Abstract:Supercapacitors (SCs) are widely recognized as excellent clean energy storage devices. Accurate state of health (SOH) estimation and remaining useful life (RUL) prediction are essential for ensuring their safe and reliable operation. This paper introduces a novel method for SOH estimation and RUL prediction, based on a hybrid neural network optimized by an improved honey badger algorithm (HBA). The method combines the advantages of convolutional neural network (CNN) and bidirectional long-short-term memory (BiLSTM) neural network. The HBA optimizes the hyperparameters of the hybrid neural network. The CNN automatically extracts deep features from time series data and reduces dimensionality, which are then used as input for the BiLSTM. Additionally, recurrent dropout is introduced in the recurrent layer to reduce overfitting and facilitate the learning process. This approach not only improves the accuracy of estimates and forecasts but also significantly reduces data processing time. SCs under different working conditions are used to validate the proposed method. The results show that the proposed hybrid model effectively extracts features, enriches local details, and enhances global perception capabilities. The proposed hybrid model outperforms single models, reducing the root mean square error to below 1%, and offers higher prediction accuracy and robustness compared to other methods.

Abstract:With significant expansion in wind farm capacity, wake disturbances from upstream wind turbines have emerged as a detrimental factor, adversely affecting the generated power of downstream units. However, the conventional power prediction models usually neglect the wake effect between adjacent wind turbines. To bridge this gap, this paper proposes a novel power prediction model that considers the wake effect and its boundary layer compensation, to enable joint spatial and temporal wind power prediction for wind farms. Firstly, a two-dimensional convolutional neural network is adopted to extract the key features and reconstruct wind power prediction data. Secondly, utilizing historical data, a long short-term memory algorithm is employed to investigate the correlation between elemental characteristics and wind data. Subsequently, a 3D-Gaussian Frandsen wake model that accounts for the wake effect and boundary layer compensation in wind farms is developed to precisely cal-culate the spatial wind speed distributions. Consequently, these distributions allow the power outputs of wind tur-bines in wind farms to be estimated more accurately via the rotor equivalent wind speed. Finally, several case studies are conducted to validate the effectiveness of the proposed method. The results demonstrate that the suggested approach yields favorable outcomes in predicting both wind speed and wind power.

Abstract:Large-power industrial power supply system is generally recognized as high energy-consuming, while the rectifier transformer is the key for efficiency promotion. This article proposes a novel transformer winding design strategy with the aims of harmonic elimination and loss reduction. By means of the innovative impedance matching design, the harmonic magnetic potential self-balance between the secondary windings is realized for harmonic suppression. Considering the impedance constraint and the margin of structural parameters, the method of resistance loss reduction is explored for the proposed transformer by optimizing the dimension pa-rameters. The transformer-filter combination is first modeled to reveal the filtering principle and obtain the impedance precondition. Then, the transformer efficiency is optimized within the constraints. At last, the trans-former prototype together with the power supply system is tested. The experimental results show that the trans-former efficiency can be improved by 0.81 %, and the current total harmonic distortion at primary side is dropped by 50%.

Abstract:Uncertainties on both the source and de-mand side pose challenges for the day-to-day management of distributed power systems, such as microgrids (MGs). To harness the potential for flexible scheduling within MGs and to explore the economic and environmental advantages of cooperative interactions between multiple MGs, this paper develops a two-stage power-sharing model for day-ahead and intraday operations across multi-MGs. The intraday stage, which takes into account the prediction errors caused by uncertainty, refines the day-ahead results by factoring in real-time fluctuations in renewable energy sources and loads, changes in equip-ment status, and the scheduling of various demand re-sponse resources. At the end of the intraday optimal scheduling process, the profits from MGs are allocated according to a generalized Nash equilibrium. A case study involving three MGs within a distribution network, ex-amining both island and cooperative operation strategies, demonstrates the practicality and effectiveness of the proposed approach.

