International Transactions on Electrical Energy Systems最新文献

Transmission towers serve as crucial safety pillars within the power transmission system, and their damage can lead to severe consequences. The structural failure of a tower undergoes a process from the initiation of local damage to overall failure, emphasizing the importance of conducting detailed local safety research. This paper introduces a nonlinear damage analysis method rooted in the continuous damage theory, specifically designed for critical areas of transmission towers. A material subroutine for elastic-plastic-damage constitutive equations is developed using commercial software, and thorough verification ensures the accuracy of both the subroutine and the algorithm. The proposed algorithm is then applied to analyze the damage in critical areas of a tower, simulating the plasticity-damage coupling evolution of the main leg during the collapse of the transmission tower. Regarding the treatment of bolt connections in the local model, it indicates that there is a small difference between the contact model and the rigid-joint model results. Taking computational efficiency into consideration, it is recommended to employ rigid-joint model to simulate the evolution of damage. The presented example illustrates damage occurring on the outer side of the main leg, ultimately leading to lateral damage under the combined influence of bending and torsion. This research offers a novel method for investigating the failure mechanisms of transmission towers under extreme weather conditions and proposes precise reinforcement strategies.

This survey provides a comprehensive review of underwater cable detection and tracking literature, identifying key problem types and highlighting unique underwater challenges. It emphasizes the critical role of underwater cable detection in global communications and energy infrastructures, addressing complexities like low visibility and variable sea conditions. The analysis compares the efficacy of various models, particularly deep learning approaches like CNNs and Transformers, in adapting to underwater imagery challenges. A new roadmap for efficient cable detection and tracking systems is proposed, focusing on multimodal data integration and nonoptical detection methods. Importantly, the study includes performance evaluations of state-of-the-art models on custom underwater datasets, offering practical insights. The survey’s findings are validated through an implementation of an underwater object-tracking model incorporating effective algorithms from the literature.

This paper introduces a novel hybrid controller designed for a wind turbine power generation system (WTPGS) that utilizes a permanent magnet synchronous generator (PMSG). This hybrid controller combines the adaptability of an adaptive neuro-fuzzy inference system (ANFIS) with the simplicity of a proportional-integral (PI) controller. The PI controllers are traditionally used for stability and noise handling. ANFIS adds adaptability, making it more suitable to cope with the variable nature of wind energy. The primary objective of this hybrid strategy is to augment the overall control performance and reliability of PMSG-based WTPGS when encountered with continuous variable wind conditions. However, implementing the PI controller alone with the WTPGS often suffers from high overshoot and sluggish response in nonlinear systems like WTPGS. In contrast, ANFIS controllers offer superior performance to PI and other artificial intelligence controllers but are still susceptible to noise issues. In this paper, the proposed WTPGS system is designed in MATLAB/Simulink software where a hybrid controller (ANFIS-PI) is implemented in the machine-side converter (MSC) and grid-side converter (GSC) of a variable speed PMSG-based wind turbine to enhance its performance subjected to wind variations. The hybrid controller is implemented in such a way that the ANFIS controller is implemented in the outer layers while the PI controller is applied in the inner layers of both MSC and GSC. The simulation results for this hybrid controller in the MSC outperform those of the conventional PI controller. They demonstrate minimal overshooting and settling time, maintaining consistent stability even when subjected to various test signals at different intervals. Similarly, the GSC also surpasses conventional PI controllers, achieving a significant 6.4% reduction in maximum overshoot and a decrease of 4.36 seconds in settling time. This highlights its strong suitability for wind turbine applications.

