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Abstract The insulators of overhead power lines play a crucial role in maintaining the reliability of transmission and distribution networks. Because they are exposed to harsh and dynamic environmental conditions, it is essential to investigate the impact of environmental parameters such as pollution, inclined angle with the cross arm, and temperature on the dielectric performance of the insulators of overhead lines. Conventionally, the effect of such parameters can be investigated through experimental measurements of the insulator flashover voltage. However, this approach is costly and time-consuming and calls for the isolation of the lines to conduct the test, causing interruption to the entire grid. As such, there is an essential need to develop a new methodology to quantify the flashover voltage of overhead insulators operating under various environmental conditions, which is the main aim of this paper. The Central Composite Design is employed to develop a mathematical correlation between the insulator flash over voltage as a dependent variable and three environmental parameters: pollution level, inclined angle, and temperature as independent variables. The robustness of the developed equation is validated through extensive experimental measurements of the insulator’s flash overvoltage under various conditions. Results reveal a good agreement between the actual and predicted flashover voltage using the developed correlation, as the absolute error for all investigated samples is less than 6%.

Health index is an essential tool to evaluate the condition of power transformers. Generally, there are two methodologies to evaluate the health index of power transformers: conventional methods and machine learning-based approaches. The main challenge in employing the health index to assess transformer’s condition is the lack of supporting data, which hinders its ability to accurately reflect the actual health condition of the transformers. While several studies on estimating the transformers health index using machine learning techniques can be found in the literatures, not much attention was given to the cumulative impact of multiple data unavailability on the calculation’s accuracy. This paper presents a comprehensive analysis of health index predictions using two machine learning methods: specifically, regression and classification against conventional methods of weighting and scoring. Furthermore, the paper presents some strategies to overcome the unavailability of multiple data. Six methods are presented and evaluated across 15 missing data scenarios to assess their effectiveness compared with scenarios featuring complete data. Results reveal that Gradient Boosting Regression has the most accurate accuracy in predicting the health index with a 95%, assuming complete data, and 97.87% considering data unavailability. The paper also presents an economic analysis to highlight the profitability associated with predicting missing data compared with the certainty level and evaluation using asset owner interpretation to assess the feasibility of the proposed method. The main contribution of the paper is the presentation of a comprehensive view for asset owners to make informed decisions in case of data unavailability.

Reliable operation of oil-immersed power transformers is crucial for electrical transmission and distribution networks. However, the aging of high voltage assets including power transformers along with the increasing of load demand have heightened the importance of adopting cost-effective asset management strategies. Dissolved gas analysis (DGA) has been recognized as a valuable diagnostic tool for detecting potential faults and monitoring the condition of oil-immersed power transformers. Traditional offline DGA method involves periodic sampling and laboratory analysis, which often results in delayed detection and response to emerging faults. To address these limitations, online DGA approach has been emerged to provide real-time monitoring and continuous data acquisition. This paper presents a new asset management approach for mineral oil-immersed power transformers by analysing the online DGA data using convolutional neural networks. The proposed approach provides real time solutions to classify emerging fault type and predict transformer health deterioration level with high accuracy. Results show that the accuracy of fault diagnostics of the proposed approach is approximately 87%.

Towards addressing the concerns of conventional power systems including reliability and security, establishing modern Smart Grids (SGs) has been given much attention by the global electric utility applications during the last few years. One of the key advantageous of SGs is its ability for two-way communication and bi-directional power flow that facilitates the inclusion of distributed energy resources, real time monitoring and self-healing systems. As such, the SG employs a large number of digital devices that are installed at various locations to enrich the observability and controllability of the system. This calls for the necessity of employing Internet of Things (IoT) to achieve reliable integration of all digital devices and proper tracing of various apparatuses in the grid. In this paper, the communication technology, architectural design, cutting-edge applications, and protocols of IoT-assisted SG systems are comprehensively reviewed. The main concerns, future challenges, and research gaps of IoT-assisted smart grid systems are highlighted in detail. Based on this review, key findings are concluded to pave the path for further research directions on IoT-assisted SG systems.

The very fast transient overvoltage/overcurrent (VFTO/VFTC) signals generated during disconnectors switching operation may result in a significant threat to the reliability of power grids. While there are several commercial types of disconnectors, there is still a lack of understanding of the electromagnetic interference characteristics that is generated during the very fast transient process of the disconnectors. Based on the transient voltage and current data of disconnector capacitive small current test, this paper extracts the characteristics of the corresponding macropulse and monopulse waveforms. Also, the paper presents a comprehensive statistical analysis of the transient waveform characteristic data through probability density function fitting and Kolmogorov–Smirnov (K–S) test of the sample cumulative empirical distribution. The statistical results show that the probability density distribution of the switching operation macropulse waveform follows generalized extreme value and inverse gaussian distribution. The maximum peak values of VFTO and VFTC monopulse are found to be 50 kV and 370 A with a respective rise time at the range of nano- and micro-seconds.​ The frequency bands of VFTO and VFTC macro waveforms are basically distributed within 16 MHz. Results of this paper can pave the way into a more scientific basis for the design of disconnectors considering the strong electromagnetic disturbance and the formulation of system level electromagnetic compatibility standard tests, ensure the safe and stable operation of the power grid.

