Doaa Khalil Ibrahim

Using recent information and communication technologies for monitoring and management initiates a revolution in the smart grid. These technologies generate massive data that can only be processed using big data tools. This paper emphasizes the role of big data in resolving load forecasting, renewable energy sources integration,
and demand response as significant aspects of smart grids. Meters data from the Low Carbon London Project is investigated as a case study. Because of the immense stream of meters’ readings and exogenous data added to load forecasting models, addressing the problem is in the context of big data. Descriptive analytics are developed using Spark SQL to get insights regarding household energy consumption. Spark MLlib is utilized for predictive analytics by building scalable machine learning models accommodating meters’ data streams. Multivariate polynomial regression and decision tree models are preferred here based on the big data point of view and the literature that ensures they are accurate and interpretable. The results confirmed the descriptive
analytics and data visualization capabilities to provide valuable insights, guide the feature selection process, and enhance load forecasting models’ accuracy. Accordingly, proper evaluation of demand response programs and integration of renewable energy resources is accomplished using achieved load forecasting results.

Interconnected transmission systems are increasingly spreading out in HV networks to enhance system efficiency, decrease reserve capacity, and improve service reliability. However, the protection of multi-terminal lines against Broken Conductor Fault (BCF) imposes significant difficulties in such networks as the conventional distance relays cannot detect BCF, as the BCF is not associated with a significant increase in current or reduction in voltage. Traditionally, the earth fault relays in transmission lines may detect such fault; Nonetheless, it suffers from a long delay time. Moreover, many of the nearby earth fault relays detect the BCF causing unnecessary trips and badly affecting the system stability. In this article, a novel single-end scheme based on extracting transient features from current signals by discrete wavelet transform (DWT) is proposed for detecting BCFs in interconnected HV transmission systems. The suggested scheme unit (SSU) is capable of accurately detecting all types of BCFs and shunt high impedance faults (SHIFs). It also adaptively calculates the applied threshold values. The accurate selectivity in multi-terminal lines is achieved based on a fault directional element by analyzing transient power polarity. The SSU discriminates between internal/external faults effectively utilizing the time difference observed between the first spikes of aerial and ground modes in the current signals. Different fault scenarios have been simulated on the IEEE 9-Bus, 230 kV interconnected system. The achieved results confirm the effectiveness, robustness, and reliability of SSU in detecting correctly BCFs as well as the SHIFs within only 24.5 ms. The SSU has confirmed its capability to be implemented in interconnected systems without any requirement for communication or synchronization between the SSU installed in multi-terminal lines.

In fast-growing isolated microgrids (IMGs), load frequency control (LFC) ensures optimal power quality for end
users. Stochastic grids, notably with renewable energy resources (RESs), require robust and intelligently designed LFC schemes. Thus, this research presents a novel cascade-loop controller combining a fractional order proportional derivative with a filter and a fractional order-proportional tilt integral derivative (FPDN-FPTID)
to improve LFC for single and multi-area IMGs. Recent dandelion optimization adjusts FPDN-FPTID controller
settings. Anti-windup keeps the controller out of the non-linear zone for low inertia IMGs. It concerns various
sources’ maximum generating rates. The two-area IMG model shows its potential and scalability. Extensive
MATLAB/Simulink simulations show that the FPDN-FPTID controller outperforms numerous published controllers, either single or cascade-loop, in minimum error criteria, undershoots/ overshoots/settling times, frequency, and tie-line power deviation following load and RES variations. Finally, the sensitivity study indicates
the suggested controller stabilizes the system despite ±25 % parameter changes.

