Power Grid Network Research Articles

Active learning and relevance vector machine in efficient estimate of basin stability for large-scale dynamic networks.

The interconnectivity between constituent nodes gives rise to cascading failure in most dynamic networks, such as a traffic jam in transportation networks and a sweeping blackout in power grid systems. Basin stability (BS) has recently garnered tremendous traction to quantify the reliability of such dynamical systems. In power grid networks, it quantifies the capability of the grid to regain the synchronous state after being perturbated. It is noted that detection of the most vulnerable node or generator with the lowest BS or N-1 reliability is critical toward the optimal decision making on maintenance. However, the conventional estimation of BS relies on the Monte Carlo (MC) method to separate the stable and unstable dynamics originated from the perturbation, which incurs immense computational cost particularly for large-scale networks. As the BS estimate is in essence a classification problem, we investigate the relevance vector machine and active learning to locate the boundary of stable dynamics or the basin of attraction in an efficient manner. This novel approach eschews the large number of sampling points in the MC method and reduces over 95% of the simulation cost in the assessment of N-1 reliability of power grid networks.

What adaptive neuronal networks teach us about power grids.

Power grid networks, as well as neuronal networks with synaptic plasticity, describe real-world systems of tremendous importance for our daily life. The investigation of these seemingly unrelated types of dynamical networks has attracted increasing attention over the past decade. In this paper, we provide insight into the fundamental relation between these two types of networks. For this, we consider well-established models based on phase oscillators and show their intimate relation. In particular, we prove that phase oscillator models with inertia can be viewed as a particular class of adaptive networks. This relation holds even for more general classes of power grid models that include voltage dynamics. As an immediate consequence of this relation, we discover a plethora of multicluster states for phase oscillators with inertia. Moreover, the phenomenon of cascading line failure in power grids is translated into an adaptive neuronal network.

Modeling and Simulation of Cascading Failures in Transportation Systems during Hurricane Evacuations

Effective and timely evacuation is critical in alleviating the impact of hurricanes. As such, evacuation models are often sought to support the preparedness of evacuations. One important task in the modeling process is to evaluate exogenous factors that cause transportation system capacity loss during evacuation. Typical factors include direct damage to the roadway network due to storm surge and cascading impacts because of other facilities failures. For example, power outage can lead to signal failure and subway suspension. This paper aims to develop a macroscopic simulation-based approach to study the capacity loss of the roadway network in evacuation due to signal loss as a consequence of power outage. In particular, to simulate the case in which traffic signals lose power, a capacity-reduction model from signalized intersections to unsignalized (all-way stop control) intersections was developed and calibrated using microscopic model created in SUMO and Synchro. We used the downtown Manhattan as a case study area and created a hypothetical power-grid network in terms of neighborhoods. Six scenarios were built to simulate power loss of different neighborhoods. The simulation results give insights on how cascading failures of power network affect roadway network and evacuation process.

SIMULATION AND ANALYSIS OF DGA ANALYSIS FOR POWER TRANSFORMER USING ADVANCED CONTROL METHODS

In any power system or grid network transformer device use for power from one circuit to another circuit without changing the frequency with high efficiency levels. Due to number of usages Transformer protection is very important now a days for electric supply which is fault free, efficiency and to increase the transformer life cycle. This paper is a brief discussion about the concentration of different gases like CO, CO2, H2, C2H6, C2H4, C2H2 and CH4 related faults which is known as DGA analysis with the help of various classical techniques gives different conditions for the same sample unit. This paper presents MATLAB simulation of ANN and Machine learning based high accuracy design techniques for DGA analysis and the results are compared with the classical techniques like Key Gas Method, IEC Ratio method, Duval triangle Method and Rogers Ratio Method, but in this paper we have done only Duval triangle Method for Comparison using Matlab. The Simulation result of the proposed methods shows that overall DGA analysis using Machine learning algorithm is better than conventional Duval triangle method Performance.

SIMULATION AND ANALYSIS OF DGA ANALYSIS FOR POWER TRANSFORMER USING ADVANCED CONTROL METHODS

In any power system or grid network transformer device use for power from one circuit to another circuit without changing the frequency with high efficiency levels. Due to number of usages Transformer protection is very important now a days for electric supply which is fault free, efficiency and to increase the transformer life cycle. This paper is a brief discussion about the concentration of different gases like CO, CO2, H2, C2H6, C2H4, C2H2 and CH4 related faults which is known as DGA analysis with the help of various classical techniques gives different conditions for the same sample unit. This paper presents MATLAB simulation of ANN and Machine learning based high accuracy design techniques for DGA analysis and the results are compared with the classical techniques like Key Gas Method, IEC Ratio method, Duval triangle Method and Rogers Ratio Method, but in this paper we have done only Duval triangle Method for Comparison using Matlab. The Simulation result of the proposed methods shows that overall DGA analysis using Machine learning algorithm is better than conventional Duval triangle method Performance.

