The increasing penetration of wind generation requires performance evaluation methods that extend beyond average annual energy production. Temporal delivery characteristics, such as monthly dispersion and exposure to low-production periods, can influence both technical robustness and economic sensitivity. Building upon a previously developed probabilistic and entropy-based assessment framework, this study evaluates the robustness of delivery-oriented performance metrics for onshore and offshore wind units under parametric and economic uncertainty. Using high-resolution operational data from four wind units (three onshore and one offshore), the analysis incorporates percentile sensitivity, threshold variation in low-production exposure, bootstrap-based uncertainty intervals, and Monte Carlo simulation of economic inputs including CAPEX, operation and maintenance costs, and discount rate. The results indicate that variations in percentile definitions and stochastic economic assumptions modify absolute performance values but do not substantially alter the relative positioning between offshore and onshore units. Averaged over 2022–2024, the analyzed offshore unit exhibited a lower monthly energy dispersion coefficient (CVE=0.255) than the analyzed onshore units (CVE=0.368), corresponding to an approximate 30% reduction in relative variability. The offshore unit also showed lower mean low-production exposure (LPE=0.526 versus 0.581 for onshore units) and consistently lower amplification of robustness-adjusted LCOE under conservative delivery assumptions. These results indicate that the analyzed offshore unit retains stronger delivery robustness and lower economic sensitivity across the tested parameter ranges. The proposed robustness-validation framework complements conventional yield-based assessments and provides additional insight for risk-aware evaluation of wind generation assets in renewable-dominated power systems.

Induction machines play a crucial role in industrial applications, making preventive maintenance combined with fault diagnosis techniques essential for ensuring reliable operation. One of the most widely used diagnostic methods for induction machines is Motor Current Signature Analysis (MCSA). However, this technique has certain limitations, particularly in the detection of incipient or small faults. Another well-established technique is Motor Square Current Signature Analysis (MSCSA), which overcomes some of the limitations of MCSA by extracting additional fault-related information from the motor current signals. This paper proposes a new diagnostic technique, designated MSCSA-APT (Motor Square Current Signature Analysis–Alternative Park Transform), based on the spectral analysis of motor currents. Compared with the conventional MSCSA method, the proposed approach provides additional information from the frequency-domain analysis, thereby improving fault detection capability. The method is based on the square of the motor square current signal and employs an Alternative Park Transform (APT) to enhance the extraction of fault signatures. Simulation and experimental results are presented to validate the proposed approach. Although the method has been evaluated for the identification of different types of faults, it is particularly effective in detecting stator short-circuit faults.

Wind energy plays a key role in the global shift towards renewable energy, requiring accurate prediction models for integration with power grids and effective energy distribution. This study validates the accuracy of wind speed forecasts from three widely used sources – European Centre for Medium-Range Weather Forecasts (ERA5), Modern-Era Retrospective Analysis for Research and Applications, MERRA-2 (NASA), and the Wind Atlas – against actual power generation data from the WindFloat Atlantic offshore wind farm near Viana do Castelo, Portugal, over the years 2022 and 2023. The results show that NASA’s forecasts were the most precise, with annual relative errors of 5 % for 2022 and 1.6% for 2023, outperforming the other models. This analysis underscores the importance of validated forecasting models to enhance renewable energy management through multi-year data for precise local calibration. The findings also emphasize the necessity of consistent short-term load forecasting models for reliable daily energy production. Overall, this research demonstrates that combining global wind datasets with local validation improves offshore wind prediction accuracy. In this context, NASA’s dataset emerges as the most reliable for operational and planning purposes in offshore renewable energy systems.

In recent years, the demand for renewable energy sources, particularly wind power, has increased significantly. In Portugal, government projections estimate a 10 GW increase in offshore wind capacity by 2030, tripling the current installed capacity. This expansion, alongside the repowering of existing onshore wind farms, will introduce substantial challenges for power networks. Some wind farms will connect to the transmission grid, while many others will be integrated into the distribution network, where infrastructure limitations could lead to operational constraints. Given the expected rise in power flow pressure, reinforcement of transmission and distribution networks will be required. Initially, the energy evacuation will rely on existing networks, which may face significant constraints under extreme load conditions. Therefore, contingency analysis, particularly under "n-1" scenarios, is crucial to assess grid resilience. This paper presents a real-case study analyzing the contingency impacts of integrating offshore and onshore wind farms into an existing 60 kV distribution network. The results highlight significant infrastructure vulnerabilities, principally line overloads in specific scenarios, emphasizing the urgent need for targeted network reinforcements, real-time monitoring systems, and dynamic optimization strategies to ensure stable wind power integration and prevent cascading failures.

