Entire Grid Research Articles

Detecting Malicious False Frame Injection Attacks on Surveillance Systems at the Edge Using Electrical Network Frequency Signals.

Over the past few years, the importance of video surveillance in securing national critical infrastructure has significantly increased, with applications including the detection of failures and anomalies. Accompanied by the proliferation of video is the increasing number of attacks against surveillance systems. Among the attacks, False Frame Injection (FFI) attacks that replay video frames from a previous recording to mask the live feed has the highest impact. While many attempts have been made to detect FFI frames using features from the video feeds, video analysis is computationally too intensive to be deployed on-site for real-time false frame detection. In this paper, we investigated the feasibility of FFI attacks on compromised surveillance systems at the edge and propose an effective technique to detect the injected false video and audio frames by monitoring the surveillance feed using the embedded Electrical Network Frequency (ENF) signals. An ENF operates at a nominal frequency of 60 Hz/50 Hz based on its geographical location and maintains a stable value across the entire power grid interconnection with minor fluctuations. For surveillance system video/audio recordings connected to the power grid, the ENF signals are embedded. The time-varying nature of the ENF component was used as a forensic application for authenticating the surveillance feed. The paper highlights the ENF signal collection from a power grid creating a reference database and ENF extraction from the recordings using conventional short-time Fourier Transform and spectrum detection for robust ENF signal analysis in the presence of noise and interference caused in different harmonics. The experimental results demonstrated the effectiveness of ENF signal detection and/or abnormalities for FFI attacks.

Do lower electricity storage costs reduce greenhouse gas emissions?

In the electricity sector, innovation in large-scale storage is anticipated to reduce costs and improve performance. The effect on greenhouse gas emissions of lower storage costs depends on the interactions between storage and the entire grid. The literature has disagreed on the role of storage in reducing emissions. In this paper we present a stylized model, which suggests that the effect of storage costs on emissions depends on the supply responsiveness of both fossil and renewable generators. Under common conditions in the United States, lower storage costs are more likely to reduce emissions when wind investment responds to equilibrium electricity prices and when solar investment does not. Simulations of a computational model of grid investment and operation confirm these intuitions. Moreover, because of its effect on coal and natural gas–fired supply responsiveness, introducing a carbon dioxide emissions price may increase the likelihood that lower storage costs reduce emissions.

Simulation Analysis of Distribution Characteristics of Urban Rail Transit Stray Current in Power System

The stray current generated by urban rail transit could cause DC magnetic bias problems in transformers round the subway, threatening the safe and stable operation of transformers and AC grid systems. In order to solve such problem, the static distribution model of subway stray current, the multi-layer block soil model of Shenzhen and the simulation model of Shenzhen AC power system were established. The variation of the ground potential of the AC power grid substation and the distribution characteristics of the neutral point DC current of the transformer caused by the stray current were calculated and analyzed. The results show that the change of ground potential and DC current through transformer neutral point is related to the operating conditions of the subway. For the autotransformer, it is pointed out that the installation of the capacitive blocking device only at the DC over-standard site cannot eliminate such DC magnetic bias problems, and it is necessary to properly arrange the blocking device from the perspective of the entire regional power grid.

A self-adaptive and fault-tolerant routing algorithm for wireless sensor networks in microgrids

Abstract With the expanding development of power grids, a microgrid is proposed, which is located on the edge of the entire grid. The edges of wireless sensor networks (WSNs) in a MG are in edge computing environments. For initialization, we propose an edge-adaptive and fault-tolerant routing algorithm. The routing is constructed using particle swarm optimization and consists of the inter-cluster structure and the intra-cluster structure. To improve stability, we take the distance, energy and traffic into account. The cluster head nodes are adaptive and adjust their communication ranges according to the network conditions and the current load. In addition, we also consider the failure of sensors. The sensors can address accidental malfunctions autonomously and quickly. Finally, we evaluate the performance of the algorithm through the simulation. The algorithm proposed in this paper shows better performance than other existing algorithms.

