Intelligent Energy Management System (iEMS) and Balanced Profile
Patent Information
- Application Number
- JP2024547699
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2022-02-11
- Filing Date
- 2023-02-06
- Publication Date
- 2025-02-21
Smart Images
Figure 2025505263000001_ABST
Abstract
Description
[Technical field]
[0001] The present invention relates to energy management systems, and more particularly to energy management systems for marine vessels, and methods for evaluating energy management in such systems. [Background technology]
[0002] Currently, onboard energy management systems (EMS) are becoming more complex, offering the option to configure power plants in different modes. Selecting among the various producers the one that produces the energy to meet the load demand while limiting losses and operational costs is crucial. As the load profile is stochastic in nature, real-time optimization is required to minimize operational costs and maximize energy efficiency.
[0003] Over the last decade, regulations regarding pollutant emissions have become stricter. This has led to new thinking in various areas of the shipping industry. Many shipping and shipping companies are looking for more energy-efficient systems with lower carbon dioxide emissions and improved fuel consumption. This is achieved by minimizing the use of diesel or gasoline-powered engines and generators, also known as generator sets (gensets), and supplementing them with renewable energy sources. Renewable energy can come in a variety of forms, including batteries, solar panels, wind, and fuel cells. However, integrating these energy sources with the traditional ship power management system has been a challenge. There are several reasons for this, including installation space, the region in which the ship operates, different voltage levels, and different control systems.
[0004] Among various renewable energy sources, batteries have long been attracting attention among researchers due to their compact size, ease of installation and maintenance, rapid charging, and ability to respond to instantaneous power demand. In addition, DC power distribution has many advantages over AC power distribution. For example, DC systems do not require synchronization, reduce cabling, and eliminate the need for bulky transformers. DC systems also eliminate reactive power circulation and harmonics.
[0005] Traditional approaches to managing power generation and distribution are insufficient to profitably operate ships with complex power systems. High-level monitoring systems that include data collection and analysis, forecasting future operational needs, decision-making strategies, and calculation of optimal set points are therefore essential. Traditional power management systems only ensure power balance and reliability of the ship's power grid.
[0006] In recent years, digitalization in the shipping industry has received a lot of attention in order to make operations more environmentally friendly and sustainable. With this growing awareness of environmental protection and ship safety, maritime authorities and governments around the world are working hard to promote the efficiency and safety of ship operations at sea. This has led to the need to keep ship power systems as efficient as possible to ensure high reliability under a wide range of operating conditions, with minimal costs and emissions for the safety of the ship, its operators and passengers. Examples of such systems are described in US Pat. No. 9811099, US Pat. No. 10170912 and EP Pat. No. 3865335. Here, measurements and registrations of available consumers, such as thrusters and onboard equipment, are combined with knowledge of generators, battery capacity, etc. Dynamic positioning systems may also be used to compensate for planned power consumption as a way to reduce the load on the system. Further known solutions include prediction of motion and compensation for the predicted motion as described in WO 2015 / 028636 and Norwegian Patent No. 337927, as well as compensation for the dynamic operating window of the generator in the system as described in WO 2015 / 028621. Summary of the Invention [Problem to be solved by the invention]
[0007] The objective of this invention is to introduce an intelligent Energy Management System (hereinafter referred to as "iEMS") that focuses on optimal system performance, taking into account various objectives such as fuel saving, emission reduction, maintenance costs, equipment health, minimal power usage, and safety.
[0008] The iEMS must provide decision making capabilities to manage the power / energy demand during various operational states such as vessel dynamic positioning (DP), maneuvering, transit, waiting and anchorage and assist in scheduling generation from generator sets or other renewable energy sources based on load levels. The torque and speed limitations of the generator sets, dynamic weather conditions and the nature of the operation are used to determine the load set points of the producers. The iEMS knowledge-based learning also helps in forecasting future load demands, thus reducing stress on the producers and preventing power instability.
[0009] Another object of the present invention is to provide a marine power unit system which is safe for power management and optimal for energy distribution / storage, taking into consideration the characteristics of the equipment.
