Grid State Research Articles

Simulation inversion analysis of simultaneous commutation failure of Zhaoyi and Yindong DC

Abstract Aim at the three times simultaneous commutation failure of two Multi-outfeed and Multi-infeed DC Systems in a Similar Path of Ningxia, the Ningxia power grid model is built in Power System Analysis Software Package (PSASP) with actual parameters, and the accident inversion simulation is carried out based on the operation state of the power grid before the fault. Based on the inversion simulation, the simulation results were compared with the measured PMU data. The results showed that the trend of simulation and the measured curve is consistent, and the simulation was slightly more serious than the actual fault, but the overall error was less than 10%, which verified the accuracy of the simulation model and method, and provided a basis for the subsequent simulation analysis of the power grid operation mode.

Synchronized States of Power Grids and Oscillator Networks by Convex Optimization

Synchronization is essential for the operation of ac power systems: all generators in the power grid must rotate with fixed relative phases to enable a steady flow of electric power. Understanding the conditions for and the limitations of synchronization is of utmost practical importance. In this article, we propose a novel approach to computing and analyzing the stable stationary states of a power grid or a network of Kuramoto oscillators in terms of a convex optimization problem. This approach allows us to systematically compute stable states where the phase difference across an edge does not exceed π/2. Furthermore, the optimization formulation allows us to rigorously establish certain properties of synchronized states and to bound the error in the widely used linear power flow approximation. Published by the American Physical Society 2024

Power data analysis and mining technology in smart grid

This study proposes a smart grid model named “GridOptiPredict”, which aims to achieve efficient analysis and processing of power system data through deep fusion of deep learning and graph neural network, so as to improve the intelligent level and overall efficiency of power grid operation. The model integrates three core functions of load forecasting, power grid state sensing and resource optimization into one, forming a closely connected and complementary framework. Through carefully designed experimental scheme, the practical value and effectiveness of “Grid OptiPredict” model are fully verified from three aspects: accuracy of load forecasting, sensitivity of power grid state sensing and efficiency of resource allocation strategy. Experimental results show that the model has significant advantages in prediction accuracy, model stability and robustness, resource optimization, security, information security, social and economic benefits and user experience.

A new model reduction method based PBC control for grid-connected inverter with LCL-filter

Passivity-based control (PBC) exhibits robustness against parameters shift, providing a substantial level of stability. However, for the LCL-filtered grid-connected inverter (GCI), the conventional PBC (called C-PBC) controller has a narrow control bandwidth due to the control time delay, resulting in poor dynamic performance, especially in the weak grid state. This paper proposes a new PBC (called P-PBC) method to increase the closed-loop bandwidth of the system by setting an appropriate feedback proportional coefficient between the grid-side current and the inverter-side current. By extending the closed-loop bandwidth of the system, the proposed P-PBC method offers improved dynamic performance, particularly in challenging grid conditions. In addition, a state observer is also employed to reduce sensors, which saves costs and enhances the system's reliability. A 110 V/50 Hz/3 kW/3-phase experimental setup has been developed using the dSPACE DS1202 platform to validate the effectiveness of the proposed control method.

ENGINEERING CHALLENGES AND SOLUTIONS IN SMART GRID INTEGRATION WITH ELECTRIC VEHICLES

This paper explores the critical engineering challenges and innovative solutions for successfully integrating intelligent grids and electric vehicles (EVs), emphasizing the increasing need for a resilient and adaptive electrical grid as global EV adoption accelerates. It comprehensively examines the technological, infrastructural, and regulatory obstacles that must be addressed to ensure seamless integration, focusing on advanced energy management systems, grid stability amidst fluctuating demand, and incorporating renewable energy sources. The study delves into the infrastructural requirements, including the expansion of charging networks, upgrades to transmission and distribution systems, and the implementation of vehicle-to-grid (V2G) technologies, while also analyzing the necessary regulatory and policy frameworks, stressing the importance of clear standards, incentives, and public-private collaboration. The paper offers a forward-looking perspective on overcoming current challenges by reviewing recent advancements in innovative grid technology—such as high-capacity energy storage and artificial intelligence (AI) use for predictive maintenance and load balancing. It highlights the need for interdisciplinary collaboration among engineers, policymakers, and industry leaders to develop a cohesive strategy for future energy distribution while underscoring the role of AI in optimizing grid performance, predicting energy consumption patterns, and enhancing overall efficiency. Ultimately, the paper provides a comprehensive analysis of the current state of smart grid and EV integration, offering actionable insights for stakeholders and concluding with recommendations for future research and development priorities, with a strong emphasis on continued innovation and cooperation to achieve a sustainable and resilient energy future.

