Cyber-physical Microgrid Research Articles

Communication Topology Optimization for Time-Delay Cyber–Physical Microgrids Under Distributed Control

Communication Topology Optimization for Time-Delay Cyber–Physical Microgrids Under Distributed Control

Probabilistic Modeling of Cyber-Physical Microgrid Systems to Evaluate the Reliability and Resiliency Implications of Cyber Attacks

Probabilistic Modeling of Cyber-Physical Microgrid Systems to Evaluate the Reliability and Resiliency Implications of Cyber Attacks

Improved stability analysis of delayed frequency regulation systems for a cyber-physical microgrid

Improved stability analysis of delayed frequency regulation systems for a cyber-physical microgrid

Advanced Machine Learning-Driven Security and Anomaly Identification in Inverter-Based Cyber-Physical Microgrids

In a landscape dominated by the digitalization of energy networks, safeguarding cyber-physical microgrids and against breaches anomalies emerges as a critical imperative. This study pioneers inventive methodologies for Intelligent Detection of Intrusions and Anomalies in Inverter-centric Cyber-Physical Microgrids, leveraging state-of-the-art machine learning paradigms. Using the combined power of LSTM and Convolutional Neural Networks, our suggested approach finds breaches and aberrations in microgrid structures with remarkable accuracy and efficiency. Additionally, we integrate the effectiveness of Gradient Boosting Machines to enhance the overall detection capabilities. Experimental findings underscore the efficacy of our machine learning-driven approach, with CNN achieving a precision of 95%, recall of 92%, and F1-score of 93% in intrusion detection, while LSTM attains a precision of 93%, recall of 91%, and F1-score of 92% in anomaly identification. Furthermore, GBM contributes to achieving an overall efficiency of 96%. Our approach not only bolsters microgrid resilience but also lays the foundation for a secure energy future. In an increasingly interconnected world, the integration of these sophisticated techniques not only bolsters microgrid resilience but also lays the foundation for a secure energy future. By embracing innovation at the convergence of machine learning and energy systems, we stride toward fortified and dependable cyber-physical microgrids.

Residual-Based Detection of Attacks in Cyber-Physical Inverter-Based Microgrids

This paper discusses the challenges faced by cyber-physical microgrids (MGs) due to the inclusion of information and communication technologies in their already complex, multi-layered systems. The work identifies a research gap in modeling and analyzing stealthy intermittent integrity attacks in MGs, which are designed to maximize damage and cancel secondary control objectives. To address this, the paper proposes a nonlinear residual-based observer approach to detect and mitigate such attacks. In order to ensure a stable operation of the MG, the formulation then incorporates stability constraints along with the detection observer. The proposed design is validated through case studies on a MG benchmark with four distributed generators, demonstrating its effectiveness in detecting attacks while satisfying network and stability constraints.

Consensus-based secondary control for DC microgrids with communication delays via a networked predictive control strategy

In today’s cyber–physical microgrid systems, the consensus-based secondary control is generally utilized to settle the voltage deviation and rough current allocation issues at the primary control level. However, time delays follow inevitably the introduction of sparse communication networks, and most existing works adopt passive tolerance approaches. To actively alleviate the unavoidable delay effect in microgrids’ communication networks, a networked predictive control (NPC) strategy is proposed for an islanded DC microgrid subject to time delays in this paper. Firstly, the predictive approaches for both voltage and current are developed based on the cyber–physical microgrid model. Unlike the practice of passively tolerating time delays, the NPC strategy is proposed to actively compensate for the effect of communication delays by estimating real-time voltage and current values using the previously obtained prediction models. Moreover, to prove the generality of the developed method, the microgrid systems’ stability can be derived from the Schur stability of the closed-loop system, thus the DC microgrid can achieve voltage regulation and proportional current sharing simultaneously. Finally, the performance of our method against the time delay effect is validated by extensive experiments on an islanded 48-V DC microgrid system, in terms of its feasibility, delay tolerance ability, and robustness to load changes and communication faults. Experimental results demonstrate the effectiveness and superiority of the NPC strategy.

A new approach for reliability assessment of the smart microgrids using k-shortest path algorithms

The reliability of cyber-physical microgrids (MGs) is crucial in the development of smart grids. The reliability of MGs can be affected by cyber network failures, which have a significant impact on the physical components. Since MGs have interdependent cyber and physical elements, a smart MG can be viewed as a system of n dependent two-mode components. This paper proposes an approach to finding the k most likely configurations of the system. The method involves three phases. Firstly, the multi-mode model is obtained for physical components, considering the operation and topology of the cyber network. Then, the problem is transformed to finding the k most likely state of a system consisting of n independent multi-mode components. In the second phase, a transformation using new transformed metrics is applied. This results in the problem being converted to finding the k shortest path, which can be solved using efficient algorithms. Finally, the states are evaluated using a DC load flow, and reliability indices such as loss of load probability (LOLP) and expected energy not supplied (EENS) are calculated. Moreover, we have incorporated the dynamic thermal rating (DTR) system into our proposed model, addressing the safe enhancement of system component ratings. The results indicate that the most probable states of the system are related to the failure of distribution generators. The most severe events occur due to failure in the cyber network, and cyber network malfunction has a higher effect on EENS compared to LOLP. Additionally, we observe a significant enhancement in reliability indices when considering the DTR system over the static thermal rating (STR) system. This approach is efficient in reliability calculation using fewer system states.