Robustness of the Chinese power grid to cascading failures under attack and defense strategies

The power grid system is one of the most important and complex infrastructure systems in our society. Catastrophic blackouts caused by cascading failures, however, still occur and lead to devastating effects. Here, for the first time, we investigate the robustness of the Chinese power grid (CPG) system under various attack and defense scenarios by considering cascading failures. The simulation results illustrate that the effect of the loading level of the power grid network has a large influence on the cascading process. When the initial load of the edge is smaller (α ≤ 1.6), the ascending order strategy of the edge collective influence is the most effective attack strategy for triggering cascading failures over the CPG network. While when the initial load of the edge is larger (α≥1.6), the high load strategy appears to outperform the other strategies. To protect the national power grid from cascading failure, two approaches can be adopted: one approach is to improve the network load capacity, and the other approach is to protect the critical edges. The efforts needed to improve the cascading robustness of the CPG network within these two approaches are quantified. Our results can guide protective measures to avoid large cascading failures and are useful for the design of a robust national power grid network.

Machine learning based false data injection in smart grid

Smart Grid is the seamless integration of advance digital communication network, state of the art control technologies, and power system infrastructure working together as an entity to ensure the reliability, sustainability, and stability of the power infrastructure. Digital communication network with is the key to the reliability of Smart Grid as all control actions are deemed upon the data transmitted by a communication network. With false data, however, the same digital communication network can lead to anomalies like abnormal disruptions, load shedding, malicious attacks and power theft. Robust False data injection attack methods proposed till now demand for the complete knowledge of interconnected power grid network topology. In this paper, three network topology independent techniques for false data injection into the smart grid are proposed based on linear regression, linear regression with time stamp, and by using delta thresholds. To make injected false data more unlikely to be detected, it is constructed to fill up the missing measurements in real-time data. The robustness of proposed attack algorithms are stated by state-of-the-art defence techniques, i.e. Bad Data Detection, AC State estimation, Support Vector Machine, and Temporal Behaviours based False data detection.

Data-driven control of complex networks

Our ability to manipulate the behavior of complex networks depends on the design of efficient control algorithms and, critically, on the availability of an accurate and tractable model of the network dynamics. While the design of control algorithms for network systems has seen notable advances in the past few years, knowledge of the network dynamics is a ubiquitous assumption that is difficult to satisfy in practice. In this paper we overcome this limitation, and develop a data-driven framework to control a complex network optimally and without any knowledge of the network dynamics. Our optimal controls are constructed using a finite set of data, where the unknown network is stimulated with arbitrary and possibly random inputs. Although our controls are provably correct for networks with linear dynamics, we also characterize their performance against noisy data and in the presence of nonlinear dynamics, as they arise in power grid and brain networks.

Enhancing White Rabbit Synchronization Stability and Scalability Using P2P Transparent and Hybrid Clocks

Time synchronization faces an increasing performance demand nowadays. This is particularly common in different segments, such as new scientific infrastructures, power grid, telecommunications or fifth-generation (5G) wireless networks. All of them share the need for a time synchronization technology offering a very low synchronization error along large networks. The new version of the IEEE-1588-2019 protocol offers a considerable performance improvement thanks to the high accuracy profile based on white rabbit (WR). The original WR implementation guarantees a synchronization accuracy below 1 ns for a small number of cascaded nodes. This contribution evaluates the performance degradation when a considerable number of devices are deployed in cascaded configurations. In order to improve the performance, different delay measurement implementations have been evaluated in a series of experiments. The results prove the considerable benefits of our implementation against the current WR implementation.