Review on the Evaluation and Improvement Measures of the Carrying Capacity of Distributed Power Supply and Electric Vehicles Connected to the Grid

With the increasing number of distributed power sources such as photovoltaic power and wind power and electric vehicles connected to the grid, the structure and operation state of the traditional distribution network have undergone great changes. Therefore, through the establishment of a distributed power grid-connected evaluation system, it has become an important research topic to evaluate the access of distributed power and the carrying capacity of electric vehicles in the distribution network of new power systems. Firstly, the situation of distributed power supply and electric vehicles and the impact of grid connection are introduced. Secondly, the traditional distribution network carrying capacity evaluation method is studied and introduced. Then, the distribution network carrying capacity evaluation considering uncertainty is reviewed and investigated. Finally, in view of the lack of existing research, further research is needed, aiming to provide a reference for the realization of distributed power supply and grid-connected EV carrying capacity evaluation and a scientific basis for the future operation planning of distributed power supply and EV integration into the distribution network.

Risk Assessment Method for Distributed Power Distribution Networks Considering Network Dynamic Reconstruction

A new safety assessment framework has been proposed to address the operational risks of the integration of wind power and photovoltaic grid, which integrates the characteristics of distributed power sources with the dynamic reconfiguration requirements of the distribution grid. The framework comprehensively considers the impacts of wind power and photovoltaic output uncertainties, as well as load fluctuations, on the stability of the distribution grid. It also evaluates the safety under different operational states of the distribution grid. Using Halton sequence sampling technology to accurately simulate the output of distributed power sources and the status of system components, combined with CPLEX optimisation for solving, a dynamic reconfiguration model is constructed to address potential faults in the distribution grid. Introducing the combined weighting method, a comprehensive risk assessment system for voltage violations, power flow violations, and load shedding has been constructed. The effectiveness of this method has been validated through simulations on the IEEE33 bus and IEEE118 bus systems, providing new insights to improve the safety and reliability of distribution grids.

A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems

The integration of renewable energy sources (RES) into smart grids has been considered crucial for advancing towards a sustainable and resilient energy infrastructure. Their integration is vital for achieving energy sustainability among all clean energy sources, including wind, solar, and hydropower. This review paper provides a thoughtful analysis of the current status of the smart grid, focusing on integrating various RES, such as wind and solar, into the smart grid. This review highlights the significant role of RES in reducing greenhouse gas emissions and reducing traditional fossil fuel reliability, thereby contributing to environmental sustainability and empowering energy security. Moreover, key advancements in smart grid technologies, such as Advanced Metering Infrastructure (AMI), Distributed Control Systems (DCS), and Supervisory Control and Data Acquisition (SCADA) systems, are explored to clarify the related topics to the smart grid. The usage of various technologies enhances grid reliability, efficiency, and resilience are introduced. This paper also investigates the application of Machine Learning (ML) techniques in energy management optimization within smart grids with the usage of various optimization techniques. The findings emphasize the transformative impact of integrating RES and advanced smart grid technologies alongside the need for continued innovation and supportive policy frameworks to achieve a sustainable energy future.

Design of Power Outage Monitoring and Warning System Based on Internet of Things

With the rapid development of distribution network and the high popularity of distributed generation, the research on risk assessment of distribution network is accelerated. When quantifying the degree of harm caused by abnormal situations, it is necessary to consider the severity of the harm and the impact on society to measure the severity of power failure and reduce the loss caused by power risk. To provide data support for the fine perception of the situation and its development trend, the monitoring system constructed by the power microservice further improves the ability and accuracy of sudden fault prediction. In this paper, through the monitoring system information to explore the transmission line fault early warning, the data analysis model is established. The particle swarm optimization algorithm and BP neural network are used to build the early warning model to predict the potential fault situation and cause analysis of the power grid under various indicators in a certain period in the future, which is to provide more scientific and reasonable risk management and control for the power grid. Through the statistical analysis of all kinds of information data collected by the power microservice, a power monitoring system based on early warning of power outage risk is established to enhance the operation adaptability of the power grid system under uncertain conditions and prevent the occurrence of tripping blackouts so that this study has an important function of intelligent decision-making. The experimental results show the feasibility and high efficiency of this study. The comparison of algorithm results shows that the performance of this method is improved by more than 10%. The stability is improved by 17%. The response time to various situations is shortened by 32% to judge the abnormal operation state of the power grid more efficiently. It can make emergency preparations in time, reduce the damage caused by accidents to the power grid and provide practical tools for the power grid to further enhance the disaster prevention ability of transmission equipment and effectively resist risk events.

