Power Grid Topology Research Articles

A power grid topology detection method based on edge graph attention neural network

Against the backdrop of growing power demand and market liberalization, operators optimize power transmission and system load balance through network reconfiguration by switching lines. However, the complex node connections and dynamic load variations exceed the capabilities of traditional algorithms, and data noise may cause detection errors, affecting grid dispatch and stability. To address this issue, a solution is proposed based on the Edge Graph Attention Neural Network (EGAT) model, providing an in-depth analysis of the reconfigured grid structure. The model employs a multi-head attention mechanism to integrate node and edge features from different layers, enhancing feature fusion and extracting critical topological information, thereby improving the accuracy and robustness of grid topology detection after reconfiguration. The method was tested on IEEE 14-bus, IEEE 39-bus, and IEEE 118-bus systems, achieving detection accuracies of 92.30 %, 90.14 %, and 87.25 %, respectively, significantly outperforming other neural network methods. However, the complexity of the model may result in additional computational overhead.

Data-driven smart grid carbon emission control methods using graph-based power flow computing

In order to solve the problem of large-scale power grid, complex connection relationship, and large demand for electricity carbon factor calculation resources, this paper proposes an algorithm that is easy to model the power grid topology and has high computational efficiency to implement the application of “One Electricity Carbon Chart”. In this paper, the graph computing component is used to realize the graph modelling, distributed storage, and high-performance calculation of the electric carbon factor for the data representing the topology of the power grid, such as the power grid table, AC line segment table, and AC line end table. This paper realizes the rapid grid topology graph modelling of EMS dispatching system data, so that the grid structure and active power flow can be intuitively displayed and dynamically studied. This paper also realizes the distributed parallel calculation of electric carbon factor in the case of a ring network, which solves the problem of difficult analysis and calculation of large-scale power grid topology. We conduct experiments on real datasets, and the experimental results demonstrate the effectiveness of the proposed method. In addition, the research on the “One Electricity Carbon Chart” makes the carbon emission of power plants depend on the power flow of the power grid and transfers the accounting method, realizing the calculation of the carbon emission factor of the power grid at the plant level, and provides a new perspective for the sharing of carbon emission responsibilities in the power system.

Tight big-Ms for Optimal Transmission Switching

This paper addresses the Optimal Transmission Switching (OTS) problem in electricity networks, which aims to find an optimal power grid topology that minimizes system operation costs while satisfying physical and operational constraints. Existing methods typically convert the OTS problem into a Mixed-Integer Linear Program (MILP) using big-M constants. However, the computational performance of these approaches relies significantly on the tightness of these big-Ms. In this paper, we propose an iterative tightening strategy to strengthen the big-Ms by efficiently solving a series of bounding problems that account for the economics of the OTS objective function through an upper-bound on the generating cost. We also discuss how the performance of the proposed tightening strategy is enhanced if reduced line capacities are considered. Using the 118-bus test system we demonstrate that the proposed methodology outperforms existing approaches, offering tighter bounds and significantly reducing the computational burden of the OTS problem.

Mitigation Strategy of Neutral-Point DC for Transformer Caused by Metro Stray Currents

Metro stray currents flowing into neutral-point-grounded transformers can cause serious direct current (DC) bias. Affected by both metro train and urban power grid operations, transformer neutral-point DC caused by metro stray current is complex, dynamic, and time-varying, which changes the DC bias risk level of transformers. Thus, just installing blocking devices (BDs) at transformer neutral points with high instantaneous DC may make it difficult to comprehensively mitigate DC bias and lead to increased BD installation. To solve this, through optimizing BD installation placements, a mitigation method for transformer DC bias is proposed. In the proposed method, the DC bias risk level and BD installation number are included in the constraint and objective functions. To evaluate the risk level of transformer DC bias, four indicators are proposed considering the distribution characteristics of neutral DC. To optimize the BD installation placements, the effects of both the metro train dynamic operation and the operation topology of the urban power grid on neutral DCs are considered. The Monte Carlo method is used to sample the train operation conditions and a relation matrix among transformers is established. Applying the method to a certain power system, the result of BD installation placements shows that the transformers supplying the metro system must have BDs installed at their neutral points.

