A method and system for optimizing governor parameters in a pumped storage power station.

Abstract

This invention discloses a method and system for optimizing governor parameters in pumped storage power stations, relating to the field of pumped storage power station control technology. The method includes: constructing a multi-dimensional evaluation system, defining seven indicators (unit safety, speed response, power response, etc.) for comprehensive evaluation of power regulation performance; based on historical power station operating data, statistically analyzing the probability of occurrence of various initial operating conditions and load changes, generating a weighted factor matrix, and quantifying the weight contribution of high-frequency and extreme operating conditions to the optimization objective; parameter optimization and decision-making: using a hydro-mechanical coupling model to simulate the performance of different governor parameter combinations (KP=0.1~1.9, KI=0.1~0.7) under 30 typical operating conditions, normalizing and comprehensively scoring the simulation results based on the TOPSIS algorithm, and selecting the parameter scheme with the highest total score. This invention solves the problems of single indicators and insufficient adaptability to operating conditions in traditional governor parameter selection, achieving a synergistic improvement in unit safety and regulation stability through multi-dimensional evaluation and probability-driven optimization.

Description

Technical Field

[0001] This invention relates to the field of pumped storage power station control technology, and in particular to a method and system for optimizing the parameters of a pumped storage power station governor. Background Technology

[0002] Pumped storage power stations undertake frequency regulation and peak shaving tasks in new power systems, and their small fluctuation conditions (such as power regulation) occur frequently. Currently, governor parameter settings have the following problems: 1. Single-indicator orientation: Traditional methods focus on speed stability, neglecting unit safety (such as guide vane wear, water hammer pressure) and power response characteristics (such as reverse regulation, regulation accuracy). 2. Poor adaptability to operating conditions: Parameter optimization does not consider the probability distribution of actual operating conditions, and extreme or low-frequency conditions can easily lead to control failure. 3. Insufficient global optimization: Parameter selection relies on experience or simple rules, lacking a systematic and comprehensive decision-making method. Traditional PID governor parameter schemes struggle to balance multi-dimensional performance indicators; frequent fluctuations in guide vane movement can easily induce equipment fatigue, and regulation accuracy is relatively low.

[0003] Therefore, how to take into account the probability distribution of actual operating conditions and take into account multi-objective optimization in the process of selecting the optimal speed governor parameters is an important problem that urgently needs to be solved. Summary of the Invention

[0004] This invention provides a method and system for optimizing governor parameters in a pumped storage power station, which takes into account the probability distribution of actual operating conditions and multi-objective optimization during the selection of governor parameters.

[0005] This invention provides a method for optimizing governor parameters in a pumped-storage power station, comprising the following steps: acquiring historical operating data of the pumped-storage power station and determining multiple regulation scenarios, each regulation scenario being an operating scenario where the pumped-storage power station adjusts from an initial operating condition to a target operating condition; establishing a hydro-mechanical coupling model of the pumped-storage power station corresponding to each set of governor parameters through simulation using multiple preset sets of governor parameters; acquiring multiple index data describing the unit's safety, speed response, and power response performance under each regulation scenario in the hydro-mechanical coupling model corresponding to each set of governor parameters; experimentally obtaining the actual operating probability of each index of the pumped-storage power station under each regulation scenario as a weight, and performing a weighted summation of the index data under all regulation scenarios to obtain a comprehensive optimization target value under the hydro-mechanical coupling model corresponding to each set of governor parameters; and scoring the comprehensive optimization target value corresponding to each set of governor parameters using a multi-attribute decision method, and selecting the governor parameter combination with the highest score as the optimal governor parameters for the pumped-storage power station.

[0006] Furthermore, the simulation establishes a hydro-mechanical coupling model of the pumped storage power station corresponding to each set of governor parameters. The specific steps include: simulating the water diversion system modeled by the method of characteristics, the full characteristic curves of the pump-turbine processed by Suter transform, and the synchronous generator model constructed by Simulink to construct the hydro-mechanical coupling model; setting multiple sets of governor parameters for the hydro-mechanical coupling model, wherein the governor parameters include: KP values ​​ranging from 0.1 to 0.9, and KI values ​​ranging from 0.1 to 0.7.

