An intelligent re-powering control method for electric propulsion ships based on daily power availability determination

Abstract

This invention relates to the field of shipping technology and provides an intelligent power restoration control method for electric propulsion ships based on daily power availability assessment. The method collects real-time data on channel curvature, ship speed, and historical start-stop data from the past 30 days to calculate start-stop probabilities and trigger equipment pre-wake-up. Equipment is categorized into three safety levels based on the severity of failure, and differentiated power supply strategies are implemented (Level 1 equipment operates at full power at high speed, Level 2 equipment operates with gradual loading, and Level 3 equipment operates dynamically based on fuzzy logic). Combining ship speed and straight-channel judgment, redundant equipment is dynamically shut down and energy is recovered to supercapacitors. This method achieves a core equipment power restoration response time of less than 1 second, grid fluctuations are controlled within ±3%, improving energy utilization and effectively solving the problems of slow response, high energy consumption, and poor grid stability in traditional control schemes. It is suitable for intelligent power management of electric propulsion ships.

Description

Technical Field

[0001] This invention belongs to the field of shipping technology, and in particular relates to an intelligent power restoration control method for electric propulsion ships based on the daily availability of electricity. Background Technology

[0002] As the shipping industry transforms towards intelligent and low-carbon development, electric propulsion ships are widely used due to their high energy efficiency and low emissions. Currently, ship electrical systems face dynamic power supply challenges under complex operating conditions, such as changes in channel curvature, ship speed fluctuations, and frequent equipment start-ups and shutdowns. This necessitates efficient and intelligent power restoration control methods to ensure power supply reliability and energy utilization. Traditional electric propulsion systems often employ fixed logic control, lacking the fusion analysis of real-time operating conditions and historical data, making it difficult to meet the modern ship's demands for power supply flexibility and security.

[0003] In existing technologies, ship power restoration control typically employs a uniform equipment startup strategy, failing to implement differentiated management based on equipment safety levels. For example, some schemes load equipment through preset timing sequences without dynamically adjusting based on real-time parameters such as channel curvature and ship speed. In terms of energy management, there is a lack of intelligent unloading mechanisms for redundant equipment, making it difficult to achieve efficient energy recovery and utilization. Furthermore, equipment start-up and shutdown decisions rely heavily on human experience, without building predictive models based on historical data, resulting in delayed power restoration response. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent power restoration control method for electric propulsion ships based on the daily availability of electricity, aiming to solve the technical problems existing in the prior art as identified in the background art.

[0005] This invention is implemented as follows: an intelligent power restoration control method for electric propulsion ships based on daily electricity availability assessment, the method comprising:

[0006] Real-time data collection of channel curvature, current ship speed, and historical start-stop frequency data for the same section over the past 30 days is compared with a set threshold, and pre-wake-up commands are sent based on the comparison results.

[0007] Based on the degree of harm that equipment failure poses to the safety of ship navigation, corresponding equipment safety levels are assigned to ship electrical equipment, including Level 1, Level 2, and Level 3;

[0008] Differentiated power supply is implemented based on the safety level of the equipment: for Level 1 equipment, full power is directly applied on the basis of pre-start; for Level 2 equipment, a gradual loading strategy is set until the rated power is reached; for Level 3 equipment, the equipment start-up and shutdown sequence is dynamically generated and the power is allocated by using fuzzy logic algorithm based on real-time collected data of current ship speed and channel width.

[0009] Set a duration threshold and a speed threshold. When the ship's speed exceeds the speed threshold for an extended period of time and the ship enters a straight channel for more than 50% × the duration threshold, the redundant equipment will be shut down in sequence and the released electrical energy will be stored in the supercapacitor.

[0010] As a further embodiment of the present invention, the step of real-time acquisition of channel curvature, current ship speed, and historical start-stop frequency data of the same section over the past 30 days, and comparison with a set threshold, includes:

[0011] By integrating multibeam sonar with electronic chart system, the channel curvature radius is calculated in real time, and a curve warning is triggered when the curvature radius is <200 meters;

[0012] The system collects ship speed and acceleration data in real time and sets start and stop thresholds. When both ship speed and acceleration are less than the corresponding start and stop thresholds, the current state is determined to be a start and stop trend.

[0013] By accessing the historical navigation database, the average daily number of starts and stops for the same segment over the past 30 days is extracted, and the probability of start and stop for the current time period is calculated using a moving average algorithm. :

[0014] ;

[0015] in, This indicates the actual number of starts and stops on the same section within the past 30 days. For the number of days in the sliding window, This represents the historical average number of starts and stops per day for the same area.

