A design method for the power supply system of a pumped storage power station

Abstract

The present invention discloses a method for designing a plant power system of a pumped-storage power station, which relates to the technical field of pumped-storage power stations and includes the following steps: S1, design of a multi-redundant power supply architecture; S2, intelligent bus connection and flexible switching mechanism; S3, intelligent classification and dynamic calculation of plant power loads; S4, intelligent protection and panoramic monitoring of the plant power system; the plant power system of the pumped-storage power station in this application effectively reduces the probability of power outage accidents through a multi-redundant power supply architecture, intelligent bus connection and switching mechanism, and intelligent protection devices, and can still ensure continuous power supply to key equipment in complex fault scenarios, greatly improve the reliability of the plant power system, and provide solid guarantee for the safe and stable operation of the power station.

Description

Technical Field

[0001] The present invention relates to the technical field of pumped storage power stations, and in particular to a design method for a power system of a pumped storage power station. Background Art

[0002] A pumped-storage power station utilizes off-peak electricity to pump water from a lower reservoir to an upper reservoir for storage. During peak power or emergencies, the water is released from the upper reservoir to generate electricity. The plant power system is a crucial component of a pumped-storage power station, responsible for providing reliable power to the station's generators, auxiliary equipment, lighting systems, and other components. As its core support component, the plant power system is directly related to the safety and stability of the station's operations.

[0003] However, traditional plant power system designs have numerous drawbacks, including poor power supply reliability and difficulty coping with complex fault scenarios; power quality affected by various factors, impacting equipment lifespan and operating efficiency; high operating and maintenance costs, relying on extensive manual inspections and routine repairs; and limited adaptability to diverse operating conditions, making it impossible to dynamically and accurately match power demand. As pumped-storage power plants evolve toward larger capacities and higher parameters, an innovative plant power system design approach is urgently needed to overcome these challenges.

[0004] Therefore, it is necessary to design a design method for the power supply system of a pumped storage power station to solve the above technical problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a design method for a power system of a pumped storage power station to solve the problems of poor power supply reliability and limited adaptability to different working conditions in the prior art power system mentioned in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for designing a power system for a pumped storage power station, comprising the following steps:

[0007] S1. Multi-redundant power supply architecture design:

[0008] Connect the low-voltage side of main transformer A to underground busbar I, and the low-voltage side of main transformer B to underground busbar III. During normal operation, the main transformer converts the high voltage to 10kV to supply the corresponding busbar. Intelligent algorithms are introduced to monitor the main transformer load and equipment power demand in real time to dynamically allocate power. Underground busbar III is constructed, connecting both the local power supply and the station's diesel generator power supply as a backup system. Energy storage buffer modules are installed to absorb current surges and store excess power.

[0009] S2. Intelligent busbar connection and flexible switching mechanism:

[0010] An intelligent interconnecting switch is installed between underground busbars I, II, and III. In the event of a fault, it automatically detects the fault type and location, and then automatically or manually closes the switch according to a preset strategy, switching the normal power supply to the faulty busbar.

[0011] S3. Intelligent classification and dynamic calculation of power load in power plants:

[0012] The plant's power load is divided into four categories: core and critical, important support, general operation, and non-essential interruptible load. A model integrating big data and artificial intelligence algorithms is used to collect equipment operating data in real time. This data is then dynamically predicted and accurately calculated using a deep neural network algorithm, taking into account equipment operating conditions, seasons, and plant scheduling plans.

[0013] S4. Intelligent protection and panoramic monitoring of the plant power system:

[0014] Deploy intelligent protection devices that integrate advanced algorithms and adaptive strategies. In addition to traditional functions, they also add harmonic monitoring suppression and unbalanced current protection, and use machine learning algorithms to automatically adjust action parameters. Build a panoramic monitoring system based on the Internet of Things, big data, and cloud computing. Deploy sensors at key locations within the factory to collect equipment operation, power, and environmental data and upload them to the cloud platform. Operators access the cloud platform through terminals to achieve monitoring, control, and alarms.

