In order to provide intelligent services, the Internet-of-Things (IoT) facilitates millions of smart devices to be enabled with network connectivity to sense, collect, process, and exchange information. Based on the communication infrastructure, the IoT can allow cyber-physical devices, such as an electric vehicle, to sense, monitor, and control from the remote control center in real time. Unfortunately, the traditional communication infrastructure is vulnerable to cyber attacks, so it is a challenging task for the IoT to explore these applications. In order to overcome the problem, this article proposes an algorithm for monitoring and controlling the electric vehicle using the IoT communication network considering false data-injection attacks. First, the driverless electric vehicle with onboard vision system is represented by a state-space framework. As the electric vehicle and monitoring control center are far away, the IoT-embedded smart sensors and actuators are used to measure and control the system states. The vehicle sensing information is transmitted to the control center over an unreliable communication channel where attacks happen. Based on the mean square error principle, the optimal state estimation algorithm is derived to know and visualize the vehicle states. In order to regulate the vehicle states, the optimal control algorithm is designed based on the semidefinite programming approach. The simulation results show that the proposed algorithms are able to properly estimate and regulate the vehicle states within a short period of time.

Human beings and control centers are usually far away from each other, so cyber attacks on the sensor measurements can lead to loss of user privacy, information, and trust. Driven by this motivation, this letter proposes an Internet of Things (IoT)-based human motion estimations algorithm under cyber attacks. The sensing measurements are transmitted to the control center over an unreliable communication channel where cyber attack occurs. Based on the mean squared error, the optimal state estimation algorithm is derived to estimate human motions. Simulation results show that the proposed method provides significant performance improvement compared with the existing approach.

This paper designs a distributed state estimation scheme considering cyber attacks using the internet of things (IoT) technologies. The IoT sensors are utilised to get synchronous generator information. After locally estimating the system states, the attack occurs during transmission of sensors information to the remote estimator. In the fusion center, the convex optimization problem is developed to estimate the generator states. Lastly, the feedback controller is proposed to regulate the generator states. Numerical results demonstrate that the developed algorithm can properly estimate and regulate the generator states.

This paper develops a state estimation and stabilisation scheme for monitoring and controlling the wind turbine. Basically, the estimation scheme is designed considering the Bayesian tree network. The estimated system states are corrected in the forward and backward direction of this network where the estimation errors are enforced to minimise. Therefore, the estimated system state converges to the actual states as time goes by. Furthermore, the optimal feedback controller is designed. Interestingly, the proposed algorithms are applied to the environment-friendly wind turbine, and it shows that the developed methods can effectively estimate and stabilise the turbine states.

This paper develops a distributed state estimation approach for smart grids. Particularly, the designed filter is developed in an interconnected way where the packet dropouts take place between estimators. The error function between true and estimated states is written in compact form, then it can be transformed into the linear matrix inequality (LMI). After solving the LMI problem, the desired gains are determined for the smart grid state estimation. The proposed method is applied to the IEEE 14-bus system where system state and input matrices are obtained from the Holt-Winters approach.

Wireless energy harvesting has emerged as an appealing solution to prolong the lifespan of energy-constrained wireless networks and been regarded as a main functional unit for almost all perpetual communications. This paper proposes a channel coding-based Internet of Things communication network, and state estimation as well as stabilization algorithms for wireless power transfer systems. After modeling the energy harvesting module like wireless energy transfer systems as a state-space framework, Web-enabled smart sensors are deployed to obtain measurements. The sensing information is transmitted to the nearby base station for collection and digital communication under the condition of packet losses. A recursive systematic convolutional code is used to add redundancy into the quantize system states. After digital modulation, the signal is transmitted to a control center for system state estimation and stabilization. The optimal gain of the state estimation scheme is determined after minimizing the mean squared errors. Afterward, a stabilization algorithm is designed considering packet losses in the communication networks using the semidefinite programming approach. Furthermore, the convergence of the developed estimation algorithm is proved. Numerical simulation results demonstrate, considering packet loss, sensor faults, disturbances, and delay into account, that the proposed algorithms can estimate and stabilize the system states within a short period of time.

In contrast to traditional centralized estimation methods, this letter proposes a distributed dynamic state estimation method for microgrids incorporating distributed energy resources. Specifically, distributed filter structure is designed in an interconnected way, where packet losses obviously occur between them. Then the error function is written in a compact form using the matrix property of the Kronecker product. Afterwards, it can be transformed into a linear matrix inequality. Finally, the local and neighboring gains for the distributed estimator are effectively computed after solving the convex optimization problem. Simulation result shows that the proposed method can well estimate the system state within 10 iterations.

