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Due to the non-linear nature of most of the loads, there is a greater demand for reactive power in the distribution network. Further, it wasted high energy on distribution and dropped the bus voltages. The integration of the distribution static synchronous compensator (DSTATCOM) mechanism into the power distribution sector increases load capacity, compensates reactive power, and reduces power losses while enhancing the voltage profile and stability. This research utilized the improved bacterial foraging search algorithm (IBFA) for the optimum sizing and placement of the DSTATCOM in the distribution sector to minimize power losses and improve voltage stability and profile. The power flow model is developed using the back-forward load flow technique. The outputs obtained are compared with those from the conventional bacterial foraging algorithm (BFA) and the base case scenario. The optimum size of DSTATCOM computed is 3697.3kVAr with the optimal placement at bus 19. The percentage drop in power losses obtained with the installation of DSTATCOM using an improved bacterial search algorithm (IBFA) is 49.021% as compared to the base case scenario. Similarly, the IBFA approach resulted in a 12.8% enhancement in voltage stability and a 3.32% improvement in voltage profile as compared to the base case scenario. The results obtained clearly indicate the efficiency and applicability of the applied IBFA technique to the optimum placing and sizing of DSTATCOM in the radial power distribution sector for minimizing power loss and improving the voltage profile and stability.

The transformer health index (HI) concept has been used as an important part of management resources and is implemented for the state assessment and ranking of Power transformers. The HI state is estimated based on many power transformer oil parameters. However, the main problem in the HI procedure as a diagnostic method is the presence of routine measurements and accurate test results. The power transformer HI prediction is carried out in this work using 1361 dataset samples collected from two different utilities. The proposed model is used to predict and diagnose the HI state of the power transformer by using a convolutional neural network (CNN) approach. The imbalance between the training dataset sample classes produces a good prediction of the class with a major number of samples while a low detection of the class has a minor number of samples. The oversampling approach is used to balance the training samples to enhance the prediction accuracy of the classification methods. The proposed CNN model predicts the HI of the power transformers after applying the oversampling approach to the training dataset samples. The results obtained with the proposed CNN model are compared with those obtained with the optimized machine learning (ML) classification methods with the superiority of the CNN results. Feature reductions are applied to minimize testing time, effort, and costs. Finally, the proposed CNN model is checked with uncertain noise in full and reduced features of up to ±25% with a good prediction diagnosis of the power transformer HI.

While much attention was given in the literature to improve the fault ride through (FRT) capability of a doubly-fed induction generator (DFIG)-based wind energy conversion system (WECS) during various faults at the grid side, not much attention was given to investigate and improve the performance of DFIG-WECS under ferroresonance conditions. This paper investigates the impact of ferroresonance on the overall performance of a DFIG-WECS. The paper also presents a new technique to improve the high voltage ride through capability of the investigated system under such condition. The proposed technique relies on incorporating a superconducting coil within the dc-link of the DFIG grid-side and rotor-side converters. Energy exchange between the coil and the system is controlled through regulating the duty cycle of the dc-chopper interfacing the coil with the dc-link. In this regard, a simple but yet effective proportional-integral (PI) controller is employed to regulate the dc-chopper duty cycle. Parameters of the PI controller are optimized using elephant herding optimization algorithm. Simulation results attest the effectiveness of the proposed controller in improving the overall performance of a DFIG-WECS during ferroresonance events.