The accuracy of voltage transformer (VT) measurements is imperative for the security and reliability of power systems and the equitability of energy transactions. The integration of a substantial number of electric vehicles (EVs) and their charging infrastructures into the grid poses new challenges for VT measurement fidelity, including voltage instabilities and harmonic disruptions. This paper introduces an innovative transformer measurement error prediction model that synthesizes Multivariate Variational Mode Decomposition (MVMD) with a deep learning framework integrating Bidirectional Temporal Convolutional Network and Multi-Head Attention mechanism (BiTCN-MHA). The paper is aimed at enhancing VT measurement accuracy under fluctuating load conditions. Initially, the optimization of parameter selection within the MVMD algorithm enhances the accuracy and interpretability of bi-channel signal decomposition. Subsequently, the model applies the Spearman rank correlation coefficient to extract dominant modal components from both the decomposed load and original ratio error sequences to form the basis for input signal channels in the BiTCN-MHA model. By superimposing predictive components, an effective prediction of future VT measurement error trends can be achieved. This comprehensive approach, accounting for input load correlations and temporal dynamics, facilitates robust predictions of future VT measurement error trends. Computational example analysis of empirical operational VT data shows that, compared to before decomposition, the proposed method reduces the Root-Mean-Square Error (RMSE) by 17.9% and the Mean Absolute Error (MAE) by 23.2%, confirming the method’s robustness and superiority in accurately forecasting VT measurement error trends.

Accurate interpretation of dissolved gas analysis (DGA) measurements for power transformers is essential to ensure overall power system reliability. Various DGA interpretation techniques have been proposed in the literature, including the Doernenburg Ratio Method (DRM), Roger Ratio Method (RRM), IEC Ratio Method (IRM), Duval Triangle Method (DTM), and Duval Pentagon Method (DPM). While these techniques are well documented and widely used by industry, they may lead to different conclusions for the same oil sample. Additionally, the ratio-based methods may result in an out-of-code condition if any of the used gases fall outside the specified limits. Incorrect interpretation of DGA measurements can lead to mismanagement and may lead to catastrophic consequences for operating power transformers. This paper presents a new interpretation technique for DGA aimed at improving its accuracy and consistency. The proposed multi-method approach employs s scoring index and random forest machine learning principles to integrate existing interpretation methods into one comprehensive technique. The robustness of the proposed method is assessed using DGA data collected from several transformers under various health conditions. Results indicate that the proposed multi-method, based on the scoring index and random forest; offers greater accuracy and consistency than individual conventional interpretation methods alone. Furthermore, the multi-method based on random forest demonstrated higher accuracy than employing the scoring index only.

Peer-to-peer (P2P) energy trading has attracted a lot of attention and the number of electric vehicles (EVs) has increased in the past couple of years. Toward sustainable mobility, EVs meet the standard development goals (SDGs) for attaining a sustainable future in the transport sector. This development and increasing number of EVs creates an opportunity for prosumers to trade electricity. Considering this opportunity, this review article aims to provide an in-depth analysis of P2P energy trading of EVs using blockchain in centralized and decentralized networks, which enables prosumers to exchange energy directly with one another. The paper is aimed to provide the reader with a state-of-the-art review on the P2P energy trading for EVs, considering different blockchain algorithms that are practically implemented or still in the research phase. Moreover, the paper presents blockchain applications, current trends, and future challenges of EVs’ energy trading. P2P energy trading for EVs using blockchain algorithms can be successfully implemented considering real-time scenarios and economically benefits smart sustainable societies.

This paper suggests a method for active power sharing between several dispatchable and dispersed generation units in a microgrid with one or more connections to the main grid. The dispatchable power sources within the microgrid must promptly and adequately make up for natural demand changes and the intermittent nature of renewable energy resources for optimal operation. In microgrids with one or more grid connectivity, the paper presents a novel active power-sharing strategy that achieves the above-mentioned goal effectively. The suggested method’s robustness is verified under various microgrid operating circumstances. Results confirmed the proposed method’s adaptability to different operating conditions in the real world. Active power-sharing approaches are examined for a microgrid with several dispatchable distributed generation units (DDGU) operating in grid-interconnected and independent/isolated modes. Multiple grid connections at different points of the microgrid are investigated and recommended approaches are appraised. On a microgrid with one or more links to the conventional grid, this paper provides a novel and trustworthy approach for active power sharing across several DDGU. The proposed active power-sharing method is an effort to establish a feasible power-sharing technique that can be categorised for all, or most, types of interconnected microgrids. The outcomes demonstrate how the suggested technique can be adjusted to fit a range Results demonstrate how the proposed technique can be adjusted to fit a wide range of real life operational circumstances of microgrids.

