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Energy distribution technique is an essential obligation of an intelligent household system to assure optimal and economical operation. This paper considers a small-scale household system detached from the power grids consisting of some electrical components in day-to-day life. Optimal power distribution generated from a photovoltaic system is vital for ensuring economic and uninterrupted power flow. This paper presents an optimal energy distribution technique for a small-scale smart household system to ensure uninterrupted and economical operation. A photovoltaic (PV) system is considered as the primary generation system, and a battery energy storage system (BESS) is viewed as a backup power supply source. The actual load and PV generation data are used to validate the proposed technique collected from the test household system. Two different load profiles and photovoltaic power generation scenarios, namely summer and winter scenarios, are considered for case studies in this research. An actual test household system is designed in MATLAB/Simulink software for analyzing the proposed technique. The result reveals the effectiveness of the proposed technique, which can distribute the generated power and utilize the BESS unit to ensure the optimal operation. An economic analysis is conducted for the household system to determine the economic feasibility. The capital investment of the system can be returned within around 5.67 years, and the net profit of the system is 2.53 times more than the total capital investment of the system. The proposed technique can ensure economical operation, reducing the overall operating cost and ensuring an environment-friendly power system. The developed strategy can be implemented in a small-scale detached interconnected smart household system for practical operation to distribute the generated energy optimally and economically.

The detection of microorganisms like Pseudomonas are very important as they trigger an infection in human blood, lungs, and different parts of the body causing various ailments. In this paper, a surface plasmon resonance (SPR) biosensor based on photonic crystal fiber (PCF) has been proposed to detect the presence of Pseudomonas bacteria with attractive performance characteristics. The sensor is designed using a simple circular lattice of PCF, coated with a thin chemically stable gold layer. The performance investigation of the sensor is numerically carried out by using a finite element (FE) based simulation tool where the highest wavelength and amplitude sensitivity are found as 20,000 nm/RIU and 1380 RIU −1 , respectively. The sensor shows an excellent spectral resolution of the highest value of 5.26×10−6 RIU, ensuring the capability of identifying a very small change in analyte refractive index (RI) within the range of 1.33 to 1.42. The performance investigation is also carried out altering the diameter of air holes, pitch, and gold layer thickness to explore the variation in phase matching conditions due to the change in structural parameters. As the sensor is adept at detecting the sample with high sensitivity and sensing resolution, the proposed sensor can be highly efficient in detecting Pseudomonas bacteria as well as other organic compounds, and biological analytes.

When using high strength materials in concrete partially encased steel (CES) composite columns, the major issues encountered are strain incompatibility of high strength steel and high strength concrete, premature concrete spalling and explosive brittle failure with poor ductility. In order to improve the compressive behaviour and ductility of CES columns, Engineered Cementitious Composites (ECC) encasement is proposed as a confinement and protection layer for concrete and steel, respectively. In order to evaluate the effectiveness of ECC cover on performance of CES columns, compressive behaviours of ECC-CES short columns were experimentally investigated to study the effects of material strengths, ECC encasement thickness, steel section shape and column's aspect ratio. Findings were discussed and reported in terms of failure behaviours, load-deformation responses, post-peak ductility and toughness. Test results suggested that while only an insignificant increase of axial compressive resistance was observed, ECC encasement improved failure behaviour of ECC-CES columns by effectively controlling brittleness and explosive spalling of high strength concrete. ECC-CES generally showed improved ductility and energy absorption capacity but the strength enhancements were minimal. Reliability of current design codes in predicting the column ultimate strength was investigated and a new approach based on effective stress method was proposed. Finally, a 3D non-linear numerical model was also developed to predict the compressive behaviours of the proposed form of columns and a small scale parametric study was conducted.