Worldwide, the emerging trend of hybrid renewable energy sources integration in modern power systems is increasing due to privileged prices and clean electricity supply. However, the optimal planning of rural hybrid systems is a challenging and complex task, especially when different alternatives and sustainability aspects are considered. This paper develops an integrated decision-making approach for the optimal planning of a 100% renewable energy supply system comprising solar, wind, hydro, and biomass sources in a rural area located in Pakistan. An hourly-based design optimization analysis of twelve on/off-grid electrification alternatives is performed. The optimization model simultaneously addresses five sustainability criteria related to economy, reliability, ecology, society, and topography aspects. Furthermore, a novel hybrid decision-making model has developed to identify the unique best configuration with on-grid and off-grid options. The proposed model combines fuzzy analytic hierarchy process, multi-objective optimization based on ratio analysis technique for order of preference by similarity to an ideal solution, and evaluation based on distance from average solution methods. The results reveal that the solar-hydro-biomass battery with a life cycle cost of 10.9 M$ is the top-ranking off-grid system. When the hybrid system is connected to the grid, the solar-hydro-battery has found the most appropriate design with a life cycle cost of 12.96 M$. Both scenarios have a negligible capacity shortage of 0.09%. Ecologically, the optimal off-grid system produces only 408.37 kg/yr of CO2 due to the significant energy share of solar and hydro sources (99.3%). The optimal on-grid system produced the minimum CO2 with 29,177.89 kg/yr compared to other alternatives. Also, employing the optimal on/off-grid designs require land area and jobs of 96.6 m2, 14 jobs, and 118 m2, 15 jobs, respectively. Overall, the developed approach with the presented case study offers a valuable benchmark and guidelines for investors and stakeholders to create realistic investment plans for the energy industry looking to push efficient inducements to encourage the high dissemination of renewables.

Solar photovoltaic (PV) is the most promising renewable energy source available on Earth. Three topologies based on a switched-inductor capacitor and non-isolated high-step-up Cuk converter have been proposed for solar PV. These topologies of the Cuk converter have higher boosting ability than conventional Cuk and boost converters and can reduce the voltage stress of the main switch. A small voltage rating and on-state resistance can give higher efficiency of the converter. The voltage boosting ability of all three topologies was compared to each other and with a conventional Cuk converter. The boosting capability of the third converter was 11 times at 0.75 duty cycle with a solar PV source. These converters do not use a coupled inductor and transformer, which leads to less volume, reducing coupling/core saturation loss, and thus the cost of the converter. A solar PV system of 12 volts was used for boosting with these converters for analysis of the feasibility of use with renewables. The three topologies of the switched-inductor and switched-capacitor (SLSC) Cuk converter were designed and simulated in MATLAB/Simulink to evaluate their effectiveness.

In this paper, a wind turbine service sustainability is achieved by assessing grounding grids under steady-state and lightning-based transient conditions. An effective grounding grid is designed concerning IEC61400-24 standard, where Taif city, Saudi Arabia is considered a farming place. However, high soil resistivity is a challenge during the design. To accomplish the study, apparent resistivities are measured using Wenner method, in the field. A framework of multi-layer soil modeling is performed using the Sunde model, and it is optimally solved using the genetic algorithm. Using the optimal stratified soil, the wind turbine grounding system is assessed considering grounding resistances, electric potentials, and step voltages. The break-point effect is evaluated considering the designed grounding system. Then, the lightning-based transient study is investigated for both the healthy grid and break-point considerations. COMSOL Multiphysics and ATP/EMTP were used to accomplish simulations and modeling. The results ensure the effectiveness design of the assessed grounding system.

In this paper, maintenance process for on-load tap changer (OLTC) would be enhanced using nanofluid that developed by adding nanoparticles (titanium dioxide, TiO2) to the insulating oil. The occurrence of the internal arcs inside OLTC resulted in the formation of carbon atoms on the inner bonding points during its operation. Over time, these points became unusable since the contact resistance value was high. To increase the efficiency of the maintenance process, infrared radiation was used to excite the additive TiO2 atoms to attract carbon atoms. Electrical tests were performed before and after the proposed maintenance process according to IEC standards. The results showed significant differences in the maintenance process with and without nanoparticles. A prediction model to identify the OLTC state was constructed based on support vector machine classifier. The results revealed the ability of the predicted model to predict the OLTC state with accuracy 100%.

