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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.

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.

Aiming at mitigating the fluctuation of distributed photovoltaic power generation, a segmented compensation strategy based on the improved seagull algorithm is proposed in this paper. In this regard, a hybrid energy storage system comprising a lithium battery and supercapacitor is utilized. The internal power distribution of the hybrid energy storage system is adjusted using wavelet packet decomposition, and the state of charge is employed to adapt the primary power distribution. The start and end times for charging and discharging are determined by combining the time of use, electricity price, state-of-charge information, and load size at night to realize the economic operation of the system. The opposing search operator strategy and mutation operation are used to improve the seagull algorithm, optimize the controller parameters of the DC/DC converter, and improve its response time. Combined with the historical measured data of a distributed photovoltaic in Hubei Province, simulation results show that the proposed strategy can effectively smoothen the fluctuation of distributed photovoltaic generated power while reducing the charging and discharging frequencies of the energy storage system, hence improving its stability and service life.

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.

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. [ABSTRACT FROM AUTHOR]

The low efficiency of the electromagnetic forming is one of the obstacles for the advancement of this technology. Aiming at solving this issue, this paper presents a double tube-task electromagnetic forming technology method by employing a single driving coil to achieve tube compression and expansion at the same time. On the basis of electromagnetic forming principles and the law of energy conversion, this paper presents a new electromagnetic forming model for double tubes. The magnetic flux density distribution, electromagnetic force distribution and forming efficiency of the tube are analyzed and the effect of the equivalent pulse width of the driving coil current on the forming efficiency is investigated. Results show that in order to maintain the same plastic strain energy of the tube, the initial discharge energy required by the proposed double tube model is 25 kJ which is smaller than that required by a traditional single tube model (33.58kJ) and the efficiency can be improved by about 34.3%. The double tube model attains the maximum forming efficiency near the optimal current pulse width and can improve the low efficiency of traditional electromagnetic forming to a certain extent and provide more possibilities for this technological process.

Tube uneven radial deformation along the axial direction is one of the main technical problems of tube electromagnetic bulging, which restricts the wide advancement of this technology. In order to improve the uniformity of tube radial deformation, a tube electromagnetic bulging method based on concave magnetic field shaper is presented in this paper. Based on the theory of tube electromagnetic bulging and magnetic field shaper, an electromagnetic structure coupling finite element model is built using COMSOL commercial software. Obtained results show that the proposed concave magnetic field shaper can alter the magnetic field distribution on the tube to shape the produced electromagnetic force in a concave profile so that the tube can exhibit more uniform bulging effect. In the case study investigated in this paper, the maximum uniform region increases from 23.7mm to 56.2mm when the concave magnetic field shaper is used. Obviously, the introduction of concave magnetic field shaper can effectively solve the problem of the tube uneven radial deformation during electromagnetic bulging process, and promote more industrial applications of this technology.

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.

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.

Health index has been widely accepted as a powerful tool for monitoring the condition of power transformer insulation system based on various diagnostic parameters. While this approach has been extensively discussed in the literature, not much attention was given to provide effective solutions to the uncertainty in the used data. According to CIGRE 761, data quality issues may arise due to measurement accuracy as well as incompleteness and unavailability of the required data. Therefore, this article presents the implementation and evaluation of a certainty level model for transformer insulation system health index to deal with data uncertainty. The impact of data unavailability on the health index results is also investigated. Certainty level of the health index is determined by the criticality level of available data, and is reported along with the health index result. A method to handle unavailable data by predicting the oil interfacial tension (IFT) using Random Forest approach is also presented. The proposed certainty level model is designed to accommodate the predicted value of missed data into the health index model while considering its prediction accuracy. The robustness of the developed model is validated through its application in assessing the health condition of six in-service power transformers. The results indicate that by including the proposed certainty level and the prediction approach to eliminate the issue of uncertain and missed diagnostic data, an asset management decision can be taken on operating power transformer fleets with high level of confidence.

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).

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%.

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.