Detection-Triggered Recursive Impact Mitigation against Secondary False Data Injection Attacks in Cyber-Physical Microgrids

Detection-Triggered Recursive Impact Mitigation against Secondary False Data Injection Attacks in Cyber-Physical Microgrids

Notice of Violation of IEEE Publication Principles: Washout Filter-Based Distributed Resilient Current Control for Cyber-Physical Microgrids

Notice of Violation of IEEE Publication Principles: <br><br>“Washout Filter-Based Distributed Resilient Current Control for Cyber-Physical Microgrids," <br>by X. Lu and J. Lai, <br> in IEEE Systems Journal, Early Access <br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <br><br>This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. <br><br>"A Combined Distributed Cooperative Method and Washout Filter-Based Method for Power Sharing and Voltage Balancing in Bipolar DC Microgrids" <br> by Ramin Babazadeh-Dizaji, Mohsen Hamzeh, Nima Mahdian Dehkordi, <br>submitted to IEEE Transactions on Energy Conversion, August 2020. <br><br> <br/> This article proposes a washout filter-based distributed resilient current control scheme that can achieve current sharing of distributed generations (DG) and voltage restoration of dc bus almost surely under communication channel noise interferences. Since the cyber channels are exposed to inherent noise interferences, the stability and reliability of cyber-physical dc microgrids may terribly be reduced. To dispel the adverse influences of noise interferences, a novel discrete time washout filter is developed. Accordingly, a discrete-time-distributed noise-resiliency control algorithm is developed for dc microgrids based on washout filters, in which only the current state variable is allowed to exchange among DGs with the local decentralized voltage controller. This makes the proposed control mode different from the conventional parallel mode usually requiring a leader–follower consensus-based voltage control by exchanging two state variables (i.e., terminal voltage and current output). Thus, the proposed control mode decreases the complexity to the formulation and enhances the robustness to the leader communication failure. By introducing stochastic theory and Lyapunov stability function, the sufficient conditions considering noise interferences is derived to ensure the stability operation of the whole closed-loop dc network. The obtained results show the effectiveness of the proposed strategy by a tested dc microgrid in OPAL-RT real-time simulator.

Self-Healing Secure Blockchain Framework in Microgrids

Blockchain has recently been depicted as a secure protocol for information exchange in cyber-physical microgrids. However, it is still found vulnerable to consensus manipulation attacks. These stealth attacks are often difficult to detect as they use kernel-level access to mask their actions. In this paper, we firstly build a trusted and secured peer-to-peer network mechanism for physical DC microgrids’ validation of transactions over Distributed Ledger. Secondly, we leverage from a physics-informed approach for detecting malware-infected nodes and then recovering from stealth attacks using a self-healing recovery scheme augmented into the microgrid Blockchain network. This scheme allows compromised nodes to adapt to a reconstructed trustworthy signal in a multi-hop manner using corresponding measurements from the reliable nodes in the network. Additionally, recognizing the possible threat of denial-of-service attacks and random time delays (where information sharing via communication channels is blocked), we also integrate a model-free predictive controller with the proposed system that can locally reconstruct an expected version of the attacked/delayed signals. This supplements the capabilities of Blockchain, enabling it to detect and mitigate consensus manipulation attempts, and network latencies.

Machine learning based smart intrusion and fault identification (SIFI) in inverter based cyber-physical microgrids

