Muhammad Adnan Khan, Sundus Munir, Muhammad Nadeem Ali
et al.
The prediction of building energy usage is crucial for improving decision-making and energy conservation in the construction industry. Accurate forecasts of energy consumption enhance resource optimization, particularly in smart cities where interconnected devices manage operations autonomously. These devices consume substantial energy, making energy optimization essential. The advent of smart homes has heightened the need for intelligent applications in healthcare, asset management, security, automation, and energy management to understand residents’ behaviors and predict future demands. Optimizing energy grids and power stations is necessary to minimize environmental impacts. Additionally, creating energy-efficient buildings can significantly reduce overall energy consumption, also contributing to lower carbon emissions, reducing energy waste, and overall ecological sustainability. Deep Learning (DL) methods, particularly Bidirectional Long Short-Term Memory (BLSTM) networks, are recognized for their effectiveness in prediction tasks, including energy consumption forecasting. Predicting energy demand is crucial as smart cities continue to integrate advanced technologies for efficient resource management. Many research studies focused on monthly or annual prediction. This research employs a B-LSTM network to forecast household energy consumption using hourly, daily, weekly, and monthly data, especially within smart homes, which generalizes across multiple time scales. The Individual Household Electric Power Consumption (IHEPC) dataset is a diverse and large collection of energy consumption data from smart homes, for testing predictive models. The proposed Energy Consumption Prediction models including extreme gradient boosting (XGBoost), categorial boosting (CatBoost), Gradient Boosting, BLSTM used in this study and demonstrated a lower error rate compared to previous approaches. The findings of this study are valuable for policymakers and leaders to make more informed energy investment decisions. Future work will explore the scalability of the model for larger and more diverse datasets.
ABSTRACT The permanent magnet synchronous motor (PMSM) serves as the power source for the electric power steering (EPS) system, which directly influences the steering feeling. Therefore, the control performance of the PMSM drive is crucial for the EPS system's overall performance. The position measurement angle error (contains measurement delay angle and periodic angle error) plays a significant role in PMSM drives, especially when the motor is rotating in the flux weakening (FW) region. In this paper, the impact of the position angle error on the performance of PMSM drives for EPS across a wide speed range is thoroughly examined. The effect of the measurement delay angle is analysed by considering the voltage and current constraint boundaries of the motor at different rotation speeds. Then, calibration and compensation of the position measurement delay angle are presented. Furthermore, the impact of the periodic angle error on current/torque ripple has been elaborated, and a sensorless control scheme is proposed to mitigate the current/torque ripple caused by such periodic error. According to the experimental results, it has been determined that the motor can operate stably over a wide speed range with an appropriate compensation of the measurement delay angle, and the current/torque ripple caused by the periodic angle error can be effectively eliminated with the proposed sensorless control scheme. As a result, it is confirmed that the analysis and discussion, as well as corresponding compensation methods, demonstrated in this paper can improve the steering feeling of the EPS.
The power grid is faced with challenges arising from the integration of electric vehicle charging, and the reliability of electric vehicle charging services is also affected by grid failures. To address the difficulties in reliability assessment caused by multi-dimensional characteristics such as randomness and sequentiality, temporal evaluation of electric vehicle charging services reliability considering photovoltaic-storage-charging integration is proposed in this paper. Firstly, the fault characteristics of distribution networks under electric vehicle grid integration are analyzed, and a distribution network fault and reliability evaluation model integrating electric vehicle charging load is constructed. Secondly, a dynamic optimization strategy for energy storage based on model predictive control is proposed, by which the reliability of charging services under both grid-connected and off-grid modes, considering distribution network faults and coordinated operation of photovoltaic-storage-charging is enhanced. Finally, multi-dimensional charging service reliability indices and a calculation method based on the sequential Monte Carlo simulation are proposed. It is shown by simulation results that the proposed evaluation indices can quantify the reliability of electric vehicle charging services reliability from multi-dimensional, and the proposed coordinated optimization strategy for photovoltaic-storage-charging can significantly improve the reliability level of electric vehicle charging services under different operation modes and fault scenarios.
Ehsan Farokhipour, Mohammad Amin Chaychi Zadeh, Sajjad Zohrevand
et al.