The increasing penetration of wind power—driven by the expansion of offshore projects and the repowering of existing onshore installations—poses novel challenges for power system operators. While wind energy is currently integrated without curtailment and considered fully dispatchable, its inherent variability introduces growing concerns due to its rising share in installed capacity relative to conventional sources. In Portugal, wind energy already accounts for approximately 30% of the total installed capacity, with projections reaching 38% by 2030, making it the country’s second largest energy source. In the context of the 2050 carbon neutrality targets, quantifying and managing wind power uncertainty has become increasingly important. This study proposes an integrated methodology to analyze and compare the uncertainty of onshore and offshore wind generation using real-world high-resolution data (15 min intervals over a three-year period) from three onshore and one offshore wind turbine. The framework combines statistical characterization, probabilistic modeling with zero-inflated distributions, entropy-based uncertainty quantification (using Shannon, Rényi, Tsallis, and permutation entropy), and an uncertainty-adjusted Levelized Cost of Energy (LCOE). The results show that although offshore wind energy involves higher initial investment, its lower temporal variability and entropy levels contribute to superior economic reliability. These findings highlight the relevance of incorporating uncertainty into economic assessments, particularly in electricity markets where producers are exposed to penalties for deviations from scheduled generation. The proposed approach supports more informed planning, investment, and market strategies in the transition to a renewable-based energy system.

This paper proposes an automated approach to the technology selection of High-Voltage Alternating Current (HVAC) Offshore Substations (OHVS) for the integration of Oil & Gas (O&G) production and Offshore Wind Farms (OWF) based on Artificial Intelligence (AI) techniques. Due to the complex regulatory landscape and project diversity, this is enacted via a cost decision-model which was developed based on Knowledge-Based Systems (KBS) and incorporated into an optioneering software named Transmission Optioneering Model (TOM). Equipped with an interactive dashboard, it uses detailed transmission and cost models, as well as a technological and commercial benchmarking of offshore projects to provide a standardized selection approach to OHVS design. By automating this process, the deployment of a technically sound and cost-effective connection in an interactive sandbox environment is streamlined. The decision-model takes as primary inputs the power rating requirements and the distance of the offshore target site and tests multiple voltage/rating configurations and associated costs. The output is then the most technically and economically efficient interconnection setup. Since the TOM process relies on equivalent models and on a broad range of different projects, it is manufacturer-agnostic and can be used for virtually any site as a method that ensures both energy transmission and economic efficiency.

Neutral Point Clamped Asymmetric-Half-Bridge (NPC-AHB) has been proposed as one of the power converter topologies for the Switched Reluctance Machine (SRM) drive. This topology is characterized by multilevel operation and the capability to operate in fault tolerant mode, which is most indicated for use in applications that require high reliability. However, one fundamental aspect associated with fault tolerance operation is the necessity to diagnose and identify a fault in the power semiconductors of the converter. Thus, this paper proposes a new approach for the detection and diagnosis of multiple faults in the power semiconductors of this converter. The proposed approach is based on an image analysis, namely through the discrimination of the different eccentricities that will appear. This identification is done through the use of proposed normalized indexes that are developed from the entropy analysis. The proposed approach will allow discriminating multiple power semiconductors in fault, as well as between open and short circuit conditions. The performance and capability of the proposed approach will be tested using a laboratory system.

The Switched Reluctance Machine (SRM) needs a drive for its operation, highlighting the essential role of maintaining its health for proper machine functioning. In this context, this paper proposes a new power converter topology for the SRM drive that provides fault-tolerant capability. However, since fault-tolerant topologies require an important amount of extra switches, the proposed topology was designed with the purpose of minimizing them. Compared to other similar topologies, it requires nearly half of the number of switches. Another specific characteristic of this drive is that it allows to provide several voltage levels to improve the machine's performance. The operation of the drive and machine under normal and fault-tolerant conditions will be presented. On the other hand, the claimed characteristics of the proposed system will be verified by several simulation studies. These studies will show that the machine can operate normally even when there are switches under fault.

Recently, it has been verified that conventional methods for detecting rotor electrical faults in induction motors can be challenging. This is because various fault scenarios can obscure the fault signatures, resulting in false negative alarms, or numerous benign conditions that produce signatures resembling those of actual faults, leading to false positive alarms. Thus, in pursuit of dependable fault detection, a trend has emerged involving the examination of the stator current during motor start-up. To enhance reliability, this paper proposes a novel method based on this concept. The proposed method is based on the use of the S-Transform. By applying the S-Transform to start-up currents, characteristic patterns emerge that can diagnose the induction motor faults. Simulation and experimental results are showcased for both a normal motor and a motor experiencing a fault to demonstrate the efficacy of the suggested approach.