A novel optimization model for dynamic power grid design and expansion planning considering renewable resources

Growing demand for electricity has made power grid design and expansion planning one of the main challenges in power industry management. In recent years, reconfiguration of existing power grid along with the adoption of renewable power generation leads to a significant reduction in expansion costs and GHG emissions. This paper offered a novel optimization model to address the design and planning of power grid expansion in a dynamic environment. Besides capacity planning, the model also determines location and time to construct new facilities. This research aims to satisfy demand by considering reduction in net present value of costs and increase in network efficiency. Electricity tariff and cost of load shedding differ according to different power consumers (i.e., residential, commercial, industrial and agricultural). Due to inherent intermittency in renewable energy resources and their subsequent impact on the entire power grid, different scenarios are generated, and the model is solved using the sample average approximation method. Eventually, validation of the proposed model and sensitivity analysis is carried out through a real case study in Iran. Computational results demonstrate the practicality of the stochastic model and show integration of renewable power plants would decrease the transmission and sub-transmission network costs.

Seed limitation in an Amazonian floodplain forest.

We monitored a close-spaced grid of 289 seed traps in 1.44ha for 8.4yr in an Amazonian floodplain forest. In a tree community containing hundreds of species, a median of just three to four species of tree seeds falls annually into each 0.5-m2 establishment site. The number of seed species reaching a given site increased linearly with time for the duration of themonitoring period, indicating a roughly random arrival of seed species in a given site-year. The number of seed species captured each year over the entire grid ranged from one-third to one-half of the total captured over the 8.4yr of monitoring, revealing a substantial temporal component of variation in the seed rain. Seed rain at the 0.5-m2 scale displayed extreme spatial variability when all potentially viable seeds were tallied, whereas the rain of dispersed seeds was scant, more nearly uniform, and better mixed. Dispersal limitation, defined as failure of seeds to reach establishment sites, is ≥99% per year for a majority of species, explaining why seed augmentation experiments are often successful. Dispersal limitation has been evoked as an explanation for distance-dependent species turnover in tropical tree communities, but that interpretation contrasts with the fact that many Amazonian tree species possess large geographical ranges that extend for hundreds or thousands of kilometers. A better understanding of the processes that bridge the gap between the scales of seedling establishment and the regulation of forest composition will require new methodologies for studying dispersal on scales larger than those yet achieved.

O v] λλ1213.8,1218.3 emission from extended nebulae around quasars: contamination of Ly α and a new diagnostic for AGN activity in Ly α emitters

ABSTRACT We investigate the potential for the emission lines O v] λλ1213.8,1218.3 and He ii λ1215.1 to contaminate flux measurements of Ly α λ1215.7 in the extended nebulae of quasars. We have computed a grid of photoionization models with a substantial range in the slope of the ionizing power law (−1.5 < α < −0.5), gas metallicity (0.01 < Z/Z⊙ < 3.0), gas density (1 < nH < 104 cm−3), and ionization parameter (10−5 < U < 1.0). We find the contribution from He ii λ1215.1 to be negligible, i.e. <0.1 of Ly α flux, across our entire model grid. The contribution from O v] λλ1213.8,1218.3 is generally negligible when U is low (≲10−3) and/or when the gas metallicity is low (Z/Z⊙ ≲ 0.1). However, at higher values of U and Z we find that O v] can significantly contaminate Ly α, in some circumstances accounting for more than half the total flux of the Ly α + He ii + O v] blend. We also provide means to estimate the fluxes of O v] λλ1213.8,1218.3 and He ii λ1215.1 by extrapolating from other lines. We estimate the fluxes of O v] and He ii for a sample of 107 Type 2 active galaxies at z > 2, and find evidence for significant (≥10 per cent) contamination of Ly α fluxes in the majority of cases (84 per cent). We also discuss prospects for using O v] λλ1213.8,1218.3 as a diagnostic for the presence of AGN activity in high-z Ly α emitters, and caution that the presence of significant O v] emission could impact the apparent kinematics of Ly α, potentially mimicking the presence of high-velocity gas outflows.

The optimization of networking method for the system protection communication networks based on the delay analysis

The real-time requirements of the data transmission for the power communication systems are very high, and the system protection demands the communication delay more stringent. The consequences of the malfunction or refusal of the grid control system caused by the communication delay are much more serious than those of other communications. Because the grid stability control system involves the entire system, the malfunction or refusal may lead the entire grid to be disassembled or collapsed. A new networking method is proposed based on the analysis of the existing power communication networking technology in this paper. And the delay of this new networking method is analyzed by analyzing the mechanism of the delay generation. So that the adaptability of the new networking method in the system protection communication networks is determined from the delay level. It is intended to provide the reference and guidance for the construction of a high-real-time system protection communication networks.

