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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.

With the global trend to develop digital substation automation systems, measurement devices are required to be reliable, of small size and light weight and of acceptable accuracy in a wide frequency band. This article presents a combined high voltage direct current measurement method which comprises the above-mentioned features that makes it suitable for smart grids protection and control applications. The proposed measurement method utilizes Hall sensor array for dc current measurement. DC voltage is measured using a voltage divider circuit while the harmonic currents are measured using a square Rogowski coil made of four straight bars along with a high precision digital integration algorithm. A four-spectral line interpolation fast Fourier transform algorithm based on trapezoidal convolution window is proposed to improve the extraction accuracy of the dc and harmonic components from the measured signal. Experimental results show that the error variation of the proposed method is less than 0.098% for voltage measurement while it is less than 0.104% for current measurement. The harmonic measurement ratio error is less than 0.2% and the angle error is less than 8'.

Wind Turbine Generator (WTG) has become one of the most popular renewable-based power generation that is broadly connected to electricity grids worldwide. Till the year 2019, the total WTGs installed worldwide reached about 650.8GW. The WTG is interfaced to the electricity grid through power electronic converters with a proper control algorithm to facilitate a smooth power delivery as well as maintaining the system voltage and frequency stability during wind intermittency. However, power grids are usually subjected to load expansion which affects the stiffness of the grid and hence its stability. A weak electricity grid exhibits voltage instability that may affect the performance of WTGs and in some cases may lead to serious damages to the wind turbines and the entire system. In this paper, superconducting magnetic energy storage (SMES) technology based on fuzzy logic controller is implemented to effectively resolve this issue and improve the overall performance of WTGs. Hysteresis current and fuzzy logic-based control system is proposed to control the energy exchange between the SMES coil and the investigated system. Results show the effectiveness of the SMES to improve the overall system performance and along with the fault ride-through capability of the doubly-fed induction generator (DFIG).

A transmission line fault generates transient high frequency travelling waves (TWs) that propagate through the entire network. The fault location can be determined by recording the instants at which the incident waves arrive at various points in the network. In single end-based methods, the incident wave arrival time and its subsequent reflections from the fault point are used to identify the fault location. In heavily branched distribution networks, the magnitude of the traveling wave declines rapidly as it passes through multiple junctions that cause reflection and refraction to the signal. Therefore, detecting the first incident wave from a high impedance fault is a significant challenge in the electrical distribution networks, in particular, subsequent reflections from a temporarily fault may not be possible. Therefore, to identify a high impedance or temporary faults in a distribution network with many branches, loads, switching devices and distributed transformers, multiple observers are required to observe the entire network. A fully observable and locatable network requires at least one observer per branch or spur which is not a cost effective solution. This paper proposes a reasonable number of relatively low-cost voltage TW observers with GPS time-synchronization and radio communication to detect and timestamp the TW arrival at several points in the network. In this regard, a method to optimally place a given number of TW detectors to maximize the network observability and locatability is presented. Results show the robustness of the proposed method to detect high impedance and intermittent faults within distribution networks with a minimum number of observers.

Frequency Response Analysis (FRA) is the most reliable technique currently used to evaluate the mechanical integrity of power transformers. While the measurement devices have been well developed over the past two decades, interpretation of the FRA signatures is still challenging regardless of the several papers published in this regard. This paper adds an attempt to understand the power transformer FRA signatures through experimental and simulation analyses. In this context, experimental FRA measurements are conducted on a 33/11 kV, 30 MVA transformer under various faults, including winding deformation, the short circuit turns, loss of clamping, and bushing fault. At the same time, the high-frequency transformer model that comprises series capacitance, self-inductance, series resistance, and mutual inductance is simulated using MATLAB / Simulink to compare simulation and experimental results. The correlation between physical circuit parameters and various faults facilitates a better understanding of each fault’s effect on the FRA signature. To quantify the impact of such faults, correlation coefficient, the absolute sum of logarithmic error, standard deviation, and sum square error are calculated with respect to the healthy signature at three frequency regions. Results show that using statistical coefficients over three frequency ranges of the FRA signature facilitates better fault identification and quantification.

Power transformer is a critical and expensive asset in electric transmission and distribution networks. It is essential to monitor the health condition of all power transformer fleet in such networks to avoid unwanted outages. The health index (HI) is a quick and efficient way to assess the condition of power transformers based on multi-criteria. While Power transformer HI method has been well presented in the literature, not much attention was given to handle the uncertainty and reliability of this method due to unavailability of used data. Therefore, this paper aims to tackle this issue through employing Artificial Intelligence (AI)-based techniques to reveal the health condition of power transformers with high accuracy and at the same time handling data uncertainty. The proposed HI approach assesses the power transformer insulation system based on oil quality, dissolved gas analysis (DGA), and paper condition. In this regard, collected data from 504, 150-kV transformers are used to establish the proposed AI-models. Seven AI algorithms including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Artificial Neural Network (ANN), Adaptive Boosting (AdaBoost), and Decision Tree are investigated. A performance comparison of the proposed AI-based HI models is carried out using the scoring-weighting-based HI method as the reference. Results show that RF model provides the best performance in predicting power transformer HI with an accuracy of 97.3%.

