International Transactions on Electrical Energy Systems最新文献

Microcontrollers (μCs) are extremely useful in renewable energy (RE) converters, where numerous onboard control actions need to be executed at low cost. This paper focusses on a cost-effective implementation of a fuzzy controller (FC) for the regulation of converters that are normally employed in RE applications such as solar, wind, and tidal. The μC realization has been achieved through simplification of a dual-input FC (DFC) into a single-input FC (SFC) using the signed distance approach, followed by the piece-wise linear (PWL) simplification of SFC named as piece-wise-linear single input FC (PWL-SFC). Despite the elimination of the fuzzification, knowledge inference, and defuzzification stages, PWL-SFC exhibits a similar control performance to that of DFC. The proposed PWL-SFC is tested through modeling and simulation using the MATLAB Simulink platform and experimentally validated through a low-cost dsPIC μC. The results reveal that the proposed PWL-SFC requires negligible tuning effort and uses three orders of magnitude less computational power compared to DFC.

The objective of this paper lies in the realization of a three-level F-type inverter (3L-FTI) as a shunt active filter in an EV-proliferated environment. The switches are triggered using segment reduced space vector pulse width modulation (SVPWM). This modulation technique provides a lower number of switching transitions than existing PWM strategies. Consequently, the inverter switches experience a decrease in both switching stress and switching losses. A 3L-FTI is a diode-free structure that reduces the harmonics in the source current with a high power factor (PF), where instantaneous reactive power (IRPT) theory is employed to generate the reference currents from the utility grid. In contrast to traditional three-level inverters, two-thirds of switches in 3L-FTI can tolerate a voltage stress equal to half of the DC input voltage. While studying the behaviour of this shunt active filter, with three different nonlinear loading conditions, the current total harmonic distortion (THD) is reduced from 28.43% to 2.13% after compensation, which is under 5% of IEEE standard 519-2014. Therefore, the 3L-FTI controlled by segment reduction SVPWM can be considered as better candidate for active filter in an EV proliferated distribution system.

The rapid development of renewable energy sources has led to critical voltage problems in sending-end systems, necessitating reactive power auxiliary devices and corresponding control strategies. This paper proposes a novel model predictive excitation controller for synchronous condenser, coordinated with the wind farms (WFs) without communication, to provide reactive power and mitigate voltage fluctuations. The proposed controller predicts the future behavior of the system and determines the optimal control input using model predictive control (MPC) algorithm with extended and linearized state space model of sending-end system. An extended state observer (ESO) is designed to estimate the reactive power output of the WFs for noncommunication coordination and to account for unmeasurable disturbances, providing the estimated states to the model predictive excitation controller. The effectiveness of suppressing voltage fluctuations and providing sufficient reactive power support is verified through time-domain simulations in MATLAB/Simulink, compared with the traditional excitation controller.

Several approaches have been introduced to manage congestion in transmission lines while simultaneously reducing the generation cost of power systems. Two of these approaches, namely, generation rescheduling and transmission switching are used together in this article. Changing the system topology through optimal transmission switching (OTS) is an important and active research area in power systems for this purpose. Essentially, OTS is a mixed-integer nonlinear programming (MINLP) problem that is inherently nonlinear and nonconvex. Therefore, solving this nonlinear problem poses significant challenges for researchers. On one hand, there is no guarantee of reaching a global optimal solution, and on the other hand, issues such as the lack of convergence and increased solution time have made it difficult to solve the OTS problem. Linearizing the OTS problem provides a guaranteed method for reaching a comprehensive optimal solution. This study presents a new linear mathematical model for the OTS problem. The proposed model is solved using mixed-integer linear programming (MILP), which accurately determines the opening or closing status of transmission lines and the number of lines that should be opened to reduce the generation and congestion costs in the network. To this aim, a linear programming and piecewise approximation, along with Taylor’s series approximation method, is used to linearize the generation cost function, and AC optimal power flow equations. To reduce the solution time of the OTS problem without losing accuracy, a congestion cost index is used based on decreasing the total congestion cost of transmission lines, as well as the production cost of generators. The proposed model is implemented on IEEE 9-bus and IEEE 118-bus standard test systems. Also, in order to analyze the reliability of the system before and after switching, two methods of contingency analysis and calculation of the LOLP index have been used. The obtained results show that transmission switching can reduce the generation cost and the total power system congestion cost as well.