Research on data-driven model for power grid fault diagnosis fusing topological quantification information

In increasingly complex power grid operation scenarios and fault modes, rapid and accurate fault identification is highly important for improving the reliability of power systems. Faced with the massive amount of available power grid data, the rapid development of artificial intelligence technology provides a powerful tool for power grid fault diagnosis. However, existing data-driven diagnosis methods lack quantitative power grid topology change representations, cannot integrate power grid fault topology with alarm information, and have limited effectiveness in terms of diagnosing complex faults. To address these issues, a data-driven power grid fault diagnosis model that integrates topological quantitative features is proposed. By studying the changes in the topological connection relationships of equipment before and after a power grid fault and the topological connectivity in a power failure zone, based on the basic topological network indicators in graph theory, a representation of the quantitative features of the power grid fault topology is achieved, and these quantitative features and the alarm information are integrated to construct a data-driven power grid fault diagnosis model. The model uses the light gradient boosting machine algorithm to determine the types of complex faults and accurately identify faulty equipment, addressing the lack of model diagnosis effects considered in previous studies. Finally, the accuracy and effectiveness of the model are verified by using simulated fault cases.

Collaborative scheduling method of power grid energy side based on carbon verification data mining algorithm

In the process of the collaborative scheduling of the grid energy-consuming side, the voltage value is low, and the utilization rate of collaborative scheduling resources of the grid energy-consuming side is poor. Therefore, a collaborative scheduling method for the grid energy-consuming side based on a carbon verification data mining algorithm is proposed. Based on the structure of the data management system, the related data structure lookup formula is set to determine the collaborative scheduling task data of the power grid energy side. Under the carbon verification data mining algorithm, the power grid topology structure is constructed, the scheduling target parameters are processed, and the coordinated scheduling of the power grid energy side is realized. The experimental results show that this method can effectively improve the voltage value of each node, and the voltage value of all nodes can reach above the qualified value and improve the utilization rate and energy-saving performance, which has a certain effectiveness.

Vulnerability Analysis of Power Transmission Grids Subject to Cascading Failures

Cascading failures are a major threat to interconnected systems, such as electrical power transmission networks. Typically, approaches proposed for devising optimized control strategies are demonstrated with reference to a few test systems of reference (IEEE systems). However, this limits the robustness of the proposed strategies with respect to different power grid structures. Recently, this issue has been addressed by considering synthetic networks randomly generated for mimicking power transmission grids’ characteristics. These networks can be used for investigating the vulnerability of power networks to cascading failures. In this work, we propose to apply a recent algorithm for sampling random power grid topologies with realistic electrical parameters and further extend it to the random allocation of generation and load. Integration with a realistic cascade simulation tool, then, allows us to perform thorough statistical analyses of power grids with respect to their cascading failure behavior, thus offering a powerful tool for identifying the strengths and weaknesses of different grid classes. New metrics for ranking the control and mitigation effort requirements of individual cascade scenarios and/or of grid configurations are defined and computed. Finally, genetic algorithms are used to identify strategies to improve the robustness of existing power networks.

Validating a UK Geomagnetically Induced Current Model Using Differential Magnetometer Measurements

AbstractExtreme space weather can damage ground‐based infrastructure such as power lines, railways and gas pipelines through geomagnetically induced currents (GICs). Modeling GICs requires knowledge about the source magnetic field and the electrical conductivity structure of the Earth to calculate ground electric fields during enhanced geomagnetic activity. The electric field, in combination with detailed information about the power grid topology, enable the modeling of GICs in high‐voltage (HV) power lines. Directly monitoring GICs in substations is possible with a Hall probe, but scarcely realized in the UK. Therefore we deployed the differential magnetometer method (DMM) to measure GICs at 12 sites in the UK power grid. The DMM includes the installation of two fluxgate magnetometers, one directly under a power line affected by GICs, and one as a remote site. The difference in recordings of the magnetic field at each instrument yields an estimate of the GICs in the respective power line segment via the Biot‐Savart law. We collected data across the UK in 2018–2022, monitoring HV line segments where previous research indicated high GIC risk. We recorded magnetometer data during several smaller storms that allow detailed analysis of our GIC model. For the ground electric field computations we used recent magnetotelluric (MT) measurements recorded close to the DMM sites. Our results show that there is strong agreement in both amplitude and signal shape between measured and modeled line and substation GICs when using our HV model and the realistic electric field estimates derived from MT data.