[0007] Furthermore, the experiment obtains the actual operating probability of each indicator of the pumped storage power station under each of the aforementioned regulation scenarios as a weight, as shown in the formula: Weight = P (Initial operating condition) × P (Target operating condition); in, P (Initial operating condition) represents the weighting factor weight of the initial operating condition. P (Target operating condition) represents the weighting factor weight of the target operating condition.

[0008] Furthermore, the aforementioned various indicator data specifically include: The data includes unit safety indicators consisting of guide vane mileage and maximum water hammer pressure; speed response indicators consisting of speed ITAE and speed fluctuation peak; and power response indicators consisting of power reverse adjustment, regulation rate, and regulation accuracy.

[0009] This invention provides an optimization system for governor parameters in a pumped storage power station, comprising: The system comprises the following modules: a scenario construction module for acquiring historical operating data of pumped storage power stations and determining multiple regulation scenarios, each scenario representing the operation of the pumped storage power station from its initial operating condition to a target operating condition; a model simulation module for simulating and establishing a hydro-mechanical coupling model of the pumped storage power station corresponding to each set of preset governor parameters; a governor parameter optimization module for acquiring multiple index data describing the unit's safety, speed response, and power response performance under each regulation scenario in the hydro-mechanical coupling model corresponding to each set of governor parameters; using the actual operating probability of each index of the pumped storage power station under each regulation scenario as a weight, and performing a weighted summation of the index data under all regulation scenarios to obtain the comprehensive optimization target value under the hydro-mechanical coupling model corresponding to each set of governor parameters; and using a multi-attribute decision method to score the comprehensive optimization target value corresponding to each set of governor parameters, and selecting the governor parameter combination with the highest score as the optimal governor parameters for the pumped storage power station.

[0010] This invention provides a method and system for optimizing the parameters of a pumped storage power station governor. Compared with the prior art, its advantages are as follows: In the hydro-mechanical coupling model corresponding to each set of governor parameters, multiple index data describing the unit's safety, speed response, and power response performance are obtained under each regulation scenario. Each regulation scenario is the operation scenario of the pumped storage power station adjusting from the initial operating condition to the target operating condition. The actual operating probability of each index of the pumped storage power station under each regulation scenario is used as the weight, and the index data under all regulation scenarios are weighted and summed to obtain the comprehensive optimization target value under the hydro-mechanical coupling model corresponding to each set of governor parameters. Based on the comprehensive optimization target value corresponding to each set of governor parameters, a multi-attribute decision method is used for scoring, and the governor parameter combination with the highest score is taken as the optimal governor parameters of the pumped storage power station. In the process of selecting the best governor parameters, the probability distribution of the actual operating conditions is taken into account, and multi-objective optimization is also taken into account. Attached Figure Description

[0011] Figure 1 A flowchart provided for an embodiment of the present invention; Figure 2 The diagram shows some indicators of the power regulation process provided in the embodiments of the present invention, wherein (a) represents a diagram of speed response indicators and (b) represents a diagram of power response indicators; Figure 3 A flowchart for optimizing control parameters during power regulation provided in an embodiment of the present invention; Figure 4 The TOPSIS evaluation results are provided for different schemes in scenarios 1 and 2 of the present invention. Detailed Implementation

[0012] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0013] See Figure 1 This invention provides a method for optimizing the parameters of a governor in a pumped storage power station, comprising the following steps: Step 1: Obtain historical operating data of the pumped storage power station and determine multiple adjustment scenarios. Each adjustment scenario is an operating scenario in which the pumped storage power station adjusts from the initial operating condition to the target operating condition.

