In the power system, the application of the signals transmitted from the sensors to the controller can significantly improve the dynamic performance. However, cyber-attacks and time delays exist widely in the communication network, which has a considerable effect on stability, operation, and control. In this paper, an H_ load frequency control (LFC) scheme for multi-area power systems under cyber-attacks and time-varying delays is investigated. First, false date injection attacks (FDIA), as well as denial of service (DoS) attacks, are viewed as two typical cyber-attacks and introduced into the multi-area power system. Then, the state-space equations of the LFC power system under these two attacks and time-varying delays are analyzed and modeled. Furthermore, by using Lyapunov stability theory and linear matrix inequality (LMI) method, an H_ LFC scheme for attacked and delayed power systems is developed. The purpose of the proposed controller is to guarantee the closed-loop system exponentially mean-square stable with prescribed H_ performance. Finally, a three-area LFC system is employed to demonstrate the effectiveness of the proposed mechanism. The simulation results verified that the control performance of an attacked and delayed system can be improved significantly via the designed controller.

There are lots of cyber-attack, especially false data injection attacks, in modern power systems. This attack can circumvent traditional residual-based detection methods, and destroy the integrity of control information, thus hindering the stability of the power system. In this paper, a novel Spatio-temporal detection mechanism is proposed to evaluate and locate false data injection attacks. In the proposed method, temporal correlation and spatial correlation are analyzed by cubature Kalman filter and Gaussian process regression, respectively, to capture the dynamic features of state vectors. Then, a deep convolutional neural network is trained to depict the functional relationship between Spatio-temporal correlation functions and the output, which is set as the detection indicator to access whether the power system under attack or not. Furthermore, the performance of the proposed mechanism is evaluated with comprehensive numerical simulation on IEEE 39-bus test system. The results of the case studies showed that the proposed method can achieve 99.84%-100% accuracy.

Recently, various strategies for energy management have been proposed to improve energy efficiency in smart grids. One key aspect of this is the use of microgrids. To effectively manage energy in a residential microgrid, advanced computational tools are required to maintain the balance between supply and demand. The concept of load disaggregation through non-intrusive load monitoring (NILM) is emerging as a cost-effective solution to optimize energy utilization in these systems without the need for extensive sensor infrastructure. This paper presents an energy management system based on NILM and the Internet of Things (IoT) for a residential microgrid, including a photovoltaic (PV) plant and battery storage device. The goal is to develop an efficient load management system to increase the microgrid’s independence from the traditional electrical grid. The microgrid model is developed in the electromagnetic transient program PSCAD/EMTDC to analyze and optimize energy performance. Load disaggregation is obtained by combining artificial neural networks (ANNs) and particle swarm optimization (PSO) to identify appliances for demand-side management. An ANN is applied in NILM as a load identification task, and PSO is used to optimize the ANN algorithm. This combination enhances the NILM technique’s accuracy, which is verified using the mean absolute error method to assess the difference between the predicted and measured power consumption of appliances. The NILM output is then transferred to consumers through the ThingSpeak IoT platform, enabling them to monitor and control their appliances to save energy and costs.

A decent vehicle-to-grid (V2G) control scheme improves voltage stability and grid reliability of distribution networks (DNs) by providing reactive power and ancillary services. However, unknown malicious attacks in cyber layers and uncertain EV charging/unplugging in physical layers will disturb the measurement process, leading to controller parameters’ mismatching and severe voltage violations. This paper proposes a robust V2G voltage control scheme for DNs investigating cyber attacks and EV customer interruptions. Firstly, the impact of cyber attacks is characterized by time-varying uncertain matrices function with Lebesgur measurable elements, while the EV customer interruptions are represented by Signum variables. Then, a new state-space equation for V2G voltage control in DNs is constructed by incorporating above matrices function and Signum variables. Moreover, a robust voltage controller is proposed for measurement disordered V2G systems to suppress voltage violations while achieving H∞ performance. By utilizing linear matrix inequality (LMI) techniques, sufficient conditions for system closed-loop exponentially mean-square stability and the controller parameter tuning are presented. Case studies conducted on modified IEEE European low voltage test feeder compare two voltage deviation indices of the proposed controller with those of existing methods, verifying the effectiveness of the proposed approach against measurement anomalies.

With increasing requirements of online monitoring of Overhead Transmission Lines (OTL) in smart grids, contact-free measurement technologies have been updated these decades. However, the contactless sensors in past research are still limited to the application environment like wind deflection. This paper proposes an advanced detection approach with a novel structure to enhance the possibility of implementation and reduce cost. Three pairs of detection probes arranged in a curved pattern can collect differential voltage signals based on the principle of electric field radiation. With three-phase vector decomposition, the 9 unknown variables including voltage to be measured and status of lines (sag and wind deflection) in 9 equations can be constructed. The voltages to be measured can be calculated by solving this nonlinear equation system by iteration. Also, the position of the device installation is optimized by Particle Swarm Optimization (PSO) algorithm regarding accuracy. The whole measurement device can be easily implemented on the towers according to the distance of insulation requirement regardless of the position of lines. The simulation of level 110 kV is investigated and a model system is built to measure a lower-level voltage for verification. The results show that the proposed non-contact OTL voltage approach can reach the desired accuracy with low costs and monitor the status of lines.