Weihao Hu

• Aalborg University

Poor voltage regulation capacity is a main limitation of resonant switched capacitor (ReSC) converter. To achieve voltage regulation, a universal segmented hysteresis control (SHC) strategy for ReSC converters with multiple voltage conversion ratios is proposed in this paper. Based on the inherent discrete voltage gain characteristics of this type...

This letter develops a novel multi-agent deep reinforcement learning (MADRL)-based local control method that can achieve coordinated scheduling of large-scale PV inverters using local information. This is achieved by the development of a system state inference-aided actor structure for each agent and implementation of random sequential updating wit...

The utilization of repurposed second-life batteries from electric vehicles in DC microgrids presents a sustainable and cost-effective solution. However, efficiently integrating these batteries within DC microgrids poses two main challenges: (1) poor consistency among retired battery packs; (2) fluctuations in the DC bus voltage. Traditional approac...

Frequent and sizable voltage fluctuation, a common issue faced by the modern distribution system (DS), could lead to potential equipment failures and power interruption. This brings huge negative impact on the power supply reliability of the DS. Existing voltage regulation methods typically rely on the precise physical parameters, complete measurem...

To overcome the limitation of voltage regulation in resonant switched capacitor topology and introduce the soft switching characteristics into AC/DC converter, a dual-resonant step-up AC/DC converter based on fixed frequency pulse overlapping modulation is presented in this paper. The voltage regulation is achieved by regulating the overlapping tim...

The variability of renewable energy within microgrids (MGs) necessitates the smoothing of power fluctuations through the effective scheduling of internal power equipment. Otherwise, significant power variations on the tie‐line connecting the MG to the main power grid could occur. This study introduces an innovative scheduling strategy that utilizes...

Controlling a 3L-NPC inverter-fed PMSM drive is complicated because of the deviation of the neutral point voltage. That the DC-link voltage is not balanced on its upper and lower capacitors tends to result in the inaccuracy in the synthesis of the expected space voltage vectors. A hybrid SVPWM and MPTC method is proposed for this problem. Based on...

This article proposes a robust multiarea distribution system state estimation method for interval estimation of state variables based on a physics-informed decentralized graphical representation network and Gaussian process (GP)-aided multiarea state estimators. The real-time and pseudomeasurements are first cast to a graph with tree topology and a...

This paper develops a robust physics-informed state estimation method for the distribution network with inaccurate topology information. An aggregated k-nearest neighbor graph is first derived as the feature graph according to the inaccurate topology and measurement features. Then, graph propagation and aggregation are performed by an adaptive mult...

The anomalous measurements pose significant challenges for the secure and economical operation of multiple microgrids (MMGs). However, existing works still cannot effectively address this problem. Therefore, this paper proposes a robust decentralized multi-agent deep reinforcement learning (MADRL) control approach by developing a novel actor networ...

Precise multienergy load forecasting (MELF) significantly contributes to the stable and economic operation of integrated energy systems (IES). However, existing MELF approaches exhibit three primary limitations: (i) naively aggregate all input features without explicit mechanisms to capture complex coupling relationships between multiple energy loa...

The forecasting of of pseudo-measurements play an important role in distribution system state estimation (DSSE). This paper proposes robust DSSE method based on forecasting-aided graphical learning method. The nodal power consumption models are first built to produce pseudo-measurements based on deep neural network. Then, the pseudo-measurements an...

A novel fault location algorithm for three-terminal line (TTTL) via two-terminal phasor measurement units (PMUs) is presented in this paper. A proposed angular compensation factor (ACF) is imposed into the fault loop equations to compensate for the angular displacement between the tie-point and the fault path currents. To predict the ACF, a trained...

With the development of advanced metering infrastructure (AMI), large amounts of electricity consumption data can be collected for electricity theft detection. However, the imbalance of electricity consumption data is violent, which makes the training of detection model challenging. In this case, this paper proposes an electricity theft detection m...

Dynamic performance is a critical index for three-phase dual active bridge (3ph-DAB) converter. However, the presence of dc bias in inductor current poses significant effects, such as overcurrent stress and increased power loss for 3ph-DAB converter. Additionally, the intricate structure and three control variables in variable duty cycles (VDCs) mo...

Power forecasting of newly built photovoltaic (PV) sites faces huge challenges owing to the lack of sufficient training samples. To this end, this paper proposes an unsupervised zero-label learning method for power generation forecasting of newly built PV sites without relying on any historical power output data. The main idea is to extract invaria...

The distributed nature of power electronic components parameters can affect the desired output voltage of the CLLC-type dual active bridge (DAB) converters, especially in mass production with limited budgets. To minimize inconsistency for CLLC-type DAB converters against manufacturing tolerance in large-scale applications, this article proposes a n...