Load frequency control (LFC) is vital for isolated microgrids (IMGs), especially when uncertain renewable energy sources (RESs) are present. Enhancing LFC schemes relies mainly on three tracks. Adding new resources to IMG structures is the first track, designing novel controller structures for LFC schemes is the second, and the third is improving controller tuning procedures in the LFC schemes. This research suggests an innovative multi-objective formula (MOF) for controller tuning that combines a novel error criterion termed the integral-square time absolute error of frequency change with the integral-square of IMG controllers' signals. Tested IMG includes multi-sources of diesel engine generators, fuel cells, battery energy storage technologies, and RESs (like photovoltaic and wind turbines). The proposed MOF tuning is evaluated compared to four different objective functions, which are the integral-absolute error (IAE), integral-time absolute error (ITAE), integral-square error (ISE), and integral-time square error (ITSE). The proportional-integral-derivative (PID), fractional-order, and cascade PID controllers are implemented to appraise the proposed MOF extensively against all these single objectives. Statistical analyses are accomplished comprehensively to verify the effectiveness of the artificial rabbits' optimization algorithm (ARO) in competition with other recent optimization algorithms to tune different controllers utilized in the LFC schemes of the examined IMG based on tested objectives. Therefore, ARO is applied to optimize controller settings by combining system nonlinearities with IMG sources' maximal generation rate constraints. The comparative analysis considers settling times, overshoots, IAE, ITAE, ISE, and ITSE performance indices. MATLAB/Simulink simulations confirmed the ability of the suggested MOF tuning to stabilize the system and keep improving performance indices, significantly attaining the minimum settling time even for massive three-type load fluctuations. The first type is step disturbances ranging between 0.1 and 0.25 p.u, the second is varying step disturbances every 5 s, and the third is severe dynamic random load shifts from −0.2 to 0.2 p.u. In addition, the MOF outperforms other competitors' tuning formulas while adding fluctuations of RESs with load disturbances. Furthermore, the robustness analysis is conducted for the applied controllers based on the proposed MOF tuning approach by changing the IMG nominal parameters with ±25 % and adding system nonlinearities. The analysis ensured its efficacy in preserving system stability. Finally, the stability test in the frequency domain using the MATLAB/control design tool verified system stability when different LFC controllers were tuned based on the proposed MOF tuning.

In rapidly expanding isolated microgrids (IMGs), load frequency control (LFC) should ensure optimal power
quality for end users. In particular, renewable energy sources (RES) require robust and intelligently designed LFC systems for their stochastic nature. This study presents a novel single-loop
controller that combines a fractional order-proportional beside a tilt integral derivative with fractional order (FPTID) to improve the LFC of multi-source IMGs. Diesel generators, fuel cells, battery storage devices, and RES (solar and wind power generation) are included in the evaluated IMG. Recent geometric mean optimization adjusts FPTID controller parameters. Extensive MATLAB/Simulink simulations reveal that the FPTID controller outperforms numerous previously published controllers regarding the minimum error criteria, undershoots/overshoots/settling times, and frequency deviation in response to load and RES variations.

The extracted power from wind is clean, plentiful, and completely renewable. All over the world, researchers keep looking for the best layouts of wind parks to maximize captured energy. To design wind farms suitably, forecast their performance, and understand the strain loads of wind turbines, there is a persistent need to catch a perfect wake model. Wind turbine wakes are one of the most vital factors in the meteorology of wind power due to reducing the power production and the necessity to raise the downstream capacity of wind turbines. This study is divided into two main aspects: firstly, enhancing the optimal layout for the wind turbines at a farm using Marine Predators Algorithm (MPA). The Jensen wake model is applied to get the extracted power for each turbine, which is one of the mutual analytic models used to reach the optimized layout. By comparing the performance of the proposed algorithm with the previous studies achieved by several techniques, the obtained results revealed that the MPA achieves promising results. Secondly, the proposed algorithm is applied for four sites in Egypt as the fraction of occurrence for the selected locations has been adequately calculated using wind speed over five years.

Isolated microgrids (IMGs) power remote areas. However, IMG may lower the frequency stability and increase frequency excursions with low system inertia. Load frequency management ensures system stability. Thus, the paper proposes a novel multi-objective tuning strategy to improve IMG's load frequency control (LFC) and take the microgrid controller's control signals into account. Diesel engine generator, fuel cell, battery energy storage system, and renewable energy sources (RESs) like photovoltaic and wind systems make up the IMG. Conventional controllers such as proportional-integral (PI) and proportional integral derivative (PID) are classically tuned based on the standard error criteria as a traditional single-objective tuning approach. Due to the low inertia of the system and the stochastic nature of RES, they cannot act as required under different operating scenarios. Therefore, the PI and PID controllers are tuned using the proposed multi-objective-based tuning approach to reduce the frequency deviations. In addition, anti-windup is applied to the enhanced classic controllers to keep them distant from the nonlinear zone and beyond the source's physical constraints. The proposed tuning process also considers the maximum practical generation rates for different sources. The recent Artificial Rabbits Optimization (ARO) algorithm is applied to simultaneously adjust the controller parameters for several controlled sources in IMG. Extensive simulations in MATLAB and Simulink confirm the effectiveness of the proposed approach to keep the system stable even when facing high levels of disturbances. In addition, accomplishing sensitivity analysis, severe +-25% changes to the system's parameters guarantee that the proposed tuning strategy keeps the system stable.

