Being one of the most multifaceted cyber-physical systems, smart grids (SGs) are arguably more prone to cyber-threats. A covert data integrity assault (CDIA) on a communications network may be lethal to the reliability and safety of SG operations. They are intelligently designed to sidestep the traditional bad data detector in power control centers, and this type of assault can compromise the integrity of the data, causing a false estimation of the state that further severely distresses the entire power system operation. In this paper, we propose an unsupervised machine learning-based scheme to detect CDIAs in SG communications networks utilizing non-labeled data. The proposed scheme employs a state-of-the-art algorithm, called isolation forest, and detects CDIAs based on the hypothesis that the assault has the shortest average path length in a constructed random forest. To tackle the dimensionality issue from the growth in power systems, we use a principal component analysis-based feature extraction technique. The evaluation of the proposed scheme is carried out through standard IEEE 14-bus, 39-bus, 57-bus, and 118-bus systems. The simulation results show that the proposed scheme is proficient at handling non-labeled historical measurement datasets and results in a significant improvement in attack detection accuracy.

The I–V curve serves as an effective representation of the inherent nonlinear characteristics describing typical photovoltaic (PV) panels, which are essential for achieving sustainable energy systems. Over the years, several PV models have been proposed in the literature to achieve the simplified and accurate reconstruction of PV characteristic curves as specified in the manufacturer’s datasheets. Based on their derivation, PV models can be classified into three distinct categories: circuit-based, analytical-based, and empirical-based models. However, an extensive analysis of the accuracy of the reconstructed curves for different PV models at the maximum power point (MPP) has not been conducted at the time of writing this paper. The IEC EN 50530 standard stipulates that the absolute errors within the vicinity of MPP should always be less than or equal to 1%. Therefore, this review paper conducts an in-depth analysis of the accuracy of PV models in reconstructing characteristic curves for different PV panels. The limitations of existing PV models were identified based on simulation results obtained using MATLAB and performance indices. Additionally, this paper also provides suggestions for future research directions.

Communications technologies are an integral part of efficient monitoring and reliable control in smart grids, but enhanced reliance on these technologies heightens the risk of cyber assaults. Recently, a new type of stealth, or covert, assault in smart grid networks has been discovered, which cannot be ascertained by legacy bad-data detectors using state estimation. Due to the delay-sensitive nature of smart grid networks, swift detection of abnormal changes is immensely desired. In this paper, we propose two Euclidean distance-based anomaly detection schemes for covert cyber-assault detection in smart grid communications networks. The first scheme utilizes unsupervised-learning over unlabeled data to detect outliers or deviations in the measurements. The second scheme employs supervised-learning over labeled data to detect the deviations in the measurements. Unlike the classic detection test, the proposed schemes tackle an unknown sample with low computational complexity, leading to a shorter decision time. To improve detection accuracy and further reduce the computational complexity and the associated time delay, we employ a genetic algorithm-based feature selection method to choose the distinguishing optimal feature data subset as input to both of the proposed schemes. The evaluation is carried out through the standard IEEE 14-bus, 39-bus, 57-bus and 118-bus test systems. Simulation results show that compared to the existing feature extraction-based detection schemes, the proposed schemes show significant improvement in covert cyber deception assault-detection accuracy.

As one of the most diversified cyber-physical systems, the smart grid has become more decumbent to cyber vulnerabilities. An intelligently crafted, covert, data-integrity assault can insert biased values into the measurements collected by a sensor network, to elude the bad data detector in the state estimator, resulting in fallacious control decisions. Thus, such an attack can compromise the secure and reliable operations of smart grids, leading to power network disruptions, economic loss, or a combination of both. To this end, in this paper, we propose a novel idea for the reconstruction of sensor-collected measurement data from power networks, by removing the impacts of the covert data-integrity attack. The proposed reconstruction scheme is based on a latterly developed, unsupervised learning algorithm called a denoising autoencoder, which learns about the robust nonlinear representations from the data to root out the bias added into the sensor measurements by a smart attacker. For a robust, multivariate reconstruction of the attacked measurements from multiple sensors, the denoising autoencoder is used. The proposed scheme was evaluated utilizing standard IEEE 14-bus, 39-bus, 57-bus, and 118-bus systems. Simulation results confirm that the proposed scheme can handle labeled and non-labeled historical measurement data and results in a reasonably good reconstruction of the measurements affected by attacks.

In this paper, a sound event detection method is proposed for estimating three types of bathroom activities—showering, flushing, and faucet usage—based on the sounds of water usage in the bathroom. The proposed approach has a two-stage structure. First, the general sound classification network YAMNet is utilized to determine the existence of a general water sound; if the input data contains water sounds, W-YAMNet, a modified network of YAMNet, is then triggered to identify the specific activity. W-YAMNet is designed to accommodate the acoustic characteristics of each bathroom. In training W-YAMNet, the transfer learning method is applied to utilize the advantages of YAMNet and to address its limitations. Various parameters, including the length of the audio clip, were experimentally analyzed to identify trends and suitable values. The proposed method is implemented in a Raspberry-Pi-based edge computer to ensure privacy protection. Applying this methodology to 10-min segments of continuous audio data yielded promising results. However, the accuracy could still be further enhanced, and the potential for utilizing the data obtained through this approach in assessing the health and safety of elderly individuals living alone remains a topic for future investigation.

The research in industry and academia on smart grids is predominantly focused on the regulation of generated power and management of its consumption. Because transmission of bulk-generated power to the consumer is immensely reliant on secure and efficient transmission grids, comprising huge electrical and mechanical assets spanning a vast geographic area, there is an impending need to focus on the transmission grids as well. Despite the challenges in wireless technologies for SGs, cognitive radio networks are considered promising for provisioning of communications services to SGs. In this paper, first, we present an IEEE 802.22 wireless regional area network cognitive radio-based network model for smart monitoring of transmission lines. Then, for a prolonged lifetime of battery finite monitoring network, we formulate the spectrum resource allocation problem as an energy efficiency maximization problem, which is a nonlinear integer programming problem. To solve this problem in an easier way, we propose an energy-efficient resource-assignment scheme based on the Hungarian method. Performance analysis shows that, compared to a pure opportunistic assignment scheme with a throughput maximization objective and compared to a random scheme, the proposed scheme results in an enhanced lifetime while consuming less battery energy without compromising throughput performance.