Power Grid State Research Articles

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.

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.

Regional Interferences to Photovoltaic Development: A Polish Perspective

The stability of energy generation is crucial for the functioning of every country. Currently, the EU policy is moving towards becoming independent of fossil energy sources, which can be replaced with sources that are not exhaustible, for example, energy from the sun. Public awareness of renewable energy is increasing. People are willing to invest in natural solutions. However, planning large photovoltaic farm projects is difficult due to complex location requirements. The study aimed to analyse the interferences/barriers to be considered when searching for a suitable location to install a photovoltaic farm. The analysis was conducted for the territory of Poland. The study used a literature and local legislation query and the Delphi method. The Delphi method identified the most important interferences from the investor’s perspective. Eleven interferences have been identified, classified into legal, spatial, technical, social, and financial groups. Several are locally determined and only exist in selected locations (e.g., technical determinants of the power grid condition, etc.). In contrast, others are unitary (e.g., concerns about the impact of PV on human health, etc.). The decision-makers are aware of the existing interferences/barriers, and the proposed administrative, legal, and technical solutions marginally mitigate barriers. System solutions are recommended, allowing an easier way to find a suitable location for a PV system.

Dynamic scheduling of electricity demand for decentralized EV charging systems

The rapid growth of electric vehicles (EVs) has brought forth new challenges to the power grid. Further, the simultaneous charging of EVs could lead to peak demand, potentially causing overloading, voltage swings, and other grid-related problems. To address these issues and lower the high energy costs faced by EV owners and grid operators, EV charging must be optimized. The study proposes an innovative strategy that utilizes decentralized charging systems to lessen the impact of EVs on the grid. A decentralized EV scheduling strategy offers scalability. Thus, making it suitable for a large EV population and it remains resilient to the dynamic arrivals of the EVs. The approach aims to balance the load on the grid and improve the effectiveness of charging operations. To achieve this, a convex optimization problem has been developed to effectively regulate the charging procedure, taking into account the distinct attributes of each EV. The mechanism operates by dividing time into several intervals. Each electric vehicle in the system autonomously adjusts its charging rate during the assigned time slots, with the goal of minimizing individual charging expenses. Moreover, the system demonstrates flexibility in deciding when to charge and discharge, allowing prioritization based on individual EV battery levels and power grid conditions. As a result, the cost analysis was conducted using the number of EVs and the average group size. A comparison of computational times between centralized and decentralized systems was undertaken to demonstrate the efficacy of the system.

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.

An improved IPT-PLL technology for single-phase grid-connected inverters in complex power grid conditions

Aiming at the common problems of frequency variations and harmonics in complex power grids, an improved inverse Park transform phase locked loop (IPT-PLL) technology for single-phase converters suitable for micro grid systems is proposed. Firstly, in the phase detection of PLL, the α component of Park transformation is selected as the reference voltage, and its orthogonal component is constructed using a 1/4 fundamental period delay method. Secondly, Lagrange interpolation polynomials are introduced to approximate fractional delay to solve the problem of delay calculation errors caused by frequency changes. Thirdly, in order to compensate for the poor ability of traditional proportional integral (PI) regulators, multi resonant controllers are superimposed to suppress low order harmonic disturbances. Finally, the design method and system performance of the PI regulator and each resonant controller are analyzed theoretically. The experimental results show that the proposed improved IPT-PLL method has strong adaptability to complex power grids. It can significantly improve the tracking performance of power grid frequency, suppress the interference of low order harmonics and DC bias. And it has good dynamic and static performance.

An improved gossip-based distributed control strategy of multi-objective combined economic emission dispatch in smart grids with stochastic communication link failures

This paper proposes a distributed control strategy, leveraging gossip-based communication, for combined economic emission dispatch (CEED) in smart grids. It optimizes economic dispatch while considering separate objectives for SO2, NOx, CO2 emissions. The network is modeled as a Bernoulli process to describe stochastic communication link failures. Utilizing the Analytic Hierarchy Process to determine the weights between multiple objectives in the CEED to aid decision-makers at thermal power plants in selecting appropriate solutions based on the actual conditions of power grid. Simulation results demonstrate the strategy effectiveness.