Converting from a complete fossil fuel energy system to a decarbonized one is crucial to mitigating climate
change and protecting human health. Hybrid energy sources are better than producing energy from a single
technology. The combination of renewable energy and fuel generators allows users to cover seasonal fluctuations of resources and protects them from the unpredictability of fuel prices and supply. Nonetheless, the large-scale industrial demand presents a real challenge due to its consistency throughout the day and the intermittent nature of renewable sources. This research proposes a novel approach for optimal hybrid energy decarbonization for any demand type in general and industrial demand in particular. The proposed framework is developed by integrating the Hybrid Optimization of Multiple Energy Resources (HOMER) for the simulations of Hybrid Configurations (HCs), the Electrical Transient Analysis Program (ETAP) for the stability studies, and the Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) method to rank the resulting configurations.
An industrial oil and gas complex with high wind and solar resource availability is adopted as a study case. It is
located in Abu Rudeis, Egypt, and currently utilizes only 11 Gas Turbine Generators (GTGs) to generate electricity. Five different HCs are investigated, including PV and wind systems. The proposed approach considers technical, environmental, economic, and socio-political criteria, with a total of 21 sub-criteria, and reveals that incorporating a wind farm of nine 2-MW wind turbines with the GTGs is the Optimal Hybrid Configuration (OHC).

Nowadays, the challenging errand is enhancing the wind energy system (WES) performance to be more competitive and economically viable. One of the best ways to enhance the performance of the doubly-fed induction generator (DFIG)-based-WES is the optimization of the proportional-integral (PI) controllers for the variable frequency converter system. Many objectives with different optimization techniques have been used in literature to achieve optimal performance. Each choice has its advantages and disadvantages. This paper presents a new design approach for better performance of PI controllers and, hence DFIG over a wide range of operating conditions through two main themes. The first is by introducing a new multi-objective formulation, while the second is utilizing recent optimization techniques like Grey Wolf Optimizer and Whale Optimization Algorithm. Four PI controllers are optimized using a traditional objective function and the proposed multi-objective formulation. Two are related to the Rotor Side Converter (RSC), named power regulator, and the main rotor side converter current regulator. The other two PI controllers related to Grid Side Converter (GSC) are the DC-link voltage regulator and the main grid-side converter current regulator. A performance comparison is held through normal and abnormal operating conditions on a simulation model of a 6 MW wind farm located in Jabal Alzayt along the Red Sea Coast in Egypt and directly connected to the grid. The results confirmed the effectiveness of the proposed approach to help the DFIG-based-WES to agree with the Egyptian Grid Code during disturbances compared with the traditional objective formulation.

In Egypt, the wind market increases quickly to make it one of the top countries in the Middle East. This
study discusses the viability of wind resources and the economic assessment for four locations in Egypt:
Ras El-Hekma, Farafra, Nuweiba, and Aswan through two stages. In the first stage, the optimal hub height
for some wind turbines has been calculated by using Equilibrium Optimizer (EO) algorithm to achieve
maximum wind energy with overall minimum cost. The second stage, the economic assessment has been
evaluated by using such turbines to calculate the cost of energy (COE) compared to the global and
Egyptian production costs of wind energy. Developed MATLAB programs are applied for statistical analysis
of wind data. The results have shown that Ras El-Hekma’s average wind speed is higher than other
sites and its wind energy potential is the best. Moreover, the economic assessment for selected locations
turns out that Ras El-Hekma by using EWT-DW61/22 turbine has the lowest COE.

