Machine Learning in Wireless Sensor Networks for Smart Cities:A Survey

Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications

A Novel Fault Classification Approach for Photovoltaic Systems

Photovoltaic (PV) energy has become one of the main sources of renewable energy and is currently the fastest-growing energy technology. As PV energy continues to grow in importance, the investigation of the faults and degradation of PV systems is crucial for better stability and performance of electrical systems. In this work, a fault classification algorithm is proposed to achieve accurate and early failure detection in PV systems. The analysis is carried out considering the feature extraction capabilities of the wavelet transform and classification attributes of radial basis function networks (RBFNs). In order to improve the performance of the proposed classifier, the dynamic fusion of kernels is performed. The performance of the proposed technique is tested on a 1 kW single-phase stand-alone PV system, which depicted a 100% training efficiency under 13 s and 97% testing efficiency under 0.2 s, which is better than the techniques in the literature. The obtained results indicate that the developed method can effectively detect faults with low misclassification

Intelligent transition control between grid-connected and standalone modes of three-phase grid-integrated distributed generation systems

This paper proposes an intelligent seamless transition controller for smooth transition between grid-connected (GC) and standalone modes of distributed generation (DG) units in the grid. The development of this seamless controller contributes to two main processes in the transition modes: the synchronization process and an islanding process. For the synchronization process, the stationary reference frame phase-locked loop (SRF-PLL) associated with the voltage source inverter (VSI) is modified using the frequency, voltage deviation, and phase angle information. Furthermore, the islanding process is classified as intentional and unintentional islanding scenarios for achieving efficient transition control. Here, the intentional islanding process is achieved with the information that is available in the system due to the planned disconnection. For the unintentional islanding process, a fuzzy inference system (FIS) is used to modify the conventional droop control using the information of change in active power, voltage, and frequency. To identify the action of the proposed approach during the transition process, numerical simulations are conducted with the hardware-in-loop (HIL) simulator by developing a 10kWp three-phase grid-connected DG system. The results identified the efficient control of the VSI for both islanding and grid connection processes. In the islanding conditions, the proposed controller provides advantage with less detection and disconnection time, and during synchronization, it instantly minimizes the phase-angle deviation to achieve efficient control

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms

Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of action. But with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow, monitoring and maintenance are a few of the prime concerns. These problems were addressed widely in the literature, but most of the research has drawbacks due to long detection time, and high misclassification error. Hence to overcome these drawbacks, and to develop an accurate monitoring approach, this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic (PV) system and highlighted along with a brief overview on existing fault detection methodology. Based on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance requirement. Further, the system was tested with a 4 kWp grid-connected PV system, and a decision tree-based algorithm was developed for the identification of a fault. The results identified 94.7% training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation

Simulation and Analysis of Electric Control System for Metal Halide High Intensity Discharge Lamps

Abstract — Metal Halide HID lamps are becoming popular because of its high efficacy. The operating characteristic of Metal Halide (MH) HID lamps is complex as it has several stage of operation. The objective of this paper is to design an electric control system for metal halide high intensity discharge (HID) lamps using half bridge inverter. The LCC resonant mode is used to provide the sufficient voltage and current to the MH lamp during its ignition and normal running condition. An adaptive control method is used to regulate the lamp power. A close loop current control method is used. In this close loop Type-3 regulator is used as a compensator. The variation in lamp power with and without loop is discussed. The stability of the close loop is also analysed. PSIM Simulation Software is used to do this analysis

Fault Ride Through approach for Grid-Connected Photovoltaic System

This paper presents a fault ride-through approach for grid-connected photovoltaic (PV) systems, aimed at improving the system's response during voltage sags and limiting the maximum inverter current during symmetrical faults. A constant active current reactive power injection approach was developed for low-voltage ride-through (LVRT) operation of grid-connected solar PV inverters in low voltage grids. The method manages the active and reactive power references and satisfies grid code requirements while also addressing tripping problems caused by overcurrent. The approach improves the speed response of the power converters during voltage sags, resulting in better dynamic grid support. The authors evaluated the approach using a 2-kW single-phase grid-connected PV system and demonstrated its ability to inject the required reactive power during a fault to improve the voltage profile and achieve dynamic grid support requirements. The accuracy of the proposed methodology is identified to be 98.3%, and the detection time is reduced by a reasonable margin in accordance with the grid standards. Overall, the fault ride-through approach can enhance the performance and reliability of grid-connected PV systems, particularly in low voltage grids

Modeling and Optimisation of a Solar Energy Harvesting System for Wireless Sensor Network Nodes

The Wireless Sensor Networks (WSN) are the basic building blocks of today’s modern internet of Things (IoT) infrastructure in smart buildings, smart parking, and smart cities. The WSN nodes suffer from a major design constraint in that their battery energy is limited and can only work for a few days depending upon the duty cycle of operation. The main contribution of this research article is to propose an efficient solar energy harvesting solution to the limited battery energy problem of WSN nodes by utilizing ambient solar photovoltaic energy. Ideally, the Optimized Solar Energy Harvesting Wireless Sensor Network (SEH-WSN) nodes should operate for an infinite network lifetime (in years). In this paper, we propose a novel and efficient solar energy harvesting system with pulse width modulation (PWM) and maximum power point tracking (MPPT) for WSN nodes. The research focus is to increase the overall harvesting system efficiency, which further depends upon solar panel efficiency, PWM efficiency, and MPPT efficiency. Several models for solar energy harvester system have been designed and iterative simulations were performed in MATLAB/SIMULINK for solar powered DC-DC converters with PWM and MPPT to achieve optimum results. From the simulation results, it is shown that our designed solar energy harvesting system has 87% efficiency using PWM control and 96% efficiency ( η s y s ) by using the MPPT control technique. Finally, an experiment for PWM controlled SEH-WSN is performed using Scientech 2311 WSN trainer kit and a Generic LM2575 DC-DC buck converter based solar energy harvesting module for validation of simulation results