Enhancement of Power Quality of Single Stage Grid Connected PV System by Using Takagi-Sugeno-Kang Fuzzy Controllers

Grid connected solar power plants are widely established in many places worldwide. Photovoltaic (PV) based grid connected solar plants are attracting recently due to improved in controlling of power converters. Single stage grid connected systems can reduce number of converters connected in power plants which resultant in reduce cost of the system. However, DC to DC converters are generally used in PV systems to enhance the operation of maximum power point for best utilization. The inverters also can be using to extract maximum power from PV systems through new controlling techniques in power electronics devises. Therefore an extraDC to DC converter is not required to make PV at its maximum power point condition. However, this technology can be used for small scale solar power plants since all PV arrays in solar power plant cannot be received same irradiance. Takagi-Sugeno-Kang (TSK) fuzzy controlleris having significant priority than proportional plus integral controllers when rapid changes are having in input. Hence, TSK based single stage controller is developed in this paper for grid connected 1MW solar plant. Generally distribution system is connected with unbalanced loads, hence these unbalanced loads will create forcefully unbalanced currents in electrical grid. Unbalanced grid currents further create many problems to other loads. Therefore, the proposed controller is designed to help making grid currents balanced during unbalanced local loads. Further, the inverter can compensate reactive power demanded by local loads to minimize reactive power supplied by grid. Extensive results are presented and evaluated through hardware-in-loop on the platform of OPAL-RT to enhance the performance of proposed controller for 1MW grid connected solar plant

Electric Vehicle Energy Management: Charging in Sustainable Urban Settings for Smart Cities

The evolution of smart cities has ushered in a new era of sustainable urban living, with energy management at its core. This paper offers a comprehensive review of the energy-related planning and operational models within the smart city framework, categorizing them into five primary intervention areas: generation, storage, infrastructure, facilities, and transport. The intricate relationship between smart cities and electric vehicles (EVs) is explored, emphasizing the need for robust charging infrastructures and forecasting peak loads. As the adoption of EVs surges, challenges such as power grid strain, voltage fluctuations, and power losses become more pronounced. Innovative solutions leveraging machine learning, including techniques like LSTM, DNN, and SVM, have been proposed to manage EV charging, ensuring efficiency and minimizing costs. Furthermore, the integration of EVs into smart cities is not without its challenges. Beyond the technical aspects, economic, social, and environmental challenges arise, necessitating a holistic approach for seamless integration. This review underscores the importance of a multifaceted strategy, encompassing all aspects of EV integration, to realize the vision of truly sustainable and smart urban centres

Neural Network Models for Wind Power Forecasting in Smart Cities- A review

Urbanization’s relentless advance intensifies the quest for sustainable energy sources, with smart cities leading the shift toward sustainability. In these innovative urban landscapes, wind power is pivotal in the clean energy paradigm. Efficient wind energy utilization hinges on accurate wind power forecasting, essential for energy management and grid stability. This review explores the use of neural network models for wind power forecasting in smart cities, driven by wind power’s growing importance in urban energy strategies and the expanding role of artificial neural networks (ANNs) in wind power prediction. Wind power integration mitigates greenhouse gas emissions and enhances energy resilience in urban settings. However, wind’s inherently variable nature necessitates precise forecasting. The surge in ANN use for wind power forecasting is another key driver of this review, as ANNs excel at modelling complex relationships in data. This review highlights the synergy between wind power forecasting and neural network models, emphasizing ANNs’ vital role in enhancing the accuracy of wind power predictions in urban environments. It covers neural network fundamentals, data preprocessing, diverse neural network architectures, and their applicability in short-term and long-term wind power forecasting. It also delves into training, validation methods, performance assessment metrics, challenges, and prospects. As smart cities champion urban sustainability, neural network models for wind power forecasting are poised to revolutionize urban energy systems, making them cleaner, more efficient, and more resilient

Designing a Renewable Energy System for Industrial IoT with Artificial Intelligence

This paper reviews the integration of renewable energy systems with Industrial IoT (IIoT) through Artificial Intelligence (AI). It examines various studies focusing on the design and monitoring of solar-powered wireless sensor nodes in diverse IIoT settings, particularly outdoors. A proposed distributed network architecture, underpinned by open-source technologies, aims for efficient solar power harvesting and data acquisition on solar radiation and ambient parameters. This data aids in devising estimation techniques to predict solar panel voltage outputs, optimizing energy utilisation of solar-powered sensor nodes. The discourse extends to photovoltaic plants, emphasising continuous monitoring and fault detection for operational safety and reliability. Reviewed works advocate embedding AI and IoT for remote sensing, fault detection, and diagnosis, addressing challenges posed by undetectable faults. Furthermore, the paper explores AI’s transformative potential in the broader energy sector, impacting electricity production, distribution, energy storage, and efficiency. The synergy of AI, IIoT, and renewable energy systems is underscored as a conduit for enhancing energy management, operational transparency, and deploying cost-effective solutions for complex industrial challenges, significantly bolstering the efficiency and intelligence of industrial production and services