Static Synchronous Series Compensator (SSSC) allows dynamic control capabilities of transmitted power. Unfortunately, inserting SSSC in transmission lines (TLs) disturbs impedance-based distance relays as their directionality and reachability are affected by overreaching or underreaching. This paper proposes an approach for protecting double-circuit TLs compensated with SSSC. It relies on the centralized wide-area protection architecture to calculate a proposed driven index: the rate of change of positive sequence active power difference. Phasor measurement units (PMUs) at TL ends estimate voltage and current phasors to calculate the positive sequence active power at TL ends and send them to the system protection center that evaluates the index and gives the trip decision or not. Ensuring the sensitivity, dependability, and security of the approach is essential. So, the overall scheme integrates two other algorithms. One is based on the polarities of the incremental power at both ends to determine whether the fault is internal or external. The other evaluates the phase angle of the integrated impedance to deactivate the power swing-blocking function for fault detection. The scheme's effectiveness is validated comprehensively through extensive simulation tests for fault and system conditions. The results show that the proposed approach is fast, secure, selective, and reliable.

This paper introduces a novel online adaptive protection scheme to detect and diagnose broken bar faults (BBFs) in induction motors during steady-state conditions based on an analytical approach. The proposed scheme can detect precisely adjacent and non-adjacent BBFs in their incipient phases under different inertia, variable loading conditions, and noisy environments. The main idea of the proposed scheme is monitoring the variation in the phase angle of the main sideband frequency components by applying Fast Fourier Transform to only one phase of the stator current. The scheme does not need any predetermined settings but only one of the stator current signals during the commissioning phase. The threshold value is calculated adaptively to discriminate between healthy and faulty cases. Besides, an index is proposed to designate the fault severity. The performance of this scheme is verified using two simulated motors with different designs by applying the finite element method in addition to a real experimental dataset. The results show that the proposed scheme can effectively detect half, one, two, or three broken bars in adjacent/non-adjacent versions and also estimate their severity under different operating conditions and in a noisy environment, with accuracy reaching 100% independently from motor parameters.

Nowadays, integrating large scale renewable energy sources, like solar PV parks, raises challenges for Load Frequency Controllers (LFC). The output of PV varies continuously, which requires a robust LFC deals
logically without continuous tuning and parameters optimization. In this paper, a fuzzy logic controller (FLC) is proposed to act as the main LFC instead of the traditional proportional–integral–derivative (PID) controller. The dynamic performance of FLC is enhanced by optimizing its parameters for different cost functions using particle swarm optimization technique (PSO). Another two FLCs will be added to PV system to act as an output controller instead of maximum power point tracker (MPPT) to enhance the overall system performance. To increase system reliability, a fast active power source called redox flow battery (RFB) is added in the proposed model as a frequency stabilizer. RFB can deeply discharge up to 90% with theoretically limitless number of duty cycles and has fast time response for severe load changes. The
importance of these proposed controllers side by side with RFB is to avoid disconnecting solar parks during
heavy cloudy days while preserving on maximizing its output during these periods. The superiority of the proposed FLC is examined by evaluating its performance compared to another control approach called PID-P (PID controller with P controller in the inner feedback loop). Finally, a comprehensive sensitivity analysis is also presented to investigate the controller robustness for extensive changes in power system parameters and loading.

Motors are the higher energy-conversion devices that consume around 40% of the global electrical generated energy. Induction motors are the most popular motor type due to their reliability, robustness, and low cost. Therefore, both condition monitoring and fault diagnosis of induction motor faults have motivated considerable research efforts. In this paper, a comprehensive review of the recent techniques proposed in the literature for broken bar faults detection and diagnosis is presented. This paper mainly investigates the fault detection methods in line-fed and inverter-fed motors proposed after 2015 and published in most relevant journals and conferences. The introduced review has deeply discussed the main features of the reported methods and compared them in many different aspects. Finally, the study has highlighted the main issues and the gaps that require more attention from researchers in this field.