[0010] Yet another perspective is that, based on statistics, there is a high percentage of ships that are not operated efficiently due to operators with poor conventional thinking and knowledge of the system. Ship operators generally have a lot of redundancy in their equipment, thinking that it will prevent accidents. However, maritime authorities report that most accidents are caused by human error. In commercial and passenger ships, risk analysis methods are used as decision support tools for the improvement of maritime safety, including the protection of life, health, property, and the marine environment. They also help to strike a balance between various technical and operational constraints, such as human factors, maritime safety, protection of the marine environment, and operational costs. However, these risk analyses are often based on statistical data of past events, which rely on recorded data of ship accidents. The improvement of maritime safety relies on accurate and comprehensive statistical data. Therefore, lack of accurate information leads to inaccurate risk analysis.
[0011] Meanwhile, for Dynamic Positioning (DP) vessels following IMO guidelines, Failure Mode and Effect Analysis (FMEA) studies are important technical exercises used to determine the worst case failures and critical single point failures of the vessel. Such FMEA reports are a prerequisite for obtaining DP Class 2 and 3 notations. Compared to oceangoing vessels, the safety element of DP vessels is specific to DP activities. Currently, incidents in DP systems are recorded with reporting, investigation and closure procedures. Owners and operators of DP vessels are encouraged to share lessons learned from DP incidents with the wider DP community. Similarly, vendors of DP systems and equipment are encouraged to share information on unexpected malfunctions, features and failures identified in operation.
[0012] In summary, every typical ship operation is complex in nature and is carried out according to operational and safety procedures predefined by the operator and / or flag state authorities. The systems involved in the operation are interlinked in several complex patterns, making it difficult for the operator to foresee the future impact of any action taken without being able to articulate the inherent possible impact on the safety of the ship. Therefore, the objective of the present invention is to simplify the information presented for the operator or, in some cases, for machine learning algorithms trained to handle the situation automatically. [Means for solving the problem]
[0013] The object of the invention is achieved by a system for controlling a vessel as claimed in the appended claims.
[0014] Therefore, the present invention proposes a system with a novel connector between the Operational Technology (OT) layer and the Information Technology (IT) layer. This connector establishes a secure and reliable data exchange mechanism between the OT and IT layers. The decisions calculated by the iEMS operating in the IT layer are then processed by the Balance Profile. Dynamic robustness is provided by the Balance Profile in terms of energy efficiency and redundancy margin indices. Based on the weighted scores calculated from the energy efficiency and redundancy margin components, an optimal decision is sent from the IT layer to the OT layer when the Balance Profile function gives the best combined index. In this way, optimal power plants including generator sets and other renewable energy sources, thrusters, and propeller setups can be autonomously configured in real time by calculating the redundancy and / or load on the system and providing an index indicative of the optimal operation of the system.
[0015] The EMS in the OT layer has sub-modes such as boost mode, peak shaving mode, PTI / PTO mode, etc. When the composite index obtained from the balance profile falls within the allowable limit, the EMS automatically switches to the sub-mode corresponding to the optimal power plant and ship operation mode.
[0016] The solution according to the invention therefore provides a system and mechanisms that are beneficial to the ship's performance in terms of both efficiency and safety (redundancy margins), such as providing recommendations to optimize power generation and distribution in a safe manner. The invention also improves the decision-making capabilities of the iEMS in combination with the balancing profile in order to configure the ship's power plants according to operations that are performed autonomously in an ideal configuration suited to the current and future weather conditions and operating location. The proposed unique connector helps to transfer selected recommendations from the balancing profile in the IT layer to the control system in the OT layer, which allows to eliminate human involvement in the decision-making process and increase its effectiveness.
[0017] By using an online cost optimizer, optimal load set points are set for the various producers or generators. In this specification, the term "producer" or "generator" is used to refer to the same type of devices such as generator sets, batteries, and fuel cells, which can be monitored using available sensors or detectors, and the term "consumer" may refer to thrusters, winches, or other onboard equipment that may affect power distribution and consumption on the vessel. The optimal power unit configuration / connection is used to improve overall fuel consumption and determine the efficiency of the system.