Offshore wind energy has the potential to be associated with hydrogen production to overcome certain disadvantages, such as the high cost of electrical transmission systems. In this work, two hydrogen producing systems are modelled, one with the electrolyzer offshore, the other with the electrolyzer onshore, along with a conventional offshore wind farm. To do so, each component is individually modelled and combined to construct the systems. Furthermore, an hourly optimisation algorithm is used to control the operation of the systems and a neural network is implemented to forecast day ahead power production and electricity price, so that regulation costs could be modelled. This study extends the existing literature by modelling the regulation costs in the day ahead electricity market using neural networks to provide day ahead forecasts along with analysing the flexibility of using an electrolyzer coupled with an offshore wind farm. Furthermore, innovative floating offshore wind turbines were considered, enabling the assessment for offshore hydrogen production in deeper waters. Results show that, for the present case study, the onshore electrolyzer system is always more economically interesting than the offshore electrolyzer system, mainly due to its ability of purchasing electricity from the grid. The first has a levelized cost of hydrogen of 5.84 €/kg, 3.42 €/kg and 2.57 €/kg for 2020, 2030 and 2050, respectively, compared to 8.98 €/kg, 4.37 €/kg and 2.68 €/kg.

Growing renewable energy deployment worldwide has sparked a shift in the energy landscape with far-reaching geopolitical ramifications. Hydrogen’s role as an energy carrier is central to this change, facilitating global trade and the decarbonisation of hard-to-abate sectors. This analysis offers a new method for optimally sizing solar/wind-to-hydrogen systems in specifically suitable locations. These locations are limited to the onshore and offshore regions of selected countries, as determined by a bespoke geospatial analysis developed to be location-agnostic. Furthermore, the research focuses on determining the best configurations for such systems that minimise the cost of producing hydrogen, with the optimisation algorithm expanding from the detailed computation of the classic levelised cost of hydrogen. One of the study’s main conclusions is that the best hybrid configurations obtained provide up to 70% cost savings in some areas. Such findings represent unprecedented achievements for Italy and Portugal and can be a valuable asset for economic studies of this kind carried out by local and national governments across the globe. These results validate the optimisation model’s initial premise, significantly improving the credibility of this work by constructively challenging the standard way of assessing large-scale green hydrogen projects.

In the context of actual electrical energy distribution systems, DC microgrids are beginning to become very significant. There are several possible structures for this kind of microgrids. However, there are two structures that have been emerged as the most adopted, namely the unipolar and the bipolar DC microgrids. In this way, their interconnection could be fundamental for several applications. Since these microgrids can be characterized by very different voltage levels, the interconnection must take this into consideration. So, in this paper, a new bidirectional DC DC converter with a high voltage step-up/down ratio is proposed for the interconnection of these two DC microgrids. The converter is also characterized by a non-isolated configuration and is able to transfer energy between the DC microgrids in a way that supports the balance of the bipolar infrastructure. Simulation studies of the proposed solution for transferring energy in both directions are presented. The results show that the proposed solution is in accordance with what was expected.

This paper proposes a fault diagnosis scheme employing the Stockwell transform (ST) for a multilevel converter in a switched reluctance motor (SRM) drive. The goal of this research is to enhance the reliability and performance of SRM drives by accurately detecting and diagnosing faults within the multilevel converter. The Stockwell transform is utilized to decompose the current signals of the SRM into time-frequency representations, providing comprehensive insights into the signal characteristics under both normal and faulty conditions. By analyzing these representations, specific fault signatures are identified, facilitating precise fault detection and localization. The effectiveness of the proposed scheme is validated through several simulations tests conducted under various fault scenarios (focusing on the most likely or most critical faults), including open-circuit and short-circuit faults. The results indicate that the ST-based approach achieves high accuracy and robustness in fault diagnosis, thereby significantly improving the fault-tolerant capability of SRM drives. Moreover, this method can be a valuable tool for predictive maintenance and real-time monitoring in industrial applications, contributing to the improvement of reliable and efficient SRM drive systems.

Despite the widespread usage of high-voltage alternating current (HVAC) for the connection of offshore wind farms (OWF), its use to power-from-shore (PFS) offshore oil and gas (O&G) production sites is often not feasible. Its limitations for long-distance subsea transmission are usually found at 50–70 km from shore and might be even shorter when compared commercially to a direct-current (DC) alternative or conventional generation. Therefore, this research paper aims to address the standardization of offshore transmission with a particular focus on the high-voltage direct current (HVDC) alternative. While the distance is typically not a limiting factor when using DC, and the voltages used are rather standard, the concept of power envelopes can be quite useful in addressing the high variability of offshore site power requirements and setting a design baseline that would lead to improved lead time. In this article, a full back and front-end genetic optioneering model purposely built from the ground up in Python language is used to #1 define up to three DC power envelopes that would cater to most of the candidate’s requirements and #2 provide the lowest cost variance. The results will demonstrate that this can be achieved at a minor overall cost expense.