Analytical Linearization of Aerodynamic Loads in Unsteady Vortex-Lattice Method for Nonlinear Aeroelastic Applications

This paper presents the analytical linearization of aerodynamic loads (computed with the unsteady vortex-lattice method), which is formulated as tangent matrices with respect to the kinematic states of the aerodynamic grid. The loads and their linearization are then mapped to a nonlinear structural model by means of radial-basis functions, allowing for a two-way strong interaction scheme. The structural model comprises geometrically exact beams formulated in a director-based total Lagrangian description, circumventing the need for rotational degrees of freedom. The structural model is spatially discretized into finite elements and temporally discretized with the help of an implicit scheme that identically preserves momenta and energy. The resulting nonlinear discrete equations are solved by applying Newton’s method, requiring calculating the Jacobians of the whole aeroelastic system. The correctness of the linearized loads is then shown by direct comparison with their numerical counterparts. In addition, we employ our strongly coupled aeroelastic model to investigate the nonlinear static and dynamic behavior of a suspension bridge. With this approach, we successfully investigate the numerical features of the aeroelastic system under divergence and flutter conditions.

Multi-rate measurement fusion for power system state estimation

Abstract In recent years, the estimation of power systems has become increasingly critical. The introduction of Synchronized PMU has significantly enhanced the dynamic estimation capabilities of power systems. However, given the widespread deployment and usage of SCADA systems in the power grid, it is challenging to entirely replace SCADA systems with PMU systems in a short period. Therefore, utilizing a hybrid approach that combines the two systems for power state estimation is of utmost importance. This article achieves the integration of multi-rate and multi-sensor information in the power system by employing predictive interpolation on SCADA to match the sampling rate of PMU. The performance of power grid state estimation surpasses that of a single SCADA deployment.

Circuit-theoretic joint parameter-state estimation—Balancing optimality and AC feasibility

AC State Estimation (ACSE) is widely recognized as a practical approach for determining the grid states in steady-state conditions. It serves as a fundamental analysis to ensure grid security and is a reference for market dispatch. As grid complexity increases with rapid electrification and decarbonization, there is a growing need for more accurate knowledge of the grid operating state. However, existing ACSE algorithms have technical gaps. Critically, current ACSE algorithms are susceptible to erroneous system parameters, which are assumed to be fixed in traditional approaches. In this paper, we build a novel circuit-theoretic joint parameter-state estimation algorithm to address this limitation. The innovative algorithm builds an analogous equivalent circuit of the grid with states and certain parameters unknown. It solves a circuit-constrained optimization to estimate the most likely grid states and parameters given a set of measurements. Further, it quantifies the goodness of the estimated output by formulating tight convex envelopes around the original non-convex problem to quantify the quality of estimates. We compare the various proposed approaches on systems with up to 2869 nodes while demonstrating a tradeoff between solution optimality and model fidelity.

Securing the green grid: A data anomaly detection method for mitigating cyberattacks on smart meter measurements

Smart meters, being a vital component in the advanced metering infrastructure (AMI), provide an opportunity to remotely monitor and control power usage and act like a bridge between customers and utilities. The installation of millions of smart meters in the power grid is a step forward towards a green transition. However, it also constitutes a massive cybersecurity vulnerability. Cyberattacks on AMI can result in inaccurate billing, energy theft, service disruptions, privacy breaches, network vulnerabilities, and malware distribution. Thus, utility companies should implement robust cyber-security measures to mitigate such risks. In order to assess the impact of cybersecurity breaches on AMI, this paper presents a cyber-attack scenario on grid measurements obtained via smart meters and assesses the stochastic grid estimations under attack. This paper also presents an efficient method for the detection and identification of anomalous data within the power grid by leveraging the distance between measurements and the confidence ellipse centered around the estimated value. To assess the proposed method, a comparative analysis is done against the chi-square test for detection and the largest normalized distribution test for the identification of bad data. Furthermore, by using a Danish low-voltage grid as a base case, this paper introduces two test cases to evaluate the performance of the proposed method under single and multiple-node cyber-attacks on the grid state estimation. Results show a notable improvement in accuracy when using the proposed method. Additionally, based on these numerical results, protective countermeasures are presented for the grid.

