Optimal Observer Synthesis for Microgrids With Adaptive Send-on-Delta Sampling Over IoT Communication Networks

State estimation is one of the main challenges in the microgrids, due to the complexity of the system dynamics and the limitations of the communication network. In this regard, a novel real-time event-based optimal state estimator is introduced in this paper, which uses the proposed adaptive send-on-delta (SoD) non-uniform sampling method over wireless sensors networks. The proposed estimator requires low communication bandwidth and incurs lower computational resource cost. The threshold for the SoD sampler is made adaptive based on the average communication link delay, which is computed in a distributed form using the event-based average consensus protocol. The SoD non-uniform signal sampling approach reduces the traffic over the wireless communication network due to the events transmitted only when there is a level crossing in the measurements. The state estimator structure is extended on top of the traditional Kalman filter with the additional stages for the fusion of the received events. The error correction stage is further improved by optimal reconstruction of the signals using projection onto convex sets (POCS) algorithm. Finally, an Internet of things (IoT) experimental platform based on LoRaWAN and IEEE 802.11 (WiFi) protocols is developed to analyse the performance of the state estimator for the IEEE 5 Bus case study microgrid

Microgrid Optimal State Estimation Over IoT Wireless Sensor Networks With Event-Based Measurements

In a microgrid, real-time state estimation has always been a challenge due to several factors such as the complexity of computations, constraints of the communication network and low inertia. In this paper, a real-time event-based optimal linear state estimator is introduced, which uses the send-on-delta data collection approach over wireless sensors networks and exhibits low computation and communication resources cost. By employing the send-on-delta event-based measurement strategy, the burden over the wireless sensor network is reduced due to the transmission of events only when there is a significant variation in the signals. The state estimator structure is developed based on the linear Kalman filter with the additional steps for the centralized fusion of events data and optimal reconstruction of signals by projection onto convex sets. Also for the practical feasibility analysis, this paper developed an Internet of things prototype platform based on LoRaWanprotocol that satisfies the requirements of the proposed state estimator in a microgrid

Forecast Based Consensus Control for DC Microgrids Using Distributed Long Short-Term Memory Deep Learning Models

In a microgrid, renewable energy sources (RES) exhibit stochastic behavior, which affects the microgrid continuous operation. Normally, energy storage systems (ESSs) are installed on the main branches of the microgrids to compensate for the load-supply mismatch. However, their state of charge (SoC) level needs to be balanced to guarantee the continuous operation of the microgrid in case of RES unavailability. This paper proposes a distributed forecast-based consensus control strategy for DC microgrids that balances the SoC levels of ESSs. By using the load-supply forecast of each branch, the microgrid operational continuity is increased while the voltage is stabilized. These objectives are achieved by prioritized (dis)charging of ESSs based on the RES availability and load forecast. Each branch controller integrates a load forecasting unit based on long short-term memory (LSTM) deep neural network that adaptively adjusts the (dis)charging rate of the ESSs to increase the microgrid endurability in the event of temporary generation insufficiencies. Furthermore, due to the large training data requirements of the LSTM models, distributed extended Kalman filter algorithm is used to improve the learning convergence time. The performance of the proposed strategy is evaluated on an experimental 380V DC microgrid hardware-in-the-loop test-bench and the results confirm the achievement of the controller objectives

Distributed control strategy for DC microgrids based on average consensus and fractional-order local controllers

A novel distributed secondary layer control strategy based on average consensus and fractional-order proportional-integral (FOPI) local controllers is proposed for the regulation of the bus voltages and energy level balancing of the energy storage systems (ESSs) in DC microgrids. The distributed consensus protocol works based on an undirected sparse communication network. Fractional-order local controllers increase the degree of freedom in the tuning of closed-loop controllers, which is required for DC microgrids with high order dynamics. Therefore, here, FOPI local controllers are proposed for enhanced energy balancing of ESSs and improved regulation of the bus voltages across the microgrid. The proposed control strategy operates in both islanded and grid-connected modes of a DC microgrid. In both modes, the average voltage of the microgrid converges to the microgrid desired reference voltage. The charging/discharging of ESSs is controlled independent of the microgrid operating mode to maintain a balanced energy level. The performance of the proposed distributed control strategy is validated in a 38- V DC microgrid case study, simulated by Simulink real-time desktop, consisting of 10 buses and a photovoltaic renewable energy source

