Sparse Adversarial Learning for FDIA Attack Sample Generation in Distributed Smart

False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad data.Existing FDIA detection methods usually employ complex neural networkmodels to detect FDIA attacks.However,they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse,making it difficult for neural network models to obtain sufficient samples to construct a robust detection model.To address this problem,this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge,which can effectively bypass the detectionmodel to threaten the power grid system.A generative adversarial network(GAN)framework is first constructed by combining residual networks(ResNet)with fully connected networks(FCN).Then,a sparse adversarial learning model is built by integrating the time-aligned data and normal data,which is used to learn the distribution characteristics between normal data and attack data through iterative confrontation.Furthermore,we introduce a Gaussian hybrid distributionmatrix by aggregating the network structure of attack data characteristics and normal data characteristics,which can connect and calculate FDIA data with normal characteristics.Finally,efficient FDIA attack samples can be sequentially generated through interactive adversarial learning.Extensive simulation experiments are conducted with IEEE 14-bus and IEEE 118-bus system data,and the results demonstrate that the generated attack samples of the proposed model can present superior performance compared to state-of-the-art models in terms of attack strength,robustness,and covert capability.

Grid tie inverter energy stabilizing in smart distribution grid with energy storage

With the growing deployment of smart distribution grid,it has become urgent to investigate the smart distribution grid behavior during transient faults and improve the system stability.The feasibility of segmenting large power grids and multiple smart distribution grids interconnections using energy storage technology for improving the system dynamic stability was studied.The segmentation validity of the large power grids and smart distribution grid inverter output interconnections power system using energy storage technology was proved in terms of theoretical analysis.Then,the influences of the energy storage device location and capacity on the proposed method were discussed in detail.The conclusion is obtained that the ESD optimal locations are allocated at the tie line terminal buses in the interconnected grid,respectively.The effectiveness of the proposed method was verified by simulations in an actual power system.

Research on IPv6 Transition Evolvement and Security Architecture of Smart Distribution Grid Data Communication System

Smart distribution grid needs data communication systems as a support to complete their important functions. The smart distribution grid of the data and information are increasingly adopting internet protocol and Ethernet technology. The IP addresses are more and more important for the smart distribution grid equipment. The current IPv4 protocol occupies a dominant position; therefore, the challenges of the evolution to IPv6 and network security are faced by data communication systems of the smart distribution grid. The importance of data communications network and its main bearer of business were described. The data communications network from IPv4 to IPv6 evolution of the five processes and four stages of the transition were analyzed. The smart distribution grid data communications network security and types of their offensive and defensive were discussed. And the data communications network security architecture was established. It covers three dimensions, the security level, the communications network security engineering and the communications network security management. The security architecture safeguards the evolution to IPv6 for the smart distribution grid data communication systems.

A calculation method for a power user's CIC under specific conditions in smart distribution grid

This paper presents a practical method for calculating a power user’s customer interruption costs(CIC)under specific conditions.This novel method has been developed,based on the CIC results predicted by Lawrence Berkeley National Laboratory(LBNL),so that the key factors,such as customer type,customer size,interruption occurrence time and interruption duration can be considered.As compared to the LBNL method,the method proposed here is easy to understand and easy to execute with an acceptable error.It lays a solid foundation for further investigation of distributed generators and demand response in assessing reliability value of smart distribution grid(SDG).The effectiveness of the proposed method is confirmed through the assessment of RBTS-Bus2.

A novel PLC channel modeling method and channel characteristic analysis of a smart distribution grid

Power line carrier(PLC)technology plays an increasingly important role in the realization of cost-effective communication in a smart distribution grid.No current channel modeling method is universally applicable to more complex topologies that may emerge in smart grids,such as ring and mesh topologies.This paper presents a novel PLC channel modeling method based on the information node concept,and the universality and feasibility of the proposed method are demonstrated with applications in modeling networks with ring and mesh topologies.The factors that affect the channel characteristics of the networks and the laws that govern their behaviors for different types of topologies are analyzed.The validity and effectiveness of the proposed method are proven using simulation and laboratory tests.This paper provides the necessary theoretical basis and technical means to design the PLC modulation method for smart distribution grids.

Linear diophantine uncertain linguistic-based prospect theory approach for performance evaluation of islanded microgrid-system scenarios

Renewable-energy-based hybrid microgrids can aid in achieving one of the United Nations Sustainable Development Goals,i.e.‘Affordable and clean energy’.However,experts may be faced with the challenge of selecting the best one for the electrification of an area.To avoid the challenge and realize the ultimate goal of the United Nations,the present study,therefore,proposes a novel pros-pect theory-based decision-making approach to help experts in opting for the best microgrid scenario.The proposed decision-making framework considers the risk appetite of the decision-maker,a quintessential aspect of the process.Linear diophantine uncertain lin-guistic sets are used to model the linguistic evaluations from the experts.The information from different experts is aggregated using a linear diophantine uncertain linguistic power Einstein-weighted geometric operator.Finally,the prospect-theory-based TOmada de Decisao Interativa Multicriterio approach is employed to evaluate the performance of the available microgrid scenarios and hence opt for the best microgrid scenario.The proposed framework has been used to evaluate the performance of seven possible microgrid scenarios and hence select the best one that can be implemented for rural electrification of a remote village in Assam,India.The microgrid scenario consisting of a photovoltaic-wind turbine-fuel cell-battery converter(MG_(3))has been revealed to be the best scen-ario among the seven considered microgrid scenarios.The validity of the obtained ranking results has been adjudged through a com-prehensive evaluation regarding the attenuation factor and the weights of the criteria.Moreover,previous case studies have also been solved using the proposed methodology and the results reveal a good correlation between the obtained ranking results.

