Imputation of missing data in photovoltaic panel monitoring system

In scientific research, data acquisition and processing play a fundamental role. In photovoltaic systems, given their nature, this process presents deficiencies due to various factors such as the dispersion of the installed modules, climatic conditions or the amount of information that must be obtained, so the processes of data acquisition, storage and processing are very important. The present research developed a data acquisition, storage and processing system for photovoltaic systems, following the European standards IEC 60904 and IEC 61724 for data acquisition, Fog Computing for information storage and finally Machine Learning was used for processing. The results showed that the KNN-based model obtained a SCORE of 99.08%, MAE of 25.3 and MSE of 93.16. Concluding that the KNN-based model is the most robust model for data imputation in PV system monitoring

A Fault-Tolerant P-Q Decoupled Control Scheme for Static Synchronous Series Compensator

Control of nonlinear devices in power systems relies on the availability and the quality of sensor measurements. Measurements can be corrupted or interrupted due to sensor failure, broken or bad connections, bad communication, or malfunction of some hardware or software (referred to as missing sensor measurements in this paper). This paper proposes a fault-tolerant control scheme (FTCS) for a static synchronous series compensator (SSSC). This FTCS consists of a sensor evaluation and (missing sensor) restoration scheme (SERS) cascaded with a P-Q decoupled control scheme (PQDC). It is able to provide effective control to the SSSC when single or multiple crucial sensor measurements are unavailable. Simulation studies are carried out to examine the validity of the proposed FTCS. During the simulations, single and multiple phase current sensors are assumed to be missing, respectively. Results show that the SERS restores the missing data correctly during steady and transient states, including small and large disturbances, and unbalanced three-phase operation. Thus, the FTCS continuously provides effective control to the SSSC with and without missing sensor measurements

In-Field Solar Panel Assessment and Fault Diagnosis

Photovoltaic energy is a green energy that suit from small houses to high-power stations spanning large areas. In such large areas, monitoring individual panels can be a tedious task, especially if it was required to identify operational faults of these panels. Photovoltaic 4.0 technology depend on collecting data from each station and feeding them to a central processing system that can analyze operation data and hopefully locate when a fault happens. In such method, it is crucial to be accurate as much as possible and for measuring device to be accurate as well to have a clear judgement. In this work, we build an analysis module at the center of a photovoltaic 4.0 station implemented in the American University in Cairo. The model is comprehensive in nature and is capable of modelling from individual cell level to the whole panel level as well as dealing with measurement issues to have a good judgement at the end. The used model is based on single-diode model of a solar panel and is capable of modelling solar panels in different environmental conditions and is validated against datasheet and actual measurement. Source code for the analysis module and the dataset are provided. It was shown that Laudani’s method of parameter extraction is more successful compared to Stonelli’s method and translating circuit parameters at different environmental conditions proved to be successful and matching to datasheets. Besides, it provided sufficient predictions without need to an actual weather station. The proposed analysis module provided insights about dusty conditions and irregularities that may exist in solar panel characterizer

Cybersecurity Strategy against Cyber Attacks towards Smart Grids with PVs

Cyber attacks threaten the security of distribution power grids, such as smart grids. The emerging renewable energy sources such as photovoltaics (PVs) with power electronics controllers introduce new potential vulnerabilities. Based on the electric waveform data measured by waveform sensors in the smart grids, we propose a novel cyber attack detection and identification approach. Firstly, we analyze the cyber attack impacts (including cyber attacks on the solar inverter causing unusual harmonics) on electric waveforms in distribution power grids. Then, we propose a novel deep learning based mechanism including attack detection and attack diagnosis. By leveraging the electric waveform sensor data structure, our approach does not need the training stage for both detection and the root cause diagnosis, which is needed for machine learning/deep learning-based methods. For comparison, we have evaluated classic data-driven methods, including -nearest neighbor (KNN), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN). Comparison results verify the performance of the proposed method for detection and diagnosis of various cyber attacks on PV systems

