Hydroelectric generating set active power distributed control system under multi-station centralized control

Abstract

The invention discloses a hydroelectric generating set active power distributed control system under multi-station centralized control. The system comprises a data acquisition system, a centralized control system, an energy management controller and a hydroelectric generating set. The data acquisition system acquires historical active power data and operation state data of all hydroelectric generating sets; based on a preset linear or nonlinear objective function and a constraint condition, the centralized control system performs iterative calculation by using the data acquired by the data acquisition system through a numerical optimization algorithm to obtain an optimal active power distribution scheme among all stations in the hydroelectric generating set; the feasibility of the optimal active power distribution scheme is evaluated through stability analysis; wherein the objective function is to minimize the total energy consumption of a power station or balance the output change, and the constraint condition city is determined according to the limitation condition of safe and stable operation of a power grid, the limitation of a power transmission line and / or the safe operation boundary of each station unit; the centralized control system determines an adjustment instruction of each station unit according to the optimal active power distribution scheme, and sends the adjustment instruction to the energy management controller of the corresponding station for real-time regulation and control so as to ensure the consistency of active power output among multiple stations; the energy management controller compares the difference between the current actual active power and the preset value based on the data monitored by the system in real time, and timely adjusts the control strategy to ensure the stability of the system. According to the technical scheme, the active power distributed control system of the hydroelectric generating set under multi-station centralized control can realize intelligent adjustment and optimization of the hydroelectric generating set through the synergistic effect of the components and the technology, so that the efficiency, the reliability and the sustainability of the whole hydroelectric system are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of power system automation control, and in particular to a distributed control system for active power of hydropower units under multi-station centralized control. Background Art

[0002] The active power distributed control system of hydropower units under multi-station centralized control is a system that uses a centralized control system of multiple control sites to monitor and manage the operation of hydropower units in real time. The advantage of this system is that it can achieve unified management and optimization of multiple hydropower units and improve the stability and economy of the entire power grid.

[0003] In active power distributed control systems, some advanced communication technologies and data processing methods are usually needed to achieve data exchange and coordination between various sites. For example, wireless sensor networks, remote communication technologies, and machine learning algorithms can be used to quickly transmit data between different sites and accurately analyze and predict power load changes.

[0004] Distributed control of active power of hydropower units under multi-station centralized control is an advanced control strategy that aims to improve the working efficiency and stability of the entire power network through the coordinated management of multiple hydropower stations. Its core is to disperse the traditional centralized control to each workstation and use communication means such as local area network to realize real-time data exchange and information sharing, so that each site can autonomously adjust the operating status according to the needs of the system and its own conditions, especially the distribution of active power; however, in this process, it also faces problems such as data transmission delays or errors, immature coordination mechanisms between stations, and possible response lags in extreme situations. Through the synergy of the above components and technologies, although the system aims to realize intelligent regulation and optimization of hydropower units and further improve the efficiency, reliability and sustainability of the overall hydropower system, the above challenges need to be solved in practical applications to better play a role. Summary of the invention

[0005] In view of this, an embodiment of the present application provides a distributed control system for active power of hydropower units under multi-station centralized control, which at least partially solves the problems existing in the prior art.

[0006] In an embodiment of the present application, a distributed control system for active power of a hydropower unit under multi-station centralized control is provided, comprising: a data acquisition system, a centralized control system, an energy management controller and a hydropower unit;

[0007] The data acquisition system collects historical active power data and operating status data of all hydropower units;

[0008] The centralized control system uses the data collected by the numerical optimization algorithm data acquisition system to iteratively calculate the optimal active power distribution plan between various sites in the hydropower unit based on the pre-set linear or nonlinear objective function and constraints, and evaluates the feasibility of the optimal active power distribution plan through stability analysis;

[0009] The objective function is to minimize the total energy consumption of the power station or the change in balanced output, and the constraints are determined according to the constraints of safe and stable operation of the power grid, the restrictions of the power transmission lines and / or the safe operation boundaries of the units at each site;

[0010] The centralized control system determines the adjustment instructions for each site unit according to the optimal active power distribution plan, and sends the adjustment instructions to the energy management controller of the corresponding site for real-time control and processing to ensure the consistency of active power output among multiple sites;

[0011] The energy management controller compares the current actual active power with the preset value based on the real-time monitoring data of the system, and adjusts the control strategy in time to ensure the stability of the system.

[0012] Optionally, in a possible implementation of the present application, the objective function is recorded as F(x), and the establishment process thereof is specifically as follows:

[0013] Based on the minimization of the total square difference of active power output between each site, i.e., Σ(Si–Sj)^2, as part of the objective function, Si and Sj are the active power output of any two sites respectively;

[0014] Adding the weight k results in a new objective function F(x) = Σk*(Si–Sj)^2+C, where C is the rate of change of electric energy and the weight k represents the importance of the system for the priority adjustment of a specific site;

[0015] When ensuring that the active power balance constraint B(Si, t) between all sites is met, that is, ΣSi=total load demand+system spinning reserve, the optimal active power allocation scheme is calculated, denoted as Si*.

[0016] Optionally, in a possible implementation of the present application, the specific implementation of adding the weight K is as follows:

[0017] Define the sites with a specific priority as Class A, and the sites other than Class A as Class B;

[0018] For Class A sites, a larger weight k_A>>k is set, while Class B sites maintain a relatively small weight;

[0019] The weight value is adjusted to ensure that the power output of the Class A site is more preferentially approached to the target value when the active power balance condition between power stations is met, i.e., ΣSi=ΣS_load;

[0020] If the weight value k does not meet the priority requirement after adjustment, the weight needs to be readjusted until the goal of active power distribution between sites is met.