Accurate short-term load forecasting (STLF) is essential for power grid systems to ensure reliability, security and cost efficiency. Thanks to advanced smart sensor technologies, time-series data related to power load can be captured for STLF. Recent research shows that deep neural networks (DNNs) are capable of achieving accurate STLP since they are effective in predicting nonlinear and complicated time-series data. To perform STLP, existing DNNs use time-varying dynamics of either past load consumption or past power correlated features such as weather, meteorology or date. However, the existing DNN approaches do not use the time-invariant features of users, such as building spaces, ages, isolation material, number of building floors or building purposes, to enhance STLF. In fact, those time-invariant features are correlated to user load consumption. Integrating time-invariant features enhances STLF. In this paper, a fuzzy clustering-based DNN is proposed by using both time-varying and time-invariant features to perform STLF. The fuzzy clustering first groups users with similar time-invariant behaviours. DNN models are then developed using past time-varying features. Since the time-invariant features have already been learned by the fuzzy clustering, the DNN model does not need to learn the time-invariant features; therefore, a simpler DNN model can be generated. In addition, the DNN model only learns the time-varying features of users in the same cluster; a more effective learning can be performed by the DNN and more accurate predictions can be achieved. The performance of the proposed fuzzy clustering-based DNN is evaluated by performing STLF, where both time-varying features and time-invariant features are included. Experimental results show that the proposed fuzzy clustering-based DNN outperforms the commonly used long short-term memory networks and convolution neural networks.

Abstract With the rapid increase of renewable energy generation worldwide, real‐time information has become essential to manage such assets, especially for systems installed offshore and in remote areas. To date, there is no cost‐effective condition monitoring technique that can assess the state of renewable energy sources in real‐time and provide suitable asset management decisions to optimize the utilization of such valuable assets and avoid any full or partial blackout due to unexpected faults. Based on the Internet of Things scheme, this paper represents a new application for the Supervisory Control and Data Acquisition (SCADA) system to monitor a hybrid system comprising photovoltaic, wind, and battery energy storage systems. Electrical parameters such as voltage, current, and power are monitored in real‐time via the ThingSpeak website. Network operators can control components of the hybrid power system remotely by the proposed SCADA system. The SCADA system is interfaced with the Matlab/Simulink software tool through KEPServerEX client. For cost‐effective design, low‐cost electronic components and Arduino Integrated Development Environment ATMega2560 remote terminal unit are employed to develop a hardware prototype for experimental analysis. Simulation and experimental results attest to the feasibility of the proposed system. Compared with other existing techniques, the developed system features advantages in terms of reliability and cost‐effectiveness.

The development of DC custom power protection devices is still in infancy that confines the sensitive loads integrated into medium-voltage (MV) and low-voltage (LV) DC networks. Considering the DC doubly-fed induction generator (DC-DFIG) based wind energy conversion system (WECS), this paper proposes a dual active bridge (DAB) based DC unified power quality conditioner (DC-UPQC) with the integration of super-conducting magnetic energy storage (SMES) to maintain the terminal voltage of DC-DFIG and regulate the current flow. The principle of the proposed DC-UPQC has three parts, i.e., parallel-side DAB (PDAB), series-side DAB (SDAB), and SMES, used for the voltage compensation, current and power regulation, and energy storage, respectively. The circuit principle of the PDAB and SDAB and the modeling of SMES are analyzed in this paper. A DC dual control strategy is also proposed to deal with the DC voltage oscillation generated by the AC-side asymmetrical fault. A case study of DC-DFIG interfaced with DC power grid is carried out, integrated with the proposed SMES-based DC-UPQC to verify the high-power applications of the proposed structure. Finally, an experiment is implemented, and the results demonstrate the correctness of the theoretical analysis and the feasibility of the proposed structure.

Voltage fluctuation and power mismatch which are caused by the decentralization, randomness, and intermittence of distributed energy resource along with load variability cause severe impacts on the security and quality of power network operation. Aiming at alleviating this issue, the structure of an AC/DC hybrid microgrid based on solid-state transformer is presented in this paper. A proper control coordination is developed to guarantee stable and reliable operation of the system. An energy management strategy is proposed to coordinate the power flow among the solid-state transformer, AC microgrid, DC microgrid and energy storage and to effectively suppress the fluctuation of the DC bus voltage. A novel adaptive droop control for the energy storage is proposed to prolong the supercapacitor life and achieve an optimum economic benefit to consumers. The droop coefficient is obtained by fuzzy logic controller of which the state of charge of the supercapacitor and unit-time electricity charge are assumed to be the input parameters. Simulation attest the feasibility of the proposed control coordination and energy management strategy.