The paper presents a machine learning based Smart Intrusion and Fault Identification (SIFI) method to identify the cyber-physical abnormalities in an inverter-based cyber-physical microgrid (CPM). The SIFI method utilizes an ensemble classifier (EC) to assimilate the decisions from three distinct classifiers (C4.5 decision tree, random forest (RF), and forest by penalizing attributes (FPA)). The proposed method employs a voting mechanism to classify and localize abnormal events for improved accuracy. The paper investigates the effects of cyberattacks (Denial-of-Service (DoS) and Malicious Data Injection (MDI) attacks) and physical abnormalities caused by line faults in microgrids. To train the classifiers, SIFI employs dataset with statistical attributes extracted from measurements. The renewable alternative power systemssimulation (RAPSim) software tool is utilized to model the proposed system. The effectiveness of the presented model is assessed with respect to mean value error (MVE). The efficiency of the classifier is demonstrated by comparing its performance with other classifiers in terms of the MVE. For physical abnormalities identification and localization, the approach provides enhanced outputs with the MVE of 0.157% and 0.162% correspondingly. For identifying MDI anomalies, the model provides better results with a lower error rate. For DoS anomaly detection, the model provides better classification performance with 0.136% error. The extensive empirical analysis proves that the anticipated ensemble classifier-based SIFI model yields the minimum MVE for identifying physical faults and cyberattacks. Furthermore, the effectiveness of the proposed method is identified by considering a case study in which the SIFI method outperforms well for the MDI and DoS cyber-attacks in the CPM.

Cyber-Secure Global Energy Equalization in DC Microgrid Clusters Under Data Manipulation Attacks

Cyber physical microgrids (MGs) are vulnerable to data manipulation attacks which can lead to significant imbalance in the MG. For instance, data manipulations by cyber adversaries may cause to higher loading of batteries with low state-of-charge. This will lead to degradation of the cycle-life of batteries unless timely intervention is done for malware detection. Therefore, a cyber-secure global energy equalisation of battery energy storage systems in multiple interconnected direct current (DC) MG clusters is proposed in this article for efficient utilisation of batteries. Cyber security is achieved in the framework through the proposed multi-level detection and mitigation paradigm aided by event generators. Firstly, it is the maiden attempt to propose and design a secure global energy equalisation framework for MG clusters. Secondly, the proposed event generators enable segregated defensive actions for leader and follower nodes in the network. This is unlike previously reported works wherein the defense mechanism has been generalised for every node. Further, the results indicate that the proposed event generators help in maintaining continuity of power to critical loads in the system. Extensive time-domain simulations are performed in MATLAB/Simulink under various practical scenarios of ad- versarial intrusions. Controller hardware-in-loop tests employing real-time digital simulator (RTDS) and field programmable gate array (FPGA) based WAVECT Controller are also performed for real-time validation of the proposed technique. Finally, for testing scalability of the algorithm, it is extended to 20 DC-DC converters.

Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review

The importance of and need for cyber security have increased in the last decade. The critical infrastructure of the country, modeled with cyber-physical systems (CPS), is becoming vulnerable because of a lack of efficient safety measures. Attackers are becoming more innovative, and attacks are becoming undetectable, thereby causing huge risks to these systems. In this scenario, intelligent and evolving detection methods should be introduced to replace basic and outworn methods. The ability of artificial intelligence (AI) to analyze data and predict outcomes has created an opportunity for researchers to explore the power of AI in cyber security. This article discusses new-age intelligence and smart techniques such as pattern recognition models, deep neural networks, generative adversarial networks, and reinforcement learning for cyber security in CPS. The differences between the traditional security methods used in information technology and the security methods used in CPS are analyzed, and the need for a transition into intelligent methods is discussed in detail. A deep neural network-based controller that detects and mitigates cyber attacks is designed for microgrid systems. As a case study, a stealthy local covert attack that overcomes the existing microgrid protection is modeled. The ability of the DNN controller to detect and mitigate the SLCA is observed. The experiment is performed in a simulation and also in real-time to analyze the effectiveness of AI in cyber security.

Distributed Discrete-Time Secondary Cooperative Control for AC Microgrids With Communication Delays

This paper presents a droop-based distributed discrete-time secondary cooperative control scheme for AC microgrids with communication delays, which can regulate the voltage and frequency of all distributed generators (DGs) and ensure that each DG obtains active power sharing accurately. Unlike the ideal communication among DGs, the delays caused by low-band-bandwidth communication networks generally lead to potential oscillation and endanger the system stable operation. To this end, the proposed control protocol aims to compensate the general communication delays for cyber-physical microgrids actively by using predictive control theory while realizing voltage and frequency recovery as well as active power sharing precisely. Morever, each DG only needs to share information with its neighbors in the proposed scheme, and sufficient conditions for achieving stability of the closed-loop control systems are derived. The effectiveness of the control strategy is also verified by simulation studies and some experiments on OPAL-RT platform.

