A multi-energy complementary high-voltage direct-current micro-grid system applied to a drilling platform

Abstract

The application discloses a multi-energy complementary high-voltage direct-current micro-grid system applied to a drilling platform, relates to the technical field of new energy micro-grid and intelligent energy management, and comprises a high-voltage direct-current bus, which is a 1500V direct-current bus and is used for micro-grid circuit control, and the high-voltage direct-current bus comprises the following: a photovoltaic power generation unit connected to the high-voltage direct-current bus through a current collecting device and used for converting solar energy into direct-current electric energy and feeding the direct-current electric energy into the bus; a methanol generator set connected to the high-voltage direct-current bus through a rectifying device and used for providing base load electric energy and supplementary electric energy; and a hybrid energy storage unit comprising a lithium battery energy storage unit and a super capacitor energy storage unit, wherein the lithium battery energy storage unit and the super capacitor energy storage unit are connected to the high-voltage direct-current bus through respective bidirectional DC / DC converters, respectively, and are used for cooperatively suppressing power fluctuation of the bus. The application realizes the coordination of electric energy among the photovoltaic power generation, the methanol generator set and the energy storage system through intelligent regulation.

Description

Technical Field

[0001] This invention relates to the field of new energy microgrids and intelligent energy management technology, and in particular to a multi-energy complementary high-voltage DC microgrid system applied to drilling platforms. Background Technology

[0002] As the global energy transition deepens, the energy supply for drilling platforms in remote areas is receiving increasing attention. Currently, both onshore and offshore drilling platforms generally rely on diesel or methanol generator sets as their sole power source. This power supply model suffers from prominent problems such as high fuel transportation costs, large carbon emissions, and severe noise pollution.

[0003] In recent years, photovoltaic power generation technology and energy storage technology have developed rapidly. The evolution of photovoltaic modules and energy storage systems towards 1500V high-voltage direct current systems has become an important trend. High-voltage direct current technology has significant advantages in reducing line losses and improving system efficiency.

[0004] However, existing photovoltaic-storage-diesel microgrid systems still face many technical bottlenecks when applied to drilling platforms:

[0005] First, 1500V high-voltage DC systems have extremely high requirements for insulation protection, arc protection, and equipment compatibility. Existing control strategies do not fully consider the transient stability of high-voltage systems under impact loads.

[0006] Secondly, the power surges during the start-up and shutdown of equipment such as the top drive and winch of the drilling platform are drastic. Such impact loads can easily cause millisecond-level voltage drops or overshoots on the 1500V DC bus, seriously threatening the safe operation of the equipment.

[0007] Third, when multiple high-power PCS units are connected in parallel in a 1500V high-voltage system, the high voltage level means that even a small phase difference can lead to significant circulating current. The existing current sharing control accuracy is insufficient and cannot meet the requirements of engineering applications.

[0008] Fourth, hybrid energy storage units such as lithium battery clusters and supercapacitor groups are mostly low-voltage modules. Direct connection to a 1500V high-voltage bus requires a highly efficient and reliable DC / DC converter. Existing converter topologies have low efficiency, are difficult to dissipate heat, and lack compatibility design for high-voltage systems.

[0009] In addition, existing systems generally lack a rapid arc detection and safety disconnection linkage mechanism for 1500V high-voltage environments, resulting in significant safety hazards.

[0010] Therefore, there is an urgent need to develop a 1500V high-voltage DC microgrid system and its control method that can adapt to the complex load characteristics of drilling platforms, integrate multi-energy complementary technology, and have high reliability and high safety. Summary of the Invention

[0011] To address the aforementioned technical problems, the present invention provides a multi-energy complementary high-voltage DC microgrid system for drilling platforms, comprising a 1500V high-voltage DC bus for microgrid circuit layout and control, including:

[0012] The photovoltaic power generation unit is connected to the high-voltage DC bus via a combiner device to convert solar energy into DC power and feed it into the bus.

[0013] The methanol generator set is connected to the high-voltage DC bus via a rectifier to provide base load power and supplementary power.

[0014] The hybrid energy storage unit includes a lithium battery energy storage unit and a supercapacitor energy storage unit. The lithium battery energy storage unit and the supercapacitor energy storage unit are respectively connected to the high-voltage DC bus through their respective bidirectional DC / DC converters to work together to smooth out power fluctuations of the bus.

[0015] Multiple PCS units are connected in parallel. The DC side of each PCS is connected to the high-voltage DC bus, and the AC side of each PCS is connected in parallel to output AC power to the drilling platform load.