[0021] Optionally, in a possible implementation of the present application, in the verification of ensuring that the active power balance condition between sites is satisfied:

[0022] If ΣSi≠ΣS_load and the absolute value of the deviation |ΣSi-ΣS_load|>Eps, Eps is a minimum value used to prevent misjudgment caused by inaccurate floating point numbers. In this case, it is considered that the system fails to maintain balance;

[0023] In this case, the corresponding power output needs to be adjusted according to the increase of Σ(Si–Sj)^2, and when the increase decreases, it remains unchanged until the system reaches stability;

[0024] After the active power among all sites reaches a dynamic balance, the final active power is output to the energy management controller and power output is started;

[0025] If the total power load demand still cannot be maintained after the above adjustments, consider increasing or reducing the participation of hydropower units to ensure that the entire system is in a balanced state.

[0026] Optionally, in a possible implementation of the present application, in the process of correcting the active power output value of the site, the increase amount based on Σ(Si–Sj)^2 is:

[0027] When the increment of the sum difference Σ(Si–Sj)^2 between sites exceeds a given threshold M, the power output is adjusted;

[0028] The rate of change of electric energy between sites C = (Si-Sk) / T, where Sk represents the active power distribution at the time of the most recent adjustment, and T is the interval between the two most recent adjustments, in hours, which is used to measure the rate of change;

[0029] If the power change rate of the power station reaches the maximum limit R, R is set according to the equipment performance. Even if the increment exceeds the given threshold M, the power station output cannot be further increased. Instead, the next-level site should be selected for compensation to ensure stable operation of the site while reducing the impact of mechanical fatigue and wear.

[0030] Optionally, in a possible implementation of the present application, when optimization is performed based on a pre-set linear or nonlinear objective function, a conditional judgment is included in the optimization process: when the iterative error of the optimization result is less than or equal to a predetermined tolerance ε, the iteration is terminated and it is considered that the optimal solution has been found.

[0031] Optionally, in a possible implementation of the present application, the termination of the iteration process is further specifically limited to: comparing the optimal solution of the last iteration with the solution obtained in the penultimate iteration, if the following is satisfied: (the optimal solution of the last iteration minus the absolute value of the solution of the penultimate iteration) / the optimal solution of the last iteration ≤ β under preset conditions, where 0<β≤01 is the convergence accuracy threshold, then the calculation iteration process is terminated.

[0032] Optionally, in a possible implementation of the present application, the selection of β under preset conditions needs to take into account real-time changes in the system. For example, β is dynamically corrected based on the dynamic load changes of the power generation system. The threshold is appropriately increased during periods of large load fluctuations, and β takes a larger value, such as 01. The threshold is appropriately lowered during periods of relatively stable load, and β takes a smaller value, such as 001, to ensure that the algorithm can both find a solution quickly and ensure the stability of the solution, that is: when the system is at peak load, set β = 01, and readjust the β value in the peak and valley load stable area, such as setting it to 001.

[0033] Optionally, in a possible implementation of the present application, the specific steps of comparing the difference between the current actual active power and the set reference value based on the data monitored in real time by the system and timely adjusting the control strategy to ensure the stability of the system include:

[0034] Perform real-time power monitoring and compare it with the set reference value to obtain the deviation;

[0035] Quickly analyze the current grid load change trend and possible deviations based on actual needs;

[0036] Adjust the preset power reference curve to adapt to changes in demand or supply caused by external factors to ensure overall stability;

[0037] Enable adaptive algorithms to automatically adjust parameter settings to eliminate persistent deviations and improve response sensitivity;

[0038] Abnormal alarm management is implemented to respond promptly to serious deviations from normal values ​​or the early stages of failures to ensure a smooth transition of the entire system.

[0039] Optionally, in a possible implementation of the present application, the specific steps of determining the adjustment instruction of each site unit according to the optimal active power allocation scheme include:

[0040] Analyze the increment or decrement requirements in the optimal active power allocation scheme, calculate the adjustment time and adjustment range based on the actual regulation performance of each site, establish the adjustment instruction sequence considering the dynamic response process, encode the above parameters into a specific communication message, and form a complete adjustment command for transmission.

[0041] In an embodiment of the present application, a distributed control system for active power of hydropower units under multi-station centralized control is provided, including: a data acquisition system, a centralized control system, an energy management controller and a hydropower unit; the data acquisition system collects historical active power data and operating status data of all hydropower units; the centralized control system uses a numerical optimization algorithm based on a pre-set linear or nonlinear objective function and constraint conditions, and uses the data collected by the data acquisition system to iteratively calculate the optimal active power distribution scheme between each site in the hydropower unit, and evaluates the feasibility of the optimal active power distribution scheme through stability analysis; wherein the objective function is to minimize the total energy consumption of the power station or balance the output change, and the constraint conditions are determined according to the restrictive conditions for the safe and stable operation of the power grid, the restrictions of the power transmission lines and / or the safe operation boundaries of the units at each site; the centralized control system determines the adjustment instructions of each site unit according to the optimal active power distribution scheme, and sends the adjustment instructions to the energy management controller of the corresponding site for real-time control processing to ensure the consistency of active power output between multiple stations; the energy management controller compares the difference between the current actual active power and the predetermined value based on the data monitored in real time by the system, and adjusts the control strategy in time to ensure the stability of the system. Through the above technical solution, it can be solved that through the synergistic effect of the above components and technologies, the active power distributed control system of the hydropower units under multi-station centralized control can realize the intelligent adjustment and optimization of the hydropower units, thereby improving the efficiency, reliability and sustainability of the entire hydropower system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings exemplarily illustrate the embodiments and constitute a part of the specification, and together with the text description of the specification, are used to explain the exemplary implementation of the embodiments. The embodiments shown are for illustrative purposes only and do not limit the scope of the claims. In all drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0043] Figure 1 A schematic diagram of a system architecture of a distributed control system for active power of a hydropower unit under multi-station centralized control in an embodiment of the present application;

[0044] Figure 2 It is a control flow diagram of a distributed control system of active power of a hydropower unit under multi-station centralized control in an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0046] It should be noted that the descriptions involving "first", "second", etc. in the embodiments of the present application are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0047] In the description of the present application, it should be understood that the numerical labels before the steps do not indicate the order in which the steps are executed, but are only used to facilitate the description of the present application and to distinguish each step, and therefore should not be understood as a limitation on the present application.

