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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 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.

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.

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.

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.

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.

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.

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

The short-term variability of photovoltaic (PV) system-generated power due to ambient conditions, such as passing clouds, represents a key challenge for network planners and operators. Such variability can be reduced using a geographical smoothing technique based on installing multiple PV systems over certain locations at distances of meters to kilometers. To accurately estimate the PV system’s generated power during cloud events, a variability reduction index (VRI), which is a function of several parameters, should be calculated precisely. In this paper, the Wavelet Transform Technique (WTT) along with Adaptive Neuro Fuzzy Inference System (ANFIS) are used to develop new models to estimate the PV system’s power output during cloud events. In this context, irradiance data collected from one PV system along with other parameters, including ambient conditions, were used to develop the proposed models. Ultimately, the models were validated through their application on a 0.7 km2 PV plant with 16 rooftop PV systems in Brisbane, Australia.

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

The significant increase in electronic devices has resulted in serious harmonic pollutions in electricity grids. In addition, due to the resonance of the system capacitance and inductance, the harmonic current may be amplified dramatically when it goes through the transmission line which is harmful to the normal operation of electrical equipment. To suppress the voltage and current harmonics resonance, many control strategies of active power filter (APF) are proposed. Nevertheless, these strategies cannot attenuate the amplified harmonic voltage and current due to resonance. In this paper, the harmonic propagation characteristics are investigated on a long transmission line and a new control strategy called VI-APF based on current-controlled and voltage-controlled current sources connected in parallel is presented. According to the simulation results, the proposed control strategy can suppress the total harmonic distortion to a low level. Finally, the effectiveness of VI-APF is demonstrated through the simulation on the IEEE 33-bus distribution system.

Several window functions are currently applied to improve the performance of the discrete Fourier transform (DFT) harmonic detection method. These window functions exhibit poor accuracy in measuring the harmonic contents of a signal with high-order and weak-amplitude components when the power frequency fluctuates within a small range. In this paper, a minimum side-lobe optimization window function that is aimed at overcoming the abovementioned issue is proposed. Moreover, an improved DFT harmonic detection algorithm based on the six-term minimum side-lobe optimization window and four-spectrum-line interpolation method is proposed. In this context, the minimum side-lobe optimization window is obtained by optimizing the conventional cosine window function according to the optimization rules, and the characteristics of the new proposed window are provided to analyze its performance. Then, the proposed optimization window function is employed to improve the DFT harmonic detection algorithm based on the six-term minimum side-lobe optimization window and four-spectrum-line interpolation method. The proposed technique is used to detect harmonics of an electricity gird in which the six-term minimum side-lobe optimization window is utilized to eliminate the influence of spectrum leakage caused by nonsynchronous sampling of signal processing. The four-spectrum-line interpolation method is employed to eliminate or mitigate the fence effect caused by the inherent measurement error of the DFT method. Simulation experiments in two complex conditions and an experiment test are carried out to validate the improved performance of the proposed window. Results reveal that the six-term minimum side-lode optimization window has the smallest peak side lobe when compared with existing windows, which can effectively reduce the interaction influence of spectrum leakage, improve the measurement accuracy of the DFT harmonic detection method, and meet the standard requirement of harmonic measurement in complex situations.

Dissolved gas analysis (DGA) of transformer oil is considered to be the utmost reliable condition monitoring technique currently used to detect incipient faults within power transformers. While the measurement accuracy has become relatively high since the development of various off-line and on-line measuring sensors, interpretation techniques of DGA results still depend on the level of personnel expertise more than analytical formulation. Therefore, various interpretation techniques may lead to different conclusions for the same oil sample. Moreover, ratio-based interpretation techniques may fail in interpreting DGA data in case of multiple fault conditions and when the oil sample comprises insignificant amount of the gases used in the specified ratios. This paper introduces an improved approach to overcome the limitations of conventional DGA interpretation techniques, automate and standardize the DGA interpretation process. The approach is built based on incorporating all conventional DGA interpretation techniques in one expert system to identify the fault type in a more consistent and reliable way. Gene Expression Programming is employed to establish this expert system. Results show that the proposed approach provides more reliable results than using individual conventional methods that are currently adopted by industry practice worldwide.

Condition monitoring and early fault diagnosis for wind turbines have become essential industry practice as they help improve wind farm reliability, overall performance and productivity. If not detected and rectified at early stages, some faults can be catastrophic with significant loss or revenue along with interruption to the business relying mainly on wind energy. The failure of Wind turbine results in system downtime and repairing or replacement expenses that significantly reduce the annual income. Such failures call for more systematized operation and maintenance schemes to ensure the reliability of wind energy conversion systems. Condition monitoring and fault diagnosis systems of wind turbine play an important role in reducing maintenance and operational costs and increase system reliability. This paper is aimed at providing the reader with the overall feature for wind turbine condition monitoring and fault diagnosis which includes various potential fault types and locations along with the signals to be analyzed with different signal processing methods.