The prediction of fault type in transformers at an early stage is an important aspect for power system reliability. Checking the transformer status begins with the dissolved gas analysis (DGA) test. Rogers' four-ratio and IEC 60599 standard methods are commonly DGA-based techniques that used to detect the transformer faults. These methods are very easy to implement, but their diagnostic accuracies are very poor. The target of this work is to build a new PSO-FS optimization platform identifying the optimal limits of gas ratios and corresponding rules for each fault type to enhance the diagnostic accuracies of Rogers' four-ratio and IEC 60599 code methods. In the proposed platform, PSO optimization technique specifies the gas ratio limits and corresponding rules where the fuzzy system identifies the fault types. Based on the results of the optimization process, the new codes that used to diagnose the fault types were developed. The performance of the proposed approach investigated using 481 collected datasets. The overall accuracies of the Rogers' four-ratio and IEC code models enhanced from 47.19 and 55.09 to 85.65 and 85.03, respectively. However, when datasets due to mixtures of faults (samples with undetermined or misidentified faults) are removed from the calculations, the accuracies of the PSO-FS methods are not significantly better than when using existing DGA methods.

The transformer fault diagnosis based on dissolved gas analysis is greatly affected by the uncertainties existing in measured data during oil sampling, handling and storage. This work aims to develop an efficient code matrix based on dissolved gas percentages for accurate fault diagnosis considering measurement uncertainties. Fuzzy system is utilized to produce the rules that map the limits of gas ratios for different fault types. Each gas percentage range is divided into three regions represented by three fuzzy memberships. The fuzzy system is then developed to relate the gas percentages to the fault type. The membership limits are then optimized by using heuristic algorithms in order to maximize the accuracy. Hybrid Grey Wolf Optimization (HGWO) algorithm is utilized to produce the diagnostic code matrix. The proposed code limits the impact of measurement uncertainties on the output fault diagnosis. Different levels of measurement uncertainties up to 20% are considered to validate the effectiveness of the new code in improving the diagnostic accuracy. The accuracies during the training and testing processes attained 97.45% and 95.45%, respectively, with the maximum uncertainty level of 20%. Moreover, randomly selected case studies representing various fault types are investigated using the proposed method. For each case study, various levels of uncertainties are imposed on the original data. The proposed method proved its easiness towards diagnosing transformer faults and robustness against measurement uncertainties.

This paper presents the results of research and development of capacitive-based sensors of rotating shaft vibration for fault diagnostic systems of powerful turbines and hydro generators. It showed that diagnostic systems with special sensors are the key to increasing the reliability of powerful turbines and hydro generators. The application of sensors in monitoring systems was considered, and the requirements for the sensors used were analyzed. Structures of concentric capacitive-based sensors of rotating shaft vibration based on the measurement of the capacitance value from the distance to the metal surface were proposed. The design scheme was created for determining electrode dimensions of the rotating shaft vibration capacitive-based sensors with concentric electrodes, and analytical dependences were obtained. The calculation results allow the selection of optimal parameters of the active and guard electrodes. Analytical and computer simulation methods determined the response functions of the capacitive sensors. Analytical calculations and simulation results using 3D FEM were used to find the response functions of the sensors. The calculation of the characteristics of the capacitive-based sensors of rotating shaft vibration is presented. The study of the influence of fringe effects was carried out using the obtained results of the modeling and analytical calculations.

In recent studies, the competitiveness of the Newton-S-Iteration-Process (Newton-SIP) techniques to efficiently solve the Power Flow (PF) problems in both well and ill-conditioned systems has been highlighted, concluding that these methods may be suitable for industrial applications. This paper aims to tackle some of the open topics brought for this kind of techniques. Different PF techniques are proposed based on the most recently developed Newton-SIP methods. In addition, convergence analysis and a comparative study of four different Newton-SIP methods PF techniques are presented. To check the features of considered PF techniques, several numerical experiments are carried out. Results show that the considered Newton-SIP techniques can achieve up to an eighth order of convergence and typically are more efficient and robust than the Newton–Raphson (NR) technique. Finally, it is shown that the overall performance of the considered PF techniques is strongly influenced by the values of parameters involved in the iterative procedure.