Short-term irradiance variability because of the passing clouds of unknown size, direction, and speed is a key issue for power grid planners because of the unexpected fluctuation in the generated power of photovoltaic (PV) systems. In order to handle this issue, several models have been presented in the literature to estimate the variability of the PV systems output power during cloudy and partial shading events. However, the estimation error of all presented models in the literature is relatively high. To comply with the utility guidelines of limiting the PV systems generated power variability to a level less than 10% of the PV systems capacity per minute, a more accurate power estimation model is essential to precisely calculate the required energy storage backup. In this article, a new model to estimate the output power of a group of rooftop PV systems during cloudy events using only one sensor is proposed. In this regard, two new strategies are adopted. First, day time is divided into three-time segments in which each segment exhibits almost the same weather conditions. Second, the data of each time series are analyzed using the wavelet transform to divide them into high and low-frequency modes. The frequency modes are then used to train a gene expression programming model to estimate the entire generated power of the PV systems. The proposed model features higher accuracy than other models presented in the literature. A sensitivity analysis is performed to quantify the effect of various parameters on the accuracy of the proposed model whose robustness is validated through practical data collected from a group of distributed rooftop PV systems in Brisbane, QLD, Australia. Results reveal that the maximum average mean absolute error of the proposed model is 7.49%, whereas it is 12.12% for the existing models in the literature.

The penetration of photovoltaic systems (PVs) to existing power grids is increasing as they are considered attractive options for electricity generation in distribution networks. This paper focuses on estimating the total power generated by a group of neighbouring PVs, spread over a distribution network using a single pyranometer for measuring the solar irradiance. A new empirical-based model that employs the Gene Expression Programming (GEP) technique is proposed to correlate the distribution of the PVs and the irradiance measured by the pyranometer and estimate the total power generated by the PVs. The geographic variability reduction index has been considered in developing the proposed model that also employs a Wavelet Transform technique to enhance its accuracy. The effective performance of the proposed model is validated using real data collected by the Solar Project at the University of Queensland, Brisbane, Australia. Results reveal that the proposed technique yields more accurate results when compared with other existing approaches in the literature.

Current industry practice to identify fault location in transmission lines is based on visual inspection, travelling waves, and line impedance measurement. Unfortunately, these techniques are only developed to detect the fault location upon its occurrence without the ability to predict abnormal events that usually precede major faults and issue a timely warning signal to avoid potential consequences for power line failures. Furthermore, the current fault locating techniques exhibit some drawbacks that limit their wide practical implementation. This includes cost, access to required data, and low accuracy when employed for specific power line topologies. This study is aimed at presenting and validating a new cost-effective technique based on the line voltage–current characteristics to predict and identify the location of various abnormal and fault events in real-time. By measuring the currents and voltages at both ends of the line, a unique line fingerprint can be identified. Any change in this fingerprint can be detected and analysed by a software installed in the control centre in real-time to identify the location, type, and level of the abnormal events or emerging faults. Robustness of the proposed technique is assessed through simulation analysis conducted on various case studies along with a practical case study.

Winding deformation is a common problem that takes place within operating power transformers due to various internal and external conditions. Due to their progressive nature, winding deformations should be detected and rectified as soon as they emerge. Unfortunately, the only reliable technique to detect such faults at this stage is the frequency response analysis which is conducted offline. This has motivated researchers to propose alternative techniques to detect power transformer incipient winding and core deformations in real time. One of the promising online techniques proposed in the literature is the V – I Lissajous pattern method. While this technique was validated through simulation analysis, its feasibility for practical applications has not been explored yet. This paper takes a further step toward the field implementation of this technique through practical measurements on a custom-made transformer to assess its sensitivity, repeatability, and antinoise capability. Also, a new compensation approach is proposed to eliminate the effect of the transformer loading variation on the V – I Lissajous pattern. For the practical application of this technique, a hardware setup has been developed and installed to monitor the V – I characteristics of an operating power transformer within a 220-kV substation in real time.

Due to the low converters rating and cost of doubly fed induction generator (DFIG) along with its ability to function under variable wind speed, DFIG has been widely employed in wind energy conversion systems (WECSs). Unfortunately, the performance of DFIG is sensitive to the variation in the operating conditions and disturbance events at the grid side. This includes wind gust, voltage fluctuation and faults at the point of common coupling of the DFIG and the grid. In this study, a model-free adaptive control (MFAC) is developed for a unified power flow controller (UPFC) in order to improve the overall dynamic performance of a DFIG-based WECS during wind gusts and enhance the fault ride through capability of the DFIG during various disturbance events. The effective performance of the proposed controller is assessed through a comparison with a conventional proportional–integral (PI) controller optimised by a modified flower pollination algorithm. Results reveal the superiority of the proposed UPFC-MFAC technique over the conventional PI controller currently used in most of the UPFC-WECS applications.

Wind energy is one of the premier renewable energy sources that have gained popularity during the last decade. Among the currently available wind energy conversion systems (WECS), doubly fed induction generator-based technology has been widely employed due to its superior advantageous. The key features of the DFIG-based WECS include its ability to capture more wind energy and support the grid with large reactive power during short disturbance events. On the other hand, DFIG is very sensitive to grid faults which affect its fault ride through (FRT) capability. Furthermore, extreme wind gust even for short durations may lead to the violation of the low voltage ride through (LVRT) threshold limits set by worldwide transmission line operators. This situation cannot be mitigated by the turbine blades pitch controller since the response of the pitch mechanical control is much slower than the rapid dynamic change in the wind speed during short duration of wind gust events. While DFIG FRT capability has been discussed in several papers in the literature, not much attention was given to the mitigation of the effects of extreme wind gust of short duration on the DFIG performance. In this paper, the effect of various levels of wind gust on the performance of a DFIG-based wind energy conversion grid-connected system is investigated and mitigated using a new controller for superconducting magnetic energy storage (SMES) unit. A combination of hysteresis current and fuzzy logic controllers is employed to control the voltage source converter and the DC-DC chopper interfacing the SMES coil with the investigated system. Simulation results reveal the effectiveness of the proposed SMES controller that can be easily implemented within existing as well as new WECS installations.

