The use of communication links between microgrid components has made the transmitted data vulnerable to cyber-attacks. This paper presents distributed cyber-attack detection and mitigation layers integrated with the primary and secondary control loops of a meshed DC microgrid. Voltage and current cyber-attacks mitigation layers based on the Kalman Filter (KF), as a robust state estimator, are proposed. The KF is utilized to estimate accurately the actual authentic terminal voltage and current from the measurable system states. Using the discrepancies between the estimated data and the measurements under cyber-attack, controllers are assigned to set proper corrective actions. These actions secure the primary and secondary loops to achieve accurate terminal voltage regulation and current sharing, respectively, for all Distributed Generator Units (DGUs) in the microgrid. Not only accurate state estimation is required for perfect cyber-attack mitigation, but fast control response is crucial. Therefore, Adaptive Proportional Integral (API) and Fractional API (FAPI) controllers are proposed for cyber-attack mitigation layers. Simulation results are provided to evaluate the dynamic performance of the proposed strategy. Moreover, experimental work is conducted to verify the effectiveness of the proposed distributed layers and the superiority of the FAPI controller in terms of perfect compensation for different types of cyber-attacks.

The wide use of communication layers in DC microgrids to transmit voltage and current measurements of each distributed generator unit (DGU) increases the possibility of exposure to cyber-attacks. Cyber-attackers can manipulate the measured data to distort the control system of microgrids, which may lead to a shutdown. This paper proposes distributed mitigation layers for the false data injection attacks (FDIA) on voltages and currents of DGUs in meshed DC microgrids. The proposed control strategy is based on integrating two layers for cyber-attack detection and mitigation to immune the primary and the secondary control loops of each DGU. The first layer is assigned to mitigate FDIAs on the voltage measurements needed for the voltage regulation task of the primary control loop. The second layer is devoted to the mitigation of FDIAs on the DGU current measurements, which are crucial for the secondary control level to guarantee the proper current sharing of each DGU. Artificial neural networks (ANNs) are employed to support these layers by estimating the authenticated measurements. Different simulation and experimental case studies are provided to demonstrate the proposed mitigation layers’ effectiveness in detecting and mitigating cyber-attacks on voltage and current measurements. The simulation and experimental results are provided to evaluate the dynamic performance of the suggested control approach and to ensure the accurate operation of DC microgrids despite the existence of cyber-attacks on the measurements employed in the control strategy. Moreover, the control strategy succeeds to keep the maximum voltage error and the maximum error in current sharing within tolerance.

One of the challenges of inverter-based distributed generators (DGs) is to keep the voltage and frequency at their specified limits during transitions between grid-connected and islanded modes of operation. This paper presents an integrated seamless control strategy for inverter-based DGs to ensure smooth transitions between the different modes of operation. The proposed strategy is based on a deep learning neural network (DL-ANN) Proportional-Integral- Derivative (PID) controller to regulate the terminal voltage of the DG interface system. A feed-forward loop is integrated with the proposed strategy to mitigate grid harmonics by controlling the DG inverter to feed the harmonics components of non-linear loads without exceeding its capacity. Results are provided to evaluate the dynamic performance of the proposed unified control strategy under different disturbances. Finally, to demonstrate the superiority of the DL-ANN controller, a comparison is carried out with the conventional Proportional-Integral (PI) controller and the set-membership affine projection adaptive (SMAPA)-based PI controller.

Nowadays, DC microgrids control problems are considered as attracting challenges for the researchers. Thus, a distributed control strategy is presented in this paper for meshed DC microgrids enhancement. The strategy consists of two control layers, a primary control layer and a secondary control layer. The primary controller regulates the terminal voltage of each Distributed Generator Unit (DGU), while the secondary controller takes the responsibility of achieving equal current sharing between DGUs. Two Adaptive PI Controllers (APICs) are proposed for the secondary control layer to enhance the dynamic performance of the system. Validation of the proposed controllers is done under different loading conditions to show the improvement of the current sharing using the adaptive controllers.

Photovoltaic (PV) systems experience performance fluctuations due to changes in irradiance and temperature, which makes maximum power point tracking (MPPT) essential for stable grid integration. This paper investigates four adaptive MPPT controllers integrated with the incremental conductance (IC) algorithm: (i) a PID controller optimized by manta ray foraging optimization (MRFO), (ii) an Adaptive PI (API) controller, (iii) a Single Perceptron Adaptive PI (SP-API) controller, and (iv) a Set Membership Affine Projection Algorithm (SMAPA)-based PI controller. Unlike conventional or offline-trained approaches, the proposed controllers adapt in real time to environmental changes without requiring large datasets. The main novelty lies in applying SP-API and SMAPA for MPPT in PV systems for the first time, and in enhancing PID and API controllers with MRFO-based tuning for improved robustness. To evaluate performance, two case studies are conducted on a grid-connected PV system: 1- uniform irradiance and temperature variations to simulate daily operating conditions, and 2- partial shading scenarios to test adaptability under local irradiance mismatches. Results show that the SMAPA-based PI controller achieves near-ideal efficiency (~ 99.8%), minimal ripples, and negligible energy losses (~ 0.2%), followed by the SP-API controller. In contrast, the conventional PID and API controllers demonstrate weaker dynamic responses and higher losses. These findings confirm that SMAPA and SP-API offer superior adaptability and stability, making them promising solutions for reliable MPPT in grid-connected PV systems.