• Energy Research

Automatic fault detection (AFD) is a key technology to optimize the Operation and Maintenance of photovoltaic (PV) systems portfolios. A very common approach to detect faults in PV systems is based on the comparison between measured and simulated performance. Although this approach has been explored by many authors, due to the lack a common basis for evaluating their performance, it is still unclear what are the influencing aspects in the design of AFD algorithms. In this study, a series of AFD algorithms have been tested under real operating conditions, using monitoring data collected over 58 months on 80 rooftop-type PV systems installed in Germany. The results shown that this type of AFD algorithm have the potential to detect up to 82.8% of the energy losses with specificity above 90%. In general, the higher the simulation accuracy, the higher the specificity. The use of less accurate simulations can increase sensitivity at the cost of decreasing specificity. Analyzing the measurements individually makes the algorithm less sensitive to the simulation accuracy. The use of machine learning clustering algorithm for the statistical analysis showed exceptional ability to prevent false alerts, even in cases where the modeling accuracy is not high. If a slightly higher level of false alerts can be tolerated, the analysis of daily PR using a Shewhart chart provides the high sensitivity with an exceptionally simple solution with no need for more complex algorithms for modeling or clustering. 33 pages, 30 figures, preprint submitted to Elsevier Solar Energy

This study addresses the challenge of optimizing energy management in the electric vehicle industry, specifically focusing on motorsport. It particularly targets optimizing energy management during an endurance event at the Formula Student competition. The research involves detailed simulation of a complete endurance event, including developing precise track and vehicle models and their application in real-time energy management of our motorsport vehicle. The primary objective is to develop an energy reference profile that optimizes point scoring following the event’s specific rules. The energy reference profile serves as a strategic guideline for energy consumption and its regeneration throughout the endurance event. What sets this study apart is its emphasis on the real-time feedback controller’s implementation in the Formula Student prototype, FST12, specifically during the endurance event. This controller dynamically regulates the inverter’s power output, ensuring the vehicle closely follows the pre-established energy reference profile. This real-time energy management approach enhances overall performance by optimizing energy utilization for maximum scoring potential. The developed distance estimation method presented an error of less than 0.7% compared to experimental measurements. The Formula Student prototype, FST12, underwent experimental validation on a real 20 km closed-loop track. Results showed that the optimized strategy can be implemented with less than 0.5% of error in energy consumption and 6.8% of error in the obtained competing points.

The use of cobalt-iron (VaCoFe) core is investigated as an alternative to silicon-iron (FeSi) on the design of interior permanent magnet synchronous motors (IPMSM). A spoke-type IPMSM geometry is optimized considering FeSi and VaCoFe cores for a torque range up to 40 N.m, providing a general comparative analysis between materials, considering the application of a 4-motor competition vehicle’s powertrain. A genetic optimization algorithm is applied over a hybrid analytical/finite-element model of the motor to provide sufficiently accurate electromagnetic and thermal results within a feasible time. VaCoFe can result in an estimated increase of up to 5 % in efficiency for the same torque, or up to 64 % torque increase for the same efficiency level. After optimization, and using a detailed time-dependent model, a potential 3.2 % increase in efficiency, a core weight reduction of 4.1 %, and a decrease of 9.6 % in the motor’s core volume was found for the VaCoFe at 20 Nm. In addition, for the same motor volume, the VaCoFe allows an increase of 51.9 % of torque with an increase of 1.1 % of efficiency, when compared with FeSi.