One portion of the distribution system is the distribution feeder. Buses carrying more loads and having long-distance route lines are significant problems due to the presence of power loss and the increase in the overall cost of the transportation process. Hence, the overall power loss minimization is taken as the major objective. The best sizing and allocation of capacitors in a bank in the distribution line are used to minimize the total loss. BWO technique is utilized in this optimization process. The backward-forward sweep load flow is used for the computation of the power flow in MATLAB. The sensitive buses have been chosen according to the factor of loss sensitivity (LSF) and using BWO. The validity of the test has been made on the standard IEEE 34 and 85 bus radial distribution system. The simulation results in the 34-bus radial system are much different from the results of PSO, GWO, WO, and IWO. In the 85-bus radial system, the results are seen with the outcomes of PSO, WO, and BFOA. In both cases, the results of the proposed technique are found to be better than the existing methods. Therefore, the result shows that BWO can be effective for future selection to improve large distribution networks by sizing and locating the capacitors optimally.

Recently, renewable energy sources integrated with microgrid (MG) networks have provided safe, secure, and reliable power supply to both utility and industrial purposes. Power quality disturbances (PQDs) seriously affect the performance of MG networks and reduce the lifecycle of numerous sensitive devices in MG networks. Hence, this paper presents a new approach to detect and classify the PQDs using discrete wavelet transform, multiresolution analysis, and optimized-kernel support vector machine. The obtained unique features from DWT-MRA are fed to train the well-known intelligent classifiers. In the optimized-kernel SVM model, computing power is enhanced for classifying multiple PQ events based on the local density and leave-one-out (LOO) algorithm. To get higher separation in feature space, the kernel width of each sample is estimated based on the local density. By using the LOO method, an improved grid search strategy is implemented to get the penalty parameter to achieve satisfactory results. Moreover, a typical MG network is simulated in MATLAB software considering the validation of the proposed technique to address the power quality issues in MG networks, and the results of the proposed method are compared with other conventional ML classifiers. The simulation results confirm that the proposed method is more effective and accurate than other intelligent classifiers.

Microgrids (MGs) play a crucial role in modern power distribution systems, particularly in ensuring reliable and efficient energy supply, integrating renewable energy sources, and enhancing grid resilience. Voltage and frequency stability are paramount for MG operation, necessitating advanced control frameworks to regulate key parameters effectively. This research introduces a multilayer interactive control framework tailored for MGs utilizing distributed energy resources (DERs). The framework comprises primary control layers, integrating internal voltage and current controller loops, and secondary layers employing distributed finite-time control (DFTC) strategies. Through simulation studies and comparative analyses with traditional proportional-integral (PI) controllers, the effectiveness of DFTC controllers in reducing initial oscillations and improving stability is demonstrated. Major findings include the superior performance of DFTC controllers in stabilizing voltage and frequency parameters, optimizing power output, and enhancing overall operational efficiency. Additionally, insights into the operational dynamics of MG systems highlight the significance of advanced control strategies in mitigating fluctuations and ensuring system stability. Furthermore, the proposed method demonstrates significant efficacy improvements over conventional approaches. Voltage stability is enhanced with oscillation amplitudes less than 0.01 pu, active power control achieves a stable level of 0.93 pu, and frequency fluctuations are reduced to 0.004 Hz and effectively recovered to 0.002 Hz. These improvements suggest that the proposed method enhances system stability and control precision by approximately 95% compared to conventional methods, as it achieves much tighter control over voltage, active power levels, and frequency fluctuations.

In this paper, a novel switched/modulated capacitor filter scheme is proposed for enhancing vehicle-to-house (V2G) battery-charging stations utilized in electric vehicles (EVs). The novel approach is tested on two controllers with a classical optimized PID controller. The technique, which employs modified multimode weighted charging modes for fast charging, improved power quality, and minimal inrush currents, results in reduced voltage transients on the DC side and less harmonics on the AC side. An intercoupled DC-AC capacitor interface that features dual complementary switching modes is used by the switched modulated filter as a way to provide optimal pulsing in both the tuned-arm filter and capacitive compensator modes of operations. This switched intercoupled AC-DC filter compensation approach leads to enhanced power usage in EVs, along with lower AC-DC voltage transients and inrush currents.