[0015] Furthermore, as a preference, in the S1 multi-redundant power supply architecture design, the energy storage buffer module adopts a supercapacitor group, which has a fast charge and discharge response time, can absorb current spikes at the moment the backup power supply is connected, and store excess electrical energy when the power is stable.

[0016] Furthermore, as a preference, the intelligent interconnecting switch in the S2 intelligent bus interconnection and flexible switching mechanism has a fault type identification function, which is used to accurately determine the fault type in a short time by analyzing the current waveform, voltage amplitude change and power factor characteristics of the bus during a fault, and select the optimal normal power supply for switching according to a preset strategy.

[0017] Furthermore, as a preference, in the S2 intelligent busbar connection and flexible switching mechanism, the intelligent control system establishes a time series model of the power load in each area, combines the real-time power supply status and equipment operating condition data, and uses a genetic algorithm to optimize the power distribution path, so as to improve the overall operating efficiency of the plant power system.

[0018] Furthermore, as a preference, in the intelligent classification and dynamic calculation of the S3 plant power load, the power consumption of the unit control system, speed regulator system, excitation system, and emergency fire pump equipment is divided into the basis for core critical loads.

[0019] Furthermore, preferably, in the intelligent classification and dynamic calculation of the S3 plant power load, the deep neural network algorithm adopts a long short-term memory network (LSTM) model to learn the historical power consumption data of equipment in different seasons and different power station scheduling plans.

[0020] Furthermore, as a preference, the intelligent protection device in the S4 intelligent protection and panoramic monitoring of the plant power system adopts an active power filter to suppress harmonics when harmonics are detected, and reduces the harmonic content to below the range allowed by national standards, so as to ensure the power quality of the plant power system.

[0021] Furthermore, as a preference, the panoramic monitoring system in the S4 plant power system intelligent protection and panoramic monitoring collects the vibration and temperature environmental parameters of the equipment in real time by attaching sensors based on MEMS technology on the surface of the equipment, and uploads the data to the cloud platform through a wireless communication module.

[0022] Compared with the prior art, the present invention provides a design method for a power system of a pumped storage power station, which has the following beneficial effects:

[0023] 1. The present invention effectively reduces the probability of power outages through a multi-redundant power supply architecture, an intelligent busbar connection and switching mechanism, and intelligent protection devices. It can still ensure continuous power supply to key equipment in complex fault scenarios, greatly improve the reliability of the plant power system, and provide a solid guarantee for the safe and stable operation of the power station.

[0024] 2. Through intelligent classification and dynamic calculation of plant power loads, adaptive optimization operation strategies, and real-time optimization and dynamic adjustment mechanisms, the present invention enables the plant power system to accurately match the power demand of the power station under different operating conditions, achieve efficient utilization of power resources, and significantly enhance the adaptability to various complex operating conditions.

[0025] 3. The present invention monitors and suppresses power quality issues such as harmonics and unbalanced currents through intelligent protection devices, and accurately regulates the power supply and equipment operating status, thereby providing a stable and high-quality power supply environment for equipment within the factory, extending the equipment service life and improving equipment operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings are used to provide a further understanding of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application.

[0027] Figure 1 A schematic flow chart of the steps of a design method for a power system of a pumped storage power station;

[0028] Figure 2 The diagram below shows the circuit structure of a design method for the power supply system of a pumped storage power station. DETAILED DESCRIPTION

[0029] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0030] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the technical features indicated. Thus, features specified as "first" or "second" may explicitly or implicitly include one or more of such features; and in the description of this application, unless otherwise specified, "plurality" means two or more.