Using pumps operating as turbines (PATs) offers the possibility of increasing the sustainability of water and energy systems by recovering the excess energy that would be otherwise lost in pressure-reducing valves or head loss chambers. Regarding on-grid applications, there have been many research works, and PATs have been implemented in several ways. However, more research still needs to be done on optimizing the efficiency and stability of PATs operating in off-grid systems. This work contributes to the development of stable direct current (DC) off-grid electric systems based on PATs using a self-excited induction generator (SEIG). In this context, a methodology is proposed, based on the hydraulic, mechanical, and electric subsystems, to define the PAT-SEIG operational area to maximize energy conversion and system efficiency. These limits depend highly on the capacitor value, rotational speed, and electric load. In addition, an analytical model is proposed to estimate the PAT-SEIG operation under specific conditions. With this, water managers can design and optimize an off-grid PAT-SEIG system and define the best hydraulic machines, electronic equipment, and control elements to maximize energy conversion within the target of operational limits. Two micro PAT-SEIG setups were implemented in the hydraulic laboratory of IST/CERIS under typical operating conditions to validate the proposed methodology. The system’s maximum efficiency and operational limits can be adapted using different capacitor values for the excitation of the SEIG. Considering the nominal efficiencies of the system’s components, the maximum p.u. efficiency obtained for each PAT-SEIG system was between 0.7 and 0.8 p.u.

A high-performance 20 kW, 20 Nm, 8000 rpm, spoke-type interior permanent magnet motor to be integrated into a FormulaStudent electric car’s powertrain has been designed to meet demanding performance requirements for its driving cycle. This paper describes key steps in the design optimization, analysis, fabrication, and testing of this machine. Design optimization used the non-dominated sorting genetic algorithm II (NSGA-II) coupled with a hybrid analytical/finite element model to reduce the computational time, achieving a torque and efficiency of 20 Nm and 98.6%, respectively. A prototype has been constructed. The final motor design has been tested, where experimental nominal torque and efficiency have reached 18.2 Nm and 90%, respectively. Design challenges regarding the manufacturing are presented, justified, and discussed in detail. Test results were conducted to evaluate reliability and motor temperatures with and without water refrigeration at nominal current. Despite those adjustments in the optimized design, one shows that the impact on the car’s lap time was low, going from 77.3 s for the ideal optimized motor to 78.9 s for the prototyped motor.

Failure modes, effects, and criticality analysis (FMECA) is a qualitative risk analysis method widely used in various industrial and service applications. Despite its popularity, the method suffers from several shortcomings analyzed in the literature over the years. The classical approach to obtain the failure modes’ risk level does not consider any relative importance between the risk factors and may not necessarily represent the real risk perception of the FMECA team members, usually expressed by natural language. This paper introduces the application of Type-I fuzzy inference systems (FIS) as an alternative to improve the failure modes’ risk level computation in the classic FMECA analysis and its use in cyber-power grids. Our fuzzy-based FMECA considers first a set of fuzzy variables defined by FMECA experts to embody the uncertainty associated with the human language. Second, the “seven plus or minus two” criterion is used to set the number of fuzzy sets to each variable, forming a rule base consisting of 125 fuzzy rules to represent the risk perception of the experts. In the electrical power systems framework, the new fuzzy-based FMECA is utilized for reliability analysis of cyber-power grid systems, assessing its benefits relative to a classic FMECA. The paper provides the following three key contributions: (1) representing the uncertainty associated with the FMECA experts using fuzzy sets, (2) representing the FMECA experts’ reasoning and risk perception through fuzzy-rule-based reasoning, and (3) applying the proposed fuzzy approach, which is a promissory method to accurately define the prioritization of failure modes in the context of reliability analysis of cyber-power grid systems.

Sustainability and efficiency in irrigation are essential in the management of the water–energy–food nexus to reach the Sustainable Development Goals in 2030. In irrigation systems, the reduction of energy consumption is required to improve the system efficiency and consequently the sustainability indicators of the water network. The use of pumps working as turbines (PATs) has been a feasible solution to recover the excess of energy where pressure reduction valves are installed. This research demonstrates the use of PATs under steady and unsteady conditions by analyzing the application in a real irrigation networks located in Vallada (Valencia, Spain). The study shows the possibility of recovering 44 MWh/year using PATs installed upstream of the irrigation hydrants. The real behavior of the PAT operation in a stand-alone recovery energy solution allowed analysis of the flow, head and efficiency variation as a function of the rotational speed, as well as the minimum capacitance to self-excite the generator and the resistive load of the electrical circuit. The PAT limit is examined in terms of the overpressure induced by a fast closure manoeuvre of hydrants, and the runaway conditions due to the disconnection from the electrical load.