With the widespread use of variable speed drives, a robust scheme that can detect and diagnose bearing faults under fixed and variable speed conditions becomes essential for reliable operation. Unfortunately, most of the reported methods in the literature are dedicated to working under fixed speed and will face challenges under variable speed conditions. Besides, most of them require detailed bearing information that may be unavailable in the real world. Therefore, in this paper, a new scheme is proposed for bearing faults detection and diagnosis under fixed and time varying speed conditions. The proposed scheme is based on the analysis of vibration signals using the persistence spectrum that can provide images rich with health-related features largely independent from rotating speed. Then, the produced image is compared with priorly stored images of the persistence spectrum of a healthy case. This comparison is performed using the multi-scale structural similarity index, which is a robust basis for images comparison without the need for training or expert knowledge. The obtained index is compared against an adaptive threshold for fault detection. Upon detecting a fault, the persistence spectrum image is compared with that of stored different fault types for fault diagnosis. The proposed scheme is extensively validated using three experimental datasets under different speed conditions. The results show that it can detect bearing faults in an earlier stage without the need for bearing specifications or shaft speed. Moreover, it can successfully diagnose bearing faults severity with accuracy reaching 100% with the minimum required data.

The distance protection in transmission networks is vulnerable to malfunction during a power swing. Distance relays have a power swing blocking (PSB) function that prevents the relay from operating during a power swing. However, during a power swing, the relay will be able to identify and clear any fault. This paper offers a scheme for detecting symmetrical/unsymmetrical faults through power swing by estimating the maximum rate of change of a (DI- DV) ellipse circumference.
The ellipse circumference of each phase is constructed at each power frequency cycle to consistently tracking the distinction in the measured current and voltage differences between the line’s two ends. The main feature of the suggested work compared to previous works is the ability to detect numerous high impedance faults (HIFs) during single mode (slow/fast) power swing or multi-mode power swing in addition to the opportunity to detect faults during asymmetrical power swing. The suggested scheme is tested in a two-area, four-machine power system and tested also in the standard three-machine, nine-bus system using ATP software. The results reveal that the improved protective scheme performs well in fault conditions with and without power swing, and they confirm the scheme’s suitability for interconnected networks.

This paper introduces a protection scheme for interconnected networks based on proposed Directional Overcurrent Relays (DOCRs) with user-defined two-level characteristics. By getting usage of the capabilities available in modern digital DOCRs, the proposed relay will have two user-defined characteristics; one for its primary operation and another for its backup operation (two-level characteristics) to fit a specific application or system. The coordination between the proposed relays is formulated and solved as a non-linear optimization problem to minimize their operating time and reduce the thermal impact caused by short circuit currents through electrical equipment while maintaining the technical constraints.
Extensive comparative studies have been performed to ensure the effectiveness of the proposed protection
scheme. Firstly, the performance of the traditional one-level characteristic relay (COLC) with two settings is
compared to the conventional two-level characteristic relay (CTLC) with three settings. Then a further investigation is carried out by suggesting increasing the number of settings to seven, named as the user-defined two-level characteristic relay (UDTLC), and then to nine settings, named as the shifted-user-defined two-level characteristic relay (SUDTLC). Finally, different multi-objective functions with proper weighting factors are investigated to determine the most effective one with the best performance for the proposed idea.
The distribution portion of the IEEE 30-bus system has been used to test and verify the proposed characteristics extensively. The optimal coordination problem is solved using the fmincon function in MATLAB. Based on the achieved results, the proposed characteristics of UDTLC and SUDTLC guaranteed a considerable reduction in operating times. In addition, the achieved results deduced that using a different multi-objective formulation has little impact on reducing operating time due to using the proposed characteristics UDTLC and SUDTLC, which means solving the coordination problem is mainly dependent on the applied characteristics.

