Luqman Sulyman Maraaba

To operate photovoltaic (PV) systems efficiently, the maximum available power should always be extracted. However, due to rapidly varying environmental conditions such as irradiation, temperature, and shading, determining the maximum available power is a time-varying problem. To extract the maximum available power and track the optimal power point under these varying environmental conditions, maximum power point tracking (MPPT) techniques are proposed. The application of MPPT for extracting maximum power plays a crucial role in developing efficient PV systems. These MPPT techniques face several issues and limitations, particularly during partial shading conditions caused by non-uniform environmental conditions. Researchers have been focusing more on mitigating the partial shading condition in PV systems for the last few years due to the need to improve power output and efficiency. This paper provides an overview of MPPTs proposed in the literature for uniform and non-uniform environmental conditions broadly categorized as MPPT-based and circuit-based methods. The MPPT-based methods are classified as conventional, soft computing, and hybrid techniques. A critical analysis of each approach regarding tracking speed, algorithm complexity, and dynamic tracking under partial shading is discussed. The literature shows hybrid strategies provide fast-tracking speed and are efficient with a tracking efficiency of around 99% compared to conventional methods; however, their design and practical implementation are complex. This comprehensive review of MPPT methods aims to provide power utilities and researchers with a reference and guideline to select the best MPPT method for normal operation and partially shaded PV systems based on their effectiveness and economic feasibility.

The reliability of a power system is considered as a critical requirement in planning and operating the system due to the increasing demand for more reliable service with a lower frequency and duration of interruption. Hence, reliability is also considered as a major challenge in the development of future power systems as they become more advanced and complex, making the accuracy of the reliability assessment dependent on several factors such as supply and load modeling. Recent studies on power systems’ reliability and stability have focused on load modeling, where loads are either assumed to be static or dynamic, by introducing significant constraints. However, the emergence of new types of loads necessitates the development of models that can incorporate them with accuracy, as this would facilitate their effective use in flow and stability simulation studies, as well as reliability analyses. In this study, dynamic loads are modeled using a feed-forward neural network where a simulation test bed is developed in MATLAB/Simulink to generate operating data used during training and validating of the neural network model. Subsequently, Electrical Transient Analyzer Program (ETAP) software is used to verify the effect of load modeling on power system reliability assessment platform. Bus 2 of Roy Billinton Test System (RBTS) is employed as a case study to investigate the sensitivity of the reliability indices, such as System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI), on the load modeling technique with mixed loads (dynamics and statics).

Insulators are considered one of the most significant parts of power systems which can affect the overall performance of high-voltage (HV) transmission lines and substations. High-voltage (HV) insulators are critical for the successful operation of HV overhead transmission lines, and a failure in any insulator due to contamination can lead to flashover voltage, which will cause a power outage. However, the electrical performance of HV insulators is highly environment sensitive. The main cause of these flashovers in the industrial, agricultural, desert, and coastal areas, is the insulator contamination caused by unfavorable climatic conditions such as dew, fog, or rain. Therefore, the purpose of this work is to review the different methods adopted to identify the contamination level on high-voltage insulators. Several methods have been developed to observe and measure the contamination level on HV insulators, such as leakage current, partial disgorgement, and images with the help of different techniques. Various techniques have been discussed alongside their advantages and disadvantages on the basis of the published research work in the last decade. The major high-voltage insulator contamination level classification techniques discussed include machine learning, fuzzy logic, neuro–fuzzy interface, detrended fluctuation analysis (DFA), and other methods. The contamination level data will aid the scheduling of the extensive and costly substation insulator, and live line washing performed using high-pressured water. As a result, considerable benefits in terms of improved power system reliability and maintenance cost savings will be realized. This paper provides an overview of the different signal processing and machine-learning methods adopted to identify the contamination level on high-voltage insulators. Various methods are studied, and the advantages and disadvantages of each method are discussed. The comprehensive review of the islanding methods will provide power utilities and researchers with a reference and guideline to select the best method to be used for contamination level identification based on their effectiveness and economic feasibility.

Reducing costs and emissions, and improving efficiency in the electric power networks are becoming urgent. It is, therefore, necessary to apply improvements in the industry’s operation. A case in point is the current efforts to maintain acceptable consumer’s operational PF (PFopr) gauged against reference PF (PFref), where the penalties are levied on monthly averaged PFopr below PFref. The efforts can be enhanced if based on interactive involvement and participation between consumers and services providers (SP’s), mainly by the fair implementation of penalties and incentives. Enticing consumer participation enables mutual benefits. The current treatment of PF in Saudi Arabia is based on average monthly measurements of consumers’ PFopr over an extended period. In this spirit, a novel mathematical model and framework are presented which consist of a time-referenced function relating applicable tariff to PFopr, thus benefiting the SPs by reducing the capital and maintenance costs, providing flexibility to focus on the peak load periods, and rewarding incentives to the consumers maintaining PF in an acceptable range. The model was implemented on energy measurements at four industrial facilities for one year and the results were verified in terms of reduction in losses, and the resulting monetary benefits, and reduction in CO2 emissions.