On Credibility of Adversarial Examples Against Learning-Based Grid Voltage Stability Assessment

Voltage stability assessment is essential for maintaining reliable power grid operations. Stability assessment approaches using deep learning address the shortfalls of the traditional time-domain simulation-based approaches caused by increased system complexity. However, deep learning models are shown to be vulnerable to adversarial examples in the field of computer vision. While this vulnerability has been noticed by the power grid cybersecurity research, the domain-specific analysis on the requirements imposed upon effective attack implementation is still lacking. Although these attack requirements are usually reasonable in computer vision tasks, they can be stringent in the context of power grids. In this paper, we conduct a systematic investigation on the attack requirements and credibility of six representative adversarial example attacks based on a voltage stability assessment application for the New England 10-machine 39-bus power system. We show that (1) compromising about half the transmission system buses' voltage traces is a rule-of-thumb attack requirement; (2) the universal adversarial perturbations regardless of the original clean voltage trajectory possess the same credibility as the widely studied false data injection attacks on power grid state estimation, while the input-specific adversarial perturbations are less credible; (3) the prevailing strong adversarial training thwarts the universal perturbations but fails in defending certain input-specific perturbations. To advance defense to cope with both universal and input-specific adversarial examples, we propose a new approach that simultaneously estimates the predictive uncertainty of any given input of voltage trajectory and thwarts the attacks effectively.

Multi-source and Multi-level Coordination of Energy Internet under V2G based on Particle Swarm Optimization Algorithm

In order to effectively improve the excessive load of microgrid during peak hours of urban electricity consumption, a multi-source and multi-level coordination method of energy Internet under V2G based on particle swarm optimization algorithm was proposed. First, a mathematical model for V2G energy integration in microgrids was developed and a scheduling concept based on a particle-by-particle optimization algorithm was used. Second, an improved PSO algorithm is proposed and experimentally validated, and the experimental results are compared with previous particle swarm optimization algorithms. Experiments have shown that as the number of iterations increases, the value of the objective function decreases and the optimal solution can be obtained until the maximum number of iterations is reached. The iteration speed and power processing cost of the improved PSO algorithm are better than before.The original load curve is the load trough period from 23:00 to 6:00, and two load peaks occur from 12:00 to 14:00 and 19:00 to 22:00. The V2G technology basically realizes the coordinated control of microgrid electric energy and achieves the effect of peaking and valley filling. The improved algorithm has obvious improvement compared with the original power grid state. Conclusion: The application of EV V2G technology can smooth the daily load curve of power grid and coordinate the electric energy of micro-grid to achieve ``peak cutting and valley filling'', and the effect of this algorithm is more outstanding than the previous algorithm. Finally, the future development direction and suggestions of V2G technology are put forward.The power grid with V2G discharge depth limit has the ability to basically reduce and eliminate the daily peak load, so the technology has broad research space and development prospects.

Research on digital transformation and application of key business of power grid planning

As the basis for the construction and development of the power grid industry, power grid planning not only affects the development direction of power grid enterprises, but also determines the economic level, social stability and quality of life of urban residents. In the construction and development of the new era, the digital transformation of power grid planning business is an important part of the construction and improvement of the digital power grid system, which will guide the power grid planning business to develop stably in the direction of intelligence, automation and information, actively create a safe and reliable smart grid, and lay a solid foundation for improving the development level of power grid enterprises. Therefore, after understanding the current digital transformation goals and development status of power grid planning business, this paper mainly explores the specific plan for the transformation of power grid planning business of a regional power enterprise according to the structure and transformation requirements of the new power system, and finally explores the practical significance of digital transformation according to the construction effect and development goals, providing a reference for the reform and development of the power industry.