Car exhaust is one of the most common causes of ozone hole aggravation, electrical vehicles (EVs) represent a promising solution to avoid this problem. Despite the benefits of EVs, their random charging behavior causes some difficulties regarding the electric network performance, such as increased energy losses and voltage deviations. This paper aims to achieve the proper scheduling of the EVs charging process, avoid its negative impacts on the network, and satisfy the EVs users’ requirements. The EVs charging process is formulated as an optimization problem and solved using particle swarm optimization. The optimization problem formulation considers the EV arrival and departure times and the state of charge required by the user. Different strategies such as separated, accumulated, and ranked strategies with continuous or interrupted fixed charging have been applied to solve the uncoordinated EVs charging problem. These strategies are extensively tested on the modified IEEE 31 bus system (499-node network), using the combination of both Open DSS and MATLAB m-files. The simulation results confirm the effectiveness of the proposed accumulated ranked strategy with interrupted fixed charging in improving the overall power system performance. The achieved improvements include minimizing: the peak power consumed, the peak power losses, and the voltage drop. Moreover, the cost of the EVs charging in most of the feeders has been decreased to a satisfying value. A comparison between the proposed strategy and some previously reported strategies has been performed to ensure the technical and economic enhancement of the proposed strategy.

Power swing is a power system transient phenomenon that arises due to several reasons including line switching, line outage, sudden increment or decrement in load, faults, etc. Unnecessary tripping during power swing and unnecessary blocking for faults occur during power swing result in distance relay maloperation. Several cascaded outages and major worldwide blackouts have occurred due to maloperation of distance relays. This paper proposes a technique for supervising distance relays during power swing. The proposed
online technique discriminates real faults and power swing accurately. It relies on constructing a locus diagram for the current and voltage differences (ΔI-ΔV) between the two ends of the protected line. The locus is estimated at every power frequency cycle to continuously monitor the state of the line by utilizing the synchrophasor measurements at the sending and receiving ends of the line. The proposed technique is tested for two-area, four-machine power system under faults at different locations of zone-1 and zone-2 regions of distance relays, fault resistances, fault inception angles and slip frequencies using MATLAB software. The simulation results proved the superior improvement of distance relay performance for handling power swing blocking and unblocking actions.

This paper proposes an adaptive dynamic Mho distance relay based on a phase comparator scheme for protecting interconnected transmission networks compensated with a Thyristor Controlled Series Capacitor (TCSC). The proposed relay uses an impedance index factor to initiate the fault detection subroutine. The RMS of the positive sequence current of the faulted loop and the TCSC terminal current are compared for TCSC zone identification. A phase comparator for ground and phase distance elements is proposed, relying on the positive sequence voltage as a polarized memory quantity, while the operating and polarizing quantities are developed using estimated TCSC impedance to mitigate its negative impact. The proposed scheme is easy in implementation and independent on synchronized data transfer, as minimum communication requirements are needed. To evaluate the performance of the proposed scheme, extensive simulation studies were carried out on an IEEE9 bus system compensated with TCSC for different firing angles covering four modes of TCSC operations, different fault types, and fault locations. In addition, an IEEE-39 bus network, as a large interconnected system, is tested for validation purposes. The achieved results designate the precision of the proposed scheme. Moreover, the results indicate its effectiveness for fault resistance tolerance, close-in three phase faults, and stable power swing phenomenon compared with conventional relays.

Most of the published research studies for detecting induction motor broken bar faults (BBFs) use a time–frequency (t − f ) decomposition tool to characterize the fault-related components. However, the identification and the assessment of these components in (t − f ) domain require skilled user or powerful pattern recognition technique. Moreover, a relatively long starting duration is necessary. This article introduces an automated scheme to detect BBFs and distinguish fault severity in induction motors under startup conditions regardless of the user experience and even under short starting duration and in a noisy environment. This scheme is based on the analysis of the starting current using optimized Stockwell transform (ST). An active set algorithm is applied to maximize the energy concentration of the left-side harmonic (LSH) component. Then, an adaptive time–frequency filter is applied to extract the LSH component from the (t − f ) domain, where the energy of the right part of LSH (RLSH) is utilized as an effective index for BBFs detection and for discriminating BBFs severity. Both real experimental data and simulation-based tests on 0.746- and 11-kW motors are used to extensively verify the performance of the proposed scheme. The achieved results have ensured that the proposed scheme can achieve a high accuracy with the minimum data and shortest acquisition time in comparison with some recent methods in the literature.