Identification of the optimum location and capacity of Static Synchronous Compensator (STATCOM) in power grids has been given much attention to maximize its technical performance while minimizing operational cost. In this regard, a new multi-objective optimization model that minimizes power loss, enhances system stability and reduces operational cost of STATCOMs is presented in this paper. To overcome the issue of the harmony search (HS) optimization algorithm in solving high-dimensional multi-objective optimization problem, an improved differential evolution harmony search (DEHS) algorithm is proposed. In this algorithm, mutation and crossover operations are adopted instead of the original pitch adjustment operation adopted in the HS optimization algorithm, which enhances the algorithm global search ability. Moreover, opposition-based learning technique is incorporated to the process to broaden the diversity of variables and hence improving the search efficiency of the algorithm. The proposed algorithm is employed to identify the optimal allocation and sizing of multiple STATCOMs within the IEEE 30-bus system. Results reveal the superiority of the proposed optimization algorithm over the conventional multi-objectives adaptive harmony search algorithm.

In a large and complex interconnected power system with long transmission lines, the inadequate damping scheme for the inter-area oscillations may lead to system instability. This issue is commonly solved by employing a power system stabilizer (PSS) to increase the system damping. However, the use of PSS encounters some limitations that include voltage fluctuation and poor performance in damping significant oscillations that result due to three-phase faults. To improve the damping of the inter-area oscillation modes, a unified power flow controller (UPFC) along with the PSS is proposed in this paper. Simultaneous utilization of PSS and UPFC calls for proper coordination to attain an optimal damping performance. Hence, a method to obtain optimal coordination between the PSS and UPFC by employing the Firefly algorithm is proposed. To verify the effectiveness of the proposed method, the performance of the proposed damping scheme is analyzed through simulation analysis. Simulation results show the effectiveness of the proposed method in damping low as well as large oscillations in the power systems.

With the increase in doubly fed induction generator-based wind energy conversion systems (DFIG-WECS) worldwide, improving the fault ride-through (FRT) capability of the entire system has been given much attention. Enhancement of the FRT capability of a DFIG-WECS is conventionally realized by employing a flexible AC transmission system device with a proper control system. This paper presents a non-conventional method for the improvement of the FRT of DFIG-WECS, using a high-temperature superconducting coil interfaced with the DC-link of the rotor and stator side converters through a DC-chopper. A fractional-order proportional-integral (FOPI) controller is utilized to regulate the DC-chopper duty cycle in order to properly manage the power flow between the DC-link and the coil. Two optimization techniques, Harmony Search and Grey Wolf Optimizer, are employed to determine the optimum size of the superconducting coil along with the optimum parameters of the FOPI controller. The effectiveness of the two proposed optimization techniques is highlighted through comparing their performance with the well-known particle swarm optimization technique.

The Internet of Things (IoT) plays an indispensable role in present-day household electricity management. Nevertheless, practical development of cost-effective intelligent condition monitoring, protection, and control techniques for household distribution systems is still a challenging task. This paper is taking one step forward into a practical implementation of such techniques by developing an IoT Smart Household Distribution Board (ISHDB) to monitor and control various household smart appliances. The main function of the developed ISHDB is collecting and storing voltage, current, and power data and presenting them in a user-friendly way. The performance of the developed system is investigated under various residential electrical loads of different energy consumption profiles. In this regard, an Arduino-based working prototype is employed to gather the collected data into the ThingSpeak cloud through a Wi-Fi medium. Blynk mobile application is also implemented to facilitate real-time monitoring by individual consumers. Microprocessor technology is adopted to automate the process, and reduce hardware size and cost. Experimental results show that the developed system can be used effectively for real-time home energy management. It can also be used to detect any abnormal performance of the electrical appliances in real-time through monitoring their individual current and voltage waveforms. A comparison of the developed system and other existing techniques reveals the superiority of the proposed method in terms of the implementation cost and execution time.

Real-time estimation of transmission line (TL) parameters is essential for proper management of transmission and distribution networks. These parameters can be used to detect incipient faults within the line and hence avoid any potential consequences. While some attempts can be found in the literature to estimate TL parameters, the presented techniques are either complex or impractical. Moreover, none of the presented techniques published in the literature so far can be implemented in real time. This paper presents a cost-effective technique to estimate TL parameters in real time. The proposed technique employs easily accessible voltage and current data measured at both ends of the line. For simplicity, only one quarter of the measured data is sampled and utilized in a developed objective function that is solved using the whale optimization algorithm (WOA) to estimate the TL parameters. The proposed objective function comprises the sum of square errors of the measured data and the corresponding estimated values. The robustness of the proposed technique is tested on a simple two-bus and the IEEE 14-bus systems. The impact of uncertainties in the measured data including magnitude, phase, and communication delay on the performance of the proposed estimation technique is also investigated. Results reveal the effectiveness of the proposed method that can be implemented in real time to detect any incipient variations in the TL parameters due to abnormal or fault events.