Groundwater augmentation through the site selection of floodwater spreading using a data mining approach (case study: Mashhad Plain, Iran)

© 2018 by the authors. It is a well-known fact that sustainable development goals are difficult to achieve without a proper water resources management strategy. This study tries to implement some state-of-the-art statistical and data mining models i.e., weights-of-evidence (WoE), boosted regression trees (BRT), and classification and regression tree (CART) to identify suitable areas for artificial recharge through floodwater spreading (FWS). At first, suitable areas for the FWS project were identified in a basin in north-eastern Iran based on the national guidelines and a literature survey. Using the same methodology, an identical number of FWS unsuitable areas were also determined. Afterward, a set of different FWS conditioning factors were selected for modeling FWS suitability. The models were applied using 70% of the suitable and unsuitable locations and validated with the rest of the input data (i.e., 30%). Finally, a receiver operating characteristics (ROC) curve was plotted to compare the produced FWS suitability maps. The findings depicted acceptable performance of the BRT, CART, and WoE for FWS suitability mapping with an area under the ROC curves of 92, 87.5, and 81.6%, respectively. Among the considered variables, transmissivity, distance from rivers, aquifer thickness, and electrical conductivity were determined as the most important contributors in the modeling. FWS suitability maps produced by the proposed method in this study could be used as a guideline for water resource managers to control flood damage and obtain new sources of groundwater. This methodology could be easily replicated to produce FWS suitability maps in other regions with similar hydrogeological conditions

Multilayer event‐based distributed control system for DC microgrids with non‐uniform delays and directional communication

The secondary control layer of microgrids is often modelled as a multi-agent distributed system, coordinated based on consensus protocols. Convergence time of consensus algorithm significantly affects transient stability of microgrids, due to changes in communication topology, switching of distributed generations (DGs), and uncertainty of intermittent energy sources. To minimise convergence time in consensus protocol, this work proposes a multilayer event-based consensus control framework, which is resilient to communication delays and supports plug-and-play (P&P) addition or removal of DGs in DC microgrids of cellular energy systems. A novel bi-layer optimisation algorithm minimises convergence time by selecting an optimal communication topology graph and then adjusts controllers' parameters. Average consensus is achieved among distributed controllers using an event-based consensus protocol, considering non-uniform delays between agents. A realisation method has also been introduced using the directional beamforming technique for topology assignment algorithm based on modern telecommunication technologies. Provided feasibility case study has been implemented on a real-time hardware-in-the-loop (HIL) experimental testbed, to validate the performance of the proposed framework for key purposes of voltage stabilisation and balanced power-sharing in DC microgrids

Therapeutic Effect of Sodium Selenite and Zinc Sulphate as Supplementary with Meglumine Antimoniate( Glucantime®) Against Cutaneous Leishmaniasis In BALB/C Mice

Background: Successful therapy of leishmaniasis depends on effective cellular immune response. We evaluated the effectiveness of sodium selenite and zinc sulphate as known immunomodulator materials, in combination with Glucantime® in treatment of cutaneous leishmaniasis lesions resulting from Leishmania ma­jor in susceptible animal model.Methods: Thirty three female mice weighing 18-20 g at the age of 7-8 week infected with L. major were randomly divided into 3 groups: group1: treated by sodium selenite (0.35 mg/kg for 30 days), group2: treated by zinc sulphate (2 mg/kg for 30 days) and group3: treated by distilled water (0.01 ml/gr body weight for 30 days) as control. All groups received Glucantime® as a standard anti- leishmanial agent (60 mg/kg, ip) for 14 days. To assess the results of treatment measurement of lesions size and parasitological tests were done weekly.Results: The lesion sizes increased continuously in sodium selenite group .Although, in zinc group did not in­crease compared to baseline But with considering the time- group interaction there was no significant difference between zinc and control group during this study. There was no difference between lesion sizes and Leishmanial loads in the interventional and control groups, respectively.Conclusion: Sodium selenite and zinc sulphate at mentioned doses and duration of treatment did not show any treatment effect on cutaneous leishmaniasis caused by L. major in BALB/c mice. Increasing the dose of supplements and considering the follow up period after treatment can help more certain conclusion

