Pumped storage power station governor parameter optimization method and system

Abstract

The invention discloses a pumped storage power station governor parameter optimization method and system, and relates to the technical field of pumped storage power station control, and the method comprises the steps: constructing a multi-dimensional evaluation system, and defining seven indexes of a unit safety class, a rotating speed response class and a power response class, which are used for comprehensively evaluating the power regulation performance; based on historical operation data of the power station, the occurrence probability of each initial working condition and load change is counted, a weighting factor matrix is generated, and weight contributions of high-frequency and extreme working conditions to an optimization target are quantified; and parameter optimization and decision making: simulating the performance of different speed regulator parameter combinations (KP = 0.1-1.9, KI = 0.1-0.7) under 30 typical working conditions by adopting a water-electro-mechanical coupling model, carrying out normalization and comprehensive scoring on simulation results based on a TOPSIS algorithm, and screening a parameter scheme with the highest total score. According to the method, the problems of single index and insufficient working condition adaptability in parameter selection of a traditional speed regulator can be solved, and collaborative improvement of unit safety and regulation stability is realized through multi-dimensional evaluation and probability-driven optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of pumped storage power station control, and in particular to a method and system for optimizing parameters of a speed regulator of a pumped storage power station. Background Art

[0002] Pumped-storage power stations undertake frequency and peak regulation tasks in new power systems, and their small fluctuation conditions (such as power regulation) occur frequently. The current speed regulator parameter setting has the following problems: 1. Single indicator orientation: The traditional method focuses on speed stability, ignoring unit safety (such as guide vane wear, water hammer pressure) and power response characteristics (such as back-regulation and regulation accuracy). 2. Poor adaptability to operating conditions: The probability distribution of actual operating conditions is not considered during parameter optimization, and extreme or low-frequency conditions can easily lead to control failure. 3. Insufficient global optimization: Parameters are selected based on experience or simple rules, and there is a lack of a systematic and comprehensive decision-making method. The traditional PID speed regulator parameter solution is difficult to balance multi-dimensional performance indicators. The frequent fluctuations in guide vane movement can easily induce equipment fatigue, and the regulation accuracy is relatively low.

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

[0004] The embodiments of the present invention provide a method and system for optimizing the speed regulator parameters of a pumped storage power station, which can take into account the probability distribution of actual operating conditions and take into account multi-objective optimization during the process of selecting the speed regulator parameters.

[0005] An embodiment of the present invention provides a method for optimizing the speed regulator parameters of a pumped-storage power station, comprising the following steps: obtaining historical operating data of the pumped-storage power station and determining multiple adjustment scenarios, each adjustment scenario being an operating scenario in which the pumped-storage power station is adjusted from an initial operating condition to a target operating condition; simulating and establishing a hydro-electromechanical coupling model of the pumped-storage power station corresponding to each set of speed regulator parameters through multiple preset sets of speed regulator parameters; obtaining, in the hydro-electromechanical coupling model corresponding to each set of speed regulator parameters, multiple indicator data for describing the unit safety, speed response, and power response performance under each adjustment scenario; experimentally obtaining the actual operating probability of each indicator of the pumped-storage power station under each adjustment scenario as a weight, and performing weighted summation on the indicator data under all adjustment scenarios to obtain a comprehensive optimization target value under the hydro-electromechanical coupling model corresponding to each set of speed regulator parameters; scoring using a multi-attribute decision-making method based on the comprehensive optimization target value corresponding to each set of speed regulator parameters, and selecting the speed regulator parameter combination with the highest score as the optimal speed regulator parameter of the pumped-storage power station.

[0006] Furthermore, the simulation establishes a hydro-electromechanical coupling model of the pumped-storage power station corresponding to each set of speed regulator parameters. The specific steps include: constructing a hydro-electromechanical coupling model by simulating the water diversion system modeled by the characteristic line method, the full characteristic curve of the pump-turbine processed by Suter transformation, and the synchronous generator model constructed by Simulink; setting multiple sets of speed regulator parameters of the hydro-electromechanical coupling model, and the speed regulator parameters include: KP value range is 0.1 to 0.9, and KI value range is 0.1 to 0.7.

[0007] Furthermore, the experiment obtains the actual operation probability of each indicator of the pumped storage power station in each regulation scenario as a weight, and the formula is: Weight = P (Initial working condition)× P (target operating conditions); 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 multiple indicator data specifically include: Unit safety indicator data consisting of guide vane mileage and maximum water hammer pressure, speed response indicator data consisting of speed ITAE and speed fluctuation peak, and power response indicator data consisting of power reverse regulation, regulation rate and regulation accuracy.