This paper presents a new 3D analytical model based on the Fourier–Bessel series for electromagnetic modeling of the performance of slotless axial flux permanent magnet (AFPM) machines under no-load conditions. The machine geometry is divided into different domains including the permanent magnet (PM) domain, the air-gap domain, and so on. The Laplace equation in terms of scalar magnetic potential is solved in each domain, and their solutions are expressed based on the Fourier–Bessel series to accurately consider the radial variation of the air-gap magnetic field. A 2D geometry function based on the Fourier–Bessel series is introduced to accurately consider the different PM shaping in the magnet domain. The boundary condition is then used to determine the unknown constants in the general solutions. This 3D analytical model is prepared to calculate the no-load flux linkage of stator phases while considering different PM shapes and skewing effects. Two indexes including the amplitude of the fundamental component and the total harmonic distortion (THD) of no-load phase flux linkage are considered to investigate the effect of skewed PMs and other PM shapes. The capability of the proposed 3D analytical model is also presented to calculate the air-gap magnetic field due to the stator phases for determining the inductance matrix. Finally, the accuracy of the proposed 3D analytical model is verified by comparing it with the corresponding results obtained through the finite element method (FEM) and the experiment setup.

There has been expeditious development and significant advancements accomplished in the electrified transportation system recently. The primary core component meant for power backup is a lithium-ion battery. One of the keys to assuring the vehicle’s safety and dependability is an accurate remaining useful life (RUL) forecast. Hence, the exact prediction of RUL plays a vital part in the management of battery conditions. However, because of its complex working characteristics and intricate deterioration mechanism inside the battery, predicting battery life by evaluating exterior factors is exceedingly difficult. As a result, developing improved battery health management technology successfully is a massive effort. Because of the complexity of ageing mechanisms, a single model is unable to describe the complex deterioration mechanisms. As a result, this paper review is organised into three sections. First is to study about the battery degradation mechanism, the second is about battery data collections using mercantile and openly accessible Li-ion battery data sets and third is the estimation of battery RUL. The important performance parameters of distinct RUL forecast and estimation are categorised, analysed and reviewed. In the end, a brief explanation is given of the various performance error indices. This article classifies and summarises the RUL prediction by data-dependent models using machine learning (ML), deep learning (DL) and ensemble learning (EL) algorithms suggested in a last few years. The goal of this work in this context is to present an overview of all recent advancements in RUL prediction utilising all three data-driven models. This article is also followed by a categorisation of several types of ML, DL and EL algorithms for RUL prediction. Finally, this review-based study includes the pros and cons of the models.

Recently, Bangladesh experienced a system loss of 11.11%, leading to significant power cuts, largely due to faults in power transmission lines. This paper proposes the XGBoost machine learning method for classifying electric power transmission line faults. The study compares multiple machine learning approaches, including ensemble methods (decision tree, random forest, XGBoost, CatBoost, and LightGBM) and the multilayer perceptron neural network (MLPNN), under various conditions. The power transmission system is modeled using Simulink and the machine learning algorithms. In the IEEE 3-bus system, all of the learning types achieve approximately 99% accuracy in imbalanced and noisy data states, respectively, except CatBoost and decision tree, in the classification of line to line, line to line to line, line to line to ground, line to ground types of faults, and no fault. However, although all of the methods gain significant accuracy, assessing the performance results indicates that the XGBoost model is the most effective for transmission line fault classification among the methods tested, as it showed the best accuracy in the imbalanced and noisy state’s classification of faults, contributing to the development of more reliable and efficient fault detection methodologies for power transmission networks.