This article proposes an intelligent control strategy to enhance the frequency dynamic performance of interconnected multi-source power systems composing of thermal, hydro, and gas power plants and the high penetration level of wind energy. The proposed control strategy is based on a combination of fuzzy logic control with a proportional-integral-derivative (PID) controller to overcome the PID limitations during abnormal conditions. Moreover, a newly adopted optimization technique namely Arithmetic optimization algorithm (AOA) is proposed to fine-tune the proposed fuzzy-PID controller to overcome the disadvantages of conventional and heuristic optimization techniques (i.e., long time in estimating controller parameters-slow convergence curves). Furthermore, the effect of the high voltage direct current link is taken into account in the studied interconnected power system to eliminate the AC transmission disadvantages (i.e., frequent tripping during oscillations in large power systems–high level of fault current). The dynamic performance analysis confirms the superiority of the proposed fuzzy-PID controller based on the AOA compared to the fuzzy-PID controller based on a hybrid local unimodal sampling and teaching learning-based optimization (TLBO) in terms of minimum objective function value and overshoots and undershoots oscillation measurement. Also, the AOA’s proficiency has been verified over several other powerful optimization techniques, differential evolution, TLBO using the PID controller. Moreover, the simulation results ensure the effectiveness and robustness of the proposed fuzzy-PID controller using the AOA in achieving better performance under several contingencies, different load variations, the high penetration level of the wind power, and system uncertainties compared to other literature controllers adjusting by various optimization techniques.

The increase in industrial and commercial applications of photovoltaic systems (PV) has a significant impact on the increase in interest in studying the improvement of the efficiency of these systems. Estimating the efficiency of PV is considered one of the most important problems facing those in charge of manufacturing these systems, which makes it interesting to many researchers. The difficulty in estimating the efficiency of PV is due to the high non-linear current–voltage characteristics and power–voltage characteristics. In addition, the absence of ample efficiency information in the manufacturers’ datasheets has led to the development of an effective electrical mathematical equivalent model necessary to simulate the PV module. In this paper, an application for an optimization algorithm named Wild Horse Optimizer (WHO) is proposed to extract the parameters of a double-diode PV model (DDM), modified double-diode PV model (MDDM), triple-diode PV model (TDM), and modified triple-diode PV model (MTDM). This study focuses on two main objectives. The first concerns comparing the original models (DDM and TDM) and their modification (MDDM and MTDM). The second concerns the algorithm behavior with the optimization problem and comparing this behavior with other recent algorithms. The evaluation process uses different methods, such as Root Mean Square Error (RMSE) for accuracy and statistical analysis for robustness. Based on the results obtained by the WHO, the estimated parameters using the WHO are more accurate than those obtained by the other studied optimization algorithms, furthermore, the MDDM and MTDM modifications enhanced the original DDM and TDM efficiencies.

The hybrid microgrid system is considered one of the best solution methods for many problems, such as the electricity problem in regions without electricity, to minimize pollution and the depletion of fossil sources. This study aims to propose and implement a new algorithm called improved heap-based optimizer (IHBO). The objective of minimizing the microgrid system cost is to reduce the net present cost while respecting the reliability, power availability, and renewable fraction factors of the microgrid system. The results show that the PV/diesel/battery hybrid renewable energy system (HRES) gives the best solution, with a net present cost of MAD 120463, equivalent to the energy cost of MAD 0.1384/kWh. The reliability is about 3.89%, the renewable fraction is about 95%, and the power availability is near to 99%. The optimal size considered is represented as 167.3864 m2 of PV area, which is equivalent to 44.2582 kW and 3.8860 kW of diesel capacity. The study results show that the proposed optimization algorithm of IHBO is better than the artificial electric field algorithm, the grey wolf optimizer, Harris hawks optimization, and the original HBO algorithm. A comparison of the net present cost with a different fuel price is carried out, in which it is observed that the net present cost is reduced even though its quantity used is mediocre.

This paper proposes an effective algorithm; this algorithm is updating the phase-locked loop (PLL) gains according to the value of the short circuit ratio that will decrease the weak grid effect in the PLL controllers to reduce problems related to renewable energy sources and connect to the point of common coupling (PCC). Based on system parameters estimation, the proposed algorithm enhances the system stability for the distribution networks. The standard IEEE 33-bus test system is used to investigate the proposed algorithm’s effectiveness and superiority. An extensive mathematical effort is exerted to get the system’s small-signal stability (SSS) model. The tuning of the phase-locked loop and the current control loop are choosing to dynamically change according to the system variables to ensure the same system stability margin, whatever the change in the loading conditions. The MATLAB/Simulink model is used to validate the algorithm and simulate the test system.