[0016] The energy management system is communicatively connected to the photovoltaic power generation unit, the methanol generator set, the hybrid energy storage unit, and the multiple PCS parallel units, respectively, and is used to collect system operation data and issue control commands.

[0017] The safety protection unit is connected to the high-voltage DC bus and the energy management system, and is used to perform bus voltage monitoring, arc fault detection and fault isolation protection actions.

[0018] Preferably, the energy management system includes:

[0019] The data acquisition module is used to collect historical load operation data of the drilling platform, historical output data of the photovoltaic power generation unit, weather forecast data, state of charge data of the lithium battery energy storage unit, and status data of the methanol generator set to form a raw dataset.

[0020] The data preprocessing module is used to perform missing value imputation, outlier removal and normalization on the original dataset to generate a standardized data sequence;

[0021] The feature extraction module is used to perform time series feature extraction and correlation analysis on the standardized data sequence to generate system initial state information that includes load impact mode features, photovoltaic power output fluctuation features and energy storage charging and discharging features.

[0022] Preferably, the energy management system further includes:

[0023] The hybrid prediction module has built-in LSTM neural network prediction branch and LightGBM prediction branch, which are used to predict and weighted fuse the initial state information of the system to generate load power prediction sequence and photovoltaic output prediction sequence for future time periods.

[0024] The optimization scheduling module is used to perform energy management optimization scheduling based on the load power prediction sequence and the photovoltaic output prediction sequence, and generate the optimal output plan of the methanol generator set and the charging and discharging plan of the energy storage unit.

[0025] Preferably, the optimized scheduling module includes:

[0026] The load trend judgment submodule is used to judge the change trend of load power based on the load power prediction sequence. When the load power is predicted to be on the rise, it sends a power increase command to the methanol generator set in advance so that the methanol generator set can slowly climb up the power and increase the power. It also sends a discharge preparation command to the lithium battery energy storage unit to cope with the peak load.

[0027] The photovoltaic judgment submodule is used to send a power reduction command to the methanol generator set and a charging command to the lithium battery energy storage unit to absorb the excess photovoltaic power when it is predicted that the photovoltaic output will increase.

[0028] The load reduction judgment submodule is used to send a power reduction command to the methanol generator set and a discharge command to the lithium battery energy storage unit to maintain load power balance when the load power is predicted to be decreasing.

[0029] The dynamic correction submodule is used to collect the actual load power and photovoltaic output in real time during the actual operation of the system. When the deviation between the actual value and the corresponding predicted value exceeds the preset deviation threshold, the optimal output plan of the methanol generator set and the charging and discharging plan of the energy storage unit are dynamically corrected.

[0030] Preferably, the hybrid energy storage unit includes:

[0031] The lithium battery energy storage sub-unit comprises multiple lithium battery clusters to provide stable energy throughput;

[0032] The supercapacitor energy storage subunit includes multiple supercapacitor modules, used to provide instantaneous high-power compensation;

[0033] The lithium battery energy storage subunit and the supercapacitor energy storage subunit are respectively connected to the high-voltage DC bus through the bidirectional DC / DC converter;

[0034] The energy management system also includes a bus voltage monitoring module, which is used to collect the voltage signal of the high-voltage DC bus in real time and calculate the bus voltage change rate.

[0035] When the absolute value of the bus voltage change rate is greater than 100V / ms and the bus voltage drop value is greater than 20V, the supercapacitor energy storage subunit immediately discharges at maximum power with a response time of less than 1ms.

[0036] The lithium battery energy storage subunit starts discharging within 50ms after the supercapacitor energy storage subunit starts discharging, and gradually takes over the power output of the supercapacitor energy storage subunit.

[0037] When the bus voltage returns to the normal operating range and the impact load ends, the lithium battery energy storage subunit replenishes energy to the supercapacitor energy storage subunit in a constant current manner.

[0038] Preferably, the bidirectional DC / DC converter adopts a multi-level modular topology, including MMC or CHB topology. The DC input terminal of the bidirectional DC / DC converter is connected to the high-voltage DC bus and supports 1500V DC input. The DC output terminal of the bidirectional DC / DC converter is connected to the corresponding energy storage sub-unit, and the output voltage matches the rated voltage of the energy storage sub-unit.

[0039] Preferably, each PCS in the parallel PCS unit is connected to the other PCS via a high-speed fiber optic ring network, and each PCS receives the load power prediction sequence broadcast by the energy management system through the high-speed fiber optic ring network.

[0040] Each PCS includes:

[0041] The output voltage regulation submodule is used to calculate the target output power of each component based on the ratio of its rated capacity to the total parallel capacity, combined with the load power prediction sequence, and adjust the output voltage amplitude and phase in advance.