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

Renewable energy resources can be deployed locally and efficiently using the concept of microgrids. Due to the natural uncertainty of the output power of renewable energy resources, the planning for a proper operation of microgrids can be a challenging task. In addition, the information about the loads and the power consumption of them can create benefits to increase the efficiency of the microgrids. However, electrical loads can have uncertainty due to reasons such as unpredictable behavior of the consumers. To exploit a microgrid, energy management is required at the upper level of operation and control in order to reduce the costs. One of the most important tasks of the energy management system is to satisfy the loads and, in other words, develop a plan to maintain equilibrium between the power generation and power consumption. To obtain information about the output power of renewable energy resources and power consumption, deep learning can be implemented as a powerful tool, which is able to predict the desired values. In addition, weather conditions can affect the output power of renewable energy-based resources and the behavior of the consumers and, as a result, the power consumption. So, deep learning can be deployed for the anticipation of the weather conditions. This paper will study the recent works related to deep learning, which has been implemented for the prediction of the output power of renewable energy resources (i.e., PVs and wind turbines), electrical loads, and weather conditions (i.e., solar irradiance and wind speed). In addition, for possible future directions some strategies are suggested, the most important of which is the implementation of quantum computing in cyber–physical microgrids.

A quadratic convex framework with bigger freedom for the stability analysis of a cyber-physical microgrid system

A quadratic convex framework with bigger freedom for the stability analysis of a cyber-physical microgrid system

Distributed Consensus Control Supported by High Reporting Rate Meters in Inverter-Based Cyber-Physical Microgrids

Distributed Consensus Control Supported by High Reporting Rate Meters in Inverter-Based Cyber-Physical Microgrids

Detection of Malicious Attacks in Autonomous Cyber-Physical Inverter-Based Microgrids

The distributed generation capabilities of microgrids (MGs) arise as essential assets in enhancing grid resilience. The integration of distributed energy sources, controllable loads, and prosumers necessitates the deployment of potent control and communication synergies. While those synergies transform MGs into cyber-physical systems through information technologies able to sense, control, and actuate local resources and loads, they inadvertently expose MGs to cyber-attack threats. Increasing the security of critical communication and control systems against “black swan” events, i.e., high-impact low-probability cyber-physical incidents, is a major priority for MG operations. Such incidents, if left unabated, can intensify and elicit system dynamics instability, eventually causing outages and system failures. In this article, we develop an integrated approach for multiagent MG systems able to perform the detection of malicious cyber-physical attacks based on subspace methods. We employ the small-signal model of an autonomous/islanded MG and consider different attack models targeting the MG’s secondary frequency control. The attack detector is constructed via identifying the stable kernel representation of the autonomous cyber-physical MG in the attack-free case. We illustrate the impact of the attack models as well as the feasibility of the developed detection method in simulation models of the Canadian urban benchmark distribution system.

Stability Analysis of Cyber Physical Microgrid with Dynamic Demand Control considering Time Delays

Time delays are enforced into the LFC system of microgrid (MG) while transmitting the control signals from the central controller and also aggregating the controllable loads of Demand Side Response (DSR). Due to these delays, the system dynamic performance gets affected leading to system instability. The time delays may be fixed or time varying. In this paper, a cyber physical microgrid is modeled with Dynamic Demand Control (DDC) loop including time delays, and the stability of the proposed model is analyzed both for fixed and time varying delays. The impact of fixed time delays on the stability of the proposed microgrid is theoretically evaluated using Frequency Sweeping Technique (FST). To examine the delay dependent stability of the proposed system with time varying delays, including external load perturbations, a matrix stability criterion is formulated and analyzed. The novelty of the method lies in the modelling of LFC with structured load perturbations and also investigating its impact on the stability of LFC using frequency domain approach. Since the effect of load perturbations is considered for the analysis, a more realistic operating condition can be portrayed in a microgrid system. The inclusion of DDC load in LFC of the microgrid provides better transient response even with larger time delays. The accuracy of the proposed method is verified through simulation results.

Multiagent-Based Distributed Coordination of Inverter-Based Resources for Optimal Operation of Microgrids Considering Communication Failures

This paper proposes the distributed coordination of inverter-based resources, to optimize the operational cost of a microgrid system. The microgrid is considered a multiagent system, which includes a distributed generator agent and energy storage system agent. A communication network is utilized to exchange information among agents. The issue of communication failures is addressed in the proposed strategy, to ensure the stable operation of the control system. A two-level hierarchical cooperative optimization system is proposed in this paper for distributed economic dispatch. The primary controller is responsible for the frequency and voltage regulations, and the secondary controller is implemented in a diffusion-based distributed control scheme, for optimal microgrid management. The proposed control strategy consistently maintains the optimal operation and frequency, even in the event of communication failures. A five-node multiagent system including a dispatchable agent is considered. Comparative studies with the conventional consensus strategy are represented, to prove the effectiveness of the proposed diffusion strategy. To demonstrate the practical feasibility of the proposed strategy, a controller hardware-in-the-loop testbed was developed for testing the proposed cyber-physical microgrid system, in which the controllers were implemented in multiple computers and the microgrid system was implemented in Opal-RT. The real-time experiment results showed the better cost optimization performance of the proposed diffusion strategy compared with the conventional consensus strategy.