Highly Fast Innovative Overcurrent Protection Scheme for Microgrid Using Metaheuristic Optimization Algorithms and Nonstandard Tripping Characteristics

The incorporation of renewable energy microgrids brings along several new protection coordination challenges due to the new and stochastic behaviour of power flow and fault currents distribution. An optimal coordination scheme is a potential solution to develop an efficient protection system to handle the microgrid protection challenges. In this paper, new optimal Over Current (OC) relays coordination schemes have been developed using nonstandard tripping characteristics for a power network connected to renewable energy resources. The International Electrotechnical Commission (IEC) microgrid and IEEE-9 bus systems have been used as benchmark networks to test and evaluate the coordination schemes. The proposed OC relays coordination approach delivers a fast and more reliable performance under different OC faults scenarios compared to traditional approaches. In addition, to improve and evaluate the performance of the proposed coordination approach, four modern and novel metaheuristic optimization algorithms are developed and employed to solve the OC relay coordination problem, namely: Modified Particle Swarm Optimization (MPSO), Teaching Learning (TL), Grey Wolf Optimizer (GWO) and Moth-Flame Optimization Algorithm (MFO). In this paper, the modern metaheuristic algorithms have been employed to handle the impact of renewable energy on the grid, and enhance the sensitivity and selectivity of the protection system. The test cases, consider the impact of integrating the different levels of renewable energy resources (with a capacity increment of 25% and 50%) in the microgrid on the OC relays protection performance by using nonstandard and standard tripping characteristics. In addition, a comparison analysis for the modern metaheuristic algorithms with Particle Swarm Optimization (PSO) algorithm as a common and standard technique in solving coordination problems under different fault scenarios considering also the higher impedance faults are introduced. The results in all cases showed that the proposed optimal nonstandard approach successfully reduced the overall tripping time and improve the performance of the protection system in terms of sensitivity and selectivity

Day-ahead industrial load forecasting for electric RTG cranes

Given the increase in international trading and the significant energy and environmental challenges in ports around the world, there is a need for a greater understanding of the energy demand behaviour at ports. The move towards electrified rubber-tyred gantry (RTG) cranes is expected to reduce gas emissions and increase energy savings compared to diesel RTG cranes but it will increase electrical energy demand. Electrical load forecasting is a key tool for understanding the energy demand which is usually applied to data with strong regularities and seasonal patterns. However, the highly volatile and stochastic behaviour of the RTG crane demand creates a substantial prediction challenge. This paper is one of the first extensive investigations into short term load forecasts for electrified RTG crane demand. Options for model inputs are investigated depending on extensive data and correlation analysis. The effect of estimation accuracy of exogenous variables on the forecast accuracy is investigated as well. The models are tested on two different RTG crane data sets that were collected from the Port of Felixstowe in the UK. The results reveal the effectiveness of the forecast models when the estimation of the number of crane moves and container gross weight are accurate

Optimal controllers and configurations of 100% PV and energy Storage systems for a microgrid : the case study of a small town in Jordan

Renewable energy systems such as Photovoltaic (PV) have become one of the best options for supplying electricity at the distribution network level. This is mainly because the PV system is sustainable, environmentally friendly, and is a low-cost form of energy. The intermittent and unpredictable nature of renewable energy sources which leads to a mismatch between the power generation and load demand is the challenge to having 100% renewable power networks. Therefore, an Energy Storage System (ESS) can be a significant solution to overcome these challenges and improve the reliability of the network. In Jordan, the energy sector is facing a number of challenges due to the high energy-import dependency, high energy costs, and the inadequate electrification of rural areas. In this paper, the optimal integration of PV and ESS systems is designed and developed for a distribution network in Jordan. The economic and energy performance of the network and a proposed power network under different optimization algorithms and power network operation scenarios are investigated. Metaheuristic optimization algorithms, namely: Golden Ratio Optimization Method (GROM) and Particle Swarm Optimization (PSO) algorithms, are employed to find the optimal configurations and integrated 100% PV and ESS for the microgrid

Advanced coordination method for overcurrent protection relays using new hybrid and dynamic tripping characteristics for microgrid

Nowadays, the Overcurrent (OC) and Earth Fault (EF) relays coordination problem is one of the most complex and challenging concerns of power protection and network operators due to the high and volatile generation capacity of renewable energy sources in the grid. In this article, a new and dynamic optimal coordination scheme based on a novel hybrid tripping characteristic has been designed and developed for Over Current Relays (OCRs). Considering the impact of renewable energy sources such as the photovoltaic (PV) system on fault characteristic, this work presents and verifies a novel dynamic and hybrid tripping to achieve minimum tripping time and improve the OCR and EF relays coordination performance in terms of security, sensitivity, and selectivity. The proposed dynamic and hybrid scheme will help the OCRs to cover the EF events, and it has been tested under different fault scenarios compared to the literature. The IEEE-9 and IEEE-33 bus systems are implemented in the ETAP package to validate the effectiveness of the proposed hybrid characteristics against traditionally well-established IEC characteristics. Furthermore, the performance of the proposed advance and dynamic protection approach which doesn’t require a communication infrastructure is investigated for a power network with PV plants under different grid operation modes and topology to provide more robustness protection system. The results, as presented using Industrial software (ETAP), showed that the novel dynamic and hybrid tripping scheme improved the speed of the total time tripping different fault scenarios and location by more than 50% and covers all EF events compared to traditional OCR schemes from the literature. The proposed novel dynamic approach has superior performance in detecting high-impedance faults and significantly reducing the tripping time on the IEEE 33 bus network by 47%