[0018] The iEMS according to the present invention is designed to have autonomous decision-making capabilities. Therefore, a balance profile function is introduced to process the results of the cost optimizer before sending the proposed configuration to the control system. The balance profile calculates a composite index between the energy efficiency and the redundancy margin of the vessel. This may be performed according to predefined rules or alternatively based on the analysis of past incidents or recorded events, possibly in combination with user feedback relevant to the situation. [Brief description of the drawings]
[0019] The invention will now be described in more detail with reference to the accompanying drawings, which show, by way of example, the invention. [Figure 1] FIG. 1 is a diagram showing an iEMS and a balance profile in a vessel control system. [Diagram 2] FIG. 2 is a diagram illustrating a balance profile. [Diagram 3] This is a flowchart of iEMS and balance profile during DP operation. [Figure 4] Flowchart of iEMS and balance profile for DP operations including transit, waiting, berthing and maneuvering. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0020] A reliable source of power is essential for ship operation. The main function of an Energy Management System (EMS) is to safely, securely and economically manage the power generation systems on different types of ships. An EMS achieves this by using fault-tolerant energy management strategies that provide high availability of power under all working conditions.
[0021] A typical EMS will: 1. Monitor generators / producers and consumers and start or stop specific producers in response to alarm conditions. 2. Control the load sharing of the battery through the converter. 3. Maintain sufficient power even under changing electrical load conditions. 4. Carry out rapid shedding of heavy consumers when producers are overloaded / shut down. 5. Ramp control of heavy consumer machines according to the number and type of connected producer machines.
[0022] One of the core functions of an EMS is the ability to prevent power outages by avoiding power overload scenarios, and to quickly respond to sudden overload situations due to unexpected loss or shutdown of electrical production equipment.
[0023] However, with tightening emission regulations, the EMS needs to intelligently set up the powerplant depending on the type of vessel mode. The iEMS, which includes a Level 4 balance profile of the ANSI / ISA-95 model, helps with real-time optimization. Based on the vessel mode and operation, it determines the optimal powerplant, calculates a composite index for its selection, and then sends it to the EMS.
[0024] The ANSI / ISA-95 model consists of five levels, 0 to 4, and iEMS is located at level 4. Each level operates on a different time frame depending on the importance of the operations being performed. iEMS differs greatly with regard to the control layer (OT layer) and operates on a minute / hourly basis.
[0025] An overview of the various components that comprise the iEMS, including the balance profile, is shown in Figure 1. A data logging system 102 collects and logs data from the control systems installed on board the ship. The logged data is presented to the IT layer components via a data adapter service 105. The data is then input and distributed from a data distribution service 111, whose bus publishes and subscribes to information between the connected services.
[0026] The dynamic positioning system 103 hosts a power plant setup advisor component 125 that assists in determining the optimal power plant setup. The power plant setup advisor is an OS application (MFC) that loops through all possible desired setups and provides one setup to be used in each iteration. Each setup describes the state of the generator sets, the state of the batteries, and the state of the available generators, including the state of certain bus tie breakers, as well as the state of the consumers, such as the state of certain thruster connections and the state of the onboard equipment, which can be inputs to a worst case failure (WSF) loop. An optimizer is run on each setup to calculate the optimal generator set / battery setup and the cost of this power plant setup.
[0027] The powerplant setup information is defined by a configuration file or machine learning algorithms. The possible switchboards, i.e. ring bus, open bus, closed bus, 2+1 split, 2 split, n split combinations, and thruster configurations during DP operation are defined in this configuration file. The DP classes corresponding to each switchboard combination are included in the same configuration file. The potential contribution of each generator set is directly provided based on the power requirements of each connected switchboard. However, for thrusters, things get complicated as they contribute in at least two directions, typically all three directions (surge, sway, yaw). Also, thrusters are connected to different switchboards. As a result, disconnecting a thruster from a particular switchboard increases the power demand of other connected switchboards. Every connection or disconnection of a thruster requires re-running the entire WSF loop to find a new optimal generator set configuration.
[0028] The thruster demand is given in three axes: surge, sway, and yaw. With the current weather conditions and current settings, the thruster demand is calculated by the DP 103. The output from the vessel response simulator 124 is used to analyze what happens if the DP settings are changed (e.g. different headings or controller gains). The cost optimizer 125 provides long term advice on the powerplant setup, so the thrust demand needs to be smoothed so that thrust spikes do not affect the results. A low pass filter is used to smooth the thrust demand.