Identification of fault location within transmission lines is essential in order to restore service rapidly, reduce unplanned outage time, minimise revenue loss, and improve network reliability. Several transmission line fault-locating techniques based on travelling waves, apparent impedance and direct circuit analysis have been introduced in the literatures. However, these methods are either impractical or too expensive to implement. In this paper, a new online technique is introduced to accurately detect the fault location within transmission lines. The technique relies on constructing a voltage-current ( V-I ) locus diagram that could be measured at every power frequency cycle to provide up-to-date condition state of the line. The technique does not call for special equipment as it utilises the existing metering or protection devices connected to the sending and receiving ends of the transmission line to monitor the voltage and current at both ends. To assess the robustness of the proposed technique, various types of faults have been simulated on transmission lines of various lengths and voltage levels using DIgSILENT PowerFactory software. Variations in load conditions, effect of fault resistance, different line configurations are also investigated. Results reveal the ability of the proposed technique to detect fault locations in transmission lines with high accuracy. In contrary with existing fault-locating techniques that are only employed to identify the location of faults after their occurrence, the proposed technique has the potential to detect high impedance faults. Thus an early warning signal can be issued to avoid any catastrophic consequences. The proposed technique is cost effective and easy to implement for new and existing transmission line installations.

Frequency response analysis (FRA) plays an important role in the assessment of the mechanical integrity and hence, reliability of high-voltage power transformers. Wireless power transfer (WPT) system introduced by Tesla a 100 years ago based on Faraday’s law of electromagnetic induction is similar to a conventional power transformer. Therefore, the reliability of a large WPT system can also be evaluated using FRA measurement. When the WPT system is constructed, the electrical components and the desired transfer distance need to be ascertained accurately. Furthermore, the structure of the winding and the electrical characteristic of the components in WPT systems may vary due to aging or fault events. FRA measurement can examine the discrepancy between the WPT system parameters and the reference fingerprint data to provide cost-effective condition monitoring-based maintenance scheme for the WPT systems. This paper presents a detailed circuit analysis of a two-coil inductively coupled WPT model in different compensating topologies to accomplish efficient energy transfer. Then, FRA measurement is used to detect the variation in the amplitude and the phase angle of the developed circuit input impedance in a wide frequency range. In addition, displacements due to the placement of ferrite material and the variation of the transfer distance between the transmitter and receiver units are investigated through practical measurements.

Power transformers are subject to winding and core deformations due to short circuit faults and other environmental conditions. Majority of these faults are of progressive nature and should be rectified as soon as they emerge. Frequency response analysis (FRA) has been widely accepted as a reliable diagnostic tool to detect such faults. As reliable interpretation codes for FRA signatures have not been fully developed and accepted yet, researchers have put much effort to investigate the impact of various mechanical deformations on the transformer FRA signature. Because of the intrusive nature of such faults when staged on a real transformer, most of the studies in the literatures were conducted on a transformer high frequency electric circuit model. Simplifications assumed in these models by ignoring the turn-to-turn capacitance, not taking into account the detailed winding structure and not considering all mutual inductances between coils in various phases have reduced the accuracy of the obtained results. To establish reliable FRA interpretation codes, it is essential to develop electric equivalent circuit models that can yield FRA signature as close as possible to the FRA signature trend of the real transformer. This paper proposes a detailed transformer high frequency electric circuit model which considers the winding structure, inter-turn capacitance and all mutual inductances. Calculation details of the model’s parameters are presented. The accuracy of the proposed model is assessed by comparing its FRA signature with that of an equivalent transformer hardware model during healthy, short circuit disks, radial deformation and axial disks buckling fault.

With the growing pool of aged power transformers, application of the sweep frequency response analysis (SFRA) to assess power transformers' mechanical integrity has been given much attention. One of the research gaps in this field is the lack of reliable and automated techniques to interpret SFRA signatures. Conventional interpretation technique relies on visual inspection and personnel level of expertise, which may lead to inconsistent interpretation for the same signature. Furthermore, current SFRA technique fails in detecting transformer incipient mechanical deformations of low levels. To overcome these limitations, this paper presents a new three-dimensional (3D)-SFRA signature that comprises frequency, magnitude and phase angle in one plot. In contrary to the current interpretation practice that relies only on the magnitude plot, the proposed 3D signature exhibits more features, which can improve the SFRA identification accuracy. To automate and standardise the fault identification process, a digital image processing code is developed to extract some unique features from the proposed signature. The proposed technique is validated through finite element simulation analysis to detect short-circuit turns, axial displacement and radial deformation of a three-phase 40 MVA transformer and practical feasibility is assessed through its application to detect short-circuit turns of a three-phase 45 MVA transformer.

Power transformers are prone to short circuit faults and external harsh environmental conditions that may result in various internal winding and core deformations. Frequency response analysis (FRA) is the current technique widely used to detect such kind of faults. The technique relies on scanning each phase in a wide frequency range to detect any discrepancies in the winding FRA signature. Current interpretation process for FRA signatures relies on graphical analysis of the magnitude plot without paying much attention to the phase angle plot. This may result in inconsistent analysis for the FRA signature as so far, there is no standardized and automated technique for FRA signature classification. To overcome this limitation, a new polar plot signature based on the integration of the FRA magnitude and phase angle plots has been proposed in the literature. While this new approach has been validated through simulation analysis, its performance on real transformers has not been assessed yet. This paper is aimed at investigating the practical feasibility of replacing the conventional FRA signature with the polar plot and automate the fault diagnosis process using digital image processing techniques. In this context, various winding faults including short-circuit disks, disk space variation, and axial disk buckling have been implemented on a custom-made, Y/Y three phase, 50 Hz, 400 kVA power transformer. The low voltage winding of the investigated transformer comprises 12-turn helical type while the high voltage winding is made of composite structure comprising 10 disks of interleaved winding on the top, 10 disks winding in the middle and 10 disks of continuous winding in the bottom. Impact of various levels and locations of these faults on the FRA polar plot is investigated through extracting digital image texture analysis features. To ease the classification process, a unique classification metric namely image sum square max-min ratio error is calculated based on the extracted features.