As Guest Editors of this Special Issue, it was our responsibility to ensure that the contributions to the issue related to the extensive field of electromechanical energy conversion, with a special focus on the design, materials, and modeling of electrical machines [...]

Switched Reluctance Machines (SRM) are a kind of electrical machines that have been adopted in many applications due to some interesting characteristics. One of the critical aspects associated to these machines is the required drive. In the case of a fault in one of its transistors the drive operation could become compromised. Thus, fault-tolerant converters are required to mitigate this problem. In this context, this paper presents a new power converter topology for a SRM drive. This topology intends to attenuate the problem of requiring many power semiconductors. Thus, a topology characterized by a reduced amount of power semiconductors is presented in this paper. The proposed topology is based on an inverter with a dual output taking advantage from the aspect that in the SRM the winding current can be inverted without affecting its operation. Besides the description of the behavior and characteristics, the theoretical assumptions are verified by several tests. These tests are done with the system in normal and fault-tolerant operation and were carried out by computer simulations.

Unbalance or asymmetry in the distribution networks is a well-known power quality issue. This power quality problem is even more important in the context of the microgrids since the presence of unbalanced currents can deteriorate the overall operation of equipment and the grid itself. In this context, this article proposes a multilevel inverter based on a cascaded configuration of a four-leg dual inverter. This multilevel inverter is specially appropriated to be used with a photovoltaic (PV) generator connected to a four-wire low-voltage grid. The power converter scheme is based on two four-leg two-level inverters in which three of the ac-side terminals are connected to a three-phase transformer with the primary in open-end winding arrangement. The dc voltage buses of the inverters are connected to two insulated PV arrays. This configuration accepts the injection of the PV generated power into the grid in a way that provides unbalanced load compensation. Additionally, a control system, a pulsewidth modulation modulator, and a current references scheme for the proposed power converter topology are presented. The proposed PV system generator can balance several load unbalances under different operating conditions. It is concluded that the proposed converter can provide significant ancillary services to an unbalanced grid. The system is validated through experimental tests performed under different operating conditions.

Photovoltaic systems play a very important role today in the context of renewable energy sources. Thus, the teaching of this area is now very important. Many of the schools have classical laboratories that are not especially designed for the teaching of this area. Thus, a laboratory that was especially designed for the teaching of photovoltaic systems is presented in this paper. The laboratory is prepared to test the several parts of that system, such as PV panel characteristics, MPPT, integration of storage, efficiency of the system, power profile throughout the day, etc. Supporting simulation tools such as Matlab/Simulink was also used to compare the simulation results with the experimental ones. It will also be presented the learning objectives that can be achieved with this laboratory.

One of the major paradigm shifts that will be predictably observed in the energy mix is related to distribution networks. Until now, this type of electrical grid was characterized by an AC transmission. However, a new concept is emerging, as the electrical distribution networks characterized by DC transmission are beginning to be considered as a promising solution due to technological advances. In fact, we are now witnessing a proliferation of DC equipment associated with renewable energy sources, storage systems and loads. Thus, such equipment is beginning to be considered in different contexts. In this way, taking into consideration the requirement for the fast integration of this equipment into the existing electrical network, DC networks have started to become important. On the other hand, the importance of the development of these DC networks is not only due to the fact that the amount of DC equipment is becoming huge. When compared with the classical AC transmission systems, the DC networks are considered more efficient and reliable, not having any issues regarding the reactive power and frequency control and synchronization. Although much research work has been conducted, several technical aspects have not yet been defined as standard. This uncertainty is still an obstacle to a faster transition to this type of network. There are also other aspects that still need to be a focus of study and research in order to allow this technology to become a day-to-day solution. Finally, there are also many applications in which this kind of DC microgrid can be used, but they have still not been addressed. Thus, all these aspects are considered important challenges that need to be tackled. In this context, this paper presents an overview of the existing and possible solutions for this type of microgrid, as well as the challenges that need to be faced now.

One of today's well-accepted solutions for the SRM drives is based on multilevel converters. In fact, they present interesting features like an extended voltage range and the capability of fault tolerance. The guarantee of fault tolerance is fundamental in the context of preventive maintenance. However, regarding the power electronic converter, this requires a fault detection and diagnosis algorithm for failures in power semiconductors. Thus, this paper proposes a novel detection and diagnostic approach for the failure of those semiconductors. In this case, it will focus on one of the most commonly used topologies, namely the asymmetric neutral point clamped converter. This approach was developed with the purpose to develop specific patterns that are associated with each semiconductor and fault type. In this way, through the image identification of the multilevel converter current patterns, it will be possible to identify a distinct semiconductor and fault type. Several tests obtained from a simulation tool allowed to show the capability of the proposed approach.