Electrical Engineering最新文献

Tidal power plants (TPPs) and wave energy conversion systems (WECSs) are emerging as significant contributors to clean energy technologies, with the potential to address energy shortages and mitigate environmental footprints. This necessitates a thorough investigation into their role in supporting ancillary services, particularly in frequency regulation. Integrating intermittent units like TPPs into power systems increases capacity but reduces system inertia due to electronic connections. Unlike other more popular renewable sources, TPP is more consistent, highly predictable, reliable, and has a high energy density. This paper introduces a new wave energy conversion systems (WECS) model incorporated into a microgrid to assess its effects. The presence of WECS leads to a deterioration in the frequency deviation dynamics following disturbances, posing a challenge to frequency regulation services. The microgrid model encompasses a rotational power plant, an electric vehicle aggregator, a TPP, and a standalone solar plant (WECS and capacitor energy storage system (CESS) is added later in the system to see the effect of them). The study considers CESS over battery energy storage system due to its high cycle life and fast response time. The projected microgrid is optimized using a hybrid African vulture optimization salp swarm algorithm in conjunction with a new 1+Fractional order Proportional Derivative controller parallel with Fractional order Proportional Integral controller with filter (1+FOPD-FOPIF controller). The study evaluates the contribution of WECS and CESS to frequency management in microgrid system. The efficacy of these tactics is showcased through simulation-driven experiments and validated using real data reflecting the annual load variation in the Fairbank area (U.S) and for IEEE 5 bus system & IEEE 39 bus system with 60% penetration of renewable sources. For verification benchmark test functions are also used as a statistical analysis of projected optimization method and stability analysis is done for projected controller. The projected technique and controller shows better settling time results and reduces oscillations when WECS and CESS are integrated.

This article presents a novel control architecture for an enhanced closed-loop speed tracking of a DC–DC buck power converter fed Permanent Magnet DC motor (PMDC) motor in face of large exogenous load torque uncertainty. The proposed architecture combines a new self learning Zernike radial polynomial neural network (ZRNN) estimator with the backstepping controller. The design involves a computationally simple online learning based ZRNN to rapidly and accurately estimate the unknown large load torque uncertainties. The proposed control solution concurrently guarantees stability and excellent dynamic performance through an effective neural network based estimation and subsequent compensation of unanticipated load torque perturbations over a wide range. The closed loop stability of the DC–DC buck power converter driven PMDC motor and asymptotic speed tracking with the proposed neuro-adaptive controller is proved using the stability theory for non-autonomous systems. The effectiveness of the proposed controller has been investigated through experimentation on an indigenously developed laboratory prototype of 200 W under closed loop operation using digital signal processors. The tests conducted around different operating conditions include the motor start-up response, step variations in the load torque, and step changes in the reference speed. Experimental results demonstrate a significant improvement in the speed tracking performance achieving (48.13 %) reduction in the settling time and no-change in speed during start-up and load torque perturbations upto (600%), respectively. Experimental validations and extensive tests spanning over a large operating region, substantiate the theoretical claims and real-time suitability of the proposed controller for sensitive applications demanding high performance.

The paper introduces a novel decentralized electricity market framework tailored for network community microgrid systems, leveraging blockchain technology. It presents a comprehensive model that integrates blockchain with a microgrid energy management system (MEMS) to facilitate peer-to-peer (P2P) energy trading, thereby ensuring optimal power flow and mitigating line congestion. The proposed optimization model takes into account crucial factors such as line flow constraints, market clearance price (MCP) using the double auction method, and social welfare optimization for energy transactions among buyers (consumers) and sellers (prosumers). By incorporating the power transfer distribution factor (PTDF) to calculate service charges associated with distribution network usage, the model safeguards the interests of all market participants while minimizing the risk of line overload. A case study is conducted to illustrate the efficacy of the proposed model, demonstrating the tangible benefits of blockchain integration in effectively managing and optimizing decentralized energy trading within microgrid environments. The proposed blockchain model for P2P energy trading offers a compelling alternative to conventional microgrid energy trading systems. By streamlining trade execution and eliminating intermediaries, it significantly reduces transaction times, with average processing times of around 10 s, highlighting its rapid processing capabilities. Furthermore, its decentralized nature and cryptographic security mechanisms provide robust protection against tampering and fraud, ensuring the integrity of transactions. Additionally, the transparent ledger system guarantees complete audibility and fairness for all participants, distinguishing it from opaque processes typical in traditional models.

