Hybrid power ship energy recovery and optimization method and control system

Abstract

The invention provides a hybrid power ship energy recovery and optimization method and a control system. The method is used for balancing recovery schemes and carrying out optimization control according to the recovery schemes. The electric power system structure of the hybrid power ship is converted into knowledge which can be understood by an expert system through the directed graph theory, the overall reliability index of the electric power system recovery scheme is defined based on the directed graph theory, and the decision of balancing the electric power system recovery scheme is participated. Factors, such as power line transmission efficiency, switching operation times, switching operation time, distance, line reliability and overall reliability of a recovery scheme, influencing hybrid power ship power system fault recovery are calculated through a target function to participate in balancing of the recovery scheme. Furthermore, according to a capability control method with nonlinear model predictive control as a core, cost function design is carried out on a balanced recovery scheme with fuel consumption and power of a power generation device as core indexes, and energy optimization control is carried out.

Description

Technical Field

[0001] The present invention relates to the field of energy control strategies for hybrid ships, and in particular, to a method and control system for energy recovery and optimization of hybrid ships. Background Art

[0002] A ship driven by hybrid power refers to a ship that uses two or more power sources as propulsion power. Hybrid power technology has promoted the development of ships from traditional power drive modes to a more environmentally friendly drive direction, alleviated the problems of carbon dioxide and pollutant emissions, and also taken into account the issue of reducing energy consumption. Many scholars have studied ship control methods and energy control methods for hybrid power systems, hoping to effectively coordinate multiple power sources of the hybrid power system, reduce energy consumption, and lower carbon emissions.

[0003] In the published materials, the power system restoration technology, as an important technology for improving the safety of the energy control system of hybrid ships, plays a crucial role in the safe navigation of ships.

[0004] The diversity of power generation equipment on hybrid ships effectively reduces fuel consumption compared with traditional diesel-driven ships and also alleviates environmental problems. However, the existing energy control strategies are not sufficient to cope with the working conditions under the failure of on-board power generation equipment. On the one hand, the equipment operation under fault conditions will affect the safety of the whole ship, while most energy control strategies are used to optimize the energy scheduling under fault-free conditions. On the other hand, although the power system restoration technology can solve the restoration problem under fault conditions, most scholars focus on the restoration research and do not optimize the energy scheduling of the post-restoration scheme. Therefore, the combination of power system restoration technology and energy control strategy is an effective solution to solve the safety problem of hybrid ships under fault conditions.

[0005] Some scholars have conducted optimal restoration analysis on ship power systems. Through linear programming methods, the problem of reconfiguring the power system under fault conditions is transformed into an optimization problem of maximizing power transmission and minimizing switch operations. Some scholars have also tried to consider the problem of reconfiguring the ship's power system by formulating a distributed control structure and framework. Some other scholars have tried to establish a connection between graph theory and the circuit breakers of the ship's power system to quickly detect and isolate the fault area of the ship's power system to minimize the impact of the fault on the ship. Currently, improved swarm intelligence optimization algorithms can also be used to solve the power system restoration problem, but the performance of these methods varies greatly in terms of convergence speed and falling into local optima. Such methods may be difficult to apply in actual engineering applications.

[0006] The main contribution of the present invention lies in designing a method for energy recovery and optimization of a hybrid ship. First, at the first layer of the strategy, a feasible recovery scheme for the power system of the hybrid ship is inferred through the directed graph theory and the expert system. At the second layer, the nonlinear model predictive control algorithm is adopted to optimize the control of the diesel generator, shaft generator, and energy storage battery, reducing fuel consumption. Summary of the Invention

[0007] To overcome the defects of the existing energy control technology, the present invention provides a method and control system for energy recovery and optimization of a hybrid ship. Based on the closed-loop energy control of the hybrid ship, the strategy weighs the fault recovery scheme through the objective function, and performs energy optimization control on the power generation device of the hybrid ship through the nonlinear model predictive control algorithm.

[0008] To achieve the above object, the present invention provides a method for energy recovery and optimization of a hybrid ship, which includes:

[0009] Using the directed graph theory to transform the power system structure of the hybrid ship into a directed graph, and defining the overall reliability index of the power system recovery scheme through the directed graph theory; when a fault occurs, based on the reliability index, making a trade-off decision based on the influencing factors to obtain the power system recovery scheme;

[0010] Adopting the nonlinear model predictive control technology to perform energy optimization control on the power system recovery scheme to ensure the energy recovery and optimization of the power system of the hybrid ship.