The development of a new dissolved gas analysis (DGA) method often requires a comparative study to assess the accuracy of the proposed technique. This is faced with the following challenges: (i) the time and effort required to implement and validate the implementation of existing DGA methods, adds to the comparative study cost; (ii) the output states of different DGA methods are not similar, which makes it difficult to put methods side by side in a comparative study; and (iii) the availability of test data is limited. In this study, a user-friendly graphical user interface software package, DGALab, is developed to overcome these challenges. DGALab implements a unified DGA diagnosis framework to map the output states of DGA methods to uniform specifications. DGALab includes a library implementing most common DGA techniques, and includes a repository for input datasets available in the literature and collected directly from laboratories. DGALab simplifies the addition of new DGA techniques written in virtually any programming language. As a result, the process of developing a new DGA technique is greatly simplified using DGALab. To evaluate the software package results, the datasets and methods implemented therein were used to regenerate the results published in earlier research papers.

In recent years, optimal sizing and location of reactive power resources are drawing much attention to help the operators of the utilities to enhance the power system operations. Therefore, this work presents a new version of grey wolf optimiser (GWO) to solve the problem of optimal reactive power resources sizing for power system operation enhancement. The proposed method which called improved grey wolf optimiser (IGWO) can be derived by modifying the exploration–exploitation balance in the conventional GWO to enhance its rate of convergence. Also, the weighted distance strategy is employed in the proposed IGWO to overcome the drawback of the conventional GWO. Optimal reactive power resources sizing problem is non-linear and non-convex optimisation problem. To solve this problem, different objective functions are used. These objective functions are minimisation of generating cost, minimisation of transmission power loss and voltage profile improvement. The validity and superiority of the proposed IGWO method are tested using three standard IEEE systems for normal and contingency conditions. Then the results are compared with those obtained from other recently published algorithms. The simulation results show that the proposed IGWO method is more accurate and efficient than other recently published algorithms.

This paper proposes a new simple technique to detect and discriminate the abnormal states of the grid-connected photovoltaic (PV) solar system based on the rate of change of voltage and current trajectory. The design of the PV system is developed by implementation of only one diode in every PV string. The diode prevents the reverse direction of the fault current in case of faulty strings. On the other hand, the installation of a diode reduces the ability of faults detection and diagnosis depending on currents in each string. The proposed technique depends on the rate of change of voltages and current trajectory during the fault transient period. The proposed algorithm can detect various fault cases such as cell-to-cell and string to string faults in addition to partial and full shadow faults. Also, the algorithm differentiates between high and low fault cases for each fault types. Moreover, the proposed technique is characterized by high sensitivity for internal abnormal states within certain array with high security level for the external abnormal conditions and normal load changing. The proposed technique is applied for a four-array PV system, which has a power rating of 400 kW connected to an AC grid. The simulation results and validation of the proposed technique are implemented by MATLAB/Simulink toolbox. The proposed technique is experimentally applied for a small PV system of a rating of 1.25 kW to prove its validity.

Clean and renewable energy sources are the preferable power system generations for the overall world. This research aims to present a very highly integrated, economic, professional, and simple construction, clean and natural resources usage of the renewable hybrid generation system. This research performs analysis, systematic representation, evaluation, and design of the hybrid proposed system—pico-hydraulic from home usage water and photovoltaic (PV)—to generate an optimal renewable generation system using a new professional control system. Applying this proposed technique in Egypt shows that the hybrid system successfully overcame Egypt’s energy crisis. Renewable energy will rise to 8.782% by increasing 7.323% (14,408.83 GWh/Y). Besides, this system increases the power supply reliability; it gives an additional emergency supply and reduces the exhausts from other generation stations (e.g., CO2). The saving from this hybrid system is very effective for; the residential sector (subscribes), which will be ranged from 9599.298 million E£/10Ys up to 86,393.68 million E£/10Ys that equals 5399.6 million $, government to use this extra generation energy to reduce the maximum loads from various stations. A practical model has been presented with results to verify the high efficiency of the proposed system that illustrates the effective performance of the used hybrid system.

