This study investigates the vulnerability of DC microgrid systems to cyber threats, focusing on false data injection attacks (FDIAs) affecting sensor measurements. These attacks pose significant risks to equipment, generation units, controllers, and human safety. To address this vulnerability, we propose a novel solution utilizing a Non-Linear Autoregressive Network with Exogenous Input (NARX) observer. Trained to differentiate between normal conditions, load changes, and cyber-attacks, the NARX network estimates DC currents and voltages. The system initially operates without FDIAs to collect data for training NARX networks, followed by online deployment to estimate output DC voltages and currents of Distributed Energy Resources (DER). An attack mitigation strategy using a PI controller aligns NARX output with actual converter output, generating a counter-attack signal to nullify the attack impact. Comparative analysis with other AI-based methods is conducted, demonstrating the effectiveness of our approach. MATLAB simulations validate the method's performance, with real-time validation using OPAL-RT further confirming its applicability.

This paper adopts a Direct Current (DC) Microgrid with a consensus-based secondary control strategy to efficiently manage the microgrid, ensuring precise voltage regulation and fair power distribution among Distributed Energy Resources (DERs). The consensus approach employs a communication network to establish a Cyber layer for DER information exchange and harmonize the entire system to achieve a common goal. However, this cyber layer’s vulnerability could destabilize the interconnected control system and lead to the whole system being unsynchronized. The paper explores replay attacks conducted in the path of the communication link between interconnected Distributed Generation Units (DGUs), demonstrating their impact on secondary control using simulations and real-time experiments. A method leveraging Distributed Kalman Filter observers for attack detection is proposed which requires low computational demand to suit the real-time application, while an attack mitigation technique using observer-generated residues with the optimal condition is implemented. Validation is conducted through both MATLAB simulations and real-time OPAL-RT experiments, confirming the proposed system’s effectiveness.

This study introduces a secondary consensus control system to maintain precise voltage regulation among distributed generators within an islanded AC microgrid. Utilizing concepts from Graph theory, a communication network is established to facilitate cooperative control, which is essential for the system's operation. However, this communication network is susceptible to cyber attacks, particularly False Data Injection (FDI) attacks, which can disrupt consensus control and jeopardize system stability. Their impact is analyzed to evaluate the resilience of traditional consensus controllers against FDI attacks. The study underscores the critical need for timely mitigation of FDI attacks to uphold system stability. To address this challenge, a novel Deep Neural Network (DNN) based observer is proposed, capable of accurately estimating the system's state even under varying loads and attack scenarios. The proposed AI-driven approach effectively detects and mitigates FDI attacks promptly, thereby averting further degradation of the system. The performance of the proposed resilient voltage control techniques is validated through simulations conducted on an IEEE 34-bus test feeder with varying numbers of distributed energy resources (DERs) and real-time validation by OPAL-RT.

This paper addresses the challenge of improving the efficiency of solar photovoltaic systems, which often suffer from intermittent energy harvesting and suboptimal power extraction methods. Solar energy, a promising renewable source, can replace fossil fuels, but its efficiency is hindered by fluctuating power output. To tackle this issue, the study introduces a novel approach utilizing wavelet-based ripple extraction. By employing a ten-level wavelet decomposition process, this method aims to obtain smooth, ripple-free power from solar canopies. The research examines the tracking efficiency of maximum power point tracking (MPPT) and the power oscillation issue. Notably, the study compares the Perturb and Observe method with the Artificial Neural Network MPPT approach, assessing ripple power during wavelet decomposition stages. Results reveal that the proposed wavelet and signal analyzer-based technique achieves a remarkable tracking efficiency of nearly 99.9% while minimizing power fluctuations, surpassing prior studies. This innovative approach enhances the performance and efficiency of solar PV systems, ultimately contributing to the broader adoption of solar energy. Real-time validation through OPAL-RT further validates its effectiveness.

In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats.

With the rapid growth of smart grid infrastructure, secure and efficient communication between smart meters and utility servers has become crucial. The exchange of metering data, control commands, and real-time diagnostics demands robust protocols that ensure both reliability and cybersecurity. In this context, ANSI C12.22 has emerged as a standard for facilitating encrypted, addressable communications over public networks between utility backends and metering endpoints. This paper presents a practical implementation and evaluation of the ANSI C12.22 protocol, a standard designed for transporting metering data over IP-based networks. We emulate a real-world utility-to-smart meter communication setup using a high-level supervisory control node and an ARM-based node configured as a smart meter device. A secure end-to-end communication framework was developed, incorporating AES-based encryption and table-based data exchange as defined by ANSI C12.22. To assess network-level performance, we captured the packet exchange using Wireshark and analyzed the communication behavior in depth. Metrics such as Round Trip Time (RTT), throughput, request-response sizes, and inter-request gaps were extracted and visualized. The results reveal insights into latency trends, protocol efficiency, and message pacing under encrypted transactions. Our work demonstrates a low-cost, reproducible testbed for evaluating smart meter protocols, providing a valuable reference for both research and industrial deployment. This framework can serve as a foundation for further studies in smart grid security, reliability, and performance benchmarking.