Shipboard Integrated Power System Research Articles

Aperiodic Coordination Scheduling of Multiple PPLs in Shipboard Integrated Power Systems

Aperiodic Coordination Scheduling of Multiple PPLs in Shipboard Integrated Power Systems

Cost‐effective secondary voltage control in dc shipboard integrated power system

AbstractFast and dramatic voltage disturbances caused by the electric propulsion and dynamic positioning process are severe issues in the DC shipboard integrated power system (DC‐SIPS). The secondary voltage control (SVC) equipped in the diesel genset (DG) and hybrid energy storage system (HESS) becomes an effective solution. However, the conventional SVC cannot distinguish the different voltage regulation characteristics between DG and HESS in a cost‐effective way when allocating the voltage regulation responsibility (VRR). The cost optimization achieved by the conventional tertiary control cannot be used for the DC‐SIPS due to frequent and unpredictable load fluctuations. In this paper, a cost‐effective secondary voltage control is proposed to realize voltage restoration and cost minimization simultaneously. First, the voltage regulation cost model is developed considering the different voltage regulation characteristics of DG and HESS. Then, the optimization problem of VRR distribution is addressed by minimizing the total voltage regulation cost function using a quadratic programming algorithm. Moreover, the voltage regulation ability of each energy storage system is fully used with the state of charge balance. Thus, the conflict among voltage restoration, cost minimization, and SoC balance is effectively addressed. Finally, promising hardware‐in‐loop test results illustrate the effectiveness of the proposed method.

Optimal Charging Control of Energy Storage Systems for Pulse Power Load Using Deep Reinforcement Learning in Shipboard Integrated Power Systems

In this paper, the charging control of the energy storage system for the pulse power load accommodation in a shipboard integrated power system (SIPS) is formulated as an optimal control problem. The SIPS is an input-affine nonlinear system with randomness and fast dynamics. The improved twin-delayed deep deterministic policy gradient (TD3) algorithm -one of the deep reinforcement learning (DRL) algorithms, is proposed to solve this optimal control problem. The proposed DRL-based control solution considers the issues regarding the reward function design and input and ramp rate constraints handling for control variables. The proposed approach linked the optimal control and DRL framework. Test cases demonstrated that we could utilize DRL algorithms to control the nonlinear system with fast dynamics by following the specific reward function design, data sampling, and constraints handling techniques.

Multiobjective Energy Management of Multiple Pulsed Loads in Shipboard Integrated Power Systems

Shipboard integratedpower systems (SIPSs) usually have multifaceted operational objectives in engineering scenarios, and many key tasks are performed by multiple pulsed power loads (PPLs) with a high power density. Owing to the complexity of the ocean environment, the optimization of SIPS comprehensive performance remains challenging. This article proposes a multiobjective energy management strategy of multiple PPLs in SIPSs. A multiobjective energy coordination model is formulated. By identifying typical operation modes of the SIPS based on discrepant priority, the PPL utility and maneuverability are incorporated into the comprehensive regulation objective in the form of a weighted sum. In the case of limited energy supply, the service duration is further considered in the optimization objective to meet the high survivability requirements. Reasonable methods are adopted to quantitatively evaluate different performances. Moreover, to improve SIPS survivability in the face of emergencies, a system-level energy allocation scheme with maneuverability enhancement is formulated to coordinate the power consumption of different functions. A particle swarm optimization algorithm with directional guidance and selective evaluation of the nonconvex constraint is proposed to solve the optimization problem effectively. Case studies on a typical SIPS under various working scenarios are presented to validate the effectiveness of the proposed method.

Ship Integrated Power System reconfiguration research under partial observation

Shipboard Integrated Power System (IPS) has been considered as the trending technology for future Ships. To ensure the safe and stable operation not only in normal mode, but also when unknown faults occur or other emergency situation, this paper proposes a novel reconfiguration strategy under partial observation. Hybrid automata is used to descript the hybrid characteristics of SPS, then the fault-tolerant control and recoverability analysis can be performed. After that, state estimate under partial observation during fault operation is derived, as well as the reconfiguration strategy. To evaluate the performance of the reconfiguration strategy, case study is presented using a four-zone SPS faced with parallel faults, and considering the exist of unobservable states and uncontrollable events. Simulation results indicate that the proposed reconfiguration strategy is capable of determining the optimal reconfiguration strategy under partial observation with multiple faults occur.