This study explores how the voltage control of a remote part of the utility gird can be improved using more sophisticated voltage control on wind-solar farms equipped with fast communication platforms. The idea is to make renewable plant the master voltage controller during large disturbance events in the grid. This is done by proposing an enhanced voltage droop control strategy based on instantaneous reactive power consumption by monitoring the virtual impedance of the point of connection using a new customised data class model of IEC 61850 communication protocol. The conventional centralised voltage droop control strategy and the proposed instantaneous direct voltage control method are both implemented on the White Rock Solar Wind Farm in NSW, Australia and their performances are compared using both MATLAB Simulink simulations under 5% voltage step disturbances, single-phase-to-ground and three-phase-to-ground faults as well as some tests conducted in the field.

With the growing global interest in developing future smart grids, nonconventional instrument transformers have been given much attention. In contrary with the classic magnetic core current transformer, electronic current transformer (ECT) does not exhibit saturation, is of small volume, and can generate a digital signal complying with the recent digital communication protocols such as the IEC61850. However, the measurement accuracy of the ECT is influenced by the mechanical vibration caused by the adjacent circuit breaker (CB) operation and the bus bar electromagnetic forces generated due to short-circuit currents. According to the GB/T 20840.8 and the IEC 60044-8 standards, the root mean square of the ECT output current should not exceed 3% of the rated secondary current after 5-ms duration following a CB operation or a large current of a short duration. Current ECT vibration compliance test does not correlate the ECT output current with the CB or the large current signal to correctly identify their ending moment. As such, the current testing technique may reveal inaccurate outcomes. This paper proposes a new vibration testing platform for ECT in which the status of the vibration source is synchronously correlated with the ECT output signal. A status monitoring circuit is proposed and implemented to achieve synchronous acquisition and comparison of the CB status and the ECT output current. In addition, a primary current sensor is designed to accurately realize the end time of any short-time large current. A software platform is developed to correctly identify the CB breaking moment and the end of the large current. The robustness and superiority of the proposed testing platform are validated through extensive simulation and practical analyses.

The failure of subsea cables has been predominantly attributed to third party activities and cable field joints. Field joints may be unavoidable due to joining of insufficient factory-made cable lengths, cable repair, or when the cable installation process has to be abandoned due to rough weather or other unplanned events. Failures within the jointing location reveal that current quality testing of these joints may be inadequate. The failure mode in these joints is mainly a result of seawater ingress at the field joint. Current design practices for subsea cable field joints recommend offshore simulations/trials to demonstrate the long term performance of the joint under the expected mechanical loads during cable installation to demonstrate whether the planned field joining practice is satisfactory. Nevertheless, offshore simulations are costly, and therefore a set of standardized onshore testing schemes would be advantageous alternative. This paper presents an onshore testing scheme for subsea power cable deployment. The proposed testing arrangement can be employed to verify the design of the offshore field joint and to prove the functionality of the field joint under the installation loads.

This paper presents a robust adaptive backstepping control (RABC) for high-speed permanent-magnet synchronous motor (HSPMSM) drive system. The proposed RABC achieves high performance operation by incorporating an ideal backstepping controller (IBC), a recurrent radial basis function neural network (RRBFNN) uncertainty observer, and a robust controller. The Lyapunov stability theorem is utilized to design the IBC as a position controller of the HSPMSM servo drive system. To enhance the disturbance rejection capability during parameter changes, certain information is needed within the backstepping control law so that the system performance would not sorely be affected. To mitigate the need for the lumped parameter uncertainties within the backstepping controller, an online adaptive observer based on RRBFNN is designed to estimate the nonlinear parameter uncertainties. Moreover, the robust controller is intended to retrieve the remaining of the RRBFNN approximation errors. To assure the stability of the proposed RABC, the Lyapunov stability analysis is used to derive the online adaptive control laws. The performance of the proposed RABC is verified by simulation and experimental analysis under different operating conditions and parameter uncertainties. The test results validate the effectiveness of the proposed RABC scheme to achieve preferable tracking performance regardless of external disturbances and parameter uncertainties.

Electronic instrument transformers are expected to play a significant protection and control role in the future smart grids. This paper aims at improving the performance and accuracy of electronic instrument transformers that are replacing the conventional current and potential transformers in the future digital substations. One of the main issues associated with the electronic instrument transformer is its poor thermal stability that affects the accuracy of the measurements. In this paper, to overcome this issue, a new wireless temperature measurement and compensation technique is proposed. In this technique, the operating temperature of the electronic transformer is continuously monitored and its impact on the measurements is compensated in real-time. The experimental results reveal that the proposed technique can effectively improve the thermal stability and measurement accuracy of the electronic instrument transformers.

