Performance Improvement of Hybrid System Based DFIG-Wind/PV/Batteries Connected To DC And AC Grid By Applying Intelligent Control

One of the main causes of CO2 emissions is the production of electrical energy. Therefore, many researchers goal’s is to develop renewable power systems. This paper proposes a new intelligent control development of hybrid PV–Wind-Batteries. Neuro-Fuzzy Direct Power Control (NF-DPC) is invested in order to enhance system performance and generated currents quality. An improved MPPT algorithm based on Fuzzy Controller (FC) is invested for PV power optimization. In addition, a new Modified Fuzzy Direct Power Control (MF-DPC) is developed and applied to the grid side converter to control the active and reactive power by monitoring the involved active power flow and providing a unit power factor by imposing a zero reactive power. An Energy Management Algorithm (EMA) is developed to maintain energy balance, meet the DC load demand, mitigate fluctuations caused by weather condition variations (wind speed and solar irradiance), and minimize battery overcharge and deep discharge. To test the proposed hybrid microgrid system operation, the different parts of the system are modeled, the wind turbine associated to the DFIG, the photovoltaic system as well as the battery storage system. Furthermore, the associated power converters with their control strategies are also presented. Global system simulation, using MATLAB/Simulink, is carried out to validate the effectiveness of both EMA and control techniques. The obtained results show significant reduction of active/reactive power ripples and THD by about 64%, 72%, and 50%, respectively. The EMA ability to manage the energy flow, produced and requested by the load. The THD rate of all injected currents is less than 4%, meaning that the proposed controls will increase the used equipments’ life span, minimize their maintenance and then reduce the hybrid power system cost

Improvement of trajectory tracking by robot manipulator based on a new co-operative optimization algorithm

Funding Information: Funding: This work was funded by the Taif University Researchers Supporting Project under Grant TURSP-2020/345. Taif. Saudi Arabia, and also the Ministry of Science and Technology (MOST) of Taiwan (grant number: MOST 110-2222-E-011-013-) and the Center for Cyber-physical System Innovation from the Featured Areas Research Center Program in the Agenda of the Ministry of Education (MOE), Taiwan. Funding Information: Acknowledgments: The authors would like to acknowledge the financial support received from Taif University Researchers Supporting Project Number (TURSP-2020/345). Taif University, Taif, Saudi Arabia. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.The tracking of a predefined trajectory with less error, system-settling time, system, and overshoot is the main challenge with the robot-manipulator controller. In this regard, this paper introduces a new design for the robot-manipulator controller based on a recently developed algorithm named the butterfly optimization algorithm (BOA). The proposed BOA utilizes the neighboring butterflies’ co-operation by sharing their knowledge in order to tackle the issue of trapping at the local optima and enhance the global search. Furthermore, the BOA requires few adjustable parameters via other optimization algorithms for the optimal design of the robot-manipulator controller. The BOA is combined with a developed figure of demerit fitness function in order to improve the trajectory tracking, which is specified by the simultaneous minimization of the response steady-state error, settling time, and overshoot by the robot manipulator. Various test scenarios are created to confirm the performance of the BOA-based robot manipulator to track different trajectories, including linear and nonlinear manners. Besides, the proposed algorithm can provide a maximum overshoot and settling time of less than 1.8101% and 0.1138 s, respectively, for the robot’s response compared to other optimization algorithms in the literature. The results emphasize the capability of the BOA-based robot manipulator to provide the best performance compared to the other techniques.Peer reviewe

Effective Transmission Congestion Management via Optimal DG Capacity using Hybrid Swarm Optimization for Contemporary Power System Operations