[0031] See also Figure 1-Figure 2 The embodiment of the present invention provides a method for designing a power system of a pumped storage power station, which includes the following steps:

[0032] S1. Multi-redundant power supply architecture design:

[0033] Main transformer power connection and intelligent distribution: Through a specific electrical connection method, the low-voltage side of main transformer A is connected to underground busbar I, and the low-voltage side of main transformer B is connected to underground busbar III. When the main transformer is operating normally, its internal electromagnetic conversion structure stably converts the high voltage to a low voltage of 10kV, providing power to the corresponding busbar to meet the power needs of most equipment in the factory. The introduced intelligent algorithm, based on the real-time data acquisition module, obtains parameters such as the current and voltage of the main transformer load, as well as information such as the power consumption and duration of the equipment. Using algorithmic models such as fuzzy logic and neural networks, this data is deeply analyzed to calculate the proportion of power required to be distributed to each busbar. Therefore, through the intelligent power control device, power distribution is dynamically adjusted to optimize the operating efficiency of the main transformer and reduce energy loss.

[0034] Backup Power System Construction: An underground busbar III was constructed and connected to both the local power supply and the station's on-site diesel generator power supply to form a backup power system. The local power supply is accessed via a dedicated, high-speed, reliable line. In the event of a main power failure, an intelligent switching device enables a quick power switchover, ensuring continuous power supply to the plant. As the ultimate safeguard, the on-site diesel generator power supply is equipped with a rapid start-up device that can start and supply power within seconds if both the main and local power sources fail. The backup power system is equipped with an energy storage buffer module, which utilizes a supercapacitor bank composed of multiple high-performance supercapacitors. Supercapacitors have extremely fast charge and discharge response times. When the backup power supply is connected, they can quickly absorb current spikes, preventing damage to equipment caused by current surges. Once the power supply stabilizes, excess energy is stored in the supercapacitor bank through an energy management circuit, with a storage efficiency exceeding 95%, for subsequent emergency use.

[0035] S2. Intelligent busbar connection and flexible switching mechanism:

[0036] Intelligent tie switches are installed between underground busbars I, II, and III. These switches integrate high-precision current and voltage sensors, as well as advanced signal processing and analysis chips. When a busbar or its corresponding power source fails, the sensors rapidly collect characteristic data, including the busbar's current waveform, voltage amplitude changes, and power factor, and transmit this data to the signal processing chip. The chip uses a fault identification algorithm to accurately determine the fault type within 5 milliseconds. For example, a short-circuit fault can be identified by characteristics such as a sudden surge in current and a sudden drop in voltage; an overload fault can be identified by a prolonged current exceeding the rated value; and a ground fault can be determined based on characteristics such as zero-sequence current. Based on a pre-set intelligent switching strategy, such as prioritizing the operating power source closest to the faulty busbar with the lightest load, the intelligent tie switch automatically closes, or manually closes based on remote operator instructions, precisely switching power from the operating power source to the faulty busbar, ensuring continuous and stable power supply to critical loads.

[0037] An intelligent control system optimizes power distribution: Intelligent switches connect underground Busbar I and aboveground Busbar I, underground Busbar II and aboveground Busbar II, and aboveground Busbar I and aboveground Busbar II, creating a coordinated underground and underground power network. The intelligent control system utilizes a data acquisition module to capture real-time data on changes in power load in each area, including power, current, and voltage. This data also includes power status data such as main transformer load and backup power availability, as well as equipment operating status data such as equipment operating status and start / stop times. Based on this data, a time series model of power load in each area is constructed, and a genetic algorithm is used to optimize power distribution paths. By mimicking the process of biological evolution, the genetic algorithm selects, crosses, and mutates different power distribution schemes to find the optimal power distribution path. This improves the overall operating efficiency of the plant's power system by over 15%, ensuring rapid and appropriate power allocation in response to changes in power load or power fluctuations in different areas, ensuring stable system operation.