[0009] An embodiment of the present invention provides a system for optimizing parameters of a pumped storage power station governor, including: A scenario construction module is used to obtain historical operating data of the pumped-storage power station and determine multiple regulation scenarios, each of which is an operating scenario in which the pumped-storage power station is adjusted from an initial operating condition to a target operating condition; a 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 speed regulator parameters through multiple preset sets of speed regulator parameters; a speed regulator parameter optimization module is used to obtain multiple indicator data for describing the unit safety, speed response and power response performance in each regulation scenario in the hydro-mechanical coupling model corresponding to each set of speed regulator parameters; the actual operating probability of each indicator of the pumped-storage power station in each regulation scenario is obtained as a weight, and the indicator data under all regulation scenarios are weightedly summed to obtain the comprehensive optimization target value under the hydro-mechanical coupling model corresponding to each set of speed regulator parameters; according to the comprehensive optimization target value corresponding to each set of speed regulator parameters, a multi-attribute decision-making method is used to score, and the speed regulator parameter combination with the highest score is used as the optimal speed regulator parameter of the pumped-storage power station.

[0010] The embodiments of the present invention provide a method and system for optimizing the speed regulator parameters of a pumped storage power station. Compared with the prior art, the method and system have the following beneficial effects: In the hydro-mechanical-electrical coupling model corresponding to each set of speed regulator parameters, a variety of indicator data for describing the unit safety, speed response and power response performance in each of the regulation scenarios are obtained; each regulation scenario is an operating scenario in which the pumped-storage power station is adjusted from the initial operating condition to the target operating condition, and the actual operating probability of each indicator of the pumped-storage power station in each regulation scenario is used as a weight, and the indicator data in all regulation scenarios are weighted and summed to obtain the comprehensive optimization target value under the hydro-mechanical-electrical coupling model corresponding to each set of speed regulator parameters. According to the comprehensive optimization target value corresponding to each set of speed regulator parameters, a multi-attribute decision-making method is used to score, and the speed regulator parameter combination with the highest score is used as the optimal speed regulator parameter of the pumped-storage power station; in the process of selecting the speed regulator parameters, the actual operating condition probability distribution is taken into account and multi-objective optimization is taken into account. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 Flowchart provided for an embodiment of the present invention; Figure 2 Schematic diagram of some indicators of the power regulation process provided by an embodiment of the present invention, wherein (a) is a schematic diagram of speed response indicators, and (b) is a schematic diagram of power response indicators; Figure 3 A flow chart of power regulation process control parameter optimization provided by an embodiment of the present invention; Figure 4 The TOPSIS evaluation results of different solutions in scenarios 1 and 2 provided in the embodiments of the present invention are shown. DETAILED DESCRIPTION

[0012] To make the above-mentioned objects, features, and advantages of the present invention more readily apparent, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following description sets forth numerous specific details to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art may make similar modifications without departing from the scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0013] See also Figure 1 The embodiment of the present invention provides a method for optimizing the speed regulator parameters of a pumped storage power station, comprising the following steps: Step 1: Obtain historical operating data of the pumped storage power station and determine multiple regulation scenarios, each of which is an operating scenario in which the pumped storage power station is adjusted from an initial operating condition to a target operating condition.

[0014] Step 2: By using multiple preset sets of speed regulator parameters, a hydro-mechanical-electrical coupling model of the pumped storage power station corresponding to each set of speed regulator parameters is simulated and established.

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

[0016] The specific contents are as follows: S1: Construct a multi-dimensional evaluation index system covering unit safety, speed response and power response. Figure 2 A schematic diagram of some indicators.

[0017] In step S1, the unit safety indicators include guide vane mileage and maximum water hammer pressure. The guide vane mileage is calculated by integrating the absolute value of the guide vane opening change, and the formula is as follows:

[0018] .

[0019] in, GVT is the guide vane mileage, GV ( t )express t The guide vane opening at the moment, t 1 and t 2 represent the calculation start and end time respectively.

[0020] The maximum water hammer pressure is defined as the maximum deviation of the unit head from the initial value. The formula is as follows: .

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

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

[0023] in, nt exis the maximum speed, nt is the unit speed, nt initial is the initial speed of the unit.

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

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

[0026] Power response indicators include power reverse regulation, regulation rate and regulation accuracy, covering the entire cycle of power dynamic characteristics. The formula is as follows The power reverse regulation formula is: .

[0027] in, Pt inversion For power reverse regulation, Pt is the unit power, Pt initial is the initial power of the unit.

[0028] The power regulation rate formula is: .

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

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

[0031] in, Pt ra is the power regulation accuracy, Tacut and Tac They are the time when the unit output enters the output allowable deviation range and the calculation duration, PobThe target output. When the unit output enters the output allowable deviation range for a duration greater than 20 seconds, the power regulation accuracy is calculated. Tac The maximum value is 40s.

