On Cyber-Physical Security of Smart Grid: Data Integrity Attacks and Experiment Platform

A Smart Grid is a digitally enabled electric power grid that integrates the computation and communication technologies from cyber world with the sensors and actuators from physical world. Due to the system complexity, typically the high cohesion of communication and power system, the Smart Grid innovation introduces new and fundamentally different security vulnerabilities and risks. In this work, two important research aspects about cyber-physical security of Smart Grid are addressed: (i) The construction, impact and countermeasure of data integrity attacks; and (ii) The design and implementation of general cyber-physical security experiment platform. For data integrity attacks: based on the system model of state estimation process in Smart Grid, firstly, a data integrity attack model is formulated, such that the attackers can generate financial benefits from the real-time electrical market operations. Then, to reduce the required knowledge about the targeted power system when launching attacks, an online attack approach is proposed, such that the attacker is able to construct the desired attacks without the network information of power system. Furthermore, a network information attacking strategy is proposed, in which the most vulnerable meters can be directly identified and the desired measurement perturbations can be achieved by strategically manipulating the network information. Besides the attacking strategies, corresponding countermeasures based on the sparsity of attack vectors and robust state estimator are provided respectively. For the experiment platform: ScorePlus, a software-hardware hybrid and federated experiment environment for Smart Grid is presented. ScorePlus incorporates both software emulator and hardware testbed, such that they all follow the same architecture, and the same Smart Grid application program can be tested on either of them without any modification; ScorePlus provides a federated environment such that multiple software emulators and hardware testbeds at different locations are able to connect and form a unified Smart Grid system; ScorePlus software is encapsulated as a resource plugin in OpenStack cloud computing platform, such that it supports massive deployments with large scale test cases in cloud infrastructure

SSNN-based energy management strategy in grid connected system for load scheduling and load sharing

The proposed research work focused on energy management strategy (EMS) in a grid connected system working in islanding mode with the connected renewable energy resources and battery storage system. The energy management strategy developed provides a balancing operation at its output by utilizing perfect load sharing strategy. The EMS technique using smart superficial neural network (SSNN) is simulated, and numerical analyses are presented to validate the effectiveness of the centralized energy management strategy in a grid connected islanded system. A SSNN prediction model is unified to forecast the associated household load demand, PV generation system under various time horizons (including the disaster condition), EV availability, and status on EV section and distance. SSNN is one the most reliable forecasting methods in many of the applications. The developed system is also accounted for degradation battery model and its associated cost. The incorporation of energy management strategy (EMS) reduces the amount of energy drawn from the grid connected system when compared with the other optimized systems

Using Wireless Communications To Enable Decentralized Analysis and Control of Smart Distribution Systems

The smart grid is a multidisciplinary approach that aims to revolutionize the whole electricity supply chain including generation, transmission and distribution systems in order to overcome the multiple challenges currently facing the electric power grid. The smart grid could be seen as a modern electrical power grid in which information as well as electricity flows among all nodes in the system, information is continuously collected, processed and hence used to control and coordinate the different system components such as distributed generation (DG) units, capacitor banks, voltage regulating transformers, etc. Therefore distributed intelligence and two-way data communication links are essential components in implementing the smart grid vision. There are numerous research efforts that focus on implementing the smart grid vision in electrical power distribution systems, most of which only target one aspect of the distribution system control and operation, e.g. a control system for voltage control, another one for DG control, a third one for protection, etc. The coexistence of such control strategies in a distribution system raises some concerns about their overall performance, their interactions with the other control strategies, and whether these control systems can adapt to changes in distribution system connectivity. In this PhD thesis we try to address these issues by proposing an implementation of the smart grid vision for distribution systems that provides an integrated design of power systems, communication systems and control strategies. A unified and flexible framework that incorporates all the different aspects of distribution system control and operation is proposed. Distributed processing units equipped with wireless communication capabilities are used to continuously process the local data along with the data received from other nodes and forward the results to neighboring nodes in the system, which in turn process the received data and share the results with their neighbors. Consequently, changes in any of the system components (load values, status of DG etc.) are taken into account in the calculations as soon as they occur, and the results are forwarded to relevant nodes in the systems. This way the information “propagates” throughout the system resulting in a seamless control and coordination among all the system components. Simulation of the electrical power distribution and the communication systems reveal the effectiveness of the proposed framework to control and coordinate multiple capacitor banks, DG units and voltage regulating transformers with changing load levels. It also reveals the potential of the proposed framework to operate in real-time by combining the real-time measured quantities with computer analysis in order to control the different system components within the time frame of normal non-emergency operating conditions. An experimental setup is built and used as a test bed for the proposed framework in order to assess the performance of the different ideas and techniques proposed in this thesis

Hierarchical video surveillance architecture: a chassis for video big data analytics and exploration

There is increasing reliance on video surveillance systems for systematic derivation, analysis and interpretation of the data needed for predicting, planning, evaluating and implementing public safety. This is evident from the massive number of surveillance cameras deployed across public locations. For example, in July 2013, the British Security Industry Association (BSIA) reported that over 4 million CCTV cameras had been installed in Britain alone. The BSIA also reveal that only 1.5% of these are state owned. In this paper, we propose a framework that allows access to data from privately owned cameras, with the aim of increasing the efficiency and accuracy of public safety planning, security activities, and decision support systems that are based on video integrated surveillance systems. The accuracy of results obtained from government-owned public safety infrastructure would improve greatly if privately owned surveillance systems ‘expose’ relevant video-generated metadata events, such as triggered alerts and also permit query of a metadata repository. Subsequently, a police officer, for example, with an appropriate level of system permission can query unified video systems across a large geographical area such as a city or a country to predict the location of an interesting entity, such as a pedestrian or a vehicle. This becomes possible with our proposed novel hierarchical architecture, the Fused Video Surveillance Architecture (FVSA). At the high level, FVSA comprises of a hardware framework that is supported by a multi-layer abstraction software interface. It presents video surveillance systems as an adapted computational grid of intelligent services, which is integration-enabled to communicate with other compatible systems in the Internet of Things (IoT)

Secure and energy-efficient multicast routing in smart grids

A smart grid is a power system that uses information and communication technology to operate, monitor, and control data flows between the power generating source and the end user. It aims at high efficiency, reliability, and sustainability of the electricity supply process that is provided by the utility centre and is distributed from generation stations to clients. To this end, energy-efficient multicast communication is an important requirement to serve a group of residents in a neighbourhood. However, the multicast routing introduces new challenges in terms of secure operation of the smart grid and user privacy. In this paper, after having analysed the security threats for multicast-enabled smart grids, we propose a novel multicast routing protocol that is both sufficiently secure and energy efficient.We also evaluate the performance of the proposed protocol by means of computer simulations, in terms of its energy-efficient operation

Multi-time-horizon Solar Forecasting Using Recurrent Neural Network

The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This results in increased uncertainties in the grid operation, thereby negatively affecting the reliability and increased cost of operation. This research paper proposes a unified architecture for multi-time-horizon predictions for short and long-term solar forecasting using Recurrent Neural Networks (RNN). The paper describes an end-to-end pipeline to implement the architecture along with the methods to test and validate the performance of the prediction model. The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best-performing methods which use one model for each time-horizon. The proposed method enables multi-horizon forecasts with real-time inputs, which have a high potential for practical applications in the evolving smart grid.Comment: Accepted at: IEEE Energy Conversion Congress and Exposition (ECCE 2018), 7 pages, 5 figures, code available: sakshi-mishra.github.i