An adaptive control strategy for integration of wind farm using a VSC-HVDC transmission system

This study addresses the issue of integrating renewable energy sources into the power grid by proposing advanced power management strategies based on an adaptive observer. The system comprises a wind turbine farm (WTF) connected to an energy storage system via a Vienna rectifier, which serves as the voltage source converter (VSC), and is linked to the DC grid through a high-voltage direct current (HVDC) transmission line. The main objective of this study is to design a nonlinear controller to improve energy extraction from the wind power frame and enhance the stability of the power grid. The system controller is designed to operate in two modes. The main feature of this controller is its ability to achieve multi-objective control using different references. In this study, we have four references but only two control laws. Furthermore, an adaptive observer is designed to estimate the power grid requirements, allowing us to produce depending on the grid state. Finally, simulation results using MATLAB/Simulink will be displayed to demonstrate the stability and efficiency of the proposed implementation.

State estimation of variable frequency power grid of multi-electric aircraft based on adaptive enhanced complex-coefficient filter

The power grid of a multi-electric aircraft is a typical isolated island microgrid with a wide frequency range from 360 to 800 Hz. The dynamic performance of the traditional constant-frequency power grid synchronization method is insufficient in the variable frequency power grid of the multi-electric aircraft. An adaptive enhanced complex-coefficient filter-based phase locked loop (AECCF-PLL) is designed for the variable frequency power grid by integrating an adaptive module into the enhanced complex-coefficient filter-based phase locked loop to accurately estimate the state of variable frequency power grid. The relationship between the transfer function and the step response is analyzed. The relationship between the model parameters and the frequency is derived, and the frequency adaptive module is established to meet the requirements for stability and rapidity in the state estimation of the variable frequency power grid. The experimental results show that the proposed AECCF-PLL can achieve the rapid and stable estimation of the state of the variable frequency power grid when there are disturbances such as large or small frequency jumps, harmonics and slopes.

High Schmidt Number Concentration in Quantum Bound Entangled States.

A deep understanding of quantum entanglement is vital for advancing quantum technologies. The strength of entanglement can be quantified by counting the degrees of freedom that are entangled, which results in a quantity called the Schmidt number. A particular challenge is to identify the strength of entanglement in quantum states that remain positive under partial transpose (PPT), otherwise recognized as undistillable states. Finding PPT states with high Schmidt numbers has become a mathematical and computational challenge. In this Letter, we introduce efficient analytical tools for calculating the Schmidt number for a class of bipartite states called grid states. Our methods improve the best-known bounds for PPT states with high Schmidt numbers. Most notably, we construct a Schmidt number 3 PPT state in five-dimensional systems and a family of states with a Schmidt number of (d+1)/2 for odd d-dimensional systems, representing the best-known scaling of the Schmidt number in a local dimension. Additionally, these states possess intriguing geometrical properties, which we utilize to construct indecomposable entanglement witnesses.

Analysis of corrosion detection error of grounding grid under amplitude characterization

Magnetic field amplitude detection is an important method for determining the positioning and corrosion degree of the grounding grid, and the conventional analysis only determines the grounding grid status by collecting single-component signals, which makes the diagnosis results of the grounding grid status susceptible to the influence of the collection process. By considering the single-component sensor device in the attitude deflection during the actual substation detection process, the analysis of the difference between the signals measured by the sensor under different attitude deflections is performed, and the degree of influence of the attitude deflection on the magnetic field amplitude characteristics is determined. Further, by analyzing the calculation error of the attitude angle on the corrosion degree of the ground grid, the correspondence between the sensor attitude and the calculation error of the corrosion degree was obtained. Finally, in order to reduce the influence of the sensor attitude on the detection signal, it is proposed that the detection scheme of measuring the three-component sensor signal and the attitude angle at the same time which could improve the accuracy of the detection system.