Cooperative Deep Reinforcement Learning for Large-Scale Traffic Grid Signal Control.

Exploiting reinforcement learning (RL) for traffic congestion reduction is a frontier topic in intelligent transportation research. The difficulty in this problem stems from the inability of the RL agent simultaneously monitoring multiple signal lights when taking into account complicated traffic dynamics in different regions of a traffic system. Such challenge is even more outstanding when forming control decisions on a large-scale traffic grid, where the RL action space grows exponentially with the number of intersections within the traffic grid. In this paper, we tackle such a problem by proposing a cooperative deep reinforcement learning (Coder) framework. The intuition behind Coder is to decompose the original difficult RL task as a number of subproblems with relatively easy RL goals. Accordingly, we implement Coder with multiple regional agents and a centralized global agent. Each regional agent learns its own RL policy and value functions over a small region with limited actions. Then, the centralized global agent hierarchically aggregates RL achievements from different regional agents and forms the final Q -function over the entire large-scale traffic grid. The experimental investigations demonstrate that the proposed Coder could reduce on average 30% congestions in terms of the number of waiting vehicles during high density traffic flows in simulations.

On the Assessment of a Numerical Weather Prediction Model for Solar Photovoltaic Power Forecasts in Cities

Recent developments in the weather research and forecasting (WRF) model have made it possible to accurately estimate incident solar radiation. This study couples the WRF-solar modifications with a multilayer urban canopy and building energy model (BEM) to create a unified WRF forecasting system called urban WRF–solar (uWRF-solar). This paper tests the integrated approach in the New York City (NYC) metro region as a sample case. Hourly forecasts are validated against ground station data collected at ten different sites in and around the city. Validation is carried out independently for clear, cloudy, and overcast sky conditions. Results indicate that the uWRF-solar model can forecast solar irradiance considerably well for the global horizontal irradiance (GHI) with an R2 value of 0.93 for clear sky conditions, 0.61 for cloudy sky conditions, and finally, 0.39 for overcast conditions. Results are further used to directly forecast solar power production in the region of interest, where evaluations of generation potential are done at the city scale. Outputs show a gradient of power generation produced by the potential available solar energy on the entire uWRF-solar grid. In total, the city has a city photovoltaic (PV) potential of 118 kWh/day/m2 and 3.65 MWh/month/m2.

A Computationally Efficient Method for Bounding Impacts of Multiple Uncertain Parameters in Dynamic Load Models

This paper develops a computationally efficient method to bound the impact of multiple uncertain parameters in a dynamic load model. Load model trajectory sensitivity is first conducted on regional dynamics only (e.g., a large industrial load bus and its model for adequately representing the bus voltage dynamics) to identify critical and correlated load parameters. Systemwide trajectory sensitivity on the entire power system model is then evaluated for this reduced set of parameters, and finally, the impact of multiple uncertain parameters on the representation of power system dynamics is bounded. To reinforce this reasoning, we elaborate on the conceptual meaning of the load model trajectory sensitivity, its implication, and its applicability to the entire power grid analysis. This research also develops a fluctuation index of trajectory sensitivity to effectively rank and select the model parameters based on the impact of their perturbations on the system's dynamic performance. Case studies for the Korean power system demonstrate the validity and efficacy of the developed methods for adequately bounding the uncertainty impacts with reference to the comprehensive time-domain dynamic simulation approach.