Advanced Modeling, Control, and Optimization Methods in Power Hybrid Systems - 2021

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on the Energy Internet, blockchain technology and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

Advances and Technologies in High Voltage Power Systems Operation, Control, Protection and Security

The electrical demands in several countries around the world are increasing due to the huge energy requirements of prosperous economies and the human activities of modern life. In order to economically transfer electrical powers from the generation side to the demand side, these powers need to be transferred at high-voltage levels through suitable transmission systems and power substations. To this end, high-voltage transmission systems and power substations are in demand. Actually, they are at the heart of interconnected power systems, in which any faults might lead to unsuitable consequences, abnormal operation situations, security issues, and even power cuts and blackouts. In order to cope with the ever-increasing operation and control complexity and security in interconnected high-voltage power systems, new architectures, concepts, algorithms, and procedures are essential. This book aims to encourage researchers to address the technical issues and research gaps in high-voltage transmission systems and power substations in modern energy systems

Object-oriented shipboard electric power system library

The objective of this thesis is to explore the powerful capabilities of using an object-oriented modeling language to model and simulate an all electric Naval Shipboard Power System. Modelica has been used to model and simulate the shipboard power system which acts as an alternative simulation tool. The shipboard system is developed using the concept of packages. Different components like the buck converter, inverter, and AC machines have been modeled as a part of the library to develop the power system. The shipboard system has been simulated as two decoupled systems, the AC and DC systems. This research further focuses on developing a networked protection system to detect and clear faults and protect the shipboard power system from complete breakdown. A discrete supervisory controller has been designed using Petri nets as part of the protection system to control the converters and clear faults. A communication network has also been modeled for communication. Two different case studies, the open circuit test, and short circuit test were performed to test the effectiveness of the protection system and the simulation results are presented. This thesis also gives an overview of different properties of Modelica along with its advantages over other simulation tools, a detailed survey of different types of object-oriented simulation tools available, a comparison of different power electronics simulation tools, and some of the previous work done in Modelica

Machine Learning based Early Fault Diagnosis of Induction Motor for Electric Vehicle Application

Electrified vehicular industry is growing at a rapid pace with a global increase in production of electric vehicles (EVs) along with several new automotive cars companies coming to compete with the big car industries. The technology of EV has evolved rapidly in the last decade. But still the looming fear of low driving range, inability to charge rapidly like filling up gasoline for a conventional gas car, and lack of enough EV charging stations are just a few of the concerns. With the onset of self-driving cars, and its popularity in integrating them into electric vehicles leads to increase in safety both for the passengers inside the vehicle as well as the people outside. Since electric vehicles have not been widely used over an extended period of time to evaluate the failure rate of the powertrain of the EV, a general but definite understanding of motor failures can be developed from the usage of motors in industrial application. Since traction motors are more power dense as compared to industrial motors, the possibilities of a small failure aggravating to catastrophic issue is high. Understanding the challenges faced in EV due to stator fault in motor, with major focus on induction motor stator winding fault, this dissertation presents the following: 1. Different Motor Failures, Causes and Diagnostic Methods Used, With More Importance to Artificial Intelligence Based Motor Fault Diagnosis. 2. Understanding of Incipient Stator Winding Fault of IM and Feature Selection for Fault Diagnosis 3. Model Based Temperature Feature Prediction under Incipient Fault Condition 4. Design of Harmonics Analysis Block for Flux Feature Prediction 5. Flux Feature based On-line Harmonic Compensation for Fault-tolerant Control 6. Intelligent Flux Feature Predictive Control for Fault-Tolerant Control 7. Introduction to Machine Learning and its Application for Flux Reference Prediction 8. Dual Memorization and Generalization Machine Learning based Stator Fault Diagnosi

Application of Power Electronics Converters in Smart Grids and Renewable Energy Systems

This book focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller

Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts-Volume II

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications, such as hybrid and microgrid power systems based on the Energy Internet, Blockchain technology, and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above