Evaluation of Grid-Connected Photovoltaic Plants Based on Clustering Methods

Photovoltaic(PV)systems are electric power systems designed to sup-ply usable solar power by means of photovoltaics,which is the conversion of light into electricity using semiconducting materials.PV systems have gained much attention and are a very attractive energy resource nowadays.The substantial advantage of PV systems is the usage of the most abundant and free energy from the sun.PV systems play an important role in reducing feeder losses,improving voltage profiles and providing ancillary services to local loads.However,large PV grid-connected systems may have a destructive impact on the stability of the elec-tric grid.This is due to thefluctuations of the output AC power generated from the PV systems according to the variations in the solar energy levels.Thus,the elec-trical distribution system with high penetration of PV systems is subject to perfor-mance degradation and instabilities.For that,this project attempts to enhance the integration process of PV systems into electrical grids by analyzing the impact of installing grid-connected PV plants.To accomplish this,an indicative representa-tion of solar irradiation datasets is used for planning and powerflow studies of the electric network prior to PV systems installation.Those datasets contain lengthy historical observations of solar energy data,that requires extensive analysis and simulations.To overcome that the lengthy historical datasets are reduced and clus-tered while preserving the original data characteristics.The resultant clusters can be utilized in the planning stage and simulation studies.Accordingly,studies related to PV systems integration into the electric grid are conducted in an efficient manner,avoiding computing resources and processing times with easier and practical implementation.

Implementation of FPGA Based MPPT Techniques for Grid-Connected PV System

Global energy demand is growing rapidly owing to industrial growth and urbanization.Alternative energy sources are driven by limited reserves and rapid depletion of conventional energy sources(e.g.,fossil fuels).Solar photovol-taic(PV),as a source of electricity,has grown in popularity over the last few dec-ades because of their clean,noise-free,low-maintenance,and abundant availability of solar energy.There are two types of maximum power point track-ing(MPPT)techniques:classical and evolutionary algorithm-based techniques.Precise and less complex perturb and observe(P&O)and incremental conduc-tance(INC)approaches are extensively employed among classical techniques.This study used afield-programmable gate array(FPGA)-based hardware arrange-ment for a grid-connected photovoltaic(PV)system.The PV panels,MPPT con-trollers,and battery management systems are all components of the proposed system.In the developed hardware prototype,various modes of operation of the grid-connected PV system were examined using P&O and incremental con-ductance MPPT approaches.

Improved Interleaved Single-Ended Primary Inductor-Converter forSingle-Phase Grid-Connected System

The generation of electricity based on renewable energy sources,parti-cularly Photovoltaic(PV)system has been greatly increased and it is simply insti-gated for both domestic and commercial uses.The power generated from the PV system is erratic and hence there is a need for an efficient converter to perform the extraction of maximum power.An improved interleaved Single-ended Primary Inductor-Converter(SEPIC)converter is employed in proposed work to extricate most of power from renewable source.This proposed converter minimizes ripples,reduces electromagnetic interference due tofilter elements and the contin-uous input current improves the power output of PV panel.A Crow Search Algo-rithm(CSA)based Proportional Integral(PI)controller is utilized for controlling the converter switches effectively by optimizing the parameters of PI controller.The optimized PI controller reduces ripples present in Direct Current(DC)vol-tage,maintains constant voltage at proposed converter output and reduces over-shoots with minimum settling and rise time.This voltage is given to single phase grid via 1�Voltage Source Inverter(VSI).The command pulses of 1�VSI are produced by simple PI controller.The response of the proposed converter is thus improved with less input current.After implementing CSA based PI the efficiency of proposed converter obtained is 96%and the Total Harmonic Distor-tion(THD)is found to be 2:4%.The dynamics and closed loop operation is designed and modeled using MATLAB Simulink tool and its behavior is performed.

