This article proposes a multiagent (MA) deep reinforcement learning (DRL) based autonomous input voltage sharing (IVS) control and triple phase shift modulation method for input-series output-parallel (ISOP) dual active bridge (DAB) converters to solve the three challenges: the uncertainties of the dc microgrid, the power balance problem, and the current stress minimization of the converter. Specifically, the control and modulation problem of the ISOP-DAB converter is formed as a Markov game with several DRL agents. Subsequently, the MA twin-delayed deep deterministic policy gradient (MA-TD3) algorithm is applied to train the DRL agents in an offline manner. After the training process, the multiple agents can provide online control decisions for the ISOP-DAB converter to balance the IVS, and minimize the current stress among different submodules. Without accurate model information, the proposed method can adaptively obtain the optimal modulation variable combinations in a stochastic and uncertain environment. Simulation and experimental results verify the effectiveness of the proposed MA-TD3-based algorithm.

The dual active bridge (DAB) converter has become a popular isolated solution to integrate energy storage systems (ESSs) and dc microgrids (MGs). However, constant power loads (CPLs) and pulsed power loads (PPLs) may reduce system damping and cause voltage oscillations in DAB converter-based ESSs (DAB-ESSs). An artificial neural network-based active disturbance rejection control (ANN-ADRC) is proposed to regulate constant output voltage quickly and accurately under different operating conditions. Firstly, the ADRC controller is designed based on the small-signal modeling of the DAB-ESSs. Feedforward compensation and uncertainty estimations of the extended state observer (ESO) help to improve the dynamic performance and to reduce the number of current sensors. Then, after satisfying the conditions of stability analysis, the parameters of the ADRC controller are selected automatically via ANN. The ANN is trained with two inputs (ADRC controller parameters) and two outputs (performance indicators of the ADRC controller). The well-trained ANN can be used as a surrogate model to obtain the optimal solution of the objective function easily and quickly. The proposed ANN-ADRC algorithm with selected parameters is implemented and validated on the hardware experimental setup. The experimental results illustrate that the proposed controller can achieve fast dynamic performance under various operating conditions.

This letter proposes a multiagent soft actor-critic (MASAC) enabled multiagent deep reinforcement learning (MADRL) algorithm for output current sharing of the input-series output-parallel dual active bridge converter. The modular converter is partitioned into different submodules, which are treated as DRL agents of Markov games. Furthermore, all agents are executed decentralized to provide online control decisions after collaborative training. The proposed MASAC algorithm verified in a 150 V/50 V hardware prototype shows optimal dynamic performance.

For the performance modeling of power converters, the mainstream approaches are essentially knowledge-based, suffering from heavy manpower burden and low modeling accuracy. Recent emerging data-driven techniques greatly relieve human reliance by automatic modeling from simulation data. However, model discrepancy may occur due to unmodeled parasitics, deficient thermal and magnetic models, unpredictable ambient conditions, etc. These inaccurate data-driven models based on pure simulation cannot represent the practical performance in physical world, hindering their applications in power converter modeling. To alleviate model discrepancy and improve accuracy in practice, this article proposes a novel data-driven modeling with experimental augmentation (D ² EA), leveraging both simulation data and experimental data. In D ² EA, simulation data aims to establish basic functional landscape, and experimental data focuses on matching actual performance in real world. The D ² EA approach is instantiated for the efficiency optimization of a hybrid modulation for neutral-point-clamped dual-active-bridge (NPC-DAB) converter. The proposed D ² EA approach realizes 99.92% efficiency modeling accuracy, and its feasibility is comprehensively validated in 2-kW hardware experiments, where the peak efficiency of 98.45% is attained. Overall, D ² EA is data-light and can achieve highly accurate and highly practical data-driven models in one shot, and it is scalable to other applications, effortlessly.

The neutral-point-clamped (NPC) dual-active-bridge (DAB) converter with hybrid semiconductors (Si and SiC devices) is a promising topology for high-power energy storage systems. The triple-phase-shift (TPS) modulation can achieve high efficiency with zero-voltage switching (ZVS) by adjusting three phase shift angles. However, when applied to hybrid DAB topologies, switching and conduction losses are too high. In this paper, a hybrid modulation method using duty ratio and phase-shift is proposed to improve the efficiency of the hybrid NPC DAB converter. The Si and SiC devices on the primary NPC bridges work under different duty ratios with staggered switching instants so that the Si devices do not suffer from the high turn-off current, and the SiC devices benefit from less conduction time. The neutral current path is used to take over the negative current which flows through SiC body diodes under TPS. Switching and conduction losses under the proposed hybrid TPS modulation are compared. A 2-kW hardware prototype is built to verify the proposed hybrid modulation.

In the pursuit of instigating a progressive transition towards a more sustainable future, policy officials all over the world are fervently advocating the use of energy conservation techniques targeted at residential customers. Keeping this in mind, a quantitative study was conducted in this work using the data from Singapore, which aims to investigate the relationships between a resident’s pattern of energy utilisation and numerous demographic parameters as well as personality attributes. Moreover, the study was conducted with existing machine learning and data analytics approaches, including k-prototype unsupervised learning and statistical hypothesis tests. The obtained results denote a persuasive correlation between the consumption behaviour of the consumer for different appliances and factors such as income, energy knowledge, usage frequency, personality, etc. For instance, there is a higher probability of a consumer acting frugally and sparingly if they believe their energy consumption is insignificant. These findings can help policymakers identify the appropriate target populations for raising energy awareness in Singapore.