[0011] A further improvement of the present invention is that in the process of making a trade-off decision on the power system recovery scheme, the factors involved include: the transmission efficiency of the power line, the number of switch operations, the switch operation time, the line reliability, the distance, and the overall reliability of the recovery scheme.

[0012] A further improvement of the present invention is that in the process of using the directed graph theory to transform the power system structure of the hybrid ship into a directed graph.

[0013] The power system structure is converted into a directed graph G, where: the equipment and busbars in the power system structure are used as the nodes of the directed graph; the connections between the equipment are characterized by the edges of the directed graph; the attributes of each edge in the directed graph include: the transmission efficiency of the power system recovery line, the number of switch operations, the switch operation time, the distance, the reliability, and the overall reliability of the recovery scheme.

[0014] The on-state and connection relationship of the equipment during the actual operation of the ship are represented by a subgraph of the directed graph G.

[0015] A further improvement of the present invention lies in that: during the trade-off decision-making process, the candidate solutions adopted are multiple power system restoration solutions applicable to corresponding fault conditions, which are generated by an expert system through reasoning based on the operating state of the hybrid ship power system; the power system restoration solutions include corresponding restoration sub-graphs, and the restoration sub-graphs include alternative power generation equipment and line connection information.

[0016] A further improvement of the present invention lies in that: during the process of calculating each power system restoration solution according to the objective function in the trade-off decision-making process, the expression of the adopted objective function is:

[0017]

[0018] In the formula, λ1, λ2, λ3, λ4, λ5 are trade-off coefficients, k represents the number of edges in each sub-graph; n represents the total number of edges; where η k represents the number of switch operations of each edge in the restoration sub-graph w k represents the number of switch operations of each edge; t k represents the time required to operate the switch of each edge; d k represents the length of the restored line; r k represents the reliability of operating each edge, which is an inherent attribute of each edge; r represents the overall reliability of the restoration sub-graph.

[0019] A further improvement of the present invention lies in that: the energy optimization control is realized through a non-linear model predictive control algorithm, and the energy of the hybrid ship power system is optimized based on the prediction model and the feedback of the control variables, so as to reduce fuel consumption and improve the survivability of the restored hybrid system.

[0020] A further improvement of the present invention lies in that: the expert system reasons based on the operating state of the hybrid ship power system to generate power system restoration solutions applicable to different fault conditions.

[0021] The present invention also provides a hybrid ship control system for implementing the above-mentioned hybrid ship energy restoration and optimization method.

[0022] The beneficial effects of the method of the present invention are:

[0023] 1. By combining the directed graph theory and the expert system, the power system structure of the hybrid ship can be accurately transformed into understandable knowledge, enabling the system to automatically generate adapted restoration solutions according to different fault modes. The restoration solutions not only consider the operating state of the system, but also ensure the optimality of the system restoration solutions by weighing various factors. It can achieve a quick response and restoration to the most reliable state when facing various complex working conditions.

[0024] 2. Energy optimization control can be carried out on the restored power system through the non-linear model predictive control algorithm. This control algorithm can dynamically adjust the energy distribution, thereby maximizing the energy utilization efficiency while ensuring power supply and reducing fuel consumption. This helps to improve fuel economy and the survivability of the ship under fault conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a schematic diagram of the energy recovery and optimization method for the hybrid ship of the present invention;

[0026] Figure 2 is the power system structure diagram of the ship in this embodiment;

[0027] Figure 3 is the process diagram of the directed graph theory describing the topological structure of the hybrid ship power system;

[0028] Figure 4 is the equivalent internal resistance model of the battery. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The following specific examples illustrate the embodiments of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0030] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0031] For the purpose of illustration, some exemplary embodiments of the present invention are described. It should be understood that the present invention can be implemented in other ways not specifically shown in the drawings.

[0032] The present invention provides a method for energy recovery and optimization of a hybrid ship, as Figure 1The above shows the strategy framework. First, the power system topology of a hybrid ship is transformed through the theory of directed graphs, and an index for measuring the overall reliability of the fault recovery scheme is established using the concept of degree to participate in the trade-off of the recovery scheme. And by weighing the objective function of the recovery scheme, the factors affecting the power system recovery scheme are taken into consideration. Finally, energy optimization control is carried out for different fault conditions through the non-linear model predictive control algorithm.

[0033] The details are as follows:

[0034] The first layer of the strategy is used for the trade-off of the recovery scheme, including: the knowledge transformation and application based on the theory of directed graphs and the logical reasoning based on the expert system, and weighing the objective function of the recovery scheme.

[0035] The second layer of the strategy is used for the energy optimization control of the recovery scheme. It includes: the mathematical model of the hybrid system and the optimization method based on non-linear model predictive control. Among them, the mathematical model of the hybrid system includes the energy conservation model and the control model of the power generation device. This patent takes Figure 2 the shown embodiment as the modeling object.

