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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, optimal allocation of a distributed thyristor-controlled series compensator (DTCSC) in a power system is presented to minimize overload, voltage deviations, and power losses while improving system reliability. The decision variable was defined as the optimal reactance of the DTCSC in the power system, which was determined using a new meta-heuristic algorithm named the improved equilibrium optimization algorithm (IEOA). A nonlinear inertia weight reduction strategy was used to improve the performance of traditional EOA in preventing premature convergence and facilitate a quick global optimum solution. The effect of system critical line outage was evaluated for each of the considered goals. To evaluate the effectiveness of the proposed methodology, IEOA capability was compared with particle swarm optimization (PSO) and manta ray foraging optimizer (MRFO) methods. Simulations were carried out considering different scenarios on 14- and 118-bus test systems. The results showed that, for all scenarios, optimal allocation of DTCSC could result in a significant reduction in overloading, voltage deviation of network buses, as well as power losses under the condition of line outage, due to the optimal injection of reactive power. In all investigated scenarios, our results attested to the superiority of the IEOA over the traditional EOA, PSO, and MRFO in achieving a better value for the objective function. In addition, the results showed that improving reliability in the objective function could eliminate overloading, and hence, introduce further improvement in each of the objectives. [ABSTRACT FROM AUTHOR]

One of the key challenges in multilevel inverters (MLIs) design is to reduce the number of components used in the implementation while maximising the number of output voltage levels. This paper proposes a new concept that facilitates a device count reduction technique of existing cascaded MLIs. Moreover, the proposed concept can be utilised to extend existing single phase cascaded MLI topologies to three-phase structure without tripling the number of semiconductor components and input dc-supplies as per the current practice. The new generalized concept involves two stages; namely, cascaded stage and phase generator stage. The phase generator stage is a combination of a conventional three-phase two level inverter and three bi-directional switches while the cascaded stage can employ any existing cascaded topology. A laboratory prototype model is built and extensive experimental analyses are conducted to validate the feasibility of the proposed cascaded MLI concept. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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

Monitoring electrical power quality has become a priority in the industrial sector background: avoiding unwanted effects that affect the whole performance at industrial facilities is an aim. The lack of commercial equipment capable of detecting them is a proven fact. Studies and research related to these types of grid behaviors are still a subject for which contributions are required. Although research has been conducted for disturbance detection, most methodologies consider only a few standardized disturbance combinations. This paper proposes an innovative deep learning-based diagnosis method to be applied on power quality disturbances, and it is based on three stages. Firstly, a domain fusion approach is considered in a feature extraction stage to characterize the electrical power grid. Secondly, an adaptive pattern characterization is carried out by considering a stacked autoencoder. Finally, a neural network structure is applied to identify disturbances. The proposed approach relies on the training and validation of the diagnosis system with synthetic data: single, double and triple disturbances combinations and different noise levels, also validated with available experimental measurements provided by IEEE 1159.2 Working Group. The proposed method achieves nearly a 100% hit rate allowing a far more practical application due to its capability of pattern characterization. [ABSTRACT FROM AUTHOR]

The proliferation of renewable energy resources (RES), especially solar photovoltaic (PV) generation resources, causes overvoltage and line overloading in distribution networks. This study proposes a two-level volt–var control method based on multiple timescales. The on-load tap changer (OLTC) operates on an hourly timescale, to regulate the voltage on the secondary winding. In the 15-minutes timescale, PV-connected smart inverters and static var compensators (SVCs) are obliged to compensate the reactive power for the voltage control at the point of common coupling. In the multi-timescale voltage control framework, this study proposes a new multi-sectional volt–var curve (MSVVC) of a PV inverter. The objective of the MSVVC is to minimize the energy loss in the network, improve the voltage profile, and obtain the operational margin of other reactive power compensation devices. In the process of determining the optimal parameters of the MSVVC, stochastic modeling-based load flow analysis is utilized to consider the intermittency and uncertainty of RES generation. The effectiveness of the proposed method is verified on the IEEE 33-bus system in comparison with the conventional volt–var curve cases. [ABSTRACT FROM AUTHOR]

