This paper proposes a novel control design for voltage tracking of an islanded AC microgrid in the presence of nonlinear loads and parametric uncertainties at the primary level of control. The proposed method is based on the Tube-Based Robust Model Predictive Control (RMPC), an online optimization-based method which can handle the constraints and uncertainties as well. The challenge with this method is the conservativeness imposed by designing the tube based on the worst-case scenario of the uncertainties. This weakness is amended in this paper by employing a combination of a learning-based Gaussian Process (GP) regression and Tube-Based RMPC. The advantage of using GP is that both the mean and variance of the loads are predicted at each iteration based on the real data, and the resulted values of mean and the bound of confidence are utilized to design the tube in Tube-Based RMPC. The theoretical results are also provided to prove the recursive feasibility and stability of the proposed learning based Tube-Based RMPC. Finally, the simulation results are carried out on both single and multiple DG (Distributed Generation) units.Note to Practitioners—In this paper, we present a new way to control the voltage in an islanded microgrid to improve Power Quality (PQ). The method we propose is based on an online optimization technique called Tube-Based Robust Model Predictive Control. It can handle uncertainties and disturbances that occur when the microgrid operates independently, ensuring the voltage remains stable. However, there’s a challenge with this method. It tends to be too cautious because it assumes the worst-case scenario for uncertainties. To make the control more efficient, we improve it by combining a learning-based technique called Gaussian Process regression with Tube-Based RMPC. The advantage of using GP is that it predicts the uncertainty of the electrical devices based on real data. We use these predictions to design the control in Tube-Based RMPC more accurately. We also provide theoretical results to show that our new learning-based control is reliable and stable. We tested our approach through computer simulations on different scenarios with one or multiple power sources in the microgrid. The results show the effectiveness of our control design in regulating the voltage even with uncertain and nonlinear loads. Overall, this paper suggests a practical and reliable way to control the voltage in an independent microgrid using a combination of online optimization and learning techniques.

This paper proposes a non-isolated high-gain DC-DC converter capable of delivering a 10x voltage gain with a 50 percent duty cycle. The topology represents an enhanced version of the Zeta converter and aims to address issues such as discontinuous input current and low voltage gain of the conventional Zeta converters, making it suitable for renewable applications. Moreover, it offers a common ground between input source and load. Semiconductor stress is also kept below unity in terms of normalized voltage/current. The proposed converter is analyzed in both ideal and non-ideal modes, with a particular focus on sensitivity analysis regarding voltage gain and efficiency in the latter. Simulation results are provided to validate the theoretical relations expressed.

This paper proposes a novel high-gain DC-DC converter that combines a boost converter with various voltage multiplier cells. This non-isolated topology can be optimized to achieve higher voltage gains, which is crucial for applications such as pulse power water electrolyzers, water refineries, and high-voltage testing. Both ideal and non-ideal analyses have been conducted, and the requirements for the converter’s operation in continuous conduction mode are discussed. A key advantage of the proposed topology is its capability to be extended for even higher voltage gains compared to recently suggested topologies. To verify and validate the operating principle of the proposed topology, experimental results from a 200-W prototype are presented.

This article proposes a reactive power control technique to regulate voltage profiles in low voltage (LV) distribution networks with high penetration of photovoltaic (PV) systems. The proposed strategy is a combination of a local controller and a distributed cooperative reactive power controller with a dynamic leader, which tracks the location of the critical bus(es), to coordinate all PV systems for voltage regulation. In this strategy, the local controller calculates each PV system's initial reactive power ratio that is applied to the distributed control. Then, unlike the existing distributed control method for voltage control, our proposed algorithm dynamically finds the PV system at the critical bus using the local data and neighbors’ information transferred via communication links. The PV system at the critical bus serves as the leader agent and coordinates all PV systems through distributed cooperative control. Indeed, this approach can improve the voltage profile of the LV distribution network in both normal and voltage ride-through conditions with a fair use of each PV inverter's reactive power capacity. Moreover, this method preserves plug-and-play capability and is robust to network reconfiguration, communication malfunction, and variation of loads and weather conditions (variation of the maximum active power and the reactive power capacity of PV inverters). Simulation results are exhibited to validate the successfulness of the proposed control method under ideal and delayed communication networks. It should be noted that the proposed approach is scalable to multiple feeders with several branches.

In this paper, it is shown that by increasing the number of Micro-Inverters (MIs), the performance of their harmonic compensators, may be deteriorated. Therfore, to design harmonic compensators for a large number of parallel MIs, it is not enough to consider the performance of only one MI. The analysis show that, in the design of harmonic compensators, presence of filter capacitor, can cause the MIs to have capacitive behaviors. On the other hand, by increasing the number of MIs, the probable interaction frequency between the MIs and the grid, drifts down to the lower frequency range and close to the harmonic frequencies. In this situation, the interaction among the inductive grid and the capacitive MIs, at harmonic frequencies, is inevitable. As a solution, in this paper, the modified design of harmonic compensators is proposed. A complete set of simulations is also provided to evaluate the effectiveness of the proposed analysis.

Selecting the best configuration for a cyber-network system is considered as one of the major challenges in terms of the reliability evolution of smart systems because of a specific relationship between power network system and cyber network. Although producing some possible cyber networks for one simple power system and selecting the best one does not need any formulas and can be done through trial and error as performed in previous studies, producing all possible nth elements of the cyber network that observe the cyber protocols and choosing the best one has not been done before. Choosing the best configuration for any nth elements cyber network that can be connected to a bulk power network needs a robust and adequate computer algorithm considering cyber protocols and decision-making goals. In this paper, a novel method is proposed in order to introduce the best configuration for a cyber-network system to accommodate cyber protocols and have a remarkable effect on decreasing expected energy not supplied (EENS) compared with those previously studied. To this end, two mathematical concepts are proposed; a graph theory to consider cyber protocols and teaching-learning-based optimization to select the best cyber configuration with minimum EENS. During the first time, choosing the best configuration with each specific goal for nth devices of a cyber-network system is applicable by converting it into an n × n adjacency matrix and using the proposed graph theory mixed with teaching-learning-based optimization algorithm. Moreover, Monte Carlo simulation was used in this paper as one of the most precise probabilistic methods. The test results indicate that the proposed method selected for identifying the best configuration of a cyber-network system has significantly improved reliability indices compared to those in previous papers and it could be useful for every wide power-cyber network. Additionally, different types of cyber network are studied for validating the proposed method. This method is applied to the realistic feeder of the Hormozgan Regional Electric Company as a smart pilot system.