Speed of Lexical Access to Arabic and English Letters

To examining the role of cultural differences in speed of lexical access, we employed two types of Posner (1967) name matching task: Arabic and English types. We have conducted an experiment on 30 native Arabic speakers from King Saud University. The results showed that the lexical access to physically identical letters is faster than lexical access to the nominally identical letters. However, there was a significant effect of task's type in the speed of lexical access. Also, the correlations coefficients varied with task's type. In its entirety, these results suggest that the cultural aspects have a role in the speed of lexical access. Keywords: Lexical Access, long term memory, letters matching

Optimal energy controllers of energy storage systems based on load forecasting for RTG cranes network

Given the increased international trading in ports around the world, there are significant challenges facing ports such as rising energy consumption and greenhouse emissions. The electrification of Rubber Tyred Gantry (RTG) cranes is one approach used to reduce gas emissions and fuel costs at port, but has also increased the electrical demand across the electrical distribution network. This will force port operators to reinforce the low voltage network to meet this increased demand and remain within the operating constraints. An energy storage system is one potential solution to increase the energy efficiency of the low voltage distribution networks whilst avoiding expensive reinforcement of the power system. This thesis aims to highlight and address the peak demand problem in the network of electrified RTG cranes and attempts to reduce peak demand and electricity costs by optimality controlling the energy storage system by utilising load forecasts. Since there is currently lack of understanding of the volatile demand behaviour, the research begins by investigating the unique characteristics of the electrical demand of the RTG crane. This understanding is a vital tool to develop an accurate forecast model and maximise the benefits of using an energy storage system through a control system. Several short-term load forecast models have been developed based on the ARIMAX and ANN models to predict accurate day-ahead electrical R TG crane demand. The forecast results show that the highly volatile demand behaviour creates a substantial prediction challenge compared to normal residential low voltage network demand. This thesis then presents the significance of forecasting the crane demand to improve the energy performance of an electrical distribution network with an ESS by employing several optimal controllers. The novel optimal control algorithms considered for the network of RTG cranes are split into: a Model Predictive Controller (MPC) with rolling forecast system and a Stochastic Model Predictive Controller (SMPC) based on a stochastic prediction demand model. The proposed MPC and SMPC control models are compared to an optimal controller based on a fixed load forecast profile and a common and standard set-point controller. Results show that the optimal controllers based on a load forecast have improved the storage device performance for the peak reduction and cost savings compared to the traditional control algorithm. Further improvements are then presented for the receding horizon controllers, MPC and SMPC, which better treat the volatility of the crane demand and the uncertainty in the forecasts. Furthermore, an economic analysis of the results for different ESS location scenarios is presented to assess their viability

A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads

This paper aims to present the significance of predicting stochastic loads to improve the performance of a low voltage (LV) network with an energy storage system (ESS) by employing several optimal energy controllers. Considering the highly stochastic behaviour that rubber tyre gantry (RTG) cranes demand, this study develops and compares optimal energy controllers based on a model predictive controller (MPC) with a rolling point forecast model and a stochastic model predictive controller (SMPC) based on a stochastic prediction demand model as potentially suitable approaches to minimise the impact of the demand uncertainty. The proposed MPC and SMPC control models are compared to an optimal energy controller with perfect and fixed load forecast profiles and a standard set-point controller. The results show that the optimal controllers, which utilise a load forecast, improve peak reduction and cost savings of the storage device compared to the traditional control algorithm. Further improvements are presented for the receding horizon controllers, MPC and SMPC, which better handle the volatility of the crane demand. Furthermore, a computational cost analysis for optimal controllers is presented to evaluate the complexity for a practical implementation of the predictive optimal control systems

A Comparative Study of Energy Storage Systems and Active Front Ends for Networks of Two Electrified RTG Cranes

The global consumerism trend and the increase in worldwide population is increasing the need to improve the efficiency of marine container transportation. The high operating costs, pollution and noise of the diesel yard equipment is leading sea ports to move towards replacing diesel RTG cranes with electric Rubber Tyre Gantry (RTG) cranes which offer reduced environmental impact and higher energy efficiency. However, ports will require smarter solutions to meet the increased demand on the electrical distribution network due to the electrification of RTGs. This paper aims to highlight the peak demand problem in the two electrical cranes network and attempts to increase the energy saving at ports by using two different technologies: Energy Storage System (ESS) and Active Front End (AFE). This article introduces one of the first extensive investigations into different networks of RTG crane models and compares the benefits of using either AFE or ESS. The proposed RTG crane models and network parameters are validated using data collected at the Port of Felixstowe, UK. The results of the proposed RTG cranes network show a significant peak demand reduction and energy cost saving