A method of fault tracking for relay protection devices is presented in this paper. Fault tracking means that after the failure of relay protection devices, the anomalies and warning information are obtained through data-mining technology, and then, the fault tracking algorithm is used to find the cause of failure. Let us take microcomputer protection as an example: Firstly, the common failure symptoms and the prior probability of failure causes can be collected through empirical field data. Then, the concept of an event set is proposed; thus, the causes set and the symptoms set of failure can be created. According to the causal relationship between the causes set of failure and symptoms set of failure, the reasoning chain and the corresponding Bayesian network model are built. Then, the probability of failure causes can be obtained through backward reasoning to continue the tracking analysis of failure causes for relay protection devices. Since the data used in modeling are all from statistics, this method has strong applicability and represents a simple and reliable method for the timely determination and elimination of failure in a power system. [ABSTRACT FROM AUTHOR]

This article describes the issues related to the analysis of the reliability-exploitation of power supply systems in transport telematics devices (PSSs in TTDs). This paper characterizes solutions, which are applied in supply systems, and describes a PSS in a TTD from a main source and a standby one. This enables determining the dependencies denoting the probabilities of the system staying in full ability state, safety threat state, and safety unreliability state. A quality analysis of the PSS in TTD was conducted, and the indicator value of the supply continuity quality was evaluated. This indicator allows the demonstration of continuity quality of power supply (CQoPS) dependency on many quality dimensions, not just reliability. An example demonstrates the calculation of CQoPS factor for both the main and the standby power supply, employing three observations, each influencing the quality. The presented considerations in the field of quality and reliability-exploitation modeling of a PSS can be applied as well in other public utility facilities (including those classified as critical infrastructure). The character of functions performed by critical infrastructure facilities demands an operating continuity of these systems at an appropriate level. The co-first authors of the article once again address the issue of determining CQoPS factor, this time on the basis of modeling using mathematical evidence. TTD is an example in this article, because the presented methods can be used in any kind of system, especially in the power source of critical systems. [ABSTRACT FROM AUTHOR]

Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to create inconsistency related problems between samples, and teacher-given label behaviors led to more ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks, a new autoencoder has been designed as an additional source that could correlate inputs and labels by exploiting label information in a completely unsupervised learning scheme. Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data. Due to the non-stationary and sequentially driven nature of samples, recovered representations have been fed into a transfer learning, convolutional, long–short-term memory neural network for further meaningful learning representations. The assessment procedures were benchmarked against recent methods under different training datasets. The obtained results led to more efficiency confirming the strength of the new learning path. [ABSTRACT FROM AUTHOR]

In this paper an adaptive tolerant estimator using singular value decomposition is proposed for a distribution network under model uncertainty in power systems. The adaptive tolerant estimator was designed with adjusted parameters and adjusted weights to overcome the limitations of model uncertainty. The estimator that reduces the measurement errors is adaptive to fast parameter changes in complicated environments. The singular value decomposition method was combined into the state estimator, which extended the over-determined cases to under-determined cases under model uncertainty. The performance of the tolerant estimator was compared with the conventional adaptive estimator, and the tolerant estimator showed accurate estimations against model uncertainty in complicated measurement environments. [ABSTRACT FROM AUTHOR]

This paper deals with a discrete-time stochastic control model design for random failure prone and maintenance in a single machine infinite bus (SMIB) system. This model includes the practical values of failure/repair rate of transmission lines and transformers. The probability matrix is, therefore, calculated accordingly. The model considers two extreme modes of operations: the most reliable mode and the least reliable contingency case. This allows the control design which stochastically stabilizes the system under jump Markov disturbances. For adequate transient response, the proposed state feedback power system stabilizer (PSS) achieves a desired settling time and damping ratio by placing the closed-loop poles in a desired region. The control target should also be satisfied for load variations in either mode of operation. A sufficient condition is developed to achieve the control objectives via solving a set of linear matrix inequalities (LMI). Using simulation, the performance of the designed controller is tested for the system that prone to random failure/maintenance under various loading conditions. Simulation results reveal that the closed-loop poles reside within the desired region satisfying the required settling time and damping ratio under the aforementioned disturbances. The contributions of the paper are summarized as follows: (1) modeling of transition probability matrix under Markov Jumps using practical data, (2) designing a controller by compelling the closed poles into the desired region to achieve adequate dynamic performance under different load varying conditions. [ABSTRACT FROM AUTHOR]