Deep Reinforcement Learning Control for Pulsed Power Load Online Deployment in DC Shipboard Integrated Power System

Online deployment of pulsed power load (PPL) is one of the most challenging issues in DC shipboard integrated power systems (SIPSs), which leads to a multi-objective optimal control problem subject to various constraints in this paper. Since traditional model-based methods face difficulties in designing the optimal control policy and are prone to model inaccuracy and parameter uncertainty, there is an urgent need for a model-free and also high-performance control approach. Thus, a deep reinforcement learning (DRL) optimal control, which employs the twin-delayed deep deterministic policy gradient (TD3) algorithm, is presented in this paper. The DRL optimal control adopts a stack-based state observation technique to enhance learning and control performance, and it uses a multi-objective reward function design to signify the overall dynamic performance. Besides achieving the safe and fast online deployment of PPL, it also fulfills the regulation of DC bus voltage and the proportional current sharing among distributed generations (DGs). Moreover, the DRL control has an advantage in handling the ramp rate constraints of SIPS. The optimal control satisfying ramp rate constraints can be obtained through a deep learning process. The performance of the proposed DRL control is validated by case studies considering different load conditions.

Performance Comparison of Three Types of SFCL in Shipboard MVDC IPS

As medium voltage dc-based (MVDC) technology is widely considered in the shipboard integrated power system (IPS), the short fault protection of shipboard IPS is urgent. This paper suggests resistive type superconducting fault current limiter (R-SFCL), flux-coupling type SFCL (FC-SFCL), and a hybrid type SFCL (H-SFCL) alleviate fault current and improve the transient behaviors of shipboard MVDC IPS. All of these SFCLs are installed at the DC line. When pole to pole short fault occurs, resistive and inductive components play roles in current limiting. To evaluate these SFCLs and choose one for the system, theoretical analysis, simulation verification and experiments are done. According to the current limiting and economic cost index, H-SFCL is preferable in these SFCLs.

Adaptive Online Sequential Extreme Learning Machine with Kernels for Online Ship Power Prediction

With the in-depth penetration of renewable energy in the shipboard power system, the uncertainty of its output power and the variability of sea conditions have brought severe challenges to the control of shipboard integrated power system. In order to provide additional accurate signals to the power control system to eliminate the influence of uncertain factors, this study proposed an adaptive kernel based online sequential extreme learning machine to accurately predict shipboard electric power fluctuation online. Three adaptive factors are introduced, which control the kernel function scale adaptively to ensure the accuracy and speed of the algorithm. The electric power fluctuation data of real-ship under two different sea conditions are used to verify the effectiveness of the algorithm. The simulation results clearly demonstrate that in the case of ship power fluctuation prediction, the proposed method can not only meet the rapidity demand of real-time control system, but also provide accurate prediction results.

Shipboard Power Management for Failure Mode Using the Hybrid MPC Approach

Shipboard integrated power systems, the key technology of ship electrification, call for effective failure mode power management control strategy to achieve the safe and reliable operation in dynamic reconfiguration. Considering switch reconfiguration with system dynamics and power balance restoration after reconfiguration, in this paper, the optimization objective function of optimal management for ship failure mode is established as a hybrid model predictive control problem from the perspective of hybrid system. To meet the needs for fast computation, a hierarchical hybrid model predictive control algorithm is proposed, which divides the original optimization problem into two stages, and reduces the computation complexity by relaxing constraints and the minimum principle. By applying to a real-time simulator in two scenarios, the results verify the effectivity of the proposed method.