With the wide application of vegetable insulating oil in power transformers, it is essential to investigate the influence of moisture on the frequency domain dielectric spectrum (FDS) analysis in order to propose an accurate moisture assessment method for vegetable oil-paper insulation system. In this regard, various vegetable oil-paper samples are prepared in a laboratory environment. FDS curves for all samples are obtained at different moisture contents and operating temperatures. A new method to estimate the moisture content in insulation paper is proposed based on the low-frequency characteristic quantity. Also, the effect of operating temperature and water migration between the oil and paper insulation on the FDS analysis is investigated. Based on this analysis, a compensation factor is introduced to eliminate the influence of temperature on paper moisture assessment. Furthermore, an improved normalization method for the temperature effect on the FDS of vegetable oil-paper insulation with different moisture contents is proposed. The proposed moisture assessment and temperature normalization methods are validated thorough their application on an operating power transformer.

Electromagnetic forming is a well-developed high-rate forming technology. An important issue of this technology is the temperature rise of the driving coil, which can adversely affect the coil service life. This paper is aimed at addressing this issue through detailed investigation of an electromagnetic forming based on half-wave current method. On the basis of the principle of electromagnetic forming and finite element modelling, this paper compares and analyzes the temperature rise profile of the driving coil along with the forming efficiency of the workpiece in the traditional discharge, the crowbar discharge and the half-wave current discharge models. Results show that with the same system discharge parameters, the half-wave current method can reduce the Joule heating of the driving coil from 1.82kJ to 1.3kJ, which effectively reduces the temperature rise of the driving coil. At the same time, the workpiece forming efficiency is improved from 9.6% to 11.8%. Thus, the half-wave current method can solve the issue of the temperature rise of the driving coil to some extent and promote the process of industrial applications of the electromagnetic forming technology.

Non-uniform axial deformation and tube wall thinning are the main issues restricting the further development of the tube electromagnetic bulging technology. Aiming at overcoming these issues, a tube electromagnetic bulging method based on convex driving coil is proposed in this paper. An electromagnetic-structure coupling model of the tube electromagnetic expansion process is established and the influence of detailed convex coil parameters on radial and axial Lorentz forces are analyzed. Results show that with the new proposed method, the homogeneous length of the tube axial deformation is 4.7 times that of the expansion loading with conventional driving coil. Meanwhile, the issue of tube wall thinning in the conventional electromagnetic bulging method is restrained due to the produced axial electromagnetic force using the proposed convex coil. The proposed tube electromagnetic bulging based on convex coil can effectively overcome the issues currently exist in the conventional tube electromagnetic bulging and promote wide industrial application of the electromagnetic forming technology.

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.

Improving the forming efficiency at a reasonable cost has been the main challenge in the electromagnetic forming technology. Towards this aim, this paper proposes a simultaneous cost-effective double-sheet electromagnetic forming method using single driving coil. In order to highlight the features of the proposed technique, comparison of the performance of five electromagnetic forming models is presented. These models include a no-sheet model, single-sheet stationary model, single-sheet moving model, double-sheet stationary model and double-sheet moving model. For each model, electromagnetic force distribution and forming efficiency of the workpiece are analyzed. Furthermore, the influence of the pulse current width of the driving coil on the electromagnetic forming efficiency of the plate is investigated for all models. Results show that for the same system discharge parameters, electromagnetic forming efficiency of double-sheet is 21.14% whereas the single-sheet electromagnetic forming model only achieves 14.68% efficiency. Thus, the double-sheet electromagnetic forming technology can improve the low forming efficiency of a single-sheet electromagnetic forming method and at the same time, the method can form two workpieces simultaneously which makes it more cost effective than the single-sheet method.

Electromagnetic forming is a method of high-speed metal workpiece forming technology that uses electromagnetic force pulses generated from the interaction of the magnetic flux density and workpiece induced eddy current. The main challenge of this technology is the control of the eddy current and magnetic flux density to perform various forming processes. This paper is aimed at overcoming this challenge by introducing a new electromagnetic sheet forming method based on discretely driven rings. In this method, a set of discrete conducting rings are placed between the driving coil and the metal workpiece to enhance the shaping process of the workpiece. By changing the number and position of the rings, the induced eddy current can be adjusted, and different electromagnetic force distribution profiles can be realized. COMSOL software is used to develop an electromagnetic field-structure coupling model of the proposed electromagnetic forming method. The characteristics of the electromagnetic force distribution and its effect on the workpiece forming process are analyzed. Results show that the electromagnetic force distribution profile can be controlled by adjusting the rings physical parameters such as diameter, material, and position. The proposed method can effectively improve the performance of the conventional electromagnetic forming technology and has the potential for implementation in wide industry applications.

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.

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.