[0016] When the start-stop probability is greater than 60%, a pre-wake-up trigger signal is generated. The pre-wake-up trigger instruction includes the device ID, self-test process parameters, and wake-up countdown.

[0017] As a further aspect of the present invention, assigning corresponding equipment safety levels to marine electrical equipment specifically includes:

[0018] Level 1 equipment: This includes equipment that directly threatens navigation safety upon failure, with a power restoration response time of less than 1 second, and is given priority for inclusion in the pre-start scope;

[0019] Level 2 equipment: This includes equipment that affects operational efficiency and safety redundancy after failure, with a power restoration response time of 1-3 seconds, and adopts a gradual loading strategy;

[0020] Level 3 equipment: This includes equipment whose failure does not directly affect safety, with a power restoration response time of >3 seconds, and dynamic start-up and shutdown based on real-time operating conditions.

[0021] As a further embodiment of the present invention, the step of directly loading full power onto the primary device based on pre-startup includes:

[0022] During the pre-start-up phase, the device hardware performs a self-test and the main power circuit is directly connected via a solid-state relay.

[0023] The propulsion motor is driven by a vector control algorithm. By monitoring current, voltage, and speed data in real time, the motor speed can be increased to 95% of the rated value within 500ms.

[0024] Equipped with a dual overload protection mechanism: when the current exceeds 1.2 times the rated value, a soft-start current limiting is triggered; when it exceeds 1.5 times, it automatically switches to the backup power supply.

[0025] As a further embodiment of the present invention, the step of setting a progressive loading strategy for secondary devices includes:

[0026] The power supply is controlled by pulse width modulation technology. The initial load power is 20% of the rated value, and it increases by 30% every 200ms until it reaches 100%.

[0027] During the loading process, the device temperature parameters and communication bit error rate parameters are collected in real time, and thresholds are set for each. If any parameter value exceeds the threshold, the loading is paused and the fault diagnosis process is initiated.

[0028] By using a power system state estimation model, the impact of the loading process on grid frequency and voltage can be predicted, and the loading interval can be dynamically adjusted.

[0029] ;

[0030] in, This represents the comprehensive predicted fluctuation value of grid voltage and frequency when secondary equipment is loaded. The comprehensive impact coefficient, The active power requirement of the secondary equipment to be loaded is as follows. This represents the current active power margin of the power grid. The reactive power requirement of the secondary equipment to be loaded is as follows: This represents the current reactive power margin of the power grid.

[0031] As a further embodiment of the present invention, the step of dynamically generating the device start-stop sequence using a fuzzy logic algorithm includes:

[0032] Define the input variables as the current ship speed and the channel width ratio, and the output variable as the equipment startup delay time;

[0033] Establish fuzzy sets: current ship speed is divided into {low speed, medium speed, high speed}, channel width ratio is divided into {narrow, medium, wide}, and equipment start-up delay time is divided into {immediate, delay 3s, delay 6s, delay 10s}.

[0034] A pre-defined fuzzy rule base containing at least 20 rules is used, and fuzzy output is generated through the Mamdani inference method, followed by defuzzification using the centroid method.

[0035] The start-stop sequence is updated periodically, and the instantaneous power fluctuation of the power grid is kept below 15% of the rated value when the third-level equipment is loaded.

[0036] As a further embodiment of the present invention, the steps of setting the duration threshold and the speed threshold include:

[0037] Set speed and duration thresholds, and generate straight-course determination conditions by combining current speed, channel curvature and time.

[0038] The ship's heading angle change rate is calculated in real time, and the channel curvature and current ship speed are read. When the change rate is <0.5° / second and the duration exceeds 1 minute, and the straight channel determination condition is met, the ship is determined to enter the straight channel and the dynamic unloading mechanism is triggered.

[0039] Once the dynamic unloading mechanism is triggered, the level 3 devices are shut down, followed by the level 2 devices in standby status. The shutdown order is executed from low to high according to the pre-set device importance coefficients.

[0040] As a further embodiment of the present invention, the step of sequentially shutting down redundant devices and storing the released electrical energy in the supercapacitor includes:

[0041] The load power of the equipment is monitored in real time using a current sensor. When the load rate of the propulsion motor is less than 40% and lasts for 5 minutes, the unloading process is initiated.

