Grid System Research Articles

Power transformer fault diagnosis using dynamic multiscale graph modeling and M2SGCN network based on statistical fusion

Power equipment fault diagnostics hold significant importance for the stability of power grid systems. In pursuit of this objective, this paper proposes a fault diagnosis method that utilizes dynamic multiscale graph (DMG) modeling and the multiscale multi-stream GCN(M2SGCN) network, incorporating statistical fusion. Specifically, a novel DMG modeling method is proposed to derive visibility graph data and horizontal visibility graph data from vibration signals across multiple scales. Next, a comprehensive neural network architecture named M2SGCN is established to learn global and local features simultaneously, providing a more precise representation. Subsequently, a Dempster Shafer evidence theory statistical fusion technique combined with an adaptive threshold model (DSTFusion) is utilized to integrate primary decision results for enhanced fault diagnosis accuracy. In addition, two datasets obtained from single-phase and three-phase power transformers are analyzed to demonstrate the evolution process. When compared to state-of-the-art indicators such as accuracy, precision, recall, and F1 scores, the method proposed excels in multiple aspects, successfully detecting fault states before their occurrence and achieving outstanding performance.

Optimizing hydropower scheduling through accurate power load prediction: A practical case study

Hydropower stations that are part of the grid system frequently encounter challenges related to the uneven distribution of power generation and associated benefits, primarily stemming from delays in obtaining timely load data. This research addresses this issue by developing a scheduling model that combines power load prediction and dual-objective optimization. The practical application of this model is demonstrated in a real-case scenario, focusing on the Shatuo Hydropower Station in China. In contrast to current models, the suggested model can achieve optimal dispatch for grid-connected hydropower stations even when power load data is unavailable. Initially, the model assesses various prediction models for estimating power load and subsequently incorporates the predictions into the GA-NSGA-II algorithm, specifically an enhanced elite non-dominated sorting genetic algorithm. This integration is performed while considering the proposed objective functions to optimize the discharge flow of the hydropower station. The outcomes reveal that the CNN-GRU model, denoting Convolutional Neural Network-Gated Recursive Unit, exhibits the highest prediction accuracy, achieving R-squared and RMSE (i.e., Root Mean Square Error) values of 0.991 and 0.026, respectively. The variance between scheduling based on predicted load values and actual load values is minimal, staying within 5 (m3/s), showcasing practical effectiveness. The optimized scheduling outcomes in the real case study yield dual advantages, meeting both the demands of ship navigation and hydropower generation, thus achieving a harmonious balance between the two requirements. This approach addresses the real-world challenges associated with delayed load data collection and insufficient scheduling, offering an efficient solution for managing hydropower station scheduling to meet both power generation and navigation needs.

Solution to uncertainty of renewable energy sources and peak hour demand in smart grid system

Today's need for environmental sustainability prompted the smart grid to incorporate more renewable energy sources. Reducing the operational costs of power generation and emission dispatch can both be accomplished with the integration of renewable energy sources. A power system is made to manage the fluctuating nature of loads for a brief time, nevertheless. However, new difficulties arise for utilities as a result of the fluctuation and uncertainty associated with renewable sources. Another problem faced by generating plants is network reliability issues arising from the peak hour's power demand in the power system. Application of demand response technology with the help of smart meters connected at consumers' end in smart cities can help to ease the enormous peak-hour pressure on power generating units. But a hybrid power system integrated with a storage system faces the challenge of optimal utilization of the battery under uncertainties caused by intermittent generation of power and dynamic demand. This research work proposes a real-time price based hidden Markov model and an enhanced demand response technique to solve these issues in a hybrid power system. Proposed model is tested on IEEE 30-bus system with/without renewable energy sources and implemented grasshopper optimization algorithm for optimal scheduling of generating units.