[0032] S2: Generate a weighted factor matrix based on the historical operating data of the pumped storage power station and calculate the joint probability of different initial operating conditions and load changes as weights. The operating conditions include initial water head, load range, and load change. In step S2, the initial operating condition is defined as a combination of the initial head (high, medium, or low head), the load range (150 MW to 300 MW), and the load variation (±25 MW to ±150 MW). The probabilities for these combinations are derived from historical power plant operating statistics. A weighting factor matrix covers 30 typical regulation scenarios, assigning higher weights to high-frequency scenarios (e.g., a load variation of ±25 MW). Extreme scenarios (e.g., ±150 MW) are quantified through probabilistic weighting to determine their actual impact.

[0033] S3: Use the hydro-mechanical-electrical coupling model to simulate the performance of the governor parameter combination under various regulation scenarios, and weightedly combine the indicator data under each scenario to generate a comprehensive optimization target value; In step S3, a hydro-electromechanical coupling model is used, including the water diversion system modeled using the characteristic line method (including surge tank and bifurcated pipe boundary conditions), the full pump-turbine characteristic curve processed using the Suter transform, and a synchronous generator model built in Simulink. The simulation covers three hydraulic head scenarios: high, medium, and low.

[0034] The candidate set of governor parameters is 40 parameter combinations, with KP ranging from 0.1 to 1.9 (in steps of 0.3) and KI ranging from 0.1 to 0.7 (in steps of 0.1). For each parameter set, seven indicators are calculated for 30 regulation scenarios. After Z-score normalization, the weighted sum (weighted by the scenario probability) is calculated to generate the comprehensive optimization target value for each parameter combination.

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

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

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

[0038] Table 1 Weighted factor results for power generation conditions Table 2 Governor parameter combination results (2) Result calculation: Considering the comprehensive index of regulation characteristics in the high water level, medium water level and low water level scenarios, the results of each governor parameter scheme under 30 research conditions are calculated. The results under these research conditions are summed up by weight to obtain the combined results (each governor scheme corresponds to 7 indicators).

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

[0040] Taking the scheme in Table 2 as the decision object, TOPSIS is used to make a decision 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 where the guide vane is slow at first and then fast). The results are shown in Figure 4 . Among them, the left and right figures are the decision results of scenarios 1 and 2 respectively, and the green columns represent the highest-scoring solutions for different scenarios 1. The solution with the highest score in scenario 1 is solution 30, with a score of 0.6525; the solution with the highest score in scenario 2 is solution 40, with a score of 0.9988. Solution 40, which scored the highest in scenario 2, scored only 0.4771 in scenario 1, which shows that the optimal solution in scenario 2 cannot adapt to complex working conditions and does not achieve the optimal comprehensive power regulation characteristics. In order to further compare the optimal solutions in the two scenarios, this section compares the results of solutions 30 and 40 under the working conditions studied in scenario 1, which takes into account a more comprehensive approach. The results are shown in Table 3.

[0041] In Table 3, the negative numbers in the "Change" row indicate that Scheme 30 is better than Scheme 40, and the positive numbers have the opposite meaning. As can be seen from the table, out of a total of 21 indicators, Scheme 30 is better than Scheme 40 in 12 indicators, among which Scheme 40 has advantages in speed ITAE, speed fluctuation maximum and power regulation rate indicators. Scheme 30 mainly has advantages in guide vane mileage, water hammer pressure fluctuation maximum, power reverse regulation and power regulation accuracy indicators. It should be noted that for power regulation speed, because it is an extremely large indicator, the larger the indicator, the better. Through the above analysis, it can be seen that Scheme 40 has better speed response indicators and power regulation speed, which means that the guide vane movement speed under this scheme is faster and the power regulation can be completed faster.

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

[0043] In step S4: the closeness score of each parameter solution to the ideal solution is calculated based on the TOPSIS algorithm, and the solution with the highest score is the speed regulator parameter that adapts to the complex working condition changes.

[0044] An embodiment of the present invention provides a system for optimizing parameters of a pumped storage power station governor, including: The scenario construction module is used to obtain the 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 is adjusted from the initial operating condition to the target operating condition.

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

[0046] The speed regulator parameter optimization module is used to obtain multiple indicator data for describing the unit safety, speed response, and power response performance in each regulation scenario in the hydro-mechanical-electrical coupling model corresponding to each set of speed regulator parameters. The experiment obtains the actual operating probability of each indicator of the pumped storage power station in each regulation scenario as a weight, and performs weighted summation on the indicator data under all regulation scenarios to obtain the comprehensive optimization target value under the hydro-mechanical-electrical coupling model corresponding to each set of speed regulator parameters. Based on the comprehensive optimization target value corresponding to each set of speed regulator parameters, a multi-attribute decision-making method is used to score them, and the speed regulator parameter combination with the highest score is used as the optimal speed regulator parameter of the pumped storage power station.