An energy hub (EH) can abstract a multi-energy system into multiple input-output ports and is often employed to characterize the coupling relationship between different energy sources. However, the existing studies mainly focus on the modeling and capacity optimization of the internal components of the EH, and lack of research on the energy schedul...

The control of heat–electricity-integrated multiple microgrid (MMG) systems is greatly challenged by anomalous measurements and inaccurate physical electricity and heat network models. Through the systematic integration of graph surrogate models, trajectory history information, and confederate image (CI) technology based distributed multiagent deep...

The output waveform quality of modular multilevel converters (MMCs) may not be good enough when there are only a few sub-modules (SMs). This paper proposes a technique to increase the quantity of equivalent output voltage levels of MMC by changing the capacitor voltage of certain sub-modules. This method significantly improves the performance of th...

The training process of learning-based distribution system state estimation (DSSE) methods relies on accurate state variables, which typically contain unknown noise and outliers in practice. To this end, this paper proposes an adaptive noise-resistant graphical learning-based DSSE method considering the impact of inaccurate state variables. Specifi...

The design and operation of hybrid microgrids (MGs) has attracted much interest. The creation of adaptable standalone hybrid systems that can satisfy interconnected clients’ energy needs using coupled green hydrogen-ammonia has been studied less. Additionally, the capability of scaling up hydrogen generation and fuel decarbonization is little studi...

Finite control set model predictive control has poor steady-state performance due to limited voltage vectors. To improve the steady-state performance, multiple vectors are used to synthesize more accurate voltage vectors, but the selection of optimal vectors is always complex. In this paper, a two-phase model predictive direct duty-cycle control ba...

For improving the voltage regulation capacity of resonant switched capacitor converters and satisfying the requirement for high power conversion efficiency (PCE) and power density of emerging power electronic systems, a multi-ratio-multi-resonance switched capacitor (MR2-SC) converter is proposed in this paper. The studied topology can be divided i...

This letter presents a meta-learning based voltage control strategy for renewable energy integrated active distribution network. The multiple interference self-supervised method is first applied to extract features from unlabeled data. Then, an efficient channel attention convolutional neural network is adopted to select targeted information that i...

This letter presents a novel data-driven model estimation method for renewable energy source (RES) integrated system with random time delay. The proposed method exploits the theoretical properties of stochastic systems to achieve real-time estimation of the state matrix and the input matrix of a closed-loop system, and does not require any system m...

This paper proposes a robust and computationally efficient control method for damping ultra-low frequency oscillations (ULFOs) in hydropower-dominated systems. Unlike the existing robust optimization based control formulation that can only deal with a limited number of operating conditions, the proposed method reformulates the control problem into...

Electric power transmission by Tunnel transmission technology resolve limitations of overhead transmission lines restricted by complex terrain, geological conditions, and environmental protection. Tunnel transmission method involves a sophisticated line-laying procedure within a smaller measurement space. However, it remains a challenging task to m...

DC-DC converters are key topologies in many industries such as renewable energy, transportations, utility, and perform an increasingly important role in the power electronics. The research of the modulation strategies with efficiency improvement is therefore critical for the DC-DC converters. As one of the most classic DC-DC converters, the dual ac...

This paper proposes a novel multi-agent deep reinforcement learning (MADRL) approach for the energy management of multiple microgrids considering the robust voltage control under the missing measurements. Missing measurement control poses challenges to the MADRL. To address the problem, we propose a trajectory history information-utilized opponent...

The multi-directional flow of energy in a multi-microgrid (MMG) system and different dispatching needs of multiple energy sources in time and location hinder the optimal operation coordination between microgrids. We propose an approach to centrally train all the agents to achieve coordinated control through an individual attention mechanism with a...

The reactive power capacity of photovoltaic inverters can be utilised to minimise power loss and mitigate rapid voltage fluctuations in an active distribution network. This paper proposes a coordinated volt/VAR control framework that simultaneously optimises the base reactive power output of photovoltaic inverters and the voltage intercept of each...

Power-to-ammonia (P2A) technology has attracted more and more attention since ammonia is recognized as a natural zero-carbon fuel. In this context, this paper constructs a renewable energy powered multi-energy hub (MEH) system which integrates with a thermo-electrochemical effect based P2A facility. Subsequently, the energy management of proposed M...

The accurate estimation of lithium battery state of health (SOH) is very important for the safe and stable operation of the battery. Since the user’s charging process is random, it is difficult for the user to know the battery SOH through the charging segment. In this article, we proposed a lithium battery SOH estimation method of random charging p...

A probabilistic load forecasting method that can deal with sudden load pattern changes caused by abnormal events such as COVID-19 is proposed in this paper. The deep residual network (ResNet) is first applied to extract the load pattern for the normal period from historical data. When an abnormal event occurs, a Gaussian Process (GP) with a composi...