Hasil untuk "Distribution or transmission of electric power"

Shiyezi Xiang, Lin Du, Huizong Yu et al.

Environmental energy harvesting from magnetic fields offers a sustainable power solution for smart grid sensors. This study optimizes bipolar current transformer arrays for enhanced energy harvesting from microcurrents to meet load requirements. Based on the current transformer array model, a mathematical model that captures the polarity conversion characteristics is constructed. Incorporating both polarity conversion properties and power management integrated circuit limitations, a multi-constraint array optimization problem is constructed. Furthermore, a binary grey wolf optimizer is then introduced to address this optimization challenge. Our findings reveal that the optimal current transformer array configurations for primary current RMS values of 500 mA, 700 mA, and 900 mA are <inline-formula> <tex-math notation="LaTeX">$12\times 1$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$6\times 2$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$4\times 3$ </tex-math></inline-formula>, respectively, achieving the highest power duty cycles of 26.45%, 57.86%, and 100%. The energy extraction efficiencies reach 59.39%, 65.21%, and 76.26%, while energy conversion efficiencies are 89.01%, 92.55%, and 87.45% under the optimal configurations. This work provides a practical framework for designing efficient bipolar harvester arrays, ensuring stable energy supply in smart grid applications.

Qiushi Wang, Xingpeng Li

Hang Shuai, Fangxing Li

This paper presents, for the first time, a framework for Kolmogorov-Arnold Networks (KANs) in power system applications. Inspired by the recently proposed KAN architecture, this paper proposes physics-informed Kolmogorov-Arnold Networks (PIKANs), a novel KAN-based physics-informed neural network (PINN) tailored to efficiently and accurately learn dynamics within power systems. PIKANs offer a promising alternative to conventional Multi-Layer Perceptrons (MLPs) based PINNs, achieving superior accuracy in predicting power system dynamics while employing a smaller network size. Simulation results on test power systems underscore the accuracy of the PIKANs in predicting rotor angle and frequency with fewer learnable parameters than conventional PINNs. Specifically, PIKANs can achieve higher accuracy while utilizing only 50% of the network size required by conventional PINNs. Furthermore, simulation results demonstrate PIKANs&#x2019; capability to accurately identify uncertain inertia and damping coefficients. This work opens up a range of opportunities for the application of KANs in power systems, enabling efficient dynamic analysis and precise parameter identification.

Elmer O. Hancco Catata, Marcelo Vinicius De Paula, Ernesto Ruppert Filho et al.

An efficient switching method is proposed for Direct Instantaneous Torque Control (DITC) in Switched Reluctance Generators (SRG) operating at low speeds, aiming to enhance system efficiency and reduce torque ripple. In the traditional DITC strategy, the magnetization state in the outgoing phase is enabled at low operating speeds, leading to decreased efficiency and unnecessary torque ripple. The proposed DITC strategy improves efficiency at low speeds while maintaining low torque ripple levels. It prioritizes the freewheeling and demagnetization states during the outgoing period. When the back electromotive force (back EMF) is small, the magnetization state is disabled, using the freewheeling state to smoothly increase torque and the demagnetization state to decrease torque. The magnetization state is reintroduced as the back EMF increases. To implement the modified DITC, an artificial neural network is used to estimate electromagnetic torque. Experimental tests were conducted for both fixed and variable SRG speeds. The proposed method is compared with other methods in the literature. Experimental tests carried out at fixed and variable SRG speeds show that the proposed method significantly enhances efficiency by up to 20% and reduces torque ripple by up to 21% compared to existing methods.

Yufei Chen, Yong Wei, Jinxin Cao et al.

Li Yang, Jiashen Teh

Tung Trieu Duc, Anh Nguyen Tuan, Tuyen Nguyen Duc et al.

Abstract Increasing the use of renewable energy in microgrids (MGs) offers environmental and economic benefits. However, the unpredictable and intermittent nature of available resources poses challenges for optimal MG scheduling. Hybrid AC–DC microgrids provide a solution, seamlessly integrating renewables while reducing energy losses and improving power grid reliability. Additionally, incentive‐based demand response programs promote flexible energy consumption, further mitigating the variability of renewable generation and enhancing grid stability. This paper investigates the challenges and potential of high renewable penetration in hybrid AC–DC MGs, analysing the role of demand response programs in system optimization. The microgrid's energy management is modelled using MILP, while a Stackelberg game represents the demand response program. These models are integrated to optimize energy management and demand response jointly. Simulations demonstrate the cost‐saving benefits of this integrated framework, achieved through coordinated flexible resource scheduling and incentive‐based demand response programming.