[0038] S3. Intelligent classification and dynamic calculation of power load in power plants:

[0039] Basis for detailed load classification: Auxiliary power loads are divided into four categories based on their criticality to the safe operation of the power plant. Equipment such as the unit control system, speed governor system, excitation system, and emergency fire pumps are classified as core critical loads, as a power outage for these devices would directly endanger the safe operation of the power plant and potentially cause a major accident. Important lighting systems and some ventilation equipment are classified as critical support loads. These require stable power supply under normal and most exceptional circumstances to maintain the basic operating environment and certain critical functions of the power plant. General plant ventilation equipment and some auxiliary production equipment are classified as general operating loads. Under certain conditions, such as power supply constraints, the power supply strategy can be adjusted to lower their priority. Non-essential loads, such as those used for maintenance during non-critical periods, are classified as interruptible. In the event of power shortages or system anomalies, power supply to these equipment can be interrupted first to ensure the power needs of more critical equipment.

[0040] Dynamic load calculation model operation: A dynamic load calculation model that integrates big data analysis and artificial intelligence algorithms is used. Data collection sensors are deployed at key locations within the plant, such as electrical equipment, busbars, and lines, to collect real-time equipment operating data, including current, voltage, power factor, and operating time. This data is combined with equipment operating condition data, such as the different operating states of the unit during power generation or pumping; seasonal variation data, as equipment power demand may vary in different seasons due to factors such as ambient temperature and humidity; and power plant load scheduling data, such as power generation or pumping schedules for different periods of time. This data is fed into a deep neural network algorithm model, which uses a long short-term memory (LSTM) model to learn and process the data. The LSTM model, with its unique memory cell structure, effectively captures long-term dependencies in the data. It learns from historical equipment power consumption data across different seasons and power plant scheduling plans, predicting load changes within the next hour with over 90% accuracy. This provides a scientific basis for power allocation and equipment control, enabling refined management of the plant's power system.

[0041] S4. Intelligent protection and panoramic monitoring of the plant power system:

[0042] Upgrade of intelligent protection devices: Intelligent protection devices are deployed in the plant power system, integrating advanced fault detection algorithms and adaptive protection strategies. In addition to traditional functional modules such as overcurrent protection, overvoltage protection, undervoltage protection, and grounding protection, new functional modules such as harmonic monitoring and suppression, and unbalanced current protection have been added. Utilizing machine learning algorithms, the operating current, operating time, and other parameters of the protection device are automatically adjusted based on equipment and line parameters such as rated current, voltage level, and line impedance. When harmonic interference occurs in the system, the harmonic monitoring module uses algorithms such as Fourier transform to detect the harmonic content and frequency in real time. Once harmonics are detected, the intelligent protection device activates the active power filter, which generates a compensating current equal to and opposite to the harmonic current, reducing the harmonic content to below the allowable range of national standards, thereby ensuring the power quality of the plant power system and preventing harmonic damage to equipment.

[0043] Panoramic Monitoring System Construction: A panoramic power monitoring system based on the Internet of Things, big data, and cloud computing technologies will be built. MEMS-based sensors are attached to the surfaces of various electrical equipment within the plant. These sensors collect real-time environmental parameters such as vibration and temperature, as well as operational parameters such as speed and pressure. Power parameter sensors are installed on busbars and lines to collect power parameters such as current, voltage, and power. These sensors upload collected data to a cloud platform via wireless communication modules such as Bluetooth and Wi-Fi. Operations personnel can access the cloud platform anytime, anywhere through mobile apps or computer clients, enabling real-time monitoring, remote control, and fault alarms for plant power equipment. Leveraging big data analytics and visualization technologies, the uploaded data is analyzed and processed to generate equipment operating status assessment reports, power consumption trend analysis charts, and fault warning information reports. This provides strong support for operational management and decision-making. By analyzing historical data, potential equipment failure risks can be predicted, allowing for proactive maintenance planning, reducing equipment failure rates, and improving plant operations management.