[0036] In the specific implementation process:

[0037] 1) The first layer of the strategy: The trade-off of the recovery scheme

[0038] (1) The knowledge transformation and application based on the theory of directed graphs

[0039] Knowledge acquisition is one of the challenges in designing an expert system. Power equipment is like a "family tree", different attributes of the equipment are like the "male" and "female" in it, and the connection relationships between the equipment are like the "father-son relationship" and "mother-son relationship". The interconnection of the equipment constitutes the power system structure. The expert system has great advantages in solving such problems with clear logical relationships. Therefore, the application of the theory of directed graphs can be used as the source of objective knowledge for the expert system.

[0040] ① Describing the topology structure of the hybrid ship power system by the theory of directed graphs

[0041] As Figure 2 、 Figure 3 shown, each device and bus in the on-board power system are the nodes of the directed graph, and the connections between the devices are characterized by the edges of the directed graph. The transfer efficiency of the power system recovery line, the number of switch operations, the switch operation time, the distance, and the reliability, as important factors affecting the safety of the ship power system recovery, are used as the attributes of the directed graph. Taking the on-board power system structure with all connections as an example, the representation of the on-board power system directed graph is as follows:

[0042]

[0043] Wherein, G represents a graph, V represents a set of nodes, E represents a set of edges, and P rops represents a set of edge attributes. A set containing some nodes and edges is a subgraph of graph G. The representation method of the subgraph is the same as that of the parent graph, reflecting the on-off state of the equipment and the connection state between the equipment during the actual operation of the ship.

[0044] ② Overall reliability index of the recovery plan

[0045] The degree of the directed graph of the shipboard power system is divided into out-degree and in-degree. In the present invention, the physical meanings of the out-degree and in-degree are the number of energy flows between equipment, characterizing the connection state between equipment. The out-degree refers to the number of edges pointing from a node to other nodes, and the in-degree refers to the number of edges pointing from other nodes to this node.

[0046] D v = ID v + OD v (2) In the formula, D v is the degree of the directed graph, ID v is the in-degree, OD v is the out-degree, v represents a node, and v ∈ V.

[0047] The complexity of the connection of the graph is related to the degree of the node and the maximum degree. If the degree of a certain node is very high, if this node fails at this time, the impact on the power system network is relatively large, and the reliability will decrease accordingly. To avoid the occurrence of secondary faults, the reliability of the recovery plan is defined as the consideration basis. The reliability update formula of the graph is defined as follows:

[0048]

[0049] In the formula, r is the reliability of subgraph x. RMS x is the root mean square of the degree of the subgraph. D x,max is the maximum degree among the nodes of subgraph x. D v is the degree of the directed graph. N is the number of nodes in the subgraph. The physical meaning of r is the average value of the ratio of the degrees of all nodes of the directed subgraph to the maximum degree, characterizing the influence of the degree of the node on the reliability of the directed graph. The physical meaning of RMS x is the average degree of all nodes.

[0050] (2) Logical reasoning based on an expert system

[0051] The expert system reasons about the expected solutions for fault conditions by writing logical rules, restricting the recovery plan within the range of feasible recovery equipment, and avoiding unnecessary plan reasoning to save computing resources.

[0052] An expert system consists of a database, an interpreter, and an inference engine. The database of the shipboard power system is divided into a comprehensive database and a fault condition knowledge base, both of which serve as the facts and rules for the inference of the expert system. The comprehensive database serves as the basis for the interpreter to trace back. The interpreter is used to trace back the inference process of the expert system. The inference machine is used to find feasible solutions for corresponding fault conditions. Knowledge acquisition is transformed through graph theory, and rules are designed for different fault conditions. In this embodiment, production rules are used to enable the machine to understand knowledge and rules. The understanding of production rules is very simple here: when certain conditions are met, corresponding conclusions are drawn, which is expressed in the following form:

[0053]

[0054] Such as Figure 2 The ship introduced in this embodiment is assumed to have its faults divided into three categories as shown in the following table, and corresponding expected solutions are provided for each category.

[0055] Table 1 Fault Conditions and Solutions

[0056]

[0057] Taking fault condition 2 as an example, when a fault occurs: First, shut down the faulty diesel generator and cut off the line connection to the shipboard power system bus. Second, search for alternative diesel generators and their line connection information to the bus: For example, when DG1 fails, search for other available diesel generators DG2 and DG3. At this time, there may be multiple alternative solutions. Finally, calculate and compare according to the objective function for weighing the restoration plan, and search for a solution with the minimum objective function value for fault restoration.