[0042] The circulating current suppression submodule is used to monitor the output current of each PCS in real time, calculate the circulating current value between each PCS based on the virtual impedance algorithm, and dynamically correct the droop control coefficient according to the circulating current value.

[0043] Preferably, the security protection unit includes:

[0044] A DC arc fault detection device is connected to the high-voltage DC bus and is used to monitor the arc signal of the high-voltage DC bus in real time.

[0045] A 1500V DC fast disconnect switch is connected in series on the high-voltage DC bus. When the DC arc fault detection device detects an arcing signal, the fast disconnect switch performs a disconnection operation within 5ms.

[0046] The voltage over-limit protection submodule is used to monitor the voltage value of the high voltage DC bus in real time. When the voltage value exceeds the overvoltage protection setting of 1550V or is lower than the undervoltage protection setting of 1400V, the voltage protection action is triggered.

[0047] The islanding detection submodule is set in each PCS and is used to detect the grid-connected operation status. When the islanding operation status is detected, it controls each PCS to automatically shut down.

[0048] The fault information feedback submodule is used to feed back the execution results of safety protection actions to the energy management system, which then records the fault information and executes the system recovery strategy.

[0049] Preferably, the prediction period of the hybrid prediction module is the next 15 minutes; the generation frequency of the load power prediction sequence and the photovoltaic output prediction sequence matches the scheduling cycle of the energy management system.

[0050] Preferably, the normal operating voltage range of the high-voltage DC bus is 1470V to 1530V.

[0051] The present invention has at least the following beneficial effects:

[0052] 1. Solve the problem of severe voltage fluctuations in 1500V high-voltage DC systems under impact loads by achieving bus voltage stability through hybrid energy storage and coordinated control;

[0053] 2. Solve the problem of circulating current amplification and current sharing difficulties when multiple high-power PCS are connected in parallel under 1500V high voltage, and improve the parallel stability of the system;

[0054] 3. Solve the technical challenges of efficiently and safely connecting low-voltage energy storage modules such as lithium batteries and supercapacitors to the 1500V DC bus, and optimize the DC / DC converter topology and control;

[0055] 4. Resolve the contradiction between slow response of methanol generator sets and rapid load fluctuation in the existing system, and achieve optimal scheduling of methanol usage. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a schematic diagram of the modules provided in Embodiment 1 of the present invention;

[0058] Figure 2A schematic diagram of an energy management system provided in Embodiment 1 of the present invention;

[0059] Figure 3 This is a schematic diagram of the optimized scheduling module provided in Embodiment 1 of the present invention. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0062] This embodiment provides a multi-energy complementary high-voltage DC microgrid system applied to drilling platforms, such as... Figure 1 As shown, it includes:

[0063] High-voltage DC bus, which is a 1500V DC bus, is used for microgrid circuit layout and control;

[0064] The photovoltaic power generation unit is connected to the high-voltage DC bus via a combiner device to convert solar energy into DC power and feed it into the bus.

[0065] Specifically, the output terminals of each photovoltaic string are connected to a DC combiner box (i.e., combiner device). Each combiner box contains an independent fuse, reverse polarity protection diode, and DC circuit breaker for each string, providing overcurrent protection, reverse polarity protection, and string-level switching control. Simultaneously, the positive and negative terminals of multiple strings are connected in parallel within the combiner box to form one or more DC outputs. The combiner box output is connected to a DC distribution cabinet via a DC cable. The distribution cabinet is equipped with a 1500V DC disconnect switch, surge protector, and insulation monitoring device. Finally, it is connected to the high-voltage DC bus via a cable to ensure a stable feed of photovoltaic power.

[0066] The methanol generator set is connected to the high-voltage DC bus via a rectifier to provide base load power and supplementary power.

[0067] Specifically, the AC power generated by the methanol generator set is first fed into the rectifier via a step-up transformer (if the output voltage does not match the input voltage of the rectifier). The rectifier uses a three-phase bridge uncontrolled rectifier or PWM active rectifier topology to convert the AC power into a stable 1500V DC power, which is then connected to the high-voltage DC bus through a DC-side filter circuit and a fast-acting fuse.

[0068] The hybrid energy storage unit includes a lithium battery energy storage unit and a supercapacitor energy storage unit. The lithium battery energy storage unit and the supercapacitor energy storage unit are respectively connected to the high-voltage DC bus through their respective bidirectional DC / DC converters to work together to smooth out power fluctuations of the bus.

[0069] Multiple PCS units are connected in parallel. The DC side of each PCS is connected to the high-voltage DC bus, and the AC side of each PCS is connected in parallel to output AC power to the drilling platform load.