Microgrids that are integrated with distributed energy resources (DERs) provide many benefits, including high power quality, energy efficiency and low carbon emissions, to the power grid. Microgrids are operated either in grid-connected or island modes running on different strategies. However, one of the major technical issues in a microgrid is unintentional islanding, where failure to trip the microgrid may lead to serious consequences in terms of protection, security, voltage and frequency stability, and safety. Therefore, fast and efficient islanding detection is necessary for reliable microgrid operations. This paper provides an overview of microgrid islanding detection methods, which are classified as local and remote. Various detection methods in each class are studied, and the advantages and disadvantages of each method are discussed based on performance evaluation indices such as non-detection zone (NDZ), detection time, error detection ratio, power quality and effectiveness in multiple inverter cases. Recent modifications on islanding methods using signal processing techniques and intelligent classifiers are also discussed. Modified passive methods with signal processing and intelligent classifiers are addressing the drawbacks of passive methods and are getting more attention in the recently published works. This comprehensive review of islanding methods will provide power utilities and researchers a reference and guideline to select the best islanding detection method based on their effectiveness and economic feasibility.

This paper investigates the technical performance of 230 kV field-aged composite insulators in the coastal region of Saudi Arabia. Two insulator samples removed from the 230 kV line after 20 years of service are assessed at KFUPM High Voltage Lab (KHVL). Different assessment techniques, such as visual inspection, contact angle, surface pollution severity, electrical withstand, and material characterization are utilized to evaluate the insulator performance. Equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) techniques are used for the surface pollution severity measurements. Clear views of surface changes and surface hydrophobicity conditions are displayed. Scanning electron microscope (SEM), energy dispersive x-ray spectrometry (EDX), and Fourier-transform infrared spectroscopy (FTIR-IR) techniques are utilized for the material characterization. The visual inspection reveals small and big cracks in the insulator sheds. Hard breakable portion areas, many shed cuts, changed color areas, and whitish parts are visually noticed in the insulator surface. Such changes confirm the aging condition effect of the insulator units. Furthermore, the samples are subjected to laboratory clean fog tests to check the electrical performance of the samples. Informative results are given for the current condition and performance for the 20-years, field-aged insulators. Additionally, the experimental results are presented for the rest of the insulator life period. Utilization of the existing line insulators has been evaluated. Finally, discussions and recommendations for the future handling of the insulators at the 230 kV line are highlighted.

This paper presents a novel stator inter-turn fault diagnosis method for Line Start Permanent Magnet Synchronous Motors (LSPMSMs) using the frequency analysis of acoustic signals resulting from asymmetrical faults. In this method, acoustic data are experimentally collected from a 1 hp interior mount LSPMSM for different inter-turn fault cases and motor loading levels, while including the background noise. The signals are collected using a smartphone at a sampling rate of 48,000 samples per second. The signal for each case is analyzed using fast Fourier transform (FFT), which results in the decomposition of the signal into its frequency components. The results indicate that, for both no-load and full-load conditions, 39 components are observed to be affected by the faults, whereby their amplitudes increase with the fault severity. The 40-turns fault shows the highest difference in the component amplitudes compared with the healthy condition acoustic signal. Therefore, this diagnostic method is able to detect the stator inter-turn fault for interior mount LSPMSMs. Moreover, the method is simple and cheap since it uses a readily available sensor.

Stator inter-turn fault diagnosis system for electric motors is of a considerable concern due to its significant effect on industrial production. In this paper, a new method for detecting the inter-turn fault and quantifying its severity in the line start permanent magnet synchronous motor (LSMPSM) is proposed. The new method depends on monitoring the stator current during steady-state period to detect the fault. The convolutional neural network (CNN) method is proposed to correlate the motor steady-state current with the status of the motor winding conditions and detect any presence of inter-turn faults. The data used in this study is extracted from both an experimental setup of a one-horsepower LSPMSM and the corresponding verified mathematical model through several testing cases under various loading conditions. One of the main features of the proposed technique is that it does not require separate feature extraction phase. The results indicate that the proposed technique is able to detect the inter-turn fault under different loading conditions varies from 0NM to 4NM with accuracy of 97.75% for all defined fault levels. The use of steady-state current for fault detection regardless of motor load enables the proposed technique to detect the fault online without disturbing the system functionality and reliability as well as without adding any extra hardware to the system.

This paper proposes an accurate diagnosing tool that can predict the stator inter-turn size in line start permanent magnetic synchronous motor (LSPMSM). The proposed diagnosing approach is developed based on an experimentally validated mathematical model of the motor under inter-turn fault. The developed model has been tested using MATLAB® under different loading and fault size conditions. Since the stator currents and voltages are easily accessible, it is decided to use them as the key signatures for developing the diagnostic tool. Several time and frequency-based features have been extracted using motor current and voltage waveforms under different loading and fault size conditions. The developed tool has been designed to correlate the extracted features with its corresponding size of stator inter-turn fault. Finally, testing of the developed diagnosis tool shows a high accuracy of 96% in detecting the size. Moreover, the proposed diagnostic tool is examined against motor parameter variations. The results confirm the robustness of the proposed approach where the accuracy is slightly affected under a wide range of motor parameter variations.