[0058] (3) The objective function for weighing the restoration plan

[0059] When the power system resumes power supply, the transmission efficiency of the electrical equipment lines, the number of switch operations, the switch operation time, the distance, the line reliability, and the overall reliability of the restoration plan will all affect the crew's consideration of the restoration plan. Therefore, this study designs the following objective function and introduces a weighing coefficient to select a better solution among the feasible solutions.

[0060]

[0061] In the formula, λ1, λ2, λ3, λ4, λ5 are weighing coefficients, k represents the number of edges in each subgraph. n represents the total number of edges. Among them, η k Represents the number of switch operations for each edge in the restoration subgraph w k Represents the number of switch operations for each edge. t k Represents the time required to operate the switch of each edge. d kIndicates the length of the restoration line. r k Indicates the reliability of operating each edge, which is an inherent property of each edge; r represents the reliability of the overall restored subgraph, which is calculated by formula (3).

[0062] The decision-making process for weighing restoration plans is as follows.

[0063]

[0064] 2) Second layer of the strategy: Energy optimization control of the restoration plan

[0065] (1) Mathematical model of the hybrid power system

[0066] ① Energy conservation model

[0067] Such as Figure 1 , the ship's power generation equipment includes shaft generators, diesel generators, and energy storage batteries, and the electrical equipment includes side thrusters and other on-board equipment. Therefore, according to the law of conservation of energy, the energy conservation model of its power demand and the output power of the power generation device is as follows:

[0068]

[0069] In the formula, P load Is the required power of the ship's side thruster and other electrical equipment, P dg,i Is the power provided by the diesel generator. P dg,j Is the output power of the shaft generator. P bat Is the battery power. When it is greater than 0, the battery is in the discharge state, and when it is less than 0, it is in the battery charging state.

[0070] ② Mathematical model of the diesel generator

[0071] The diesel generator is mainly composed of a diesel main engine and a generator set. By fitting the rotational speed and output power of the diesel generator, the output power calculation formula of the diesel generator is as follows:

[0072]

[0073] In the formula, Is the i-th power of the rotational speed of the i-th diesel generator, a j Is The fitting coefficient in the output power function of the auxiliary generator.

[0074] The relationship between the fuel consumption rate of the diesel generator and the output power is expressed as follows:

[0075]

[0076] In the formula, m dg,i Is the fuel consumption rate of the diesel generator, is the j-th power of the output power of the auxiliary generator, b j is the fitting coefficient in the fuel consumption function of the auxiliary generator.

[0077] The upper and lower limits of the output power of the diesel generator are constrained as follows.

[0078] P dg,min ≤P dg ≤P dg,max (9)

[0079] where P dg,min is the minimum output power, P dg,max is the maximum output power.

[0080] ③ Mathematical model of shaft generator

[0081] The shaft generator mainly consists of a diesel main engine and a generator. By fitting the rotational speed and output power of the shaft generator, the following calculation formula for its output power is obtained:

[0082]

[0083] In the formula, is the j-th power of the rotational speed of the i-th diesel generator, c j is the fitting coefficient in the output power function of the auxiliary generator.

[0084] The relationship between the fuel consumption rate of the shaft generator and the output power is expressed as follows:

[0085]

[0086] In the formula, m sg,i is the fuel consumption rate of the diesel generator, is the j-th power of the output power of the auxiliary generator, d j is the fitting coefficient in the fuel consumption function of the auxiliary generator.

[0087] The upper and lower limits of the output power of the diesel generator are constrained as follows.

[0088] P sg,min ≤P sg ≤P sg,max (12) where P dg,min is the minimum output power, P dg,max is the maximum output power.

[0089] ④ Mathematical model of energy storage battery

[0090] For the energy storage battery model, to simplify the calculation, an internal resistance equivalent model is adopted, such asFigure 4 As shown, the current calculation formula is as follows:

[0091]

[0092] Wherein, I bat is the current output by the battery, V oc is the open-circuit voltage of the battery, P bat is the power output by the battery; R0 is the equivalent internal resistance of the battery. Among them, V oc Figure 4 is the open-circuit voltage of the internal resistance model shown, P bat is the output power of the energy storage battery, and R0 is the internal resistance of the energy storage battery.

[0093] The SOC calculation formula of the battery is:

[0094]

[0095] Wherein, SOC0 is the initial state of charge, Q is the battery capacity, and I bat is the current output by the battery. The SOC constraint, current constraint, upper and lower limits of charging and discharging power of the battery are as follows:

[0096]

[0097] Wherein P bat，min is the minimum charging power, P bat，max is the maximum charging power, SOC min is the minimum SOC, SOC max is the maximum SOC, I bat，min is the minimum working current, I bat，max is the maximum working current.