As an emerging and active entity in China’s electricity market, electricity selling companies call for a more reliable operational mechanism and new consumption mode to broaden their profit margins. Aiming at distributed power generation-based sales companies and by considering the participation of virtual power plants (VPPs), this paper presents the relevant operating systems in the Chinese power market. Then the paper proposes a new platform for power transactions and optimal dispatch based on a master-slave game optimization model. The model is built so that the main gamer, power sales company, can achieve maximum profit while at the same time the secondary gamer, represented by the VPP attains the lowest internal dispatching cost. The energy of both parties is linked together, and the two parties continuously exchange their own strategies and optimize the operational decisions and scheduling plans iteratively. The results of the investigated case study reveal the benefits to retail electricity companies from adopting the proposed model to aggregate and manage decentralized resources, and optimize decision-making. The platform facilitates the use of controllable loads and distributed energy sources to participate in market transactions on a large scale, and optimize the operational strategies of electric utilities.

Suspended submarine cables are prone to likely mechanical damages due to ship anchors, which represent a significant threat to the reliability of power transmission and information communication networks. This paper is aimed at presenting a detailed investigation to the dynamic response of a ship anchor impacting the suspended section a submarine cable in order to reduce the risk of cable damage. In this regard, a three-dimensional and dual nonlinear model of the anchor affecting a subsea cable is established using ABAQUS finite element analysis software. The damage effect due to the ship anchor on the suspended and buried-in-soil sections of the cable is studied. Results show that when a ship anchor hits a suspended section of the subsea cable, the mechanical stress is concentrated at the impacted point and is progressing to both sides of the suspended section with a significant cable deformation. On the other hand, the buried section of the cable suffers from a short impact process, and the deformation is relatively small. To reduce this impact effectively, a detailed technical comparison of two common dumping and filling methods is conducted, and the better protection method is proposed.

In this paper, optimized hybrid off-grid renewable energy systems (HRES) have been designed for two divisional locations in Dhaka and Khulna in Bangladesh. An analysis is conducted using five different load dispatch strategies to find the best dispatch strategy for a cost-effective and technically feasible Islanded hybrid microgrid that will support the growing power demand. A comparative analysis among the various dispatch strategies is also presented to find out the best and worst dispatch strategies for the proposed HRESs. The two HRESs consist of solar PV, a backup diesel generator, wind turbine, load demand of 23.31 kW, and a battery storage system. HOMER predictive strategy, Generator Order, Load Following, Combined Dispatch, and Cycle Charging dispatch strategy has been adopted for evaluation. The two HRESs are optimized for CO2 emission, Levelized Cost of Energy, and Net Present Cost minimization along with a feasible and stable voltage-frequency response on basis of the five dispatch techniques. The techno-economic analysis of the two HRESs is conducted using the HOMER Pro software platform. For the power system response analysis, MATLAB/Simulink is used. This study provides a complete guideline for determining the optimum component sizing ensuring optimum operation and possible costing estimation for the optimized performance of the two HRESs for different dispatch techniques. Also, a comparative study among the designed HRESs, other hybrid systems, and conventional power plants is conducted to prove the significance of this research work.

This paper provides a thorough examination of various techniques for sharing active power between multiple dispatchable generation sources distributed within an interconnected microgrid. Ideally, an interconnected microgrid should function as a consistent load or source. However, achieving this ideal operation requires compensating for natural load fluctuations and the intermittent power output from non-dispatchable energy sources within the microgrid through timely and adequate adjustments made by dispatchable generation. Numerous control and management systems have been documented in the existing literature to achieve optimal microgrid operation. This paper presents a concise comparison of most of the proposed systems published in the literature. The benefits and restrictions of the proposed methods are thoroughly evaluated under various possible operating scenarios.

