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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.

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.

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.

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.

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.

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.

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.

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]