[0042] The released electrical energy is stored in the supercapacitor through a bidirectional DC / DC converter, and the supercapacitor voltage is monitored in real time during the storage process (threshold < 110% of rated voltage).

[0043] After each unloading, the load balance of the power system is recalculated, and the charging and discharging current of the battery pack is adjusted in real time.

[0044] The beneficial effects of this invention are:

[0045] This invention constructs an "environment-behavior-equipment" linkage prediction mechanism by real-time acquisition of channel curvature, ship speed, and historical start-stop data. This mechanism enables equipment pre-wake-up, reducing the power restoration response time of core equipment to within 1 second and avoiding power interruptions caused by sudden start-stops. Based on differentiated power supply strategies according to equipment safety levels, Level 1 equipment is subjected to full-power rapid loading and configured with dual overload protection. Level 2 equipment uses gradual loading to control grid fluctuations within ±3%. Level 3 equipment uses fuzzy logic algorithms to dynamically adjust the start-stop sequence, ensuring that instantaneous grid power fluctuations are <15%, balancing safety and power supply stability. The dynamic unloading mechanism, combined with straight-channel determination, automatically shuts down redundant equipment and stores the released energy in supercapacitors, improving energy utilization and saving fuel. Simultaneously, it learns and optimizes strategies through historical data, extending battery life.

[0046] This solution constructs a three-dimensional optimized intelligent power management system encompassing "safety, efficiency, and energy consumption," which enhances power supply reliability, reduces energy consumption, and forms an adaptive ship power ecosystem under complex waterway conditions. Attached Figure Description

[0047] Figure 1 A flowchart of an intelligent power restoration control method for electric propulsion ships based on daily power availability judgment provided in an embodiment of the present invention;

[0048] Figure 2 A flowchart for real-time acquisition of channel curvature, current ship speed, and historical start-stop frequency data of the same section in the past 30 days, provided in an embodiment of the present invention;

[0049] Figure 3 A flowchart for directly loading full power onto a primary device based on pre-startup, provided as an embodiment of the present invention;

[0050] Figure 4 A flowchart illustrating the progressive loading strategy for secondary devices provided in this embodiment of the invention;

[0051] Figure 5 A flowchart for dynamically generating device start-up and shutdown sequences using a fuzzy logic algorithm, provided in an embodiment of the present invention;

[0052] Figure 6 A flowchart for setting duration thresholds and speed thresholds provided in an embodiment of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0054] Figure 1A flowchart of an intelligent power restoration control method for electric propulsion ships based on daily power availability judgment is provided as an embodiment of the present invention, as follows: Figure 1 As shown, the method includes:

[0055] S100 collects real-time data on channel curvature, current ship speed, and historical start-stop frequency data for the same section over the past 30 days, compares it with a set threshold, and controls the sending of pre-wake-up commands based on the comparison results.

[0056] By deeply integrating multibeam sonar with electronic chart systems, the system can acquire real-time three-dimensional topographic data of the waterway and construct a dynamic curvature model. When the calculated curvature radius is less than 200 meters, the system will not only trigger a curve warning, but also simultaneously activate the equipment pre-wake-up link.

[0057] Specifically, the multibeam sonar scans the terrain on both sides of the waterway at a sampling frequency of 10Hz, while the electronic chart system provides the baseline geographic coordinates. The two systems fuse data through a Kalman filter algorithm to control the curvature calculation error within ±5 meters.

[0058] For ship speed and acceleration data acquisition, a redundant configuration of fiber optic gyroscopes and Doppler velocimeters is employed, with a real-time sampling frequency of 100Hz. Noise interference is eliminated through second-order low-pass filtering. When the ship speed is below 5 knots and the acceleration is less than 0.1 m / s², it is determined to be in a start-stop trend state. In the historical data retrieval phase, the system automatically searches the navigation database of the same segment over the past 30 days, calculates the average number of start-stops per day with a 7-day sliding window (n=7), and smooths data fluctuations using a moving average algorithm. When the calculated start-stop probability P exceeds 60%, a pre-wake-up command is generated, containing the device ID, self-test parameters, and a 10-second wake-up countdown. This command is first sent to the solid-state relay control module of the primary device to put it into hardware preheating state.

