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This paper presents a two-layer framework for improving the resilience of a 118-bus active distribution network consisting of four microgrids, which includes hybrid storage systems, electric buses (EBs), and the direct load control (DLC) program. In the proposed model, the uncertainties of RESs generation, demand, and EBs' mobility are considered, and the robust optimization approach is used to tackle them. In the first layer, the planning of each microgrid is done separately and the energy purchase/sale request is sent to the control center. Then in the second layer, the control center performs the planning of the main network according to the requested program of the microgrids. Note that in this layer, the control center is able to rearrange the distribution feeder and send EBs to vital points of the network. Finally, the validity of the proposed model is evaluated through the implementation on seven case studies and the results show that the presence of hydrogen and electrical storage devices reduces forced load shedding (FLS) by 45.03% and 12.19%, respectively, during emergency situations. In addition, the results indicate that robust planning and the use of EBs for network recovery increase the resilience index by 3.35% and 3.98%, respectively. • Developing a decentralized robust framework for microgrid resilience enhancement. • Providing a two-layer model to guarantee the privacy of microgrids. • Increasing system resilience by electrical and hydrogen storage systems. • Improving system resilience by deploying EBs to vital nodes of the network. • Increasing resilience index through DFR and DLC. [ABSTRACT FROM AUTHOR]

With the emergence of smart grids, accurate very short-term load forecasting (VSTLF) has become a crucial tool for competitive energy markets. The number of behind-the-meter photovoltaic solar panels, which usually are not monitored are increasing. This could reduce the load visibility and also affects the VSTLF accuracy. While most of the research works focus on demand forecasting of large areas, the performance of VSTLF methods for the future scenario of high solar power penetration in the microgrid environment is unclear. This paper investigates the impact of high solar power integration on the forecasting accuracy of machine learning VSTLF models for microgrids. The performance of Neural Network, Support Vector Regression are evaluated for high penetration scenario and no solar penetration. The accuracy of these models is examined for three different forecasting horizons through various comparative experiments for two real-world microgrid datasets. According to simulation results, using the most relevant variables is highly recommended. Furthermore, the results demonstrate that machine-learning methods can tackle the nonlinear characteristics of net load forecasting as well as total load forecasting. These simulations show that high penetration of solar power generation could not significantly affect the accuracy of machine learning VSTLF if most relevant variables are selected and applied as model inputs. [ABSTRACT FROM AUTHOR]

This paper presents a fault detection and faulty area identification algorithm in double-circuit transmission lines (DCTLs) compensated by the generalized unified power flow controller (GUPFC) for the first time. The topology of the GUPFC-compensated DCTL is threatened by three types of faults: single-circuit faults, inter-circuit faults, and grounded double-circuit cross-country faults. In the first step, the suggested scheme can determine the time of fault in this type of topology using a very fast method that is based on the analysis of a current index called the current jump index. In the second step, the fault type and area are identified with the help of two other indices, i.e., voltage difference index and current difference index. The test network presented in this paper, which includes a DCTL and a GUPFC, along with the proposed algorithm, is implemented in the MATLAB software package. The results of the simulation and sensitivity analysis of the algorithm confirm the correct performance of the algorithm. [ABSTRACT FROM AUTHOR]

An accurate inter‐circuit fault location algorithm for double‐circuit transmission lines compensated by a generalized unified power flow controller based on phasor analysis is presented. The proposed algorithm utilizes synchronous currents and voltages of the buses on both sides of the line to accurately calculate the fault location. Using the equations of sequence component circuits, the algorithm calculates voltage and current of the fault point on both circuits. Then, using boundary conditions equations for 98 types of inter‐circuit fault on the double‐circuit transmission lines, the accurate location of the fault is obtained. The number of boundary conditions equations varies depending on the type of faulty phases of the two circuits and the number of unknowns in the current and voltage equations of the fault point. The calculation of these boundary equations are also described. The results presented in the simulation section verify the validity and accurate performance of the proposed algorithm under different fault conditions. [ABSTRACT FROM AUTHOR]

Network reconfiguration (NR) has undergone new changes to adapt to smart grid evolution. Internet of things (IoT) has found its way to smart grids especially in the distribution systems. Demand‐side management can be more efficient in the presence of IoT. IoT‐based energy management systems can have bidirectional communication with smart grids and decide according to their preferences, time‐of‐use (TOU) pricing, and their flexible devices. Therefore, customers would consume electricity more interactively. Consequently, the total load profile will be changed and a new NR approach must be adopted to meet load demands. So, the selection of critical switches must be modified to achieve the most effective network resource utilisation. For this purpose, a mixed‐integer linear programming model for feeder reconfiguration is presented considering the customers' behaviours in the IoT environment. At first, the customer manner is investigated at different prices and preferences. Then the total load profile is calculated considering the normal distribution of uncertain customer preference factors. Finally, the identification of critical switches is obtained using the proposed method. The optimisation is done in YALMIP and MOSEK toolboxes. Results show the effectiveness of the proposed method to efficient utilisation of IoT potentials and renewable energy benefits. [ABSTRACT FROM AUTHOR]

