Masud Rana

An Overview of Distributed Microgrid State Estimation and Control for Smart Grids

Given the significant concerns regarding carbon emission from the fossil fuels, global warming and energy crisis, the renewable distributed energy resources (DERs) are going to be integrated in the smart grid. This grid can spread the intelligence of the energy distribution and control system from the central unit to the long-distance remote areas, thus enabling accurate state estimation (SE) and wide-area real-time monitoring of these intermittent energy sources. In contrast to the traditional methods of SE, this paper proposes a novel accuracy dependent Kalman filter (KF) based microgrid SE for the smart grid that uses typical communication systems. Then this article proposes a discrete-time linear quadratic regulation to control the state deviations of the microgrid incorporating multiple DERs. Therefore, integrating these two approaches with application to the smart grid forms a novel contributions in green energy and control research communities. Finally, the simulation results show that the proposed KF based microgrid SE and control algorithm provides an accurate SE and control compared with the existing method.

Distributed State Estimation Using RSC Coded Smart Grid Communications

Recently, the renewable distributed energy resources (DERs) have become more and more popular due to carbon-free energy sources and environment-friendly electricity generation. Unfortunately, these power generation patterns are mostly intermittent in nature and distributed over the electrical grid, which creates challenging problems in the reliability of the smart grid. Thus, the smart grid has a strong requisite for an efficient communication infrastructure to facilitate estimating the DER states. In contrast to the traditional methods of centralized state estimation (SE), we propose a distributed approach to microgrid SE based on the concatenated coding structure. In this framework, the DER state is treated as a dynamic outer code, and the recursive systematic convolutional (RSC) code is seen as a concatenated inner code for protection and redundancy in the system states. Furthermore, in order to properly monitor the intermittent energy source from any place, this paper proposes a distributed SE method. Particularly, the outputs of the local SE are treated as measurements, which are fed into the master fusion station. At the end, the global SE can be obtained by combining local SEs with corresponding weighting factors. The weighting factors can be calculated by inspiring the covariance intersection method. The simulation results show that the proposed method is able to estimate the system state properly.

Distributed dynamic state estimation over a lossy communication network with an application to smart grids

In contrast to the traditional centralised power system state estimation methods, this paper investigates the interconnected optimal filtering problem for distributed dynamic state estimation considering packet losses. Specifically, the power system incorporating microgrids is modelled as a state-space linear equation where sensors are deployed to obtain measurements. Basically, the sensing information is transmitted to the energy management system through a lossy communication network where measurements are lost. As the system states are unavailable, so the estimation is essential to know the overall operating conditions of the electricity network. The proposed estimator is based on the mean squared error between the actual state and its estimate. To obtain the distributed estimation, the optimal local and neighbouring gains are computed to reach a consensus estimation after exchanging their information with the neighbouring estimators. Then the convergence of the developed algorithm is theoretically proved. Afterwards, a distributed controller is designed based on the semidefinite programming approach. Simulation results demonstrate the accuracy of the developed approaches under the condition of missing measurements.

Cyber attack protection and control of microgrids

Recently, the smart grid has been considered as a next-generation power system to modernize the traditional grid to improve its security, connectivity, efficiency and sustainability. Unfortunately, the smart grid is susceptible to malicious cyber attacks, which can create serious technical, economical, social and control problems in power network operations. In contrast to the traditional cyber attack minimization techniques, this paper proposes a recursive systematic convolutional U+0028 RSC U+0029 code and Kalman filter U+0028 KF U+0029 based method in the context of smart grids. Specifically, the proposed RSC code is used to add redundancy in the microgrid states, and the log maximum a-posterior is used to recover the state information, which is affected by random noises and cyber attacks. Once the estimated states are obtained by KF algorithm, a semidefinite programming based optimal feedback controller is proposed to regulate the system states, so that the power system can operate properly. Test results show that the proposed approach can accurately mitigate the cyber attacks and properly estimate and control the system states.

Renewable microgrid state estimation using the Internet of Things communication network

Given the huge concerns all over the world regarding carbon emissions from fossil fuels, energy crisis and global warming, the renewable distributed energy resources (DERs) are going to be integrated in electricity grids, which will make the energy supply more reliable and decrease transmission losses. Regrettably, one of the main practical defies in smart grid planning, control and operation with DERs is the voltage regulation at the distribution field level. This problem motivates the deployment of sensors and actuators in electricity grids so that the voltage regulation can be controlled at the desired level. To do that the measurements from the renewable microgrid state information is transmitted to an energy management center via the internet of things (IoT) based communication network. In other words, the proposed IoT communication infrastructure provides an opportunity to address the voltage regulation challenge by offering the two-way communication links for microgrid state information collection and estimation. Based on this smart grid communication infrastructure, we propose a Kalman filter based state estimation method for voltage regulation of the microgrid. Finally, the effectiveness of the Kalman filter based state estimation method is illustrated using the linear state space model of a microgrid incorporating DERs.