Optimal cost and feasible design for gridconnected microgrid on campus area using the robust-intelligence method

In this paper,a robust optimization and sustainable investigation are undertaken to find a feasible design for a microgrid in a campus area at minimum cost.The campus microgrid needs to be optimized with further investigation,especially to reduce the cost while considering feasibility in ensuring the continuity of energy supply.A modified combination of genetic algorithm and particle swarm optimization(MGAPSO)is applied to minimize the cost while considering the feasibility of a grid-connected photovoltaic/battery/diesel system.Then,a sustainable energy-management system is also defined to analyse the characteristics of the microgrid.The optimization results show that the MGAPSO method produces a better solution with better convergence and lower costs than conventional methods.The MGAPSO optimization reduces the system cost by up to 11.99%compared with the conventional methods.In the rest of the paper,the components that have been optimized are adjusted in a realistic scheme to discuss the energy profile and allocation characteristics.Further investigation has shown that MGAPSO can optimize the campus microgrid to be self-sustained by enhancing renewable-energy utilization.

Use of solar PV inverters during night-time for voltage regulation and stability of the utility grid

Photovoltaic(PV)inverters are vital components for future smart grids.Although the popularity of PV-generator installations is high,their effective performance remains low.Certain inverters are designed to operate in volt-ampere reactive(VAR)mode during the night.Yet,this approach is ineffective due to the consumption of active power from the grid(as internal losses)and the regulation necessity of the direct-current(DC)bus.This paper will demonstrate the operation of a PV inverter in reactive power-injection mode when solar energy is unavailable.The primary focus is on the design of the inverter controller with respect to the synchronous rotating frame control method.The proposed novel method enables an inverter to inject the required level of reactive power to regulate the voltage levels of the utility grid within specified limits.In the process,the inverter does not absorb active power from the grid for its internal operation.The presented model has the ability to inject≤2 kVAR of reactive power at zero power factor without absorbing active power from the grid.Simulation and hardware models of the inverter were developed and tested for different reactive loads in which the hardware model represented the real-world application.The reactive power injection of the two models ran at zero power factor and produced the expected outcomes for their corresponding independent reactive loads.Henceforth,it was evident that the proposed method can enhance the efficiency of an inverter and ensure the stability of the utility grid to which it is connected.

A cognitive spectrum allocation scheme for data transmission in smart distribution grids

As the communication needs in the smart distribution grid continue to rise,using existing resources to meet this growing demand poses a significant challenge.This paper researches on spectrum allocation strategies utilizing cognitive radio(CR)technology.We consider a model containing strong time-sensitive and regular communication service requirements such as distribution terminal communication services,which can be seen as a user with primary data(PD)and weak time-sensitive services such as power quality monitoring,which can be seen as a user with secondary data(SD).To fit the diversity of services in smart distribution grids(SDGs),we formulate an optimization problem with two indicators,including the sum of SD transmission rates and the maximum latency of them.Then,we analyze the two convex sub-problems and utilize convex optimization methods to obtain the optimal power and frequency bandwidth allocation for the users with SD.The simulation results indicate that,when the available transmission power of SD is low,Maximization of Transmission Sum Rate(MTSR)achieves lower maximum transmit time.Conversely,when the available transmission power is high,the performance of Minimization of the Maximum Latency(MML)is better,compared with MTSR.

A review on non-isolated low-power DC-DC converter topologies with high output gain for solar photovoltaic system applications

The major challenges of the high-gain DC-DC boost converters are high-voltage stress on the switch,extreme duty ratio operation,diode reverse-recovery and converter efficiency problems.There are many topologies of high-gain converters that have been widely developed to overcome those problems,especially for solar photovoltaic(PV)power-system applications.In this paper,20 high-gain and low-power DC-DC converter topologies are selected from many topologies of available literature.Then,seven prospective topologies with conversion ratios of>15 are thoroughly reviewed and compared.The selected topologies are:(i)voltage-multiplier cell,(ii)voltage doubler,(iii)coupled inductor,(iv)converter with a coupled inductor and switch capacitor,(v)converter with a switched inductor and switched capacitor,(vi)cascading techniques and(vii)voltage-lift techniques.Each topology has its advantages and disadvantages.A comparison of the seven topologies is provided in terms of the number of components,hardware complexity,maximum converter efficiency and voltage stress on the switch.These are presented in detail.So,in the future,it will be easier for researchers and policymakers to choose the right converter topologies and build them into solar PV systems based on their needs.