The concept of intentional controlled islanding (ICI) is introduced as a proactive measure to safeguard the power system against blackouts in the event of significant disturbances. It involves strategically partitioning the system into self-healing islands, thereby mitigating the impact of such disturbances. This study introduces a new framework for creating stable, controlled islands in power systems with large-scale wind power plants. The proposed islanding strategy takes into account the impact of wind power plants on the coherency grouping of generators as a constraint in the ICI problem. The proposed algorithm employs the Virtual Synchronous Motion Equation (VSME) model of asynchronous generators to replace wind power plants in power systems and groups all generators, including synchronous generators and wind turbine generators, based on their dynamic coupling. Support Vector Clustering is employed in the ICI problem to determine the coherent generator clusters as the cores of the formed islands. The algorithm can identify the optimal number of islands without prior information about the number of clusters. In this study, a Mixed Integer Linear Programming (MILP) model is formulated to address the ICI problem with the objective of minimising the power imbalance in each island after splitting while ensuring the transient stability and dynamic frequency stability of ICI. Simulation results on modified 39-bus and 118-bus test power systems demonstrate that integrating a Virtual Inertia Controller into the wind-integrated power systems results in a high-inertia power system that behaves similarly to a conventional power system with only synchronous generators during the islanding procedure.

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.

While electromagnetic forming of aluminum alloy sheet has gained several advantages, the uneven radial deformation of the sheet is still one of the main drawback of this technology that calls for reliable and cost-effective solution. Aiming at this point, this paper presents an electromagnetic bulging system equipped with a new magnetic field shaper placed between the driving coil and the object sheet to modulate the distribution of the induced eddy current in the sheet, and hence regulating the distribution of the electromagnetic force to achieve more uniform deformation. A finite element electromagnetic-structure 2D coupling model simulating this process is developed and the effects of workpiece geometry and deformation speed on the circuit, magnetic field and structure field are considered. The coupling between the electric circuit and magnetic field; magnetic field and workpiece deformation is realized to simulate the actual system of thin plate electromagnetic bulging. The performance of the proposed system is compared with that of the traditional system through investigating the profiles of the induced eddy current, electromagnetic force and forming effect. Simulation results show that the uniformity of sheet metal forming is improved by using the proposed magnetic field shaper, and the maximum uniform deformation area is increasing from 32.1 mm to 73.8 mm, which stimulates more industrial applications of sheet metal electromagnetic forming.

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.

Due to the fact that geometrical structure of tube electromagnetic bulging process with field shaper does not conform to the axisymmetric property, three-dimensional (3D) model is usually used to analyze the process which is complicated and time consuming. Therefore, on the basis of understanding the working principle of the field shaper, a two-dimensional (2D) model with current constraint is proposed in this paper to analyze the electromagnetic bulging process of a tube fitted with a field shaper. COMSOL finite element analysis software is used to develop three models for performance analysis and comparison, these are: conventional 3D model and the proposed 2D model with and without current constraint. Simulation results show that the induced eddy current density, magnetic flux density and electromagnetic body density of the two-dimensional model with current constraint are in good agreement with the results of the three-dimensional model. This provides an alternative and effective way to analyze the performance of the tube electromagnetic bulging with field shaper using a simple but yet reliable 2D model.

Tube electromagnetic bulging has been given much attention due to its superior advantages in the field of light alloy processing. However, the conventional tube electromagnetic bulging process exhibits some critical issues that restrict its wide industrial applications. This includes non-uniform axial deformation and poor forming performance such as wall thickness thinning. This article proposes a cost-effective solution for such issues through simultaneous radial and axial electromagnetic force loading using an auxiliary driving coil. In this regard, the influence of the auxiliary coil geometrical parameters on the electromagnetic force distribution and tube forming performance is investigated using electromagnetic-structure coupling model of the bulging process. The robustness of the new proposed method is validated by comparing its electromagnetic force distribution and tube forming performance with the traditional cylindrical coil method, currently used by industry practice. Numerical results show that the introduction of the auxiliary coil may effectively weaken the radial electromagnetic force at the end of the tube and solve the uneven axial deformation issue. Based on the obtained results, the maximum uniform deformation area is increased from 6.9mm to 35.1mm. The tube relative wall thinning issue is reduced and the relative wall thickness reduction of the pipe is found to be 0.01661. The axial electromagnetic force can be further improved by adjusting the geometrical parameters of the coil and the proposed electromagnetic bulging can effectively increase the uniform deformation range of the tube.

Uneven axial deformation is the current main issue of the tubes electromagnetic bulging technology which calls for innovative and cost-effective solutions. This paper presents a new method to overcome the above-mentioned issue based on employing a magnetic field shaper. The proposed magnetic field shaper is employed in the middle of the pipe and the driving coil. Induced eddy current control is realized by changing the structural parameters of the magnetic field shaper; thereby regulating the electromagnetic force distribution. The effectiveness of the proposed method is investigated through numerically comparing the electromagnetic performance of two EMF setups; traditional coil-based loading model and magnetic field shaper driving coil-based loading model proposed in this paper. The influence of the magnetic field shaper structural parameters on the tube electromagnetic forming performance is examined for optimum structure design. Results reveal that the concave profile of the radial electromagnetic force in the traditional loading method can be effectively improved using the proposed loading in this paper. The introduction of a magnetic field shaper not only changes the induced eddy current distribution, but also regulates the electromagnetic force profile in the axial-direction of the pipe which effectively enhances the uniform range of the pipe electromagnetic bulging, the maximum uniform deformation area is increased from 9mm to 35mm.