Steady-State Analysis of Grid-Connected New Energy Power Plants

In order to ease the fossil energy crunch,new energy sources need to be fully utilized.Clean energy sources such as wind,light,and nuclear energy are important tools to solve environmental and energy problems.However,in the process of researching new energy farms,there are some problems when they are integrated into the power system.In order to ensure the stability of new energy power plants,it is necessary to conduct an in-depth analysis of the grid connection technology of new energy farms.In the study,it is necessary to learn about the specific problems of the stability of the grid connection of new energy power plants,and to clarify the specific application of the grid connection technology of new energy power plants from the application principle and advantages of the grid connection technology of new energy power plants.Through simulation experiments,the positive effect of grid connection technology of new energy power plants in improving the stability of power systems was determined.

Soft-switching Grid-connected PV Inverter With Interleaved Flyback Topology

提出一种应用于单个光伏组件的软开关交错反激并网逆变器拓扑及其软开关控制策略。针对反激变压器漏感问题，提出漏感能量吸收回馈电路，实现了漏感能量吸收再利用，并实现了开关管漏源电压的钳位，提高了变换效率同时降低了开关管关断电压尖峰；提出基于数字处理器的反激变换器变开关频率谐振软开关控制策略，实现了开关管的零电压开通，同时改善了逆变器的电磁兼容特性；提出的交错并联反激逆变器有助于减小变压器和滤波器的体积，提高功率密度。详细分析变换器的工作原理，分析变开关频率谐振软开关控制方式的原理和实现条件，最后进行实验验证。

Grid-connected inverter for wind power generation system

In wind power generation system the grid-connected inverter is an important section for energy conversion and transmission, of which the performance has a direct influence on the entire wind power generation system. The mathematical model of the grid-connected inverter is deduced firstly. Then, the space vector pulse width modulation （SVPWM） is analyzed. The power factor can be controlled close to unity, leading or lagging, which is realized based on H-type current controller and grid voltage vector-oriented control. The control strategy is verified by the simulation and experimental results with a good sinusoidal current, a small harmonic component and a fast dynamic response.

Performance Assessment of a Real PV System Connected to a Low-Voltage Grid

The generation of photovoltaic(PV)solar energy is increasing continuously because it is renewable,unlimited,and clean energy.In the past,generation systems depended on non-renewable sources such as oil,coal,and gas.Therefore,this paper assesses the performance of a 51 kW PV solar power plant connected to a low-voltage grid to feed an administrative building in the 6th of October City,Egypt.The performance analysis of the considered grid-connected PV system is carried out using power system simulator for Engineering(PSS/E)software.Where the PSS/E program,monitors and uses the power analyzer that displays the parameters and measures some parameters such as current,voltage,total power,power factor,frequency,and current and voltage harmonics,the used inverter from the type of grid inverter for the considered system.The results conclude that when the maximum solar radiation is reached,the maximum current can be obtained from the solar panels,thus obtaining the maximum power and power factor.Decreasing total voltage harmonic distortion,a current harmonic distortion within permissible limits using active harmonic distortion because this type is fast in processing up to 300 microseconds.The connection between solar stations and the national grid makes the system more efficient.

Research on Control Strategy of Grid-connected Brushless Doubly-fed Wind Power System Based on Virtual Synchronous Generator Control

The brushless doubly-fed wind power system based on conventional power control strategies lacks ‘inertia’ and the ability to support grid,which leads to the decline of grid stability.Therefore,a control strategy of brushless doubly-fed reluctance generator(BDFRG) based on virtual synchronous generator(VSG) control is proposed to solve the problem in this paper.The output characteristics of BDFRG based on VSG are similar to a synchronous generator(SG),which can support the grid frequency and increase the system ‘inertia’.According to the mathematical model of BDFRG,the inner loop voltage source control of BDFRG is derived.In addition,the specific structure and parameter selection principle of outer loop VSG control are expounded.The voltage source control inner loop of BDFRG is combined with the VSG control outer loop to establish the overall architecture of BDFRG-VSG control strategy.Finally,the effectiveness and feasibility of the proposed strategy are verified in the simulation.

