Comparative Performance Study of Dissolved Gas Analysis (DGA) Methods for Identification of Faults in Power Transformer

© 2023 Abdul Wajid et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The power transformer is an essential component of the electrical network that can be used to step up and step down voltage. Dissolved gas analysis (DGA) is the most reliable method for the identification of incipient faults in power transformers. Various DGA methods are used to observe the generated key gases after oil decomposition. The main gases included are hydrogen (H2), ethylene (C2H4), acetylene (C2H2), methane (CH4), and ethane (C2H6). There is a lack of research that can compare the performance of various DGA methods in identification of faults in power transformer. In addition, it is also not clear which DGA method is optimal for identification of faults in power transformer. In this paper, the comparative performance study of seven DGA methods such as Roger’s ratio, key gas, IEC ratio, the Doernenburg ratio, the Duval triangle, three-ratio method, and the relative percentage of four gases is carried out in order to identify the optimal technique for fault identification in transformer. The data of various power transformers installed in “RAWAT” NTDC grid station, Islamabad, and “UCH-II” power station, Balochistan, are considered for the comparative analysis. This analysis shows that the three-ratio method provides better performance than other DGA methods in accurately identifying the faults in power transformers. The three-ratio method has 90% accuracy in identifying the faults in power transformer.Peer reviewe

A novel ultra local based-fuzzy PIDF controller for frequency regulation of a hybrid microgrid system with high renewable energy penetration and storage devices

A new ultra-local control (ULC) model and two marine predator algorithm (MPA)-based controllers; MPA-based proportional-integral-derivative with filter (PIDF) and MPA-based Fuzzy PIDF (FPIDF) controllers; are combined to enhance the frequency response of a hybrid microgrid system. The input scaling factors, boundaries of membership functions, and gains of the FPIDF con-troller are all optimized using the MPA. In order to further enhance the frequency response, the alpha parameter of the proposed ULC model is optimized using MPA. The performance of the pro-posed controller is evaluated in the microgrid system with different renewable energy sources and energy storage devices. Furthermore, a comparison of the proposed MPA-based ULC-PIDF and ULC-FPIDF controllers against the previously designed controllers is presented. Moreover, a vari-ety of scenarios are studied to determine the proposed controller’s sensitivity and robustness to changes in wind speed, step loads, solar irradiance, and system parameter changes. The results of time-domain simulations performed in MATLAB/SIMULINK are shown. Finally, the results demonstrate that under all examined conditions, the new ULC-based controllers tend to further enhance the hybrid microgrid system’s frequency time response

Innovative AVR-LFC design for a multi-area power system using hybrid fractional-order PI and PIDD2 controllers based on dandelion optimizer

In this article, the problem of voltage and frequency stability in a hybrid multi-area power system including renewable energy sources (RES) and electric vehicles has been investigated. Fractional order systems have been used to design innovative controllers for both load frequency control (LFC) and automatic voltage regulator (AVR) based on the combination of fractional order proportional-integral and proportional-integral-derivative plus double derivative (FOPI–PIDD2). Here, the dandelion optimizer (DO) algorithm is used to optimize the proposed FOPI–PIDD2 controller to stabilize the voltage and frequency of the system. Finally, the results of simulations performed on MATLAB/Simulink show fast, stable, and robust performance based on sensitivity analysis, as well as the superiority of the proposed optimal control strategy in damping frequency fluctuations and active power, exchanged between areas when faced with step changes in load, the changes in the generation rate of units, and the uncertainties caused by the wide changes of dynamic values

A novel primary and backup relaying scheme considering internal and external faults in HVDC transmission lines

Discrimination of different DC faults near a converter end of a DC section consisting of a filter, a smoothing reactor, and a transmission line is not an easy task. The faults occurring in the AC section can be easily distinguished, but the internal and near-side external faults in the DC section are very similar, and the relay may cause false tripping. This work proposes a method to distinguish external and internal faults occurring in the DC section. The inputs are the voltage signals at the start of the transmission line and the end of the converter filter. The difference in voltage signals is calculated and given to an intelligent controller to detect and discriminate the faults. The intelligent controller is designed using machine learning (ML) and deep learning (DL) techniques for fault detection. The long short-term memory (LSTM-) based relay gives better results than other ML methods. The proposed method can distinguish internal from external faults with 100% accuracy. Another advantage is that a primary relay is suggested that detects faults quickly within a fraction of milliseconds. Nevertheless, another advantage is that a backup relay has been designed in case the primary relay cannot operate. Results show that the LSTM-based protection scheme provides higher sensitivity and reliability under different operation modes than the conventional traveling wave-based relay

Fuzzy logic controller equilibrium base to enhance AGC system performance with renewable energy disturbances

Owing to the various sources of complexity in the electrical power system, such as integrating intermittent renewable energy resources and widely spread nonlinear power system components, which result in sudden changes in the power system operating conditions, the conventional PID controller fails to track such dynamic challenges to mitigate the frequency deviation problem. Thus, in this paper, a fuzzy PI controller is proposed to enhance the automatic generation control system (AGC) against step disturbance, dynamic disturbance, and wind energy disturbance in a single area system. The proposed controller is initialized by using Equilibrium Optimization and proved its superiority through comparison with a classical PI optimized base. Results show that the fuzzy PI controller can reduce the peak-to-peak deviation in the frequency by 30–59% under wind disturbance, compared to a classical PI optimized base. Moreover, a fuzzy PID controller is also proposed and EO initialized in this paper to compare with the PIDA optimized by several techniques in the two-area system. Results show that the fuzzy PID controller can reduce the peak-to-peak deviation in the frequency of area 1 by 30–50% and the deviation of frequency in area 2 by 13–48% under wave disturbance, compared to the classical PIDA optimized base