This paper presents the design, simulation, and experimental validation of six passive anomalous reflectors using 2-bit digital coding metasurfaces (DCMs) that can enable quasi-continuous single-beam steering for 5G n257 mm-wave applications. The design starts with a square-shaped meta-atom illuminated by a plane wave under an oblique incidence of −45°, enabling four discrete reflection phases with 90° phase increments. Each metasurface is constructed using identical meta-atoms in one direction and specific coding sequences in the orthogonal direction. Each supercell may contain equal or unequal numbers of meta-atoms per phase state, achieving anomalous reflections in the desired directions. As a demonstration, the design goals are six passive anomalous reflectors that produce six beams directed at −32°, −17°, −7°, +7°, +17°, and +32°. An expanded angular range with a minimal step size is also achieved by varying the frequency of the incident oblique beam. The full-wave simulations show good agreement with the analytical predictions. The far-field received power is measured across 27.5–29.5 GHz and compared to that of a perfect electric conductor (PEC). The measured frequency responses closely match those of the simulations, confirming the effectiveness of the proposed structures. These results are intended to facilitate a practical solution for wide-angle single-beam steering for mm-wave wireless communications with obstructed line-of-sight (LOS).
The busbar, serving as a critical power transmission component in power electronic converters, fulfills essential functions including interconnection of power devices, capacitors, terminals, and insulation. To mitigate parasitic parameters and device stresses, converter circuits must be integrated through busbars. This paper focuses on the ANPC topology composed of a 15 kV SiC metal oxide semiconductor field effect transistor (SiC MOSFET) and a series-connected 6.5 kV Si insulated gate bipolar transistor (Si IGBT), investigating optimized busbar design through dimensional arrangement, layer stacking sequence, and terminal positioning. A three-dimensional electromagnetic model of medium-voltage multi-device integrated busbars is established using finite element simulation software. Parametric analysis is conducted to optimize device spacing and layer structures, proposing a busbar layout strategy tailored for hybrid ANPC topologies. Simulation results demonstrate that the optimized design effectively reduces system parasitic while validating reasonable electric field distribution under high-frequency switching conditions. Experimental tests on a prototype platform confirm that the optimized busbar exhibits superior insulation performance at critical nodes and enhanced overall reliability compared to conventional designs.
Seyyed Morteza Ghamari, Mehrdad Ghahramani, Daryoush Habibi
et al.
Brushless DC (BLDC) motors are commonly used in electric vehicles (EVs) because of their efficiency, small size and great torque-speed performance. These motors have a few benefits such as low maintenance, increased reliability and power density. Nevertheless, BLDC motors are highly nonlinear and their dynamics are very complicated, in particular, under changing load and supply conditions. The above features require the design of strong and adaptable control methods that can ensure performance over a broad spectrum of disturbances and uncertainties. In order to overcome these issues, this paper uses a Fractional-Order Proportional-Integral-Derivative (FOPID) controller that offers better control precision, better frequency response, and an extra degree of freedom in tuning by using non-integer order terms. Although it has the benefits, there are three primary drawbacks: (i) it is not real-time adaptable, (ii) it is hard to choose appropriate initial gain values, and (iii) it is sensitive to big disturbances and parameter changes. A new control framework is suggested to address these problems. First, a Reinforcement Learning (RL) approach based on Deep Deterministic Policy Gradient (DDPG) is presented to optimize the FOPID gains online so that the controller can adjust itself continuously to the variations in the system. Second, Snake Optimization (SO) algorithm is used in fine-tuning of the FOPID parameters at the initial stages to guarantee stable convergence. Lastly, cascade control structure is adopted, where FOPID controllers are used in the inner (current) and outer (speed) loops. This construction adds robustness to the system as a whole and minimizes the effect of disturbances on the performance. In addition, the cascade design also allows more coordinated and smooth control actions thus reducing stress on the power electronic switches, which reduces switching losses and the overall efficiency of the drive system. The suggested RL-enhanced cascade FOPID controller is verified by Hardware-in-the-Loop (HIL) testing, which shows better performance in the aspects of speed regulation, robustness, and adaptability to realistic conditions of operation in EV applications.