In high voltage direct current (HVDC) systems, the occurrence of short circuits results in a rapid rise in line current, adversely affecting the interconnected alternating current (AC) grid. Particularly in voltage source-based multimodular converter (MMC) HVDC networks, such transients pose a significant threat to power converter units. Traditional relaying algorithms prove inadequate for safeguarding AC-DC-linked HVDC networks. Both the direct current (DC) and alternating current (AC) segments of such networks demand robust protection mechanisms. Signal processing-based techniques offer valuable insights during fault events, yet challenges such as noise interference, mode missing, and harmonics generation during faults persist, leading to erroneous conclusions. To address this, we introduce Synchro Squeezed Transform (SST) in this study to mitigate ambiguity in relaying algorithm decisions. SST facilitates the extraction of amplitude and effective instantaneous frequency of AC signals. The proposed method employs the Rényi entropy of time-frequency representation (TFR) as the primary logic, followed by the estimation of the spectrum-based Teager–Kaiser Energy Operator (TKEO) for DC signals as the secondary logic. These combined logics enable the identification of various AC and DC faults in Voltage Source Converter (VSC)-based bipolar HVDC networks. Simulation results, including comparisons with existing approaches, demonstrate the efficacy of the proposed methodology in enhancing fault detection and classification accuracy in AC-DC-linked HVDC networks.

Data anomaly detection in small hydropower stations is an important research area because it positively affects the reliability of optimal scheduling and subsequent analytical studies of small hydropower station clusters. Although many anomaly detection algorithms have been introduced in the data preprocessing stage in various research areas, there is still little research on effective and highly reliable anomaly detection systems for practical applications in small hydropower stations. Therefore, this paper proposes a real-time data anomaly detection system for small hydropower clusters (RDADS-SHC) considering multiple time series. It addresses the difficulties of timely detection, alerting, and management of real-time data anomalies (errors, omissions, and so on) in existing small hydropower stations. It proposes a real-time data anomaly detection algorithm for small hydropower stations integrated with the Z-score and dynamic time warping, which can detect and process abnormal information more accurately and efficiently, thereby improving the stability and reliability of data sampling. The paper proposes a Keepalived-based hot-standby RDADS-SHC deployment model with m (m ≥ 2) units. It can automatically remove and restart faulty services and switch to their standbys, which significantly improve the reliability of the proposed system, ensuring the safe and stable operation of related functional services. This paper can detect anomalous data more accurately, and the system is more stable and reliable in a cluster detection environment. The actual operation has shown that compared with existing anomaly detection systems, the architecture and algorithms proposed in this paper can detect anomalous data more accurately, and the system is more stable and reliable in the small hydropower cluster detection environment. It solves abnormal data management in small hydropower stations and provides reliable support for subsequent analysis and decision-making.

A healthy operation of photovoltaic installations (similar to all electrical systems) is always limited by breakdown, degradation due to aging, or imbalance caused by weather conditions. In this context, producing the maximum energy possible with an acceptable form factor is a significant challenge for autonomous systems, especially those connected to the grid. In this paper, we have two main issues to address. The first is determining the maximum power point of an unbalanced photovoltaic field (due to a defect or nonuniform weather conditions affecting the photovoltaic generators). For such a system, the particle swarm optimization (PSO) algorithm remains highly effective because it can easily handle the existence of multiple maxima simultaneously to provide the best possible solution. The second challenge is managing the imbalance between the three phases of the photovoltaic system. In this context, the results of conducted studies propose two approaches to balance and maximize the power supplied by the photovoltaic generator and converters. In addition, the presence of a battery storage system plays dual roles: firstly, compensating the power fluctuations due to nonuniform operating conditions between phases, and secondly, ensuring system power supply during periods of no sunlight exposure. The proposed approaches take into account the constraints imposed on DC voltages and currents to ensure optimal integration with the multilevel inverter stage (cascaded H-bridge multilevel inverters). This is achieved through selective harmonic elimination control without the need for a filtering system. A comparative study between these two approaches will be conducted to assess their advantages and disadvantages. The battery-based storage system efficiently absorbs excess energy and provides energy during deficits, thanks to a flexible control mechanism that allows easy switching between different battery groups and phases.