Anh‐Tuan Tran, Minh Phuc Duong, Nhat Truong Pham et al.

Abstract This article introduces a novel approach named HBA‐dHoSMO, which combines a continuous decentralized higher‐order sliding mode controller‐based observer (dHoSMO) with the honey badger algorithm (HBA), specifically designed for load frequency control in multi‐area power systems (MAPSs). Traditional sliding mode controllers (SMCs) employed in load frequency control of MAPSs often face challenges related to chattering and oscillations, leading to decreased robustness and stability. Additionally, tuning the parameters for these SMC designs to achieve optimal performance in MAPSs can be challenging. The HBA‐dHoSMO is proposed to address the issues of chattering and oscillations, while the optimal parameters for SMC design are obtained using HBA. The stability analysis of the entire system is conducted using linear matrix inequality and the Lyapunov stability theory, affirming the reliability and feasibility of the approach. A comprehensive set of case studies is performed under various configurations and conditions. Additionally, particle swarm optimization and tuna swarm optimization, in conjunction with SMC‐based and proportional–integral–derivative controllers, are examined for performance comparison. Simulation results demonstrate the superior performance of the proposed controller across all case studies. This is evidenced by the lowest integral time absolute error values recorded as 0.0133, 6.45 ×  10−4, and 0.0167 for single‐, two‐, and three‐area power systems, respectively.

Mostafa Kazemi Mostaghim, Mohamadmahdi Shahbazi

This paper presents a Peer-to-Peer (P2P) energy trading model in micro-grids that considers distributed solar photovoltaic systems (SPVs) and battery energy storage systems (BESS) utilizing the TLBO Algorithm. It aims to minimize customer costs and increase profit by optimizing charging, purchasing, and selling decisions. For this purpose, two scenarios are studied. In the first scenario, the primary energy system includes SPVs, loads, and BESS to optimize the charge/discharge of the energy storage systems. In the second scenario, it is assumed that in addition to the SPVs, loads, and BESS, a neighbouring fossil fuel-fired micro-grid is connected to the primary energy systems, allowing peer-to-peer (P2P) energy trading with it. According to the results, trading in the second scenarios on a winter day lead to 14.53 $ per day, compared to the first scenario with 11.53 $. In addition, the neighbouring fossil fuel-fired micro-grid in the second scenario, which has created the possibility of energy exchange between micro-grids, has led to an increase of about 21% in the profit of the primary power grid. Based on the results, this approach seemed to be helpful for micro-grid operators to make the most economical decisions.

Abolfazl Mohammadi, Moein Abedini, Mahdi Davarpanah

Abstract Even though shunt compensation of transmission lines (TLs) is a common practice to limit the overvoltages stemming from the Ferranti effect, it may cause transient and steady‐state resonant overvoltages when one phase is left open due to the single‐pole‐open condition following the temporary phase‐to‐ground (Ph‐g) fault. Furthermore, the existing shunt reactors potentially increase the duration of the secondary arc current at the fault point and hence prevent offering fast single‐pole reclosing. To exploit the mentioned issues, in recent studies, a neutral reactor is introduced, which is installed in the neutral point of the shunt reactors; nonetheless, obtaining the appropriate amount of it is remarkably challenging. In this paper, an optimal neutral reactor (ONR) is determined based on an optimization problem, which minimizes the neutral reactor cost subject to limiting both resonant overvoltages and the duration of the secondary arc current. In modelling, the Laplace domain is employed rather than the phasor domain to consider both transient and steady‐state conditions since this technique allows having better control over transient overvoltages as well as transient secondary arc.

Akhil Prasad, Scott D. Sudhoff, Todd C. Monson et al.

Geomagnetic disturbances (GMDs) give rise to geomagnetically induced currents (GICs) on the earth&#x2019;s surface which find their way into power systems via grounded transformer neutrals. The quasi-dc nature of the GICs results in half-cycle saturation of the power grid transformers which in turn results in transformer failure, life reduction, and other adverse effects. Therefore, transformers need to be more resilient to dc excitation. This paper sets forth dc immunity metrics for transformers. Furthermore, this paper sets forth a novel transformer architecture and a design methodology which employs the dc immunity metrics to make it more resilient to dc excitation. This is demonstrated using a time-stepping 2D finite element analysis (FEA) simulation. It was found that a relatively small change in the core geometry significantly increases transformer resiliency with respect to dc excitation.

Faisal Mohammad, Dong-Ki Kang, Mohamed A. Ahmed et al.