[0029] The power ramp required for each connecting switchboard depends on the calculated thrust ramp, where the thrust ramp is determined from the standard deviation of the thrust demand. This deviation is calculated recursively separately for surge, sway, and yaw. The calculated thrust standard deviation is multiplied by two to obtain the thrust ramp.
[0030] The purpose of the vessel response simulator 124 is to generate thrust demands and vessel responses based on environmental disturbances (i.e. wind, currents, and waves). These environmental data are obtained from the weather data service 118 and adapter 119.
[0031] The solution 125 of the optimization problem is sent from the DP 103 to the DP connector 106 in the IT layer via a web interface 104. The interface 104 gets a token from the IT layer, which is verified for every post from the DP 103. The traffic between 103 and 106 is encrypted using self-assigned certificates.
[0032] In 126, different services are hosted in the IT layer, subscribe to the data and publish the results to the data distribution service 111. The vessel mode service 107 automatically detects the vessel's main operating mode based on several probable signals such as number of thrusters running, number of generator sets / batteries running, shore power connection, vessel speed, water depth, draft, vessel GPS position, vessel motion, etc., and the static load analysis 108 determines the minimum static load based on the vessel's main operating mode information received from 107. Deviations from the reference static load are returned to the data distribution service 111. The powerplant advisor service 115 analyzes the results from 125 and determines the actions that need to be forwarded to the control system via the control system connector 113, e.g. DG, stopping / starting thrusters, adjusting kW / s, etc.
[0033] The efficiency and loss service 116 calculates the power loss and operational efficiency of the equipment including producers or generators and consumers. The equipment health monitoring service 128 calculates the availability of the equipment including producers and consumers based on the input from 101. This service also calculates the power loss and efficiency based on the proposed setup received from 125. The optimal power plant utilization service 109 subscribes to the results from 108, 115, 116, 107, and 128. The optimal power plant setup, minimum static load, power loss and efficiency, equipment health monitoring service, and vessel operating mode are used in 109 to determine the ideal load criteria that gives minimum fuel consumption and maximum efficiency. The weather data adapter 119 and the weather data service 118 are used to obtain the weather forecast for the geographical location of the vessel using an internet based cloud service. This weather forecast information is published and subscribed by other services, i.e. 108, 106. Using the DP connector 106, the weather parameters are forwarded to the vessel response simulator 124.
[0034] In 110, the possible fuel consumption reduction is calculated based on the results from 109 and forwarded to the balance profile 112. The fuel consumption reduction is used in 117 to calculate the achievable emission reduction and forwarded to the balance profile. In 114, the maintenance cost reduction based on the reduction in flight hours is calculated. In 126, the combined information from all the above mentioned services is used to determine a composite index score based on the achievable energy efficiency and redundancy margins.
[0035] The control system connector 113 is used to send the composite index and optimal power plant setup recommendations via 120 to the control system layer 127. The connector operates in the same mechanism as 104. The system 127 includes selection elements for selecting the primary system mode 121 and the power system mode 122. It also houses the EMS 123 for managing the power generation system safely, securely and economically. The different primary system modes 121 of the vessel are quay docked, anchored, maneuvering, slow transit, fast transit, full transit, maximum transit speed, DP propeller, DP thruster, anchor, quiet operation, etc. At 126, the vessel mode service 107 automatically determines one of the primary modes as described above.
[0036] For each main mode, 123 can configure the power plant in any of the power system modes. The power system modes vary depending on the configuration of main engines, hybrid shaft generators, auxiliary engines, energy storage units, grid converters, shore connections, etc. The composite index and optimal power plant configuration are processed by 123 to determine if a change in power system mode is required to comply with the proposal. If the composite index from the balance profile falls within acceptable limits, the adaptation is performed automatically by EMS 123.
[0037] Figure 2 shows a block diagram of a Balance Profile 200 including a Composite Index calculator service. From the Data Distribution Service 201, the proposed actions and results from 126 other services are presented in 202. Based on the recommended setup, the values of the Energy Efficiency Index 203 and the Ship Redundancy Margin Index 204 are calculated. Part by part, the Balance Profile shows an example of how the Energy Efficiency Index and the Ship Redundancy Margin Index are calculated for each individual action.