[0070] Specifically, the hybrid energy storage unit consists of two parts: a lithium battery energy storage unit and a supercapacitor energy storage unit. Each is connected to a 1500V high-voltage DC bus via its own independent high-efficiency bidirectional DC / DC converter. Since the rated voltages of the lithium battery pack and the supercapacitor pack are typically much lower than 1500V (e.g., the rated voltage of a lithium battery pack is around 700V, and the supercapacitor pack is 48V×N series), they cannot be directly connected to the high-voltage bus. Therefore, a bidirectional DC / DC converter is needed to achieve voltage level conversion and bidirectional power flow control.

[0071] The energy management system is connected to the photovoltaic power generation unit, methanol generator set, hybrid energy storage unit and multiple PCS parallel units to collect system operation data and issue control commands.

[0072] The safety protection unit is connected to the high-voltage DC bus and energy management system to perform bus voltage monitoring, arc fault detection and fault isolation protection actions.

[0073] As a further embodiment of the present invention, the energy management system includes (e.g.) Figure 2 (as shown)

[0074] The data acquisition module is used to collect historical load operation data of the drilling platform, historical output data of the photovoltaic power generation unit, weather forecast data, state of charge data of the lithium battery energy storage unit, and status data of the methanol generator set to form a raw dataset.

[0075] The data preprocessing module is used to perform missing value imputation, outlier removal and normalization on the original dataset to generate a standardized data sequence;

[0076] The feature extraction module is used to perform time series feature extraction and correlation analysis on standardized data sequences to generate system initial state information that includes load impact mode features, photovoltaic power output fluctuation features, and energy storage charging and discharging features.

[0077] Specifically, for historical load operation data, the power change curves of key equipment such as top drives, winches, and mud pumps are recorded in real time with millisecond-level sampling periods through smart meters deployed in each load circuit or sampling units built into the PCS; historical photovoltaic output data is obtained by acquiring the power generation parameters of each string through current and voltage sensors in the photovoltaic combiner box; weather forecast data is obtained by accessing key meteorological factors such as light intensity, temperature, and cloud cover through small weather stations deployed on the platform combined with external meteorological service APIs; the state of charge (SOC) of the lithium battery energy storage unit is obtained through the SOC estimation algorithm of the battery management system (BMS); and the status data of the methanol generator set is read by the unit's built-in controller, which reads its operating parameters such as output power, speed, and exhaust temperature. All of the above data are aggregated into the real-time database of the energy management system after being marked with a unified timestamp to form the raw dataset.

[0078] The data preprocessing module takes the original dataset and performs three core processes: For missing value imputation, for data missing due to communication interruptions, linear interpolation or the K-nearest neighbor algorithm based on historical data from the same period is used for imputation; for abnormal data segments missing for extended periods, they are marked and removed. For outlier removal, sampling points that significantly deviate from the normal range are identified and filtered based on the Laida criterion (3σ criterion), such as zero or full-scale values ​​caused by sensor malfunctions. For normalization, due to the large differences in the dimensions and magnitudes of various data types (e.g., power data is in the megawatt range, SOC is in percentage, and meteorological temperature is from 0 to 40 degrees Celsius), the min-max normalization method is used to map all features to the [0,1] interval, or Z-score normalization is used to make them conform to a standard normal distribution, in order to eliminate the impact of dimensional differences on subsequent prediction models. After the above processing, a standardized data sequence is generated.

[0079] The feature extraction module performs in-depth mining based on the standardized data sequence: For load data, time series feature extraction calculates statistics such as mean, variance, maximum rate of change, and peak-to-peak value within the window using the sliding window method, and extracts frequency domain features in load impact modes using Fourier transform or wavelet transform to identify the time-frequency domain signatures of typical impact events such as top drive start-up and winch braking; for photovoltaic power output data, it extracts the shape and volatility features of the power output envelope from sunrise to sunset; correlation analysis calculates Pearson correlation coefficients and mutual information coefficients between load power and photovoltaic power output, between load power and historical data, and between SOC change and load power consumption to screen out feature variables that have a significant impact on prediction. Finally, after dimensionality reduction and feature selection, the above features are fused and packaged into a multi-dimensional feature vector containing load impact mode features, photovoltaic power output fluctuation features, and energy storage charging and discharging features, i.e., system initial state information, providing standardized, high-quality input for subsequent hybrid prediction models.

[0080] As another embodiment of the present invention, the energy management system further includes (such as...) Figure 2 (as shown)

[0081] The hybrid prediction module has built-in LSTM neural network prediction branch and LightGBM prediction branch, which are used to predict the initial state information of the system and then weighted and fused to generate load power prediction sequence and photovoltaic output prediction sequence for future time periods.