Decentralised power distribution and SOC management algorithm for the hybrid energy storage of shipboard integrated power system

In the medium voltage direct current (MVDC) shipboard grid, the inherent inertial support from the DC capacitors is too small to resist step changes or fluctuations from the high power pulse load and propulsion load, which results in lower DC voltage quality. This study proposes a decentralised control algorithm for the MVDC shipboard hybrid energy storage system (HESS) to enhance the onboard survivability. This algorithm adjusts the droop control coefficient based on the bus voltage change rate adaptively, meanwhile the voltage differential signal processing and filtering are implemented by the trace differentiator. In addition, the proposed algorithm also has state-of-charge (SOC) balancing and SOC recovery abilities between multiple groups of energy storage devices. The parameter selection principle are analysed, and a variety of working conditions are simulated and verified in PSCAD. Theoretical analysis and simulation show that, HESS can achieve good power distribution and SOC management performance without communication compared to fixed droop coefficient control strategies, and the dynamic characteristics of bus voltage have been significantly improved.

Cyber-Physical Co-Simulation of Shipboard Integrated Power System Based on Optimized Event-Driven Synchronization

As the energy management system (EMS) participates in the closed-loop control of shipboard integrated power system (IPS), the information network of EMS is closely coupled with the power system and its characteristics affect power system performance significantly. To study the close-coupling relationship between the two systems, a cyber–physical co-simulation platform based on the high level architecture (HLA) framework is constructed in this paper. The proposed platform uses PSCAD and OPNET to simulate shipboard power system and information network respectively, and utilizes OPNET HLA nodes and PSCAD user-defined modules to implement co-simulation interfaces. In order to achieve a higher co-simulation precision without impairing efficiency, an optimized event-driven co-simulation synchronization method is also proposed. By pre-defining power system synchronization points and detecting information network synchronization points in the co-simulation process, both systems can be synchronized in time and the synchronization error is eliminated. Furthermore, the co-simulation efficiency is also improved by optimizing the data transmission in the synchronization process. A co-simulation model of shipboard power distribution network protection based on CAN bus communication is built and analyzed. Simulation results show that the proposed co-simulation platform and synchronization method are feasible and effective.

Real-Time Model Predictive Control for Shipboard Power Management Using the IPA-SQP Approach

Shipboard integrated power systems, the key enablers of ship electrification, call for effective power management control (PMC) to achieve optimal and reliable operation in dynamic environments under hardware limitations and operational constraints. The design of PMC can be treated naturally in a model predictive control (MPC) framework, where a cost function is minimized over a prediction horizon subject to constraints. The real-time implementation of MPC-based PMC, however, is challenging due to computational complexity of the numerical optimization. In this paper, an MPC-based PMC for a shipboard power system is developed and its real-time implementation is investigated. To meet the requirements for real-time computation, an integrated perturbation analysis and sequential quadratic programming (IPA-SQP) algorithm is applied to solve a constrained MPC optimization problem. Several operational scenarios are considered to evaluate the performance of the proposed PMC solution. Simulations and experiments show that real-time optimization, constraint enforcement, and fast load following can be achieved with the IPA-SQP algorithm. Different performance attributes and their tradeoffs can be coordinated through proper tuning of the design parameters.

The Shipboard Integrated Power System [About This Issue

Presents an editorial discusses the topics of interest for this issue of the pulbication, focusing on shipboard integrated power systems.

Sensitivity Analysis of the Shipboard Integrated Power System

AbstractStochastic sensitivity analysis is a valuable tool in ranking inputs and in investigating the degree of interaction of its components. In this paper, we present stochastic simulation results for a shipboard integrated power system and study its sensitivity. Specifically, we apply sensitivity analysis to two high‐fidelity models of shipboard subsystems, investigating open‐ and closed‐loop control of the propulsion system. The results show that different inputs are most important for the open‐ and closed‐loop control, with sensitivities that change dramatically in time as they reflect the transition from the fast electrical scales to slower mechanical scales. We also demonstrate how sensitivity analysis can be used to establish the robustness of the AC drive.