Spoofing Resilient State Estimation for the Power Grid Using an Extended Kalman Filter

Real-time monitoring of a wide-area power grid network has become feasible with time-synchronized Phasor Measurement Units (PMUs). PMUs use GPS signals for time synchronization and are susceptible to GPS Spoofing Attacks (GSAs) since civilian GPS signals are unencrypted and have low signal power of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-16</sup> Watts. We propose a novel Spoofing-Resilient State Estimator (SR-SE) that fuses time-varying GPS and PMU measurements using an Extended Kalman Filter (EKF). We design our measurement model that relates the coupling between GPS and PMU measurements to improve the performance of SR-SE. We perform software as well as Hardware-In-the-Loop (HIL) simulations to validate SR-SE against GSAs. We conduct 100 Monte Carlo (MC) simulations with IEEE 14, IEEE 39, and Illinois 200-bus test systems in software simulations. In HIL simulation, we simulate and generate GPS-PMU integrated datasets for the IEEE 14-bus test system using Real-Time Digital Simulator, PMUs, and GPS clock.

A fast cycle detection method for power grids based on graph computing

The cycle structure in a power grid may lower the stability of the network; thus, it is of great significance to accurately and timely detect cycles in power grid networks. However, detecting possible cycles in a large-scale network can be highly time consuming and computationally intensive. In addition, since the power grid's topology changes over time, cycles can appear and disappear, and it can be difficult to monitor them in real time. In traditional computing systems, cycle detection requires considerable computational resources, making real-time cycle detection in large-scale power grids an impossible task. Graph computing has shown excellent performance in many areas and has solved many practical graph-related problems, such as power flow calculation and state estimation. In this article, a cycle detection method, the Paton method, is implemented and optimized on a graph computing platform. Two cases are used to test its performance in an actual power grid topology scenario. The results show that the graph computing-based Paton method reduces the time consumption by at least 60% compared to that of other methods.

The interconnection exchange and complex systems properties in power grid network

A case study based on the real data obtained from the Polish PSE System Operator of the highest voltages electrical energy network is shown. The data about the interconnection exchange and some complex networks (graphs) parameters were examined, after the removal of selected nodes. This allowed to test selected network parameters and to show that the breakdown of only three nodes in this network can cause significant drop of its average efficiency.

Analysing smart power grid against different cyber attacks on SCADA system

The integration of information and communications technology (ICT) with traditional power grid is transforming the power grid into a more reliable and smarter cyber physical system (CPS). A supervisory control and data acquisition (SCADA) system is used to implement the cyber layer for power grid, which is responsible for real-time monitoring and control of power grid and power delivery network. A SCADA system combined with other devices creates smart power grid environment, but it also makes power grid vulnerable against different cyber-attacks as it requires connection with various kinds of open networks. Smart power grids are a extremely critical infrastructure and attack of any kind can affect socio-economical condition of the region. In this paper we present a non-complex co-simulation environment for analysing smart power grid using OMNeT++ and openDSS, which also includes two different cyber-attack scenarios. The main contribution of the paper lies in the behavioural analysis of the smart power grid environment while various attacks are performed on cyber layer of the grid and respective response of the power grid during these attacks. The findings of the proposed work show the vulnerabilities of smart power grid, as during cyber-attacks power grid was unstable and power generation was dropped significantly.

Robust Data Predictive Control Framework for Smart Multi-Microgrid Energy Dispatch Considering Electricity Market Uncertainty

With the emerging technologies for Energy Intent (EI) and data-driven applications, the conventional power grid network is undergoing a radical modernization. An efficient energy management and electricity price forecasting remains a challenging task. In this paper, a new Robust Data Predictive Control framework for Energy Management System (RDPC-EMS) is developed to overcome the uncertainty of the electricity retail price market and minimize the total operating costs for the multi-microgrids (MMG) system. The proposed framework solves the economic energy dispatch based on an accurate Electricity Price Forecasting (EPF) by an Outlier-Robust Extreme Learning Machine (OR-ELM) algorithm and a two layers cooperative Distributed Model Predictive Control (DMPC). The First level provides an optimal energy scheduling between the Distribution System Operator (DSO) and cooperative microgrids systems to minimize the operating cost based on the forecasted electricity price. In contrast, second level maintains the supply-demand balance by applying the optimal energy scheduling from the first layer through an adjustment of the distributed energy resources (DER). The electricity retail price prediction is assessed using real dataset from the Iso New England electricity market. The OR-ELM regression method shows a significant forecasting performance in terms of error metrics. For instance, the mean absolute error in the training stage 2.05% for OR-ELM with a comparison of 4.17% and 6.29% for Support Vector Regression (SVR), and Artificial Neural Network (ANN) models respectively. Finally, simulation results demonstrate the efficiency RDPC-EMS for daily operating cost reduction, with decrease of 15% for MG 1 and 16% for MG 2.