The electrification of transport has proved to be a breakthrough to uplift the sustainable and eco-friendly platform in the global sector in which electric vehicles (EVs) are considered indispensable. In particular, creating intelligent energy management in the power distribution system integrated with electric vehicle charging stations (EVCS) as a new entity is one of the most important challenging tasks. The implementation of the EVCS network infrastructure should facilitate the adoption of the spatiotemporal electricity demand for EVs. The intelligent decision for the transmission, distribution, energy allocation and charging station placement by the control center or central aggregator is only possible by correctly forecasting its usage, occupancy, and energy or charging demand. Techniques like data analytics have enabled to extract data from the EVCS on a daily basis to store and process all the recorded data. To overcome the above-mentioned challenges related to energy demand forecasting for EVCS network, this work proposes two encoder-decoder models based on convolutional long short-term memory networks (ConvLSTM) and bidirectional ConvLSTM (BiConvLSTM) in combination with the standard long short-term memory (LSTM) network. Data on energy demand from EVCS located in four different cities is used in the proposed models. All datasets are preprocessed to make them suitable for the multi-step time-series learning models in order to make the framework data-centric. The suggested architectures are built on the ConvLSTM and BiConvLSTM to extract the key features from the spatiotemporal data of the energy demand data of the EVCS distributed over the time and space. The predicted outcomes generated by the suggested strategy are compared with conventional deep learning models and traditional machine learning techniques.

Abbas Zare Ghaleh Seyyedi, Sara Mahmoudi Rashid, Ehsan Akbari et al.

Abstract Generation and transmission expansion increase the flexibility of power systems and hence their ability to deal with contingency. This paper presents a resilient‐constrained generation and transmission expansion planning (RCGTEP) model considering the occurrence of earthquakes and floods. The proposed model minimizes the investment and operation costs of resiliency sources (RSs) and resiliency (blackout) costs arising from the outage of the network against the occurrence of extreme conditions. For further consideration, uncertainties of load and RSs availability are included as a Stochastic programming model. A hybrid solver of teaching‐learning‐based optimization (TLBO) and krill herd optimization (KHO) is used to solve the proposed problem and achieve the optimal solution, including a low standard deviation in the final optimal response. The model is tested using a modified version of the IEEE 6‐Bus and IEEE 89‐Bus transmission networks. Numerical results show the potential of the mentioned approach to improve indices of operation, economics, and resiliency in the transmission network.

Reza Bekhradian, Majid Sanaye‐Pasand, Mahdi Davarpanah et al.

Abstract Electrical Arc Furnaces (EAFs) consume time‐varying power. This causes mechanical torque variations in the nearby small scale synchronous generators (SSSGs) and reduces their life expectancy. In this paper, an appropriate EAF model is implemented and verified by using field measurements. Moreover, an actual distribution system to which an SSSG is connected to supply industrial loads including an EAF is investigated based on time‐domain simulation studies. To enhance dynamic performance of the SSSG adjacent to an EAF load, two novel approaches are presented in this paper. The first method is utilized for proper adjustment of governor controller parameters. Analytical and simulation‐based studies are accomplished to determine proper parameters of the SSSG conventional governor controller to mitigate the mechanical oscillations caused by EAF. These comprehensive investigations reveal that the governor PI controller proportional parameter should be adjusted as small as possible to mitigate the SSSG oscillations; while, the oscillations are fairly independent of integrator parameter. Besides, the integrator parameter should be set as high as possible to improve the SSSG dynamic performance. Moreover, a new modified SSSG governor controller including steady‐state and transient control loops is developed and a novel control strategy is proposed to enhance steady‐state and transient performance of SSSG. Finally, robustness and effectiveness of the proposed controller and its parameters are investigated using field measured EAF current signals for various transients. It should be noted that the proposed strategies of this paper can be also applied to similar cases where a nonlinear time varying load is connected to an SSSG.

Mojtaba Partovi, Saeid Esmaeili, Morteza Aein

Abstract Electrical vehicles (EVs) are among the fastest‐growing electrical loads that change both temporally and spatially at distribution networks. Moreover, the existence of uncertain parameters, such as EVs as well as domestic loads in power networks, poses serious operational challenges for them. Accordingly, stochastic studies of system performance are a must. Against this background, this paper aims to present a stochastic multi‐objective method for the problem of simultaneous active and reactive power management as well as harmonic compensation in distribution networks in the presence of EVs and non‐linear devices (NLDs). This method minimizes costs associated with power generation and losses. Besides, it improves the total harmonic distortion of voltage (THDv) at network buses subject to network and EV constraints. In the proposed method, to strike a balance between exploration and exploitation abilities, a hybrid technique named the “PSO‐GA optimization algorithm” was used to take advantage of both the genetic algorithm (GA) and the particle swarm optimization (PSO) method. Accordingly, the effectiveness of the proposed method was examined on a standard IEEE 33‐bus distribution network populated with EVs equipped with on‐board bidirectional chargers. The results obtained showed that the proposed model improved network power quality indices as well as economic and technical issues of EVs in parking lots.