The issue of power quality is one of the prominent factors in determining the reliability of power systems. It is inevitable to consider power quality issues and include appropriate mitigation techniques in the design of any future power system project construction. Voltage sag and flicker are the two most common short disturbance types of power quality issues that frequently occur in power systems. A widely accepted mitigation strategy to enhance the power quality of disturbed networks is by connecting a flexible ac transmission system. In this paper, a superconducting magnetic energy storage (SMES) unit along with hysteresis-fuzzy controller to suppress the severe impacts of voltage sag and flicker events on the power quality of a power system is proposed, implemented, and tested. The proposed SMES configuration with hysteresis current control and fuzzy logic controller is effective, simple, and easy to implement. Simulation results show the ability of the proposed SMES controller in compensating sag and/or flicker distortions.

This paper presents a new cost-effective technique to improve the performance of doubly fed induction generator (DFIG)-based wind energy conversion systems (WECS) during wind gust and fault events through the integration of high temperature superconductor (SC) within the DC link of the rotor side and grid side converters that interface the DFIG rotor with the grid. Fractional order proportional integral (FOPI) controller is employed to control the energy exchange between the SC and the system through controlling the duty cycle of the DC chopper interfacing the SC with the DC link. Rating of the SC and parameters of the proposed FOPI controller are precisely calculated using harmony search optimization technique in order to regulate the DFIG generated power and improve its fault ride through capability during disturbance events. To validate the superiority of the proposed FOPI controller, the performance of the studied system is also investigated when a conventional PI controller is employed. Results reveal the ability of the proposed topology and controller to improve the performance of the DFIG-based WECS during wind gust and short circuit faults at the DFIG terminals.

Owing to its modular construction, ability for bi-directional power flow and suitability for AC/DC grids, solid-state transformer (SST) is expected to be the backbone of the future smart grids. One of the main drawbacks of SST is the generation of negative-sequence current component at its input stage under unbalanced distribution system which causes adverse impacts on the power quality of the electricity grids. This paper is aimed at proposing a novel unbalance compensation method based on reduced order generalized integrator to suppress the negative-sequence current. Unlike the conventional sequence compensation method that is based on dual synchronous reference frames, the new proposed method does not involve complex calculation of the command current and sequence decomposition. As such, the response speed of the compensation controller is significantly improved. Additionally, the proposed method is easy to implement when compared with the current conventional compensation technique as there is no need to inject sequence components into the grid. A simulation model of three-module cascaded SST with three-phase star connection is established in Matlab/Simulink. Several case studies are carried out under different operating conditions. Simulation results validate the feasibility of the proposed method.

Power transformers are located in the electrical transmission and distribution networks where different voltage levels are needed. The turn ratio of the low voltage and high voltage windings is mechanically controlled by an on-load tap changer or de-energized tap changer. As the tap changer is the transformer’s only moving part, it is highly susceptible to mechanical failure and aging degradation. While some diagnostic tools have been used to determine the mechanical condition of tap changer contacts, not much attention was given to use the frequency response analysis to diagnose the transformer’s tap changers’ mechanical integrity. This paper is taking one step forward into maturing the application of the frequency response analysis (FRA) technique to detect transformer tap changer faults. In this regard, two common tap changer faults are created, and experimental testing for four FRA test configurations is conducted. For a better understanding of the tap changer fault mechanism, an electrical equivalent circuit model is proposed and designed using Simulink. The simulation and implementation of the equivalent circuits using MATLAB\R2018a.

While frequency response analysis (FRA) is a well matured technique widely used by current industry practice to detect the mechanical integrity of power transformers, interpretation of FRA signatures is still challenging, regardless of the research efforts in this area. This paper presents a method for reliable quantitative and qualitative analysis to the transformer FRA signatures based on a decision tree classification model and a fully connected neural network. Several levels of different six fault types are obtained using a lumped parameter-based transformer model. Results show that the proposed model performs well in the training and the validation stages, and is of good generalization ability.

In a photovoltaic (PV)-battery integrated system, the battery undergoes frequent charging and discharging cycles that reduces its operational life and affects its performance considerably. As such, an intelligent power control approach for a PV-battery standalone system is proposed in this paper to improve the reliability of the battery along its operational life. The proposed control strategy works in two regulatory modes: maximum power point tracking (MPPT) mode and battery management system (BMS) mode. The novel controller tracks and harvests the maximum available power from the solar cells under different atmospheric conditions via MPPT scheme. On the other hand, the state of charge (SOC) estimation technique is developed using backpropagation neural network (BPNN) algorithm under BMS mode to manage the operation of the battery storage during charging, discharging, and islanding approaches to prolong the battery lifetime. A case study is demonstrated to confirm the effectiveness of the proposed scheme which shows only 0.082% error for real-world applications. The study discloses that the projected BMS control strategy satisfies the battery-lifetime objective for off-grid PV-battery hybrid systems by avoiding the over-charging and deep-discharging disturbances significantly.

The immense increase in the energy and housing demands of the world, the increased population that is estimated to reach around 9.6 billion by the year 2050 call for renewable energy systems must be integrated into buildings with cost-effective innovative storage technologies. In order to this requirement, this paper develops a building system using a new Strong Energy Storing, Smart, Adaptive, Modular Element (SESAME). The SESAME that's made from a durable, high specific heat capacity and temperature resilient geopolymer concrete is fully demountable, recyclable, and modular to allow element replace-ability, storage thermal energy and safely transforming it into electricity. The SESAME in the buildings will not only provide conventional structural functions but also act as replaceable, modular and adaptive safe thermal batteries for future sustainable cities.

