Journal of Electrical Engineering & Technology最新文献

Considering the practical issues of home ventilators and the advantages of fractional order calculus, this paper implements the fractional order proportional-integral–differential (FOPID) controller to the ventilator pressure system. Given that existing FOPID controller parameter optimization algorithms are complex and lack real-world validation, a genetic-ant colony optimization algorithm is proposed. The paper commences with fractional order calculus derivation and the principles of traditional optimization algorithms. Subsequently, this paper enhances the evolution, crossover, and mutation aspects of the genetic algorithm through theoretical analysis, while incorporating the concept of pheromones to augment the efficacy of the optimization algorithm. A new multi-objective function is proposed, accompanied by the transfer function derivation and calculation for the ventilator pressure system. Simulation experiments compare the results of traditional optimization algorithms and the Genetic-Ant Colony Algorithm (G-ACA) for various controlled objects and objective functions. Finally, the solved FOPID controllers are applied to the actual circuit of the ventilator and compared with the conventional proportional-integral-derivative controllers. The results show that the FOPID controllers optimized by the G-ACA surpass the traditional ones in simulation and practice, validating the proposed objective function.

Flexible DC grid solves the disadvantages of high line loss and small transmission capacity of traditional AC grid, but it still has the problems of difficult to extract characteristic signals and fault diagnosis. To solve this problem, a fault detection method based on empirical wavelet transform (EWT) with multiscale fuzzy entropy (MFE) and Whale algorithm optimization with convolutional neural network (WOA-CNN) is proposed. Firstly, EWT is used to decompose the fault line mode voltage signal and obtain the fault component. Then, the correlation coefficient of each component is calculated, and the components with more feature information are reconstructed. The MFE value of the reconstructed signal under different faults is calculated. Finally, the fault feature quantity is input into WOA-CNN for classification. A large number of experiments demonstrate that this method has strong anti-interference ability and high accuracy, and can reliably detect line fault under different fault types, fault positions and transition resistance conditions. Its accuracy is significantly improved comparing with CNN, PSO-CNN, K-means clustering, PSO-SVM and BP neural network, with an average of 99.5834%.

The research endeavors to investigate the incorporation of Virtual Power Plants (VPPs) into contemporary energy systems, with a particular emphasis on aggregation and optimal scheduling. The primary focus lies in examining the pivotal role of VPPs in assimilating renewable energy sources and fortifying the stability of the grid. Commencing with a comprehensive overview of VPPs, the study proceeds to delve into their immense significance in facilitating the transition towards sustainable energy futures. In addition, the detailed examination and analysis of VPPs’ technical complexities, including renewable energy production, storage solutions, and demand-side management, are thoroughly explored and scrutinized. The investigation also meticulously scrutinizes various control strategies and algorithms that have been devised to optimize VPP operation in response to the ever-fluctuating dynamics of the market as well as demand variations. In order to effectively demonstrate the efficacy of VPPs in terms of energy storage management and dynamic power adjustment based on real-time market conditions, a robust model is employed. In addition, the research’s objective is to provide insight into the incorporation of VPPs into hybrid grid systems. This will emphasize their natural ability to efficiently manage the supply and demand of energy, all while optimizing the use of renewable energy sources. The recommendations stemming from this comprehensive analysis unambiguously underscore the pressing need for advancements in control algorithms and grid technologies, which would undeniably augment the scalability and feasibility of VPP integration into modern energy systems.

Hybrid renewable energy systems (HRESs) that merge wind and solar power with energy storage offer a trustworthy and affordable alternative for remote consumers. Energy storage integrates variable wind and solar energy, while energy management enhances system reliability, reduces costs, and minimizes environmental impact. This paper proposes a novel methodology called the coronavirus herd immunity optimizer (CHIO) for modeling and sizing HRESs. The CHIO algorithm uniquely balances exploration and exploitation phases inspired by herd immunity principles, setting it apart from traditional optimization methods. It addresses the optimization problem of minimizing the system's overall net present cost, aiming to reduce the cost of energy (COE) while improving system reliability. We investigate the efficacy of the CHIO method in solving hybrid system design issues and compare its performance to other popular optimization strategies, such as cuckoo search (CS) and particle swarm optimization (PSO). The results demonstrate that CHIO achieves superior solutions to the optimization problem, producing energy with a lower COE and higher reliability compared to PSO and CS.

The predominant method for mitigating vibrations in electric vehicle (EV) propulsion motors involves utilizing electromagnetic strategies to reduce air-gap electromagnetic force (AEMF) orders. This study investigates the origins of AEMFs that significantly induce vibrations in induction motor (IM) models with 6-poles and 54-slots. Initially, it is revealed through spatial harmonic analysis of winding distribution factors that harmonics of same magnitude to the fundamental component play a crucial role in the harmonics of the armature reaction (critical order). Subsequently, it analytically examines and compares the sources of vibration orders of AEMFs. The conditions for determining the rotor slot number are provided to avoid vibration orders that induce significant vibrations. To prove this, this paper utilizes 2D-FEA to calculate AEMFs at rated operating point for both the 44-bar and 70-bar models, confirming the substantial contribution of the critical order of MMF to lower vibration orders in the 44-bar model. Conversely, the 70-bar model exhibits significantly reduced forces due to the absence of correlation between lower vibration orders and the critical order of MMF. After that, considering the modulation effect of high vibration orders caused by rotor slots and slip effects, the forces are calculated by vector summation with lower vibration orders for all operating speeds. This comparison confirms that the 44-bar model generates larger AEMFs compared to the 70-bar model. Finally, through coupling analysis, it demonstrates that the 70-bar model is advantageous in terms of vibration compared to the 44-bar model.