[0044] The above is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A design method for a power system of a pumped storage power station, characterized in that: It includes the following steps: S1. Multi-redundant power supply architecture design: Connect the low-voltage side of main transformer A to underground busbar I, and the low-voltage side of main transformer B to underground busbar III. During normal operation, the main transformer converts the high voltage to 10kV to supply the corresponding busbar. Intelligent algorithms are introduced to monitor the main transformer load and equipment power demand in real time to dynamically allocate power. Underground busbar III is constructed, connecting both the local power supply and the station's diesel generator power supply as a backup system. Energy storage buffer modules are installed to absorb current surges and store excess power. S2. Intelligent busbar connection and flexible switching mechanism: An intelligent interconnecting switch is installed between underground busbars I, II, and III. In the event of a fault, it automatically detects the fault type and location, and then automatically or manually closes the switch according to a preset strategy, switching the normal power supply to the faulty busbar. S3. Intelligent classification and dynamic calculation of power load in power plants: The power load of the power plant is divided into four categories: core and critical load, important guarantee load, general operation load and non-essential interruptible load; A model that integrates big data and artificial intelligence algorithms is used to collect equipment operating data in real time. This data is then combined with equipment operating conditions, seasons, and power station scheduling plans to dynamically predict and accurately calculate loads using a deep neural network algorithm. S4. Intelligent protection and panoramic monitoring of the plant power system: Deploy intelligent protection devices that integrate advanced algorithms and adaptive strategies. In addition to traditional functions, they also add harmonic monitoring suppression and unbalanced current protection, and use machine learning algorithms to automatically adjust action parameters. Build a panoramic monitoring system based on the Internet of Things, big data, and cloud computing. Deploy sensors at key locations within the factory to collect equipment operation, power, and environmental data and upload them to the cloud platform. Operators access the cloud platform through terminals to achieve monitoring, control, and alarms.

2. A method for designing a power system for a pumped storage power station according to claim 1, characterized in that: In the S1 multi-redundant power supply architecture design, the energy storage buffer module uses a supercapacitor group, which has a fast charge and discharge response time, can absorb current spikes at the moment the backup power supply is connected, and store excess electrical energy when the power is stable.

3. The method for designing a power system for a pumped storage power station according to claim 1, wherein: The intelligent interconnection switch in the S2 intelligent bus interconnection and flexible switching mechanism has a fault type identification function. By analyzing the current waveform, voltage amplitude change and power factor characteristics of the bus during a fault, it is used to accurately determine the fault type in a short time and select the optimal normal power supply for switching according to the preset strategy.

4. The method for designing a power system for a pumped storage power station according to claim 1, wherein: In the S2 intelligent busbar connection and flexible switching mechanism, the intelligent control system establishes a time series model of the power load in each area, combines real-time power status and equipment operating condition data, and uses a genetic algorithm to optimize the power distribution path to improve the overall operating efficiency of the plant power system.

5. The method for designing a power system for a pumped storage power station according to claim 1, wherein: In the intelligent classification and dynamic calculation of the S3 plant power load, the power consumption of the unit control system, speed governor system, excitation system, and emergency fire pump equipment is divided into the basis for core critical loads.

6. The method for designing a power system for a pumped storage power station according to claim 1, characterized in that: In the intelligent classification and dynamic calculation of the S3 plant power load, the deep neural network algorithm uses a long short-term memory network (LSTM) model to learn the historical power consumption data of equipment under different seasons and different power station scheduling plans.

7. The method for designing a power system for a pumped storage power station according to claim 1, characterized in that: The intelligent protection device described in the S4 intelligent protection and panoramic monitoring of the auxiliary power system uses an active power filter to suppress harmonics when harmonics are detected, reducing the harmonic content to below the range allowed by national standards, thereby ensuring the power quality of the auxiliary power system.

8. The method for designing a power system for a pumped storage power station according to claim 1, characterized in that: The panoramic monitoring system described in the S4 plant power system intelligent protection and panoramic monitoring collects the vibration and temperature environmental parameters of the equipment in real time by attaching sensors based on MEMS technology on the surface of the equipment, and uploads the data to the cloud platform through the wireless communication module.

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