To address the economic and risk issues of high-proportion renewable energy power systems, a two-layer optimal dispatch model for a cascaded hydropower-wind-solar joint system in the electricity market is proposed. The upper layer is aimed at minimizing the system cost and conditional value at risk to optimize the day-ahead generation plan. The lower layer constructs a real-time market leader-follower game based on the day-ahead generation results of wind, solar, and hydropower obtained from the upper layer: the market operator (leader) maximizes social welfare through electricity price decisions; the wind-solar-hydropower alliance (follower) responds to electricity prices to optimize real-time generation and uses the Shapley value method to allocate alliance benefits. Through closed-loop feedback of actual generation, the risk aversion coefficient, wind and hydropower deviation penalty coefficient are dynamically and adaptively adjusted to achieve rolling optimization. The second stage builds an optimal dispatch model for the real-time market of the wind-solar-hydropower system based on the results of the first stage, optimizing the clearing price and allocating the system ' s benefits. To address the shortcomings of traditional solution algorithms in terms of accuracy and speed, an improved Mantis Shrimp Optimization Algorithm (MShOA) is proposed, introducing inertia weights, L & eacute;vy flights, and adaptive rotation angles and step sizes in foraging, attacking, and defending, respectively, to ensure population diversity and enhance the algorithm ' s spatial search ability. Case studies show that the proposed model can effectively mitigate the fluctuations of renewable energy, reduce system risks, and improve economic efficiency.

Introduction: High-power induction motor drives (e.g., mine hoists) based on DEMMC face severe energy imbalances and power surges under extreme dynamics. This paper proposes a hierarchical coordinated control strategy to address these challenges and ensure system stability under variable-speed, heavy-load conditions. Methods: An ESO-MPC framework is established for robust torque tracking and active disturbance rejection. A dual-layer State of Charge (SOC) balancing mechanism is developed, employing fuzzy-logic-based nonlinear gain for inter-arm regulation and a novel DO-CDPWM strategy for rapid intra-arm equalization. The approach is validated via MATLAB/Simulink. Results: Simulation results demonstrate that the ESO-MPC has an ultra-fast current settling time of 2 ms (within a 2% error band) with negligible overshoot. The proposed balancing strategy suppresses intra-arm SOC deviation from 4% to 0.5% within 0.2s, while effectively eliminating second-order harmonic circulating currents. Discussion: A coordinated control strategy integrating ESO-MPC and dual-layer SOC balancing is proposed for DE-MMC mine hoists. Simulations demonstrate a 1ms current response and rapid SOC equalization (4% to 0.5%) within 0.2s. This strategy decouples motor drive from energy management, ensuring stable operation under heavy-load conditions. Conclusion: The proposed strategy effectively decouples motor drive from energy management, offering superior dynamic performance and global energy equilibrium compared to traditional PIbased methods, making it highly suitable for high-power mining applications.

ObjectivesThe fluctuation of renewable energy generation and frequent load switching may cause frequent variations in the power flow direction of energy storage systems, compromising the stability of the bus voltage and system dynamic response performance. Therefore, this study proposes a virtual inertia control strategy for DC bus voltage in integrated energy system based on electric-hydrogen hybrid energy storage.MethodsVirtual inertia control is introduced into the voltage outer loop, and model predictive control is introduced into the current inner loop. The virtual inertia parameters are combined with the voltage change rate to establish an adjustment relationship between voltage and virtual capacitance. Building on this, a power coordination control method suitable for electric-hydrogen hybrid energy storage systems is designed, and the economic feasibility of different energy storage devices is compared. Simulation models are established using MATLAB/Simulink to verify the effectiveness of the proposed strategy.ResultsThe proposed strategy reduces voltage fluctuation range to 2.2%, ensuring efficient coordinated operation of the electric-hydrogen hybrid energy storage system while improving the safety of hydrogen storage tanks. Compared with single lithium battery storage and lithium battery-supercapacitor hybrid storage solutions, the electric-hydrogen hybrid energy storage solution achieves cost reductions of 10.55% and 3.45%, respectively.ConclusionsThe proposed strategy effectively mitigates bus voltage fluctuations caused by load power fluctuations, enhances the system dynamic response capability, and contributes to the stable operation of park-level integrated energy systems.

In half-bridge converter series microgrid (HBCS-MG) systems, output fluctuations caused by varying wind speeds and solar shading induce active power imbalances among generation modules (GMs). This imbalance increases susceptibility to overmodulation distortion and restricts the active power regulation range. To address these challenges, this paper proposes a variable carrier level phase disposition SPWM (VCLPD-SPWM) strategy to enhance the active power regulation depth of GMs at the modulation level. Assuming a stable DC-link voltage for the half-bridge converters (HCs), the power distribution characteristics and switching durations of GMs under PD-SPWM are analytically examined. Subsequently, the carrier level transition points and periods for the maximum regulation range under VCLPD-SPWM are derived, alongside the corresponding power increments and negative power characteristics of each GM. Finally, theoretical calculations, simulations, and experimental results validate the feasibility and effectiveness of the proposed strategy, demonstrating its superiority over carrier phase-shifted modulation strategies.