[0082] The optimization scheduling module is used to perform energy management optimization scheduling based on the load power prediction sequence and the photovoltaic output prediction sequence, and generate the optimal output plan of the methanol generator set and the charging and discharging plan of the energy storage unit.

[0083] Specifically, the hybrid prediction module employs a hybrid prediction architecture that combines LSTM neural networks and LightGBM to achieve high-precision predictions of load power and photovoltaic output. The LSTM neural network branch leverages its powerful time-series modeling capabilities to deeply extract historical variation patterns of drilling platform loads, impact mode characteristics, and the time-series dependence of photovoltaic output. The LightGBM branch, based on gradient boosting decision trees, performs nonlinear fitting and characterization of multi-dimensional heterogeneous features such as weather forecast data, energy storage state of charge, and methanol generator operating status. After each branch completes its prediction independently, the hybrid prediction module uses an adaptive weighted fusion algorithm to dynamically adjust the fusion weights based on the prediction error performance of the two branches within the recent historical window, ultimately generating a load power prediction sequence and a photovoltaic output prediction sequence for the next 15 minutes, providing accurate input data for system optimization and scheduling.

[0084] As another embodiment of the present invention, the optimized scheduling module includes (e.g.) Figure 3(as shown)

[0085] The load trend judgment submodule is used to judge the change trend of load power based on the load power prediction sequence. When the load power is predicted to be on the rise, it sends a power increase command to the methanol generator set in advance so that the methanol generator set can slowly climb up the power and increase the power. It also sends a discharge preparation command to the lithium battery energy storage unit to cope with the peak load.

[0086] The photovoltaic judgment submodule is used to send a power reduction command to the methanol generator set and a charging command to the lithium battery energy storage unit to absorb the excess photovoltaic power when it is predicted that the photovoltaic output will increase.

[0087] The load reduction judgment submodule is used to send a power reduction command to the methanol generator set and a discharge command to the lithium battery energy storage unit to maintain load power balance when the load power is predicted to be decreasing.

[0088] The dynamic correction submodule is used to collect the actual load power and photovoltaic output in real time during the actual operation of the system. When the deviation between the actual value and the corresponding predicted value exceeds the preset deviation threshold, the optimal output plan of the methanol generator set and the charging and discharging plan of the energy storage unit are dynamically corrected.

[0089] Specifically, the load trend judgment submodule analyzes the load power prediction sequence. When it identifies a continuous upward trend in load power in the future, this submodule calculates the required power increment and ramp rate of the methanol generator set based on the predicted increase magnitude and rate. It then sends a power boost command containing the target power value and ramp time to the methanol generator set in advance, allowing the generator set to increase power smoothly and gradually, avoiding mechanical stress and fuel waste caused by sudden load increases. Simultaneously, this submodule sends a discharge preparation command to the lithium battery energy storage unit based on the predicted peak power deficit, putting it into hot standby mode to ensure the lithium battery can respond to peak power demands at any time. Secondly, the photovoltaic judgment submodule identifies the changing trend of the photovoltaic output prediction sequence. When it determines that photovoltaic output will increase, this submodule calculates the difference between the increased photovoltaic power and the current load demand. Based on this difference, it sends a corresponding power reduction command to the methanol generator set to withdraw part of the base load power, and simultaneously sends a constant current or constant power charging command to the lithium battery energy storage unit, allowing the lithium battery to absorb excess photovoltaic power and avoiding curtailment. Secondly, the load reduction judgment submodule monitors the inflection point and rate of decrease in the load power prediction sequence. When a decrease in load power is predicted, this submodule calculates the power reduction value that the methanol generator set needs to generate based on the predicted decrease magnitude, and sends a power reduction command to the methanol generator set in advance to maintain system power balance. At the same time, it sends a discharge command to the lithium battery energy storage unit, so that the lithium battery can supplement the power required by the remaining load after the power reduction of the methanol generator set, ensuring the stability of the bus voltage. Finally, the dynamic correction submodule continuously acquires the actual load power value and the actual photovoltaic output value in real time through sensors and data acquisition channels during the actual operation of the system. It compares the actual value with the predicted value in a time window and calculates the deviation percentage. When the deviation exceeds the preset deviation threshold (e.g., 10%), this submodule triggers the correction logic, replaces the predicted value with the actual value, and re-substitutes it into the optimization scheduling algorithm. It continuously updates and dynamically adjusts the optimal output plan of the methanol generator set and the charging and discharging plan of the energy storage unit, and sends the corrected control command to each execution unit in real time, thereby realizing the closed-loop correction between predictive scheduling and actual operation, ensuring that the energy management strategy is always close to the actual system state.