[0048] The distributed control system of active power of hydropower units under multi-station centralized control is a system that realizes distributed control of the active power of units by centrally controlling and monitoring the power system of multiple hydropower units.

[0049] See also Figure 1 , its basic system architecture is as follows: The active power distributed control system 10 of the hydropower unit under multi-station centralized control includes: a data acquisition system 101, a centralized control system 102, an energy management controller (Energy Management Controller, EMC) 103, a hydro-generator unit 104 and / or a user-side system 105;

[0050] The data acquisition system is responsible for collecting real-time data such as voltage, current, frequency, etc. from each hydropower unit. These data can be transmitted to the centralized control system via wired or wireless communication;

[0051] The centralized control system is the core part of the entire system, responsible for receiving and processing real-time data from the data acquisition system. At the same time, the centralized control system can also remotely control and adjust each hydropower unit according to preset logic and rules;

[0052] Each hydropower unit requires an energy management controller to control and manage it locally. The EMC can communicate with the centralized control system by wire or wireless communication, receive instructions from the centralized control system, and adjust the operating status of the unit according to actual conditions;

[0053] The hydro-generator set is the power source of the entire system and needs to be controlled by a centralized control system and EMC to ensure its safe, reliable and economical operation;

[0054] Under multi-station centralized control, the user-side system can also be connected to the distributed control system to achieve intelligent scheduling and optimization of the hydropower system.

[0055] The above is the basic system architecture of the active power distributed control system of hydropower units under multi-station centralized control. Different application scenarios may be different, but in general they all revolve around data acquisition, processing, control and energy management.

[0056] Embodiment 1

[0057] Next, referring to the attached drawings, the active power distributed control system of the hydropower unit under the multi-station centralized control of the present application is described. The control system aims to ensure the maximum efficiency and reliability of active power output under the coordinated operation of multiple hydropower stations through a series of steps. The key to this method is to divide the whole process into several interrelated stages, and respond to specific problems at each stage.

[0058] See also Figure 2 The control process of the active power distributed control system of the hydropower unit under multi-station centralized control specifically includes the following steps:

[0059] S201: The data acquisition system collects historical active power data and operating status data of all hydropower units.

[0060] The first step is to collect data using a data acquisition system. This step involves obtaining historical and current operating parameters from the hydropower units of each hydropower station. These parameters include active power levels in each period and other key indicators such as flow, speed, gate opening and other operating status data. Through this process, sufficient feedback information can be collected for subsequent optimization analysis.

[0061] S202: The centralized control system uses a numerical optimization algorithm to iteratively calculate the data collected by the data acquisition system based on pre-set linear or nonlinear objective functions and constraints to obtain the optimal active power distribution plan between the various sites in the hydropower units, and evaluates the feasibility of the optimal active power distribution plan through stability analysis.

[0062] The objective function is to minimize the total energy consumption of the power station or balance the output change, and the constraint conditions are determined according to the restriction conditions for safe and stable operation of the power grid, the restriction of the power transmission line and / or the safe operation boundary of the units at each site.

[0063] The second step after the data acquisition system acquires this data is the centralized control phase. In this phase, the control system at the central location operates according to predetermined objective equations - these equations are usually set to reduce total energy consumption or provide a more stable supply when demand changes, which can be implemented through linear or more complex nonlinear programming. The system will run one or more optimization algorithms based on the above objective equations and take into account the security constraints of the grid and other technical boundaries to find the best allocation plan among many possible options. This plan will be carefully analyzed before it is proposed to verify its impact and adaptability on maintaining the stability of the entire power plant group. For example, if the wind speed decreases on a certain day, resulting in a decrease in the supply of electricity generated by wind energy, there may be a demand for higher hydropower output, and this request will be entered into the system as an input and processed by the above control logic to determine the specific proportion of each hydropower plant that should adjust its power generation.

[0064] S203: The centralized control system determines the adjustment instructions for each site unit according to the optimal active power distribution plan, and sends the adjustment instructions to the energy management controller of the corresponding site for real-time control processing to ensure the consistency of active power output among multiple sites.

[0065] Then, after the result of step S202 is obtained, the third step, step S203, comes into play, that is, issuing instructions to each site for actual execution: the control system now issues power adjustment instructions to each power station according to the most optimized solution, the purpose of which is to make the output of the entire system as balanced as possible on the basis of meeting the immediate load requirements. These specific control commands will then be sent to the local controller of each station for further processing - this is to enable each hydroelectric power generation equipment to respond promptly and accurately, and maintain a synchronized and consistent behavior as a whole. Specifically, under the influence of certain extreme climates, such as droughts that may cause water supply shortages, this approach may be used to coordinate the reduction of the output of each power station in order to share the load, without causing excessive depletion of a single water source, resulting in unnecessary losses or even environmental damage.

[0066] S204: The energy management controller compares the current actual active power with the preset value based on the data monitored in real time by the system, and adjusts the control strategy in time to ensure the stability of the system.

[0067] In the last process, step S204, it can be seen that local management activities are taking effect. After receiving the instructions, each site will make dynamic adjustments through its own configured energy manager based on the comparison between the actual output value monitored by the system and its expected target. This includes, for example, precise switching operations on water flow valves, or changing the speed of turbines. This can ensure that a continuous and controllable working mode can be maintained at any time and in any place in the entire operation chain. In one embodiment, if a power station fails to reach the specified target power due to some fault, this information will be quickly fed back to the centralized control center and trigger a correction instruction, so as to ensure that other power stations can make timely supplements to ensure that the demand balance of the entire power network is not disturbed, and at the same time, it will not cause energy waste or other damage.

[0068] It is precisely because of the existence and synergy of these steps that when it comes to how to use these components in combination with professional technologies to effectively meet challenges, the distributed hydropower regulation and optimal allocation mechanism we have built has demonstrated its ability to achieve intelligent regulation and optimization of hydropower units, thereby improving the efficiency, reliability and sustainability of the entire hydropower system.

