to the effective power of the pump and the shaft power; A fourth calculation unit (24), configured to calculate the cooling efficiency according to the actual heat dissipation and the theoretical maximum heat dissipation; A fifth calculation unit (25) is used to calculate the thermal power according to the geothermal fluid temperature, the geothermal fluid flow rate and the geothermal fluid specific heat capacity, and to calculate the geothermal power generation efficiency according to the thermal power and the actual power generation power.

8. The renewable energy power generation control system according to claim 2, characterized in that: The power generation efficiency prediction model is a BP neural network model, and the mathematical expression of the power generation efficiency prediction model is configured as: E predicted =BPNN(the boiler ,or turbine ,or cooler ,or pump ,T e ,P,M,t) Among them, E predicted is used to represent the predicted geothermal power generation efficiency, BPNN is used to represent the BP neural network model, η boiler Used to represent the boiler heat conversion efficiency, η turbine Used to express the turbine conversion efficiency, η cooler Used to express the cooling efficiency, η pump Used to express the pump efficiency, T e It is used to indicate the temperature of geothermal fluid, P is used to indicate the pressure of geothermal fluid, M is used to indicate the mass of geothermal fluid, and t is used to indicate the time variable.

9. A renewable energy power generation control method, applied to the renewable energy power generation control system according to any one of claims 1 to 8, characterized in that: include: Step S1, the acquisition module (1) acquires multiple waste heat boiler parameters, turbine parameters, condenser parameters, working fluid pump parameters and geothermal power generation parameters of the geothermal power station; Step S2, the calculation module (2) calculates the corresponding boiler heat conversion efficiency, turbine conversion efficiency, cooling efficiency, pump efficiency and geothermal power generation efficiency according to the waste heat boiler parameters, the turbine parameters, the condenser parameters, the working fluid pump parameters and the geothermal power generation parameters; Step S3, the training module (3) introduces an initial model, and takes the heat conversion efficiency of each boiler, the conversion efficiency of each turbine, the cooling efficiency and the pump efficiency as input, and takes the corresponding geothermal power generation efficiency as output, and retrains the initial model to obtain a power generation efficiency prediction model, wherein the input of the power generation efficiency prediction model is a plurality of power generation control parameters, and the output of the power generation efficiency prediction model is the geothermal predicted power generation efficiency; Step S4, the environment detection module (4) detects multiple environmental parameters outside the geothermal power station in real time; Step S5, the function establishment unit (51) establishes an objective function according to the predicted geothermal power generation efficiency and the preset ideal power generation efficiency, the output of the objective function is the objective function value, the factor optimization unit (52) introduces a preset ant colony algorithm, takes each of the power generation control parameters as an ant individual, and generates an optimized pheromone volatilization factor and an optimized heuristic factor according to each of the environmental parameters; Step S6, the optimization unit (53) updates the pheromone update formula and the state transition probability formula according to the optimized pheromone volatilization factor and the optimized heuristic factor respectively, and continuously iterates and updates the objective function value of each ant individual using the updated state transition probability formula and the pheromone update formula, and finally takes the ant individual with the minimum objective function value obtained by iterative update as the global optimal solution, and outputs each of the power generation control parameters contained in the global optimal solution as the optimal parameter set; Step S7, the geothermal adjustment module (6) dynamically adjusts the equipment parameters of the waste heat boiler, turbine, condenser and working fluid pump in the geothermal power plant according to the optimal parameter set.

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