Development of energy management system using neural network for power electric system

1. Introduction

The international community has strengthened regulations on emissions from ships, and the IMO(International Maritime Organization) has tightened regulations on air pollution emitted from ships [1]. Accordingly, various alternatives such as EEDI(Energy Efficiency Design Index) power system improvement, low-carbon fuel, etc. have been proposed to reduce carbon dioxide [2], and the engine system is also being improved as a solution [3]. This affects not only the change of the propulsion system, but also the improve the efficiency of the power system. In the power system, the conventional parallel running load-sharing power generation system has limitations such as frequent maintenance cycle, increased emissions due to the fuel consumption increase by the low efficiency of the power source in the case of low-load operation [4]. To improve this, various fuels for engines, types of sources, and efficiency improvement measures for power generation sources have been introduced [5][6]. Among them, a hybrid power system using a battery system can also be a solution to improve system efficiency [7]. The efficiency of the system can be improved by the optimal operation of the power source and the battery system’s compensation. Comprehensive control of the battery management system linked with the existing power management system can be managed by the EMS(Energy Management System) [8]; the EMS controller is devised and used by various control methods [9]. In this study, by applying a rule-based strategy, the operation mode of the power generation system according to the load condition and battery SOC and the operation command for each generator were set. In addition, the EMS control results considering the SOC of the battery as a power source and the system without considering the SOC’s effects on the results of the EMS controller were also examined through simulation. To improve the efficiency of the system, the optimal operation point of the power source is designed to be operated in as many sections as possible, and the battery system is designed to compensate for variable load conditions.

2. Power management system and energy management system for ship

2.2 Control Strategy of Energy Management System not Considering Battery SOC

As shown in Table 1, if the battery SOC is not considered a reserve power source, it operates in five operating modes. The generator's operation command is designed to be taken as G__STOP, G__OPT, α = P_LOAD + P_{G_min} is set in consideration of the minimum generator operation output value in the design standard of the load standard to designate the initial battery charge and the charging section of the battery according to the section without considering the battery SOC as a reserve power. The output command rule for each source was created.

Table 1 :

Rule based strategy not considering battery SOC (α = PLOAD + PG_min, PG_min = 20%, G_OPT = 75% )

2.3 Control Strategy of Energy Management System Considering Battery SOC

When the battery is considered as the reserve power of the load, the overall generator output command value is reduced because the system power is compensated by the battery. As shown in Table 2, unlike Table 1, by setting β = P_LOAD − P_BATt, a different operation mode is obtained according to the load condition category. The generator operation status is set to G__STOP, G__VAR, G__OPT.

Table 2 :

Rule based strategy considering battery SOC (β = PLOAD − PBATT)

3. Design of energy management system using neural network

3.1 Specification and test environment

To design the controller for EMS according to the designed rule, the system capacity (system load, generator engine, and battery) was selected as shown in Table 3, and the NCR output of the generator engine was selected as 2,000 kW considering the engine margin. All the design specifications were calculated to control the generator engine’s output, as shown in Table 4.

Table 3 :

System specification

Table 4 :

Design specification of generator engine

3.2 Training data for neural network

To test the responsiveness and stability of the generator output command controller of the system according to the time-varying load and SOC variation, the environment shown in Figures 1 and 2 was selected as the final experimental environment.

Figure 1:

Specific load profile

Figure 2:

Specific SOC profile

Table 5 shows the neural network training values that do not consider the battery SOC as a reserve power based on the rule-based strategy for the generator output command according to the ship’s load and the battery SOC value in consideration of the simulation environment. Table 6 shows the neural network learning values considering the SOC value.

Table 5 :

Neural network training value not considering battery SOC

Table 6 :

Neural network training value considering battery SOC

The gray section indicates the operation where the battery is no longer charged from the system when the battery SOC value reaches 90% or more, and P_G__min is set to 0 at α = P_LOAD + P_G__min and trained.

3.3 Neural network application

The designed neural network controller had a single-layer neural network; the hidden layers were composed of 30 layers; and a neural network controller using Bayesian regularization was applied. As shown in Figures 3 and 4, the error in the output value of the learned neural network controller can be compensated by the battery system, so it does not affect the output or operation performance.

Figure 3 :

Result of errors in neural network training not considering battery SOC

Figure 4 :

Result of errors in neural network training considering battery SOC

3.4 Training result by neural network controller

In addition, to verify the control stability of the designed neural network, the load and battery SOC values as shown in Figures 5 and 6 that were used for learning were applied to verify that the generator output command value matches the learned value and to confirm that there are no irregular or uncontrollable areas in the control result. Figures 7 and 8 depicts the results, which are stable.

Figure 5 :

Specific load profile

Figure 6 :

Specific SOC profile

Figure 7 :

Result of neural network controller output tested in step load and step battery SOC not considering battery SOC

Figure 8 :

Result of neural network controller output tested in step load and step battery SOC considering battery SOC

3.5 Simulation

The system shown in Figure 9 was designed through Matlab/Simulink to apply the unspecified ship power load and battery SOC value as input values using the verified neural network controller to make the output command value for the generator output through the controller which prove the stability of the controller.

Figure 9 :

EMS using neural network

As shown in Figures 10 and 11, if the battery SOC is not considered, the required output of the power source is higher. However, as a result of both designs, the power generation source was designed to have a parallel operation form above a certain load exceeding the optimum operation output value of the power source.

Figure 10 :

Result of neural network controller output tested in specific load and battery SOC not considering battery SOC

Figure 11 :

Result of neural network controller output tested in specific load and battery SOC considering battery SOC

As shown in Figures 12 and 13, the command value converted from the generator output command value using the load-RPM table designed in Table 4. It is for a command as the generator angular speed command. It was confirmed that irregular values or oscillation situations did not occur.

Figure 12 :

Result of RPM command tested in specific load and battery SOC not considering battery SOC

Figure 13 :

Result of RPM command tested in specific load and battery SOC considering battery SOC

4. Conclusion

Through this study, our novel research made the following contributions and findings.

• 1) It was simulated through Matlab/Simulink to design a neural network controller that contributes to the improvement of system efficiency by applying an EMS that manages the battery hybrid system to the power system of a general merchant ship.
• 2) The error between the designed rule-based control value and the neural network controller output was minimized using the Bayesian regularization technique.
• 3) In the controller design, the output of the neural network controller according to the time-varying load is set by setting the optimal operation command of the power source according to the designer's intention and comparing the case where the battery SOC is considered a reserve power source with the case where the battery SOC is not considered under the same load condition.
• 4) It was confirmed that the control characteristics of the command value were stable in designed load and SOC conditions and in a specific test environment too.

However, for future study, the speed range of the engine should be considered for generator control in consideration of the lubricating and mechanical characteristics of the engine.

Acknowledgments

This work was supported by the Technology development program(P0016077) funded by the Ministry of SMEs and Startups(MSS, Korea).

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