This paper is interested in implementing and controlling a modified six-phase induction motor (MSPIM) when fed from a three-phase supply either via an inverter or with a direct grid connection loaded by a centrifugal pump. The main aims of using the MSPIM are to enhance motor reliability and reduce torque pulsation. A three-to-six phase transformer has been designed, implemented, and employed to enable the SPIM to be driven from a three-phase supply. It is preferable to use the three-to-six phase transformers integrated with three-phase inverter on using the six-phase inverter to generate lower values of harmonics and lower steady-state error of speed and reduce the starting current and because also it isolates the primary circuit from the secondary, and the cost will be lower compared to the design of a special six-phase inverter. Dynamic models of SPIM, three-to-six phase transformer, and three-phase variable speed drive are derived. Then, a scalar (V/F) closed-loop control of SPIM is employed, and the results are discussed. Fine-tuning of PID controllers is used to keep the motor speed tracking the reference value. A low pass filter is connected to reduce the ripple of voltage and current waveforms. An experimental setup has been built and implemented to check the possibility of controlling SPIM by a variable speed drive system fed from a three-to-six phase transformer. It is found that the proposed method can be effectively used to drive the SPIM from a three-phase supply.

The continuous rise of global electricity demand and significant dependency on fossil fuel-based centralised power plants are the main indicators of increasing greenhouse gas emissions, thus negatively affecting climate change and human health and increasing the earth temperatures. This alarming situation has demanded the transition to 100 % renewable energy to decarbonise energy use. However, the fluctuating weather resources, and high investment cost are the major challenges with renewable energy implementation. In this context, the combined integration of multiple sources with energy storage in a so-called hybrid renewable energy system was developed as a durable remedy for the previous issues. This paper proposes a joint and conceptual approach for techno-economic design and dynamic rule-based power control of an off-grid solar/wind hybrid renewable energy system integrated with a hybrid energy storage system that comprises a lithium-ion battery, lead-acid battery, and a supercapacitor. Such concurrent integration of 100 % renewable energy systems and hybrid energy storage systems is lacking in the existing literature. First, with the aid of HOMER software, the feasibility and optimisation analysis of nine different configurations was performed in 1 min resolution to find the optimal component sizes. Second, MATLAB/Simulink models were assembled for the winning design based on a dynamic rule-based strategy to investigate and analyse the system’s dynamic response, power equilibrium, DC-bus voltage supervision and load voltage/frequency control against instantaneous and dynamic changes in the load or renewable energy resources. The proposed approach was promoted and validated on an actual case study for isolated residential community electrification in Saudi Arabia, in which a multi-tier framework was adopted to accurately simulate the stochastic energy consumption of the community’s households. From the design results, the hybrid renewable energy system, which integrates solar, wind, lead-acid batteries, and converter with optimal capacities of 55 kW, 18 kW, 325 kWh and 42 kW converter, respectively, is the most cost-effective alternative with the minimum net present and energy costs of $232,423.3 and $0.3458/kWh, respectively. The system has the least unmet load with 13.5 kWh/years (0.02 %), thus ensuring maximum reliability and customer satisfaction. Meanwhile, the developed control strategy efficiently improved the dynamic response, the DC-bus voltage stability, and the load voltage/frequency during different climatological and load interruptions. Further, the use of lead-acid batteries and supercapacitors has effectively diminished the maximum overshoot of the DC-bus and load voltages by 50 % during all disturbances. In addition, the integration of supercapacitors proficiently saved 2.3–6 kW during wind speed change and 2–9 kW during simultaneous changes in the radiation and load. Overall, the presented method, together with the reported results, provides an improved understanding and highlights the extent to which the successful integration of hybrid renewable energy system and hybrid energy storage system can be used to develop reliable and sustainable energy access for off-grid areas.