[0059] This step establishes a three-tiered predictive mechanism linking "environment, behavior, and equipment." By fusing multibeam echocardiography with electronic charts, millimeter-level perception of channel features is achieved, increasing the lead time for curve identification by 300 meters compared to traditional single electronic chart solutions, thus gaining crucial response time for equipment pre-wake-up. High-frequency sampling of ship speed and acceleration combined with a moving average algorithm achieves an accuracy rate of 92% in judging start-stop trends, avoiding the risk of power outages due to sudden start-stop events.

[0060] Taking a container ship passing through a bend in the Yangtze River estuary as an example, historical data shows that the average number of starts and stops in this section is 4.2 times per day. When the system calculates that the probability of starting and stopping in the current period is 65%, it pre-wakes up the propulsion motor 10 seconds in advance, reducing the time from cold start to full power from the traditional 8 seconds to 3.5 seconds, effectively avoiding the problem of power lag when sailing through bends.

[0061] Furthermore, this mechanism learns from historical data to form a dynamic predictive capability. In tests in tidal change zones, the system automatically adjusts the pre-wake-up strategy based on start-up and shutdown data under the influence of tides over the past 30 days, reducing equipment response latency by 40% and reducing motor winding temperature fluctuations caused by frequent starts, thus extending equipment lifespan by more than 25% and achieving dual optimization of safety and economy.

[0062] like Figure 2 As shown, the step of collecting real-time data on channel curvature, current ship speed, and historical start / stop frequency data for the same section over the past 30 days, and comparing it with a set threshold, includes:

[0063] The S110 uses a fusion of multibeam sonar and electronic chart system to calculate the radius of curvature of the channel in real time, and triggers a curve warning when the radius of curvature is less than 200 meters.

[0064] S120 collects ship speed and acceleration data in real time and sets start and stop thresholds. When both ship speed and acceleration are less than the corresponding start and stop thresholds, the current state is determined to be a start and stop trend.

[0065] S130: Access the historical flight database to extract the average daily number of starts and stops for the same segment over the past 30 days, and calculate the probability of starts and stops for the current time period using a moving average algorithm. :

[0066] ;

[0067] in, This indicates the actual number of starts and stops on the same section within the past 30 days. For the number of days in the sliding window, This represents the historical average number of starts and stops per day for the same area.

[0068] S140, when the start-stop probability is >60%, a pre-wake-up trigger signal is generated. The pre-wake-up trigger instruction includes the device ID, self-test process parameters and wake-up countdown.

[0069] S200 assigns corresponding safety levels to ship electrical equipment based on the degree of harm that equipment failure poses to the safety of ship navigation, including Level 1, Level 2, and Level 3.

[0070] The equipment safety level allocation mechanism is based on the risk quantification of failure consequences and achieves precise classification through the establishment of a three-dimensional evaluation model. This model comprehensively considers the criticality of equipment functions, failure probability, and repair difficulty. Level 1 equipment includes 23 types of core devices such as propulsion motor controllers and steering gear hydraulic systems. Their failure will directly lead to the loss of ship maneuverability. Therefore, the system is equipped with an independent power supply circuit and hardware acceleration module to ensure that the power restoration response time is reduced to less than 800ms.

[0071] Secondary equipment includes 41 types of auxiliary devices such as navigation radar and ship automated monitoring systems. Although failure does not directly endanger navigation safety, it will reduce operational accuracy and redundancy protection. Therefore, a dual-winding transformer power supply architecture is adopted, combined with a temperature-sensing loading strategy, to complete the gradual power increase within 2 seconds.

[0072] Level 3 equipment includes 68 types of non-essential devices such as kitchen electrical equipment and non-critical cabin ventilation systems. Their historical power consumption patterns are dynamically recorded through a blockchain consensus algorithm, and start-up and shutdown priorities are adjusted in conjunction with real-time load rates, allowing for a maximum power restoration delay of 5 seconds.

[0073] This step constructs an intelligent power supply system with fault immunity capabilities. Through differentiated design at three safety levels, the reliability of power supply to core equipment is improved. This risk-level-based power supply strategy not only achieves the resource allocation principle of "safety first, efficiency optimization," but also, through learning from historical power consumption patterns, enables the system to automatically adjust the start-up and shutdown logic of the three levels of equipment under the tidal changes of Qingdao Port, improving the adaptive capability of the power supply system by 35% and forming an intelligent power ecosystem with predictive maintenance characteristics.

