s. golshannavaz

In smart grid paradigm, there exist many versatile applications to be fostered such as smart home, smart buildings, smart hospitals, and so on. Smart hospitals, wherein patients are the possible consumers, are one of the recent interests within this paradigm. The Internet of Things (IoT) technology has provided a unique platform for healthcare system realization through which the patients’ health-based data is provided and analyzed to launch a continuous patient monitoring and; hence, greatly improving healthcare systems. Predictive machine learning techniques are fostered to classify health conditions of individuals. The patients’ data is provided from IoT devices and electrocardiogram (ECG) data. Then, efficient data pre-processings are conducted, including data cleaning, feature engineering, ECG signal processing, and class balancing. Artificial intelligence (AI) is deployed to provide a system to learn and automate processes. Five machine learning algorithms, including Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), logistic regression, Naive Bayes, and random forest, as the AI engines, are considered to classify health status based on biometric and ECG data. Then, the output would be the most proper signals propagated to doctors’ and nurses’ receivers in regard of the patients providing them by initial pre-judgments for final decisions. Through the conducted analysis, it is shown that logistic regression outperforms the other AI machine learning algorithms with an F1 score, recall, precision, and accuracy of 0.91, followed by XGBoost with 0.88 across all metrics. SVM and Naive Bayes both achieved 0.85 accuracy, while random forest attained 0.86. Moreover, the Receiver Operating Characteristic Area Under Curve (ROC-AUC) scores confirm the robustness of Logistic Regression and XGBoost as apt candidates in learning the developed healthcare system. The conducted study concludes a promising potential of AI-based machine learning algorithms in devising predictive healthcare systems capable of initial diagnosis and preliminary decision makings to be relied upon by the clinician. What is more, the availability of biometric data and the features of the proposed system significantly contributed to primary care assessments.

Distributed generations (DGs) based on renewable energy, such as PV units, are becoming more prevalent in distribution networks due to technical and environmental benefits. However, the intermittency and uncertainty of these sources lead to technical and operational challenges. Energy storage application, uncertainty analysis, and network reconfiguration are apt therapies to resist these challenges. Energy management of modern, smart, and renewable-penetrated distribution networks is tailored here considering the uncertainties correlations. Network operation costs including switching operations, the expected energy not served (EENS) index as the reliability objective, and the node voltage deviation suppression as the technical objective are mathematically modeled. Multi-objective particle swarm optimization (MOPSO) is considered as the optimization engine. Scenario generation method and Nataf transformation are used in probabilistic evaluations of the problem. Moreover, the technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) is deployed to make a final balance between different objectives to yield a unified solution. To show the effectiveness of the proposed approach, the IEEE 33-node distribution network is put under extensive simulations. Different cases are simulated and interrogated to assess the performance of the proposed model. For different objectives dealing with different aspects of the network, remarkable achievements are attained. In brief, the final solution shows 4.50% decrease in operation cost, 13.07% improvement in reliability index, and 18.85% reduction in voltage deviation compared to the initial conditions.

This study is focused on assessing the effect of energy storage system (ESS) presence on security improvement of power systems hosting remarkable renewable energy resources. To this end, ESS presence is suitably included in security-constrained optimal power flow (SCOPF) model; the required technical amendments are hence considered. To launch a realistic model, ramping constraints of thermal units are also taken into account which limit the generators from completely responding to power shortfalls. Considering a high penetration level of renewable generations, different scenarios of outages in transmission lines and generators are simulated to measure the line outage distribution factor (LODF) and power transfer distribution factor (PTDF). also, in order to illustrate the economic impact of wind power generation curtailment and load shedding, two penalty parameters VWC and VOLL are considered in the model. Two test systems, including a PJM 5-bus system and an IEEE 24-bus RTS, are put under numerical studies to assess the possible impact of ESS on security improvement of the investigated systems. The obtained results are discussed in depth.