As one of the efforts to overcome the problem of climate change, increasing the share of renewable energy (RE) in the national energy mix has become intensive in many countries, especially after the ratification of the Paris Agreement in 2015. Although this effort can effectively reduce carbon emissions, challenges to the security of power systems with increasing RE penetration are also emerging. This paper aims to provide an overview of several security issues on power systems, along with challenges arising from the impact of inertial reduction, RE fluctuations, RE prediction errors, and fault response, addressed to researchers as a reference for further studies. Case studies of security issues experienced by several system operators (SOs) when RE penetration is high in their electrical grids are discussed as a lesson for modern power systems operations. Moreover, measures to prevent and overcome these problems are proposed, including the need for changes and development in security assessment, protection and control schemes, and more relevant services for facing system security challenges in the future.

Prognostics and health management technology for industrial equipment heavily relies on the accurate prediction of the remaining useful life (RUL). As commonly used RUL prediction approaches, the conventional convolutional neural network, and long-short term memory network are not only difficult to realize the extraction process of spatio-temporal features, but also cannot reflect the difference between the data at different moments in the RUL prediction results. Aimed to deal with these problems, a self-attention mechanism network integrating spatio-temporal feature extraction (SAMN-STFE) is proposed to predict RUL, which can deliver higher weight to the significant moments. In detail, feature selection and noise reduction are performed on the data picked up by the multiple sensors during the working process. The self-attention mechanism network assigns corresponding weights to different time points in the time window. Afterward, the spatial features are extracted by one-dimensional convolutional neural network. The temporal features are extracted by bidirectional long short-term memory networks. Ultimately, the trained SAMN-STFE can be utilized for online RUL prediction. To validate the proposed approach for predicting RUL, the dataset of aircraft turbofan engines, furnished by NASA Ames Prediction Center is employed. Experimental results represent that the proposed approach has excellent RUL prediction performance.

This paper proposes a location method based on the analysis of the time-frequency matrix for distribution networks. Firstly, the traveling wave (TW) signal measured by the detection device at the end of each feeder is decoupled to obtain the aerial-mode component of the fault TW. Then, faults are preset at particular locations, such as the main line, primary branch, and secondary branch to construct time-frequency matrices by applying continuous wavelet transform (CWT) to the measured TW signals, and a matrix comparison library is established. The feeder and branch where the fault occurs are determined by comparing the similarity between the matrix extracted from each terminal at the time of fault occurrence and the matrix in the comparison library. Finally, this paper calculates the time for the wave head to reach the measurement point by plotting the energy evolution spectrum of the specific frequency band in the time-frequency matrix, and the precise location of the fault in the distribution network is achieved through the double-ended location method.

An aggregated energy interaction and marketing strategy is developed for demand side energy communities (DSECs) with hybrid energy storage units, considering the grid friendly issue. The whole mechanism is built as a hierarchical scheme. On the upper-layer, an aggregator is responsible for managing all demand responses through a game based energy scheduling and marketing strategy. On the lower-layer, each user-level energy prosumer (EP) pursues its optimal energy economical goal by participating in aggregated energy interactions within the DSEC through the energy router. Aiming for coordinated operation with the main grid, both energy self-equilibrium and grid friendliness criteria are incorporated with the hierarchical energy interaction and marketing model. The double-layered nonlinear system model is then converted into one single-layer mixed integer linear programming model and solved by the Tabu search-Particle swarm optimization algorithm. Case studies show that with the presented energy scheduling and marketing strategy, energy costs on the end user side are significantly reduced. In the case studies, the energy cost of the three EPs decreased by 3, 8 and 3% respectively. When grid friendliness was taken into account, the DSECA’ revenue was 28% higher than before and the load rate rise from 36.46 to 48.15%.

Insulators are crucial for power transmission lines, and issues related to discharge from insulators are one of the leading causes of faults in these lines. Therefore, an algorithm that can accurately assess the severity of insulator discharge quickly and that can provide real-time monitoring at the edge is needed. In this paper, these issues are addressed by making lightweight improvements to the YOLOv8 object detection algorithm. First, the input side is enhanced by introducing the Mosaic-9 data augmentation method, which improves the algorithm’s robustness and versatility. Next, the backbone network is replaced with the GhostNet network, achieving model lightweighting. The RELU activation function is replaced with GELU to enhance convergence speed and detection accuracy. Finally, the SIoU loss function is introduced to optimize the network, resulting in the Lightweight Improved YOLOv8 algorithm for assessing the severity of insulator discharge at the edge. Experimental validation shows that this algorithm achieves an 87.6% mean average precision (mAP) and 58 frames per second inference speed on edge devices, which meets the requirements for assessing the severity of insulator discharge at the edge.

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