A Weakly Pareto Compliant Quality Indicator

In multi-objective optimization problems, the optimization target is to obtain a set of non-dominated solutions. Comparing solution sets is crucial in evaluating the performances of different optimization algorithms. The use of performance indicators is common in comparing those sets and, subsequently, optimization algorithms. A good solution set must be close to the Pareto-optimal front, well-distributed, maximally extended and fully filled. Therefore, an effective performance indicator must encompass these features as a whole and must be Pareto dominance compliant. Unfortunately, some of the known indicators often fail to properly reflect the quality of a solution set or cost a lot to compute. This paper demonstrates that the Degree of Approximation (DOA) quality indicator, is a weakly Pareto compliant unary indicator that gives a good estimation of the match between the approximated front and the Pareto-optimal front. Moreover, DOA computation is easy and fast

Two decades of condition monitoring methods for power devices

Condition monitoring (CM) of power semiconductor devices enhances converter reliability and customer service. Many studies have investigated the semiconductor devices failure modes, the sensor technologies, and the signal processing techniques to optimize the CM. Furthermore, the improvement of power devices’ CM thanks to the use of the Internet of Things and artificial intelligence technologies is rising in smart grids, transportation electrification, and so on. These technologies will be widespread in the future, where more and more smart techniques and smart sensors will enable a better estimation of the state of the health (SOH) of the devices. Considering the increasing use of power converters, CM is essential as the analysis of the data obtained from multiple sensors enables the prediction of the SOH, which, in turn, enables to properly schedule the maintenance, i.e., accounting for the trade-off between the maintenance cost and the cost and issues due to the device failure. From this perspective, this review paper summarizes past developments and recent advances of the various methods with the aim of describing the current state-of-the-art in CM research

Effect of Islanding and Telecontrolled Switches on Distribution System Reliability Considering Load and Green-Energy Fluctuations

To improve electrical distribution network reliability, some portions of the network could operate in autonomous mode, provided that the related technical issues are addressed. More specifically, when there is not a path from those portions to the primary substation due to a fault in the network, such portions could be disconnected from the main network and supplied by local generation only. Such a mode of operation is known as "intentional islanding" and its effectiveness, in terms of adequacy, depends on the ability of the local generation to meet the island's load. In fact, the ratio between the available local generation and load demand can frequently change during islanding due to load variations and, especially, due to the strongly irregular behavior of the primary energy sources of renewable generators. This paper proposes an analytical formulation to assess local generation adequacy during intentional islanding, accounting for the aforementioned variations. More specifically, the fluctuations of load and green-energy generators during islanding are modeled by means of Markov chains, whose output quantities are encompassed in the proposed analytical formulation. Such a formulation is used by the analytical equations of load points' outage rate and duration. The evaluation of the reliability indices accounts for a protection scheme based on an appropriate communication infrastructure. Therefore, a brief overview on the telecommunications technologies has been presented with reference to their suitability for the specific application. In particular, distribution network safety issues have been considered as the main concern. The results show that neglecting load and generation fluctuations leads to a strong overestimation of the ability of distributed generators to meet the island load. Through a case study it is observed that the error on the load point outage rate is greater than the one affecting the outage duration

Battery Management in a Green Fog-Computing Node: a Reinforcement-Learning Approach

In the last years, Internet is evolving towards the cloud-computing paradigm complemented by fog-computing in order to distribute computing, storage, control, networking resources, and services close to end-user devices as much as possible, while sending heavy jobs to the remote cloud. When fog-computing nodes cannot be powered by the main electric grid, some environmental-friendly solutions, such as the use of solar- or wind-based generators could be adopted. Their relatively unpredictable power output makes it necessary to include an energy storage system in order to provide power, when a peak of work occurs during periods of low-power generation. An optimized management of such an energy storage system in a green fog-computing node is necessary in order to improve the system performance, allowing the system to cope with high job arrival peaks even during low-power generation periods. In this perspective, this paper adopts reinforcement learning to choose a server activation policy that ensures the minimum job loss probability. A case study is presented to show how the proposed system works, and an extensive performance analysis of a fog-computing node highlights the importance of optimizing battery management according to the size of the Renewable-Energy Generator system and the number of available servers