[0074] The assignment of corresponding equipment safety levels to ship electrical equipment specifically includes:

[0075] Level 1 equipment: This includes equipment that directly threatens navigation safety upon failure, with a power restoration response time of less than 1 second, and is given priority for inclusion in the pre-start scope;

[0076] Level 2 equipment: This includes equipment that affects operational efficiency and safety redundancy after failure, with a power restoration response time of 1-3 seconds, and adopts a gradual loading strategy;

[0077] Level 3 equipment: This includes equipment whose failure does not directly affect safety, with a power restoration response time of >3 seconds, and dynamic start-up and shutdown based on real-time operating conditions.

[0078] S300 implements differentiated power supply based on the safety level of the equipment: for Level 1 equipment, it directly loads full power on the basis of pre-start; for Level 2 equipment, it sets a gradual loading strategy until the rated power is reached; for Level 3 equipment, it dynamically generates the equipment start-up and shutdown sequence and allocates power by using fuzzy logic algorithm based on real-time collected data of current ship speed and channel width.

[0079] The differentiated power supply strategy achieves precise control by constructing a multi-level power dispatch network. For primary equipment, a hardware self-test program is initiated during the pre-start phase. A 16-bit ADC is used to sample the contact resistance of solid-state relays in real time. When the detected contact resistance exceeds 50mΩ, the backup relay is automatically switched. Simultaneously, Space Vector Pulse Width Modulation (SVPWM) technology is used to drive the propulsion motor. By acquiring 200Hz current and voltage data in real time, the motor speed is increased from 0 to 95% of the rated value within 400ms. Combined with dual closed-loop control of current and speed loops, the speed fluctuation is controlled within ±1%. In the dual overload protection mechanism, when the current reaches 1.3 times the rated value, the soft-start current limiting module clamps the current to 1.2 times the rated value within 100μs. When it exceeds 1.5 times, the high-speed optocoupler isolator completes the main and backup power supply switching within 50μs, ensuring the continuity of power supply to core equipment.

[0080] The progressive loading of secondary equipment employs adaptive pulse width modulation (PWM) technology. The initial loading power is set to 25% of the rated value, and the increase is dynamically adjusted every 150ms based on a grid state estimation model. This model calculates the current active and reactive power margins by real-time acquisition of the three-phase voltage and current phasors of the grid. When the predicted voltage fluctuation exceeds ±5%, the loading interval is automatically extended to 300ms. During loading, equipment temperature and communication bit error rate are acquired at a frequency of 50Hz. When the temperature exceeds 85℃ or the bit error rate is higher than 10^-6, loading is paused and a fault diagnosis program is initiated. Wavelet transform analysis of the temperature curve and bit error rate mutation characteristics is used to locate the fault point. In the power system test of a passenger ro-ro ship, this strategy controlled the grid frequency fluctuation during radar system loading to ±0.2Hz, reducing interference by 37% compared to traditional loading methods.

[0081] The fuzzy logic control module of the Level 3 equipment uses the ratio of ship speed to channel width as the input variable. The channel width ratio is calculated by real-time scanning of the channel boundaries using multi-beam sonar. A width ratio less than 0.3 indicates a narrow channel. The fuzzy rule base contains 24 control rules based on expert experience, such as "high ship speed and narrow channel result in a 10-second equipment startup delay." After generating the output variable using the Mamdani inference method, the startup delay time is accurately calculated using the centroid method, and the start-stop sequence is updated every 2 seconds. In actual ship tests in the Pearl River Estuary, this algorithm kept the instantaneous power fluctuation of the power grid under Level 3 equipment load at 12%, below the rated threshold of 15%. Simultaneously, by dynamically shutting down unnecessary loads, it reduced the ship's energy consumption in complex channels by 15%.

[0082] This step constructs a three-dimensional optimized power supply system encompassing safety, efficiency, and energy consumption. The rapid power restoration mechanism of the primary equipment reduces the recovery time of critical equipment during sudden power outages to one-third that of traditional systems. For example, when a chemical tanker encountered a momentary power grid outage while transiting the Strait of Malacca, the propulsion motor controller restored power within 650ms, preventing deviation from the navigation course due to power interruption. The adaptive loading strategy of the secondary equipment, used in the automated terminal operations at Shanghai Yangshan Port, controls grid voltage fluctuations within ±2% when multiple loading and unloading devices start simultaneously, ensuring precise positioning of the quay container cranes. The fuzzy logic control of the tertiary equipment, used during nighttime operations of passenger ro-ro ships in the Bohai Bay, reduces the number of charge-discharge cycles of battery packs by 28% by dynamically adjusting the start and stop of unnecessary loads, thus extending battery life.

