Cyber-security on smart grid: Threats and potential solutions

Cyber-physical systems (CPSs) empower the integration of physical processes and cyber infrastructure with the aid of ubiquitous computation resources and communication capabilities. CPSs have permeated modern society and found extensive applications in a wide variety of areas, including energy, transportation, advanced manufacturing, and medical health. The security of CPSs against cyberattacks has been regarded as a long-standing concern. However, CPSs suffer from extendable vulnerabilities that are beyond classical networked systems due to the tight integration of cyber and physical components. Sophisticated and malicious cyberattacks continue to emerge to adversely impact CPS operation, resulting in performance degradation, service interruption, and system failure. Secure state estimation and control technologies play a vital role in warranting reliable monitoring and operation of safety-critical CPSs. This article provides a review of the state-of-the-art results for secure state estimation and control of CPSs. Specifically, the latest development of secure state estimation is summarized in light of different performance indicators and defense strategies. Then, the recent results on secure control are discussed and classified into three categories: 1) centralized secure control; 2) distributed secure control; and 3) resource-aware secure control. Furthermore, two specific application examples of water supply distribution systems and wide-area power systems are presented to demonstrate the applicability of secure state estimation and control approaches. Finally, several challenging issues are discussed to direct future research.

Secure State Estimation and Control of Cyber-Physical Systems: A Survey

This article proposes a memory-based event-triggering $H_{\infty }$ load frequency control (LFC) method for power systems through a bandwidth-constrained open network. To overcome the bandwidth constraint, a memory-based event-triggered scheme (METS) is first proposed to reduce the number of transmitted packets. Compared with the existing memoryless event-triggered schemes, the proposed METS has the advantage to utilize series of the latest released signals. To deal with the random deception attacks induced by open networks, a networked power system model is well established, which couples the effects of METS and random deception attacks in a unified framework. Then, a sufficient stabilization criterion is derived to obtain the memory $H_{\infty }$ LFC controller gains and event-triggered parameters simultaneously. Compared with existing memoryless LFC, the control performance is greatly improved since the latest released dynamic information is well utilized. Finally, an illustrative example is used to show the effectiveness of the proposed method.

Memory-Based Event-Triggering H ∞ Load Frequency Control for Power Systems Under Deception Attacks

In this article, the event-triggered security consensus problem is studied for time-varying multiagent systems (MASs) against false data-injection attacks (FDIAs) and parameter uncertainties over a given finite horizon. In the process of information transmission, the malicious attacker tries to inject false signals to destroy consensus by compromising the integrity of measurements and control signals. The randomly occurring stealthy FDIAs on sensors and actuators are modeled by the Bernoulli processes. In order to reduce the unnecessary utilization of communication resources, an event-triggered control mechanism with state-dependent threshold is adopted to update the control input signal. The main objective of this article is to design a controller such that, under randomly occurring FDIAs and admissible parameter uncertainties, the MASs achieve consensus. By utilizing stochastic analysis method, two sufficient criteria are derived to ensure that the prescribed $H_{\infty }$ consensus performance can be achieved. Then, the desired controller gains are derived by solving recursive linear matrix inequalities. Simulation results are presented to illustrate the effectiveness and applicability of the proposed control method.

Event-Triggered Consensus Control for Multi-Agent Systems Against False Data-Injection Attacks

This article investigates the switching-like event-triggered control for networked control systems (NCSs) under the malicious denial of service (DoS) attacks. First, by dividing the DoS attacks into S-interval (DoS-free case) and D-interval (DoS case), a switching-like event-triggered communication scheme (SETC) is well designed to deal with intermittent DoS attacks to improve communication efficiency while keeping the desired control performance. Second, by considering the SETC and NCSs into a unified framework, the studied system is transferred into a time-delay system. Then, under the constraint of the number of maximum allowable data dropouts induced by DoS attacks, a stability criterion and a stabilization criterion are derived, which can be used to estimate the event-triggered communication parameters and obtain the security controller gain simultaneously. Moreover, the derived stabilization criterion can also provide a tradeoff to balance communication efficiency and $H_{\infty }$ control performance. At last, a networked invert pendulum on a cart is conducted to show the effectiveness of the proposed method.

Switching-Like Event-Triggered Control for Networked Control Systems Under Malicious Denial of Service Attacks

Smart grids (SGs), which can be classified into a class of networked distributed control systems, are designed to deliver electricity from various plants through a communication network to serve individual consumers. Due to the complexity of environments, the distribution of the spatial locations and vulnerability of the communication networks, cyber security emerges to be a critical issue because millions of electronic devices are interconnected via communication networks throughout critical power facilities. This paper addresses a comprehensive security understanding of the SGs framework, attacks scenarios, detection/protection methods, estimation and control strategies from both communication and control viewpoints. Also, some potential challenges and solution approaches are discussed to deal with the threat issues of SGs. At last, some conclusions and highlight future research directions are presented.

A Survey on Security Communication and Control for Smart Grids Under Malicious Cyber Attacks

Event-triggered H∞ control for active semi-vehicle suspension system with communication constraints

The invention discloses an adaptive state estimation method for a networked multi-area power system under a data injection attack. According to the method, a new measurement equation is firstly givenfor each area under the condition of simultaneously considering a system state and an attack signal and state estimation is converted into an optimization solving problem with a constraint; a few of virtual nodes are constructed to connect a boundary system to achieve a communication constraint between adjacent areas; a Lagrange function with constraint information is given by using a principle ofvariational inequality; and a state estimation method of a variable penalty parameter is finally provided to achieve state estimation under the data injection attack, wherein the size of the penaltyparameter can be adaptively adjusted according to an estimation error and a boundary error, thereby improving the estimation accuracy and speed. The adaptive state estimation method is applied to state estimation of the large-scale multi-area power system under the data injection attack, and the accuracy and the velocity can be well ensured to meet the development requirements of a smart power grid in the future.

Adaptive state estimation method for networked multi-area power system under data injection attack

Production performance prediction of tight gas reservoirs is crucial to the estimation of ultimate recovery, which has an important impact on gas field development planning and economic evaluation. Owing to the model’s simplicity, the decline curve analysis method has been widely used to predict production performance. The advancement of deep-learning methods provides an intelligent way of analyzing production performance in tight gas reservoirs. In this paper, a sequence learning method to improve the accuracy and efficiency of tight gas production forecasting is proposed. The sequence learning methods used in production performance analysis herein include the recurrent neural network (RNN), long short-term memory (LSTM) neural network, and gated recurrent unit (GRU) neural network, and their performance in the tight gas reservoir production prediction is investigated and compared. To further improve the performance of the sequence learning method, the hyperparameters in the sequence learning methods are optimized through a particle swarm optimization algorithm, which can greatly simplify the optimization process of the neural network model in an automated manner. Results show that the optimized GRU and RNN models have more compact neural network structures than the LSTM model and that the GRU is more efficiently trained. The predictive performance of LSTM and GRU is similar, and both are better than the RNN and the decline curve analysis model and thus can be used to predict tight gas production. 

Mingjin Yang

Papers

A Survey on Security Communication and Control for Smart Grids Under Malicious Cyber Attacks

Event-triggered H∞ control for active semi-vehicle suspension system with communication constraints

Adaptive state estimation method for networked multi-area power system under data injection attack

Deep-Learning-Based Production Decline Curve Analysis in the Gas Reservoir through Sequence Learning Models