[0014] Step 2: Using multiple preset governor parameters, simulate and establish the hydro-mechanical coupling model of the pumped storage power station corresponding to each set of governor parameters.

[0015] Step 3: In the hydro-mechanical coupling model corresponding to each set of governor parameters, obtain multiple index data to describe the unit's safety, speed response, and power response performance under each regulation scenario; experimentally obtain the actual operating probability of each index of the pumped storage power station under each regulation scenario as a weight, and perform weighted summation on the index data under all regulation scenarios to obtain the comprehensive optimization target value under the hydro-mechanical coupling model corresponding to each set of governor parameters; based on the comprehensive optimization target value corresponding to each set of governor parameters, use a multi-attribute decision method to score, and take the governor parameter combination with the highest score as the optimal governor parameters of the pumped storage power station.

[0016] The details are as follows: S1: Construct a multi-dimensional evaluation index system covering unit safety, speed response, and power response. Figure 2 This is a diagram illustrating some of the indicators.

[0017] In step S1, the unit's safety indicators include guide vane mileage and maximum / minimum water hammer pressure. The guide vane mileage is calculated using the absolute value of the integral guide vane opening change, as shown in the following formula:

[0018] .

[0019] in, GVT For guide vane mileage, GV ( t )express t The guide vane opening at any given moment, t 1 and t 2 represents the start and end times of the calculation, respectively.

[0020] The maximum water hammer pressure is defined as the maximum deviation between the unit's water head and its initial value, as shown in the following formula: .

[0021] in, Ht ex This represents the maximum water hammer pressure. Ht Indicates the unit's head. Ht initial This is the initial head of the unit.

[0022] Speed ​​response metrics include peak speed fluctuation and integrated time and absolute error (ITAE). The former characterizes the maximum speed deviation, while the latter focuses on speed stability in the later stages. The formulas are as follows: The formula for the initial extreme speed value in speed response indicators is: .

[0023] in, nt exThis is the maximum speed. nt For unit speed, nt initial This is the initial speed of the unit.

[0024] The formula for the speed ITAE in the speed response index is: .

[0025] in, nt ITAE For rotational speed ITAE, t Indicates time, e ( t () indicates the difference between the rotational speed and the rated speed.

[0026] Power response metrics include power inverse regulation, regulation rate, and regulation accuracy, covering the entire lifecycle of power dynamic characteristics. The formulas are as follows: The formula for power inverse regulation is: .

[0027] in, Pt inversion For power inversion, Pt For unit power, Pt initial This represents the initial power of the generator unit.

[0028] The power regulation rate formula is: .

[0029] in, Pt sr For power regulation rate, Ts and Te The calculation starts and ends at the start and end times, respectively. The power regulation rate is calculated when the deviation between the unit output and the initial output first exceeds 5% of the power change range; the calculation ends when the unit completes 90% of the current power regulation command. Pts and Pte They are respectively Ts and Te Power at that time.

[0030] The formula for power regulation accuracy of power response indicators is: .

[0031] in, Pt ra For power regulation accuracy, Tacut and Tac These are the time it takes for the unit's output to enter the allowable deviation range and the calculation duration, respectively. PobOutput to the target. Power regulation accuracy calculation begins when the unit's output remains within the allowable output deviation range for more than 20 seconds. Tac The maximum value is 40s.

[0032] S2: Generate a weighted factor matrix based on historical operating data of pumped storage power stations, and calculate the joint probability of different initial operating conditions and load changes as weights. Operating conditions include initial head, load range and load change. In step S2, the initial operating condition is defined as a combination of initial head (high / medium / low head), load range (150MW-300MW), and load variation (±25MW-±150MW), the probability of which is obtained based on historical operating statistics of the power plant. The weighting factor matrix covers 30 typical regulation scenarios, with higher weights assigned to high-frequency scenarios (such as load variation of ±25MW), and extreme scenarios (such as ±150MW) having their actual impact quantified through probability weights.