The problems of energy shortage and environmental pollution can no longer be ignored. How to make the best of energy and improve energy efficiency has always been a concern of researchers. The rapid development of electric vehicles (EVs) has made them an energy load that cannot be ignored. On this basis, an optimal configuration model of a multienergy virtual power plant (MEVPP) considering EV access is constructed to meet the multiple energy needs. To better consider EV users’ willingness to respond, this paper combines price demand response (PDR) with incentive demand response (IDR), establishes a fuzzy response model for EV charging and discharging under the joint response strategy (JRS), and analyzes the influence of user responsiveness and large-scale EV access on MEVPP planning and operation under different incentive levels. Meanwhile, to realize the low carbon, a stepped carbon trading mechanism (SCTM) is introduced. Based on the gazelle algorithm and mixed integer linear programming (MILP), the capacity and output of the system energy equipment are jointly optimized, and the running curve of MEVPP in a typical quarter is analyzed. The example analysis shows that the joint response strategy proposed reduces the operating cost by 7.1%, and the introduction of SCTM reduces the carbon emission by 13.7%, realizing the low-carbon and economic running of MEVPP.

Multiple inverters–paralleled photovoltaic microgrid is a typical cyber-physical system with varying line impedances and unsynchronized nodes that result in unbalanced power sharing and are prone to cause circulating current. Therefore, a complex network based on a finite-time consensus pinning control method for microgrids is proposed in this paper. First, the distributed generators are regarded as agent nodes, and a small-world network model is established based on complex network theory. To overcome the subjectivity of relying on expert experience to select pinning nodes in previous pinning control methods, a selection algorithm that uses only nodes with large out-degree as pinning nodes is proposed to reduce the communication bandwidth requirement of the system. Second, the finite-time consensus algorithm and the pinning control method are integrated to form a finite-time consensus pinning control method. By introducing voltage and frequency correction in the primary control layer, the finite time consensus pinning control method is applied to design distributed secondary controllers. The finite-time stability of the system is analyzed through Lyapunov stability theory. Finally, a hardware-in-the-loop simulation platform is built in StarSim HIL. Compared to the traditional finite-time control method, the proposed method can reduce the peak deviation of nodes by at least 7.7%. The experimental results validate that the proposed method can realize the accurate sharing of active and reactive power in finite time, and the dynamic response speed of the system is significantly improved, with good robustness.

This study proposed a modified passive islanding technique that combines over/under frequency (OUF), rate of change of frequency (ROCOF), rate of change of phase angle difference (ROCPAD), and rate of change of voltage (ROCOV) for sensing of islanding and nonislanding conditions. The technique was implemented in MATLAB/Simulink and applied to the distribution network of the Federal Capital Territory (FCT) of Nigeria containing 120 buses to maintain a stable power supply to its critical loads during grid outages. The method utilized the frequency, voltage, and current signals as input to the combined method. For each of the relays, the low threshold was for a fast operation while the high threshold was for a secure operation. First, the OUF, ROCOF, ROCOV, and ROCPAD relays were combined using an AND gate to limit the relay operation during short circuits since the ROCOV measures the voltage drop and determines whether it is in grid-connected or islanding mode. After that, the higher set threshold of the ROCOF was connected using a logical OR gate to limit the operation of the relay under transients and noises. Finally, the higher set threshold of the ROCPAD was used to sense islanding in case the lower threshold failed to initially detect it. The efficacy of the technique was tested on the FCT distribution network, and the result showed successful islanding for the loss of the grid supply and loss of power supply due to fault on the 330 kV line, while the loss of 33 kV feeder and loss of power supply at a single supply point were nonislanding events. After islanding due to an outage and loss of the 330 kV line, it took 0.01138 and 0.000316 s to switch to the DES, and the optimal capacities that supplied power to the critical loads were 33.2 MW at Bus 13, 18.9 MW at Bus 120, 48.4 MW at Bus 72, and 45.1 MW at Bus 46. The total connection of the DES was able to supply 144.8 MW out of the total peak load of 600 MW which represents about 24.13% of the total system load referred to as the critical loads. Therefore, connecting DES capable of islanding operation to the distribution network improves power supply security, supply continuity to consumers, and customer satisfaction.