Edge Computing in Centralized Data Server Deployment for Network Qos and Latency Improvement for Virtualization Environment

With the advancement of Internet of Things (IoT), the network devices seem to be raising, and the cloud data centre load also raises; certain delay-sensitive services are not responded to promptly which leads to a reduced quality of service (QoS). The technique of resource estimation could offer the appropriate source for users through analyses of load of resource itself. Thus, the prediction of resource QoS was important to user fulfillment and task allotment in edge computing. This study develops a new manta ray foraging optimization with backpropagation neural network (MRFO-BPNN) model for resource estimation using quality of service (QoS) in the edge computing platform. Primarily, the MRFO-BPNN model makes use of BPNN algorithm for the estimation of resources in edge computing. Besides, the parameters relevant to the BPNN model are adjusted effectually by the use of MRFO algorithm. Moreover, an objective function is derived for the MRFO algorithm for the investigation of load state changes and choosing proper ones. To facilitate the enhanced performance of the MRFO-BPNN model, a widespread experimental analysis is made. The comprehensive comparison study highlighted the excellency of the MRFO-BPNN model

Smart Grid Sensor Monitoring Based on Deep Learning Technique with Control System Management in Fault Detection

The smart grid environment comprises of the sensor for monitoring the environment for effective power supply, utilization and establishment of communication. However, the management of smart grid in the monitoring environment isa difficult process due to diversifieduser request in the sensor monitoring with the grid-connected devices. Presently, context-awaremonitoring incorporates effective management of data management and provision of services in two-way processing and computing. In a heterogeneous environment context-aware, smart grid exhibits significant performance characteristics with the grid-connected communication environment for effective data processing for sustainability and stability. Fault diagnoses in the automated system are formulated to diagnose the fault separately. This paper developed anoptimized power grid control model (OPGCM) model for fault detection in the control system model for grid-connected smart home appliances. OPGCM model uses the context-aware power-awarescheme for load management in grid-connected smart homes. Through the adaptive smart grid model,power-aware management is incorporated with the evolutionary programming model for context-awareness user communication. The OPGCM modelperforms fault diagnosis in the grid-connected control system initially, Fault diagnosis system comprises of a sequential process with the extraction of the statistical features to acquirea sustainable dataset with effective signal processing. Secondly, the features are extracted based on the sequential process for the acquired dataset with a reduction of dimensionality. Finally, the classification is performed with the deep learning model to predict or identify the fault pattern. With the OPGCM model, features are optimized with the whale optimization model to acquire features to perform fault diagnosis and classification. Simulation analysis expressed that the proposed OPGCM model exhibits ~16% improved classification accuracy compared with the ANN and HMM model

Privacy preserved data sharing using blockchain and support vector machine for industrial IOT applications

The Industrial Internet of Things (IIoT) paradigm's fast expansion in the amount of information created from linked devices creates new opportunities for improving the service quality and applications through sharing of data. Data security is a significant problem since training and Support Vector Machine (SVM) classifier often involves compiling tagged IoT data from several organizations. Beyond causing the suppliers to lose money, disclosing sensitive information might cause significant problems. To solve these issues, introduced a secure SVM, which is a Privacy-Preserving SVM (PP-SVM) training method using block chain-based encoded IoT data, to fill the void between ideal assumptions and practical restrictions. IoT messages are encrypted before being stored on a decentralized system because Block chain offers safe and dependable data exchange platforms across several data sources. Using a homomorphic cryptographic algorithm, Paillier, reliable modules have been developed, such as protected algebraic multiplying and protected comparison. Furthermore, a protected SVM learning algorithm that only needs two conversations during a single step has been created. According to stringent security assessment, proposed approach guarantees SVM model parameters for data professionals and secrecy of sensitive information for each data source. Extensive testing backs up effectiveness of the suggested plan

Residual learning for segmentation of the medical images in healthcare

Medical workers can assess disease progression and create expedient treatment plans with the help of automated and accurate 3Dsegmentation of medical images. DCNNs (Deep convolution neural networks) have been widely used in this work, but their accuracy still needs to be increased, mostly due to their insufficient understanding of 3D environments. This study proposed three dimensional residual networks, ResUNet++, for precise segmentations of three-dimensional medical images where encoders, segmentation decoders, and context residual decoders are used. Two decoders are connected at scale utilizing context attention maps and context residual, the former explicitly learns inter-slice context data and the latter utilizes contexts as attention to increase segmentation accuracy. This model was assessed by using MICCAI 2018 BraTS dataset and, the Pancreas-CT dataset. The BrasTS and Pancreas-CT dataset scales were compared in terms of ET, WT, TC. Moreover, the proposed model was compared with/without boundary loss and validation dice score. The outcomes not only show how effective the suggested 3D residual learning approach is, but also show that the suggested ResUNet++ offers better accuracy compared to six of the top-ranking techniques used for segmenting tumors in the brain