Statistical Estimation Framework for State Awareness in Microgrids Based on IoT Data Streams

This paper presents an event-triggered statistical estimation strategy and a data collection architecture for situational awareness (SA) in microgrids. An estimation agent structure based on the event-triggered Kalman filter is proposed and implemented for state estimation layer of the SA using long range wide area network (LoRAWAN) protocol. A setup has been developed which provides enormous data collection capabilities from smart meters in order to realize an adequate level of SA in microgrids. Thingsboard Internet of things (IoT) platform is used for the SA visualization with a customized dashboard. It is shown that by using the developed estimation strategy, an adequate level of SA can be achieved with a minimum installation and communication cost to have an accurate average state estimation of the microgrid

A Distributed Event-Triggered Control Strategy for DC Microgrids Based on Publish-Subscribe Model Over Industrial Wireless Sensor Networks

This paper presents a complete design, analysis, and performance evaluation of a novel distributed event-triggered control and estimation strategy for DC microgrids. The primary objective of this work is to efficiently stabilize the grid voltage, and to further balance the energy level of the energy storage (ES) systems. The locally-installed distributed controllers are utilised to reduce the number of transmitted packets and battery usage of the installed sensors, based on a proposed event-triggered communication scheme. Also, to reduce the network traffic, an optimal observer is employed which utilizes a modified Kalman consensus filter (KCF) to estimate the state of the DC microgrid via the distributed sensors. Furthermore, in order to effectively provide an intelligent data exchange mechanism for the proposed event-triggered controller, the publish-subscribe communication model is employed to setup a distributed control infrastructure in industrial wireless sensor networks (WSNs). The performance of the proposed control and estimation strategy is validated via the simulations of a DC microgrid composed of renewable energy sources (RESs). The results confirm the appropriateness of the implemented strategy for the optimal utilization of the advanced industrial network architectures in the smart grids

Global burden of human brucellosis : a systematic review of disease frequency

BACKGROUND: This report presents a systematic review of scientific literature published between 1990-2010 relating to the frequency of human brucellosis, commissioned by WHO. The objectives were to identify high quality disease incidence data to complement existing knowledge of the global disease burden and, ultimately, to contribute towards the calculation of a Disability-Adjusted Life Years (DALY) estimate for brucellosis.METHODS/PRINCIPAL FINDINGS: Thirty three databases were searched, identifying 2,385 articles relating to human brucellosis. Based on strict screening criteria, 60 studies were selected for quality assessment, of which only 29 were of sufficient quality for data analysis. Data were only available from 15 countries in the regions of Northern Africa and Middle East, Western Europe, Central and South America, Sub-Saharan Africa, and Central Asia. Half of the studies presented incidence data, six of which were longitudinal prospective studies, and half presented seroprevalence data which were converted to incidence rates. Brucellosis incidence varied widely between, and within, countries. Although study biases cannot be ruled out, demographic, occupational, and socioeconomic factors likely play a role. Aggregated data at national or regional levels do not capture these complexities of disease dynamics and, consequently, at-risk populations or areas may be overlooked. In many brucellosis-endemic countries, health systems are weak and passively-acquired official data underestimate the true disease burden.CONCLUSIONS: High quality research is essential for an accurate assessment of disease burden, particularly in Eastern Europe, the Asia-Pacific, Central and South America and Africa where data are lacking. Providing formal epidemiological and statistical training to researchers is essential for improving study quality. An integrated approach to disease surveillance involving both human health and veterinary services would allow a better understand of disease dynamics at the animal-human interface, as well as a more cost-effective utilisation of resources