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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 (C2 H6), ethylene (C2 H4), and acetylene (C2 H2)) 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. [ABSTRACT FROM AUTHOR]

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 (Rg) 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• PD activity was measured and analyzed for various defects in gas insulated switchgear. • PD diagnosis based on PD pulse sequence was proposed. • PD diagnosis was implemented using different machine learning classification techniques. • Naïve Bayes classification exhibited the highest diagnostic accuracy. Partial discharge (PD) diagnostics in Gas Insulated Switchgear (GIS) is important for reliable and secure operation of electrical utilities. Different techniques were used for PD diagnosis in GIS. In this work, PD diagnosis in GIS is proposed based on PD pulse sequence. PD pulse sequence only requires the measurement of PD phase appearance and its corresponding instantaneous voltage. The PD diagnosis of various defect types is implemented using five optimized machine learning classification techniques: decision tree classification, ensemble methods, k-nearest neighbouring, Discriminant analysis, and Naïve Bayes classification. The features used for PD pulse sequence are the voltage change and phase angle change between successive PD pulses. Three scenarios are proposed for predicting the defect types in GIS. The first scenario is built based on the extracted features for two successive PD pulses, the second scenario is built based on the extracted features for three successive PD pulses, while the last scenario is built based on the extracted features for four successive PD pulses. The results illustrate the superior detecting accuracy of the second scenario with the proposed five ML classification techniques. The optimized ML classification techniques are implemented and carried out based on MATLAB software package. The ensemble classification method exhibited the highest accuracy for PD-based diagnosis in GIS with an overall accuracy of 97.1%. [ABSTRACT FROM AUTHOR]

• A generalized algorithm to estimate the voltage drop across the TCSC is proposed. • The distance relay terminal voltage is compensated by the estimated TCSC voltage. • This algorithm is effective for TCSC capacitive or inductive modes. • The conduction angle of the TCR is estimated for the different synchronization modes. • The proposed adaptive distance relay is tested under different stress conditions. 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. [ABSTRACT FROM AUTHOR]

• Present thermodynamic model to identify the severity of the fault. • Construct NPR model to detect the transformer fault type with its severity. • High diagnosing accuracy is observed using NPR. 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 (C 8 H 18) and eicosane (C 20 H 42), 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. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the interruption capability with the aid of a parallel capacitor branch and exploits the single current direction through a series thyristor with the DC system. Two DC current interruptions are introduced by providing superimposed oscillatory currents. The first one is that the capacitor branch (capacitor and inductor) is continuously connected in parallel with the system where the capacitor is charged from the system during the normal operation. Due to fault occurrence, the capacitor branch including an inductor produces an oscillatory current that can make zero crossing through the series thyristor. However, the interrupting current is unfortunately after the second oscillatory peak current that the fault resistance can damp the oscillatory current. By changing the charged capacitor polarity, the second breaking concept is introduced depending on a precharged capacitor by the system voltage and then injecting the oscillatory current using a self‐triggered additional thyristor. The current interruption initiation is attained using the DC current raising. The interruption performance is evaluated using a thyristor‐based converter DC system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• A new technique is proposed to detect and classify the PV system fault states. • The technique based on mapping the rate of changes of voltage and current. • The design of the PV system is implemented by installing one diode in every string. • The proposed technique is verified by modelling and experimentally. • This technique is sensitive to internal and stable for external PV array faults. 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. [ABSTRACT FROM AUTHOR]

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 CO 2 due to the significant energy share of solar and hydro sources (99.3%). The optimal on-grid system produced the minimum CO 2 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. [Display omitted] • 100% renewable energy system is established for on/off grid rural electrification. • Twelve distinctive solar/wind/hydro/biomass-based hybrid energy alternatives are optimized. • An integrated fuzzy-AHP/TOPSIS/EDAS/MOORA decision-making model is proposed. • Cost, reliability, emission, social, and topographical criteria are simultaneously considered. • Results prove the long-term sustainability of 100% renewable in rural Pakistan. [ABSTRACT FROM AUTHOR]

• Model for the design and control of an off-grid 100% renewable system is proposed. • Nine alternatives of solar/wind/different storages are modelled and optimized. • Evaluation criteria are cost, emission, and load voltage/frequency constancy. • Solar/wind/lead-acid battery shows the most economic and reliable alternative. • Lead-acid battery/supercapacitor is more efficient than battery only. 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. [ABSTRACT FROM AUTHOR]

• The analyses and reductions of backflow lightning overvoltages of the PV power plants. • High-frequency models for all PV plant components (air-termination, grounding system, surge protective devices, PV string, inverters, underground cables, and power transformers) using the ATP/EMTP simulation software. • Design the PV grounding system using COMSOL Multiphysics. • Evaluate the PV grounding system toward decreasing the backflow lightning overvoltage magnitudes. 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. [ABSTRACT FROM AUTHOR]