Propagation-Based Network Partitioning Strategies for Parallel Power System Restoration With Variable Renewable Generation Resources

Grid resiliency is defined as the ability to prepare for and adapt to changing conditions and withstand and recover rapidly from disruptions. Following large-scale disruptions of the power grid such as a complete blackout, parallel power system restoration accelerates the recovery process by allowing for simultaneous restoration in each island through network partitioning, thus enhancing grid resiliency. However, existing network partitioning strategies have not sufficiently considered variable renewable resources yet, and may not be flexible or computationally efficient to accommodate additional requirements from the evolving bulk power grid. To bridge these gaps, this study proposes a propagation-based optimization approach for power grid network partitioning. The requirements of blackstart resources, power balancing, synchrocheck relays for ties lines, and increased ramp rate requirements due to variable renewable resources are modeled in the constraints. Through propagation on the physical connectivity of grid assets, the network partitioning issue is formulated as a mixed integer linear programming (MILP) problem. The linearity ensures highly computational performance and optimality of the solutions from the proposed approach to identify the cutset edge possibilities for bulk power grid applications. Graph reduction strategies are proposed to further improve the computational performance for online or nearly online applications. Case studies based on two IEEE benchmark systems show that the proposed MILP model is able to determine the optimal cutset for network partitioning in approximately 0.28 seconds, and the proposed network reduction strategy is able to further improve the computational performance by approximately 9%.

Network overloading management by exploiting the in‐system batteries of electric vehicles

SummaryNetwork overloading and electric power shortfalls occur due to large variations in anticipated renewable electric power and electric loads, which may cause system overheating or network failure. A large portion of residential loads (RLs) comprises of electric vehicles (EVs); therefore, by using in‐system electric vehicle batteries (EVBs), above mentioned issues can be resolved. A lot of research has been done in this domain but the stochastic nature of this system has not been fully explored yet. In this article, an intelligent charging mechanism of EVs has been proposed for RLs. EVBs are charged during off‐peak hours and then batteries are utilized as an independent power source during peak hours to decrease stress on the power network. Vehicle‐to‐grid charging and discharging scenarios are considered, to optimize the rate of charging. A novel stochastic model predictive control (SMPC) framework, based on smart charging/discharging of EVBs has been proposed in this article. SMPC deals with the uncertain variations in the projected generation and demand of electric power, due to variable renewable energy and high electric loads, respectively. It deals with the discrepancy of supply, demand, and energy prices. Using SMPC, complex stochastic problems can be transformed into a simpler optimization problem, making it computation efficient and suitable for real‐time applications. To relax constraints on power demand, a worst case scenario technique has been used which reduces the computational cost of the system. Simulation results depict that controlled charging and discharging of EVBs in power grid network help reduce overshoots and undershoots of electric power demand in a smart community. Comparison of the proposed strategy has been done with benchmark algorithms, to show the competency of the proposed strategy. This study provides insight of a smart community microgrid operation and resultant trade‐offs to manage the demand and supply of electricity.

PGOpt: Multi-objective design space exploration framework for large-Scale on-chip power grid design in VLSI SoC using evolutionary computing technique

Design of the power grid network (PGN) of a VLSI chip is a challenging task because of the increase in network complexity. Due to the presence of resistances of the metal lines of the PGN, voltage drops occur in the form of IR drop, which can change the voltage level of underlying logic circuits, resulting in malfunction of the System-on-Chip (SoC). The IR drop also depends upon different reliability constraints, and violation of those constraints can deteriorate the IR drop much more. Subsequently, a significant objective while designing a PGN is to reduce the IR drop without violating the reliability constraints. IR drop also affects the timing of the critical path of the circuits. Over the past two decades, several works have been proposed to optimize the PGN by minimizing metal area considering the IR drop as a design constraint. One of the widely accepted IR drop minimization practices is by increasing the metal widths, which in turn increases the metal area. As a result, the area of the chip increases, which manifests that the primary design objectives, i.e., IR drop and metal routing area, are conflicting in nature. Therefore, these two conflicting design objectives need to be accommodated while designing the PGN in order to optimize the reliability and the yield of the chip. In this work, for the first time, we propose a multiobjective design space exploration framework for the power grid design, which deals with reliability and yield. We have studied various design aspects to determine a trade-off between these two critical conflicting design objectives by developing an optimization framework using the evolutionary algorithmic technique. Results on the standard power grid benchmarks demonstrate that our proposed framework helps in a reliable PGN design with high yield.

