The hybrid neutral-point-clamped (NPC) dual-active-bridge (DAB) topology aims to mitigate the excessive voltage and current stresses experienced by semiconductor devices in high-power applications. The hybrid duty ratio phase-shift modulation (PSM) achieves improved efficiency performance. However, previous studies have not extensively explored the intricacies of the switching characteristics. In this paper, a comprehensive analysis of the switching behavior is given, particularly focusing on the turn-off loss of Si IGBTs, based on relevant formulas and switching waveforms. To enhance clarity, a comparative analysis is conducted, contrasting the obtained results with those obtained from the triple-phase-shift (TPS) modulation method. Simulations and experiments are conducted for validation of the theoretical analysis.

This paper introduces a novel physics-informed pattern recognition (PIPR) method for open-circuit fault detection in inverters. The proposed method unfolds in three stages: model analysis, offline training, and online validation. In the first stage, we construct an analytical model of power converters. This model is subsequently used to derive fault diagnosis variables. This step is followed by the collection of training samples via simulations. The gathered samples are then fed into pattern recognition neural networks, a process enabled by the prior extraction of model information. This architecture allows for efficient training of the neural network with fewer neurons and samples. The final stage involves the detection and diagnosis of faults by a well-trained online classifier. The robustness of the proposed PIPR method in dealing with unexpected conditions in classification problems shows its potential across diverse conditions.

This paper presents an agent-based power scheduling framework for interconnected local energy communities (LECs) using the Nash bargaining solution (NBS) approach to offer a fair and financial reimbursement for changing the operation objectives of such LECs. LECs are a modern form of microgrids (MGs) which consist not only of electrical energy systems but also include thermal energy. The focus is set on the LEC operator needs with different operational objectives, where in classical MGs the island aspect often dominates the operation. We assume that in the future distribution system several LECs will exist and are, therefore, interconnected with each other. It can be assumed that the operational objectives of an LEC and the distribution system operator (DSO) may differ considerably. To align these objectives and achieve system-wide cost-optimal operation, the DSO incentivizes the LECs to deviate from their previously planned power schedules by offering a financial reimbursement that is determined using the NBS mechanism. The local and global cost saving for the proposed agent-based power scheduling framework is demonstrated on a use case scenario modeled after a real pilot microgrid.

This paper presents a Port-Hamiltonian based modelling and control approach for MVDC Shipboard Power Systems (SPSs). The Line Regulating Converters (LRCs) consist of parallel-connected submodules based on the single-phase Dual Active Bridge (DAB) topology. The load side converters (Point of Load (POL) converters) are tightly regulated buck converters, which exhibit Constant Power Load (CPL) behavior with destabilizing effect on the MVDC bus voltage. We apply here an Interconnection and Damping Assignment Passivity Based Control (IDA-PBC) strategy to the MVDC SPS and evaluate its performance by simulation in MATLAB Simulink. The analyzed scenarios consider large disturbances such as load step up, submodule disconnection and LRC module disconnection. Furthermore, we prove that the entire MVDC is Port-Hamiltonian and therefore the system stability is guaranteed by the IDA-PBC.

This paper investigates the applicability of a Data-Driven Control (DDC) approach for power electronic converters in DC microgrids to stabilize the bus voltage under large perturbations from connection/disconnection of converters. In DC microgrids, loads and generators are interfaced through power electronic converters. When tightly regulated, these loads exhibit a constant power load (CPL) behavior. CPLs have negative incremental impedance behavior which may lead to system instability. The DDC approach is applied to an MVDC microgrid and compared with a model-based non-linear controller; specifically, the Linearizing State Feedback (LSF), that was previously investigated by the authors and is used here as benchmark. Both control techniques act on the interface converters at the generator side. The results of the simulation cases show that the DDC approach has a good performance for bus voltage regulation under system reconfigurations. As opposed to the LSF controller, the DDC does not require accurate analytical models, or exchange of information among converters.

The growing share of Distributed Generators (DG) brings with it a fundamental change in dispatchability and location of the overall power generation portfolio. A significant share of DGs are renewable-based and connected to the distribution system, replacing the conventional generators placed at high voltage levels. With this fundamental shift in the power system, more active approaches to distribution system operation are required. In this paper the authors propose a model to utilize distributed flexibilities to locally balance power generation and demand, minimizing the power exchange between low and medium voltage levels. The proposed model is applied to several low voltage networks in the German distribution system. Simulation results show that the proposed model decreases the peak load in tested LV networks by 2.6-12.5 %, and leads to an overall flattening of the load profile.