Stability of hydropower units under full operating conditions considering nonlinear coupling of turbine characteristics

The nonlinearity of the hydro-turbine governing system (HTGS) will affect the regulation stability. This paper investigates the stability of HTGS considering the nonlinear coupling of hydro-turbine characteristics (NCTC) under full operating conditions. Firstly, the HTGS models considering the NCTC and power grid conditions are established. Then, the Hopf bifurcation analysis is carried out to comprehensively explain the mechanism of HTGS stability. Finally, the effect regularity of the NCTC on the stability of HTGS is summarized under the full operating conditions. The results indicate that the NCTC includes the coupling of nonlinear speed characteristic and linear head characteristic (NSHC), the coupling of nonlinear head characteristic and linear speed characteristic (NHSC), and the coupling of nonlinear head characteristic and linear opening characteristic (NHOC). The effect of NCTC on stability and dynamic response is mainly realized through the NHOC. The NHOC is favorable for stability under negative load disturbances and unfavorable for stability under positive load disturbances. A smaller unit inertia time constant or a greater flow inertia reduce the effect of NHOC on stable domain. The NHOC has different effects on stability under different operating conditions with a certain regularity. This paper provides potential support for optimizing control parameters of the HTGS.

Research on a New Synchronous Phase-Locked Method for Renewable Energy Grid-connected Converters

Background:: The phase-locked loop (PLL) is widely used to estimate synchronization information such as magnitude and phase angle of the grid voltage, which is essential for gridconnected power generation from distributed renewable energy sources. However, the harmonics in the grid often affect the accurate extraction of fundamental positive sequence, resulting in phase deviations. Methods:: A dual extended novel third-order generalized integrator (DENTOGI) is proposed and placed into the control outer loop of the PLL. This approach can be applied in the condition of the grid with DC offset voltage (DCOV). Additionally, the PLL is proposed by introducing moving average filter (MAF) and eliminating the proportional controller of the integration link to suppress high-frequency harmonics and speed up the response. Results:: A comparative experiment using Matlab/Simulink is conducted to evaluate the proposed PLL against the conventional PLLs. The experimental results confirm that the proposed PLL exhibits superior filtering performance for DC offset and harmonic components when compared to the conventional PLL. Additionally, the proposed PLL demonstrates better dynamic adjustment capabilities. Conclusion:: The main contribution of this paper is to propose a phase-locked method that can eliminate DCOV and harmonic components, which can be applied in non-ideal power grid conditions. The proposed PLL based on DENTOGI and MAF can lock the grid phase stably without errors under the grid fault involving DCOV and harmonic disturbance, it also exhibits the characteristics of fast response.

Current Transformer Condition Online Monitoring Platform Based on Big Data

Abstract As an important power equipment indispensable to the normal operation of the power grid, the good or bad working condition of the current transformer directly affects the operational efficiency of the whole power grid. In this paper, an online monitoring platform based on big data technology has been developed to monitor the current status of transformers. First of all, for the temperature of the transformer, through the decoupling of the heat generation mode of the transformer, the state monitoring method based on fiber optic temperature measurement technology and optical multiplexing technology is proposed, and the overall scheme of transformer state monitoring is designed. The data signal of the transformer’s status is collected and processed, and a fault state diagnosis model is constructed based on the decision tree algorithm. Finally, according to the data requirements of the current transformer, an online monitoring platform for transformer status has been constructed using big data technology. Through the simulation test, the variation of ratio error of this paper’s monitoring method is less than 0.60%, and the variation of phase error is less than 0.75 percent, which is in line with international requirements. The average loading time of the monitoring platform is between 123.591s~143.911s, the average loading speed is less than 13s, the average throughput is between 241ops/sec ~257ops/sec, and the average latency of the read and upload operations is 24.225ms. The current transformer condition monitoring platform in this paper has good stability, the overall performance is excellent, and it provides a good solution for the research of the current transformer condition monitoring platform provides practical support for the research of current transformer condition monitoring platform.