In view of three-phase power unbalanced caused by the output power difference of each phases microsource of the modular multilevel converter half-bridge series microgrid (MMC-MG) operating in the islanded mode, a balance control method of inter-phase power is proposed. The MMC-MG topology and three inter-phase power dispatching modes are introduced. The system three-phase output power mathematical model is established, and the mechanism of the dc circulating current to realize the inter-phase power flow is described. A dc circulating current controller based on arm virtual voltage is designed. Its output signal is superimposed on each phase modulation indices as power balance control variable. According to the output power of each microsource and the load power, the inter-phase power regulation is distributed, and the regulation is compensated by the state of charge of the energy storage device. Simulation and experiments show that the proposed control strategy can achieve inter-phase power balance control, which has no effect on the output voltage and frequency of the system. Compared with general three-phase microgrid, MMC-MG can realise inter-phase power balance by its own circulating current control, without additional investment of power balance equipment.

This research addresses the issue of low grid inertia in wind energy systems by proposing an improved control strategy that integrates model predictive control (MPC) with virtual synchronous generator (VSG) control. As traditional energy sources deplete and environmental concerns increase, wind energy is rapidly growing. However, most wind turbines in maximum power point tracking (MPPT) mode cannot support grid inertia. Energy storage systems in wind farms can provide inertia and frequency regulation support to enhance grid stability. While VSG technology improves inertia, it struggles to track grid frequency changes effectively, potentially causing oscillations. The study analyzes the virtual inertia and VSG control of the wind-storage combined power generation system, establishes a predictive model to track real-time frequency variations, and integrates the MPC optimization results into the VSG control loop. Simulations show that the proposed MPC-VSG strategy allows wind farms to simulate synchronous generator inertia, thus reducing the maximum rate of frequency change, frequency deviation, and steady-state error, while improving system inertia. The study concludes that the MPC-VSG control strategy significantly enhances the inertia level and frequency stability of wind-storage integrated power systems compared to traditional VSG control and traditional proportional integral (PI) control.

This paper proposes a multi-harmonic compensation control strategy based on the simulated annealing particle swarm optimization (SA-PSO) algorithm to address the overmodulation caused by power imbalance in wind and solar micro-sources within a half-bridge converter series Y-connection microgrid (HCSY-MG) grid-connected system. The strategy introduces appropriate higher-order harmonics such as the 3rd, 5th, 7th, and 9th harmonics, into the overmodulated units to adjust the modulation wave amplitude, ensuring that it remains within a value of 1. At the same time, corresponding inverse harmonics are injected into the non-overmodulated units to cancel out the effects of the forward harmonics. The SA-PSO algorithm is used to quickly optimize the compensation coefficients for each harmonic, expanding the modulation range and enhancing the system stability. Simulation and experimental results demonstrate that this method significantly improves the operational stability of the system under severe power imbalance conditions in micro-sources, effectively overcoming the limitations of traditional methods and validating the correctness and effectiveness of the proposed strategy.

The half-bridge converter series Y-connection microgrid (HCSY-MG), as a novel type of series-connected microgrid, has received limited attention regarding output power allocation and control. Moreover, existing power allocation and control strategies for microgrids are not directly applicable to HCSY-MG grid-connected system. To achieve the output power distribution and control of the HCSY-MG grid-connected system across various generation modules (GMs), while considering the system's strong nonlinearity constraints, a control strategy for output power distribution based on the Lagrange-proximal policy optimization (Lagrange-PPO) algorithm was proposed. First, the output power mathematical model of the HCSY-MG grid-connected system was established under the carrier disposition sinusoidal pulsewidth modulation (CD-SPWM) method. Based on the output power range of each GM and microsource output power, the corresponding relationship between each GM and the carrier layers was derived. Then, considering the strong nonlinearity in the output power constraints of each GM under the CD-SPWM strategy, the output power control problem is reformulated as a constrained Markov decision process (MDP) to reduce algorithmic complexity. To address this constrained MDP, a Lagrange-PPO algorithm is proposed. Finally, the feasibility and effectiveness of the proposed strategy, along with its superiority over conventional methods, are validated through both simulations and experimental comparisons.

Unlike the inverters in the traditional alternating-current (AC) microgrid, those in a microgrid with series microsource inverters (SMSI-MG) are connected to the power grid after being cascaded. The authors of this study first divide the control sections according to the degree of grid voltage dips and formulate a coordinated scheme to suppress fluctuations in the output powers of the SMSI-MG. For the section in which the degree of unbalanced grid voltage dips is relatively low, a current-limiting strategy that reduces the output power of the SMSI-MG through the coordinated control of the generator subunits (CCGU) is proposed. More active power can be provided by the SMSI-MG when the proposed strategy is used, than in the strategy that is based on changing the reference power, and the output reactive power of the SMSI-MG can be automatically changed with the degrees of dip and unbalance of the grid voltage. The Light Gradient-Boosting Machine (LightGBM) is used to establish a mapping relationship between the parameters characterizing overcurrent and the reduction quantity in output active power of the SMSI-MG to implement the CCGU-based current-limiting strategy. The complex collaborative control is simplified to improve the low-voltage ride through (LVRT) capability of the SMSI-MG.