Robust Dichotomy Solution-Based Model Predictive Control for the Grid-Connected Inverters with Disturbance Observer

This paper proposes a robust dichotomy-based model predictive control(DS-MPC)with a fixed switching frequency for the grid-connected inverter(GCI).The proposed fast dichotomy algorithm can select and deduce the optimal voltage vector dynamically through the space vector plane.Therefore,the proposed DS-MPC strategy could ensure dynamic performance and steady-state performance as well.Also,the current control robustness can be improved through DS-MPC with disturbance observer(DO)based on the extended Kalman filter(EKF).The novelty of this control is that the current control with fast dynamic response can be realized in the weak grid,even if the grid voltages are greatly distorted.Simulation and hardware experiments on the weak grid validate the effectiveness of the proposed DS-MPC with the EKF observer approach.

Grid System Analysis of Urban Flora of Bukhara City (Uzbekistan)

This article presents information on the study of the flora of Uzbekistan based on grid system mapping. The urban flora of the city of Bukhara was researched in it. As a result of research, the territory of Bukhara city was divided into 85 indexes based on 1 × 1 km2 grid mapping system. The diversity and density of species in the indexes are determined. The influence of anthropogenic factors on the diversity of species in the indexes is determined.

Single Current Feedback Control Strategy for Grid-connected Inverters With LCL Filters

LCL滤波器广泛应用于并网逆变器系统中,其对高次谐波具有良好的衰减作用;但LCL滤波器为低阻尼三阶系统,容易发生谐振。传统的有源阻尼方法,通过对电容电流进行采样并反馈,能够抑制系统振荡,但增加了传感器数量。提出一种单电流反馈控制策略,其采用入网电流两次微分的反馈方法,增加了系统阻尼,从而有效地抑制LCL滤波器的谐振尖峰,保证系统的稳定性;同时其对系统参数不敏感,并且不增加额外的传感器,是一种低成本高可靠性的控制策略。文中推导该控制策略下的系统传递函数,并分析系统的稳定性。最后,通过仿真和实验,验证提出的控制策略的可行性与有效性。

RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by consumers.The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning techniques.Moreover,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware.Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance.In this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious activity.The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft detection.Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for classification.The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification results.The performance evaluation of the proposed model revealed exemplary results across variousmetrics.The accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.

A Long-Time-Step-Permitting Tracer Transport Model on the Regular Latitude–Longitude Grid

If an explicit time scheme is used in a numerical model, the size of the integration time step is typically limited by the spatial resolution. This study develops a regular latitude–longitude grid-based global three-dimensional tracer transport model that is computationally stable at large time-step sizes. The tracer model employs a finite-volume flux-form semiLagrangian transport scheme in the horizontal and an adaptively implicit algorithm in the vertical. The horizontal and vertical solvers are coupled via a straightforward operator-splitting technique. Both the finite-volume scheme's onedimensional slope-limiter and the adaptively implicit vertical solver's first-order upwind scheme enforce monotonicity. The tracer model permits a large time-step size and is inherently conservative and monotonic. Idealized advection test cases demonstrate that the three-dimensional transport model performs very well in terms of accuracy, stability, and efficiency. It is possible to use this robust transport model in a global atmospheric dynamical core.

Physics-Constrained Robustness Enhancement for Tree Ensembles Applied in Smart Grid

With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and intelligence.However,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their robustness.To address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles.Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical rules.In our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical constraints.These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.

Assessment of Crop Yield in China Simulated by Thirteen Global Gridded Crop Models

Global gridded crop models(GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far only a few studies have assessed the performance of GGCMs in China, and these studies mainly focused on the average and interannual variability of national and regional yields. Here, a systematic national-and provincial-scale evaluation of the simulations by13 GGCMs [12 from the GGCM Intercomparison(GGCMI) project, phase 1, and CLM5-crop] of the yields of four crops(wheat, maize, rice, and soybean) in China during 1980–2009 was carried out through comparison with crop yield statistics collected from the National Bureau of Statistics of China. Results showed that GGCMI models generally underestimate the national yield of rice but overestimate it for the other three crops, while CLM5-crop can reproduce the national yields of wheat, maize, and rice well. Most GGCMs struggle to simulate the spatial patterns of crop yields. In terms of temporal variability, GGCMI models generally fail to capture the observed significant increases, but some can skillfully simulate the interannual variability. Conversely, CLM5-crop can represent the increases in wheat, maize, and rice, but works less well in simulating the interannual variability. At least one model can skillfully reproduce the temporal variability of yields in the top-10 producing provinces in China, albeit with a few exceptions. This study, for the first time, provides a complete picture of GGCM performance in China, which is important for GGCM development and understanding the reliability and uncertainty of national-and provincial-scale crop yield prediction in China.