ABSTRACT Under external short‐circuit conditions, transformer windings are subjected to axial vibrations induced by axial electromagnetic forces. Due to the unidirectional compressive nature of spacers, separation between winding disks and spacers may occur, threatening the axial mechanical stability of the windings. Extensive research has been conducted on axial vibration calculation models and the vibration characteristics. These efforts have led to the establishment of vibration models based on mass‐spring‐damper systems, and investigations into the effects of factors such as moisture, ageing and damping on the behaviour of spacers and the vibration process. However, neither the impact of disk–spacer separation on winding stability has been analysed, nor have the critical conditions for its occurrence been defined. In this paper, a winding vibration model incorporating the unidirectional compressive characteristics of spacers was developed to analyse changes in vibration intensity before and after the onset of disk–spacer separation. The study clarified the patterns of separation under varying short‐circuit currents, pre‐tightening forces and spacer hardness. Furthermore, a rapid evaluation method for axial stability was proposed, using disk–spacer separation as a criterion. The results identify the critical conditions for disk–spacer separation, providing a theoretical basis for improving the axial strength of transformer windings.
Recently, Verma et al. (2025) introduced a novel generalized class of Kavya-Manoharan distributions, which have demonstrated significant utility in reliability analysis and the modeling of lifetime data. This paper proposes an extension of this class by applying the power generalization technique, thereby enhancing more flexibility and applicability. We take the exponential distribution as the baseline distribution to introduce a new model capable of accommodating both monotonic and non-monotonic hazard rate functions. Our model includes eleven submodels. We present several statistical properties of the introduced model, including moments, generating and characteristic functions, mean deviations, quantile function, mean residual life function, Rényi entropy, order statistics, and reliability. To estimate the unknown model parameters, we use the maximum likelihood approach. A simulation study is conducted to assess the validity of the maximum likelihood estimator. The superiority of the new distribution is demonstrated through the use of a real data application.
Nowadays the demand for receiving the high quality electrical energy is being increasing as consumer wants not only reliable but also quality power .The usage of automated equipment are increasing and far more susceptible to disturbances compare to the previous generation equipment and information systems. With
the deregulation of the electric power energy market, the awareness concerning the quality of power has been increasing day by day among different categories of customers. Power quality is an issue that is becoming increasingly important to electricity consumers at all levels of usage. Power quality can be improved in distributed system by using shunt compensation device known as Distribution
Static Compensator (DSTATCOM). This paper covers the different topologies of Distribution Static Compensators (DSTATCOMs) and the various control methodologies, and its selection for specific applications.
Nowadays the demand for receiving the high qualityelectrical energy is being increasing as consumer wants not onlyreliable but also quality power .The usage of automated equipmentare increasing and far more susceptible to disturbances compare tothe previous generation equipment and information systems. Withthe deregulation of the electric power energy market, the awarenessconcerning the quality of power has been increasing day by dayamong different categories of customers. Power quality is an issuethat is becoming increasingly important to electricity consumers atall levels of usage. Power quality can be improved in distributedsystem by using shunt compensation device known as DistributionStatic Compensator (DSTATCOM). This paper covers the differenttopologies of Distribution Static Compensators (DSTATCOMs) andthe various control methodologies, and its selection for specificapplications.
<b>[Objective]</b> This paper focuses on developing a pneumatic-electric hybrid magnetic flux leakage(MFL) detector, examining and verifying its technical advantages to overcome challenges in using traditional air compressor-driven MFL detectors in both newly constructed long-distance natural gas pipelines and established pipelines with low fow rates or no transmission. <b>[Methods]</b> After analyzing the operational states of traditional air compressor-driven MFL detectors during inner pipeline detection, a dynamic model was constructed to simulate the motion of a pneumatic-electric hybrid inner detector within a pipeline, adhering to fundamental principles of detector mobility in gas pipelines. The MFL detector was enhanced with an active motor drive function to operate using both the blower and motor drive mechanisms. The motor was activated upon encountering abrupt changes in resistance, effectively addressing challenges such as detector blockages, pressure buildup, and acceleration caused by gas explosions, while ensuring smooth detector passage through the pipeline.<b>[Results]</b> The pneumatic-electric hybrid MFL detector developed in this study was used for the pre-commissioning inner detection of the Daqing-Harbin branch line of the China-Russia East-route Natural Gas Pipeline. It successfully detected various features at 6,930 positions along the 52 km pipeline. The subsequent excavation was performed at a position where metal loss was reported, confirming that all detection data, including defect location in mileage, type, ground positioning, length, width, depth, and clockwise direction, aligned with the excavation results. <b>[Conclusion]</b> The pneumatic-electric hybrid MFL detector has obvious advantages by using blower and its own power as the driving force, making it effective in practical engineering applications concerning safety, power performance, and data reliability. By integrating a low-pressure and low-fow blower, it effectively eliminates potential safety hazards commonly associated with high-pressure gas, prevalent in air compressor operations. The controllable speed feature ensures uniform detector movement, thereby improving data quality and detection accuracy. With the combined pneumatic and electric power, the detector navigates potential jamming points like crossings/spannings and bends swiftly, reducing the risk of blockages. This innovative detector shows promising application prospects,necessitating improvements in cost-effectiveness, system simplicity, and battery life.