A two-fold adaptive dynamic quadrilateral relay is developed in this research for protecting
Thyristor-Controlled Series Compensator (TCSC)-compensated transmission lines (TLs). By
investigating a new tilt angle and modifying the Takagi method to recognize the fault zone identifier,
the proposed relay adapts its reactive reach and resistive reach separately and independently. The
investigated tilt angle and identified fault zone use the TCSC reactance to compensate its effect
on the TL parameters and system homogeneity. Excessive tests are simulated by MATLAB on the
non-homogenous network, IEEE-9 bus system and further tests are carried out on IEEE-39 bus
system in order to generalize and validate the efficiency of the proposed approach. The designed
trip boundaries are able to detect wide range of resistive faults under all TCSC modes of operations.
The proposed approach is easy to implement as there no need for data synchronization or a high
level of computation and filtration. Moreover, the proposed adaptive dynamic relay can be applied
for non-homogeneity systems and short as well as long TLs which are either TCSC-compensated or
-uncompensated TLs.

Thyristor controlled series compensation (TCSC) is widely used in long transmission lines to mainly improve power transfer capability. However, TCSC produces complicated impedance that negatively affects distance protection operation. The wind energy generation system produces additional complexity to the distance protection performance due to the variation of wind speed and fault current level. This paper proposes an integrated scheme to change adaptively the settings of the Mho distance protection by shifting the relay characteristics considering the bad impacts of TCSC, wind power and fault resistance. The proposed scheme achieves its main stages starting from fault detection, until relay tripping decision procedure including online estimation for preliminary fault location, impedance of TCSC and fault resistance using limited communication requirements. By extensive MATLAB simulations, the performance of the proposed scheme is examined compared with the conventional Mho relays under different fault locations, fault inception angle, fault resistance, different wind power penetration, different wind speeds and different TCSC ring angles. The achieved results ensured that the proposed scheme improves significantly Mho distance relay operation and avoids under-reaching and over-reaching problems irrespective of the large shunt capacitance along the transmission line, and also without identifying the parameters of TCSC such as the capacitance, the inductance or the ring angle.

Optimizing the Photovoltaic (PV) hosting capacity (HC) considering the irradiance’s variability properly and the load during the day represents a critical matter. Typically, the high PV HC results in overvoltage and high voltage fluctuations at the point of common coupling (PCC) with the utility. In this paper, a new algorithm is proposed for enhancing PV HC by considering the smart inverter functions to overcome key PCC issues. The Volt-Var and dynamic reactive current functions of the smart inverter are suggested to increase the PV HC. Furthermore, the time of use tariff is utilized for mitigating the duck curve issue at the utility by reducing the peak to valley difference of the substation net load curve. Quasi-static timeseries simulations are performed using the OpenDSS program to prove the effectiveness of the proposed algorithm. The proposed algorithm is validated by extensive numerical analysis on the standard IEEE 123 node test feeder. Deduced outcomes are very encouraging in mitigating the overvoltage, reducing the energy losses, limiting the considerable number of on-load tap changes, and alleviating the high voltage fluctuations.

Hybrid energy storage system has essential priority in Electric Vehicle applications. Therefore, the design of an appropriate power sharing algorithm among energy storage components is necessary to improve battery thermal performance and provide extra extension of battery lifetime cycles. This paper presents an analytical study on the effect of using wavelet decomposition-based power sharing algorithm to force the high frequency component to be fed by the supercapacitor and accordingly reduces the thermal stress on the battery. The proposed approach was investigated by applying it on electric vehicle model in ADVISOR Tool/MATLAB using different driving profiles such as Urban Dynamometer Driving Schedule profile, Highway Fuel Economy Test, New York City Cycle, Los Angeles 1992 and new European driving cycle. The results declare that by using proposed power sharing algorithm, the working temperature of lithium battery decreases significantly while battery lifetime cycles increase, apparently. For urban dynamometer driving schedule, the operating temperature of lithium battery is improved much at maximum decomposition levels reaching to only 25.6 °C compared to 35 °C. In addition, the battery lifetime cycles increased from 2213 to 2585 cycles. Neural Networks pattern recognition tool is also applied to classify the driving cycle to the nearest reference cycles chosen to represent the different driving conditions which help to detect the appropriate wavelet decomposition level, achieving better battery thermal performance and battery lifetime cycles.