The Energy Bank-Roadmap for the 21st Century Green power grid

The electric power industry in the U.S. and the world is moving toward predominantly renewable energy. The electrical characteristics of most renewable resources are quite different than traditional generating systems. There is a transition from rotational generating resources to ones that are either static or electrically appear as static. The predominant renewable resources are expected to be wind and solar, where solar resources are expected to dominate due to their favorable economics. Wind and solar resources are non-dispatchable and their power production may not be coincident with demand. Storage resources therefore become an integral component of the 21st century power grid.This article proposes a new vision, road-map and business model which the author calls the “Energy Bank” toward meeting the Green energy requirements of the electric power industry in the 21st Century. The Energy Banks are envisioned to be private entities which collect and store energy at their own facilities or contract with private storage facilities within the entire power grid from transmission all the way to distribution and end consumers. This energy is then is made available for withdrawal by the depositor at a different time and location as well as in the form of physical and financial contracts to manage the price risks associated with wholesale markets.In the 21st Century, similar to money, most of the population make energy, need to store it and like to use it at the different times and locations. The Author argues that Energy Banks as private market forces can facilitate energy transactions among vast number of renewable resources which will be needed by the 21st Century Electric Power while creating a sustainable business model capable of exerting long-term pressure on the cost of electricity.

An Emissions Arbitrage Algorithm to Improve the Environmental Performance of Domestic PV-Battery Systems

Domestic PV-battery systems are rarely operated in a way which specifically maximizes environmental benefit. Consequently the studies that seriously examine such systems often find that the greenhouse gas and pollutant emissions savings of rooftop PV, though still positive, are lessened by adding a domestic battery. This study shows thatby simulating a PV-battery system with a range of sizes that this need not be inevitable. A novel algorithm was designed specifically to perform ‘emissions arbitrage’: to charge the battery when the grid emissions intensity is low and to discharge when it is high. It was found that the CO2 saved relative to the same system with PV only can more than pay back the CO2 debt of manufacturing the battery. This is true as long as the UK moves away from the present-day situation where natural gas-fired generators are nearly always the marginal generator. This work underlines the importance of both the operating strategy and the interactions between the system and the entire grid, in order to maximize the environmental benefit achievable with domestic PV-battery systems.

Estimating density of an elusive carnivore in urban areas: use of spatially explicit capture-recapture models for city-dwelling bobcats

An important first step in managing urban carnivores or the habitat in which they live to reduce risk of conflicts with humans is to understand their basic ecology and population dynamics. Traditional density estimators may be inappropriate in urban areas because of extensive areas of impermeable development but new techniques that include spatial structure could be useful within large urban metropolitan areas. Yet to date, these techniques have largely remained untested. We evaluated whether spatially explicit capture-recapture models (SECR) could provide a reliable density estimate of bobcats (Lynx rufus) in the Dallas Fort-Worth metroplex, Texas, USA. We obtained 1003 photographs of bobcats in an urbanized landscape from June–November 2014, using 41 double camera stations spaced approximately 1.05 km apart. We individually identified bobcats from their distinct pelage patterns and used SECR to predict density throughout the study area. The overall density was at least one bobcat per km2, which calculated to approximately 43 independent-aged bobcats across the entire camera grid, an estimate higher than documented bobcat densities in both rural and peri-urban studies in Texas. Our study revealed a high density of bobcats in an urban landscape despite most assumptions that bobcats require large areas of habitat and are sensitive to fragmentation.

Full electromagnetic transient simulation for large power systems

For building Global Energy Interconnection (GEI), it is necessary to implement new breakthroughs on large- power system simulation. Key routes for implementing full electromagnetic transient simulation of large-power systems are described in this paper, and a top framework is designed. A combination of the new large time step algorithm and the traditional small-time step algorithm is proposed where both parts A and B are calculated independently. The method for integrating the Norton equivalence of the power electronic system to the entire power grid is proposed. A two-level gird division structure is proposed, which executes a multi-rate parallel calculation among subsystems and element parallel calculation in each subsystem. The initialization method of combining load flow derivation and automatic trial-and-error launching is introduced. The feasibility of the method is demonstrated through a practical power grid example, which lays a foundation for further research.

Refined safety assessment of steel grid structures with crooked tubular members

Abstract For grid structures, damages of structural members such as crookedness significantly affect the overall structural safety. Traditional assessment methods can hardly evaluate the impact of crooked members in existing grid structures accurately due to difficulties lying in crookedness measurement and residual load-bearing capacity analysis. In this paper, a new method of refined model adjustment and structural assessment with crooked tubular members of steel grid roofs taken into consideration is proposed, while the crooked measurement is conducted with an image-based 3D measurement method for the first time. Firstly, the original shape of crooked members is recovered using image-based 3D measurement method. Secondly, the measured deformation of the crooked members is adjusted from the load status at the time of measurement to the zero-load status in two ways. Finally, the FE model of the entire grid structure, together with the adjusted crooked tubular members is adopted in the residual load-bearing capacity analysis considering both geometric nonlinearity and material nonlinearity. A large span steel grid roof structure containing four crooked tubular members is utilized for the illustration and validation of the proposed methodology. The proposed method inventively offers a new direction for refined assessment of existing grid structures with crooked members and provides significant potential for practical implementation.