[0090] As a further embodiment of the present invention, the hybrid energy storage unit includes:

[0091] The lithium battery energy storage sub-unit comprises multiple lithium battery clusters to provide stable energy throughput;

[0092] The supercapacitor energy storage subunit includes multiple supercapacitor modules, used to provide instantaneous high-power compensation;

[0093] The lithium battery energy storage sub-unit and the supercapacitor energy storage sub-unit are respectively connected to the high-voltage DC bus via bidirectional DC / DC converters;

[0094] The energy management system also includes a bus voltage monitoring module, which is used to acquire the voltage signal of the high-voltage DC bus in real time and calculate the bus voltage change rate.

[0095] When the absolute value of the bus voltage change rate is greater than 100V / ms and the bus voltage drop is greater than 20V, the supercapacitor energy storage sub-unit immediately discharges at maximum power with a response time of less than 1ms.

[0096] The lithium battery energy storage sub-unit starts discharging within 50ms after the supercapacitor energy storage sub-unit starts discharging, and gradually takes over the power output of the supercapacitor energy storage sub-unit.

[0097] When the bus voltage returns to the normal operating range and the impact load ends, the lithium battery energy storage sub-unit replenishes energy to the supercapacitor energy storage sub-unit in a constant current manner.

[0098] As another embodiment of the present invention, the bidirectional DC / DC converter adopts a multi-level modular topology, which includes MMC topology or CHB topology. The DC input terminal of the bidirectional DC / DC converter is connected to the high-voltage DC bus and supports 1500V DC input. The DC output terminal of the bidirectional DC / DC converter is connected to the corresponding energy storage sub-unit and the output voltage matches the rated voltage of the energy storage sub-unit.

[0099] The PCS units in the above-mentioned parallel PCS unit are connected to each other via a high-speed fiber optic ring network, and each PCS receives the load power prediction sequence broadcast by the energy management system through the high-speed fiber optic ring network.

[0100] Each PCS includes:

[0101] The output voltage regulation submodule is used to calculate the target output power of each component based on the ratio of its rated capacity to the total parallel capacity, combined with the load power prediction sequence, and adjust the output voltage amplitude and phase in advance.

[0102] The circulating current suppression submodule is used to monitor the output current of each PCS in real time, calculate the circulating current value between each PCS based on the virtual impedance algorithm, and dynamically correct the droop control coefficient according to the circulating current value.

[0103] Specifically, the load power prediction sequence broadcast by the energy management system is received via a high-speed fiber optic ring network, and the time base is unified among all PCSs using the IEEE 1588 precision clock synchronization protocol. Each PCS's internal output voltage regulation submodule reads its own rated capacity parameters, calculates the proportionality coefficient between its rated capacity and the sum of the rated capacities of all parallel PCSs, and then multiplies the power values ​​at each moment in the received load power prediction sequence by the aforementioned proportionality coefficient to obtain the target output power value corresponding to that PCS at each prediction moment. Based on this, the submodule converts the target output power value into reference commands for the output voltage amplitude and phase: according to the equivalent output impedance characteristics of the PCS, it uses the decoupling relationship between the power angle and active power, and between the voltage amplitude and reactive power in the power transfer equation to calculate the voltage phase offset required to achieve the target active power output, as well as the voltage amplitude adjustment required to maintain the bus voltage level; then, before the actual change in load power occurs, the submodule adjusts the phase of the output voltage towards the target direction several control cycles in advance, and adjusts the voltage amplitude to the calculated value, so that each PCS has already established an output state close to the steady-state requirement when the load arrives, thereby avoiding instantaneous power distribution imbalance caused by response lag.

[0104] For the circulating current suppression submodule, during the operation of each PCS, the instantaneous value of the output current of that PCS is collected in real time using a high-precision current sensor, and the active and reactive components of the output current are extracted using digital signal processing algorithms. This submodule performs circulating current calculation based on a virtual impedance algorithm: first, the instantaneous value and phase information of the output current of each PCS are exchanged between them through a high-speed fiber optic ring network; then, using the output current of one PCS as a reference, the vector difference between the output current of each other PCS and the reference current is calculated, and this vector difference is the circulating current component between each PCS. After obtaining the circulating current calculation value, this submodule compares the circulating current value with the circulating current allowable threshold. If the circulating current value exceeds the allowable range, the correction amount of the droop control coefficient is calculated based on the magnitude and direction of the circulating current: when the output current of a certain PCS is detected to be too large, the virtual impedance droop coefficient of the PCS is increased to reduce the output voltage reference point of its external characteristic droop curve, thereby forcing the PCS to reduce the output current; conversely, when the output current of a certain PCS is detected to be too small, its droop coefficient is decreased to raise its output voltage reference point, thereby increasing its output current sharing ratio.