[0069] Embodiment 2

[0070] Next, the objective function of the present application is described as F(x), and its establishment process is as follows: First, the sum of the squares of the total difference in active output of all power stations Σ(Si–Sj)^2 is minimized as a component of the objective function. Here, Si and Sj represent the real-time active power output of any two different stations. The purpose is to optimize the overall coordination of the system by minimizing the power difference between these stations and reduce the problems caused by the imbalance of power in the power stations.

[0071] After adding the weight coefficient k to the objective function, a new objective function F(x) = Σk*(Si–Sj)^2+C is formed. Here, C represents the rate of change of the system's electric energy and can be regarded as a measure of the total energy consumption or energy generation rate over a period of time. The weight k reflects the priority level when the system needs to give priority to certain specific power plants or power plant groups; according to specific conditions or emergency needs in actual operation, the k value can be dynamically adjusted to achieve the best effect.

[0072] Considering that the actual power system needs to meet the hard constraint of active power balance, the objective function must also meet the active power balance constraint B(Si,t), that is, the total power output ΣSi of all generators must be equal to the current load demand in the system plus the spinning reserve generation capacity prepared for sudden demand. Based on this constraint, the optimal output power allocation Si* of the power station is determined.

[0073] For example, in a multi-site centralized control environment, we are dealing with the active power distribution control problem of hydropower units. There are several power stations A, B, C, and D that jointly supply the power grid. Assuming that the demand of the power grid at a certain time T is P_total, and the rotation reserves of each station are R_a, R_b, R_c, and R_d, the sum of all active power plus the reserved capacity of each power station should match the demand at this time, that is, S_a+S_b+S_c+S_d+R_a+R_b+R_c+R_d=P_total. In this case, we seek the optimal output of each power station to ensure that the output difference between the power stations is small and the operation efficiency of the entire network is high.

[0074] Specifically, the parameter k can be set within a reasonable range according to the grid stability requirements, such as a value between 0.5 and 2, and the optimal value depends on the power deviation sensitivity required under specific operating conditions. In addition, the setting of the electric energy change rate C depends on historical data and the expected load growth rate or decay rate. This form of objective function can effectively guide the active output to an ideal configuration to improve grid stability and economic benefits.

[0075] Embodiment 3

[0076] Next, the specific implementation of adding weight K in this application is further described as follows:

[0077] Define the sites with a specific priority as Class A, and the sites that are not Class A as Class B. This step first clarifies the priority of each hydropower unit in the system. For example, in one embodiment, the more reliable or cost-effective hydropower units may be set to Class A due to geographical location, technical status, or operating costs, to ensure that key sites can provide the required power output when necessary.

[0078] For Class A sites, a larger weight k_A>>k is set, and the value of k can be 1, while Class B sites maintain a relatively small weight k_B. Larger or smaller here means that the weight k_A is much larger than the weight k_B of any other non-critical node. In this way, when solving the problem under the constraints, the algorithm will naturally tend to give priority to the contribution of Class A hydropower units. In an example, assuming that the k_A of Class A may be 1000 units, while that of Class B is 1 unit, such a weight difference makes the algorithm pay more attention to the working conditions of high-priority generators.

[0079] The weight value is adjusted to ensure that the power output of Class A sites is more preferentially close to the target value while satisfying the active power balance condition between power stations (i.e., ΣSi = ΣS_load). Specifically, this condition means optimizing the power dispatch strategy under the premise that the total generation ΣSi in the power system is equal to the required total load ΣS_load. The formula ΣSi represents the total power output of all hydropower generators combined, while ΣS_load refers to the overall power demand of the system. This balance is to ensure the consistency between the supply side and the demand side, and is also the basis for the stability of the power grid. The adjustment of weights makes the power output more reasonable, thereby optimizing the working efficiency and response speed of the system.

[0080] If the weight value k does not meet the priority requirements after adjustment, the weight needs to be readjusted until the goal of active power distribution between sites is met. This section emphasizes that if the initial selection of weights cannot make the high-priority Class A sites achieve the expected results, multiple iterative calculations of weights are required, through trial and error until the priority distribution requirements are met. For example, after the initial setting, it may be found that the Class A site cannot maximize the output as expected due to the existence of a Class B site, so it is necessary to increase the gap between k_A and k and re-set the weights and optimize the calculations.

[0081] Embodiment 4

[0082] Next, the verification in this application to ensure that the active power balance conditions between sites are met is further described: If ΣSi≠ΣS_load and the absolute value of the deviation |ΣSi-ΣS_load|>Eps, here ΣSi represents the sum of the total active power generated or received by all sites in the entire network, and ΣS_load represents the total active power consumed by all access sites. When the absolute value of the deviation between the two is greater than a very small preset value Eps, this minimum value usually takes into account the internal processing power of the computer and the actual measurement and signal transmission errors in the power grid. Setting Eps can effectively prevent unnecessary balance check errors due to data processing accuracy.

[0083] In the case of such a system being judged to be unbalanced, the system adjusts the power output according to Σ(Si–Sj)^2, where Si and Sj represent the active power output of two different sites. When the sum of the squares of the changes in these outputs increases, it indicates that the imbalance of the current system is increasing; and when it tends to decrease, it indicates that the balance state is being restored. Dynamic adjustments are made by continuously detecting the trend of changes in this parameter until the state change of the system tends to stabilize, that is, it no longer continues to increase or has reached an acceptable small fluctuation range.

[0084] Specifically, when Σ(Si–Sj)^2 stops increasing and enters a state of smaller fluctuations, an effective active power dynamic balance is achieved between all connected sites. At this time, the final, stable active power data is sent to the controller in the overall energy management system for recording, and the subsequent system work plan is allocated according to this data.