Lithium-ion battery state of health (SOH) estimate is crucial for the safe and reliable operation of the lithium-ion Battery management system (BMS) [1]. An improved whale algorithm optimized kernel Extreme learning machine-based SOH estimation method (IWOA-KELM) is suggested to address the issue of the low estimate accuracy of conventional estimation methods. In order to improve convergence speed and optimization algorithm accuracy, an inverse learning technique can be used to create an initial population with high adaptive values. Secondly, the capabilities of local optimization and global search are balanced by the addition of a nonlinear convergence component. Finally, the system can eventually deviate from the local optimal solution and increase search precision by adding nonlinear time-varying adaptive weight coefficients. The kernel limit learning machine's variables were optimized using the suggested IWOA algorithm, and a battery SOH estimate model based on KELM was created. The suggested model was then tested using three NASA lithium-ion battery datasets and compared to WOA-KELM and KELM, showing that the method has good estimate accuracy and robustness and that the estimation error is stable within 3%.

Aiming at the problems of high similarity of output voltage/current waveforms after different power tubes of the cascaded H-bridge multilevel inverter, fault diagnosis is difficult, and the actual operation process is affected by DC side voltage fluctuations, AC side load changes and circuit noise disturbances. In this paper, a fault diagnosis method based on EEMD-MPE cascaded H-bridge inverter is proposed. First, the voltage of each IGBT transistor after failure is subjected to ensemble empirical mode decomposition (EEMD), and the optimal intrinsic mode component (IMF) is selected on the basis of original signal and the correlation components. Multi-scale permutation entropy (MPE) is calculated from the obtained optimal natural mode components. Secondly, three datasets are constructed by adding the above three disturbance factors, and the grid search method support vector machine (GS-SVM) is applied to fault diagnosis. Finally, in order to speed up the diagnosis and reduce the dimension of fault features, Principal Component Analysis (PCA) is used to reduce the dimension of the obtained multi-scale permutation entropy data set, and the accuracy of the cumulative contribution rate of different principal components is obtained.

Aiming at the nonlinear and non‐stationary characteristics of the power load, in order to improve the accuracy of load forecasting, a short‐term load forecasting method based on variational modal decomposition and the combination of convolutional neural network and bi‐directional long short‐term memory (Bi‐LSTM) network is proposed. The load sequence is decomposed into components with different frequencies by variational modal decomposition, and each component is predicted by Bi‐LSTM. Then the convolution neural network is introduced to process multi‐source data. The load, temperature, day and other data are constructed according to the time sliding window as the input, and the convolution neural network is used to extract the effective feature vector, the eigenvector is constructed in a time series and used as the input data of Bi‐LSTM network. Taking the public data set provided by a US public utility department as an example, the proposed model is compared with other methods. The results show that the proposed method can better track the change of load and effectively improve the accuracy of short‐term load forecasting. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

To improve the utilization rate of micro sources of MMC series structured microgrid in a gridconnected operation mode, a prediction-based coordination control strategy of micro-source power of bridge arm is proposed in this paper. The connection between the output power and the delta carrier is determined by analyzing each generation unit’s output power. Each micro source’s output power is controlled independently by using the carrier-wave variable amplitude phase-shift modulation method. A finite control set-model predictive control algorithm is used to predict the next cycle’s power using the carrier amplitude of the current cycle as the controlled input. The optimal switching state for the next cycle is determined according to the coordination principle of “more work for more energy.” Through simulation, the coordination strategy can effectively control the maximum power output of the bridge arm and improve the micro source’s utilization rate.

As a novel topology of microgrid, the output voltage control of MMC half bridge series microgrid (MMC-MG) is rarely studied. In this paper, on the basis of fully analyzing the mechanism of output voltage fluctuation of MMC-MG under the condition of islanded mode, a control strategy of a hybrid energy storage system is proposed to reduce the generating module (GM) DC-link voltage fluctuation caused by the randomness of renewable energy microsource output power. Moreover, in order to further improve the stabilization of the MMC-MG output voltage and meet the requirements of fast voltage recovery and antijamming, a sliding mode controller is designed. Then, a voltage fluctuation compensation controller is designed to suppress the DC component and fundamental frequency deviation of system output voltage caused by GM DC-link voltage fluctuation. The proposed control approach is validated against simulations using MMC-MG models with 4-GM per arm. The results show that the proposed hybrid energy storage control strategy can suppress the GM DC-link voltage fluctuation, the sliding mode controller can stabilize the system output voltage when the load drastic changes, and the fluctuation compensation strategy can suppress the DC component and the fundamental frequency deviation of system output voltage.