While electromagnetic forming is widely used for aluminum alloy materials, the non-uniformity of plate radial deformation is still a key issue that restricts the wide development of this technology. This article presents a new electromagnetic-forming topology. The proposed topology comprises outer ring double-coil plates along with an inner loop driving coil of a small number of turns whose geometrical parameters are optimized to adjust the induced eddy current of the plate. Numerical simulation results show that the presence of an inner ring driving coil shapes the magnetic field in a manner that the radial electromagnetic force is concentrated close to the edge of the die which affects the center of the plate. With optimum design for the geometrical parameters of the proposed inner ring driving coil, the electromagnetic force distribution can be adjusted over the plate, resulting in an improved plate deformation with an increase in the maximum uniform deformation area from 24mm to 65mm. Results also show that the proposed inner and outer ring double coil loading method possesses superior advantages over the traditional flat spiral coil loading method.

The main factors of stress distribution in MCRs are the magnetostriction effect of the core materials and the magnetic force between gaps under DC bias excitation. This article developed a coupling model for MCRs considering Maxwell magnetic force and magnetostriction under DC flux density biases. The constitutive equations of the magnetic field and strain field are constructed based on the magnetic property curves with a new measured and analyzed method to consider DC magnetic flux biases, which is the key contributions of this study. Then, the electromagnetic vibration properties of the MCR model are calculated and analyzed. To prove the validity of the proposed method, the vibration of a 4.4 kVar-220 V MCR is tested and analyzed.

Controlling the direction and profile of the electromagnetic force (EMF) is a key of improving the tube axial-uniformity and wall thickness during the electromagnetic forming process. In this regard, this article proposes a new tube electromagnetic-bulging topology using internal negative-external positive three-coil system. This structure is aimed at realizing effective bidirectional loading of axial and radial EMFs on the tube. An electromagnetic-structure coupling two-dimensional model is proposed to study the effect of the coil structure on the EF profile, bulging profile and the tube wall-thickness. The performance of the proposed method is compared with the traditional method through several performance metrics that include the electromagnetic force distribution cloud map, the tube bulging uniformity and its wall thickness. Numerical simulation results show that by changing the coil structure, the radial EMF profile can be effectively enhanced, and the free bulging contour of the tube can be further regulated. The additional axial EMF due to the proposed structure can promote the axial-flow of the material, thereby suppressing the reduction in the tube wall-thickness which is unavoidable using the traditional method. In comparison to the traditional single-coil system bulging, the maximum axial uniformity range of the tube is increased from 6.5mm to 35.2mm, and the wall thickness of the most severely thinned area is increased from 1.43mm to 1.77mm which verifies the effectiveness of the proposed method.

Electromagnetic tube expansion has been given much attention due to its superior cold forming properties. The traditional electromagnetic coil is of single structure, which results in substantial tube wall thinning and mechanical strength reduction. This shortcoming restricts further development of the electromagnetic tube expansion technology in various industrial applications. This paper proposes an axial-radial electromagnetic force bidirectional loading method to increase the axial fluidity of the material and eliminate the wall thinning issue that currently exists in the traditional method. Furthermore, the performances of three proposed structures for the axial compression electromagnetic tube expansion using three-coil, two-coil and one-coil are analyzed and compared. In this context, the electromagnetic force distribution and the wall thinning of the tube during the expansion process are analyzed based on finite element modelling. Results show that with the proposed modified structure of the traditional single coil, the axial electromagnetic force can be substantially increased which results in reducing the tube wall thinning significantly. The proposed technique in this paper paves the way for further development and more industrial applications of the electromagnetic expansion technology.

In the conventional electromagnetic tube expansion, axial distribution of the electromagnetic force is not uniform along the workpiece. This results in uneven axial deformation of the deformed tube and restricts the full industrial implementation of this technology. In an attempt to overcome this issue, this article proposes a new method based on dual-coil electromagnetic tube compression. In this regard, an electromagnetic-structure finite element coupling model is developed to emulate the real electromagnetic tube compression process. Influence of the proposed dual-coil structure parameters on the electromagnetic force distribution is analyzed. Results show that the proposed dual-coil electromagnetic tube compression method can effectively solve the problem of uneven axial deformation in the conventional electromagnetic tube compression. The proposed method is expected to promote wide industrial applications of the electromagnetic tube forming technology.