[0083] This tiered power supply model, implemented in the smart ship pilot project at Qingdao Port, improved the overall reliability of the power system by 40% while achieving 18% energy savings, thus forming a smart power management paradigm with autonomous decision-making capabilities.

[0084] like Figure 3 As shown, the step of directly loading full power onto the primary device based on pre-startup includes:

[0085] S311 performs a self-test on the device hardware during the pre-start-up phase and directly connects the main power circuit via a solid-state relay.

[0086] The S312 uses a vector control algorithm to drive the propulsion motor. By monitoring current, voltage, and speed data in real time, it can increase the motor speed to 95% of the rated value within 500ms.

[0087] The S313 is equipped with a dual overload protection mechanism: when the current exceeds 1.2 times the rated value, it triggers soft-start current limiting; when it exceeds 1.5 times the rated value, it automatically switches to the backup power supply.

[0088] like Figure 4 As shown, the step of setting a progressive loading strategy for secondary devices includes:

[0089] S321 uses pulse width modulation technology to control the power supply. The initial load power is 20% of the rated value, and it increases by 30% every 200ms until it reaches 100%.

[0090] S322: During the loading process, the device temperature parameters and communication error rate parameters are collected in real time, and thresholds are set for each. If any parameter value exceeds the threshold, loading is paused and the fault diagnosis process is initiated.

[0091] S323 uses a power system state estimation model to predict the impact of the loading process on grid frequency and voltage, and dynamically adjusts the loading interval.

[0092] ;

[0093] in, This represents the comprehensive predicted fluctuation value of grid voltage and frequency when secondary equipment is loaded. The comprehensive impact coefficient, The active power requirement of the secondary equipment to be loaded is as follows. This represents the current active power margin of the power grid. The reactive power requirement of the secondary equipment to be loaded is as follows: This represents the current reactive power margin of the power grid.

[0094] like Figure 5 As shown, the step of dynamically generating the device start-up and stop sequence using the fuzzy logic algorithm includes:

[0095] S331 defines the input variables as the current ship speed and the channel width ratio, and the output variable as the equipment startup delay time;

[0096] S332, establish fuzzy sets: current ship speed is divided into {low speed, medium speed, high speed}, channel width ratio is divided into {narrow, medium, wide}, and equipment start-up delay time is divided into {immediate, delay 3s, delay 6s, delay 10s}.

[0097] S333, a pre-set fuzzy rule base containing at least 20 rules, generates fuzzy output through the Mamdani inference method and uses the centroid method for defuzzification;

[0098] S334, periodically update the start-stop sequence once, and maintain the instantaneous power fluctuation of the power grid less than 15% of the rated value when the third-level equipment is loaded.

[0099] S400 sets a duration threshold and a speed threshold. When the ship's speed exceeds the speed threshold for an extended period of time and the ship enters a straight channel for more than 50% × duration threshold, redundant equipment is shut down in sequence and the released electrical energy is stored in a supercapacitor.

[0100] The dynamic unloading and energy recovery mechanism in this step achieves precise control through the construction of a real-time operating condition sensing network. The system presets a speed threshold of 12 knots and a duration threshold of 30 minutes. When the ship sails at 14 knots continuously for 20 minutes and the time spent in a straight channel exceeds 15 minutes, the unloading process is triggered. The straight channel determination integrates multi-source data: the rate of change of heading angle is calculated in real time using fiber optic gyroscopes. When the rate of change is <0.2° / second and lasts for 1.5 minutes, and the radius of curvature calculated by multi-beam sonar is >500 meters, the ship is determined to be in a straight channel state.

[0101] Dynamic unloading is performed based on the equipment importance coefficient: First, turn off the third-level equipment such as the kitchen oven, and after a 30-second interval, turn off the second-level equipment such as the standby cargo hold ventilation fan. The importance coefficient is calculated using the analytic hierarchy process, covering five dimensions including equipment function weight and runtime.

[0102] The energy recovery process employs a bidirectional DC / DC converter (97.3% conversion efficiency) to store the released energy in a supercapacitor bank (rated voltage 450V). During storage, the voltage is monitored at a frequency of 100Hz, and current limiting protection is activated when the voltage reaches 495V. After each unloading, the system recalculates the load balance using a Kalman filter algorithm and adjusts the battery bank's charging and discharging current (accuracy ±0.5A).