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

[0048] S2. Generate a weighted factor matrix based on the historical operating data of the pumped storage power station, 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 weighting factor matrix are calculated as follows: Weight = P (Initial working condition)× P (Target operating conditions).

[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 is 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, simulate the performance of the governor parameter combination in various regulation scenarios through the hydro-mechanical-electrical coupling model, and combine the index data in each scenario by weight to generate a comprehensive optimization target value. Figure 2 and Figure 3 shown.

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

[0054] Among them, mean is the average value of a certain indicator deviation, and sigma is the standard deviation of a certain indicator deviation.

[0055] The final 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 scheme.

[0056] S4. Use the multi-attribute decision-making method to score the comprehensive optimization target value and screen the optimal PID parameter solution.

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

[0058] The final 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 scheme.

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

[0060] Through the collaborative design of weighted indicator scoring and extreme rule adaptation, the deep method makes the PID parameter scheme of the pumped storage power station (such as KP=1.0, KI=0.4) superior to the traditional scheme in 12 indicators, combining efficient regulation capabilities and extreme scenario safety.

[0061] The above-described embodiments merely represent several implementation methods of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A method for optimizing the speed regulator parameters of a pumped storage power station, characterized in that: The following steps are involved: Acquiring historical operating data of the pumped-storage power station and determining a plurality of adjustment scenarios, each of the adjustment scenarios being an operating scenario of the pumped-storage power station adjusted from an initial operating condition to a target operating condition; By using multiple preset speed regulator parameter groups, a hydro-mechanical-electrical coupling model of a pumped storage power station corresponding to each speed regulator parameter group is simulated and established; In the hydro-mechanical-electrical coupling model corresponding to each set of speed regulator parameters, multiple indicator data for describing the unit's safety, speed response, and power response performance in each regulation scenario are obtained. The actual operating probability of each indicator in each regulation scenario of the pumped-storage power station is obtained as a weight, and the indicator data for all regulation scenarios are weighted and summed to obtain the comprehensive optimization target value under the hydro-mechanical-electrical coupling model corresponding to each set of speed regulator parameters. According to the comprehensive optimization target value corresponding to each set of speed regulator parameters, a multi-attribute decision-making method is used to score them, and the speed regulator parameter combination with the highest score is taken as the optimal speed regulator parameters of the pumped storage power station.

2. The method for optimizing the speed regulator parameters of a pumped storage power station according to claim 1, wherein: The simulation establishes a hydro-mechanical-electrical coupling model of a pumped storage power station corresponding to each set of speed regulator parameters, and the specific steps include: The water-mechanical-electrical coupling model is constructed by simulating the water diversion system modeled by the characteristic line method, the full characteristic curve of the pump-turbine processed by Suter transform, and the synchronous generator model constructed by Simulink; Multiple groups of speed regulator parameters of the hydro-mechanical-electrical coupling model are set, wherein the speed regulator parameters include: a KP value range of 0.1 to 0.9, and a KI value range of 0.1 to 0.

7.

3. The method for optimizing the speed regulator parameters of a pumped storage power station according to claim 1, wherein: The experiment obtains the actual operating probability of each indicator of the pumped storage power station in each regulation scenario as a weight, and the formula is: Weight = P (Initial working condition)× P (target operating conditions); 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.

4. The method for optimizing the speed regulator parameters of a pumped storage power station according to claim 1, wherein: The various indicator data specifically include: Unit safety indicator data consisting of guide vane mileage and maximum water hammer pressure, speed response indicator data consisting of speed ITAE and speed fluctuation peak, and power response indicator data consisting of power reverse regulation, regulation rate and regulation accuracy.

5. A pumped storage power station governor parameter optimization system, characterized in that: include: A scenario construction module is used to obtain historical operating data of the pumped storage power station and determine multiple adjustment scenarios, each of which is an operating scenario of the pumped storage power station adjusted from an initial operating condition to a target operating condition; A model simulation module is used to simulate and establish a hydro-mechanical-electrical coupling model of a pumped storage power station corresponding to each set of governor parameters using multiple preset sets of governor parameters; The speed regulator parameter optimization module is used to obtain multiple indicator data for describing the unit safety, speed response and power response performance in each regulation scenario in the hydro-mechanical-electrical coupling model corresponding to each set of speed regulator parameters; the actual operation probability of each indicator of the pumped storage power station in each regulation scenario is obtained as a weight, and the indicator data under all regulation scenarios are weighted summed to obtain the comprehensive optimization target value under the hydro-mechanical-electrical coupling model corresponding to each set of speed regulator parameters; according to the comprehensive optimization target value corresponding to each set of speed regulator parameters, a multi-attribute decision-making method is used to score, and the speed regulator parameter combination with the highest score is used as the optimal speed regulator parameter of the pumped storage power station.

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