Melting performance analysis of finned metal foam thermal energy storage tube under steady rotation

The latent heat thermal energy storage (LHTES) technology based on solid-liquid phase change material (PCM) is of great significance for the efficient utilization of thermal energy. To address the issues of slow thermal response and non-uniform melting of the LHTES technology, a hybrid heat transfer enhancement method combined with finned metal foam and steady rotation is proposed in this work. An enthalpy-porosity model considering non-Darcy porous effects and mechanical rotation is established based on the local thermal equilibrium assumption and the fixed grid system. Four different structures (uniform metal foam, graded metal foam, finned metal foam with uniform porosity, and finned metal foam with graded porosity) are investigated firstly to identify the optimal structural arrangement under the same volume of PCM. Numerical results demonstrate that the LHTES units strengthened by the finned metal foam with graded porosity achieve the shortest melting time and largest thermal energy storage rate (TESR). The graded porous structure reduces thermal resistance, rotation enhances flow and heat transfer inside the container, and the fins expand the heat source area. Moreover, the effect of different fin types, graded porosities, and rotational speeds are further considered. The findings suggest that the increase of these three parameters does not correspond to better thermal performance; instead, there is an optimal value. Compared with the base case, the optimal configuration (fin length of 20 mm, fin width of 1.5 mm, porosity gradient of 2%, and rotational speed of 0.5 rpm) can shorten the melting time by 46.68%, increase the TESR and Nu by 74.06% and 69.02%, respectively. This paper validates the feasibility of the hybrid heat transfer enhancement method with finned metal foam and steady rotation, which can offer new insights into the engineering practice of the LHTES technology.

Electric vehicles and the operation of multi-carrier energy systems in the smart grid: a review study

Smart grids have been connected to energy hubs (EHs) with a range of energy carriers and storage capacity to increase the efficiency, flexibility, and dependability of the energy system. One of the most challenging problems in smart grids nowadays is the scheduling of EHs. Although the interaction of several devices, such as EHs and electric vehicle charging stations (EVCSs), can help improve their performance, developing such intricately interwoven systems is difficult. In this paper, with the help of several papers, the impact of EVCSs on the optimal operation of EHs is investigated, despite the uncertainty surrounding electric vehicle charging behaviours. Additionally, the formulation concerning the energy hub's component parts, electric vehicles (EVs) in a single hub, and interconnected hubs is investigated. In addition, some simulation results from reviewed papers are presented and analyzed in this paper. The main goal of this paper is to analyze the effects of EVCS on the optimal management of EHs. Furthermore, some models of energy hubs integrated by EVCS are presented, and a numerical analysis is conducted to examine the function of EVCS in conjunction with electrical and thermal storage systems.

Higher-order Granger reservoir computing: simultaneously achieving scalable complex structures inference and accurate dynamics prediction

Recently, machine learning methods, including reservoir computing (RC), have been tremendously successful in predicting complex dynamics in many fields. However, a present challenge lies in pushing for the limit of prediction accuracy while maintaining the low complexity of the model. Here, we design a data-driven, model-free framework named higher-order Granger reservoir computing (HoGRC), which owns two major missions: The first is to infer the higher-order structures incorporating the idea of Granger causality with the RC, and, simultaneously, the second is to realize multi-step prediction by feeding the time series and the inferred higher-order information into HoGRC. We demonstrate the efficacy and robustness of the HoGRC using several representative systems, including the classical chaotic systems, the network dynamical systems, and the UK power grid system. In the era of machine learning and complex systems, we anticipate a broad application of the HoGRC framework in structure inference and dynamics prediction.

Dynamic modelling and a dual vector modulated improved model predictive control with auto tuning feature of active front‐end converters for distributed energy resources

AbstractThe operational performance of grid‐connected active front‐end converters (AFEs) faces challenges arising from the intricate interplay among phase‐locked loop (PLL) non‐linearities, grid impedance, and conventional control strategies, resulting in compromised stability. This study introduces a refined approach to dynamic model predictive control (MPC) by integrating recursive least squares (RLS) for the precise estimation of physical model parameters, thereby addressing stability concerns. Unlike conventional methodologies, the proposed enhanced RLS‐based MPC approach, equipped with an auto‐tuning feature, allows for the design of controllers without a prerequisite understanding of exact external dynamics. Notably, this technique exhibits exceptional disturbance rejection capabilities. The evaluation of the cost function at each sampling interval facilitates the determination of optimal switching states based on predicted variables. Gate pulses for the switches of the AFEs are generated accordingly. Employing a simulation platform, the proposed control structure's performance across varied conditions is comprehensively assessed, encompassing alterations in grid impedance and system non‐linearities. The method adeptly integrates inherent non‐linearities within the system, showcasing exceptional robustness in diverse dynamic scenarios. To further substantiate the efficacy of the proposed control system over conventional approaches, simulation results are validated using a laboratory hardware platform equipped with Typhoon HIL and dSPACE real‐time emulators, providing tangible evidence of the proposed control system's effectiveness in real‐world hardware setups. The multifaceted approach, encompassing precise parameter estimation, predictive control, auto‐tuning, disturbance rejection, robust design, and real‐time evaluation, collectively establishes a resilient foundation for enhancing and maintaining the overall stability of the system across diverse operating scenarios.