Publisher Copyright: AuthorManaging transmission congestion had been a major problem with growing competition in the power networks. Accordingly, competitiveness emerges through the network's reconfiguration and the proliferation of secondary facilities. Congestion of transmission lines is a critical issue, and their regulation poses a technical challenge as the power system is deregulated. Therefore, the present research illustrates a multi-objective strategy for reaching the optimal capabilities of distributed generators (DG) like wind power plants and geothermal power-producing plants to alleviate congestion throughout the transmission network. Goals such as congestion management during power delivery, power loss reduction, power flow improvement with the enhancement of voltage profile, and investment expenditure minimization are considered to boost the network's technological and economic reliability. The congestion management is achieved using the locational marginal price (LMP) and calculation of transmission congestion cost (TCC) for the optimal location of DG. After identification of congested lines, DG is optimally sized by particle swarm optimization (PSO) and a newly proposed technique that combines the features of modified IL-SHADE and PSO called hybrid swarm optimization (HSO) which employs linear population size reduction technique which improves its performance greatly by reducing the population size by elimination of least fit individuals at every generation giving far better results than those obtained with PSO. In addition, optimal rescheduling of generations from generators has been done to fulfill the load demand resulting in alleviation of congested lines thereby enhancing the performance of the network under investigation. Furthermore, the performance of the proposed methodology of HSO and PSO has been tested successfully on standard benchmark IEEE-30 & IEEE-57 bus configurations in a MATLAB environment with the application of MATPOWER power system package.Peer reviewe

Application of a novel metaheuristic algorithm based two-fold hysteresis current controller for a grid connected PV system using real time OPAL-RT based simulator

Grid connected photovoltaic (GCPV) rooftop systems have been considered as fast development and promising renewable energy sources due to low maintenance cost, secure investment, noise-free and do not require additional space for installation. Various factors considered for the installation of GCPV are mathematical models of PV module, sizing methods based on techno-economic objectives, PV panels and configurations, selection of the final optimum configuration and environmental criteria. Selection of appropriate controller and its optimal design for power electronic based converters also plays a crucial role in the performance of GCPV. Therefore in this article, a two-fold hysteresis current controller (TFHCC) based on an Improved Arithmetic Optimization Algorithm (IAOA) is introduced and investigated for the first time, for a GCPV system to minimize the switching loss and total harmonic distortion (THD). A novel multi-objective function considering switching frequency and current error is proposed by assigning appropriate weights to obtain the optimal values of duty cycle and hysteresis bands using IAOA, AOA, Forensic Based Investigation (FBI), Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. TFHCC utilizes zero level of the inverter by properly switching it on for a half cycle only and either on or off for the other cycle. Comparative performance analysis of the optimal TFHCC obtained with different algorithms is presented and it is proved that IAOA based TFHCC exhibits substantial reductions in variation and magnitude of the average switching frequency by 2.82 kHz and THD by 0.65%. Initially, the study is carried out with MATLAB Simulink environment and then experimentally validated with real time simulator based on OPAL-RT 4510

Accurate Insulating Oil Breakdown Voltage Model Associated with Different Barrier Effects

In modern power systems, power transformers are considered vital components that can ensure the grid’s continuous operation. In this regard, studying the breakdown in the transformer becomes necessary, especially its insulating system. Hence, in this study, Box–Behnken design (BBD) was used to introduce a prediction model of the breakdown voltage (VBD) for the transformer insulating oil in the presence of different barrier effects for point/plane gap arrangement with alternating current (AC) voltage. Interestingly, the BBD reduces the required number of experiments and their costs to examine the barrier parameter effect on the existing insulating oil VBD. The investigated variables were the barrier location in the gap space (a/d)%, the relative permittivity of the barrier materials (εr), the hole radius in the barrier (hr), the barrier thickness (th), and the barrier inclined angle (θ). Then, only 46 experiment runs are required to build the BBD model for the five barrier variables. The BBD prediction model was verified based on the statistical study and some other experiment runs. Results explained the influence of the inclined angle of the barrier and its thickness on the VBD. The obtained results indicated that the designed BBD model provides less than a 5% residual percentage between the measured and predicted VBD. The findings illustrated the high accuracy and robustness of the proposed insulating oil breakdown voltage predictive model linked with diverse barrier effects.Peer reviewe

A comprehensive analytical exploration and customer behaviour analysis of smart home energy consumption data with a practical case study