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. The PIKANs present 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 a single-machine infinite bus system and a 4-bus 2- generator system underscore the accuracy of the PIKANs in predicting rotor angle and frequency with fewer learnable parameters than conventional PINNs. Furthermore, the simulation results demonstrate PIKANs 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 determination of grid dynamics and precise parameter identification.
Ahmad Mohammad Saber, Amr Youssef, Davor Svetinovic
et al.
Recently, there has been a growing interest in utilizing machine learning for accurate classification of power quality events (PQEs). However, most of these studies are performed assuming an ideal situation, while in reality, we can have measurement noise, DC offset, and variations in the voltage signal's amplitude and frequency. Building on the prior PQE classification works using deep learning, this paper proposes a deep-learning framework that leverages attention-enabled Transformers as a tool to accurately classify PQEs under the aforementioned considerations. The proposed framework can operate directly on the voltage signals with no need for a separate feature extraction or calculation phase. Our results show that the proposed framework outperforms recently proposed learning-based techniques. It can accurately classify PQEs under the aforementioned conditions with an accuracy varying between 99.81%$-$91.43% depending on the signal-to-noise ratio, DC offsets, and variations in the signal amplitude and frequency.
Tasnuba Binte Jamal, Samiul Hasan, Omar I. Abdul-Aziz
et al.
This study presents an agent-based model (ABM) developed to simulate the resilience of a community to hurricane-induced infrastructure disruptions, focusing on the interdependencies between electric power and transportation networks. In this ABM approach, agents represent the components of a system, where interactions within a system shape intra-dependency of a system and interactions among systems shape interdependencies. To study household resilience subject to a hurricane, a library of agents has been created including electric power network, transportation network, wind/flooding hazards, and household agents. The ABM is applied over the household and infrastructure data from a community (Zip code 33147) in Miami-Dade County, Florida. Interdependencies between the two networks are modeled in two ways, (i) representing the role of transportation in fuel delivery to power plants and restoration teams' access, (ii) impact of power outage on transportation network components. Restoring traffic signals quickly is crucial as their outage can slow down traffic and increase the chance of crashes. We simulate three restoration strategies: component based, distance based, and traffic lights based restoration. The model is validated against Hurricane Irma data, showing consistent behavior with varying hazard intensities. Scenario analyses explore the impact of restoration strategies, road accessibility, and wind speed intensities on power restoration. Results demonstrate that a traffic lights based restoration strategy efficiently prioritizes signal recovery without delaying household power restoration time. Restoration of power services will be faster if restoration teams do not need to wait due to inaccessible roads and fuel transportation to power plants is not delayed.
The inverse power method is a numerical algorithm to obtain the eigenvectors of a matrix. In this work, we develop an iteration algorithm, based on the inverse power method, to numerically solve the Schrödinger equation that couples an arbitrary number of components. Such an algorithm can also be applied to the multi-body systems. To show the power and accuracy of this method, we also present an example of solving the Dirac equation under the presence of an external scalar potential and a constant magnetic field, with source code publicly available.
Abstract To achieve the transient overvoltage suppression of doubly fed induction generator (DFIG) terminals and the point of common coupling (PCC) in the wind farm (WF), an adaptive overvoltage control strategy based on data‐driven approach is proposed. Firstly, the PCC reactive power demand and DFIG overvoltage threshold are derived, respectively. Secondly, three regression models are developed through surface fitting to determine three variables: the maximum overvoltage of each DFIG, the PCC maximum overvoltage, and the reactive power demand of each DFIG. Subsequently, the K‐means clustering method is adopted to group all DFIGs, and then the reactive power requirement of the PCC is proportionally distributed to each group of DFIGs. Lastly, a coordinated control strategy of DFIGs and static var generator (SVG) is designed based on the values of three regression models, which can adaptively adjust the reactive power absorbed by each DFIG and SVG. In particular, the de‐load control is adopted to enlarge the maximum reactive power capacity of DFIG. The simulation results show that the proposed control strategy can timely and effectively restore the transient overvoltage of the wind farm to the normal range during the gird failure, ensuring the stable operation of the wind farm.