This paper presents a new investigation to detect various faults within the three-phase star and delta induction motors (IMs) using a frequency response analysis (FRA). In this regard, experimental measurements using FRA are performed on three IMs of ratings 1 HP, 3 HP and 5.5 HP in normal conditions, short-circuit fault (SC) and open-circuit fault (OC) conditions. The SC and OC faults are applied artificially between the turns (Turn-to-Turn), between the coils (Coil-to-Coil) and between the phases (Phase-to-Phase). The obtained measurements show that the star and delta IMs result in dissimilar FRA signatures for the normal and faulty windings. Various statistical indicators are used to quantify the deviations between the normal and faulty FRA signatures. The calculation is performed in three frequency ranges: low, middle and high ones, as the winding parameters including resistive, inductive and capacitive components dominate the frequency characteristics at different frequency ranges. Consequently, it is proposed that the boundaries for the used indicators facilitate fault identification and quantification.

Due to the uncertainty of the nodal power caused by the varying renewable energies and the variety of loads, the line power of the distribution network (DN) is uncertainty also. In extreme scenarios, the line power may exceed the loading limits and incur overload violations. In this paper, a risk analysis specifically for overload violations based on the security region of the DN is established. This method takes the N-0 security of the DN as the reference to determine the bidirectional security region and violation distances. The calculation of the probability distribution of the overload violation in the distribution lines is established according to the distribution of node injections of the DN by using the semi-invariant algorithm. By referring to the security boundaries, the optimization model of the anti-violation strategy to minimize the cost of anti-violation is derived, by which the severity of violation risk events is obtained accordingly. Assessment of the risk cost is built with the CVaR index for violation events. Based on the above algorithms, the risk-tolerated scheduling model of the DN is arrived at with the objective of minimizing the comprehensive risk cost and operating cost. Finally, the validity of the proposed method is verified by a modified IEEE 33-node example.

Frequency Response Analysis (FRA) technique has been recognized by worldwide utilities as a matured technology to assess the mechanical integrity of power transformers. While some industrial critical assets such as induction motors have the same construction principle as power transformers, the application of FRA technique to induction motors has not yet been fully explored. This paper presents analogical experimental studies for the application of FRA on power transformers and induction motors. For a consistent analogy, the FRA technique has been employed to detect short and open circuit turns in both appliances, which helps explore a wider scope of the FRA applications on rotating machines. In this regard, experimental FRA measurements are performed on an 11/0.415 kV, 500 kVA, three-phase distribution transformer and a 5.5 HP three-phase induction motor. Several short and open circuit faults are staged on the windings of both tested equipment and the FRA signature is recorded and compared with the reference signature at no fault. To quantify the impact of faults on the FRA signature, several statistical indicators are used and threshold limits for these indicators are proposed to automate the interpretation process. Results reveal a good correlation between the FRA signatures of induction motors and power transformers that attests to the feasibility of using FRA technique to detect various faults within large rotating machines.

Transformer insulation paper is a key indicator for transformer remaining operational life. Paper decomposition is evaluated using the degree of polymerization (DP) which calls for samples of insulation paper from operating transformers. Since collecting such paper samples is extremely difficult, other indicators have been used to indirectly reveal the DP value of insulation paper. This includes dissolved gases in transformer oil such as carbon oxides and hydrocarbon gases, furan compounds, methanol, ethanol, and moisture along with some oil characteristics such as interfacial tension. However, for the same oil sample, these individual parameters lead to different DP values. This is attributed to the lack of accuracy of the established mathematical and artificial intelligence models correlating DP with each of the above mentioned individual parameters. This paper presents a self-learning method to estimate the DP value of transformer insulation paper based on multiple transformer oil aging parameters. The proposed method comprises data processing, fuzzy c-means and linear regression. Results reveal that estimating the DP value based on multiple aging parameters is more accurate than estimating it using one single parameter as per the current practice. The proposed method not only helps to understand the correlation between multiple oil aging parameters and the DP value of paper insulation, but also promotes the establishment of more accurate life assessment models for power transformers based on these oil aging parameters.

Dissolved gas in oil analysis (DGA) is one of the most reliable condition monitoring techniques, which is currently used by the industry to detect incipient faults within the power transformers. While the technique is well matured since the development of various offline and online measurement techniques along with various interpretation methods, no much attention was given so far to the oil sampling time and its correlation with the transformer loading. A power transformer loading is subject to continuous daily and seasonal variations, which is expected to increase with the increased penetration level of renewable energy sources of intermittent characteristics, such as photovoltaic (PV) and wind energy into the current electricity grids. Generating unit transformers also undergoes similar loading variations to follow the demand, particularly in the new electricity market. As such, the insulation system within the power transformers is expected to exhibit operating temperature variations due to the continuous ramping up and down of the generation and load. If the oil is sampled for the DGA measurement during such ramping cycles, results will not be accurate, and a fault may be reported due to a gas evolution resulting from such temporarily loading variation. This paper is aimed at correlating the generation and load ramping with the DGA measurements through extensive experimental analyses. The results reveal a strong correlation between the sampling time and the generation/load ramping. The experimental results show the effect of load variations on the gas generation and demonstrate the vulnerabilities of misinterpretation of transformer faults resulting from temporary gas evolution. To achieve accurate DGA, transformer loading profile during oil sampling for the DGA measurement should be available. Based on the initial investigation in this paper, the more accurate DGA results can be achieved after a ramping down cycle of the load. This sampling time could be defined as an optimum oil sampling time for transformer DGA.