Kun Yao, Shuangshuang Fan, Ying Wang et al.

Abstract Anomaly detection of steam turbines is to recognize infrequent instances within sensor data that plays a vital role in stable power supply. Machine learning models have been applied to diagnose the faults of turbine and verified useful for identifying engine problem. To detect anomalies of steam turbines with machine learning methods, here, an approach called hierarchical pre‐warning strategy is proposed that combines clustering methods with classification methods. Three different clustering methods, K‐means, Isolation Forest and Local Outlier Factor, are chosen to separate anomalies from normal data. Since clustering results cannot give unanimous decision, the clustering instances are labelled with three classes, real anomalies, suspected anomalies and normal data, according to their overlapping recognition. Subsequently, five classification algorithms, k‐nearest neighbour, support vector machine, decision tree, random forest and gradient boosting decision tree, have been examined to train the labelled data set. The classification results illustrate that gradient boosting decision tree and random forest are much more precise to detect real anomalies of steam turbines. The real anomalies identified by clustering methods have been classified into suspected anomalies by this approach that is more practicable and consistent with ground truth.

Bystrík Dolník, Ľuboš Šárpataky, Iraida Kolcunová et al.

Insulators are one of the many components responsible for the reliability of electricity supply as part of transmission and distribution lines. Failure of the insulator can cause considerable economic problems that are much greater than the insulator cost. When the failure occurs on the transmission line, a large area can be without electricity supply or other transmission lines will be overloaded. Because of the consequences of the insulator’s failure, diagnostics of the insulator plays a significant role in the reliability of the power supply. Basic diagnostic methods require experienced personnel, and inspection requires moving in the field. New diagnostic methods require online measurement if it is possible. Diagnostic by measuring the leakage current flowing on the surface of the insulator is well known. However, many other quantities can be used as a good tool for diagnostics of insulators. We present in this article results obtained on the investigated porcelain insulators that are one of the most used insulation materials for housing the insulator’s core. Leakage current, dielectric loss factor, capacity, and electric charge are used as diagnostic quantities to investigate porcelain insulators in different pollution conditions and different ambient relative humidity. Pollution and humidity are the main factors that decrease the insulator´s electric strength and reliability.

Deepak Ramasubramanian, Parag Mitra, Papiya Dattaray et al.

Hossein Hosseini, Saeed Afsharnia

Abstract In this paper a novel method is proposed to estimate the rotor speed of synchronous generators in steady state and dynamic conditions using phasor measurement unit's data. This method uses the synchronous generator model to find the relationship between generator rotor speed and frequencies at different buses of the power system. The principles of the proposed method are first presented through a simple network consisting a generator connected to a transmission line. Afterward the general formulation is developed. To demonstrate the effectiveness of our method, IEEE 9‐bus and IEEE 39‐bus power systems are used as the benchmark systems. Different tests are carried out under three conditions including, normal condition, presence of measurement errors and uncertainty of the power system parameters. As well, the estimation results are compared with the results of other estimation methods. The results show the high accuracy of the proposed method in different conditions.

Amirhossein Nasri, Amir Abdollahi, Masoud Rashidinejad

Abstract High impact–low probability (HILP) incidents, such as hurricanes, usually and gravely damage electric distribution networks. A resilient distribution network must have an ability to recover itself with a fast restoration methodology against the effects of HILP events. Due to improve the electric distribution network resiliency against HILP incidents, this paper suggests a probabilistic–proactive distribution network operation model based upon the chaos theory (P‐PDNOMC(. Here, a resilience index based upon operational cost and load shedding cost is employed to decrease the effects of a hurricane as an HILP incident. P‐PDNOMC is formulated as a framework that consists of a novel hurricane modelling and operation scheduling in normal and emergency conditions with uncertainties considering. It should be noted that a prediction approach based on the chaos theory and the least‐squares support vector machine (LS‐SVM) is also designed to consider the provisional and spatial behaviour of the hurricane. Furthermore, this paper proposes a novel optimization framework for damaged lines determination based upon the P‐PDNOM C (DLsP‐PDNOM C) by considering multi‐zone and multi‐period damaged equipment budget constraints. Here, the load shedding cost is applied to present the efficiency of the proposed model. The numerical results indicate the resiliency enhancement of the electric distribution network in the face of HILP incidents.