A hybrid Linear Quadratic Regulator (LQR) and Proportional-Integral (PI) control for a MicroGrid (MG) under unbalanced linear and nonlinear loads was presented and evaluated in this paper. The designed control strategy incorporates the microgrid behavior, low-cost LQR, and error reduction in the stationary state by the PI control, to reduce the overall energetic cost of the classical PI control applied to MGs. A Genetic Algorithm (GA) calculates the parameters of LQR with high-accuracy fitness function to obtain the optimal controller parameters as settling time and overshoot. The gain values of the classical PI controller were determined through the improved LQR values and geometrical root locus. When MG operates in the grid-tied mode under unbalanced conditions, the controller performance of the Current Source Inverter (CSI) of the MG is considerably affected. Consequently, the CSI operates in a negative-sequence mode to compensate for unbalanced current at the Point of Common Coupling (PCC) between the MG and the utility grid. The study cases involved the reduction of the negative-sequence percentage in the current at the PCC, mitigation of harmonics in the current signal injected by the MG, and close related power quality issues. All these cases have been analyzed by implementing an MG connected at the PCC of a low-voltage distribution network. A numerical model of the MG in Matlab/Simulink was implemented to verify the performance of the designed LQR-PI control to mitigate or overcome the power quality concerns. The extensive simulations have permitted verifying the robustness and effectiveness of the proposed strategy.

Phasor measurement units (PMUs) play an important role in many applications of power systems. In order to ensure a reliable performance, the phasor estimation algorithm has to satisfy ...

Technological innovations in electrical systems require the development of testing and validation tools that provide a safe environment and allow configuring different operating conditions. In this regard, power hardware-in-the-loop (PHIL) simulation is a suitable tool that interacts with physical components knowing as hardware under test (HuT) according to a mathematical model. In this work, a PHIL simulation for both electrical source analysis and load studies as HuT using linear and nonlinear loads is carried out. It is performed by means of an ideal transformer model (ITM) as interface algorithm along with a feedback stage using digital filtering, where a state-space control is simulated and implemented in a real-time hardware and a voltage source inverter (VSI) is used as a power amplifier. Different loads are used to validate the PHIL implementation such as resistive load, resistive-inductive load, and a nonlinear load by considering a dc motor. In this work, two HuT are used, a transformer arrangement and a power inverter.

Renewable energy technologies play a key role in mitigating climate change and advancing sustainable development. Among these, photovoltaic (PV) systems have experienced significant growth in recent years. However, shading, one of the most common faults in PV modules, can drastically degrade their performance. This study investigates the application of convolutional neural networks (CNNs) for the automated detection and classification of shading faults, including multiple severity levels, using current–voltage (I–V) curves. Four scenarios were simulated in Simulink: a healthy module and three levels of shading severity (light, moderate, and severe). The resulting I–V curves were transformed into grayscale images and used to train and evaluate several custom-designed CNN architectures. The goal is to assess the capability of CNN-based models to accurately identify shading faults and discriminate between severity levels. Multiple network configurations were tested, varying image resolution, network depth, and filter parameters, to explore their impact on classification accuracy. Furthermore, robustness was evaluated by introducing Gaussian noise at different levels. The best-performing models achieved classification accuracies of 99.5% under noiseless conditions and 90.1% under a 10 dB noise condition, demonstrating that CNN-based approaches can be both effective and computationally lightweight. These results underscore the potential of this methodology for integration into automated diagnostic tools for PV systems, particularly in applications requiring fast and reliable fault detection.

The electric spring (ES) is a contemporary device that has emerged as a viable alternative for solving problems associated with voltage and power stability in distributed generation-based smart grids (SG). In order to study the integration of ESs into the electrical network, the steady-state simulation models have been developed as an essential tool. Typically, these models require an equivalent electrical circuit of the in-test networks, which implies adding restrictions for its implementation in simulation software. These restrictions generate simplified models, limiting their application to specific scenarios, which, in some cases, do not fully apply to the needs of modern power systems. Therefore, a robust steady-state model for the ES is proposed in this work to adequately represent the power exchange of multiples ESs in radial micro-grids (µGs) and renewable energy sources regardless of their physical location and without the need of additional restrictions. For solving and controlling the model simulation, a modified backward–forward sweep method (MBFSM) is implemented. In contrast, the voltage control determines the operating conditions of the ESs from the steady-state solution and the reference voltages established for each ES. The model and algorithms of the solution and the control are validated with dynamic simulations. For the quasi-stationary case with distributed renewable generation, the results show an improvement higher than 95% when the ESs are installed. On the other hand, the MBFSM reduces the number of iterations by 34% on average compared to the BFSM.

Phasor measurement units (PMUs) are important elements in power systems to monitor and know the real network condition. In order to regulate the performance of PMUs, the IEEE Std. C37.118.1 stablishes two classes—P and M, where the phasor estimation is carried out using a quadrature oscillator and a low-pass (LP) filter for modulation and demodulation, respectively. The LP filter plays the most important role since it determines the accuracy, response time and rejection capability of both harmonics and aliased signals. In this regard and by considering that the M-class filters are used for more accurate measurements, the IEEE Std. presents different M-class filters for different reporting rates (when a result is given). However, they can degrade their performance under frequency deviations if the LP frequency response is not properly considered. In this work, a unified model for magnitude compensation under frequency deviations for all the M-class filters is proposed, providing the necessary values of compensation to improve their performance. The model considers the magnitude response of the M-class filters for different reporting rates, a normalized frequency range based on frequency dilation and a fitted two-variable function. The effectiveness of the proposal is verified using both static and dynamic conditions for frequency deviations. Besides that, a real-time simulator to generate test signals is also used to validate the proposed methodology.