Comprehensive energy efficiency optimization algorithm for steel load considering network reconstruction and demand response

Industrial loads are usually energy intensive and inefficient. The optimization of energy efficiency management in steel plants is still in the early stage of development. Considering the topology of power grid, it is an urgent problem to improve the operation economy and load side energy efficiency of steel plants. In this paper, a two-level collaborative optimization method is proposed, which takes into account the dynamic reconstruction cost, transmission loss cost, energy cost and demand response benefit. The upper level objective is the optimization of topology in the grid structure to optimize the power loss and dynamic reconstruction costs of the grid. The lower level is the energy cost considering demand response, real time price and dynamic demand response price. Firstly, the mathematical models of stable load, impact load and the steel production line load are built. The key parameters are identified by the Back Propagation neural network algorithm according to the actual production data. Secondly, considering the constraints of grid structure and load operation capacity, the impact of dynamic grid loss and real-time dynamic electricity price on the energy efficiency of the whole grid are analyzed in depth. The optimal operation model considering the dynamic reconfiguration and grid tramission loss of distribution network is built. Taking a steel plant park in Northeast China as an example, it is proved that the optimization model can improve energy efficiency on the load side by optimizing energy consumption and demand response participation time on load side. The energy cost is reduced by 17.77% on the load side, the network loss is reduced by 1.8%, and the operating cost of the power grid is reduced by 26.2%, which has a positive effect on improving energy utilization efficiency, reducing distribution network loss, and improving overall economic efficiency.

Toward dynamic stability assessment of power grid topologies using graph neural networks.

To mitigate climate change, the share of renewable energies in power production needs to be increased. Renewables introduce new challenges to power grids regarding the dynamic stability due to decentralization, reduced inertia, and volatility in production. Since dynamic stability simulations are intractable and exceedingly expensive for large grids, graph neural networks (GNNs) are a promising method to reduce the computational effort of analyzing the dynamic stability of power grids. As a testbed for GNN models, we generate new, large datasets of dynamic stability of synthetic power grids and provide them as an open-source resource to the research community. We find that GNNs are surprisingly effective at predicting the highly non-linear targets from topological information only. For the first time, performance that is suitable for practical use cases is achieved. Furthermore, we demonstrate the ability of these models to accurately identify particular vulnerable nodes in power grids, so-called troublemakers. Last, we find that GNNs trained on small grids generate accurate predictions on a large synthetic model of the Texan power grid, which illustrates the potential for real-world applications.

A Low Voltage Prediction Based on LSTM-BP Combined Model for Distribution Station Areas

The timely management of low voltage issues in the distribution network relies heavily on accurate predictions of voltage in the station areas. Many current methods for voltage prediction require complex data collection involving power grid topology parameters and electricity information. However, these methods suffer from drawbacks such as the need for extensive data, limited real-time performance, and significant prediction errors. Hence, this study proposes an approach to voltage prediction utilizing a combined LSTM-BP model. Initially, an analysis of the mechanism of low voltage formation in the station areas reveals that the primary factor influencing node points voltage is the power consumption of users. Subsequently, the LSTM neural network is employed to forecast short-term load curves in the distribution station areas, while the self-learning capability of the BP neural network is utilized to establish the correlation between users’ power and their corresponding voltage levels. By effectively combining the above two neural network models, the historical load’s data can be used to accurately and quickly predict the future low voltage situation in the station areas. Finally, taking station areas as the research object and comparing the actual voltage data with the voltage data predicted in this paper, the results show that within the 1V error range, the prediction accuracy is 99.8%. In contrast to the conventional voltage prediction approach, the method put forward in this research paper enables instantaneous and precise anticipation of low voltage levels without relying on details such as station configuration, line characteristics, or user voltage information.

Analysis and Evaluation of Bearing Capacity of Distributed Photovoltaic Connected to Hunan Power Grid

The development of distributed power in Hunan Province is mainly photovoltaic, with an average annual growth rate of 16.4% in the past five years. By the end of October 2022, Hunan’s distributed photovoltaic installed capacity is 3.06 million kilowatts, accounting for 54.6% of the total photovoltaic installed capacity, which exceeds the centralized photovoltaic installed capacity. Due to the rapid large-scale development of distributed photovoltaics, it is common that the actual operation scale exceeds the contemporaneous planning. In addition, Hunan distributed photovoltaic access is concentrated in northern and southern Hunan, while in southern Hunan, due to a large number of local clean energy, a large number of distributed photovoltaic access will increase the output of terminal power supply, which can seriously cause the grid power flow to reverse. This paper takes the power grid topology in southern Hunan as an example of carrying out the bearing capacity assessment of regional distributed photovoltaic access to the power grid. This method guides the rational layout of distributed photovoltaics and stably ensures the operation of the Hunan power grid.

