Microwave nondestructive testing (MNDT) has shown great potential in detecting defects in various materials. This is due to it being safe and noninvasive. Safety is essential for the operators as well as the specimens being tested. Being noninvasive is important in maintaining the health of critical structures and components across various industries. In this paper, a review of MNDT methods is given with a comparison against other NDT techniques. First, the latter techniques are described, namely testing using a dye penetrant, ultrasound, eddy currents, magnetic particles, or radiography. Next, an overview of various microwave NDT methods is provided through a review of the applications, advantages, and limitations of each technique. Further, a detailed review of emerging MNDT techniques like microwave microscopy, active microwave thermography, and chipless radio frequency identification is presented. Next, a brief description of current and emerging algorithms employed in MNDT is discussed, with emphasis on those using artificial intelligence. By providing a comprehensive review, this article aims to shed light on the current state of MNDT, thus serving as a reference for subsequent innovations in this rapidly evolving domain.

Smart grid cybersecurity is a critical research challenge due to society’s dependence on reliable electricity. Existing research primarily addresses cybersecurity by focusing on the optimal placement of phasor measurement units (PMUs) to ensure topological observability and minimize system costs, followed by developing AI-based attack detection algorithms. However, these studies fail to simultaneously consider system cost, loss in system observability, and false data injection attack (FDIA) detection performance. Thus, this paper proposes a novel approach by formulating this issue as a tri-objective functions optimization problem. The proposed approach optimizes PMU allocation to maximize topological observability and minimize system cost while improving the FDIA detection performance using machine learning. Specifically, the k-Nearest Neighbors (KNN) model’s Brier loss is used as an objective function within the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) optimization framework to represent the FDIA detection performance. To demonstrate the proposed approach’s efficacy, it is tested on the IEEE 38-bus distribution system. To verify the strength of the developed KNN classifier, we examined it using seven different metrics: accuracy, brier loss, F1-score, elapsed time, learning curve, receiver operating characteristic curve (ROC) curve, and confusion matrix. The simulation results show that the KNN model achieved superior attack classification performance with a top accuracy of 99.99% and a minimal Brier loss of 9.9478×10-4 on the ±0.2% PMU observation tolerance dataset. These results highlight the success of our framework in concurrently optimizing attack detection performance, topological observability, and system cost.