Abstract:This paper introduces a tri-state modulation technique for a soft-switching bidirectional DC-DC con-verter (BDC). This method maintains the soft-switching condition and introduces a freewheeling interval that reduces the rise and fall times of the inductor current, effectively suppressing inductor current ripples. Additionally, the tri-state modulation provides an extra degree of freedom, enabling optimization for reduced operating losses. The paper details the operation principles of tri-state modulation in both buck and boost modes and discusses optimization strategies for minimizing losses. An experimental setup is developed to validate the tri-state modulation approach, where switching waveforms and efficiency are measured. The experimental results con-firm that the proposed method achieves soft-switching conditions, suppresses inductor current ripples, and provides higher efficiency compared to conventional hard-switching BDC and typical soft-switching BDC.

Abstract:Multienergy loads in integrated energy systems (IESs) exhibit strong volatility and randomness, and existing multitask sharing methods often encounter negative migration and seesaw problems when addressing complexity and competition among loads. In line with these considerations, a short-term multienergy load joint prediction method based on seasonal-trend decomposition using LOESS (STL) and convolutional progressive layered extraction (CPLE) is proposed, called STL-CPLE. First, STL is applied to model regular and uncertain load information into interpretable trend, seasonal, and residual components. Then, joint modeling is performed for the same type of components of multienergy loads. A one-dimensional convolutional neural network (1DCNN) is constructed to extract deeper feature information. This approach works in concert with the progressive layered extraction sharing method, and convolutional shared and task-specific experts are developed to acquire common and distinctive representations of multienergy loads, respectively. Task-specific parameters are gradually separated through progressive routing. Finally, a subtask network is built to learn temporal dependencies using long short-term memory (LSTM). Simulation validation is performed on the IES dataset at the Tempe campus of Arizona State University, and the experiments show that the STL-CPLE method exhibits higher prediction accuracy than do the other methods.

Abstract:Shore-to-ship power (SSP) technology is an effective way for developing sustainable maritime transportation systems. Its implementation requires attractive pricing and incentive policies. This paper proposes a time-of-use (TOU)-based pricing strategy for SSP services considering the ship behaviors. A hierarchical pricing framework is first proposed to characterize the interactions among government regulators (GR), seaport authorities (SA) and ships, which is formulated as a tri-level two-loop Stackelberg game model. On the ship-side, each ship is treated as an independent stakeholder and the queuing process caused by limited number of berths is considered. Correspondingly, a coupled voyaging-berthing-queuing (CVBQ) model is established for each individual ship to accurately formulate their voyage scheduling, berth selection, queuing behaviors and power dispatch. The optimal CVBQ decision of each ship depends on its queuing time, which cannot be known before all ships make their decisions. To this end, a first-come-first-serve (FCFS)-based ship queuing algorithm is developed to chronologically derive the optimal decisions of all ships. The proposed hierarchical pricing model is solved iteratively by combining heuristics and commercial solvers. Case studies demonstrate the effectiveness of the proposed method.

Abstract:Traditional transient angle stability analysis methods do not fully consider the spatial characteristics of the network topology and the temporal characteristics of the time-series disturbance. Hence, a data-driven method is proposed in this study, combining graph convolution network and long short-term memory network (GCN-LSTM) to analyze the transient power angle stability by exploring the spatiotemporal disturbance characteristics of future power systems with high penetration of renewable energy sources (wind and solar energy) and power electronics. The key time-series electrical state quantities are considered as the initial input feature quantities and normalized using the Z-score, whereas the network adjacency matrix is constructed according to the system network topology. The normalized feature quantities and network adjacency matrix were used as the inputs of the GCN to obtain the spatial features, reflecting changes in the network topology. Subsequently, the spatial features are inputted into the LSTM network to obtain the temporal features, reflecting dynamic changes in the transient power angle of the generators. Finally, the spatiotemporal features are fused through a fully connected network to analyze the transient power angle stability of future power systems, and the softmax activation cross-entropy loss functions are used to predict the stability of the samples. The proposed transient power angle stability assessment method is tested on a 500 kV AC-DC practical power system, and the simulation results show that the proposed method could effectively mine the spatiotemporal disturbance characteristics of power systems. Moreover, the proposed model has higher accuracy, higher recall rate, and shorter training and testing times than traditional transient power angle stability algorithms.