[0038] Table 1 shows a situation where an offshore vessel performs DP operations with two split switchboards. All thrusters operating on the two switchboard segments are operational and enabled in the DP system. The composite indices calculated for each operation from the balance profile are as follows:
[0039] [Table 1]
[0040] In this case, the energy efficiency and redundancy margin score is the average of each element's individual scores, but may be weighted differently either automatically through analysis of past situations or by the operator. The composite index is determined by setting baseline and target values for the energy efficiency and redundancy margin scores. It then maps the two scores into a common index range and assigns them dynamic weights depending on the mode of operation. Finally, it sums up the combined weighted score values of each score. The weights may be predetermined or dynamically adjusted based on machine learning algorithms and continuously sampled information related to the system's performance.
[0041] In the situation in Table 1, the composite index is very low, which is not optimal and requires adjustments in the system, such as increasing the number of generators or other production machines in the system. This increases the redundancy margin, but at the expense of increased fuel consumption and emissions. Thus, the composite index balances the different aspects of the system. Depending on the available equipment, the system can suggest different adjustments to the system and calculate the composite index for each of them. This allows the system or the operator to make the best choice based on the composite index.
[0042] Table 2 below shows an offshore vessel in transit operation with two main engines and two propellers operating in diesel mechanic mode and one auxiliary engine connected to the main switchboard in a single closed switchboard configuration. The vessel is cruising at 10 knots.
[0043] The composite index calculated from the balance profile is as follows:
[0044] [Table 2]
[0045] Again, the system proposes different solutions based on the available resources in the system and calculates the composite index for different conditions, allowing the solution that provides the optimal value of the composite index (the highest in this example) to be selected.
[0046] In more detail, the score is calculated using several parameters, as shown in Table 1 for DP and Table 2 for transit operation. Each parameter has a weighted score depending on the ship operation mode. The weighted score ranges from 0 to 1. In 204, a score of 0 indicates a very high redundancy margin and a score of 1 indicates a very low redundancy margin. In 203, a score of 0 indicates an inefficient system and a score of 1 indicates an optimal system. These scores are then combined to generate a unified score in 205. In the composite index, base and target values for energy efficiency and redundancy margin are defined. The base and target values are dynamically adjusted depending on the ship operation mode. This composite index is validated against the limit values defined in 206 before a decision is made regarding forwarding to the control system connector. To pass the validation, the service health status and the composite index value in 126 must be within the acceptable limits.
[0047] FIG. 3 shows an exemplary process flow diagram of iEMS and balance profile for DP operations. The process starts at 303, where a determination of the mean change in weather is made. The limits for determining the mean change in weather, i.e. wind, current, and waves, are configurable, and a weather forecast is received (301) and posted (303). At 304, power plant setting changes are determined, such as bus tie breaker status, generator set status, thruster status, power system mode changes, etc. At 305, changes in DP settings are checked, such as heading changes, controller gains, etc. At 306, a change in operation mode is detected. If any of the conditions at 303, 304, 305, and 306 are met, a power plant advisor 307 is initiated. The power plant advisor as described above runs a power plant loop to determine the optimal setup with the minimum number of generator sets, batteries, and thrusters based on the selected DP class without exceeding the WSF conditions.
[0048] The results from 307 are post-processed in Optimal Power Plant Utilization, where power losses, efficiency, minimum static load, equipment health and availability, and power system modes are used to determine the ideal load criteria for production machines that minimize fuel consumption and emissions. The output of 308 is used in the Balance Profile block at 309, where a composite index is calculated. At 310, the composite index score is compared to acceptable limits, and the results of 308 are then forwarded to the control system via connector 312, once approved at 311. If the score exceeds the limits, the process returns to 309 and continues the same sequence for the next proposal in the pipeline.
[0049] In parallel, as shown in Figure 3, a data distribution service 313 continuously publishes and collects data from various services hosted on the IT layer. The results from these services are used in 308.
[0050] FIG. 4 shows an exemplary process of iEMS and a flow diagram of the balance profile during non-DP operation. The process starts at 403, where a change in the average weather is detected based on the received weather forecast 401, and a change in the power plant setup, including a change in power system mode (404) or a change in operation mode (405), is used to initiate optimal power plant utilization services. Different power system modes for different operation modes are defined in a configuration file. An optimizer using the average load profile and peak load requests runs iterations over the defined modes to determine the best setup that provides the minimum energy usage. The selected configuration should also provide the required efficiency.