This paper reports an experimental and numerical study on the flexural behaviour of Engineered Cementitious Composites and Lightweight Concrete (ECC–LWC) encased slender High Strength Steel (HSS) composite beams. Four simply supported slender HSS composite beams with different encasement materials (concrete or ECC–LWC) and configurations (fully or partially encased) were first tested under four–point bending. Three–dimensional nonlinear finite element (FE) models were then developed and validated against the test results. Experimental results obtained showed that the two ECC–LWC fully encased slender beams carried higher loading with improved ductility than the concrete fully encased slender beam. Moreover, test results of the ECC–LWC fully encased slender beams confirmed that the encasement successfully eliminated both local buckling and global lateral torsional buckling of the slender HSS I–section. However, the partially encased beam in which encasement was only applied to the compression flange was failed by lateral torsional buckling before its top flange was yielded. A parametric study was also carried out using the validated FE model to further investigate the effectives of different encasement parameters on the flexural behaviour of ECC–LWC fully encased slender HSS composite beams. Finally, a simplified analytical solution is proposed for estimating the flexural strength of the fully encased beams.

This paper presents the results of an experimental study on the compressive behaviour of engineered cementitious composites and concrete encased steel (ECC-CES) composite columns. Two configurations of ECC-CES composite columns based on fully and partially concrete encasement were considered. A total of eleven short columns with different ECC and concrete encasing configurations were tested under pure compression. The effects of ECC strength, concrete strength and column configuration on the column compressive behaviour were investigated and reported in terms of failure modes, load-deformation curves, ductility and toughness. In addition, in order to study the confinement effect of different thickness ECC covers on high strength concrete (HSC), three ECC encased HSC short columns without encased steel section were also tested. The experimental results were compared with the ultimate strength predictions from different design codes for the tested columns. It was found that current design guidelines were generally conservative. Therefore, new equations with modified factors to predict the ultimate strength of ECC-CES columns were proposed. Finally, a comparison of performance of ECC-CES with conventional CES columns suggested that the ECC encasement could provide an alternative way to confine concrete core in columns applications.

This paper presents an experimental investigation on the behaviour of a new form of engineered cementitious composites (ECC) encased concrete-steel composite stub columns. The proposed column section uses ECC encasement as a potential confinement layer to control the premature concrete spalling and explosive brittle failure of concrete encased steel composite columns. In this study, twelve stub columns including two bare steel and ten composite sections are tested under concentric compression. The effects of some key parameters such as material strengths, steel section type and column section configuration on the performance of proposed column sections were investigated in terms of failure behaviour, load deformation response, toughness and ductility. It was found that ECC encasement improved the compressive failure behaviour of encased composite columns and enhanced their ductility and toughness. Strain analysis was performed to trace the strain development and damage patterns of different materials. Finally, a simple equation to estimate ultimate strength of proposed columns was proposed which gave good predictions agreed well with test results.

Although high-strength concrete (HSC) has higher compressive strength than normal-strength concrete (NSC), its application in column construction is often limited by its brittleness and limited post-peak ductility. In this preliminary study, hybrid fibre engineered cementitious composite (ECC) is proposed as a potential encasement material to confine the HSC core and to increase the column's ductility. The behaviours of the proposed ECC-encased concrete stub column under axial compression are studied experimentally. Thirty stub columns, including six NSC/HSC, six ECC and 18 ECC-encased NSC/HSC (ECC-NSC/HSC) are tested under axial compression. The effects of ECC strength, ECC encasement thickness, concrete strength and cross-section shapes (circular, square and rectangular) are then investigated and reported in terms of failure modes, strength ratios, post-peak ductility and energy dissipation capacity of the stub columns. It is found that, when comparing with NSC/HSC stub columns, although no significant improvement in the ultimate strength was observed, the ECC encasement improved the performance of ECC-NSC/HSC stub columns by reducing brittleness and improving the post-peak behaviour during failure.