Grid-tied inverters are essential for seamlessly integrating sustainable energy resources into the electrical grid, yet they also introduce harmonics due to the inherent switching operations of power electronics. While inductance-capacitance-inductance (LCL) filters effectively limit these harmonics, enhancing overall system performance, they come with their own set of challenges. These include design complexity, potential resonance at high frequencies leading to system instability, and variations in grid impedance. This paper addresses the design complexities by employing a circle search algorithm to optimize the LCL filter and control system parameters. Additionally, a continuous time-based adaptive filtering algorithm, the half-quadratic criterion, is implemented to dynamically adjust the gain of the inner capacitor current feedback damping loop and the gains of a proportional resonant controller to address the resonance and grid impedance variation issues. These algorithms aim to minimize a constrained multi-objective optimization function based on total harmonic distortion, including high-frequency harmonic distortion and the absolute amplitude error between the measured and reference currents. The system is tested using MATLAB/SIMULINK and real-time Typhoon HIL simulations. The findings illustrate that the proposed control scheme significantly enhances the damping region by suppressing the resonance frequency in the higher frequency band. Furthermore, the results demonstrate that the proposed control loop maintains robustness against fluctuations in grid-side impedance, accommodating increases up to 400% and decreases down to 75%. The system achieves a nearly negligible steady-state error and maintains a transient error below 0.1% throughout step changes in reference current.

Small-scale photovoltaic (PV), battery energy storage systems (BESS), and electric vehicle charging stations have all been proposed and implemented as part of an integrated system in numerous cities worldwide to develop sustainable urban efficiency and dramatically increase the rate of utilization of solar energy resources. To scale PV and BESS and define BESS’s charging/discharging pattern, this manuscript demonstrates a grid-connected photovoltaic/battery energy storage/EV charging station optimization model (PBES). To minimize the cost of electricity, this study provides an optimization model for a grid-connected PBES. To solve this model, GOA-BESA is used. The model's optimal size and energy management technique are determined. Therefore, this manuscript proposes an intelligent search technique that combines the gazelle optimization algorithm (GOA) and is improved by utilizing the bald eagle search algorithm (BESA) which is named the improved gazelle optimization algorithm (IGOA). The IGOA is employed to simulate EV charging patterns and to calculate the EV charging demand at each time interval. By then the performance of the proposed methodology will be evaluated using MATLAB, and then, the proposed technique will be compared with existing techniques.

The operational performances of the conventional permanent magnet synchronous motor drive systems are affected by complex work conditions, sudden load changes, and external disturbances. For the nonlinear control of a permanent magnet synchronous motor, a passive control method based on ADRC and PCHD with disturbance observation is proposed. The disturbance of the current loop is estimated and compensated by the disturbance observer, and the feedback control law is obtained by interconnection and damping configuration, thus the port-controlled Hamiltonian with dissipation based on perturbation observation is designed. With the introduction of active disturbance rejection control, the system has stronger robustness to external disturbance. The experimental results show that compared with the PI control and ADRC method, the proposed method reduces overshoot effectively and improves the response speed of the system. The control system has the better performance of anti-disturbance.