Smart and efficient EV charging navigation scheme in vehicular edge computing networks

With the increasing number of electric fast charging stations (FCSs) deployed along roadsides of both urban roads and highways, the long-distance travel of electric vehicles (EVs) becomes possible. The EV charging navigation scheme is significant for the quality of user experience. However, the variable conditions of both power grid and traffic networks make it a serious challenge. In this paper, we propose an efficient EV charging navigation scheme while considering both the electric and computation resource sharing. With the support of vehicular edge computing networks in intelligent transportation systems (ITSs), EVs perform both the flexible power load and edge computing nodes. When the traffic network in the established route starts to become congested, EVs can select to enter the nearest FCS. In addition to being supplemented by electric resources, EVs also benefit by sharing their own computing resource with FCSs. We formulate the EV charging navigation as a mixed integer programming problem, the EV moving route planning, FCS selection, and staying time in FCSs are optimized, to balance the relationships among the traveling time, traveling cost and reward. To address the influence caused by the randomness of traffic conditions and charging prices, a two-stage charging navigation algorithm combined with A∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A^{*}$$\\end{document} algorithm and deep reinforcement learning (DRL) is proposed, with a novel designed reward function. Eventually, numerous experimental results show the effectiveness of the proposed schemes.

Control Strategy of Three-level Active Neutral Point Clamped Grid Connected Inverters in Unbalanced Power Grid

Traditional phase-locked loops in three-level active neutral point-clamped inverter grid-connected systems for photovoltaic power generation exhibit a deficiency in the decoupling of positive and negative sequences under unbalanced power grid conditions. To address this issue, this study proposes the implementation of the fourth-order generalized integrator into the phase-locked loop to facilitate positive and negative sequence decoupling. Initially, a 3L-ANPC inverter grid connection system is established. However, under unbalanced grid voltage conditions, the PLL effect of the Decoupled Double Synchronous Reference Frame Phase-Locked Loop is found to be suboptimal, leading to considerable fluctuations and distortions in the output frequency signal. To address these complications, we propose an optimized phase-locked loop structure. In this improved design, the PLL first utilizes the FOGI to filter out multiple harmonics in the grid voltage prior to decoupling the positive and negative sequence voltage. This process effectively mitigates the effect on the PLL, thereby enabling rapid and precise tracking of the grid voltage. Simulation analysis is conducted by using Matlab/Simulink to substantiate the effectiveness of the proposed PLL, demonstrating its ability to reliably lock onto the frequency and phase of the fundamental voltage under conditions of unbalanced power grid voltage.

Real-time simulation for detailed wind turbine model based on heterogeneous computing

The deployment of sophisticated and costly large-capacity wind turbines continues to rise. It is urgent for precise real-time simulation to accurately identify and address potential problems. However, the existing solutions, which joint various simulation platforms, suffer from the disadvantages of complex architecture and high cost. This paper proposes a CPU-FPGA heterogeneous computing-based real-time simulation platform for detailed wind turbine model (DWTM). DWTM encompasses the turbine model (covering the inflow wind, aerodynamic, and mechanical dynamic), the electrical model (covering the electromagnetic transient), and the control system. The real-time operating system is introduced to guarantee the real-time performance of the turbine model and control system in the CPU. FPGA is used to perform real-time electrical model calculation. Furthermore, a general FPGA solver for electromagnetic transients is designed. It can accommodate various circuit topologies without the need for recompiling the FPGA code. Finally, the real-time simulation performance and accuracy are verified. The coupled dynamics between the turbine and electrical models are investigated under multiple power grid and wind conditions.

Geometric deep learning for online prediction of cascading failures in power grids

Past events have revealed that widespread blackouts are mostly a result of cascading failures in the power grid. Understanding the underlining mechanisms of cascading failures can help in developing strategies to minimize the risk of such events. Moreover, a real-time detection of precursors to cascading failures will help operators take measures to prevent their propagation. Currently, the well-established probabilistic and physics-based models of cascading failures offer low computational efficiency, hindering them to be used only as offline tools. In this work, we develop a data-driven methodology for online estimation of the risk of cascading failures. We utilize a physics-based cascading failure model to generate a cascading failure dataset considering different operating conditions and failure scenarios, thus obtaining a sample space covering a large set of power grid states that are labeled as safe or unsafe. We use the synthetic data to train deep learning architectures, namely Feed-forward Neural Networks (FNN) and Graph Neural Networks (GNN). With the development of GNNs, improved performance is achieved with graph-structured data, and GNNs can generalize to graphs of diverse sizes. A comparison between FNN and GNN is made and the GNNs inductive capability is demonstrated via test grids. Furthermore, we apply transfer learning to improve the performance of a pre-trained GNN model on power grids not seen in the training process. The GNN model shows accuracy and balanced accuracy above 96% on selected test datasets not used in the training. Conversely, the FNN shows accuracy above 85% and balanced accuracy above 81% on test datasets unseen during training. Overall, the GNN model is successful in determining, if one or several simultaneous outages result in a critical grid state, under specific grid operating conditions.

Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms

This paper proposes novel architectures for spatio-temporal graph convolutional and recurrent neural networks whose structure is inspired by the physics of power systems. The key insight behind our design consists in deriving the so-called graph shift operator (GSO), which is the cornerstone of Graph Convolutional Neural Network (GCN) and Graph Recursive Neural Network (GRN) designs, from the power flow equations. We demonstrate the effectiveness of the proposed architectures in two applications: in forecasting the power grid state and in finding a stochastic policy for foresighted voltage control using deep reinforcement learning. Since our design can be adopted in single-phase as well as three-phase unbalanced systems, we test our architecture in both environments. For state forecasting experiments we consider the single phase IEEE 118-bus case systems; for voltage regulation, we illustrate the performance of deep reinforcement learning policy on the unbalanced three-phase IEEE 123-bus feeder system. In both cases the physics based GCN and GRN learning algorithms we propose outperform the state of the art.

Laplace-Domain Hybrid Distribution Model Based FDIA Attack Sample Generation in Smart Grids

False data injection attack (FDIA) is a deliberate modification of measurement data collected by the power grid using vulnerabilities in power grid state estimation, resulting in erroneous judgments made by the power grid control center. As a symmetrical defense scheme, FDIA detection usually uses machine learning methods to detect attack samples. However, existing detection models for FDIA typically require large-scale training samples, which are difficult to obtain in practical scenarios, making it difficult for detection models to achieve effective detection performance. In light of this, this paper proposes a novel FDIA sample generation method to construct large-scale attack samples by introducing a hybrid Laplacian model capable of accurately fitting the distribution of data changes. First, we analyze the large-scale power system sensing measurement data and establish the data distribution model of symmetric Laplace distribution. Furthermore, a hybrid Laplace-domain symmetric distribution model with multi-dimensional component parameters is constructed, which can induce a deliberate deviation in the state estimation from its safe value by injecting into the power system measurement. Due to the influence of the multivariate parameters of the hybrid Laplace-domain distribution model, the sample deviation generated by this model can not only obtain an efficient attack effect, but also effectively avoid the recognition of the FDIA detection model. Extensive experiments are carried out over IEEE 14-bus and IEEE 118-bus test systems. The corresponding results unequivocally demonstrate that our proposed attack method can quickly construct large-scale FDIA attack samples and exhibit significantly higher resistance to detection by state-of-the-art detection models, while also offering superior concealment capabilities compared to traditional FDIA approaches.

Optimal Scheme of Remote Backup Automatic Switch Applied to Renewable Energy Grids

The support of renewable energy to the bus voltage during the grid fault period will cause the failure action of the backup automatic switch (BPAS). Moreover, the improper coordination of renewable energy and source supply lines in the process of BPAS operation will lead to the asynchronous close of a circuit breaker, which seriously threatens the stable operation of the power system. To solve the above problems, an optimal operation strategy applied to the remote BPAS is proposed in this paper. Firstly, the operation logic and characteristics of internal BPAS and remote BPAS are analyzed. Secondly, the mechanism of failure action and asynchronous close of a circuit breaker in regional power grids with renewable energy sources are analyzed. Then, to improve the speed and reliability of the BPAS, a strategy of remote BPAS operation suitable is proposed. To avoid the problem of failure action, by monitoring the low-frequency condition of the regional power grid, the strategy accelerates the bus voltage loss to shorten the operation time of the BPAS. Moreover, the operation conditions of BPAS are optimized to avoid the asynchronous close of the circuit breaker between renewable energy and source supply lines. Finally, the experimental results in the 110 kV transformer substation verify that the improved remote BPAS strategy can effectively improve the reliability and quickness of the grid connecting to renewable energy.