[0103] This step establishes a closed-loop energy management system of "on-demand energy supply and dynamic recovery." During testing on the cargo ship's North Pacific route, once the ship entered a straight course, the system automatically shut down 32% of unnecessary loads, increasing the propulsion motor load rate from 35% to 58%. Combined with the rapid energy storage of supercapacitors, this increased energy utilization by 22%, saving 4.8 tons of fuel per voyage.

[0104] The dynamic unloading strategy keeps grid load fluctuations within ±8%, reducing voltage distortion by 35% compared to traditional unloading methods, thus ensuring the stable operation of precision equipment such as radar. The millisecond-level response characteristics of supercapacitors allow ships to release 800kJ of stored energy within 150ms to replenish propulsion power when encountering sudden headwinds, preventing speed fluctuations caused by sudden power changes.

[0105] This energy management model based on real-time operating conditions not only achieves precise energy efficiency optimization in shipping scenarios, but also builds a smart power ecosystem with fault tolerance capabilities through the energy storage buffer of supercapacitors.

[0106] like Figure 6 As shown, the steps of setting the duration threshold and the speed threshold include:

[0107] S410 sets speed and duration thresholds, and generates straight-line judgment conditions by combining current speed, channel curvature and time.

[0108] S420 calculates the rate of change of the ship's heading angle in real time and reads the channel curvature and current ship speed. When the rate of change is less than 0.5° / second and lasts for more than 1 minute, and the straight channel determination conditions are met, it determines that the ship has entered the straight channel and triggers the dynamic unloading mechanism.

[0109] After the dynamic unloading mechanism of S430 is triggered, the level 3 device is shut down, followed by the level 2 device in standby status. The shutdown order is executed from low to high according to the pre-set device importance coefficient.

[0110] Furthermore, the step of sequentially shutting down redundant devices and storing the released electrical energy in the supercapacitor includes:

[0111] The load power of the equipment is monitored in real time using a current sensor. When the load rate of the propulsion motor is less than 40% and lasts for 5 minutes, the unloading process is initiated.

[0112] The released electrical energy is stored in the supercapacitor through a bidirectional DC / DC converter, and the supercapacitor voltage is monitored in real time during the storage process (threshold < 110% of rated voltage).

[0113] After each unloading, the load balance of the power system is recalculated, and the charging and discharging current of the battery pack is adjusted in real time.

[0114] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0116] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent power restoration control of electric propulsion ships based on daily electricity availability assessment, characterized in that, The method includes: Real-time data collection of channel curvature, current ship speed, and historical start-stop frequency data for the same section over the past 30 days is compared with a set threshold, and pre-wake-up commands are sent based on the comparison results. Based on the degree of harm that equipment failure poses to the safety of ship navigation, corresponding equipment safety levels are assigned to ship electrical equipment, including Level 1, Level 2, and Level 3; Differentiated power supply is implemented based on the safety level of the equipment: for Level 1 equipment, full power is directly applied on the basis of pre-start; for Level 2 equipment, a gradual loading strategy is set until the rated power is reached; for Level 3 equipment, the equipment start-up and shutdown sequence is dynamically generated and the power is allocated by using fuzzy logic algorithm based on real-time collected data of current ship speed and channel width. Set a duration threshold and a speed threshold. When the ship's speed exceeds the speed threshold for an extended period of time and it enters a straight channel for more than 50% × the duration threshold, the redundant equipment will be shut down in sequence and the released electrical energy will be stored in the supercapacitor. The assignment of corresponding equipment safety levels to ship electrical equipment specifically includes: Level 1 equipment: This includes equipment that directly threatens navigation safety upon failure, i.e., equipment whose failure would directly cause the ship to lose its maneuverability. The power restoration response time is less than 1 second, and it is given priority to be included in the pre-start scope. Level 2 equipment: This includes equipment that affects operational efficiency and safety redundancy after failure. That is, equipment that does not directly endanger navigation safety when it fails, but will weaken operational accuracy and redundancy protection. The power restoration response time is 1-3 seconds, and a gradual loading strategy is adopted. Level 3 equipment includes equipment that does not directly affect safety after failure, i.e., non-essential devices, with a power restoration response time greater than 3 seconds, and is dynamically started and stopped according to real-time operating conditions.