Air-core coil current transformer is a key piece of equipment in the digital substation development. However, it is more vulnerable to various faults when compared with the traditional electromagnetic current transformer. Aiming at understanding the effect of various parameters on the performance of the air-core coil current transformer, this paper investigates the influence of these factors using the maximum information coefficient. The interference mechanism of influencing factors on the transformer error is also analyzed. Finally, the Stacking model fusion algorithm is used to predict transformer errors. The developed base model consists of deep learning, integrated learning and traditional learning algorithms. Compared with gated recurrent units and extreme gradient boosting algorithms, the prediction model based on stacking model fusion algorithm proposed in this paper features higher accuracy and reliability which helps improve the performance and safety of future digital substations. [ABSTRACT FROM AUTHOR]

The extensive usage of power electronic components creates harmonics in the voltage and current, because of which, the quality of delivered power gets affected. Therefore, it is essential to improve the quality of power, as we reveal in this paper. The problems of load voltage, source current, and power factors are mitigated by utilizing the unified power flow controller (UPFC), in which a combination of series and shunt converters are combined through a DC-link capacitor. To retain the link voltage and to maximize the delivered power, a PV module is introduced with a high gain converter, named the switched clamped diode boost (SCDB) converter, in which the grey wolf optimization (GWO) algorithm is instigated for tracking the maximum power. To retain the link-voltage of the capacitor, the artificial neural network (ANN) is implemented. A proper control of UPFC is highly essential, which is achieved by the reference current generation with the aid of a hybrid algorithm. A genetic algorithm, hybridized with the radial basis function neural network (RBFNN), is utilized for the generation of a switching sequence, and the generated pulse has been given to both the series and shunt converters through the PWM generator. Thus, the source current and load voltage harmonics are mitigated with reactive power compensation, which results in attaining a unity power factor. The projected methodology is simulated by MATLAB and it is perceived that the total harmonic distortion (THD) of 0.84% is attained, with almost a unity power factor, and this is validated with FPGA Spartan 6E hardware. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the increasing demand for renewable energy power generation systems, high-power DC transmission technology is drawing considerable attention. As a result, stability issues associated with high power DC transmission have been highlighted. One of these problems is the fault current that appears when a fault occurs in the transmission line. If the fault current flows in the transmission line, it has a serious adverse effect on the rectifier stage, inverter stage and transmission line load. This makes the transmission technology less reliable and can lead to secondary problems such as fire. Therefore, fault current must be managed safely. DC circuit breaker technology has been proposed to solve this problem. However, conventional technologies generally do not take into account the effects of fault current on the transmission line, and their efficiency is relatively low. The purpose of this study is to introduce an improved DC circuit breaker that uses a blocking inductor to minimize the effect of fault current on the transmission line. It also uses a ground inductor to efficiently manage the LC resonant current and dissipate residual current. DC circuit breakers minimize adverse effects on external elements and transmission lines because the use of elements placed on each is distinct. All of these processes are precisely verified by conducting simulation under 200 MVA (±100 kV) conditions based on the VSC-based HVDC transmission link. In addition, the mechanism was explained by analyzing the simulation results to increase the reliability of the circuit in this paper. [ABSTRACT FROM AUTHOR]

The conventional online battery impedance measurement method works by perturbing the duty cycle of the DC-DC power converter and measuring the response of the battery voltage and current. This periodical duty cycle perturbation will continuously generate large voltage ripples at the output of power converters. These large ripples will not easily be removed due to the high amplitude and wide frequency range and would be a challenge to meet tight output regulation. To solve this problem, this paper presents a new online battery impedance measurement technique by inserting a small switched resistor circuit (SRC) into the converter. The first contribution of this work is that the perturbation source is moved from the main switch to the input-side of the converter, so the ripples are reduced. The analysis and experimental results of the proposed method show a reduction of 16-times compared with the conventional method. The second contribution tackles the possible change of the battery state of charge (SOC) during the online battery measurement process, which will inevitably influence the impedance measurement accuracy. In this proposed method, battery impedance at multiple frequencies can be measured simultaneously using only one perturbation to accelerate measurement speed and minimize possible SOC change. The experimental impedance results coincide with a high-accuracy laboratory battery impedance analyzer. [ABSTRACT FROM AUTHOR]