Optimal operation of microgrids with demand-side management based on a combination of genetic algorithm and artificial bee colony

An important issue in power systems is the optimal operation of microgrids with demand-side management. The implementation of demand-side management programs, on the one hand, reduces the cost of operating the power system, and on the other hand, the implementation of such programs requires financial incentive policies. In this paper, the problem of the optimal operation of microgrids along with demand-side management (DSM) is formulated as an optimization problem. Load shifting is considered an effective solution in demand-side management. The objective function of this problem is to minimize the total operating costs of the power system and the cost of load shifting, and the constraints of the problem include operating constraints and executive restrictions for load shifting. Due to the dimensions of the problem, the simultaneous combination of a genetic algorithm and an ABC is used in such a way that by solving the OPF problem with an ABC algorithm and applying it to the structure of the genetic algorithm, the main problem will be solved. Finally, the proposed method is evaluated under the influence of various factors, including the types of production units, the types of loads, the unit uncertainty, sharing with the grid, and electricity prices all based on different scenarios. To confirm the proposed method, the results were compared with different algorithms on the IEEE 33-bus network, which was able to reduce costs by 57.01%.Web of Science1411art. no. 675

Novel and accurate mathematical simulation of various models for accurate prediction of surface tension parameters through ionic liquids

Ionic Liquids (ILs) as a novel class of liquid solvent simultaneously carry the positive characteristics of both molten salts and organic liquids. Remarkable positive properties of ILs have such as low vapor pressure and excellent permittivity have encouraged the motivation of researchers to use them in various applications over the last decade. Surface tension is an important physicochemical property of ILs, which its experimental-based measurement has been done by various researchers. Despite great precision, some major shortcomings such as high cost and health related problems caused the researchers to develop mathematical models based on artificial intelligence (AI) approach to predict surface tension theoretically. In this research, the surface tension of two novel ILs (bis [(trifluoromethyl) sulfonyl] imide and 1,3-nonylimidazolium bis [(trifluoromethyl) sulfonyl] imide) were predicted using three predictive models. The available dataset contains 45 input features, which is relatively high in dimension. We decided to use AdaBoost with different base models, including Gaussian Process Regression (GPR), support vector regression (SVR), and decision tree (DT). Also, for feature selection and hyper-parameter tuning, a genetic algorithm (GA) search is used. The final R2 -score for boosted DT, boosted GPR, and boosted SVR is 0.849, 0.981, and 0.944, respectively. Also, with the MAPE metric, boosted GPR has an error rate of 1.73E-02, boosted SVR has an error rate of 2.35E-02, and it is 3.36E-02 for boosted DT. So, the ADABOOST-GPR model was considered as the primary model for the research

A resilience-oriented bidirectional anfis framework for networked microgrid management

This study implemented a bidirectional artificial neuro-fuzzy inference system (ANFIS) to solve the problem of system resilience in synchronized and islanded grid mode/operation (during normal operation and in the event of a catastrophic disaster, respectively). Included in this setup are photovoltaics, wind turbines, batteries, and smart load management. Solar panels, wind turbines, and battery-charging supercapacitors are just a few of the sustainable energy sources ANFIS coordinates. The first step in the process was the development of a mode-specific control algorithm to address the system’s current behavior. Relative ANFIS will take over to greatly boost resilience during times of crisis, power savings, and routine operations. A bidirectional converter connects the battery in order to keep the DC link stable and allow energy displacement due to changes in generation and consumption. When combined with the ANFIS algorithm, PV can be used to meet precise power needs. This means it can safeguard the battery from extreme conditions such as overcharging or discharging. The wind system is optimized for an island environment and will perform as designed. The efficiency of the system and the life of the batteries both improve. Improvements to the inverter’s functionality can be attributed to the use of synchronous reference frame transformation for control. Based on the available solar power, wind power, and system state of charge (SOC), the anticipated fuzzy rule-based ANFIS will take over. Furthermore, the synchronized grid was compared to ANFIS. The study uses MATLAB/Simulink to demonstrate the robustness of the system under test

Review on unidirectional non-isolated high gain DC-DC converters for EV sustainable DC fast charging applications

Modern electrical transportation systems require eco-friendly refueling stations worldwide. This has attracted the interest of researchers toward a feasible optimal solution for electric vehicle (EV) charging stations. EV charging can be simply classified as Slow charging (domestic use), Fast charging and Ultrafast charging (commercial use). This study highlights recent advancements in commercial DC charging. The battery voltage varies widely from 36V to 900V according to the EVs. This study focuses on non-isolated unidirectional converters for off-board charging. Various standards and references for fast off-board charging have been proposed. Complete transportation is changed to EVs, which are charged by the grid supply obtained by burning natural fuels, contributing to environmental concerns. Sustainable charging from sustainable energy sources will make future EV completely eco-friendly transportation. The research gap in complete eco-friendly transit is located in interfacing sustainable energy sources and fast DC EV charging. The first step towards clean, eco-friendly transportation is identifying a suitable converter for bridging the research gap in this locality. A simple approach has been made to identify the suitable DC-DC converter for DC fast-charging EVs. This article carefully selected suitable topologies derived from Boost, SEPIC, Cuk, Luo, and Zeta converters for clean EV charging applications. A detailed study on the components count, voltage stress on the controlled and uncontrolled switches, voltage gain obtained, output voltage, power rating of the converters, switching frequency, efficiency obtained, and issues associated with the selected topologies are presented. The outcome of this study is presented as the research challenges or expectations of future converter topologies for charging