[0033] S3: Simulate the performance of the speed governor under various regulation scenarios by using a water-mechanical-electric coupling model, and weight and merge the index data under each scenario to generate a comprehensive optimization target value; In step S3, a water-turbine coupled model is used, including a water intake system modeled using the method of characteristics (including surge tanks and bifurcation pipe boundary conditions), full characteristic curves of the pump-turbine system processed by Suter transform, and a synchronous generator model constructed using Simulink. The simulation covers three head scenarios: high, medium, and low.

[0034] The candidate set of governor parameters is KP=0.1~1.9 (step size 0.3) and KI=0.1~0.7 (step size 0.1), totaling 40 parameter combinations. For each parameter combination, 7 indicators are calculated for 30 regulation scenarios. After Z-score normalization, the indicators are weighted and summed (weights are scenario probabilities) to generate the comprehensive optimization target value for each parameter combination.

[0035] In step S3, in order to make the obtained governor parameters as adaptable as possible to the actual operating conditions and obtain the optimal governor parameters, a weighting factor of the pumped storage power station is introduced to represent the probability in the actual operation of the power station. The weighting factors of different operating conditions are coupled into the calculation results as weights.

[0036] The process of selecting the optimal governor parameters is as follows: Figure 3 As shown.

[0037] (5) First, set up the scenario: determine the governor parameter scheme and the output change scenario. The former is described in detail in Table 2, and the latter consists of the initial working condition and the final working condition. These two working conditions are combinations of the working condition points in Table 1 (excluding the case where the initial working condition and the final working condition are the same), for a total of 30 working conditions for study.

[0038] Table 1 Weighting factor results for power generation operating conditions Table 2 Result of Governor Parameter Combination (2) Result calculation: Under the scenarios of high water level, medium water level and low water level, the comprehensive index of regulation characteristics is considered, and the results of each governor parameter scheme under 30 research conditions are calculated. The results under these research conditions are summed by weight to obtain the combined result (7 indexes for each governor scheme).

[0039] (3) Result decision: Using the indicators under high, medium and low water levels as the decision layer, TOPSIS is introduced to score 40 governor parameter schemes. The scheme with the highest score is the governor parameter that adapts to complex working conditions.

[0040] Using the schemes in Table 2 as the decision-making objects, TOPSIS was used to make decisions on the results of scenarios 1 and 2 (scenario 1 is the inflection point position corresponding to the optimal maximum head of the volute; scenario 2 is the extreme case of the guide vane going from slow to fast). The results are shown in [Table 2]. Figure 4 The left and right figures show the decision results for scenarios 1 and 2, respectively. The green bars represent the highest-scoring solutions in different scenarios 1. The highest-scoring solution in scenario 1 is solution 30, with a score of 0.6525; the highest-scoring solution in scenario 2 is solution 40, with a score of 0.9988. The highest-scoring solution 40 in scenario 2 only scores 0.4771 in scenario 1, indicating that the optimal solution in scenario 2 is not suitable for complex operating conditions and does not achieve optimal comprehensive power regulation characteristics. To further compare the optimal solutions in the two scenarios, this section provides a detailed comparison of the results of solutions 30 and 40 under the more comprehensive operating conditions of scenario 1, as shown in Table 3.

[0041] In Table 3, negative numbers in the "Change" row indicate that Scheme 30 is superior to Scheme 40, while positive numbers have the opposite meaning. The table shows that out of a total of 21 indicators, Scheme 30 outperforms Scheme 40 in 12. Specifically, Scheme 40 has advantages in speed ITAE, maximum speed fluctuation, and power regulation rate. Scheme 30 mainly excels in guide vane mileage, maximum water hammer pressure fluctuation, power reversal, and power regulation accuracy. It should be noted that for power regulation speed, because it is a very large indicator, a higher value is better. The above analysis shows that Scheme 40 has superior speed response indicators and power regulation speed, indicating that the guide vane operates faster under this scheme, allowing for quicker power regulation.