This paper proposes sliding mode SVPWM current tracking control method for shunt active power filter (SAPF). Different from the conventional hysteresis SVPWM (HSVPWM), the SVPWM method based on sliding mode control (SMC) optimizes the method of judging the area where the current error vector is located during the reference current tracking control. The simulation results show that when the system reaches a steady state, the sliding mode SVPWM control method proposed can make the shunt active power filter have better active filtering performance and robustness than HSVPWM method.

To minimum the capacity of rotor-side SMES and reduce the cost, the capacity ratio between energy storage converter (ESC) and RSC are redesigned. Dual control strategy is utilized into ESC controller to eliminated the influence of negative sequence component of rotor current during voltage unbalance. Two kinds of control target are analyzed in detail and the control diagram in ESC has been designed. Simulation has been done to validate the effectiveness of proposed method. Simulation results express that the controller can precisely carry out the two control targets.

This paper presents a coordinate power control of solid state transformer (SST) and Superconducting Magnetic Energy Storage (SMES) for microgrid. Under normal working conditions, the SST will control the power exchange among the AC micro grid, SMES and the DC micro grid. SMES stores the excess energy from the grid and distributed energy resource (DER). When short-time grounding fault occurs on dc side, SMES is controlled to stabilize the DC side voltage by charging and discharging, improving fault ride-through capability of Microgrid. And a Matlab/Simulink model is demonstrated to validate the correctness of the coordinate control method.

In this paper, the ant colony optimization (ACO) technique is employed to precisely tune the parameters of a superconducting magnetic energy storage unit and power system stabilizer to damp the oscillations in a Single Machine Infinite Bus system during transient and disturbance events. In this regard, an objective function to minimize the integral time absolute error is developed and solved using the ACO. System performance is assessed through its frequency response and the machine's rotor angle along with the eigenvalue characteristic for each control combination scheme. Results reveal the effectiveness of the proposed controller.

Nowadays, the structural elements of buildings are static, irreplaceable, and designed solely for load-bearing purposes. Concerns about the environment, climate change and energy call for the creation of innovative components for future green buildings, the intelligent use of structural elements can provide promising solutions. This paper proposed a ground-breaking Strong, Energy Storing, Smart, Adaptive, Modular Elements (SESAMEs) for solar power supply system in green buildings. This element will not only provide conventional structural functions but also act as replaceable, modular and adaptive safe thermal batteries for future sustainable cities.

The electronic voltage transformer (EVT) has received much attention with the recent global trend to establish smart grids and digital substations. One of the main issues of the EVT is the deterioration of its performance with long-term operation which affects the control and protection systems it is employed for and hence the overall reliability of the power grids. This calls for the essential need for a reliable technique to regularly assess the accuracy of operating EVT in real-time. Unfortunately, traditional calibration methods cannot detect the incipient EVT performance change in real-time. As such, this paper presents a new online method to evaluate the accuracy of the EVT. In this regard, the Q-statistic is calculated based on the recursive principal components analysis (RPCA) using the output data of EVT to map up the changes of metering error on the electric–physics relationship. By employing the output data of the EVT along with the power grid characteristics, the performance of the EVT is evaluated without the need for a standard transformer, as per the current industry practice. Results show that the proposed method can assess the EVT with a 0.2 accuracy class.

The interconnection system faces inter-area oscillation challenges that affect power system stability. Conventional Power System Stabilizer (PSS) cannot provide effective damping to the inter-area oscillations. This paper deals with a Proportional Integral (PI) controller as a damping control scheme of the Unified Power Flow Controller (UPFC) to improve the damping of inter-area oscillation in a multimachine system. The proposed PI Controller-based UPFC is tuned using the Firefly Algorithm (FA), and the performance is compared with PSS. Simulation results show that the proposed PI Controller-based UPFC provides a better stability margin than PSS under the different conditions and effectively reduces the overshoot by 53%, accelerating the settling time by 74%, and minimizing the area graph by 85%.

Charging a group of series-connected batteries of a PV-battery hybrid system exhibits an imbalance issue. Such imbalance has severe consequences on the battery activation function and the maintenance cost of the entire system. Therefore, this paper proposes an active battery balancing technique for a PV-battery integrated system to improve its performance and lifespan. Battery state of charge (SOC) estimation based on the backpropagation neural network (BPNN) technique is utilized to check the charge condition of the storage system. The developed battery management system (BMS) receives the SOC estimation of the individual batteries and issues control signal to the DC/DC Buck-boost converter to balance the charge status of the connected group of batteries. Simulation and experimental results using MATLAB-ATMega2560 interfacing system reveal the effectiveness of the proposed approach.

Battery storage systems are subject to frequent charging/discharging cycles which reduce the operational life of the battery and reduce system reliability on the long run. As such, several Battery Management Systems (BMS) have been developed to maintain system reliability and extend the battery's operative life. Accurate estimation of the battery State of Charge (SOC) is a key challenge in the BMS due to its non-linear characteristics. This paper presents a comprehensive review on the most recent classifications and mathematical models of the SOC estimation. Future trends of the SOC estimation methods are also presented.

This paper proposes a generalized 3-phase cascaded multilevel inverter (CMLI) along with a simplified space vector pulse width modulation (SVPWM) based on 2-level inverter (2LI) topology. While conventional cascaded H-bridge inverters (CHBIs) call for a number of DC sources which increase the cost and complexity of their implementation, the proposed CMLI requires one DC source only. On the other hand, the complexity of SVPWM lies in the switching diagram that consists of a large number of triangles comprising uneven numbers of switching vectors. The generalized 3-phase CMLI proposed in this paper is aimed at reducing the number of switching vectors of each triangle. The hypothesis of the proposed CMLI-SVPWM is firstly illustrated through extensive simulation analysis using MATLAB/SIMULINK software. Feasibility of the proposed inverter is then validated through experimental measurements on a hardware prototype. Results reveal the superiority of the proposed inverter over existing ones in terms of the quality of the output voltage and current waveforms and the overall efficiency.

