Predictive current controllers based on finite control set-model predictive control (FCS-MPC) have been extensively used in power converters. One of the clear advantages of FCS-MPC is that several control targets and constraints can be included in a cost function and simultaneously controlled. In order to establish the importance of one controlled target in relation to the others, weighting factors are used. Once the weighting factors have been tuned, they remain unchanged. This paper presents a neural network-based novel approach to the problem, in which weighting factors are tuned online as a function of several merit figures and references. This adaptive method updates online the weighting factors in the cost function when either the merit figures or the references change to boost the performance of the controller. This strategy is validated through simulations and experiments are carried out on a three-level neutral point clamped converter. The results are compared with the conventional FCS-MPC, which is based on static cost functions.

Encouraged by the considerable cost reduction, small-scale solar power deployment has become a reality during the last decade. However, grid integration of small-scale photovoltaic (PV) solar systems still remains unresolved. High penetration of Renewable Energy Sources (RESs) results in technical challenges for grid operators. To address this, Virtual Power Plants (VPPs) have been defined and developed to manage distributed energy resources with the aim of facilitating the integration of RESs. This paper introduces a hybrid irradiance forecasting approach aimed at facilitating the integration of PV systems into a VPP, especially when a historical irradiance dataset is exiguous or non-existent. This approach is based on Artificial Neural Networks (ANNs) and a novel similar hour-based selection algorithm, has been tested for a real PV installation, and has been validated also considering irradiance measurements from an aggregation of ground-based meteorological stations, which emulate the nodes of a VPP. Under a reduced historical dataset, the results show that the proposed similar hour-based method produces the best forecasts with regard to those obtained by the ANN-based approach. This is particularly true for one-month and two-month datasets minimizing the mean error by 16.32% and 9.07% respectively. Finally, to demonstrate the potential of the proposed approach, a comparative analysis has been carried out between the hybrid method and the most used benchmarks in the literature, namely, the persistence method and the method based on similar days. It has been demonstrated conclusively that the proposed model yields promising results regardless the length of the historical dataset.

This article presents a hybrid fault diagnosis framework for DC–DC converters in photovoltaic (PV) systems, combining digital twin (DT) modelling and detection with machine learning anomaly classification. The proposed method addresses both hardware faults such as open and short circuits in insulated-gate bipolar transistors (IGBTs) and diodes and sensor-level false data injection attacks (FDIAs). A five-dimensional DT architecture is employed, where a virtual entity implemented using FMI-compliant FMUs interacts with a real-time emulated physical plant. Fault detection is performed by comparing the real-time system behaviour with DT predictions, using dynamic thresholds based on power, voltage, and current sensors errors. Once a discrepancy is flagged, a second step classifier processes normalized time-series windows to identify the specific fault type. Synthetic training data are generated using emulation models under normal and faulty conditions, and feature vectors are constructed using a compact, interpretable set of statistical and spectral descriptors. The model was validated using OPAL-RT Hardware in the Loop emulations. The results show high classification accuracy, robustness to environmental fluctuations, and transferability across system configurations. The framework also demonstrates compatibility with low-cost deployment hardware, confirming its practical applicability for fault diagnosis in real-world PV systems.

Solar energy penetration has been on the rise worldwide during the past decade, attracting a growing interest in solar power forecasting over short time horizons. The increasing integration of these resources without accurate power forecasts hinders the grid operation and discourages the use of this renewable resource. To overcome this problem, Virtual Power Plants (VPPs) provide a solution to centralize the management of several installations to minimize the forecasting error. This paper introduces a method to efficiently produce intra-day accurate Photovoltaic (PV) power forecasts at different locations, by using free and available information. Prediction intervals, which are based on the Mean Absolute Error (MAE), account for the forecast uncertainty which provides additional information about the VPP node power generation. The performance of the forecasting strategy has been verified against the power generated by a real PV installation, and a set of ground-based meteorological stations in geographical proximity have been used to emulate a VPP. The forecasting approach is based on a Long Short-Term Memory (LSTM) network and shows similar errors to those obtained with other deep learning methods published in the literature, offering a MAE performance of 44.19 W/m² under different lead times and launch times. By applying this technique to 8 VPP nodes, the global error is reduced by 12.37% in terms of the MAE, showing huge potential in this environment.

Since the advent of the microgrid (MG) concept, almost two decades ago, the energy sector has evolved from a centralized operational approach to a distributed generation paradigm challenged by the increasing number of distributed energy resources (DERs) mainly based on renewable energy. This has encouraged new business models and management strategies looking for a balance between energy generation and consumption, and promoting an efficient utilization of energy resources within MGs and minimizing costs for the market participants. In this context, this paper introduces an efficient management strategy, which is aimed at obtaining a fair division of costs billed by the utilities, without relying on a centralized utility or MG aggregator, through the design of a local event-based energy market within the MG. This event-driven MG energy market operates with blockchain (BC) technology based on smart contracts for electricity transactions to both guarantee veracity and immutability of the data and automate the transactions. The event-based energy market approach focuses on two of the design limitations of BC, namely the amount of information to be stored and the computational burden, which are significantly reduced while maintaining a high level of performance. Furthermore, the prosumer data is obtained by using IEC 61850 standard-based commands within the BC framework. By doing so, the system is compatible with any device irrespective of the manufacturer implementing the IEC 61850 standard. The advantages of this management approach are considerable for: MG participants, in terms of financial benefits; the MG itself, as it can operate more independently from the main grid; and the grid since the MG becomes less unpredictable due to the internal energy exchanges. The proposed strategy is validated on an experimental setup employing low-cost devices.

Condition monitoring of high voltage power lines through self-powered sensor systems has become a priority for utilities with the aim of detecting potential problems, enhancing reliability of the power transmission and distribution networks and mitigating the adverse impact of faults. Energy harvesting from the magnetic field generated by the alternating current flowing through high voltage lines can supply the monitoring systems with the required power to operate without relying on hard-wiring or battery-based approaches. However, developing an energy harvester, which scavenges the power from such a limited source of energy, requires detailed design considerations, which may not result in a technically and economically optimal solution. This paper presents an innovative simulation-based strategy to characterize an inductive electromagnetic energy harvester and the power conditioning system. Performance requirements in terms of the harvested power and output voltage range, or level of magnetic core saturation can be imposed. Different harvester configurations, which satisfy the requirements, have been produced by the simulation models. The accuracy and efficiency of this approach is verified with an experimental setup based on an energy harvester, which consists of a Si-steel magnetic core and a power conditioning unit. For the worst-case scenario with a primary current of 5 A, the maximum power extracted by the harvester can be as close as 165 mW, resulting in a power density of 2.79 mW/cm³.