In this paper, a robust adaptive dynamic surface control (RADSC) is proposed with a recurrent cerebellar model articulation controller (RCMAC) based on the function link neural network (FLNN) for two-axis X-Y table derived by two permanent-magnet synchronous motors (PMSMs) servo drives. Initially, an optimal computed torque controller (OCTC) is used to ensure the stability of the two-axis X-Y table system. But there may be destroyed in the control performance due to the presence of parameter uncertainties. This might be due to the fact that linear optimal control possesses inherent robustness within a specified spectrum of model uncertainties. Due to the above reasons, the RADSC is developed according to the system requirements in order to increase the robustness of the control system. In the proposed control scheme, a dynamic surface controller (DSC) and an RCMAC uncertainty observer with a robust controller are combined to constitute the RADSC scheme. In the proposed RADSC, the DSC is utilized to get rid of the complexity of explosion present in the backstepping design. Furthermore, the RCMAC uncertainty observer is developed such that it can approximate the nonlinear parameter uncertainty terms online, whereas the robust controller is designed to recover the residual of the approximation error of the RCMAC. Based on the Lyapunov stability analysis, the online adaptive control laws are derived. The experimental results confirm that the x-axis and y-axis motions are controlled separately, whereas it can be concluded that the proposed RADSC's dynamic behaviors accomplish robust tracking performance though there were parameter uncertainties.

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.

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.

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.

A power transformer is one of the most critical and expensive assets in electric power systems. Failure of a power transformer would not only result in a downtime to the entire transmission and distribution networks but may also cause personnel and environmental hazards due to oil leak and fire. Hence, to enhance a transformer’s reliability and extend its lifespan, a cost-effective and reliable condition monitoring technique should be adopted from day one of its installation. This will help detect incipient faults, extend a transformer’s operational life, and avoid potential consequences. With the global trend to establish digital substation automation systems, transformer online condition monitoring has been given much attention by utilities and researchers alike. Several online and offline condition monitoring techniques have been recently proposed for oil-immersed power transformers. This paper is aimed at providing a state-of-the-art review for the various condition monitoring technologies used for oil-immersed power transformers. Concept of measurements and analysis of the results along with the future trend of condition monitoring techniques are presented.

This paper presents a mechanism for active power sharing among multiple dispatchable and distributed generation units within a micro grid comprising one or multiple interconnections with the main grid. Ideally, a micro grid should act as a constant load or a constant voltage source when connected to the main grid. However, to achieve ideal operation, natural load variations and the intermittency of renewable energy sources within the microgrid need to be adequately and timely compensated for by dispatchable power sources. While several control algorithms have been reported in the literature to achieve ideal microgrid operation, the majority of the proposed methods assumed a micro grid with a single interconnection to the main grid. In the real world, micro grids may have to maintain multiple live links with the main grid for several technical and operational reasons such as reliability, power-dispatch restriction, and operational limitations requirements. Therefore, a new method of active power sharing is proposed in this paper, which is equally effective for micro grids with one or multiple grid connections. The robustness of the proposed method is examined under different microgrid operating conditions. The results reveal the flexibility of the proposed method to adapt under various real-world operating conditions.

The industry has widely accepted Frequency Response Analysis (FRA) as a reliable method to detect power transformers mechanical deformations. While the FRA technique has been recommended in recent literature as a potential diagnostic method to detect internal faults within rotating machines, detailed feasibility studies have not been fully addressed yet. This paper investigates the feasibility of using the FRA technique to detect several short circuit faults in the stator winding of three-phase induction motors (TPIMs). In this regard, FRA testing is conducted on two sets of induction motors with various short circuit faults. Investigated faults include short circuits between two phases, short circuit turns within the same phase, phase-to-ground, and phase-to-neutral short circuit. The measured FRA signatures are divided into three frequency ranges: low, medium, and high. Several statistical indicators are employed to quantify the variation between faulty and healthy signatures in each frequency range. Experimental results attest the feasibility of the FRA technique as a diagnostic tool to detect internal faults in rotating machines, such as induction motors.

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.

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 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.