[0085] For example, in one embodiment, there is a network system consisting of four hydropower station nodes. During real-time operation, it is found that the actual input power (ΣSi) of all stations is 1 MW more than the required total power demand (ΣS_load), and the deviation value of 1 MW far exceeds the set minimum value Eps = 0.01MW. At this time, it is determined that an imbalance has occurred. The adjustment algorithm observes the change of Σ(Si-Sj)^2 over time, and tries to adjust the output several times in a row to reduce the imbalance until the square difference approaches zero or no longer continues to increase.

[0086] Assuming that the system output still meets the unbalanced condition after this, and the above-mentioned adjustment still cannot effectively achieve the supply and demand balance of the total power load, as part of the next step, further adjustment decisions must be made on the hydropower units to increase the power generation units or reduce some operating capacity. The goal of this is to keep the entire hydropower station network operating normally in a state of active power balance to ensure that users have a continuous and reliable power supply.

[0087] Embodiment 5

[0088] Next, the process of correcting the active power output value of the site in this application is described. Based on the increase in Σ(Si–Sj)^2, when the increment of the total difference Σ(Si–Sj)^2 between the sites exceeds a given threshold M, the power output is adjusted; this mechanism is intended to dynamically evaluate and correct the power-frequency deviations between the power stations in the power grid system. Σ(Si–Sj)^2 calculates the sum of the squares of the difference between the active output of each station in the network and the output of its paired power station. This indicator can help determine the load imbalance between sites. When the imbalance intensifies to a preset level (M), measures must be taken to adjust the site output. The design of this formula is based on a choice based on minimizing errors, which helps to improve the stability and balance of the power system.

[0089] Then, in actual operation, the rate of change of electric energy C = (Si-Sk) / T is measured and used to determine the speed of power change at the site. Here, Sk represents the specific active power allocated to the site after the previous adjustment, Si represents the actual output power value that needs to be allocated at present, and T represents the time between the last power adjustment and this adjustment, usually measured in hours. The rate of change calculated by the formula helps to understand the working condition and response speed of the generator set.

[0090] For example, in a multi-site grid-connected power supply system, if there are ten hydropower stations cooperating with each other and sharing information to jointly maintain the stability of the grid frequency, at a certain time, the system detects a sharp change in Σ(Si–Sj)^2, which means that the power output mismatch between different sites has reached a critical point (M) that must be corrected; then the active power of the power station is adjusted to ensure the stability of the entire power system.

[0091] Furthermore, during the adjustment process, if it is found that the power change rate C reaches the upper limit of the maximum adjustment rate (R) allowed by the mechanical performance of the power station, even if the imbalance still needs to be corrected, the output of a single site cannot be increased to avoid excessive adjustment frequency damaging the equipment. At this time, the next secondary site with adjustment capability should be found to share the load change demand.

[0092] Specifically, assuming that a hydropower station has adjusted its frequency for multiple times in succession and its power is close to the limit adjustment speed, even though the detection system still shows that the power frequency in the system needs further adjustment (i.e., Σ(Si–Sj)^2 is greater than M), considering the risk of equipment wear, the power output of this power station will be stopped from being further increased, and another power station will be activated to perform frequency correction.

[0093] Each formula is set up based on the premise of ensuring the smooth operation of the power grid. M, as the difference threshold, should be set to ensure the reliable operation of the power grid while taking into account the requirements for power quality. For a statement like C = (Si-Sk) / T, the key is to ensure that the rate of change does not exceed the maximum limit for the safe operation of a specific device, that is, the maximum rate R in the formula, which is determined by the parameters provided by the equipment manufacturer.

[0094] Embodiment 6

[0095] Next, the optimization based on a pre-set linear or nonlinear objective function in this application is described, and conditional judgment is included in the optimization process: when the iterative error of the optimization result is less than or equal to the predetermined tolerance ε, the iteration is terminated and it is considered that the optimal solution has been found. The first step is to define the objective function in the active power distributed control method of the hydropower unit. The objective function may contain multiple sub-goals, such as minimizing the power difference between generator sets, improving the overall energy efficiency of the system, or ensuring the frequency stability of the system. On this basis, the objective function F(x) is established, where x is a set of control variables (such as the active power output of each generator set).

[0096] In the second step, step S202, an optimization algorithm is set to solve the defined objective function. Here, a gradient method, a genetic algorithm or other applicable global search techniques can be selected to perform optimization calculations. Each algorithm will approach the direction of minimization in different ways.

[0097] Entering the third step, step S203, at this stage, a preset tolerance ε, one of the stop criteria, is defined to measure whether the error between each round of optimization iterations reaches an acceptable level. For example, in the case of multi-station centralized monitoring, if the error ΔP of the power allocation scheme between two consecutive cycles is less than ε, the algorithm stops running, and the result obtained at this time is considered to be the optimal solution under the current conditions. The choice of ε here depends on the application requirements and accuracy. It is usually quantitatively determined in engineering practice based on historical experience and specific application requirements. For example, the value range of ε can be between 0.001 and 0.00001 watts / second (MW / second). The selection of the optimal value depends on the required power resolution and tolerance to sensitivity to changes.

[0098] As a specific example, in a simulation case there is a network of hydroelectric units with several turbines. When using a specific algorithm to optimize the power distribution of this power network, a target model based on the total system power loss as the objective function is set, and ε is used as the threshold for judging the completion of the optimization. After the initial configuration, the iterative calculation begins. Whenever the next possible better state is calculated, the solution is updated and the error value is recorded. If at the nth iteration, the new solution obtained by the algorithm has only a negligible change compared to the previous one (that is, less than or equal to the pre-defined tolerance ε), then the system determines that the current solution is close enough to the optimal value and stops subsequent calculations, announcing that a satisfactory balance point has been found, completing the optimization of the entire power distribution process.

[0099] Embodiment 7

[0100] Next, the further specific limitations of the above-mentioned termination iteration process in this application are described as follows: First, the optimal solution generated by the last iteration process and the solution obtained by the second-to-last iteration immediately before it need to be obtained. This means that the corresponding solution needs to be recorded at each step of the iteration process; and at the checkpoint of the termination condition, it is necessary to ensure that at least two complete iterations have been completed so that the difference between the solutions of two consecutive iterations can be compared.

