Recent technological advances in communication networks, intelligent devices, power electronics, and phasor measurement units have significantly transformed the operation of modern power systems. This evolution gave rise to smart grids, which enable the flow of real-time information on the operational state of the grid and of control commands across multiple communication infrastructures, using a variety of protocols and standards, between control centers and devices deployed throughout the grid’s physical structure. At the same time, it has exposed power systems to new challenges and threats, due to the vulnerabilities inherited from the different components they integrate. Attackers have a variety of attacks at their disposal, by which they can disturb the availability of electricity as well as cause damage to the smart grid’s physical structure. Therefore, cybersecurity has become an important aspect of the smart grid concept. This field of research has attracted the attention of many researchers, and in the last decade or so, the number of studies on the cyber-physical security of smart grids has surged significantly. Proportionally, an important number of survey papers were published as well. It has therefore become more difficult to navigate literature on the topic of smart grid cyber-physical security due to the large number of papers, the complexity of the grid’s structure, and the variety of attacks, resolution methods, and techniques. To address this issue, in this work, we present a comprehensive review of existing literature reviews on the topic of smart grid cyber-physical security. We reviewed 100 survey papers, which were categorized into general surveys, attack-specific surveys, method-specific surveys, and component-specific surveys. We discussed and highlighted research tendencies in terms of attacks and methods used to protect smart grids. Additionally, we presented an overview of the different research challenges and possible future directions.

Term Rewriting Systems (TRSs) are used in compilers to simplify and prove expressions. State-of-the-art TRSs in compilers use a greedy algorithm that applies a set of rewriting rules in a predefined order (where some of the rules are not axiomatic). This leads to a loss of the ability to simplify certain expressions. E-graphs and equality saturation sidestep this issue by representing the different equivalent expressions in a compact manner from which the optimal expression can be extracted. While an e-graph-based TRS can be more powerful than a TRS that uses a greedy algorithm, it is slower because expressions may have a large or sometimes infinite number of equivalent expressions. Accelerating e-graph construction is crucial for making the use of e-graphs practical in compilers. In this paper, we present Caviar, an e-graph-based TRS for proving expressions within compilers. The main advantage of Caviar is its speed. It can prove expressions much faster than base e-graph TRSs. It relies on three techniques: 1) a technique that stops e-graphs from growing when the goal is reached, called Iteration Level Check; 2) a mechanism that balances exploration and exploitation in the equality saturation algorithm, called Pulsing Caviar; 3) a technique to stop e-graph construction before reaching saturation when a non-provable pattern is detected, called Non-Provable Patterns Detection (NPPD). We evaluate caviar on Halide, an optimizing compiler that relies on a greedy-algorithm-based TRS to simplify and prove its expressions. The proposed techniques allow Caviar to accelerate e-graph expansion for the task of proving expressions. They also allow Caviar to prove expressions that Halide’s TRS cannot prove while being only 0.68x slower. Caviar is publicly available at: https://github.com/caviar-trs/caviar.

This paper proposes a three-phase metaheuristic-based approach for induction machine bearing failure detection and diagnosis. It consists of extracting and processing different failure types features to set up a knowledge base, which contains different failure types. The first phase consists in pre-processing the measured signals by aggregating them and preparing the data in exploitable formats for the clustering. The second phase ensures the induction machine operating mode diagnosis. A measured signals clustering is performed to build classes where each one represents a health state. A variable neighborhood search (VNS) metaheuristic is designed for data clustering. Moreover, VNS is hybridized with a classical mechanics-inspired optimization (CMO) metaheuristic to balance global exploration and local exploitation during the evolutionary process. The third phase consists of two-step failure detection, setting up a knowledge base containing different failure types, and defining a representative model for each failure type. In the learning step, different class features are extracted and inserted in the knowledge base to be used during the decision step. The proposed metaheuristic-based failure detection diagnosis approach is evaluated using PRONOSTIA and CWR bearing data experimental platforms vibration and temperature measurements. The achieved results clearly demonstrate the failure detection and diagnosis, efficiency, and effectiveness of the proposed metaheuristic approach.

In the last decades, the availability constraint as well as learning and deteriorating effects were introduced into the production scheduling theory to simulate real-world case studies and to overcome the limitation of the classical models. To the best of our knowledge, this paper is the first in the literature to address the permutation flowshop scheduling problem (PFSP) with flexible maintenance under learning and deterioration effects to minimize the makespan. Firstly, we address the PFSP with flexible maintenance and learning effects. Then, the deteriorating effect is also considered. Adaptive artificial bee colony algorithms (ABC) enhanced with Q-learning are proposed, in which the Nawaz–Enscore–Ham (NEH) heuristic and modified NEH heuristics are hybridized with a maintenance insertion heuristic to construct potential integrated initial solutions. Furthermore, a Q-learning (QL)-based neighborhood selection is applied in the employed bees phase to improve the quality of the search space solutions. Computational experiments performed on Taillard’s well-known benchmarks, augmented with both prognostic and health management (PHM) and maintenance data, demonstrate the effectiveness of the proposed QL-driven ABC algorithms.