Algebraic multigrid for the nonlinear powerflow equations

AbstractIn a recent article, one of the authors developed a multigrid technique for coarse‐graining dynamic powergrid models. A key component in this technique is a relaxation‐based coarsening of the graph Laplacian given by the powergrid network and its weighted graph, which is represented by the admittance matrix. In this article, we use this coarsening strategy to develop a multigrid method for solving a static system of nonlinear equations that arises through Ohm's law, the so‐called powerflow equations. These static equations are tightly knitted to the dynamic model in that the full powergrid model is an algebraic‐differential system with the powerflow equations describing the algebraic constraints. We assume that the dynamic model corresponds to a stable operating powergrid, and thus, the powerflow equations are associated with a physically stable system. This stability permits the coarsening of the powerflow equations to be based on an approximate graph Laplacian, which is embedded in the powerflow system. By algebraically constructing a hierarchy of approximate weighted graph Laplacians, a hierarchy of nonlinear powerflow equations immediately becomes apparent. This latter hierarchy can then be used in a full approximation scheme (FAS) framework that leads to a nonlinear solver with generally a larger basin of attraction than Newton's method. Given the algebraic multigrid (AMG) coarsening of the approximate Laplacians, the solver is an AMG‐FAS scheme. Alternatively, using the coarse‐grid nodes and interpolation operators generated for the hierarchy of approximate graph Laplacians, a multiplicative‐correction scheme can be derived. The derivation of both schemes will be presented and analyzed, and numerical examples to demonstrate the performance of these schemes will be given.

Robust power grid network design considering EM aging effects for multi-segment wires

This paper presents a number of power grid network design and optimization techniques that consider the electromigration (EM) effects for multi-segment interconnect wires. First, we consider a new EM immortality constraint due to EM void saturation volume for multi-segment interconnects. It helps reduce conservativeness in the EM-aware on-chip power grid design. Along with the EM nucleation phase immortality constraint, we show that both EM immortality constraints can be naturally integrated into the existing programming based power grid optimization framework. Second, to mitigate the overly conservativeness of the immortality constrained optimization methods, we further explore three strategies: we first size up failed wires to meet one of the immortality conditions subject to the design rules; second, we consider the EM-induced aging effects on power supply networks for a target lifetime, which allows some short-lived wires to fail and optimizes the remaining wires; third, we propose a large change sensitivity-based optimization scheme to perform localized fixing based on recently proposed coupled EM-IR drop analysis method. Numerical results on a number of IBM-format power grid networks demonstrate that the new method can reduce more power grid area compared to the existing EM immortality constrained optimizations. Moreover, the new method is able to optimize power grids with nucleated wires, which would not be possible with the existing methods. Results also show the sensitivity-based localized power girds fixing can fix EM-induced IR drop violations in a few minutes for synthesized power grid networks from ARM core designs.

Grid Integration of Solar and Solar/Wind Hybrid Mini-Grid Projects: A Case of Solar/Wind Hybrid Mini-Grid Project Implemented by AEPC

Alternative Energy Promotion Center (AEPC) has been scaling up renewable energy technologies through Mini-grid power supply in Nepal’s off-grid areas in order to fulfill community electricity demands for both households and electricity-based enterprises. It is likely that, in some of the off-grid renewable energy (RE) mini-grid service areas, Nepal Electricity Authority (NEA) grid line might reach in near future. The objective of this study is to highlight success stories of solar and solar/wind mini-grids implemented by AEPC and for their longer-term sustainable operation grid integration policy of isolated mini-grid systems will require. Till the date AEPC has successfully electrified more than 20 rural communities through solar and solar/wind hybrid mini-grid systems. Some of these isolated mini-grid projects will seek grid interconnections in near future due to NEA distribution system expansion trend. When the isolated mini-grids will be connected to the national grid power network in future, the mini-grid can feed surplus electricity to the local utility grid line and will receive deficit electricity from the local utility grid system to serve the consumers connected to the local grid network. The methodology applied to this study is assessment of existing solar and solar/wind mini-grid projects, technological intervention required for grid integration, reviewing grid-connection policy of distributed renewable generations in the country and recommendation of relevant policy option for grid connection of rural mini-grids. Grid integration of isolated solar and solar/wind mini-grids will have positive impacts to the sustainability of the present isolated mini-grids, will help in retaining existing mini-grids end users, energy produced by mini-grid systems will be optimally utilized, the existing mini-grid power will contribute to stabilize weak national grid electrical parameters. The outcome of the study will recommend need of policy options as well as technological interventions in order to do grid integration of solar and solar/wind hybrid mini-grid systems in Nepal.