The paper describes a case study of the safety hydraulic system damage in the working of a longwall in a Polish coal mine. The safety elements are a component of the powered roof supports which secure the shield against damage during rock burst incidents. The damage event, which occurred in the hydraulic system during the mining process, caused the uncontrolled lowering of the powered roof support height during the mining process. The uncontrolled lowering of a shield may cause the danger of the loss of the stability along the longwall working in the form of a rock burst and collapses and may represent a serious and immediate danger to the safety and health of employees. Based on the results of the computational fluid dynamics methods (CFD) analysis of the safety elements in the hydraulic system of longwall 2-leg shield, the causes of damage were diagnosed and presented. The CFD and the strength analysis by the finite element method (FEM) were used for numerical modeling. The diagrams and maps of changes of parameters having an impact on the damage mechanism in safety elements of the hydraulic leg were developed based on the results of model tests. The forecasted values of stress distributions in the safety system of the hydraulic leg have made it possible to identify the reasons of the damage causes, verified by real observations. [ABSTRACT FROM AUTHOR]

Distributed energy resources (DERs), including renewable energy resources (RESs) and electric vehicles (EVs), have a significant impact on distribution systems because they can cause bi-directional power flow in the distribution lines. Thus, the voltage regulation and thermal limits of the distribution system to mitigate from the excessive power generation or consumption should be considered. The focus of this study is on a control strategy for DERs in low-voltage DC microgrids to minimize the operating costs and maintain the distribution voltage within the normal range based on intelligent scheduling of the charging and discharging of EVs, and to take advantage of RESs such as photovoltaic (PV) plants. By considering the time-of-use electricity rates, we also propose a 24-h sliding window to mitigate uncertainties in loads and PV plants in which the output is time-varied and the EV arrival cannot be predicted. After obtaining a request from the EV owner, the proposed optimal DER control method satisfies the state-of-charge level for their next journey. We applied the voltage sensitivity factor obtained from a load-flow analysis to effectively maintain voltage profiles for the overall DC distribution system. The performance of the proposed optimal DER control method was evaluated with case studies and by comparison with conventional methods. [ABSTRACT FROM AUTHOR]

Fault diagnosis under the condition of data sets or samples with only a few fault labels has become a hot spot in the field of machinery fault diagnosis. To solve this problem, a fault diagnosis method based on deep transfer learning is proposed. Firstly, the discriminator of the generative adversarial network (GAN) is improved by enhancing its sparsity, and then adopts the adversarial mechanism to continuously optimize the recognition ability of the discriminator; finally, the parameter transfer learning (PTL) method is applied to transfer the trained discriminator to target domain to solve the fault diagnosis problem with only a small number of label samples. Experimental results show that this method has good fault diagnosis performance. [ABSTRACT FROM AUTHOR]

Contingency analysis (CA) is a well-known function in power system planning and operation. In accordance with CA results, the system operator dispenses information regarding static security of the power system (overloads and/or voltage outside tolerable limits). However, classic CA with remedial action schemes cannot distinguish safe operating regimes from potentially dangerous ones in terms of voltage (in)stability. In fact, voltage instability is considered as one of the major threats leading to power system insecurity. Therefore, in this study an enhanced contingency analysis (ECA) is presented where the classical CA is extended with static voltage analysis based on the modal analysis. The article presents a dedicated methodology for the proposed ECA tool, with special emphasis on the analysis of corrective measures provided by the system operator, intended for enhancing power system security (regulation transformer action, distributed generation and energy storage). Also the influence of the load model was analyzed by simulation and the main conclusions are presented. The study demonstrated the advantages that distributed generation resources and energy storage can provide in the context of voltage stability. Also, the simulations acknowledged the importance of correct load modeling, since over or under estimation of a certain load-type component can result in too optimistic or too pessimistic power system operation limits. [ABSTRACT FROM AUTHOR]