Effective detection of electricity theft is essential to maintain power system reliability. With the development of smart grids, traditional electricity theft detection technologies have become ineffective to deal with the increasingly complex data on the users’ side. To improve the auditing efficiency of grid enterprises, a new electricity theft detection method based on improved synthetic minority oversampling technique (SMOTE) and improve random forest (RF) method is proposed in this paper. The data of normal and electricity theft users were classified as positive data (PD) and negative data (ND), respectively. In practice, the number of ND was far less than PD, which made the dataset composed of these two types of data become unbalanced. An improved SOMTE based on K-means clustering algorithm (K-SMOTE) was firstly presented to balance the dataset. The cluster center of ND was determined by K-means method. Then, the ND were interpolated by SMOTE on the basis of the cluster center to balance the entire data. Finally, the RF classifier was trained with the balanced dataset, and the optimal number of decision trees in RF was decided according to the convergence of out-of-bag data error (OOB error). Electricity theft behaviors on the user side were detected by the trained RF classifier.

Wind energy has been identified as the second dominating source in the world renewable energy generation after hydropower. Conversion and distribution of wind energy has brought technology revolution by developing the advanced wind energy conversion system (WECS) including multilevel inverters (MLIs). The conventional rectifier produces ripples in their output waveforms while the MLI suffers from voltage balancing issues across the DC-link capacitor. This paper proposes a simplified proportional integral (PI)-based space vector pulse width modulation (SVPWM) to minimize the output waveform ripples, resolve the voltage balancing issue and produce better-quality output waveforms. WECS experiences various types of faults particularly in the DC-link capacitor and switching devices of the power converter. These faults, if not detected and rectified at an early stage, may lead to catastrophic failures to the WECS and continuity of the power supply. This paper proposes a new algorithm embedded in the proposed PI-based SVPWM controller to identify the fault location in the power converter in real time. Since most wind power plants are located in remote areas or offshore, WECS condition monitoring needs to be developed over the internet of things (IoT) to ensure system reliability. In this paper, an industrial IoT algorithm with an associated hardware prototype is proposed to monitor the condition of WECS in the real-time environment.

While several recent studies have focused on eliminating the imbalance of energy stored in series-connected battery cells, very little attention has been given to balancing the energy stored in parallel-connected battery cells. As such, this paper aims at presenting a new balancing approach for parallel LiFePO 4 battery cells. In this regard, a Backpropagation Neural Network (BPNN) based technique is employed to develop a Battery Management System (BMS) that can assess the charging status of all cells and control its operations through a DC/DC Buck-Boost converter. Simulation results demonstrate the effectiveness of the proposed approach in balancing the energy stored in parallel-connected battery cells in which the state of charge (SoC) estimation error is found to be only 1.15%.

Frequency response analysis (FRA) is considered as the most popular and reliable method to detect mechanical deformations within power transformers. Despite this popularity, interpretation of FRA signatures has not yet been standardized worldwide. Correct interpretation of FRA signatures results in reliable diagnosis of the transformer mechanical integrity which facilitates timely and proper remedial action. As a further step towards the full understanding of the analysis of FRA signatures, this paper presents a k-means method to cluster power transformers under different fault types. In this regard, series of FRA measurements has been conducted on various transformer models under different fault types. Then, a feature based on interval maximum to global maximum (IMGM) is extracted from the obtained FRA measurements to facilitate data clustering. Using grasshopper optimization algorithm (GOA), the centers of the clusters are determined and by applying the data obtained from operating transformers, the performance of the proposed method is evaluated and compared under different cases.

This paper presents a novel three-phase DC-link multilevel inverter topology with reduced number of input DC power supplies. The proposed inverter consists of series-connected half-bridge modules to generate the multilevel waveform and a simple H-bridge module, acting as a polarity generator. The inverter output voltage is transferred to the load through a three-phase transformer, which facilitates a galvanic isolation between the inverter and the load. The proposed topology features many advantages when compared with the conventional multilevel inverters proposed in the literatures. These features include scalability, simple control, reduced number of DC voltage sources, and less devices count. A simple sinusoidal pulse-width modulation technique is employed to control the proposed inverter. The performance of the inverter is evaluated under different loading conditions and a comparison with some existing topologies is also presented. The feasibility and effectiveness of the proposed inverter are confirmed through simulation and experimental studies using a scaled down low-voltage laboratory prototype.

The penetration of photovoltaic systems (PVs) is increasing and they are considered attractive options for electricity generation in distribution networks. This study focuses on estimating the total power generated by a group of neighboring PVs, spread in a locality while using a single pyranometer for measuring the solar irradiance. A new model has been proposed which employs the Gene Expression Programming (GEP) technique for developing a correlation between the distribution of the PVs and the irradiance measured by the pyranometer in estimating the total power generated. The proposed technique considers the geographic variability reduction and employs a Wavelet Transform technique for the calculations. The effective performance of the proposed model is validated using the real data collected by the Solar Project at the University of Queensland, Brisbane, Australia. The studies reveal that the proposed technique yields more accurate results against the other existing approaches.