A risk analysis and prediction model of electric power GIS based on deep learning

In the distribution and supply system of electric power, the regional-based grids and users are diverse and complicated, which leads to the association between operation of the power systems and its geographic information much more closely. Geographic information systems (GIS) have been becoming an indispensable part of the power information management system (PIMS). By combining the aid from equipment dynamic analysing in GIS and with the deep learning of nonlinear network structure, the complex functional models are able to simulate the situation of power grid equipment more efficiently. Based on this model, we are able to predict the risk of entire power grid and provide decision support for the grids management. We have collected multiple sets of historical grid-runtime data that come from provincial power grid systems as the input of the model, and combined with the prior standard training data to improve the accuracy of the risk prediction model, the methods demonstrate that the model has a highly prediction accuracy and full capability of achieving better results contrasted with other modern optimisation algorithms.

Notice of Violation of IEEE Publication Principles: Multi-Area Distributed State Estimation Strategy for Large-Scale Power Grids

Distributed state estimation system is beneficial in power grids with complex structures and large quantities of measurements because of its advantages, such as high computational efficiency and enhanced reliability. This study investigates a novel multi-area distributed state estimation algorithm for large-scale interconnected power systems. The state estimation model of the entire power grid is effectively decomposed into a group of estimation models that can be locally solved using an area estimator. The global information required by an estimator in solving its estimation model is effectively synthesized as two average values, and an average consensus-based algorithm is developed for an area estimator to access the required global information in a distributed fashion. Bad data are identified and eliminated after the convergence of the iterative estimation process. Simulation results of the IEEE 14-bus test system are provided to show the correctness and effectiveness of the proposed algorithm. Solution accuracy, computational efficiency and state estimation speed are compared with those of existing centralized, hierarchical and distributed methods by the numerical example in the IEEE 300-bus test system.

Identification and Estimation of Harmonic Sources Based on Compressive Sensing

Nodes in electric distribution networks are greatly differentiated and are very often nonlinear and/or unbalanced. They can create significant harmonic pollution, with harmonics that inevitably spread along the grid. Monitoring harmonic propagation and correlated power quality phenomena requires specific measurement devices and methodologies. Nevertheless, because of the unavailability of a rapid diffusion of synchronized and dedicated devices (due to technical and economic reasons) on every node and branch of the network, estimating the harmonic status of the entire grid by means of a complete or even redundant monitoring system can be practically unfeasible. A more feasible, though always meaningful, goal can thus be pursued, that is estimating the main harmonic sources in the network, rather than its complete harmonic status. This approach, of course, can be based on a simpler and cheaper upgrade of the distributed monitoring system. Even more, by considering the common scenario where the number of significant harmonic sources is lower than the number of loads connected to the grid, specific estimation procedures can be defined to further reduce the complexity of the monitoring system. In this scenario, this paper presents an efficient compressive sensing harmonics detector (CSHD) for the identification and the estimation of the principal pollution sources. The proposed CSHD method is validated by means of appropriate tests performed on an example of distribution grid.

DC voltage margin adaptive droop control strategy of VSC‐MTDC systems

In this study, direct current (DC) voltage margin control strategy and droop control strategy of voltage source converter multi-terminal direct current (VSC-MTDC) are introduced firstly, and according to the characteristics of two control strategies, an adaptive droop control strategy of DC voltage margin is proposed. When the power fluctuation of DC grid is relatively smaller, some of converter stations which connect to the power generations systems are involved in power coordination to balance the power margin of the entire DC grid. When the power of DC grid suddenly changes and the power fluctuation is relatively larger, the converter stations that adopt the adaptive droop control strategy adjust power and can avoid some of them to lose the adjust ability due to full load and power huge fluctuation as far as possible. Finally, the simulation based on MATLAB validates the effectiveness of the strategy.