Explore the vast range of titles available.

The quantity and variety of parameters involved in the failure evolutions in solder joints under a thermo-mechanical process directs the reliability assessment of electronic devices to be frustratingly slow and expensive. To tackle this challenge, we develop a novel machine learning framework for reliability assessment of solder joints in electronic systems; we propose a correlation-driven neural network model that predicts the useful lifetime based on the materials properties, device configuration, and thermal cycling variations. The results indicate a high accuracy of the prediction model in the shortest possible time. A case study will evaluate the role of solder material and the joint thickness on the reliability of electronic devices; we will illustrate that the thermal cycling variations strongly determine the type of damage evolution, i.e., the creep or fatigue, during the operation. We will also demonstrate how an optimal selection of the solder thickness balances the damage types and considerably improves the useful lifetime. The established framework will set the stage for further exploration of electronic materials processing and offer a potential roadmap for new developments of such materials.

The emergence of smart cities demands a fundamental transformation in how energy is managed, positioning smart grid technologies as a key driver in urban progress [...]

Accurate solar radiation forecasting is essential to operate power systems safely under high shares of photovoltaic generation. This paper compares the performance of several machine learning algorithms for solar radiation forecasting using endogenous and exogenous inputs and proposes an ensemble feature selection method to choose not only the most related input parameters but also their past observations values. The machine learning algorithms used are: Support Vector Regression (SVR), Extreme Gradient Boosting (XGBT), Categorical Boosting (CatBoost) and Voting-Average (VOA), which integrates SVR, XGBT and CatBoost. The proposed ensemble feature selection is based on Pearson coefficient, random forest, mutual information and relief. Prediction accuracy is evaluated based on several metrics using a real database from Salvador, Brazil. Different prediction time-horizons are considered: 1 h, 2 h and 3 h ahead. Numerical results demonstrate that the proposed ensemble feature selection approach improves forecasting accuracy and that VOA performs better than the other algorithms in all prediction time horizons.

Modern power delivery systems are rapidly evolving with high proliferation of power-electronic (PE)-interfaced distributed energy resources (DERs). Compared to the conventional sources of generation, the PE-interfaced DERs, e.g., solar and wind resources, are attributed substantially different characteristics such as lower overload capability and limited frequency response patterns. This paper focuses on effective management and control mechanisms for PE-interfaced DERs in power distribution systems with high penetration of renewables, particularly under fault, voltage-sag, load variations, and other prevailing conditions in the grid. Aiming at the solutions to enhance the system performance resilience, we introduce an advanced model predictive control (MPC) based scheme to control the DER units, minimize the impact of transients and disruptions, speed up the response and recovery of particular metrics and parameters, and maintain an acceptable operation condition. The performance of the suggested control scheme is tested on a modified IEEE 34-bus test feeder, where the proposed solution demonstrates its effectiveness to minimize the system transient during faults, with an enhanced grid-edge and system-wide resilience characteristics in voltage profiles.

Blockchain technology brings about an opportunity to maintain decentralization in several applications, such as cryptocurrency. With the agents of a decentralized system operating independently, it calls for a consensus protocol that helps all nodes to agree on the state of the ledger. Most of the existing blockchains rely on Proof of Work (PoW) as the underlying consensus algorithm, resulting in a significant amount of electricity power consumption. Furthermore, it demands the miner to buy specific computation devices. Besides, a protocol to gather the society-related taxes such as public education funding and charities is lacking in existing consensus algorithms. In response, this paper proposes a new consensus algorithm, namely Proof of Humanity (PoH) aiming at gathering society-related taxes. According to PoH, the probability that an agent becomes a leader depends on its donations to non-profit accounts. Therefore, PoH encourages miners to donate money and gain mining power, its incentives, and transaction fees. The associated bureaucracy model is introduced briefly to address the required ecosystem for real case implementation of PoH. A distributed random variable generation algorithm is presented in this paper which ensures that the randomly selected leader is neither predictable nor adjustable. It is demonstrated that the proposed blockchain is totally robust against forking and possesses a high level of propagation speed, which ensures the scalability. Simulations show that the proposed blockchain network does not fail even in adverse scenarios where the majority of nodes refuse to propagate valid blocks. Besides, simulations reveal a suitable average block creation duration.

Allocation of the power losses to distributed generators and consumers has been a challenging concern for decades in restructured power systems. This paper proposes a promising approach for loss allocation in power distribution systems based on a cooperative concept of game-theory, named Shapley Value allocation. The proposed solution is a generic approach, applicable to both radial and meshed distribution systems as well as those with high penetration of renewables and DG units. With several different methods for distribution system loss allocation, the suggested method has been shown to be a straight-forward and efficient criterion for performance comparisons. The suggested loss allocation approach is numerically investigated, the results of which are presented for two distribution systems and its performance is compared with those obtained by other methodologies.

The price‐based unit commitment (PBUC) problem aims to optimise the power generating units' schedules to meet the system demand with the objective to maximise the generation companies' (GENCOs') profit. State‐of‐the‐art PBUC models have taken into account exogenous uncertainties in renewable generation, demand, and price signals. This study proposes a novel PBUC problem formulation with endogenous or decision‐dependent uncertainty (DDU) in the elastic portion of the demand. The proposed PBUC model is formulated as a mixed‐integer non‐linear programming (MINLP) problem with non‐convex continuous relaxation. A concavification approach is developed to reformulate the non‐convex MINLP model as an equivalent mixed‐integer quadratic programming (MIQP) model whose continuous relaxation is convex. Case studies considering GENCOs owning and operating 3, 12, 19, and 40 generating units demonstrate the efficacy of the proposed DDU‐aware PBUC formulation on the GENCOs' anticipated profits.

The evidence of ongoing rising temperatures has been accumulating for years. It is, therefore, essential to utilize recent climate data to inform future investment decisions. Perhaps nowhere is this more important than in improving the aging power grid infrastructure. This work presents a novel data-driven robust optimization approach to guide transmission expansion planning and transmission capacity expansion planning decisions focused on mitigating the effects of globally and regionally rising temperatures. The proposed methodology is tested on a large-scale realistic test case of the power transmission grid in Arizona, and two classes of valid inequality are used to accelerate the computation time. The effects of temperature are modeled at a regional level in the feastured test case using a k -means clustering method. Results demonstrate that more accurate regional temperature modeling results in more focused investment plans.

Power grid response against high‐impact low‐probability (HILP) events could be enhanced by (a) hardening mechanisms to boost its structural resilience and (b) corrective recovery and mitigation analytics to improve its operational resilience. Planning for structural resilience and attempts to find the optimal location of the Tie switches in radially operated power distribution networks that enable harnessing the network topology for maximised resilience against HILP disasters are focussed. This goal is achieved through a novel resilience‐oriented multi‐objective decision making platform, which employs a k‐PEM based probabilistic power flow (PPF) algorithm. The proposed framework offers a decision making analytic embedded with the fuzzy satisfying method (FSM) that characterises the system resilience features, such as robustness, restoration agility, load criticality, and recovered capacity, to assess different network reconfiguration options and select the optimal solution for implementation. The aforementioned resilience features are formulated in nodal level and then aggregated over the entire system to characterise the system‐level objective functions. The performance of the suggested framework is analysed on the IEEE 33‐Bus test system under a designated HILP event, and the applicability on larger networks has been verified on the IEEE 69‐bus test system. The results demonstrate the efficacy and applicability of the proposed framework in boosting the network resilience against future extremes.