The distribution power subsystems are usually interconnected to each other, so the design of the interconnected optimal filtering algorithm for distributed state estimation is a challenging task. Driven by this motivation, this paper proposes a novel consensus filter based dynamic state estimation algorithm with its convergence analysis for modern power systems. The novelty of the scheme is that the algorithm is designed based on the mean squared error and semidefinite programming approaches. Specifically, the optimal local gain is computed after minimizing the mean squared error between the true and estimated states. The consensus gain is determined by a convex optimization process with a given suboptimal local gain. Furthermore, the convergence of the proposed scheme is analyzed after stacking all the estimation error dynamics. The Laplacian operator is used to represent the interconnected filter structure as a compact error dynamic for deriving the convergence condition of the algorithm. The developed approach is verified by using the renewable microgrid. It shows that the distributed scheme being explored is effective as it takes only 0.00004 seconds to properly estimate the system states and does not need to transmit the remote sensing signals to the central estimator.

This paper explores the optimal filtering problem for microgrid state estimation considering packet losses in the internet of things (IoT) networks. The considered IoT networks collect microgrid information from smart sensors and send control messages to actuators where sensors and actuators are linked over a lossy network. Explicitly, the distribution power system incorporating renewable distributed energy resources such as wind turbine is represented as a state-space model where IoT element such as smart sensors are deployed to obtain measurements. This sensing information is transmitted to the fusion center through a lossy IoT communication network where measurements are lost. This paper proposes an optimal estimator based on the mean squared error between the actual states and its estimate. Afterwards, a state feedback controller is designed based on the semidefinite programming approach under the condition of packet dropouts in the IoT network. The efficacy of the proposed approaches are demonstrated by presenting an environment-friendly microgrid model incorporating distributed energy resources.

Due to dramatically rising energy demand worldwide power system is often run near the operational and technical limits, where unexpected trivial disturbances can cause possibly massive blackouts. Cyber attacks on smart grid communication networks are one of the impending threats to cause large-scale cascading outage. In contrast to the traditional cyber attack protection techniques, this paper presents a recursive systematic convolutional code based defending technique from the signal processing perspective. This code introduces redundancy in the system for protecting the grid information. Furthermore, an optimal control law is designed to stabilize the power network. Specifically, the performance index for control is converted to a convex semidefinite programming problem. The proposed controller can work well for any initial values. The efficacy of the developed approach is verified through numerical simulations. Results show that the proposed strategy has stronger attack protection performance and the controller can stabilize the grid in a fairly short time. This approach provides a fundamental framework for the design of the smart grid energy management system and reliable communication infrastructure scheme with renewable integration applications.

The penetration of renewable distributed energy resources such as wind turbine has been dramatically integrated in distribution networks due to clean, sustainable, and economical green energy. Due to intermittent wind speed, the power generation patterns vary which can risk the distribution network operation. So, it is intrinsically required to monitor the wind turbines in a distributed way. This paper presents an adaptive-then-combine distributed dynamic approach for monitoring the grid under lossy communication link between the wind turbines and energy management system. Firstly, the wind turbine is represented by a state-space linear equation, with sensors deployed to obtain system state information. Based on the mean squared error principle, an adaptive approach is proposed to estimate the local state information. The global estimation is designed by combining estimation results with weighting factors which are calculated by minimizing estimation error covariance based on semi-definite programming. Finally, the convergence analysis indicates that estimation error is gradually decreased, so the estimated state converges to the actual state. The efficacy of the developed approach is verified using the turbine model. The research is valuable for green energy technologies, households and interconnected industrial information societies, and also provides the knowledge towards a smart and secure green energy future.

The penetration of renewable distributed energy resources, such as wind turbine, has been dramatically increased in distribution networks. Due to the intermittent property, the wind power generation patterns vary, which may risk distribution network operations. So, it is intrinsically necessary to monitor wind turbines in a distributed way. This paper presents an adaptive-then-combine distributed dynamic approach for monitoring the grid under lossy communication links between the wind turbines and energy management system. First, the wind turbine is represented by a state-space linear model, with sensors deployed to obtain the system state information. Based on the mean squared error principle, an adaptive approach is proposed to estimate the local state information. The global estimation is designed by combining estimation results with weighting factors which are calculated by minimizing the estimation error covariance based on semidefinite programming. Finally, the convergence analysis indicates that the estimation error is gradually decreased, so the estimated state converges to the actual state. The efficacy of the developed approach is verified using the wind turbine and the IEEE 6-bus distribution system.