High hybrid power converter performance using modern‐optimization‐methods‐based PWM strategy

Recently, interest in DC networks and converters has increased due to the high number of applications in renewable energy systems. Consequently, the importance of improving the efficiency of the hybrid converters has been highlighted. An optimal control strategy is a significant solution to handle the challenges of controlling the hybrid interleaved boost–Cuk converter. In this article, a modern‐optimization‐methods‐based PWM strategy for a hybrid power converter is developed. In order to improve the performance of the hybrid converter, four modern optimization algorithms—namely, Manta ray foraging optimization (MRFO), Marine Predators Algorithm (MPA), Jellyfish Search Optimizer (JS), and Equilibrium Optimizer (EO)—are employed to minimize the input current ripple under different operation scenarios. The results of the proposed modern optimization algorithms have shown more efficient converter performance and balanced power‐sharing compared with conventional strategies and the literature on optimization algorithms such as Differential Evolution (DE) and Particle Swarm Optimization (PSO). In addition, the results of all operation cases presenting the proposed optimal strategy successfully reduced the input current ripple and improve the performance of power‐sharing at the converter compared with the conventional methods

Optimised sustainable energy supply alternatives for Libyan utilities under unsubsidised tariff conditions

With growing electricity demand and fossil fuel concerns, renewable energy (RE) solutions are becoming increasingly important. This paper explores sustainable energy alternatives to address the critical energy instability at an educational utility, namely the College of Electrical and Electronics Technology (CEET) in Benghazi, with potential applications for different Libyan sectors, including community areas and commercial entities. Four configurations were evaluated: standalone PV with storage, hybrid PV/wind/storage, grid-connected PV, and grid/diesel. The study aims to identify the optimal setup by minimising the net present cost (NPC) and levelised cost of energy (LCOE) over the project's operational period across varying fossil electricity and diesel rates. Sensitivity analysis indicates that higher diesel and grid electricity prices ($1.00/L and$0.10/kWh) reduce the LCOE of the hybrid system to $0.12/kWh, making it competitive with grid-based options. The study provides practical insights into addressing Libya's energy challenges using technically and economically feasible RE strategies

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

Stochastic optimal energy management system for RTG cranes network using genetic algorithm and ensemble forecasts

In low voltage networks, Energy Storage Systems (ESSs) play a significant role in increasing energy cost savings, peak reduction and energy efficiency whilst reinforcing the electrical network infrastructure. This paper presents a stochastic optimal management system based on a Genetic Algorithm (GA) for the control of an ESS equipped with a network of electrified Rubber Tyre Gantry (RTG) cranes. The stochastic management system aims to improve the reliability and economic performance, for given ESS parameters, of a network of cranes by taking into account the uncertainty in the RTGs electrical demand. A specific case study is presented using real operational data of the RTGs netwrok in the Port of Felixstowe, UK, and the results of the stochastic control system is compared to a standard set-point controller. In this paper, two forecast data sets with different levels of accuracy are used to investigate the impact of the crane demand forecast error in the proposed ESS control system. The results of the proposed control strategies indicate that the stochastic management system successfully increases the electric energy cost savings, the peak demand reductions and successfully outperforms a comparable set-point controller

Power management system for RTG crane using fuzzy logic controller

In this research, there are two major objectives have been investigated for a Rubber Tyred Gantry (RTG) crane system: energy consumption reduction and decrease the stress on the primary source. These objectives can be met by using an advance control system that reads the status of the crane and outputs a power reference value which is fed to the storage device. This paper presents Fuzzy Logic Controller (FLC) approach to maximise the potential benefits of adding energy storage units to RTG cranes. In this work, FLC is described and simulated, with the results analysed to highlight the behaviour of the storage in association with the specific control system. An actual collected data at the Port of Felixstowe, UK has been used to develop the crane and ESS models and test the proposed control strategies in this paper. Furthermore, a comparison analysis between the FLC and the standard control system (PI) for RTG crane and ESS applications will be presented with respect to energy consumption, fuel saving and the control impact on the energy device. The simulation results of the FLC control strategy for the collected data shows that it successfully increases the energy savings by 32% and outperforms the PI controller by 26%

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