An overview of distributed microgrid state estimation and control for smart grids

© 2015 by the authors; licensee MDPI, Basel, Switzerland. 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

Cyber attack protection and control in microgrids using channel code and semidefinite programming

© 2016 IEEE. The smart grid has been considered as a nextgeneration power system to modernize the traditional grid to improve its security, connectivity and sustainability. Unfortunately, the grid is susceptible to malicious cyber attacks, which can create serious technical, economical and control problems in power network operations. In contrast to the traditional cyber attack minimization techniques, this paper proposes a recursive systematic convolutional (RSC) code and Kalman filter based method in the context of microgrids. 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, a semidefinite programming based optimal feedback controller is proposed to regulate the system states. Test results show that the proposed approach can accurately mitigate the cyber attacks and properly estimate and control the system states

Microgrid state estimation and control using Kalman filter and semidefinite programming technique

The design of environment-friendly microgrids at the smart distribution level requires a stable behaviour for multiple state operations. This paper develops a Kalman filter based optimal feedback control method for the microgrid state estimation and stabilization. First, the microgrid is modelled by a discrete-time state space equation. Then the cost-effective smart sensors are deployed in order to obtain the required system information. From the communication point of view, the recursive systematic convolution code is adopted to add the redundancy in the system. At the end, the soft output Viterbi decoder is used to recover the system information from the noisy measurements and transmission uncertainties. Thereafter, the Kalman filter is utilized to estimate the system states, which acts as a precursor for applying the control algorithm. Finally, this paper proposes an optimal feedback control method to stabilize the microgrid based on semidefinite programming. The performance of the proposed approach is demonstrated by extensive numerical simulations

Distributed State Estimation over Unreliable Communication Networks with an Application to Smart Grids

© 2017 IEEE. 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

An Adaptive-Then-Combine Dynamic State Estimation Considering Renewable Generations in Smart Grids

© 1983-2012 IEEE. 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

Microgrid protection and control through reliable smart grid communication systems

© 2016 IEEE. 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

Distributed State Estimation Using RSC Coded Smart Grid Communications

© 2013 IEEE. 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 condition monitoring of renewable microgrids using adaptive-then-combine algorithm

© 2016 IEEE. 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

Modelling the Interconnected Synchronous Generators and its State Estimations

© 2018 IEEE. In contrast to the traditional centralized power system state estimation approaches, this paper investigates the optimal filtering problem for distributed dynamic systems. Particularly, the interconnected synchronous generators are modeled as a state-space linear equation where sensors are deployed to obtain measurements. As the synchronous generator states are unknown, the estimation is required to know the operating conditions of large-scale power networks. Availability of the system states gives the designer an accurate picture of power networks to avoid blackouts. Basically, the proposed algorithm is based on the minimization of the mean squared estimation error, and the optimal gain is determined by exchanging information with their neighboring estimators. Afterward, the convergence of the developed algorithm is proved so that it can be applied to real-time applications in modern smart grids. Simulation results demonstrate the efficacy of the developed algorithm

Aged leaves effect on essential components in green and oolong tea

Tea leaf encompasses essential components like caffeine, polyphenol, lipid etc. The study was conducted to find out the essential constituents in green and oolong tea from aged tea leaves during the year of 2012. Tea leaves with different plucked aged were collected from Lackatoorah Tea Estate at sylhet district in Bangladesh. Collected leaves were processesed to make desirable samples for biochemical analysis in the laboratory of Department of Food Enginnering and Tea Technology, SUST. Results revealed that the essential constituents like moisture, caffeine, polyphenol, lipid, protein, ash, ascorbic acid, acidity and pH value in green tea made from different aged leaves were found slightly higher than oolong tea, ranged from 6.38±1.06 to 3.49±0.59%; 4.91±0.82 to 1.49±0.24%; 30.88±5.15 to 18.23±3.04%; 7.50±1.25 to 9.58±1.59%; 13.15±2.19 to 17.33±2.88%; 3.87±1.65 to 7.86±1.31%; 48.4±8.05 to 21.3±3.55(mg); 2.13±0.68 to 1.18±0.19% and 5.52±0.11 to 5.97±0.18%, respectively. Similarly, the moisture, caffeine, polyphenol, lipid, protein, ash, ascorbic acid, acidity and pH value from different aged leaves were found in oolong tea from 6.19±1.04 to 2.98±0.49%; 4.68±0.78 to 1.11±0.19%; 20.89±3.48 to 8.23±1.37%; 6.40±1.07 to 9.13±1.52%; 13.03±2.17 to 17.19±2.86%; 3.44±0.58 to 7.57±1.27% 6.44±1.08 to 0.98±0.17 (mg); 2.02±0.34to 1.02±0.17% and 5.53±0.11 to 5.97±0.20%, respectively. Therefore, young tea leaves (i.e. 5 to 8 days tea leaves) should be plucked for considering useful constituents in processed green tea and oolong tea. DOI: http://dx.doi.org/10.3329/ijarit.v3i2.17845 Int. J. Agril. Res. Innov. & Tech. 3 (2): 54-58, December, 201