A DRL-Based Load Shedding Strategy Considering Communication Delay for Mitigating Power Grid Cascading Failure

Successive failures in power transmission lines can cause cascading failures in the power grid, which may eventually affect large parts of the power grid and even cause the power grid system to go down. Collecting and transmitting primary equipment information and issuing load-shedding action commands in the power grid depend on the power communication network. With the help of the power communication network, we can better observe the situation of the power grid in real time and provide a guarantee for the regular working of the power grid. However, the communication network also has the problem of communication delay causing latency in load-shedding action. On the premise of preserving the key physical properties and operational characteristics of the power grid, this paper uses the IEEE 14 and 30 bus systems as examples to establish a direct current (DC) power flow simulation environment. We establish a communication network model based on the power grid topology and the corresponding communication channels. For the problem of cascading failures occurring in the power grid after transmission line failures, a load-shedding strategy using soft actor-critic (SAC) based on deep reinforcement learning (DRL) was developed to effectively mitigate cascading failures in the power grid while considering the impact of communication delay. The corresponding communication delay is obtained by calculating the shortest communication path using the Dijkstra algorithm. The simulation verifies the feasibility and effectiveness of the SAC algorithm to mitigate cascading failures. The trained network can decide on actions and give commands quickly when a specific initial failure is encountered, reducing the scale of cascading failures.

Critical Node Identification Method of Power Grid Based on the Improved Entropy Weight Method

It is very important to accurately identify the critical nodes of the power grid for its safe and stable operation. In this paper, a method for identifying the critical nodes of the power grid based on the improved entropy weight method (IEWM) is proposed, and the IEWM corrects issues with the information overlap between evaluation indices and inconsistency between the entropy weight (EW) and entropy value (EV). First, considering the power grid topology and operating conditions, structural factor evaluation indices and state factor evaluation indices are established. On this basis, the IEWM is used to assign weights to nodes with different voltage levels, which strengthens the consideration of node voltage levels in the identification method of critical nodes and makes the results more accurate and effective. Simulation experiments of IEEE 30-bus and IEEE 118-bus systems verify the accuracy of the critical node identification method proposed in this paper.

Leveraging Power Grid Topology in Machine Learning Assisted Optimal Power Flow

Machine learning assisted optimal power flow (OPF) aims to reduce the computational complexity of these non-linear and non-convex constrained optimization problems by consigning expensive (online) optimization to offline training. The majority of work in this area typically employs fully connected neural networks (FCNN). However, recently convolutional (CNN) and graph (GNN) neural networks have also been investigated, in effort to exploit topological information within the power grid. Although promising results have been obtained, there lacks a systematic comparison between these architectures throughout literature. Accordingly, we introduce a concise framework for generalizing methods for machine learning assisted OPF and assess the performance of a variety of FCNN, CNN and GNN models for two fundamental approaches in this domain: regression (predicting optimal generator set-points) and classification (predicting the active set of constraints). For several synthetic power grids with interconnected utilities, we show that locality properties between feature and target variables are scarce and subsequently demonstrate marginal utility of applying CNN and GNN architectures compared to FCNN for a fixed grid topology. However, with variable topology (for instance, modeling transmission line contingency), GNN models are able to straightforwardly take the change of topological information into account and outperform both FCNN and CNN models.

Hidden Markov Chain Model-Based Intelligent Distributed Optimization Scheduling Operation Error Prevention Method for Main Network

After the implementation of the intelligent distributed optimal dispatching mode of the main network, the number of operations is more frequent, resulting in the problem of operation error, which affects the reliability and stability of the power grid operation. In this paper, a hidden Markov chain (HMC) model-based intelligent distributed optimization scheduling operation error prevention method for the main network is proposed. In this study, the power grid topology model is established through continuous access, which lays the foundation for the dynamic tracking of dispatching operation instructions. The hidden Markov chain model is used to estimate the operational risk of scheduling. Combining with the association mining theory, the anti-error detection rules between risk and anti-error strategy are established, and the anti-error strategy is implemented. The results show that under the application of the operation error prevention method, the operation error prevention response time difference and the voltage fluctuation range of the power grid reach the minimum. Through this study, it is expected to provide a reliable reference for dispatchers to make more effective dispatching decisions and correct dispatching errors in time.