Advanced MPC Control Strategy for Active Front-End Converters in Distributed Energy Resources

The stability of grid-connected active front-end converters (AFEs) is often compromised by the intricate interplay between inherent converter nonlinearities and grid dynamics. This article presents a novel approach to dynamic model predictive current control (MPC) by leveraging recursive least squares (RLS) to accurately estimate the properties of a physical model. This estimation mitigates stability issues, setting the stage for improved control. Unlike conventional methods, our proposed RLS-based MPC, enriched with an auto-tuning feature, empowers controller development without the upfront need for precise external dynamics. This not only ensures high disturbance rejection but also maintains a high-fidelity control performance, rendering the approach versatile for applications where obtaining or predicting precise external dynamics is challenging. At each sampling interval, a cost function is applied to predicted variables to discern optimal switching states. Through extensive simulation studies covering diverse grid impedance changes and system nonlinearities, we evaluate the controller’s effectiveness. To underscore its superiority over traditional controls, simulation results are validated on a laboratory hardware platform equipped with Typhoon HIL and dSPACE real-time emulators, further attesting to the robustness and practical viability of our proposed approach.

Techno-economic analysis of commercial-scale 15 MW on-grid ground solar PV systems in Bakalia: A feasibility study proposed for BPDB

Renewable energy systems, such as solar power, are becoming increasingly important worldwide due to the limited supply of non-renewable energy sources. Solar power stands out as a practical choice for electricity generation because it's simple to set up and costs less than other renewable options. Solar-based on-grid or grid-tied systems are more effective compared to other PV grid systems due to their more reasonable installation system, favorable maintenance, and less complex system. This study was designed with a solar-based grid-tied system. This study evaluates the performance and economic viability of a 15 MW on-grid photovoltaic (PV) system in Bakalia Char, Chittagong, Bangladesh, and will propose this study for the Bangladesh Power Development Board (BPDB). Developing a clean energy system in Bangladesh is the main purpose of this study. This system performed efficiently, with an 84.03 % performance ratio, produced energy of 21,510.186 MWh/year and proved economically attractive with a 4.5-year payback period, a competitive electricity cost of 0.024 USD/kWh, and a 389 % return on investment(ROI%). Using software like PVsyst and SketchUp ensures precise system design and optimal module placement. Also, use a better PV panel system whose efficiency is higher than that of another PV panel system designed for a similar project. This system boosts local electricity production and aligns with sustainable energy goals. Its success is a valuable model for future solar projects in similar regions facing energy challenges. The system also shows significant environmental benefits, with a projected reduction of approximately 252,168.5 tons of CO2 emissions over its operational lifespan.

Spherical Gravity Forwarding of Global Discrete Grid Cells by Isoparametric Transformation

For regional or even global geophysical problems, the curvature of the geophysical model cannot be approximated as a plane, and its curvature must be considered. Tesseroids can fit the curvature, but their shapes vary from almost rectangular at the equator to almost triangular at the poles, i.e., degradation phenomena. Unlike other spherical discrete grids (e.g., square, triangular, and rhombic grids) that can fit the curvature, the Discrete Global Grid System (DGGS) grid can not only fit the curvature but also effectively avoid degradation phenomena at the poles. In addition, since it has only edge-adjacent grids, DGGS grids have consistent adjacency and excellent angular resolution. Hence, DGGS grids are the best choice for discretizing the sphere into cells with an approximate shape and continuous scale. Compared with the tesseroid, which has no analytical solution but has a well-defined integral limit, the DGGS cell (prisms obtained from DGGS grids) has neither an analytical solution nor a fixed integral limit. Therefore, based on the isoparametric transformation, the non-regular DGGS cell in the system coordinate system is transformed into the regular hexagonal prism in the local coordinate system, and the DGGS-based forwarding algorithm of the gravitational field is realized in the spherical coordinate system. Different coordinate systems have differences in the integral kernels of gravity fields. In the current literature, the forward modeling research of polyhedrons (the DGGS cell, which is a polyhedral cell) is mostly concentrated in the Cartesian coordinate system. Therefore, the reliability of the DGGS-based forwarding algorithm is verified using the tetrahedron-based forwarding algorithm and the tesseroid-based forwarding algorithm with tiny tesseroids. From the numerical results, it can be concluded that if the distance from observations to sources is too small, the corresponding gravity field forwarding results may also have ambiguous values. Therefore, the minimum distance is not recommended for practical applications.