[0042] Table 3. Quantitative comparison of results between Scheme 30 and Scheme 40 under the study conditions. S4: Use a multi-attribute decision-making method to score the comprehensive optimization target value and select the optimal solution for PID parameters.

[0043] In step S4: The closeness score between each parameter scheme and the ideal solution is calculated based on the TOPSIS algorithm. The scheme with the highest final score is the governor parameter that adapts to complex working conditions.

[0044] This invention provides an optimization system for governor parameters in a pumped storage power station, comprising: The scenario construction module is used to acquire historical operating data of pumped storage power stations and determine multiple adjustment scenarios. Each adjustment scenario is an operating scenario in which the pumped storage power station adjusts from the initial operating condition to the target operating condition.

[0045] The model simulation module is used to simulate and establish a hydro-mechanical coupling model of a pumped storage power station corresponding to each set of preset governor parameters.

[0046] The governor parameter optimization module is used to obtain various index data describing the unit's safety, speed response, and power response performance under each regulation scenario in the hydro-mechanical coupling model corresponding to each set of governor parameters. The actual operating probability of each index of the pumped storage power station under each regulation scenario is obtained experimentally and used as a weight. The index data under all regulation scenarios are weighted and summed to obtain the comprehensive optimization target value under the hydro-mechanical coupling model corresponding to each set of governor parameters. Based on the comprehensive optimization target value corresponding to each set of governor parameters, a multi-attribute decision method is used for scoring, and the governor parameter combination with the highest score is taken as the optimal governor parameters for the pumped storage power station.

[0047] A specific example is as follows: This embodiment discloses a method for optimizing the parameters of the governor in a pumped storage power station. The specific steps are as follows: S1. Construct a multi-dimensional evaluation index system covering unit safety, speed response, and power response. Unit safety indexes include guide vane mileage and maximum water hammer pressure. Speed ​​response indexes include speed ITAE and peak speed fluctuation. Power response indexes include power reverse adjustment, regulation rate, and regulation accuracy.

[0048] S2. Generate a weighted factor matrix based on historical operating data of pumped storage power stations, and calculate the joint probability of different initial operating conditions and load changes as weights. The operating conditions include initial head, load range and load change.

[0049] The weights of the weighted factor matrix are calculated as follows: Weight = P (Initial operating condition) × P (Target operating condition)

[0050] in,P (Initial operating condition) represents the weighting factor weight of the initial operating condition. P (Target operating condition) represents the weighting factor weight of the target operating condition. The weighting factor weight of each operating condition can be obtained by querying Table 1.

[0051] For example, when the initial load is 300MW, 270MW, and 240MW, the corresponding probabilities are 23%, 26%, and 18%, respectively.

[0052] S3. The performance of the speed governor under various regulation scenarios is simulated using a water-mechanical-electric coupling model, and the index data under each scenario are weighted and merged to generate a comprehensive optimization target value. For example... Figure 2 and Figure 3 As shown.

[0053] The simulation process includes the following constraints: parameter KP ranges from 0.1 to 1.9, KI ranges from 0.1 to 0.7, the simulation conditions cover high, medium, and low head scenarios, and the load variation ranges from -150MW to +150MW; the simulation results need to be normalized using Z-score, the formula is: The index deviation nom = (x - mean) / sigama.

[0054] Where mean is the average deviation of a certain indicator, and sigama is the standard deviation of the deviation of a certain indicator.

[0055] The final generated optimal PID parameters are KP=1.0 and KI=0.4, which reduces the guide vane mileage by 13% and the water hammer pressure by 12.7% compared with the traditional solution.

[0056] S4. Use a multi-attribute decision-making method to score the comprehensive optimization target value and select the optimal solution for PID parameters.

[0057] The multi-attribute decision method is the TOPSIS algorithm, and its scoring formula is: Score = ∑(Normalized index × weight)

[0058] The final generated optimal PID parameters are KP=1.0 and KI=0.4, which reduces the guide vane mileage by 13% and the water hammer pressure by 12.7% compared with the traditional solution.