[0105] As another embodiment of the present invention, the security protection unit includes:

[0106] A DC arc fault detection device is connected to the high-voltage DC bus to monitor the arc signal of the high-voltage DC bus in real time.

[0107] A 1500V DC fast disconnect switch is connected in series on the high-voltage DC bus. When the DC arc fault detection device detects an arcing signal, the fast disconnect switch will perform a disconnection operation within 5ms.

[0108] The voltage over-limit protection submodule is used to monitor the voltage value of the high voltage DC bus in real time. When the voltage value exceeds the overvoltage protection setting of 1550V or is lower than the undervoltage protection setting of 1400V, the voltage protection action is triggered.

[0109] The islanding detection submodule is set in each PCS and is used to detect the grid-connected operation status. When the islanding operation status is detected, it controls each PCS to automatically shut down.

[0110] The fault information feedback submodule is used to feed back the execution results of safety protection actions to the energy management system, which then records the fault information and executes the system recovery strategy.

[0111] It should be noted that the prediction period of the hybrid prediction module is the next 15 minutes; the generation frequency of the load power prediction sequence and the photovoltaic output prediction sequence is matched with the scheduling cycle of the energy management system.

[0112] Secondly, the normal operating voltage range of the high-voltage DC bus is 1470V to 1530V.

[0113] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A multi-energy complementary high-voltage DC microgrid system applied to drilling platforms, comprising a 1500V high-voltage DC bus for microgrid circuit layout and control, characterized in that, include: The photovoltaic power generation unit is connected to the high-voltage DC bus via a combiner device to convert solar energy into DC power and feed it into the bus. The methanol generator set is connected to the high-voltage DC bus via a rectifier to provide base load power and supplementary power. The hybrid energy storage unit includes a lithium battery energy storage unit and a supercapacitor energy storage unit. The lithium battery energy storage unit and the supercapacitor energy storage unit are respectively connected to the high-voltage DC bus through their respective bidirectional DC / DC converters to work together to smooth out power fluctuations of the bus. Multiple PCS units are connected in parallel. The DC side of each PCS is connected to the high-voltage DC bus, and the AC side of each PCS is connected in parallel to output AC power to the drilling platform load. The energy management system is communicatively connected to the photovoltaic power generation unit, the methanol generator set, the hybrid energy storage unit, and the multiple PCS parallel units, respectively, and is used to collect system operation data and issue control commands. The safety protection unit is connected to the high-voltage DC bus and the energy management system, and is used to perform bus voltage monitoring, arc fault detection and fault isolation protection actions.

2. The multi-energy complementary high-voltage DC microgrid system applied to a drilling platform according to claim 1, characterized in that, The energy management system includes: The data acquisition module is used to collect historical load operation data of the drilling platform, historical output data of the photovoltaic power generation unit, weather forecast data, state of charge data of the lithium battery energy storage unit, and status data of the methanol generator set to form a raw dataset. The data preprocessing module is used to perform missing value imputation, outlier removal and normalization on the original dataset to generate a standardized data sequence; The feature extraction module is used to perform time series feature extraction and correlation analysis on the standardized data sequence to generate system initial state information that includes load impact mode features, photovoltaic power output fluctuation features and energy storage charging and discharging features.

3. The multi-energy complementary high-voltage DC microgrid system applied to a drilling platform according to claim 2, characterized in that, The energy management system also includes: The hybrid prediction module has built-in LSTM neural network prediction branch and LightGBM prediction branch, which are used to predict and weighted fuse the initial state information of the system to generate load power prediction sequence and photovoltaic output prediction sequence for future time periods. The optimization scheduling module is used to perform energy management optimization scheduling based on the load power prediction sequence and the photovoltaic output prediction sequence, and generate the optimal output plan of the methanol generator set and the charging and discharging plan of the energy storage unit.