[0051] At 408, the composite index from the balance profile block 407 is compared to limits to determine if a recommendation can be forwarded to the control system via 409 and 410. Any deviation against acceptable limits causes the sequence of forwarding advice to the control system to be aborted. The flow returns to 407 to repeat the procedure based on the next advice in the queue. As mentioned above, other services hosted in the IT layer perform calculations based on real-time measurements received via the data delivery service (412).
[0052] The present invention relates to a hierarchical structure based on the industry accepted ANSI / ISA-95 model. The physical components are located at level 0 of the model, where processes are performed in milliseconds or microseconds. Measurements from level 0 are sent to controllers at level 1, where actions are performed in seconds. At level 2, monitoring and control are performed in seconds. The EMS is located at level 3, where PMS operation and operation mode selection are performed. The iEMS, including the balance profile, is located at the level between 3 and 4, i.e. 3.5. The link between level 3.5 and level 3 is the connector of this novel control system. At level 5, business planning and logistics are performed. This level is not related to the mechanisms defined in this application.
[0053] In summary, the present invention relates to a system and related method for controlling a ship including multiple devices, the devices including multiple producers / power generators and consumers further comprising sensors for monitoring the activity of the devices. The monitoring may be a per se known function implemented in a particular device or may be obtained using suitable available sensors. The system also comprises at least one processor for calculating device score values representative of the performance of the devices based on the sensor measurements and known characteristics of each device. The system also comprises a control unit connected to the devices and configured to calculate an efficiency score value based on the device score values. The efficiency score value defines the efficiency of the system.
[0054] The system is also configured to calculate a system redundancy based on the operation modes and / or loads of the devices, calculate a redundancy margin index indicative of the redundancy of the devices of the system according to a predefined score for each operation mode associated with the redundancy of the associated device, and calculate an efficiency score value and a redundancy score value of the system. The system is further configured to calculate a composite index based on the efficiency score value and the redundancy margin index.
[0055] The system is configured to include a set of setup configurations for devices included in the system, to calculate a composite index for at least one alternative device setup in an instance where the composite index is outside a predetermined range, and to indicate a preferred setup based on a comparison between the composite index values, and to select an optimal operating mode for the system by selecting the highest composite index value.
[0056] Therefore, the corresponding method is calculating a score value for each device; calculating an efficiency score value based on the score values of the devices, the efficiency score value defining an efficiency of the system; calculating the system redundancy based on the operating mode and / or load of each device, calculating a redundancy margin index indicating the redundancy of the device according to a predetermined score for each operating mode, and calculating efficiency and redundancy values; calculating a composite index based on the efficiency score value and the redundancy margin index; where the vessel includes a set of setup configurations for devices included in the system, and where the system calculates a composite index for at least one alternative device setup in an instance where the composite index is outside of a predetermined range and indicates a preferred setup based on a comparison between the composite index values.
[0057] The predefined scores for each of the operational modes may be based on a statistical analysis of past stored data from corresponding situations, and the predefined scores and / or predefined ranges may be obtained from a machine learning unit that analyses the performance of the system. Preferably, the scores and ranges obtained from the machine learning unit are based on an analysis of past data sampled in past cases and / or on feedback registered from a system operator.
[0058] The comparison of the composite indices may include a comparison of calculated or computed scores, indices, and alternative device setups provided by the machine learning unit to optimize system efficiency and redundancy based on individual evaluations of the devices, in addition to the efficiency and redundancy score values.
[0059] The sensors in the system may also include one or more sensors for monitoring the vessel environment such as waves, currents, wind, etc., and / or for monitoring the vessel behavior such as vessel position, movement, speed, heading, etc. For this reason, the system may weight the scores depending on the situation, for example prioritizing redundancy in severe weather conditions. For the same purpose, the system may also be configured to directly receive information about the vessel environment, such as weather forecasts, or may be configured to take into account environmental information by a vessel response simulator, whereby inputs related to future conditions of the environment may be provided to the system.
[0060] Devices in the system may be defined as individual entities within the system or may be grouped into clusters, such as a cluster for power generation, a cluster for thrusters, etc. This allows the system to calculate a common device score for each cluster.