Study on Joint Method of 3D Acoustic Emission Source Localization Simplex and Grid Search Scanning

Acoustic emission(AE)source localization is a fundamental element of rock fracture damage imaging.To improve the efficiency and accuracy of AE source localization,this paper proposes a joint method comprising a three-dimensional(3D)AE source localization simplex method and grid search scanning.Using the concept of the geometry of simplexes,tetrahedral iterations were first conducted to narrow down the suspected source region.This is followed by a process of meshing the region and node searching to scan for optimal solutions,until the source location is determined.The resulting algorithm was tested using the artificial excitation source localization and uniaxial compression tests,after which the localization results were compared with the simplex and exhaustive methods.The results revealed that the localization obtained using the proposed method is more stable and can be effectively avoided compared with the simplex localization method.Furthermore,compared with the global scanning method,the proposed method is more efficient,with an average time of 10%–20%of the global scanning localization algorithm.Thus,the proposed algorithm is of great significance for laboratory research focused on locating rupture damages sustained by large-sized rock masses or test blocks.

Self-Attention Spatio-Temporal Deep Collaborative Network for Robust FDIA Detection in Smart Grids

False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams.Data-driven features,however,cannot effectively capture the differences between noisy data and attack samples.As a result,slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks.To address this problem,this paper designs a deep collaborative self-attention network to achieve robust FDIA detection,in which the spatio-temporal features of cascaded FDIA attacks are fully integrated.Firstly,a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes,and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node,which guides the network to pay more attention to the node information that is conducive to FDIA detection.Furthermore,the bi-directional Long Short-Term Memory(LSTM)network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal selfattention mechanism to describe the time correlation of data and assign different weights to different time steps.Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information,efficiently distinguish power grid noise from FDIA attacks,and adapt to diverse attack intensities.Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator(NYISO)in IEEE 14,IEEE 39,and IEEE 118 bus systems,and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness.

Novel cyber-physical collaborative detection and localization method against dynamic load altering attacks in smart energy grids

Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.

Virtual Power Plants for Grid Resilience: A Concise Overview of Research and Applications

The power grid is undergoing a transformation from synchronous generators(SGs) toward inverter-based resources(IBRs). The stochasticity, asynchronicity, and limited-inertia characteristics of IBRs bring about challenges to grid resilience. Virtual power plants(VPPs) are emerging technologies to improve the grid resilience and advance the transformation. By judiciously aggregating geographically distributed energy resources(DERs) as individual electrical entities, VPPs can provide capacity and ancillary services to grid operations and participate in electricity wholesale markets. This paper aims to provide a concise overview of the concept and development of VPPs and the latest progresses in VPP operation, with the focus on VPP scheduling and control. Based on this overview, we identify a few potential challenges in VPP operation and discuss the opportunities of integrating the multi-agent system(MAS)-based strategy into the VPP operation to enhance its scalability, performance and resilience.

Intelligent Power Grid Load Transferring Based on Safe Action-Correction Reinforcement Learning

When a line failure occurs in a power grid, a load transfer is implemented to reconfigure the network by changingthe states of tie-switches and load demands. Computation speed is one of the major performance indicators inpower grid load transfer, as a fast load transfer model can greatly reduce the economic loss of post-fault powergrids. In this study, a reinforcement learning method is developed based on a deep deterministic policy gradient.The tedious training process of the reinforcement learning model can be conducted offline, so the model showssatisfactory performance in real-time operation, indicating that it is suitable for fast load transfer. Consideringthat the reinforcement learning model performs poorly in satisfying safety constraints, a safe action-correctionframework is proposed to modify the learning model. In the framework, the action of load shedding is correctedaccording to sensitivity analysis results under a small discrete increment so as to match the constraints of line flowlimits. The results of case studies indicate that the proposed method is practical for fast and safe power grid loadtransfer.