Distribution system operators in rural areas of Germany are frequently facing imminent equipment overloading caused by the feed-in of local renewable Distributed Generation (DG). A grid operator’s last resort to maintain system stability and avoid protection tripping is to temporarily curtail local feed-in until hosting capacity in the network has caught up with the demand. Due to technological limitations in today’s networks the volume of curtailed energy can be greater than what would strictly be necessary. This paper presents a case study of a 110 kV overhead line in Avacon’s network and demonstrates the limitations of today’s approach to DG curtailments, especially the relative coarse granularity of control steps. The authors develop a novel control algorithm for emergency curtailments that takes advantage of technological improvements and describes the architecture for a successful deployment at the example of Avacon’s network and SCADA. The authors compare the amount of curtailed energy under today’s best practice with the theoretical optimum and the novel approach.

In the European demonstration Project InterFLEX, five different demonstration sites are realized with a focus on flexibility services from energy generation and demand. The use of flexibility for grid and generation-supply balance management is a key factor in renewable-based electricity systems. However, the way such flexibility services are activated from an Information and Communication Technology (ICT) perspective requires harmonization across Europe. As a step on this way, this paper proposes a methodology based on the Smart Grid Architecture Model (SGAM) to study cross-demonstrator ICT interoperability and show its application in the InterFLEX context. This is a multi-player environment with Distribution System Operators (DSOs), aggregators, Electric Vehicle (EV) charging operators and other actors, providing a representative overview of contemporary flexibility use and implementation options. In particular, a SGAM clustering approach is set up in order to group devices and actors within common entities. The results of this analysis show that the use-cases and interfaces are comparable between the demos, however the chosen solutions and protocols are manifold and very demonstrator-specific. Future candidates for future selection of interfaces and protocols from an InterFLEX point of view are discussed.

In this paper we propose a sequence-to-sequence machine learning architecture for time-series forecasting based on recurrent neural networks. This architecture can be used as a general purpose forecasting method and is evaluated for the application of short-term electric load forecasting in this paper. The proposed sequence-to-sequence architecture1combines elements of auto-regressive forecasting techniques with multivariate regression by including exogenous variables for each forecasted time step as well as previous values when inferring forecasts. We assess the proposed architecture on a load data set provided by the Global Energy Forecasting Competition. The conclusion is that it outperforms other machine learning forecasting techniques as well as time-series analysis methods.1The implementation is available at: https://github.com/HenWil13/ieeeINDIN18

This paper presents a case study of different control approaches for a single phase Dual Active Bridge (DAB) based MVDC Shipboard power system (SPS). The Line Regulating Converters (LRCs) are based on DAB topology and the load side converters or Point of Load Converters (POLs) are based on buck topology. The tightly controlled POLs exhibit a constant power load (CPL) behaviour, which has a destabilising effect on the bus voltage. This paper also considers and investigates the impact of measurement uncertainties on stability and bus voltage quality. In this work, the linear control strategies LQI, H2, H∞and MPC are designed and implemented for the DAB converter. Large signal test cases such as load side, source side connections or disconnections are performed and the performance of the proposed robust control strategies are compared.

This paper proposes the application of advanced modelling and control strategies for a single phase Dual Active Bridge (DAB) based Medium Voltage DC (MVDC) Shipboard Power System (SPS). In these systems the tightly controlled point of load (POL) converters exhibit a constant power load (CPL) behaviour, which has a destabilising effect on the bus voltage. In this work, a Multiple Input Single Output (MISO) state space plant model based on switching function for the triple phase shift (TPS) modulated DAB converter is derived. For obtaining the MISO model the generalised state space averaging (GSSA) technique was used. A H∞mixed sensitivity controller is designed to tackle the negative impedance characteristics of tightly regulated Point of Load (POL) converters, presence of measurement uncertainty and high current stress. This paper also presents a detailed description of the design of weighting functions required for the mixed sensitivity formalism. The effectiveness of the proposed modelling scheme and control strategy is studied through system level simulations. A comparative study with the previously proposed TPS control strategy is performed.

The concept of an all-electric ship, while offering unprecedented advantages from the point of view of efficiency and flexibility of operation, has introduced new challenges in terms of stability and power flow control. The advent of a full power electronics power system has raised new questions from the point of view of system dynamics, particularly when dealing with the new medium-voltage direct current distribution. The overall goal of guaranteeing a secure operation of the power system has brought researchers to consider two main approaches: reducing the dynamics of the large load to operate in a range of dynamics compatible with traditional generation systems, or making the generator set smarter through its power electronics interface. This paper compares these approaches to stable operation, focusing on the latter considered more in line with the progress of technology and in general more appealing.

We present an approach to viewpoint invariant hand detection which merges model based representation of shape and exact curve matching with graph search in order to achieve a very low false alarm rate system able to work in real time. The method proposed makes few assumptions on the articulated object nature and can be applied to recognize other articulated objects as well.