Over the years, the automation of traditional power grids has been taking place to overcome the difficulties such as blackouts, outages, demand-side management, load profiling, enhancing customer participation, etc. This automation enables the traditional grids to be transformed into smart grids. Smart homes/buildings are key sub-categories of smart grids. The advanced metering infrastructure connected to them continuously captures and stores the energy consumption data as datasets. Usually, understanding the structure of data and the behaviour of customers from energy consumption datasets is a tedious task. There are some literature works tried to explore various smart home energy consumption datasets as well as investigate customer behaviour, however, most of these methods are complex in implementation. Hence, this paper proposes a simple approach for the comprehensive exploration of the smart home energy consumption dataset. This approach can be used for any similar smart home dataset that contains numerical data. Further, using the exploration results, this paper analyzes the customers’ energy consumption behaviour by identifying peak hours in communication and electrical perspectives. To implement the proposed approach, an energy consumption dataset ‘Tracebase’ is considered as a case study. The exploration of the considered dataset results in 2356 files distributed among various directories. For customer behaviour analysis, the energy consumption data of all 43 appliances (with more than 95 million records) is considered from the “complete” directory of the “Tracebase” dataset. This analysis revealed the peak hours as hour-23 from the communication perspective and hour-9 from the electrical perspective. These represent the customer behaviour in terms of their participation in the power network, which further helps for better grid operations

Precise transformer fault diagnosis via random forest model enhanced by synthetic minority over-sampling technique

Funding Information: The authors appreciate Taif University Researchers Supporting Project TURSP 2020/34, Taif University, Taif, Saudi Arabia for supporting this work. Further, this study is partly funded by DIPA Polinema. Publisher Copyright: © 2023Power transformers are considered one of the power system's most critical and expensive assets. In this regard, it is vital to assess the fault within the power transformer considering numerous operational aspects. In the literature, dissolved gas analysis (DGA) is the routine in-service test for power transformers and one of the most important tests to ensure sufficient system reliability. Specifically, this test can detect dissolved gases in transformer oil which are then interpreted to detect the fault type of the transformer. Previous studies reported that the graphical Duval pentagon is one of the most accurate and consistent DGA interpretation techniques. However, it still has limitations on the complexity of the implementation in large amounts of data. To cover these issues, this study mitigates the limitation and complexity of implementing the graphical Duval Pentagon Method (DPM) in large amounts of data. To reach this goal, we develop a precise machine-learning-based fault identification model by employing the Random Forest algorithm with Synthetic minority over-sampling technique (SMOTE) preprocessing. The proposed Random Forest models with SMOTE perform satisfactorily in diagnosing faults for the evaluation dataset, with a total accuracy of 96.2% for DPM1 and 96.5% for DPM2. The proposed models were also compared to other machine learning algorithms, performing better both in classification accuracy and consistency due to uncertainty.Peer reviewe

A parametric study with experimental investigations of expanded graphite on performance measure of EDM process of Ni55.8Ti SMA

The present work focuses on the impact of expanded graphite (EG) nano-powder along with spark-on-duration (Ton) and spark-off-duration (Toff), and current as factors on increasing material removal rate (MRR), reduction of surface roughness (SR), tool wear rate (TWR), dimensional deviation (DD), and surface defects for Ni55.8Ti. Taguchi’s design having 4 factors at 3 levels was employed to perform the experimental trials. ANOVA has successfully validated the developed regression equations. EDM factors of PC, Toff, current, and Toff were found to be the largest contributing factors with the involvement of 76.91 %, 38.40 %, 34.36 %, and 44.54 % for MRR, TWR, SR, and DD respectively. TLBO algorithm was used in the present work to tackle the conflicting situation and to optimize the response variables. The simultaneous optimization conducted through the Teaching-learning-based optimization technique has yielded optimal parameters setting of Ton at 7 µs, Toff at 5 µs, PC at 1.5 g/L, and current at 10 A by giving optimal response values at MRR of 42.82 mm3/min, TWR of 0.4039 mm3/min, SR of 3.71 µm, and DD of 92.65 µm. Lastly, Scanning electron microscopy was utilized to check EG nano-powder significance on the machined parts' surface morphology