Development of constant switching frequency model predictive control for DFIG-based wind energy conversion system in standalone and grid connected operation

This paper presents a constant switching frequency (CSF) model predictive control (MPC) for a doubly-fed induction generator (DFIG)-based wind energy conversion system (WECS) in standalone and grid-connected operations. The system comprises a cascade connection of a rotor side converter (RSC) and a grid side converter (GSC) with a common DC-link. A battery energy storage system (BESS) is also connected to the common DC-link via a bi-directional dc–dc converter (BDDC). The RSC is controlled through CSF-model predictive current control (CSF-MPCC) scheme in both standalone mode (SAM) and grid-connected mode (GCM) of operations. The GSC control employs the CSF-MPCC scheme in grid-connected operation, whereas CSF-model predictive voltage control (CSF-MPVC) scheme is implemented in standalone operation. Moreover, the power balance is achieved by controlling the BDDC using MPCC to discharge or charge the BESS in response to surplus or deficient power generation. The effectiveness of the proposed control approach for DFIG-based WECS in both SAM and GCM is verified through MATLAB-based simulation and experimental results under different dynamic conditions like variable wind velocity changing from 12 m/s to 8 m/s, different grid demands from 3 kW to 1.5 kW and load change of 4 kW to 6.5 kW. Moreover, the robustness of the proposed control is corroborated by parametric sensitivity, time delay, and grid unbalance, along with its low-voltage ride-through (LVRT) capability during an LLLG fault.

Deformation analysis of prefabricated structures in the shaking table test based on density point clouds

ABSTRACT In this paper, a point cloud extraction method based on the template grid was proposed for deformation analysis. Firstly, coordinate alignment was carried out by a coarse-to-fine strategy to ensure that the point clouds are in a unified coordinate system. Then, the points in the grid are subsequently converted into a universal grid system and the center according to the grid border is carried out to serve as the feature points for deflection detection. The deformation was determined by analyzing the deviation under different working conditions, and the maximum deformation under different earthquake magnitudes was verified in the experiment.

Soft Computing Technique for Harmonic Mitigation of Enhancing Grid-Integrated Photovoltaic (PV) System Power Quality

This abstract explores the use of soft computing techniques for harmonic mitigation in renewable energy integration, particularly for photovoltaic (PV) systems in power grids. Traditional methods often fail to effectively mitigate harmonics, leading to system performance compromise and grid instability. Soft computing's adaptability and ability to handle complex systems offer promising solutions. The study explores the use of soft computing techniques like artificial neural networks, fuzzy logic, and genetic algorithms to mitigate harmonics in PV systems. ANNs learn complex patterns, fuzzy logic handles uncertain data, and genetic algorithms optimize mitigation strategies for varying grid conditions and system dynamics. The study demonstrates the effectiveness of soft computing-based harmonic mitigation techniques in improving power quality indices like total harmonic distortion and voltage stability. It emphasizes the importance of integrating advanced computational approaches in PV systems for seamless grid integration and promoting sustainable renewable energy integration. Key Word: Renewable Energy sources, PV systems, harmonics, fuzzy logic controller, ANNs

Grid-connected Systems Powered by Solar Energy Implemented with Fuzzy based Voltage Source Converters

Solar energy systems connected to the electrical grid are known as grid-tied solar power systems or solar power-connected grid systems. Grid-tied solar inverters are only capable of producing active electricity since they are made to produce power at a unity power factor. The grid alone can meet the load’s requirement for reactive power. Reactive power drawn from the grid relative to active power has increased significantly with the sharp rise in the deployment of distributed energy resources based on renewable energy. This has an impact on the grid’s power quality. The amount of reactive power that the grid must supply will decrease if the grid-tied solar inverter is designed to be intelligent enough to provide reactive power in addition to active power. This work’s primary goal is to generate the necessary pulses for a three-phase inverter, which will aid in producing reactive power and ultimately help achieve the work’s primary objective. Fuzzy logic is a complex approach that takes advantage of the flexibility and adaptability of fuzzy systems to manage Voltage Source Converters (VSCs) for three-phase inverter operation. The primary circuit is simulated in the MATLAB/Simulink environment, giving the desired results.