Design and Analysis of New Type of Magnetically Controlled Reactor

In recent years, various new types of magnetic materials have emerged, especially in the field of nanotechnology. The application of material composite technology has formed a new type of dual-phase composite magnetic material. Under the control of an external magnetic field, the material can achieve functional conversion between the hard magnetic phase and the soft magnetic phase, with magnetic permeability, non-magnetic permeability, and excitation ability. In this paper, it is proposed to use this material as the basic material for the magnetic state control of the reactor core, break through the bondage of the traditional reactor core to the performance of the controllable reactor, and innovatively form a new type of reactor structure. By adding a detection and control system, according to the working state of the power grid, the function conversion of the nano adjustable magnetic material is effectively adjusted to realize the automatic adjustment of the magnetic state of the reactor. Firstly, the preparation and material properties of dual-phase composite magnetic materials were studied. The design and preparation process from magnetic powder to bulk magnet were given, and the magnetic properties were measured. Secondly, a new 380 V/100 kVar magnetically controlled reactor was designed by using dual-phase composite magnetic material to form a composite core of magnetically controlled reactor on silicon steel sheet. The simulation analysis of electromagnetic characteristics and volt-ampere characteristics was carried out to verify the correctness of the proposed scheme.

A decentralized coordinated control scheme for the DC network with a multiport converter

For complex networks with multiple subgrids formed by solid-state transformers, existing studies primarily focus on coordination within the networks in islanded mode, overlooking cooperation in grid-connected mode. To address this gap, this paper proposes a coordinated control scheme for the triple active bridge (TAB) converter in the grid-connected DC network. By establishing a droop relationship between the input power and the voltage difference, the TAB converter facilitates coordination between the utility grid and the DC network. The energy exchange with the utility grid is not only regulated by the TAB converter but also adjusted according to the utility grid state. So, the proposed scheme effectively facilitates coordination between the higher- and lower-level networks, strengthening system stability. Besides, the proposed scheme employs a unified control structure, which has power sharing and voltage regulation capabilities in grid-connected mode, as well as operating in islanded mode. This control structure enables the TAB converter to seamlessly transition across different operation modes, avoiding mode switching process and reducing the control complexity. Moreover, the proposed scheme is decentralized which negates the necessity for communication links, bolstering the system reliability. Lastly, the feasibility of the proposed scheme is analyzed in theory and validated by the hardware-in-loop tests.

Leveraging the power of machine learning and data balancing techniques to evaluate stability in smart grids

Modernizing traditional power grids into smart grids represents a significant advancement in improving efficiency and sustainability within the energy sector. However, integrating renewable energy sources and the intricate network of interconnected devices has presented new challenges, especially in grid stability. Anticipating and maintaining grid stability ensures a continuous energy supply and prevents potential disruptions. This research paper explores the smart grid stability issue through machine learning. Two distinct scenarios are examined. In the first scenario, the problem is approached as a binary classification task, where grid states are categorized as stable or unstable, a well-established method for addressing grid stability. In the second scenario, an effort is made to predict continuous values that reflect the degree of grid stability. These continuous values are subsequently mapped to a conditional function, facilitating stability detection based on a predefined threshold. To address data imbalance, two balancing strategies, specifically the Synthetic Minority Over-sampling Technique (SMOTE) and K-means SMOTE, are employed in each scenario. The analysis covers a range of regressors and classifiers, focusing on tree-based models, boosting algorithms, ensemble learners, and Multilayer Perceptrons (MLP). The investigation results demonstrate that combining the K-means SMOTE strategy with the CatBoost Classifier in the first scenario achieves the highest accuracy, reaching an impressive 99.6%, and an exceptional ability to detect grid instability, achieving 100% accuracy. Furthermore, the results of the second scenario are promising, with accuracy rates reaching 99.4%, thanks to the utilization of a stacking ensemble learner. Notably, this study introduces Explainable AI, a groundbreaking initiative in this field, to delve into the inner workings of the proposed models and enhance their transparency. This pioneering effort marks a significant step forward in understanding and optimizing the predictive capabilities of machine learning models applied to smart grid stability.