The aging of power transformers causes several defects and damages in the insulating system, especially in the insulating paper. The degradation of the insulating paper generates dissolved gases in the insulating oil, which are measured by gas chromatography and used as an indicator of the insulation status. The state of the insulating paper can be identified based on the degree of polymerization (DP) measurement. In some cases, when the measurement of DP is difficult, estimating DP can be accomplished through gathering information about some of the testing parameters, such as the dissolved gases (DGA), breakdown voltage (BDV), oil interfacial tension (IF), oil acidity (ACI), moisture content (MC), oil color (OC), dielectric loss (Tan δ), and furans concentration specifically (2-furfuraldhyde (FA)). The statistical tools (correlation and multiple linear regression), based on 131 transformer samples, can be used to build a relation linking DP and one or more of the previous parameters to identify the insulating paper status and the percentage of remaining life of the transformer. The results indicated that it is difficult to build a mathematical model to relate between the DP and the testing variables, except with FA, where the trend of DP with FA is more obvious than with other variables. The empirical formula to compute DP based on the FA concentration was developed and gave promising results to compute DP and the remaining life of the power transformers. [ABSTRACT FROM AUTHOR]

The objective of this work is to analyze disturbances in the environment caused by anthropic activities in the oil and gas extraction sector. Methodologically, focusing on environmental vulnerability (EV), hydrocarbons (oil and gas) are considered through a qualitative and quantitative analysis of environmental impacts, including the research of Environmental Impact Studies and procedures like EIA/RIMA (institutional Environmental Impact Reports in Brazil). This study focuses on the operation and demobilization of the offshore drilling activity and the installation and operation of the Santos Basin pre-salt oil and gas production (Stages 1, 2, and 3). The criteria addressed in the EIA/RIMAs are used, focusing on those that correlate with EV and oil and gas extraction. Impacts for long-term, permanent, partially reversible, or irreversible disturbances are filtered, totaling 53 impacts (31 effective/21 potential). We concluded that the criteria and methodologies of EIAs vary between stages. At times, the variation is so drastic that the same impact can have a completely different rating from one stage to another, despite referring to the same area. This condition makes it impossible to define a single vulnerability index for the pre-salt venture. This work does not offer a concrete resolution, but exposes the EV issue and its inconsistencies. [ABSTRACT FROM AUTHOR]

The conversion of the electrical energy into the mechanical is usually realized by a motor, power electronics and cascade control. The relative stability (Θ -stability), i.e., the displacement of its eigenvalues of this system is analyzed for a drive with a BLDC motor. The influence of changing the basic parameters of the motor and power supply system on the drive operation is considered. 4th order closed-loop transfer-function of the cascade control is presented, where boundaries of the transfer-function coefficients are used. The cascade system which uncertainty of the resistance, inductance, flux and gain parameters is analyzed. Theoretical calculations for the cascade control, simulations and laboratory tests are included in the article. [ABSTRACT FROM AUTHOR]

In this paper, different Pulse Width Modulation (PWM) strategies for operating with a low-switching frequency, a topology that combines Conventional Matrix Converters (CMCs), and Coupled Reactors (CRs) are presented and discussed. The principles of the proposed strategies are first discussed by a conceptual analysis and later validated by simulation. The paper shows how the combination of CMCs and CRs could be of special interest for sharing the current among these converters' modules, being possible to scale this solution to be a modular system. Therefore, the use of coupled reactors allows one to implement phase shifters that give the solution the ability to generate a stair-case load voltage with the desired power quality even the matrix converters are operated with a low-switching frequency close to the grid frequency. The papers also address how the volume and weight of the coupled reactors decrease with the growth of the fundamental output frequency, making this solution especially appropriate for high power applications that are supplied at high AC frequencies (for example, in airport terminals, where a supply of 400 Hz is required). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]