Microgrid State Estimation: A Distributed Approach

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.

Distributed State Estimation Over Unreliable Communication Networks With an Application to Smart Grids

In contrast to the traditional centralized power system state estimation methods, this paper investigates the interconnected optimal filtering problem for distributed dynamic state estimation considering packet losses. Specifically, the power system incorporating microgrids is modeled as a state-space linear equation where sensors are deployed to obtain measurements. Basically, the sensing information is transmitted to the energy management system through a lossy communication network where measurements are lost. This can seriously deteriorate the system monitoring performance and even lose network stability. Second, as the system states are unavailable, so the estimation is essential to know the overall operating conditions of the electricity network. Availability of the system states provides designers with an accurate picture of the power network, so a suitable control strategy can be applied to avoid massive blackouts due to losing network stability. Particularly, the proposed estimator is based on the mean squared error between the actual state and its estimate. To obtain the distributed estimation, the optimal local and neighboring gains are computed to reach a consensus estimation after exchanging their information with the neighboring estimators. Then, the convergence of the developed algorithm is theoretically proved. Afterward, a distributed controller is designed based on the semidefinite programming approach. Simulation results demonstrate the accuracy of the developed approaches under the condition of missing measurements.

Architecture of the Internet of Energy Network: An Application to Smart Grid Communications

Due to the global warming and energy crisis, the renewable distributed energy resources, such as wind turbines, are integrated into the grid. We model an AC microgrid with energy generating units, local loads, and electronic devices. Then, the set of non-linear differential equations are expressed as a state-space model. As the microgrid is located in the customer premises or remote areas, its condition needs to monitor in real-time. So, the smart sensor requires to deploy around the microgrid, and its sensing information transmits to the energy management system via the Internet as the sensing information is a massive amount of data. Combining the Internet of Things elements, such as sensors (Internet emended), and the Internet as a transmission medium will form the Internet of Energy, which is considered as a sign interest nowadays. Basically, the energy management center estimates the microgrid states to know the operating conditions of these foreseeable intermittent resources. For estimating the microgrid states, the H-infinity-based Mimi-max filter is proposed, which will no need to know the exact process and measurement noise statistics. Simulation results show that the proposed approach can well estimate the system states compared with the existing Kalman filter. As a result, this framework will assist to design a suitable microgrid framework and provides effective dynamic state estimations.

Modelling the Microgrid and Its Parameter Estimations Considering Fading Channels

As the electricity demand continues to grow and renewable energy resources are incorporated into the grid, an effective dynamic state estimation is required for monitoring and grid synchronization. This paper proposes a microgrid state estimation approach considering the unreliable communication channels. Particularly, the renewable microgrid incorporating distributed energy resources, such as solar panels, are represented as a state-space linear model. Then, the wireless sensor network is adopted to sense the microgrid states. For long-distance transmission, we propose an innovative smart grid infrastructure, which is easy to design and offers a reliable two-way communication. In this infrastructure, the modulated signal is transmitted over an unreliable channel, which causes signal distortions. After demodulation and dequantization, the received signal at the energy management system is used the fading Kalman filter algorithm. Using the tunable forgetting factor in the predicted error covariance step, this algorithm aims to minimize the estimation errors so the estimated states reflect the true system states. Simulations results indicate that the developed approach estimates the system states within 0.25 seconds.

Distributed condition monitoring of renewable microgrids using adaptive-then-combine algorithm

This paper explores the problem of distributed state estimation including packet losses for the environment-friendly renewable microgrid incorporating electricity generating circuits. The problem is becoming critical due to the global warming, increasing green house gas emissions, and practical infeasibility with computational burden of the large-scale centralized power system monitoring. To address the impending problem, a novel distributed microgrid state estimation algorithm is derived in the context of microgrids. Specifically, after modelling the microgrid, this paper proposes a local microgrid state estimation algorithm considering packet losses. Then a novel optimal weighting factor calculation method for the global state estimation is proposed. Particularly, it can automatically adjust the optimal weighting factors for different sensor measurements based on the observation quality, improving the estimation accuracy of the global estimation. Simulations show that the desired state estimation accuracy is achievable.