The use of high strength steel (HSS) in the construction of concrete encased steel (CES) composite columns is often limited by the strain incompatibility issue between HSS and concrete at peak-load. This study proposes an alternative approach to confine the high strength concrete with Engineered Cementitious Composite (ECC) to improve its compatibility with high strength steel. The main purpose of this study is to experimentally evaluate the axial compressive performance of the proposed composite column cross-section configuration. Behaviours of fifteen short columns including twelve ECC-CES columns are investigated in terms of failure modes, load-deformation curves, ductility and energy absorption capacity. The test parameters included ECC and concrete strengths, ECC cover thickness, steel section shape and column section's aspect ratio. It was found that ECC generally improved the failure behaviour of high strength steel CES columns and increased the deformation and energy absorption capacity. On average ECC-CES columns showed around 12% and 8% higher ductility and toughness than control concrete column, respectively. A detailed 3D nonlinear finite element model was developed and validated against experimental results. Applicability of current design codes to predict the ultimate strength of ECC-CES columns was also evaluated. Finally, a method to calculate the ECC-CES column's capacity considering effective material stresses at peak-load was proposed.

This study proposes a method of using Polyvinyl Alcohol Engineered Cementitious Composites (PVA-ECC) encasement to provide continuous restraints along the compression flange of High Strength Steel (HSS) section so that it will reach its sectional plastic moment resistance under bending without lateral restraint. In order to demonstrate the effectiveness of the proposed method, experimental and numerical investigations were carried out to study the flexural strength of the ECC encased HSS beams (ECC-HSS beams). Six simply supported beams fabricated with identical HSS sections but with different encasement configurations were tested until failure. Flexural resistance and failure modes of the ECC-HSS beams were compared with similar bare HSS and normal concrete (NC) encased HSS beams (NC-HSS beams). It was found that when compared with the bare HSS and NC-HSS beams, a significant enhancement in flexural resistance was achieved for the ECC-HSS beams. More importantly, this study confirmed that the compressive ECC layers was crushed after the compression flanges were yielded and therefore successfully prevented the onset of lateral torsional buckling. Besides the flexural responses, the interfacial slip behaviours along the compression flange of the HSS section were also studied. Finally, a finite element (FE) model was developed and validated against the experimental results.

© 2018 Elsevier Ltd When designing light weight protective structures it is important to consider the threat from cased charges. In this research, a steel panel constructed using standard steelwork sections and a steel-concrete sandwich panel were loaded using a cased pipe charge containing 15 kg of TNT equivalent explosive. Central deflections and damage images were collected and were employed to validate finite element (FE) simulations. These were used to compare models using different stand-offs and two simplified smeared loading methods. The results showed that the influence of the localised damage was limited for the loading range considered.

This paper studies the flexural and bond-slip behaviour of composite beams fabricated by encasing universal steel beams with Polyvinyl Alcohol-Engineered Cementitious Composite (PVA-ECC) and Light Weight Concrete (LWC). Four-point bending tests were conducted on one bare steel and four composite beams with different ECC and LWC encasement configurations. Test results showed that the ECC and LWC encasements could enhance the flexural strength and ductility of bare steel beams significantly. Furthermore, it was found that the weight of the encased beams could be further reduced by either replacing the bottom ECC layer with LWC or even only encasing the compression flange of the steel section without reducing the flexural strength of the beam significantly. The bond-slip behaviour between the ECC matrix and the steel section was also investigated. The experimental study is complemented by a non-linear finite element (FE) model which was validated against the test results. A small scale parametric study was then conducted by using the validated FE model to investigate the performances of beams formed by the steel sections with yield strengths ranging from 350 MPa to 960 MPa.

The resistance of structural elements to cased charges is an important area of research since the blast waves and projectiles loads can combine to cause overall structural bending failures and to reduce the stiffness of the components through direct localised damage. In this paper, the effects of cased charges blasts on sandwich steel and concrete panels were assessed. An experiment with a pipe charge was summarised. Its results were then used to validate a finite element (FE) model of the blast test. In this, both loading types were modelled using empirical and analytical methods available in literature. The initial deflections and the damage extents were simulated accurately, verifying the techniques used in the model. A quasistatic test was then simulated using the damaged results. The predicted yield capacity was compared with that of an undamaged sample. The results showed that the yield loads were similar, indicating that the projectile local damage did not affect the resistance of the structure. Therefore, it is likely that equivalent pressures could be used to calculate the dynamic effects of the combined loading, avoiding the need to include accurate models of the fragments and significantly simplifying the design process.