Study of Load Adjustment Strategy for Nuclear Power Units Focusing on Rankine Cycle: Flexibility–Environment–Economy

The demand for the power grid system’s capacity to undergo peak-shaving is increasing as the proportion of renewable energy rises. In China, nuclear power units usually only provide a base load operation in the view of safety and economic considerations, but they do not provide load adjustment services, which undoubtedly increases the pressure of grid load adjustment. In this paper, a novel flexibility load adjustment strategy of the CHP nuclear unit is studied, which is achieved by introducing the thermal storage tank (TST) into the Rankine cycle without changing the output of the nuclear reactor. The AP1000 pressurized water reactor nuclear power unit for combined heat and power is taken as an example, and the thermodynamic model is established through the water vapor equation. Furthermore, the reference system is simulated for the goal of minimizing the imbalance between power supply and demand, and the flexibility–environment–economy benefits are evaluated. The results show that the heat storage/release of the TST may achieve power output flexible adjustment of the nuclear unit, and the power imbalance of the reference energy system is reduced from 1107.99 MWh to 457.24 MWh, a reduction of 58.73%. The introduction of a 600 MWh TST can enable the reference unit to contribute 335 MWh of peak electricity within the reference day. From the perspective of replacing the power generation output increment of coal-fired power units with equal amounts, it can achieve a reduction of 106.09 tons of coal consumption in the case day, which means that 277.73 tons of CO2 emissions can be reduced. The profit of the reference unit can be improved by CHY 70,125 via participating in load adjustment in the case day if following the time-of-use electricity price.

Smartgrid-based hybrid digital twins framework for demand side recommendation service provision in distributed power systems

Electricity consumers face challenges in selecting an optimal energy-saving plan, and this is a sustainability problem. To set consumers focus on sustainable energy management, developments around ”Industry 4.0” are needed to achieve an optimal balance between cost and energy consumption with a focus on cutting-edge machine-learning models and smart services introduction. Energy modelling is crucial for confronting the challenges introduced by the energy transition. This research is to promote digital twin (DT) technology in smart energy grids. The hybrid digital twin model is developed based on smart grid end-users’ electrical components. This approach emulates the component specification of the reference smart grid system and encourages a reduction in net energy consumption. Additionally, the application of AI connects smart meters, IoT devices, and the assets of the smart grids to the DT recreation of demand-side end-users for energy efficiency recommendation provision; this setup improves energy management, energy efficiency, and the usage of renewable energy resources. The novelty of this study is that the recent scope of demand-side recommendation schemes has been extended in the direction of cutting-edge Industry 4.0 hybrid DTs. Additionally, a prototype system and its functional characteristics have been proposed with the potential to pave the path for utilization in microgrid environments. Simulation results show a unique level of parallelism between the reference system and the developed model, along with a 36.8% reduction in net energy consumption following implementation of the recommendation advice.

Regulating local chemical environment in O3-type layered sodium oxides by dual-site Mg2+/B3+ substitution achieves durable and high-rate cathode

The O3-Na0.85Ni0.2Fe0.4Mn0.4O2 layered oxide cathode material possesses the advantages of high specific capacity, low cost, and simple synthesis. However, sluggish kinetics and complicated phase transition caused by the large size difference between Na+ and tetrahedral gaps lead to poor rate and cycling performance. Therefore, a scalable and feasible strategy was proposed to modulate local chemical environment by introducing Mg2+ and B3+ into O3-Na0.85Ni0.2Fe0.4Mn0.4O2, which can distinctly improve kinetic transport rate as well as electrochemical performance. The capacity retention of O3-(Na0.82Mg0.04)(Ni0.2Fe0.4Mn0.4)B0.02O2 (NFMB) increases from 43.3% and 12.4% to 89.5% and 89.0% at 1 C and 3 C after 200 cycles, respectively. Moreover, the electrode still delivers high rate capacity of 93.9 mAh/g when current density increases to 10 C. Mg2+ ions riveted on Na layer act as a “pillar” to stabilize crystal structure and inhibit structural change during the desodiumization process. B3+ ions entering tetrahedral interstice of the TM layer strengthen the TM-O bond, lower Na+ diffusion energy barrier and inhibits the slip of TM layer. Furthermore, the assembled full batteries with the modified cathode material deliver a high energy density of 278.2 Wh/kg with commercial hard carbon as anode. This work provides a strategy for the modification of high-performance SIB layered oxide materials to develop the next-generation cost-effective energy storage grid systems.