4. A multi-energy complementary high-voltage DC microgrid system for drilling platforms according to claim 3, characterized in that, The optimized scheduling module includes: The load trend judgment submodule is used to judge the change trend of load power based on the load power prediction sequence. When the load power is predicted to be on the rise, it sends a power increase command to the methanol generator set in advance so that the methanol generator set can slowly climb up the power and increase the power. It also sends a discharge preparation command to the lithium battery energy storage unit to cope with the peak load. The photovoltaic judgment submodule is used to send a power reduction command to the methanol generator set and a charging command to the lithium battery energy storage unit to absorb the excess photovoltaic power when it is predicted that the photovoltaic output will increase. The load reduction judgment submodule is used to send a power reduction command to the methanol generator set and a discharge command to the lithium battery energy storage unit to maintain load power balance when the load power is predicted to be decreasing. The dynamic correction submodule is used to collect the actual load power and photovoltaic output in real time during the actual operation of the system. When the deviation between the actual value and the corresponding predicted value exceeds the preset deviation threshold, the optimal output plan of the methanol generator set and the charging and discharging plan of the energy storage unit are dynamically corrected.

5. A multi-energy complementary high-voltage DC microgrid system for drilling platforms according to claim 3, characterized in that, The hybrid energy storage unit includes: The lithium battery energy storage sub-unit comprises multiple lithium battery clusters to provide stable energy throughput; The supercapacitor energy storage subunit includes multiple supercapacitor modules, used to provide instantaneous high-power compensation; The lithium battery energy storage subunit and the supercapacitor energy storage subunit are respectively connected to the high-voltage DC bus through the bidirectional DC / DC converter; The energy management system also includes a bus voltage monitoring module, which is used to collect the voltage signal of the high-voltage DC bus in real time and calculate the bus voltage change rate. When the absolute value of the bus voltage change rate is greater than 100V / ms and the bus voltage drop value is greater than 20V, the supercapacitor energy storage subunit immediately discharges at maximum power with a response time of less than 1ms. The lithium battery energy storage subunit starts discharging within 50ms after the supercapacitor energy storage subunit starts discharging, and gradually takes over the power output of the supercapacitor energy storage subunit. When the bus voltage returns to the normal operating range and the impact load ends, the lithium battery energy storage subunit replenishes energy to the supercapacitor energy storage subunit in a constant current manner.

6. A multi-energy complementary high-voltage DC microgrid system for drilling platforms according to claim 5, characterized in that, The bidirectional DC / DC converter adopts a multi-level modular topology, which includes MMC or CHB topology. The DC input terminal of the bidirectional DC / DC converter is connected to the high-voltage DC bus and supports 1500V DC input. The DC output terminal of the bidirectional DC / DC converter is connected to the corresponding energy storage sub-unit and the output voltage matches the rated voltage of the energy storage sub-unit.

7. A multi-energy complementary high-voltage DC microgrid system for drilling platforms according to claim 3, characterized in that, The PCS units in the parallel PCS unit are connected to each other via a high-speed fiber optic ring network, and each PCS receives the load power prediction sequence broadcast by the energy management system through the high-speed fiber optic ring network. Each PCS includes: The output voltage regulation submodule is used to calculate the target output power of each component based on the ratio of its rated capacity to the total parallel capacity, combined with the load power prediction sequence, and adjust the output voltage amplitude and phase in advance. The circulating current suppression submodule is used to monitor the output current of each PCS in real time, calculate the circulating current value between each PCS based on the virtual impedance algorithm, and dynamically correct the droop control coefficient according to the circulating current value.

8. A multi-energy complementary high-voltage DC microgrid system for drilling platforms according to claim 1, characterized in that, The security protection unit includes: A DC arc fault detection device is connected to the high-voltage DC bus and is used to monitor the arc signal of the high-voltage DC bus in real time. A 1500V DC fast disconnect switch is connected in series on the high-voltage DC bus. When the DC arc fault detection device detects an arcing signal, the fast disconnect switch performs a disconnection operation within 5ms. The voltage over-limit protection submodule is used to monitor the voltage value of the high voltage DC bus in real time. When the voltage value exceeds the overvoltage protection setting of 1550V or is lower than the undervoltage protection setting of 1400V, the voltage protection action is triggered. The islanding detection submodule is set in each PCS and is used to detect the grid-connected operation status. When the islanding operation status is detected, it controls each PCS to automatically shut down. The fault information feedback submodule is used to feed back the execution results of safety protection actions to the energy management system, which then records the fault information and executes the system recovery strategy.

9. A multi-energy complementary high-voltage DC microgrid system for drilling platforms according to claim 3, characterized in that, The prediction period of the hybrid prediction module is the next 15 minutes; the generation frequency of the load power prediction sequence and the photovoltaic output prediction sequence is matched with the scheduling cycle of the energy management system.

10. A multi-energy complementary high-voltage DC microgrid system applied to a drilling platform according to claim 1, characterized in that, The normal operating voltage range of the high-voltage DC bus is 1470V to 1530V.

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