[0098] ⑤ Optimization method based on nonlinear model predictive control

[0099] In the present invention, the state variables are selected as x = [SOC, m dg , m sg T , the control variable is u(k) = [I bat , ω dg,1 , ω dg,2 , ω dg,3 , ω sg,1 , ω sg,2 T , and the output variable is y = [SOC, m dg , m sg T . m dg = ∑m dg,i , m sg = ∑m sg,i The meaning of which is m​​​dg , m sg represents the total fuel consumption of the diesel generator and the total fuel consumption of the shaft generator. The non-linear model is given by the following formula:

[0100]

[0101] The cost function is designed in the following form:

[0102]

[0103] where (SOC i -SOC ref ) 2 represents the penalty measure for the battery SOC deviation, which can ensure to avoid excessive discharge changes in the energy demand during the entire operation cycle. SOC ref is the reference input of the battery SOC; is the penalty term for minimizing fuel consumption; is the minimized system input; (SOC N -SOC ref,N ) 2 , is the terminal constraint; is the penalty coefficient; u m is the control variable, which satisfies the corresponding constraints. Finally, the energy conservation of the ship is added to the strategy in the form of an equality constraint.

[0104] The above embodiments are only illustrative of the principles and effects of the present invention, and are not used to limit the present invention. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed by the present invention should still be covered by the claims of the present invention.

Claims

1. A method for energy recovery and optimization of a hybrid ship, characterized in that: The power system structure of the hybrid ship is transformed into a directed graph by using the directed graph theory, and the overall reliability index of the power system recovery plan is defined through the directed graph theory; when a fault occurs, according to the reliability index, a trade-off decision is made based on influencing factors to obtain a power system recovery plan; The nonlinear model predictive control technology is adopted to perform energy optimization control on the power system recovery plan to ensure the energy recovery and optimization of the hybrid ship power system.

2. The energy recovery and optimization method for a hybrid ship according to claim 1, wherein: During the process of making a trade-off decision on the power system recovery plan, the factors involved include: power line transfer efficiency, number of switch operations, switch operation time, line reliability, distance, and the overall reliability of the recovery plan.

3. The hybrid ship energy recovery and optimization method according to claim 2, wherein: During the process of transforming the power system structure of the hybrid ship into a directed graph by using the directed graph theory: The power system structure is converted into a directed graph G, where: the equipment and busbars in the power system structure are used as the nodes of the directed graph; the connections between the equipment are characterized by the edges of the directed graph; the attributes of each edge in the directed graph include: power system recovery line transfer efficiency, number of switch operations, switch operation time, distance, reliability, and the overall reliability of the recovery plan; The on - state and connection relationship of the equipment during the actual operation of the ship are represented by a sub - graph of the directed graph G.

4. The energy recovery and optimization method for a hybrid ship according to claim 3, characterized in that: During the trade - off decision - making process, the candidate solutions adopted are multiple power system recovery plans applicable to the corresponding fault conditions generated by the expert system through reasoning based on the operating state of the hybrid ship power system; the power system recovery plan includes the corresponding recovery sub - graph, and the recovery sub - graph includes alternative power generation equipment and line connection information.

5. The energy recovery and optimization method for a hybrid ship according to claim 4, characterized in that: During the process of calculating each power system recovery plan according to the objective function in the trade - off decision - making process, the expression of the objective function adopted is: where λ1, λ2, λ3, λ4, λ5 are trade-off coefficients, k represents the number of edges in each subgraph; n represents the total number of edges; where η k represents the number of switch operations w for each edge in the restored subgraph k represents the number of switch operations for each edge; t k represents the time required to operate the switch of each edge; d k represents the length of the restored line; r k represents the reliability of operating each edge, which is an inherent property of each edge; r represents the overall reliability of the restored subgraph.

6. The energy recovery and optimization method for a hybrid ship according to claim 1, wherein The energy optimization control is realized through the nonlinear model predictive control algorithm, and the energy of the hybrid ship power system is optimized based on the feedback of the prediction model and control variables to reduce fuel consumption and improve the survivability of the recovered hybrid system.

7. The energy recovery and optimization method for a hybrid ship according to claim 1, wherein The expert system reasons based on the operating state of the hybrid ship power system to generate power system recovery plans applicable to different fault conditions.

8. A hybrid ship control system for implementing the hybrid ship energy recovery and optimization method according to any one of claims 1 to 7.

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