An internet of things-based healthcare system performing on a prediction approach based on random forest regression

To predict physiological indicators, such as heart rate, blood pressure, and body heat sensors, this study develops an internet of things (IoT)-based healthcare approach performing on random forest regression models and mean square error (MSE). Machine learning approaches such as random forest design is trained to predict factors like age, heart rate, and recorded physiological measures using a dataset generated by sensors with Raspberry Pi. The precision and dependability of the models are assessed by contrasting the predictions with the physiological degrees produced by sensors. IoT-enabled models and sensors are useful for a variety of healthcare monitoring tasks, such as early anomaly detection and quick assistance for medical interventions. It is seen that the proposed model could provide appropriate predictions that are in line with common datasets demonstrated by the results. Moreover, there is strong agreement between the sensor readings and the predicted values for the considered parameters showcasing the outperformance of the proposed healthcare system

Transient stability enhancement in multiple‐microgrid networks by cloud energy storage system alongside considering protection system limitations

Abstract The gas turbine synchronous generators (GTSGs) are widely deployed as distributed generations (DGs) in countries with massive natural gas production owing to their low prices. However, due to the low inertia time constants of these synchronous‐based DGs, they are more susceptible to power grid faults which stands as a transient stability issue in networks with multiple microgrids (MGs). On the other hand, the cloud energy storage system (CESS) is a new concept that centralizes the individual distributed energy storage of one or more MGs. Here, the employment of CESS with synchronverter grid connections creates a suitable opportunity for improving the transient stability of the network by providing higher inertia. This is while the fault current contribution of the synchronverter‐based CESS imperils protection constraints in these networks. Therefore, a proper protection coordination index (PCI) is considered in the conducted study to identify the optimal size of the synchronverter‐based CESS through a two‐stage optimization algorithm that preserves the protection constraints among protective devices. Finally, the transient stability of the network with synchronverter‐based CESS is assayed by calculation of the critical clearing time (CCT) for faults. Numerical studies are carried out on the IEEE 33‐bus test system. Results are discussed in depth

A comprehensive stochastic energy management system in reconfigurable microgrids

This paper addresses the joint stochastic energy and reserve scheduling problem in microgrids (MGs). The established approach proposes a novel high-performance energy management system (EMS) making use of automatically controlled switches (ACSs). Accordingly, besides the optimal scheduling of active elements namely distributed generations (DGs) and responsive loads (RLs), the optimal topology of the network for each of the scheduling intervals is determined as well. Likewise, the effects of the reconfiguration process in probable variations of the scheduled energy patterns in DGs, RLs, and grid purchases are thoroughly assessed to highlight the alterations in unallocated capacities of these resources. Moreover, the uncertainties associated with both the load and wind speed forecasting errors are suitably accommodated through the reserve allocations. The proposed optimization procedure is formulated as a mixed-integer non-linear problem and resolved using a genetic algorithm (GA). The effectiveness of the projected framework is verified utilizing a typical MG, and the obtained numerical results are discussed in depth. Copyright © 2016 John Wiley & Sons, Ltd

A new hybrid control technique for operation of DC microgrid under islanded operating mode

This study proposes a novel combined primary and secondary control approach for direct current microgrids, specifically in islanded mode. In primary control, this approach establishes an appropriate load power sharing between the distributed energy resources based on their rated power. Simultaneously, it considers the load voltage deviation and provides satisfactory voltage regulation in the secondary control loop. The proposed primary control is based on an efficient droop mechanism that only deploys the local variable measurements, so as to overcome the side effects caused by communication delays. In the case of secondary control, two different methods are devised. In the first, low bandwidth communication links are used to establish the minimum required data transfer between the converters. The effect of communication delay is further explored. The second method excludes any communication link and only uses local variables. Accordingly, a self-sufficient control loop is devised without any communication requirement. The proposed control notions are investigated in MATLAB/Simulink platform to highlight system performance. The results demonstrate that both proposed approaches can effectively compensate for the voltage deviation due to the primary control task. Detailed comparisons of the two methods are also provided.Peer reviewe

A two-stage robust-intelligent controller design for efficient LFC based on Kharitonov theorem and fuzzy logic

This paper proposes an efficient load frequency control (LFC) approach based on robust and intelligent methods. Practically speaking, proportional-integral (PI) controller is widely deployed in LFC structure. Basically, the parameters of PI controller are adjusted based on trial-and-error or classic control methods. In such manners, robust performance of PI controller cannot be guaranteed in disturbances including load changes or parameter variations. In this research, at the first stage, the gain values of PI controller are tuned in an offline manner based on Kharitonov theorem which strengthens the validity of the controller against the variations in time constants of turbine and governor. As another aspect of uncertainty, power system loading demand is changed ceaselessly. To accommodate such conditions, at the second stage, the initial gain values based on Kharitonov theorem are adapted in an online manner based on fuzzy logic approach. The fuzzy controller, as an aspect of intelligence, adapts the proportional and integral gains through appropriate membership functions in an online fashion. Frequency deviation and its derivative are selected as efficient input signals for the fuzzy controller. Detailed numerical studies are conducted to assess performance of the proposed approach. Results demonstrate a reliable frequency performance against different uncertainties