Deep learning and multidimensional signal fusion are utilized to fully extract fault features and integrate them into effective signals to cope with special cases in bearing fault diagnosis. Current mainstream data fusion methods only utilize vibration signals, and the vast majority of signal analysis is limited to the time domain. In addition, in the mainstream data fusion scheme, the fusion capability of the signal collector is relatively low, and the correlation and compatibility between the data cannot be guaranteed. In order to further improve the judging ability of signal features, this paper proposes a bearing fault diagnosis model based on multi-dimensional signals and multi-analysis domain. In this model, a multi-dimensional signal data model with multiple analysis domains is used for feature extraction and fusion. And the independent networks are classified according to their functions, and a single network is used to establish a data feature fusion system, while other networks extract features from different sensors. To ensure the fusion of signal acquisition from different analysis domains, multiple fusion nodes are added between the layers of the fusion network and an attention mechanism is introduced to self-weight the different features. Through experiments, technical comparisons were conducted to improve the efficiency of feature recognition and the accuracy of defect classification, and to verify the effectiveness and feasibility of the proposed method.

Linear synchronous motors, with advantages such as high thrust density, fast speed, and strong dynamic response capabilities, are widely used in industrial automation, aerospace, military, transportation, and other fields. The use of vector control in linear synchronous motors can achieve static decoupling of current, but the dynamic coupling relationship still exists. As the speed increases, the impact of dynamic coupling becomes increasingly severe, leading to a decrease in the dynamic performance of the system. Traditional current decoupling control methods, such as current feedback decoupling control and current deviation decoupling control, are sensitive to motor parameters and cannot solve the current decoupling problem caused by changes in inductance parameters during motor operation. Therefore, this paper proposes a current decoupling control strategy based on an improved extended state observer (ESO). By observing the coupling term using the improved ESO and combining it with feedforward control for corresponding compensation, current decoupling control is achieved without relying on accurate inductance parameters, thereby reducing the sensitivity of the strategy to parameters. Furthermore, the stability of the improved ESO was demonstrated using Lyapunov stability theory in the paper. Simulation and experiments have verified that the current decoupling control strategy based on the improved ESO can effectively reduce the dynamic coupling in vector control, enhance the control performance, and significantly improve the system’s robustness.

In order to address the security threats posed by the volatility and stochasticity of large-scale distributed wind power, this paper proposes an attention-based hybrid deep learning approach for more efficient and accurate wind power sequence prediction. Firstly, the Pearson correlation coefficient (PCC) is used to identify the main meteorological variables as input sequences. Secondly, the intrinsic complete ensemble empirical mode decomposition with adaptive noise is used to decompose the sequence of wind power. Then, the hidden information such as wind speed, wind direction, and wind magnitude are extracted by bidirectional temporal convolutional networks (BiTCN), and the acquired information is inputted into bidirectional gated recurrent units (BiGRU) optimized by a multi-head self-attention mechanism for prediction. Finally, the predicted values of each component are summed to obtain the final prediction results. By comparing with the other 12 models, the results show that the two-scale integrated model of BiTCN and BiGRU can obtain better prediction accuracy. Compared with other benchmark models, the RMSE of this paper's model is reduced by more than 9.4%, indicating that this paper's model can fit the wind power data better and achieve better prediction results.

In response to the imperative of achieving net-zero emissions, Multi-Energy Microgrids (MEMGs) have emerged as pivotal infrastructures. This study advocates for precise scheduling of integrated energy resources within MEMGs, incorporating energy conversion facilities and optimizing a hybrid Demand Response (DR) scheme. The integration of hydrogen-based technologies, such as hydrogen power transmission units, hydrogen storage systems (HSSs), fuel cells, and battery electric vehicles (BEVs), offers unprecedented opportunities to mitigate carbon emissions effectively. The approach leverages a novel multi-objective optimization method, the Horse Herd Optimization Algorithm (HOA), complemented by fuzzy sampling and Pareto criteria, to address complex objectives including minimizing operational costs and emissions. The developed energy management model facilitates continuous control mechanisms for MEMG operators, accommodating both flexible and inflexible energy demands. Importantly, the study navigates uncertainties in electricity market prices, energy demand, and renewable power generation through robust stochastic modeling and multiple probabilistic scenarios. This study achieves a significant 18% reduction in operational costs and a remarkable 25% decrease in greenhouse gas emissions, leveraging advanced technologies like HSSs, fuel cells, and BEVs within MEMGs. The integration of these technologies also enables up to 15% improvement in energy efficiency and a 12% increase in revenue generation through optimized energy trading strategies.