Modern Optimal Controllers for Hybrid Active Power Filter to Minimize Harmonic Distortion

Nowadays, AC distributed power networks are facing many challenges in guaranteeing and improving the required level of power quality indices in power networks with increasing nonlinear, time-variable and unbalanced loads. Power networks can benefit from avoiding and minimizing different AC problems, such as frequency fluctuation and Total Harmonic Distortions (THDs), by using power filters, such as Hybrid Active Power Filters (HAPFs). Therefore, attention towards responsible power quality indices, such as Total Harmonic Distortion (THD), Power Factor (P.F) and Harmonic Pollution (HP) has increased. THD and HP are important indices to show the level of power quality at the network. In this paper, modern optimization techniques have been employed to optimize HAPF parameters, and minimize HP, by using a nature-inspired optimization algorithm, namely, Whale Optimization Algorithm (WOA). The WOA algorithm is compared to the most competitive powerful metaheuristic optimization algorithms: Manta Ray Foraging Optimization (MRFO), Artificial Ecosystem-based Optimization (AEO) and Golden Ratio Optimization Method (GROM). In addition, the WOA, and the proposed modern optimization algorithms, are compared to the most competitive metaheuristic optimization algorithm for HAPF from the literature, called L-SHADE. The comparison results show that the WOA algorithm outperformed all other optimization algorithms, in terms of minimizing harmonic pollution, through optimizing parameters of HAPF; therefore, this paper aims to present the WOA as a powerful control model for HAPF

Innovative Characterization and Comparative Analysis of Water Level Sensors for Enhanced Early Detection and Warning of Floods

In considering projections that flooding will increase in the future years due to factors such as climate change and urbanization, the need for dependable and accurate water sensors systems is greater than ever. In this study, the performance of four different water level sensors, including ultrasonic, infrared (IR), and pressure sensors, is analyzed based on innovative characterization and comparative analysis, to determine whether or not these sensors have the ability to detect rising water levels and flash floods at an earlier stage under different conditions. During our exhaustive tests, we subjected the device to a variety of conditions, including clean and contaminated water, light and darkness, and an analogue connection to a display. When it came to monitoring water levels, the ultrasonic sensors stood out because of their remarkable precision and consistency. To address this issue, this study provides a novel and comparative examination of four water level sensors to determine which is the most effective and cost-effective in detecting floods and water level fluctuations. The IR sensor delivered accurate findings; however, it demonstrated some degree of variability throughout the course of the experiment. In addition, the results of our research show that the pressure sensor is a legitimate alternative to ultrasonic sensors. This presents a possibility that is more advantageous financially when it comes to the development of effective water level monitoring systems. The findings of this study are extremely helpful in improving the dependability and accuracy of flood detection systems and, eventually, in lessening the devastation caused by natural catastrophes

Business analytics in industry 4.0: a systematic review

Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions

Analysis of RTG crane load demand and short-term load forecasting

The increasing numbers of international trading ports around the world are facing significant energy and environmental challenges such as rising energy consumption and greenhouse emissions. To understand the energy demand behaviour of ports or cranes, several simulation studies have been carried out using data from the Port of Felixstowe in the UK. The aim of this paper is to propose a 24-hours active power forecast model and analysis tools for a single electrified RTG crane. This model could be a potential solution to these energy consumption and management problems. The crane data has been collected for 30 days and analysed in terms of the daily demand usage, the number of crane moves and the weight of containers. Two different forecast methods, ARIMAX and Artificial Neural Network have been used to forecast highly stochastic, non-smooth and very volatile active crane power demand. The results indicate that the ANN forecast model is more accurate according to the mean absolute percentage error (MAPE) results during the testing period

Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems

This article presents a study of optimal control strategies for an energy storage system connected to a network of electrified Rubber Tyre Gantry (RTG) cranes. The study aims to design optimal control strategies for the power flows associated with the energy storage device, considering the highly volatile nature of RTG crane demand and difficulties in prediction. Deterministic optimal energy management controller and a Model Predictive Controller (MPC) are proposed as potentially suitable approaches to minimise the electric energy costs associated with the real-time electricity price and maximise the peak demand reduction, under given energy storage system parameters and network specifications. A specific case study is presented in to test the proposed optimal strategies and compares them to a set-point controller. The proposed models used in the study are validated using data collected from an instrumented RTG crane at the Port of Felixstowe, UK and are compared to a standard set-point controller. The results of the proposed control strategies show a significant reduction in the potential electricity costs and peak power demand from the RTG cranes