In this study, the flexural behaviour of a new form of Engineered Cementitious Composite (ECC) - Light Weight Concrete (LWC) encased steel composite beams is studied. Four simply supported ECC-LWC encased steel composite beams were tested to investigate the effects of ECC cover thickness on the ultimate load carrying capacities and failure modes. The test results indicated that the ductility of ECC enhanced the residual strength of encased steel member even after the crushing of the top ECC cover. It is demonstrated that the structural performance of traditional steel-concrete composite beams could be improved by replacing normal concrete with ECC and LWC as the encasing materials. In addition to flexural strength enhancement, it is found that the combined use of ECC and LWC could improve the overall ductility of the beams and reduce their weights. To complement the experimental study, a three-dimensional nonlinear finite element model is developed to simulate the behaviour of ECC-LWC encased steel composite beams. The model was validated by comparing the numerical results to the experimental results. A parametric study was then conducted using the model to investigate the influence of the ECC compressive strength, the ECC tensile strength, the LWC compressive strength and the steel yield strength on stiffness, flexural resistance and ductility of ECC-LWC encased steel composite beams.

© 2014 Chinese Association of Automation. 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.

© 2005-2012 IEEE. 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.

© 2018 IEEE. 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.

© 2017 IEEE. 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.

© 2017 This paper proposes a novel distributed 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 sub-optimal local gain. Furthermore, the convergence of the proposed schemed is analysed 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 mathematical dynamic model of the renewable microgrid. It shows that the proposed distributed scheme is effective to properly estimate the system states.

© 2016 Given the significant concerns regarding carbon emissions from fossil fuels, global warming and energy crisis, renewable distributed energy resources (DERs) are going to be integrated in smart grids, which will make the energy supply more reliable and decrease the costs and transmission losses. Unfortunately, one of the key technical challenges in power system planning, control and operation with DERs is the voltage regulation at the distribution level. This problem stimulates the deployment of smart sensors and actuators in smart grids so that the voltage can be stabilized. The observation from the microgrid incorporating DERs is transmitted to the control center via wireless communication systems. In other words, the proposed communication infrastructure provides an opportunity to address the voltage regulation challenge by offering the two-way communication links for microgrid state information collection, estimation and stabilization. Based on the communication infrastructure, we propose a least square based Kalman filter algorithm for state estimation and an optimal feedback control framework for stabilizing the microgrid states. Specifically, we propose to optimize the performance index by using semidefinite programming techniques in the context of smart grid applications. At the end, the efficacy of the developed approaches is demonstrated using a microgrid incorporating multiple DERs.

© 2017 In contrast to the traditional centralised power system state estimation methods, this paper investigates the optimal filtering problem for distributed dynamic systems. Specifically, the interconnected synchronous generators are modelled 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 overall operating conditions of large-scale complex power networks. Basically, the proposed algorithm is based on the minimization of the mean squared estimation error, and the optimal gain is computed by exchanging information with their neighboring estimators. Afterwards, the convergence of the developed algorithm is proved so that it can be applied to real-time applications in modern smart grids. Finally, simulation results demonstrate the efficacy of the developed approach.

© 2017 ICIC International. This paper addresses the problem of distributed energy management system (EMS) design considering network-induced delays. First of all, the distribution power network incorporating solar panels is modelled as a discrete-time linear state-space equation and its measurements/control information is obtained using a set of sensors and actuators. These electronic devices send the observation information to the EMS via un-reliable communication links. In order to stabilize the system, we propose discrete-time distributed static output feedback control strategies based on the linear matrix inequality and semi definite programming approaches. Using the Taylor series method, delays are integrated into the closed loop system so that the proposed controller runs with less energy resources and computation. Simulation results demonstrate that the proposed method is able to stabilize the system in a fairly short time.

© 2016 IEEE. 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.

© 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.

© 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.

© 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.

© 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.

© 2016 IEEE. 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.

© 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.

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.

© 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.

© 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.