[0101] Then the difference is calculated, that is, the difference between the optimal solution of the last iteration and the result of the second-to-last iteration. The absolute value is taken as the positive value to ensure that the difference is positive; then the result is used as the numerator, and the optimal solution obtained in the last iteration is used as the denominator to construct a ratio to measure the degree of closeness between two consecutive results. The ratio must meet the condition that the ratio should not be greater than the preset threshold β to determine whether the termination condition is met, where β defines the convergence standard.

[0102] β is a percentage-based accuracy threshold that represents the maximum allowable difference between algorithm iteration results, and its value is in the range of (0, 0.01]. For example, when 0.001≤β≤0.003, it can be understood that when the relative error between the solutions produced by two consecutive iterations is less than 0.1%-0.3%, the algorithm is considered to have converged. At this time, it can be considered that a sufficiently accurate target value has been reached without the need for repeated optimization.

[0103] In a specific application scenario of distributed control of active power of hydropower units, for example, there are multiple hydropower stations distributed in a river basin, and each hydropower station is responsible for a certain amount of load regulation. In order to achieve efficient and stable operation of the entire power grid, a global collaborative control system is needed to optimize the joint scheduling of these units. This process can solve the optimal configuration solution through the above-mentioned iterative process. If after multiple rounds of iterations, the power output under the last optimal configuration does not differ from the previous power output by more than 0.01% of the total target output, it can be judged that the optimization has reached the required precision standard according to the settings, so the process of iterative search for new solutions can be stopped. This method can effectively balance the relationship between computing resource consumption and the final solution, improving the optimization effect while controlling the computing overhead.

[0104] Embodiment 8

[0105] Next, it is described that the selection of β under the preset conditions in this application needs to take into account the real-time changes of the system. For example, β is dynamically corrected based on the dynamic load changes of the power generation system. The threshold is appropriately increased during periods of large load fluctuations, and β takes a larger value, such as 0.01. The threshold is appropriately lowered during periods of relatively stable load, and β takes a smaller value, such as 0.001, to ensure that the algorithm can both find a solution quickly and ensure the stability of the solution, that is: when the system is at peak load, set β = 0.01, and readjust the β value in the peak and valley load stable area, such as setting it to 0.001.

[0106] This description refers to the role that parameter β plays in a multi-station centralized control system and how it changes dynamically in response to load fluctuations within the power system. The first step is to adjust the set value of parameter β based on the load situation. When the instantaneous load of the power generation system fluctuates greatly, which is usually one of the characteristics of peak power consumption, increasing parameter β to a higher value, such as setting β to 0.01, is intended to speed up the active power control algorithm to find a balance and respond quickly to sudden changes so that it can quickly adapt to new requirements of the power grid.

[0107] The second step is to reduce the parameter β to a lower level, such as setting β to 0.001, during the period when the post-peak load is flat. This ensures that the frequency of the power system is more stable during the period of stable load, and enables the system to accurately maintain or approach the target operating point, and obtain better control performance and energy saving benefits under low dynamic load conditions.

[0108] For example, in one embodiment, a reservoir power plant consisting of three large hydropower units adopts the above scheme to implement the control logic. Assuming that the active load reaches a peak at noon due to the sharp increase in industrial production and residential air conditioning use in the region and causes instantaneous fluctuations in the grid voltage, β is automatically adjusted up to 0.01 at this time. Due to the higher β value, the control system can identify load changes more quickly and make appropriate power adjustments to cope with sudden demands. When it enters the middle of the night or the load returns to stability, β drops to 0.001, allowing the unit to maintain a stable power supply in a more energy-efficient manner and minimize operating costs.

[0109] The selection of the β parameter mentioned here involves a trade-off in control theory - the balance between convergence speed and stability. A higher β value is conducive to approaching the control target faster; however, too high a β value may cause system oscillation. The optimal value needs to be set flexibly according to the actual system characteristics and changes in load patterns, so the design of the parameter adjustment strategy must take into account different load conditions and the specific characteristics of the generator set.

[0110] Embodiment 9

[0111] Next, the specific steps of comparing the difference between the current actual active power and the set reference value based on the data of real-time monitoring of the system in this application, and adjusting the control strategy in time to ensure the stability of the system are described. First, the system will perform real-time power monitoring and collect the active power actually generated by the current hydropower station through sensors or other data acquisition means. Compare these real-time acquired values ​​with the reference values ​​that have been set (i.e., the predetermined target power level or power generation plan target) to determine the size of the deviation between the two. This step is the basis for the system to perform self-inspection and is also the prerequisite for subsequent dynamic control. For example, in a multi-station centralized control scenario, the total output power generated by each hydropower unit is monitored in real time by the central controller and compared with the target power generation plan specified by the dispatching agency, so as to quickly understand whether there is a problem of imbalance between supply and demand.

[0112] Next comes the analysis of the changing trends of the grid load. This part involves not only the identification of the deviation between the currently measured active power and the expected target value, but also the assessment of the possibility of load increase or decrease in the future, because this prediction helps to prepare appropriate control measures in advance to cope with potential changes in demand or power supply. Specifically, when it is detected that the demand for electricity in a certain area has increased due to climate change, this trend can be predicted through data analysis, and responses can be made to avoid the pressure that may be caused by this increase. For example, when the air conditioning load is expected to increase before the hot season begins, the control system can infer the upcoming demand based on the data of the past few years and begin to take preparatory measures to adjust the generator output level.

[0113] This is followed by an adjustment of the original power reference curve so that it can change with the influence of external factors and maintain the balance of the overall system. This means that the system will change the power output plan originally set for the hydropower station based on the results of the load analysis in the previous stage to meet the changing needs of the actual situation and keep the supply and demand consistent across the power grid. For example, if the power demand on a certain evening is suddenly much less than previously estimated, the power generation rate of the unit can be slowed down to match this new low demand environment.