Voltage sag assessment of large power grid based on branch change influence domain

The change of a large-scale power grid topology leads to the change of node voltage sag assessment results. Thus, it is necessary to re-simulate massive line fault scenarios. However, the simulation time of the existing assessment methods is too long to ensure the practicability of the project. To solve the problems above, we propose a voltage sag assessment method for a large-scale power grid based on the branch change influence domain. Firstly, the branch change influence index is constructed to quantify the influence law of the branch change on the node voltage sag level. Secondly, based on the different influence degrees of each region, an adaptive electrical distance threshold selection method is proposed and then the range of the branch change influence domain is divided in order to construct and simulate the fault scene in the branch change influence domain. Finally, the historical simulation results are updated by using the simulation results of the branch change influence domain fault, and the severity of the node voltage sag after the topology change is calculated. Through the example of the IEEE118 node system and the actual grid structure of a city in Fujian, it has been proved that the proposed method can effectively reduce the fault simulation time by up to 96% and improve the efficiency of the voltage sag assessment of a large-scale power grid.

Graph convolutional network-based security-constrained unit commitment leveraging power grid topology in learning

Security-constrained unit commitment (SCUC) is a complex optimization problem in power system operation, which is computationally intensive. To bring significant time-savings, this paper presents a graph convolutional network (GCN)-based SCUC approach (GCN-SCUC) using the information of power grid topology. Instead of tackling the mixed integer linear programming (MILP)-based SCUC (MILP-SCUC), the GCN learner predicts the unit decisions first, and then the MILP-SCUC problem is transformed into a continuous convex one. Numerical experiments are performed on the modified IEEE-30 and IEEE-118 systems to verify the feasibility of our approach both in terms of accuracy and computation time. Moreover, compared with the state-of-the-art MILP-SCUC, the proposed approach achieves speedups of between 13x and 17x on different testing examples with high accuracy.

Inter-Object Navigation of Unmanned Aerial Vehicles to Increase the Efficiency and Accuracy of Inspection of Power Lines

The purpose of the work is to improve the accuracy and efficiency of a power line section inspection for a fault detection using unmanned aerial vehicles. The goal was achieved by using a unified computing and measurement platform on multicopter and aircraft drones and by simplifying the interaction between them and by using the inter-object navigation sensors. The most significant results were the development of a method of route planning by drones over different parts of the power grid and a method of inter-object navigation. The drone route planning problem was represented by a multiagent variation of the classical traveling salesman problem and was solved by the ant colony method. The method of inter-object navigation was distinguished by the representation of the power grid topology by high and low intensity graphs, involving a different number and types of drones in the inspection process. The application of the developed methods made it possible to increase the accuracy of power line inspections by 27-73%, and the efficiency by 2-8 times. Solving the problem of multicriteria optimization of the drone team flight route planning made it possible to reduce the cost of monitoring critical infrastructure facilities while improving its efficiency and accuracy. Thus, the conducted research has shown the effectiveness of the proposed approach for the monitoring of power facilities, route selection, number and composition of search teams. The direction for further research is to improve the ant algorithm.

Repair time factor estimation of the distribution network and the value of reliability indexes analysis

The System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) must be consistently improved, according to the Distribution System Operator (DSO). However, to measure the reliability and sustainability of the distribution network (DN) at the same time and guarantee customers' demands and uninterrupted power supply, the calculation methodology for reliability must be changed due to the rising demand for electrical consumers and the changing and installation of new technologies. This study demonstrates that different techniques, power grid topologies, and network zoning approach evaluations provide varied outcomes when computing reliability indices. The reliability indices, SAIDI and SAIFI, fault intensity evaluation, and weakness DN zones were evaluated using network zoning and Customer Minutes Interrupted (CMI) and repair time calculation methodologies. The results show that the capacity and the repair time have a direct impact on reliability indices. A detailed evaluation of the related elements may reveal the interconnectedness of the factors that assure reliability. By balancing the interests of numerous parties and guaranteeing the customer's demands, installing more renewable energy sources and assuring distributed generation, after assessing the environmental requirements, network downtime will be reduced at the same time and the level of consumer satisfaction will increase.