[0061] The system may also include a user interface, including an interface such as a display and keyboard, to allow an operator to select a setup based on the composite index, where the composite index is visualized for the operator to simplify the selection or to provide feedback from the user evaluating performance. The selections and feedback are registered by the system and used, for example, by machine learning algorithms, to optimize future automated selections or future score and index calculations.
Claims
1. 1. A system for controlling a marine vessel including a number of devices, comprising: The device includes a number of electrical power generators and consumers; the device further includes a sensor for monitoring activity of the device; The system includes at least one processor for calculating a device score value for the device; The system further comprises a control unit connected to the device and configured to calculate an efficiency score value defining an efficiency of the system based on the device score value; The system is configured to calculate a redundancy of the system based on an operation mode and / or a load of the device, calculate a redundancy margin index indicating a redundancy of the device of the system according to a predetermined score of each of the operation modes, and calculate an efficiency score value and a redundancy score value; The system is further configured to calculate a composite index based on the efficiency score value and the redundancy margin index; the system includes a set of setup configurations for the devices included in the system, and is configured to, in an instance where the composite index is outside a predetermined range, calculate a composite index for at least one alternative device setup, and indicate a preferred setup based on a comparison between values of the composite index. system.
2. The system of claim 1 , wherein the predetermined score for each of the operational modes is based on a statistical analysis of past stored data from a corresponding situation.
3. The system of claim 1 or 2, wherein the predetermined score and / or the predetermined range are obtained from a machine learning unit.
4. The system of claim 3 , wherein the predetermined score and the predetermined range obtained from the machine learning unit are based on an analysis of past data sampled from past cases and / or feedback registered from an operator of the system.
5. 5. The system of claim 1, wherein the comparison of the composite indices further comprises a comparison of computed or calculated scores, indices, and alternative device setups provided by a machine learning unit to optimize efficiency and redundancy of the system.
6. The system of any one of claims 1 to 5, wherein the sensors further include one or more sensors for monitoring the vessel environment such as waves, currents, wind, and / or for monitoring the vessel behavior such as vessel position, movement, speed, heading, etc.
7. A system as claimed in any one of claims 1 to 6, wherein the system is configured to receive information about the vessel environment, such as a weather forecast, and configured to take into account information about the vessel environment by a vessel response simulator, whereby inputs relating to future conditions of the environment are provided to the system.
8. The system of any one of claims 1 to 7, wherein the devices are grouped into clusters, such as a cluster for power generation and a cluster for thrusters, and the system calculates a common device score for each cluster.
9. A system according to any preceding claim, wherein the system includes a user interface including a display for enabling an operator to select a set-up based on the composite index.
10. The system of any one of claims 1 to 9, wherein the power generator comprises at least one of a generator set, a fuel cell, and a battery.
11. 1. A method for controlling a marine vessel including a number of devices, the devices including a number of electrical power generators and consumers, and sensors for monitoring activity of the devices, comprising: - calculating a score value for each of said devices; - calculating an efficiency score value defining an efficiency of the system based on the score values of the devices; - calculating the redundancy of the system based on the operating modes and / or loads of each of the devices, calculating a redundancy margin index indicative of the redundancy of the devices according to a predetermined score predefined for each of the operating modes, and calculating the efficiency and redundancy values; - calculating a composite index based on said efficiency score value and said redundancy margin index; Including, the vessel including a set of setup configurations of the devices included in the system, the system including, in an instance where the composite index is outside a predetermined range, calculating a composite index of at least one alternative device setup and indicating a preferred setup based on a comparison between values of the composite index. method.
12. The method of claim 11 , wherein the predetermined score for each of the operational modes is calculated based on a statistical analysis of past stored data from corresponding situations.
13. The method of claim 11 or 12, wherein the predetermined score and / or the predetermined range are obtained from a machine learning unit.
14. 14. The method of claim 13, wherein the predetermined score and the predetermined range obtained from the machine learning unit are based on an analysis of past data sampled from past cases and / or feedback registered from an operator of the system.
15. 15. The method of any one of claims 11 to 14, wherein the comparison of the composite indices further comprises a comparison of computed or calculated scores, indices, and alternative device setups provided by a machine learning unit to optimize efficiency and redundancy of the system.