In order to improve the conversion efficiency of photovoltaic cells and reduce the energy loss, the maximum power point tracking (MPPT) method needs to be studied. Aiming at the problem that the tracking speed and steady-state accuracy of the traditional perturbation observation method (P&O) cannot be balanced, and misjudgment occurs when the environment changes greatly, a variable-step P&O control strategy that can adapt to the environmental changes is proposed. Firstly, the short-circuit current under current illumination is obtained by using the characteristics of the photovoltaic cell similar to the constant current source when it first starts, and the reference voltage of the maximum power point (MPP) is derived by the fixed current method. Secondly, when the illuminance changes abruptly, the power correction method is proposed, and the variable step size adjustment strategy is given. Finally, a fractional order proportion integration differentiation (FOPID) controller based on linear extended state observer (LESO) is designed, which can further track and compensate the reference voltage output by the algorithm. Simulation results show that the proposed control strategy can improve the steady-state accuracy and tracking speed, and effectively improve the output power of photovoltaic cells.
Lightning is one of the main causes of voltage sags in power grid. Accurate estimation of the severity of voltage sags caused by lightning can provide a basis for developing optimal management plans and siting sensitive users. In this paper, a data-driven self-learning estimation method for the severity of voltage sags is proposed. Firstly, based on the mechanism of voltage sags caused by lightning, the parameters involved in mining are selected by the monitoring information in lightning location system and power quality monitoring system. Secondly, the influence of discretization results on the accuracy of rules is reduced, and the number of discretization intervals for different parameters is determined by using discretization evaluation indexes. Then, to solve the problem of low efficiency of mining algorithm when the grid database changes dynamically, the incremental learning-based association rule mining algorithm to continuously update the mined rules, which gives it the ability of self-learning. Finally, a weighted Euclidean distance based on the integrated assignment method is proposed to evaluate the severity of voltage sags in real scenarios. The results of the empirical analysis by monitoring data of a regional power grid and simulation data of IEEE 30-node prove that the method in this paper can accurately mine valuable rules in reality and realize the severity assessment of voltage sags of the concerned nodes.
Recently solar panels are gaining popularity in the field of non-conventional energy sources for generating green and clean electric power. On the negative side, the photovoltaic efficiency is reduced with an increase in ambient temperature. The production of energy is dropped by 0.33% for every degree Celsius above STC. Consequently, the electric power which is generated by the solar panel may not be sufficient to run the load. It is important to realize that in some applications, such as standalone electric vehicles, space for providing an additional solar panel to compensate for the decremented output power may not be feasible. By implementing the cooling arrangements, this excessive heat might be reduced. Several cooling techniques have been implemented, named as active and passive methods. This article presents a review on maximizing the efficiency of the solar panel by utilizing different cooling methods and by integrating TEG with solar panels.
With the rapid development of renewable energy,the role of energy storage has become increasingly prominent. In terms of sizing problems of shared energy storage to provide primary frequency regulation for multiple renewable energy stations,an optimal energy storage configuration method is proposed aiming to minimize the total cost of shared energy storage investors. Firstly,the empirical distribution of historical frequency data is fitted,and the result is used to generate frequency data to further configure the energy storage. Then,based on the frequency data,an optimal configuration model of energy storage is developed which meets the requirements of primary frequency regulation. The primary frequency regulation constraints,energy storage rate characteristics constraints,and primary frequency regulation participation rate constraints and so on are considered in the optimal configuration model. The model is a mixed-integer linear programming model which can be solved by mature solvers. Finally,the proposed method is simulated and analyzed according to the actual frequency data,and the configuration results of the lithium-ion battery and flywheel are compared. The results show that the total cost can be reduced by installing shared energy storage compared with independent energy storage. Although the capacity of the lithium-ion battery energy storage systems is significantly greater than that of flywheel energy storage systems,the total cost of lithium-ion battery energy storage systems is lower.