The variable performance of the thyristor-controlled series compensator (TCSC) system represents a vital challenge for conventional distance relays. Most existing algorithms are based on the measured data at two ends of the TCSC-compensated lines. However, some of single-end algorithms need to modify the distance relay for the series capacitor (SC) only. This paper introduces a generalized algorithm to estimate the voltage drop across the TCSC. The distance relay uses the estimated TCSC voltage drop for terminal voltage compensation. The second objective of the proposed algorithm is to estimate the conduction angle of the thyristor-controlled reactor (TCR) branch under different modes of operation. This algorithm is effective for different TCSC modes such as the capacitive or the inductive modes, in addition to the method of firing angle of synchronization current or voltage synchronization modes. The main benefit of the proposed algorithm is presenting a parameter-less distance scheme for TCSC-compensated transmission line. A 400-kV overhead transmission line test system is simulated in MATLAB/SIMULINK package® and the numerical modeling is implemented in the MATLAB® script in order to evaluate the proposed algorithm effectiveness. The proposed technique is tested under several stress conditions that may face the conventional distance relay. The results illustrate the effective performance of the proposed algorithm.

In this study, a conditional probability scheme is designed to diagnose transformer incipient faults based on the percentage of five dissolved gases (hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), and acetylene (C2H2)) with respect to their summation. Based on the fault features, the point probabilities of each fault type occurrence and non-occurrence are computed. Then, the conditional probability of certain fault occurrence is estimated to specify the probabilistic indication of each fault type occurrence. Multivariate normal probability density function is considered to point out the conditional probability of the fault occurrence. It is implemented in different scenarios to define the fault type (partial discharge, low energy discharge, high energy discharge, low thermal, medium thermal, and high thermal) where the best scenario is selected. The proposed technique has the merits of simplicity and ease of implementation. It is assessed through the analysis of 403 dissolved gas sample dataset collected from the Egyptian electric utility as well as from credited literatures. The proposed probabilistic technique is evaluated in comparison with other methods in literatures. Also, it is validated against uncertainty in DGA data up to 20%. The results reveal the ability and reliability of the proposed technique for transformer fault diagnosis.

In this study, an evolutionary strategy is presented to get the optimal grounding grid design to minimise the design cost, the touch and step voltages of the grounding grid to ensure the economical constraint as well as the safety conditions. The proposed technique is used for one and two-layer soil. This technique is based on the comparison between the quality factors of the initial grid dimension according to the cost, touch and step voltages with the other quality factors obtained from new generations (offspring) of the grid dimensions. Effects of top layer thickness, number of grid meshes, vertical rods number and length, and the soil resistivity of the top and bottom layers on grounding resistance (R g) and earth surface potential are studied. Different case studies are introduced to evaluate the performance of the proposed technique and to provide the knowledge about the grid dimensions that is substantial for the economical design of the grounding grids.

Dissolved Gas Analysis (DGA) is one of the most common techniques to detect the incipient faults in the oil-filled power transformers. In this paper, a new approach of DGA technique is proposed to overcome the conflict that takes place in the traditional interpretation techniques for transformer fault diagnosis. The new approach is based on the analysis of 386 dissolved gas samples data set that collected from the Egyptian electric utility chemical laboratory as well as from credited literatures. These data sets are used to build the technique model and also as a tested data set to get the technique’s accuracy. The new approach DGA diagnoses the transformer fault types based on the gas concentration percentage limit of the sum of main five gases (Hydrogen (H 2 ), Methane (CH 4 ), Ethan (C 2 H 6 ), Ethylene (C 2 H 4 ), and Acetylene (C 2 H 2 )) and some suggested gases ratios depending on the sample data set analysis. The validation of the proposed approach of DGA technique is satisfied by comparing its results with the results of the IEC Standard Code, Duval triangle and Rogers methods for the collected data set. The results refer to the ability and reliability of the new approach in transformer faults diagnostic.

In this paper, a smart drive system of the induction motor (IM) is proposed and adapted for applications in electric vehicles (EVs). Objectively, the EV drive systems are robust over wide speed and torque ranges. The proposed drive system is independent of encoder (encoderless) and concerned with the torque control drive (TCD) and indirect rotor field-oriented control (IRFOC) using the sliding mode observer (SMO). This arrangement of monitoring system and control techniques are smartly integrated for the IM applications in EVs. The encoderless technique utilizes SMO to estimate the stator current, rotor flux angle, and rotor speed. The SMO is verified in motoring mode at very low and zero speed conditions. The accelerator pedal is utilized for TCD to generate the reference torque required to accelerate the EV by the driver. The rotor flux angle is estimated based on IRFOC method. The laboratory waveforms illustrate the robustness of the encoderless control of the IM-based torque control drive system in electrical vehicle applications at very low speed using SMO. The laboratory waveforms prove the validity of SMO with encoderless control of a smart dive system of the IM in EV applications under load torque/speed variations.