Abstract:Fully harnessing the ocean wave's renewable energy resources could benefit coastal countries. However, ocean wave energy harvesting systems encounter several challenges, i.e., marine uncertainties, long-distance mainte-nance, power fluctuations, irregular wave currents, non-linear generator dynamics, turbine limitations, cost optimization, and power smoothing issues. To overcome these challenges, this paper proposes a new multi-stage con-trol design approach for performance evaluation of the os-cillating water column (OWC)-based ocean wave energy conversion (OWEC) system. The first stage optimizes the Wells turbine by implementing an efficient airflow control strategy. It achieves maximum power-harvesting ability by eliminating stalling phenomena. In the second stage, we investigate the robustness of the permanent magnet syn-chronous generator-based OWEC system by designing adaptive back-stepping controllers, taking into account the Lyapunov stability theory. It accomplishes precise speed regulation for optimal power extraction while delivering reduced delay response and percentage errors. To ensure the OWEC system's availability, the third stage incorporates fault-ride-through capabilities. It executes a fault reconfig-urable control for a parallel converter configuration, elimi-nating only the faulty leg instead of the entire power con-verter. In the fourth stage, a supercapacitors-based energy management system achieves power smoothing, even when the OWC plant output power fluctuates. We accomplish this by implementing a model predictive control strategy. Finally, the Matlab/Simulink results verify that the presented mul-ti-stage control for the OWC OWEC system is an effective design approach, offering an optimal, robust, reliable, and power-smoothing solution.

Abstract:The integration of distributed generations (DG), such as wind turbines and photovoltaics, has a significant impact on the security, stability, and economy of the distribution network due to the randomness and fluctuations of DG output. Dynamic distribution network reconfiguration (DNR) technology has the potential to mitigate this problem effectively. However, due to the non-convex and nonlinear characteristics of the DNR model, traditional mathematical optimization algorithms face speed challenges, and heuristic algorithms struggle with both speed and accuracy. These problems hinder the effective control of existing distribution networks. To address these challenges, an active distribution network dynamic reconfiguration approach based on an improved multi-agent deep deterministic policy gradient (MADDPG) is proposed. Firstly, taking into account the uncertainties of load and DG, a dynamic DNR stochastic mathematical model is constructed. Next, the concept of fundamental loops (FLs) is defined and the coding method based on loop-coding is adopted for MADDPG action space. Then, the agents with actor and critic networks are equipped in each FL to real-time control network topology. Subsequently, a MADDPG framework for dynamic DNR is constructed. Finally, simulations are conducted on an improved IEEE 33-bus power system to validate the superiority of MADDPG. The results demonstrate that MADDPG has a shorter calculation time than the heuristic algorithm and mathematical optimization algorithm, which is useful for real-time control of DNR.

Abstract:The advancement accomplished in power systems over the last decade has enabled the extensive integration of renewable energy sources. It has resulted in enhanced efficiency and reliability of the system by meeting the load demand from small, local sources known as distributed generators (DGs). Consequently, this has led to the concept of microgrids (MGs). Nevertheless, there are operational challenges such as bidirectional power flow, fluctuations in fault current level, and protection issues such as blinding, false tripping, and unintentional islanding. Synchronous generator-based distributed generators (SGDGs) may experience a loss of synchronism across the generators due to undesirable events, such as abrupt changes in demand or faults. Similarly, voltage instability concerns may arise with inverter-based distributed generators (IDGs). This paper provides a thorough review of the concepts of critical clearing time (CCT) and grid code compliance in relation to SGDGs and IDGs, respectively. It provides a comprehensive analysis of the existing literature on several protection strategies used for reducing the adverse effects of DG integration. It highlights the characteristics, benefits, and constraints of these schemes. Finally, this paper presents the conclusion and outlines the potential areas for future study in the field of protective relaying methods, specifically addressing the issues posed by current power systems.