The rapid industrial growth in solar energy is gaining increasing interest in renewable power from smart grids and plants. Anomaly detection in photovoltaic (PV) systems is a demanding task. In this sense, it is vital to utilize the latest updates in machine learning technology to accurately and timely disclose different system anomalies. This paper addresses this issue by evaluating the performance of different machine learning schemes and applying them to detect anomalies on photovoltaic components. The following schemes are evaluated: AutoEncoder Long Short-Term Memory (AE-LSTM), Facebook-Prophet, and Isolation Forest. These models can identify the PV system’s healthy and abnormal actual behaviors. Our results provide clear insights to make an informed decision, especially with experimental trade-offs for such a complex solution space.

Securing cyber-physical systems (CPS) and Internet of Things (IoT) systems requires the identification of how interdependence among existing atomic vulnerabilities may be exploited by an adversary to stitch together an attack that can compromise the system. Therefore, accurate attack graphs play a significant role in systems security. A manual construction of the attack graphs is tedious and error-prone, this paper proposes a model-checking-based automated attack graph generator and visualizer (A2G2V). The proposed A2G2V algorithm uses existing model-checking tools, an architecture description tool, and our own code to generate an attack graph that enumerates the set of all possible sequences in which atomic-level vulnerabilities can be exploited to compromise system security. The architecture description tool captures a formal representation of the networked system, its atomic vulnerabilities, their pre-and post-conditions, and security property of interest. A model-checker is employed to automatically identify an attack sequence in the form of a counterexample. Our own code integrated with the model-checker parses the counterexamples, encodes those for specification relaxation, and iterates until all attack sequences are revealed. Finally, a visualization tool has also been incorporated with A2G2V to generate a graphical representation of the generated attack graph. The results are illustrated through application to computer as well as control (SCADA) networks.

The need for an efficient power source for operating the modern industry has been rapidly increasing in the past years. Therefore, the latest renewable power sources are difficult to be predicted. The generated power is highly dependent on fluctuated factors (such as wind bearing, pressure, wind speed, and humidity of surrounding atmosphere). Thus, accurate forecasting methods are of paramount importance to be developed and employed in practice. In this paper, a case study of a wind harvesting farm is investigated in terms of wind speed collected data. For data like the wind speed that are hard to be predicted, a well built and tested forecasting algorithm must be provided. To accomplish this goal, four neural network-based algorithms: artificial neural network (ANN), convolutional neural network (CNN), long short-term memory (LSTM), and a hybrid model convolutional LSTM (ConvLSTM) that combines LSTM with CNN, and one support vector machine (SVM) model are investigated, evaluated, and compared using different statistical and time indicators to assure that the final model meets the goal that is built for. Results show that even though SVM delivered the most accurate predictions, ConvLSTM was chosen due to its less computational efforts as well as high prediction accuracy.

An engineered system is designed to deliver certain performance related to its quality-of-service, and while doing so, it must also maintain stable operation. Resilience of a system is its ability to continue to offer system performance stably, while withstanding any adverse events. Motivated by this concept, we propose to measure the resilience level of a power system by quantifying its stability level as measured by: transient stability margin (TSM), critical clearance time (CCT), relay margin (RM), and load security margin (LSM), as well as its performance level as measured by: load loss (LL) and recovery/repair time (RT) while being exposed to adverse events. For comparability, we also propose a normalization for each of the 6 measures to a number in the unit interval [0, 1], which is scale-invariant, and further probabilistically average each of those across all possible sequences of faults (of a specified length) against their occurrence probabilities to arrive at a set of 6 unit-interval valued indices. New polynomial complexity algorithms (in the number of generators) are proposed for estimating TSM (in form of volume of region of stability) and CCT; new quadratic program formulation for precise computation of RM is developed and implemented; also, new security and stability informed notions of LSM and LL are introduced and implemented by extending continuation power flow. Such quantification of resilience levels provides a numerical measure to compare the relative abilities of different power grids to withstand the impact of sequences of adverse events. The proposed approach is illustrated by computing and comparing the resilience of three similar power system topologies differing only in the location of generators. The framework is further validated by implementing it on the IEEE 30-bus test system.

Measuring resiliency of smart grid systems is one of the vital topics towards maintaining a reliable and efficient operation under attacks. This paper introduces a set of factors that are utilized for resiliency quantification of microgrid (MG) systems. The level of resilience (LoR) measure is determined by examining the voltage sag percentage, the level of performance reduction (LoP_R) as measured by percentage of reduction of load served, recovery time (RT), which is the time system takes to detect and recover from an attack/fault, and the time to reach Power Balance state (T_b) during the islanded mode. As an illustrative example, a comparison based on the resiliency level is presented for two topologies of MGs under an attack scenario.

Cyber-attacks threaten the safety of cyber physical systems (CPSs) as a result of the existence of weaknesses in the multiple structural units constituting them. In this paper, three cyber physical systems case studies of a pressurized water nuclear power plant (NPP), an industrial control system (ICS), and a vehicular network system (VNS) are examined, formally presented, and implemented utilizing Architecture Analysis and Design Language, determining system design, links, weaknesses, resources, potential attack instances, and their pre-and post-conditions. Then, the developed plant models are checked with a security property using JKind model checker embedded software. The attack graphs causing plants disruptions for the three applications are graphically visualized using a new graphical user interface (GUI) windows application.