Dissolved gas analysis (DGA) is used to diagnose power transformer fault based on the concentration of dissolved gases and the ratios between them. These gases are generated in oils as a result of electrical and thermal stresses, but these DGA techniques cannot identify the severity of the fault types. In IEEE Standard C57.104, the maintenance action is taken based on the total dissolved combustible gases, which is not sufficient because it ignores the importance of the gas type and its change rate. Thermodynamic theory using different starting decomposing materials, namely, n-octane (C8H18) and eicosane (C20H42), is used to estimate the severity of transformer fault types. Two scenarios are suggested with different data transformation techniques to enhance neural pattern-recognition (NPR) method accuracy for predicting transformer fault types and their severity classes. The proposed scenarios are built based on 446 samples collected from the laboratory and literature. Results refer to the role of the starting decomposing material on the severity of the transformer fault and illustrate that the proposed model has a higher accuracy (92.8%) compared with other DGA methods for diagnosing transformer fault types and superior accuracy (99.1%) to predict fault severity class.

This study proposes a new fault diagnosis algorithm for a photovoltaic (PV) solar system implemented on the bases of diode distribution and two classifiers based on ensemble bagged trees and neural pattern recognition techniques. The proposed approach is designed by adding two diodes connected to the upper and lower terminals of each PV string. The two diodes are connected to prevent the reverse direction of faulted string currents, thereby preventing high injecting fault currents. Given that the string fault current is prevented owing to diode interaction, this dynamic interaction provides new states during the fault periods considered to detect string faults. Owing to the distributed diodes, system voltage is not reduced during fault disturbances and voltage is not considered an input to the proposed detection techniques. Monitoring currents at the upper and lower ends of each string is required as input for the proposed approaches to detect string fault types. The proposed approaches are used to detect string fault types, such as cell-to-string negative terminal, cell-to-cell in the same string, and string-to-string faults. The proposed model is applied for 400 kW, and the PV system consists of 4 arrays interconnected with 1200 V AC grid built-in MATLAB/Simulink. Experimental results validate the newly suggested approaches.

Photovoltaic (PV) systems are subjected to lightning strikes that contribute to losing their sustainable electrification service. Furthermore, they are subjected to backflow lightning overvoltages due to the installation in high soil resistivity areas, however, such the study is not aforementioned well in the literature. Therefore, the analyses and reductions of backflow lightning overvoltages are investigated in this paper, considering a PV power plant as an example installed in Taif city, KSA. All PV plant components are modeled using high-frequency models, in which they are such as air-termination, grounding system, surge protective devices, PV string, inverters, underground cables, and power transformers. To decrease the lightning overvoltages in the PV power plant, a modified PV grounding system design is introduced and evaluated. The evaluation is performed considering step voltage profiles using COMSOL Multiphysics and backflow lightning overvoltage magnitudes at different points in PV plant using ATP/EMTP. The results provide the motivation of studying backflow lightning overvoltages in PV plants and evidence of the efficacy of the proposed grounding system design in reducing the overvoltage magnitudes.

The interconnections between different AC systems using HVDC can be carried out by different configurations: single-infeed (SIF), multi-infeed (MIF) and multi-terminal (MT) configurations. As it is necessary to model the different configurations of AC-DC systems, a generalized model suitable for both SIF, MIF, and radial MT configurations is presented in this paper. The Newton-Raphson method used for the AC load flow calculations is modified and implemented to be suitable for AC-DC power flow calculations for different AC-DC system configurations. The proposed model is implemented on different AC-DC system configurations using MATLAB script-file programs. The proposed procedure of considering the DC system as a voltage dependent load easily provides robustness DC effect implementation on the AC Jacobian matrix as well as fast and reliable solution. Such enhancement is approved via a widely evaluated comparative study.