[0114] The next step is to introduce adaptive algorithms, which can automatically fine-tune control strategy parameters to reduce continuous deviations. The parameter optimization algorithm involved in this process can continuously update itself to more accurately control power, allowing the control system to react immediately whenever unexpected changes occur. In the above case, if a higher-than-expected consistency error is continuously monitored for a period of time, the adaptive algorithm may decide to slow down the speed of the hydropower generator or reduce the water flow to reduce output, and continue to fine-tune in the process until the ideal error threshold is reached.

[0115] The final component is the abnormal alarm management system. It detects situations that exceed the set safety range and notifies management personnel to intervene immediately. This process is the last line of defense to ensure system safety, and is particularly critical in extreme conditions or when the system is about to face the risk of being unable to recover on its own. If any situation that does not conform to normal operation is found during monitoring, the system will issue an early warning signal, such as excessive temperature, uneven pressure, or unstable output, allowing on-site engineers to solve possible problems as soon as possible to ensure stable operation of the power plant.

[0116] Embodiment 10

[0117] Next, the specific steps of determining the adjustment instructions of each site unit according to the optimal active power allocation scheme in the present application are described.

[0118] The first step is to analyze the increment or decrement requirements in the optimal active power allocation scheme. In this step, it is necessary to clarify the total power adjustment required by the grid dispatch. This adjustment may be a positive number to increase the power generation, or a negative number to reduce the power supply. For example, at a certain moment, due to a sudden increase in load, the dispatch center may order the total system to increase power by 10MW. This demand is regarded as a global increment or decrement.

[0119] Next, the actual regulation performance of each site needs to be combined to evaluate the respective response speed and regulation range limits to achieve the overall power target. This step involves considering variables such as response time (t_res) and maximum regulation power (P_max), where t_res refers to the time interval in which the site can take action after receiving the signal, which generally ranges from milliseconds to seconds; and P_max represents the upper limit of the site's power adjustment capacity in the shortest unit time. For example, at a hydropower site A, the fastest response time is set at 0.5 seconds, and the maximum adjustable power change is 500KW / second. Through such indicators, it is possible to estimate how long it will take to complete a given adjustment.

[0120] Establishing the adjustment command sequence is to formulate a list of operations in order to achieve the overall increase or decrease after clarifying what kind of adjustments each station can make. This process takes into account the dynamic response of the power grid, that is, the adjustment of each power station may affect the real-time balance of the entire power system. In this example, it is assumed that hydropower station B takes 30 seconds to reach full load, so the order of issuing adjustment orders to station B should be before its downstream power station or leave enough waiting time to ensure that overload will not occur.

[0121] Finally, the results of the above calculations are converted into machine code, that is, the values ​​required for adjustment at each site and the execution time are encoded into a data packet of a specific format for transmission. This encoding process ensures that the information is distortion-free and reaches the target device accurately. Finally, the power station receives and processes these specific adjustment commands and starts to make corresponding changes in active power to meet the scheduling requirements.

[0122] As for why the parameters of the formula and their optimal values ​​are set in this way, it is because they must be done based on the actual capabilities of the equipment. Otherwise, the instructions will be impossible to implement or will exceed the safe operating boundaries of the power plant, causing system instability.

[0123] The distributed control system of active power of hydropower units under multi-station centralized control of this application includes several core steps and technical points, aiming to provide a solution that can improve the operating efficiency and system stability of hydropower units. First of all, from the data level, the entire control system uses a data acquisition system to collect relevant data of all hydropower units. These data cover historical active power data and current operating status information of the units. By summarizing these data, the centralized control system can accurately understand the working conditions of each power generation site and make corresponding adjustment strategies.

[0124] The task of the centralized control system is the core of the core. It uses numerical optimization algorithms to calculate the most suitable active power distribution method among the units based on the collected data and pre-defined objective functions (such as reducing total energy consumption or maintaining power output balance) and the actual constraints of power grid operation (such as power grid security and technical parameter restrictions of each power generation point, etc.). This distribution not only depends on the optimization results, but also undergoes stability analysis to evaluate whether the implementation in actual operation is feasible.

[0125] Furthermore, based on the optimal power allocation results, the control system issues the required adjustment commands for each specific unit, and the energy management controller at each site makes precise adjustments based on real-time data supervision. Real-time data supervision here includes comparing the difference between the current power generation level and the target setting value. After any deviation is found, adjustment actions will be taken in time, which not only ensures the stability of power output but also enhances the consistency of power distribution between multiple power stations.

[0126] Through such a process design - from centralized information collection and decision support to decentralized execution at each site - the control system effectively solves the problems of slow regulation response and high energy consumption caused by delayed information transmission in the past, and improves the energy utilization efficiency, operational reliability and environmental friendliness of the entire hydropower system. In this way, while ensuring that the basic needs of electricity users are met, it can also cope with the strong volatility of renewable energy and increase the comprehensive benefits of the power supply system.

[0127] Obviously, those skilled in the art should understand that the modules or steps of the above-mentioned embodiments of the present application can be implemented by general-purpose computer devices, they can be concentrated on a single computer device, or distributed on a network composed of multiple computer devices, optionally, they can be implemented by executable program codes of computer devices, so that they can be stored in a storage device and executed by the computer device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0128] It should be noted that the above are only preferred embodiments of the present application, and the patent protection scope of the present application is not limited thereto. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A distributed control system for active power of hydropower units under multi-station centralized control, characterized in that: include: Data acquisition systems, centralized control systems, energy management controllers and hydropower units; The data acquisition system collects historical active power data and operating status data of all hydropower units; The centralized control system uses the data collected by the data acquisition system of the numerical optimization algorithm to iteratively calculate the optimal active power distribution scheme between the various sites in the hydropower unit based on the pre-set linear or nonlinear objective function and constraint conditions, and evaluates the feasibility of the optimal active power distribution scheme through stability analysis; The objective function is to minimize the total energy consumption of the power station or the change in balanced output, and the constraint conditions are determined according to the restriction conditions for safe and stable operation of the power grid, the restriction of the power transmission line and / or the safe operation boundary of the units at each site; The centralized control system determines the adjustment instructions for each site unit according to the optimal active power allocation scheme, and sends the adjustment instructions to the energy management controller of the corresponding site for real-time control processing to ensure the consistency of active power output among multiple sites; The energy management controller compares the current actual active power with the preset value based on the real-time monitoring data of the system, and adjusts the control strategy in time to ensure the stability of the system.