An Overview of Strengths and Weaknesses in Using MOSFET Experience for Modeling GaN HEMT

GaN high electron mobility transistors (HEMTs) represent an emerging and key enabling technology for obtaining highly efficient and compact power electronic systems. The use of circuit models of power devices is essential for the optimal design of power converters, but while they have been deeply investigated for power MOSFETs and IGBTs, GaN HEMT models are still in their early stages. This paper first discusses the main similarities and differences between conventional MOSFETs and GaN HEMTs in terms of the datasheet information that the device manufacturers use to obtain the behavioral models that they usually provide as Spice-like netlists. Then, it highlights the strengths and weaknesses of using the behavioral models of MOSFET for GaN HEMT. To achieve this aim, a study of the existing GaN HEMT models revealed the lack of a proper modeling strategy for the dynamic conduction resistance, which is the most critical aspect of HEMT modeling. The difficulty is due to the dependence of the dynamic conduction resistance on quantities related to the application, which is a behavior absent in power MOSFETs. Consequently, future research efforts on GaN HEMT modeling must face this issue

Optimal Switch Placement Considering the Regulatory Period

Summary The introduction of reward/penalty mechanisms by the national regulation authorities has strongly increased the utilities' interest in distribution systems reliability improvement. Usually, the regulator correlates such mechanisms with the annual number and/or duration of interruptions per customer, requiring the utilities to reach a target value for these reliability indicators each year for a given regulatory period. Installing switches along the distribution network positively affects reliability but does involve utility costs. Therefore, utilities need a method to schedule annually the switches' placement to obtain the required annual reliability improvement in a cost-effective manner. This paper proposes a simple and effective model of the optimization problem that enables the distribution network operator to try to achieve uniform levels of improvement in distribution system reliability indices during the regulatory period. In this perspective, the paper presents a way to provide the optimal number, type, and position of the switches that must be installed each year to achieve both the targeted annual reliability improvement and the cheapest investment cost during the overall regulatory period. The proposed approach is formulated as a multiobjective optimization problem and is solved using a genetic algorithm. The proposed approach is useful for the distribution network operator since it is able to provide the overall minimum investment cost required for each desired reliability level to be reached annually over the regulatory period, as well as the maximum annual reliability that can be achieved for each overall economic investment by performing only one single optimization

Distortion Due to the Zero Current Detection Circuit in High Power Factor Quasi-Resonant Flybacks

In a high-power factor quasi-resonant Flyback, an ideal zero current detection (ZCD) circuit and control circuitry enable the power switch turn-on in the exact instant a zero ringing current is reached after demagnetization. A nonzero current at the turn-on instant affects the input current shape and; consequently, affects its Total Harmonic Distortion (THD). This paper firstly deeply analyzes the effect on the distortion due to a nonideal ZCD circuit. After, some typical implementations of the ZCD circuit and their effect on the THD are analyzed, identifying their pros and cons. Finally, some experimental results are obtained to validate the analytical investigation

Distortion Due to the Zero Current Detection Circuit in High Power Factor Quasi-Resonant Flybacks

In a high-power factor quasi-resonant Flyback, an ideal zero current detection (ZCD) circuit and control circuitry enable the power switch turn-on in the exact instant a zero ringing current is reached after demagnetization. A nonzero current at the turn-on instant affects the input current shape and; consequently, affects its Total Harmonic Distortion (THD). This paper firstly deeply analyzes the effect on the distortion due to a nonideal ZCD circuit. After, some typical implementations of the ZCD circuit and their effect on the THD are analyzed, identifying their pros and cons. Finally, some experimental results are obtained to validate the analytical investigation

Multi-Objective Optimization of Thin-Film Silicon Solar Cells with Metallic and Dielectric Nanoparticles

Thin-film solar cells enable a strong reduction of the amount of silicon needed to produce photovoltaic panels but their efficiency lowers. Placing metallic or dielectric nanoparticles over the silicon substrate increases the light trapping into the panel thanks to the plasmonic scattering from nanoparticles at the surface of the cell. The goal of this paper is to optimize the geometry of a thin-film solar cell with silver and silica nanoparticles in order to improve its efficiency, taking into account the amount of silver. An efficient evolutionary algorithm is applied to perform the optimization with a reduced computing time