Numerical study for slamming loads on the bow of a ship-type FPSO model under breaking waves

Numerical analyses were performed to investigate the characteristics of the slamming impact loads on the bow-flare of a ship-type floating production storage and offloading (FPSO) model under breaking waves. To estimate the slamming impact load, a series of breaking wave simulations was performed. From 2D wave flume tests, the grid aspect ratio, convection scheme, turbulence model, effect of surface tension, and grid dependencies were investigated. Through a series of numerical simulations, the grid system and numerical schemes were determined, and the slamming impact load simulations were conducted. It could be observed that the slamming impact loads and motions of the FPSO model in the numerical simulations were in good agreement with the experimental results. In order to investigate the relation between the slamming impact load and free motion of the FPSO model, the breaking impact simulations were performed under fixed positions and the free motion of the FPSO model. From the results, the relation between the relative angle and slamming impact load, as well as the relation between the position of the FPSO model and impulse response, are discussed.

Adjoint-based shape optimization for radiative transfer in porous structure for volumetric solar receiver

Due to their large surface areas, porous structures are often used for volumetric solar receivers for efficient utilization of the solar energy. However, the precise simulation and optimization of radiative heat transfer inside porous structures are still challenging due to their complex geometries. In the present study, an adjoint-based shape optimization framework for radiative transfer in porous structures is first proposed. Specifically, an arbitrary complex geometry of a porous structure is represented by a level-set function embedded in a Cartesian grid system. The volume penalization method combined with the discrete ordinate method is employed to simulate a forward radiative transfer problem in a porous structure. Then, a cost functional for an adjoint analysis is defined by the volume average of the divergence of the radiative heat flux, which corresponds to the total heat absorbed by the porous structure with a negative sign. The adjoint equations for minimizing the cost functional, and the corresponding shape update formula are derived and implemented in an open-source software, OpenFOAM. The developed adjoint-based shape optimization framework is applied to three types of initial porous structures, i.e., SC Kelvin, BCC Kelvin, and square honeycomb models. For all the initial porous models, the front surface commonly extends towards the direction of the incident radiation, and consequently notable improvements of the absorption efficiency can be achieved through the optimization. In addition, a systematic parametric survey for the initial shape of the SC Kelvin model is conducted to reveal the impacts of the number of unit cells, the solid emissivity, and the solid temperature on the optimal shapes and the resulting absorption efficiencies.

Bidirectional mapping between rhombic triacontahedron and icosahedral hexagonal discrete global grid systems

ABSTRACT The icosahedron is currently the mainstream polygon in research and application of discrete global grid systems (DGGS). However, compared to the rhombic triacontahedron (RT), the icosahedron has disadvantages, such as lower sphere-fitting accuracy, greater projection distortion, and difficulty in incorporating the matrix structure for geospatial data storage. More importantly, the special positional relationship between the rhombic triacontahedron and the Earth enables it to effectively support event simulations related to geographical locations. To this end, bidirectional mapping of the hexagonal grid between the RT and icosahedron was proposed, which can efficiently integrate the existing datasets and algorithms of icosahedral DGGS into RT DGGS, thereby achieving seamless conversion between heterogeneous grid systems. We established geometric and topological correlations between the RT and icosahedron, abstracted the spatial algebraic structures of hexagonal grids on the two different polygons, and constructed mapping relationships between them. Finally, conversion between heterogeneous grid indices was achieved using dual quaternions. Experiments revealed that the proposed method was 3.9150 and 2.8151 times more efficient at grid conversion from RT to icosahedron and from icosahedron to RT, respectively, than was a method using latitude/longitude coordinates as a medium.