2. The active power distributed control system of a hydroelectric generator set according to claim 1, characterized in that: The objective function is recorded as F(x), and its establishment process is as follows: Based on the minimization of the total square difference of active power output between each site, i.e., Σ(Si–Sj)^2, as part of the objective function, Si and Sj are the active power output of any two sites respectively; Adding the weight k results in a new objective function F(x) = Σk*(Si–Sj)^2+C, where C is the rate of change of electric energy and the weight k represents the importance of the system for the priority adjustment of a specific site; When ensuring that the active power balance constraint condition B(Si, t) is satisfied between all sites, that is, ΣSi=total load demand+system spinning reserve, the optimal active power allocation scheme is calculated and recorded as Si*.

3. The active power distributed control system of a hydroelectric generator set according to claim 2, characterized in that: The specific implementation of adding weight K is as follows: Define the sites with a specific priority as Class A, and the sites other than Class A as Class B; For Class A sites, a larger weight k_A>>k is set, while Class B sites maintain a relatively small weight; The weight value is adjusted to ensure that the power output of the Class A site is more preferentially approached to the target value when the active power balance condition between power stations is met, i.e., ΣSi=ΣS_load; If the weight value k does not meet the priority requirement after adjustment, the weight needs to be readjusted until the goal of active power distribution between sites is met.

4. The active power distributed control system of a hydroelectric generator set according to claim 3 is characterized in that: In the verification to ensure that the active power balance conditions between sites are met: If ΣSi≠ΣS_load and the absolute value of the deviation |ΣSi-ΣS_load|>Eps, Eps is a minimum value used to prevent misjudgment caused by inaccurate floating point numbers. In this case, it is considered that the system fails to maintain balance; In this case, the corresponding power output needs to be adjusted according to the increase of Σ(Si–Sj)^2, and when the increase decreases, it remains unchanged until the system reaches stability; After the active power among all sites reaches a dynamic balance, the final active power is output to the energy management controller and power output is started; If the total power load demand still cannot be maintained after the above adjustments, consider increasing or reducing the participation of hydropower units to ensure that the entire system is in a balanced state.

5. The active power distributed control system of a hydroelectric generator set according to claim 4, characterized in that: In the process of correcting the active power output value of the site, the increase based on Σ(Si–Sj)^2 is: When the increment of the sum difference Σ(Si–Sj)^2 between sites exceeds a given threshold M, the power output is adjusted; The rate of change of electric energy between sites C = (Si-Sk) / T, where Sk represents the active power distribution at the time of the most recent adjustment, and T is the interval between the two most recent adjustments, in hours, which is used to measure the rate of change; If the power change rate of the power station reaches the maximum limit R, R is set according to the equipment performance. Even if the increment exceeds the given threshold M, the power station output cannot be further increased. Instead, the next-level site should be selected for compensation to ensure stable operation of the site while reducing the impact of mechanical fatigue and wear.

6. The active power distributed control system of a hydroelectric generator set according to claim 1, characterized in that: When the optimization is performed based on a preset linear or nonlinear objective function, the optimization process includes conditional judgment: when the iteration error of the optimization result is less than or equal to the predetermined tolerance ε, the iteration is terminated and it is considered that the optimal solution has been found.

7. The active power distributed control system of a hydroelectric generator set according to claim 6, characterized in that: Further specific limitations on the termination of the iteration process include: comparing the optimal solution of the last iteration with the solution obtained in the penultimate iteration, if the following condition is satisfied: (the optimal solution of the last iteration minus the absolute value of the solution of the penultimate iteration) / the optimal solution of the last iteration ≤ β under preset conditions, where 0<β≤0.01 is the convergence accuracy threshold, then the calculation iteration process is terminated.

8. The active power distributed control system of a hydroelectric generator set according to claim 7, characterized in that: The selection of β under the preset conditions needs to take into account the real-time changes of the system. β is dynamically modified based on the dynamic load changes of the power generation system. The threshold is appropriately increased during periods of large load fluctuations, and β takes a larger value. The threshold is appropriately lowered during periods of relatively stable loads, and β takes a smaller value, to ensure that the algorithm can find a close solution quickly and ensure the stability of the solution.

9. The active power distributed control system of a hydroelectric generator set according to claim 1, characterized in that: The specific steps of comparing the difference between the current actual active power and the set reference value based on the data of real-time system monitoring and timely adjusting the control strategy to ensure the stability of the system include: Perform real-time power monitoring and compare it with the set reference value to obtain the deviation; Quickly analyze the current grid load change trend and possible deviations based on actual needs; Adjust the preset power reference curve to adapt to changes in demand or supply caused by external factors to ensure overall stability; Enable adaptive algorithms to automatically adjust parameter settings to eliminate persistent deviations and improve response sensitivity; Abnormal alarm management is implemented to respond promptly to serious deviations from normal values ​​or the early stages of failures to ensure a smooth transition of the entire system.

10. The active power distributed control system of a hydroelectric generator set according to claim 1, characterized in that: The specific steps of determining the adjustment instructions of each site unit according to the optimal active power allocation scheme include: Analyze the increment or decrement requirements in the optimal active power allocation scheme, calculate the adjustment time and adjustment range based on the actual regulation performance of each site, establish an adjustment instruction sequence considering the dynamic response process, encode the above parameters into a specific communication message, and form a complete adjustment command for transmission.

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