[0059] The advanced methods include: extreme scenario adaptation rules: when the load change exceeds 100MW, the guide vane segmentation time interval ΔT>10s is forced to be used first fast and then slow to avoid the power reverse adjustment amount exceeding the threshold.

[0060] The deep approach, through the collaborative design of weighted index scoring and extreme rule adaptation, enables the PID parameter scheme (such as KP=1.0, KI=0.4) of pumped storage power stations to outperform traditional schemes in 12 indicators, combining efficient regulation capability with safety in extreme scenarios.

[0061] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for optimizing the parameters of a governor in a pumped storage power station, characterized in that, Includes the following steps: Historical operating data of pumped storage power stations are acquired and multiple adjustment scenarios are determined. Each adjustment scenario is an operating scenario in which the pumped storage power station adjusts from the initial operating condition to the target operating condition. By using multiple preset speed governor parameters, a hydro-mechanical coupling model of the pumped storage power station corresponding to each set of speed governor parameters is established through simulation. In the hydro-mechanical coupling model corresponding to each set of governor parameters, various index data for describing the unit's safety, speed response and power response performance under each regulation scenario are obtained, including: unit safety index data composed of guide vane mileage and water hammer pressure maximum value, speed response index data composed of speed ITAE and speed fluctuation peak value, and power response index data composed of power reverse regulation amount, regulation rate and regulation accuracy. The product of the actual operating probability of the initial operating condition and the actual operating probability of the target operating condition is used as the weight of the adjustment scenario. The index data under all adjustment scenarios are weighted and summed to obtain the comprehensive optimization target value under the water-turbine-electric coupling model corresponding to each set of governor parameters. Based on the comprehensive optimization target value corresponding to each group of governor parameters, a multi-attribute decision method is used to score them, and the governor parameter combination with the highest score is taken as the optimal governor parameters for the pumped storage power station.

2. The method for optimizing the parameters of a pumped storage power station governor as described in claim 1, characterized in that, The simulation establishes a hydro-mechanical coupling model of the pumped storage power station corresponding to each set of governor parameters. The specific steps include: A water-turbine coupled model was constructed by simulating the water diversion system modeled by the method of characteristics, the full characteristic curves of the pump-turbine processed by Suter transform, and the synchronous generator model built by Simulink. Multiple sets of governor parameters are set for the water-electric coupling model. The governor parameters include: KP values ​​ranging from 0.1 to 0.9, and KI values ​​ranging from 0.1 to 0.

7.

3. A system for optimizing the parameters of a governor in a pumped storage power station, characterized in that, include: The scenario construction module is used to acquire historical operating data of pumped storage power stations and determine multiple adjustment scenarios. Each adjustment scenario is an operating scenario in which the pumped storage power station adjusts from the initial operating condition to the target operating condition. The model simulation module is used to simulate and establish a hydro-mechanical coupling model of the pumped storage power station corresponding to each set of governor parameters using multiple preset sets of governor parameters. The governor parameter optimization module is used to acquire various index data describing the unit's safety, speed response, and power response performance under each regulation scenario in the hydro-mechanical coupling model corresponding to each set of governor parameters. These include: unit safety index data composed of guide vane mileage and maximum water hammer pressure; speed response index data composed of speed ITAE and speed fluctuation peak; and power response index data composed of power counter-regulation, regulation rate, and regulation accuracy. The module uses the product of the actual operating probability of the initial operating condition and the actual operating probability of the target operating condition as the weight of the regulation scenario, and performs a weighted summation of the index data under all regulation scenarios to obtain the comprehensive optimization target value under the hydro-mechanical coupling model corresponding to each set of governor parameters. Based on the comprehensive optimization target value corresponding to each set of governor parameters, a multi-attribute decision method is used for scoring, and the governor parameter combination with the highest score is taken as the optimal governor parameters for the pumped storage power station.

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