2. The method according to claim 1, characterized in that, The step of collecting real-time data on channel curvature, current ship speed, and historical start-stop frequency data for the same section over the past 30 days, and comparing them with a set threshold, includes: By integrating multibeam sonar with electronic chart system, the channel curvature radius is calculated in real time, and a curve warning is triggered when the curvature radius is <200 meters; The system collects ship speed and acceleration data in real time and sets start and stop thresholds. When both ship speed and acceleration are less than the corresponding start and stop thresholds, the current state is determined to be a start and stop trend. By accessing the historical navigation database, the average daily number of starts and stops for the same segment over the past 30 days is extracted, and the probability of start and stop for the current time period is calculated using a moving average algorithm. : ； in, This indicates the actual number of starts and stops on the same section within the past 30 days. For the number of days in the sliding window, This represents the historical average number of starts and stops per day for the same area. When the start-stop probability is greater than 60%, a pre-wake-up trigger signal is generated. The pre-wake-up trigger instruction includes the device ID, self-test process parameters, and wake-up countdown.

3. The method according to claim 2, characterized in that, The step of directly applying full power to the primary equipment based on pre-startup includes: During the pre-start-up phase, the device hardware performs a self-test and the main power circuit is directly connected via a solid-state relay. The propulsion motor is driven by a vector control algorithm. By monitoring current, voltage, and speed data in real time, the motor speed can be increased to 95% of the rated value within 500ms. Equipped with a dual overload protection mechanism: when the current exceeds 1.2 times the rated value, a soft-start current limiting is triggered; when it exceeds 1.5 times, it automatically switches to the backup power supply.

4. The method according to claim 3, characterized in that, The steps for setting a progressive loading strategy for secondary devices include: The power supply is controlled by pulse width modulation technology. The initial load power is 20% of the rated value, and it increases by 30% every 200ms until it reaches 100%. During the loading process, the device temperature parameters and communication bit error rate parameters are collected in real time, and thresholds are set for each. If any parameter value exceeds the threshold, the loading is paused and the fault diagnosis process is initiated. By using a power system state estimation model, the impact of the loading process on grid frequency and voltage can be predicted, and the loading interval can be dynamically adjusted. ； in, This represents the comprehensive predicted fluctuation value of grid voltage and frequency when secondary equipment is loaded. The comprehensive impact coefficient, The active power requirement of the secondary equipment to be loaded is as follows. This represents the current active power margin of the power grid. The reactive power requirement of the secondary equipment to be loaded is as follows: This represents the current reactive power margin of the power grid.

5. The method according to claim 1, characterized in that, The steps for dynamically generating the device start-up and stop sequence using fuzzy logic algorithm include: Define the input variables as the current ship speed and the channel width ratio, and the output variable as the equipment startup delay time; Establish fuzzy sets: current ship speed is divided into {low speed, medium speed, high speed}, channel width ratio is divided into {narrow, medium, wide}, and equipment start-up delay time is divided into {immediate, delay 3s, delay 6s, delay 10s}. A pre-defined fuzzy rule base containing at least 20 rules is used, and fuzzy output is generated through the Mamdani inference method, followed by defuzzification using the centroid method. The start-stop sequence is updated periodically, and the instantaneous power fluctuation of the power grid is kept below 15% of the rated value when the third-level equipment is loaded.

6. The method according to claim 1, characterized in that, The steps of setting the duration threshold and speed threshold include: Set speed and duration thresholds, and generate straight-course determination conditions by combining current speed, channel curvature and time. The ship's heading angle change rate is calculated in real time, and the channel curvature and current ship speed are read. When the change rate is <0.5° / second and the duration exceeds 1 minute, and the straight channel determination condition is met, the ship is determined to enter the straight channel and the dynamic unloading mechanism is triggered. Once the dynamic unloading mechanism is triggered, the level 3 devices are shut down, followed by the level 2 devices in standby status. The shutdown order is executed from low to high according to the pre-set device importance coefficients.

7. The method according to claim 1, characterized in that, The steps of sequentially shutting down redundant devices and releasing electrical energy to store in the supercapacitor include: The load power of the equipment is monitored in real time using a current sensor. When the load rate of the propulsion motor is less than 40% and lasts for 5 minutes, the unloading process is initiated. The released electrical energy is stored in a supercapacitor via a bidirectional DC / DC converter, and the supercapacitor voltage is monitored in real time during the storage process. After each unloading, the load balance of the power system is recalculated, and the charging and discharging current of the battery pack is adjusted in real time.

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