In this study, on the basis of a new agent‐based situational awareness platform, the amount of severity in closeness to voltage instability occurrence is determined. In the proposed platform (with the aid of the agents of the disturbance detection unit that are responsible for perception of environmental events and understanding the current situation and the agents of the early enough instability prediction unit that predicts instability occurrence), the novelty is in creating the agents of clustering units that are responsible for prediction of the future status, i.e. severity in closeness to voltage instability occurrence through determining the severity class. This clustering unit maps the reactive power‐based features obtained from unconstrained power flow analyses, to the instability time; without any need of post‐disturbance data, i.e. the method belongs to just‐after‐disturbance algorithms. Then, it estimates the severity of the closeness to instability for any test scenario according to its membership degree related to the pre‐defined severity clusters. The simulation results show acceptable overall accuracy (up to 99%) in performing early warning task for operators of power systems. [ABSTRACT FROM AUTHOR]

The rapid growth and development of manufacturing technology for controlling processors and high-power semiconductor devices has led to the enhancement of flexible AC transmission system (FACTS) controllers, thus improving the performance of power systems. The generalized unified power flow controller (GUPFC) is today introduced as the most advanced generation of the FACTS family, which is based on voltage source converter (VSC). It can, hence, control all the five parameters of a power system given above. The paper, for the first time, presents the accurate modeling of different parts of the control system and power electronics of the 72-pulse VSC-based GUPFC in detail to satisfy operation constraints and improve power quality in terms of harmonic level reduction. There is no need to use active or passive filters in the proposed model for reducing harmonic level of the GUPFC. In this regard, the calculation of four main parameters, namely D_alpha, alpha, angular speed, and sigma, as inputs of the pulse-firing system for the 72-pulse VSC is performed dynamically and optimally to meet all expected constraints in terms of operation, power quality, and harmonic reduction of a network with a GUPFC. The presented modeling is implemented on a 230 kV GUPFC-compensated double-circuit transmission line in MATLAB/Simulink environment, and the desired results of the presence of the 72-pulse VSC-based GUPFC are shown to meet the desired constraints of operators. [ABSTRACT FROM AUTHOR]

In this study, a simple, fast, and adaptive network reduction procedure is presented for long‐term voltage stability analyses, to speed up the simulation time. The proposed procedure consists of two parts including disturbance detection based on data from synchronised measurement devices; and the rest, up to the final estimated values, based on simulations and calculations. In turn, simulations and calculations result in an adaptive dynamic‐oriented determination of external sub‐network (according to the disturbance occurred) and substituting an appropriate equivalent for that subsystem. The external subsystem is determined without any need to measure post‐disturbance data, while the detection criteria are calculated by simulations at 0.8 s after disturbance occurrence. The results have shown considerable reduction in the simulation time while providing acceptable accuracy in delivering the time of voltage instability occurrences, especially when applying the proposed error compensation procedure. [ABSTRACT FROM AUTHOR]

• A multiple chance-constrained model for microgrid scheduling is proposed. • The model considers unwanted islanding and outage of DGs and batteries. • The model considers uncertainty of renewable energies and demand. • A novel probabilistic formulation is proposed to determine the required reserve. • A novel index is proposed to evaluate the sufficiency of the reserve. Maintaining power balance under both normal and emergency conditions is one of the most important challenges in optimal scheduling of microgrids (MGs). In this paper, a multiple chance-constrained scheduling model is developed for optimal scheduling of microgrid considering the most important factors that affect the power balance of the MG such as the uncertainty of demand and renewable resources, sudden outage of distributed generators (DGs), and unwanted islanding. Due to the growing penetration and size of batteries, the outage of batteries is also considered. Simultaneous consideration of all above-mentioned factors complicates the scheduling problem. Hence, to reduce the complexity of the model, the chance constraints are reformulated innovatively by adding controllable variables to the chance constraints. In addition, to determine the required reserve in different conditions of MG operation (i.e. normal operation, outage of each DG, outage of each battery, and unwanted islanding), individual probability distribution functions (PDFs) are formulated. Based on these PDFs, a probabilistic index entitled probability of reserve sufficiency (PRS) is introduced. The model maintains a certain amount of PRS in different conditions of MG operation. Finally, the model is examined on a test microgrid for different case studies and the results are analyzed to evaluate the model. [ABSTRACT FROM AUTHOR]

This paper deals with a control design based on amplitude adaptive notch filter (AANF) for a four-leg distributed static compensator (DSTATCOM) in a three-phase four-wire distribution grid to overcome current-related problems of power quality. Extracted reference currents of DSTATCOM are obtained using AANF because of its simple structure, exact measuring of frequency and amplitude, suitable estimation of the desired signal, and capability of tracking the changes of the input signal amplitude. To improve the dynamic performance of DSTATCOM, two fuzzy logic controllers are utilized to regulate DC link voltage and the voltage of point of common coupling (PCC). Furthermore, an adaptive hysteresis band current controller is applied for generating the gate pulses of IGBT switches. The proposed control scheme is robust to power system oscillations, especially when the main voltage suffers from disturbances and unbalancing. Different surveys are performed to study the efficacy of the proposed method, and results are verified through the simulation results in MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

In this study, the proposed algorithm is a help for utilities to find out when voltage instability will happen, as soon as they realise that a disturbance may/will result in voltage instability. In this regard, the data gathered, on‐line, by a wide area monitoring system (WAMS) is supposed to be processed in an energy management system, where, finally, voltage instability inception time will be determined. The proposed algorithm employs k‐nearest neighbour algorithm as an instance‐based learning algorithm. This estimation uses only pre‐disturbance information and exact disturbance data, so it is fast enough for real‐time applications. A comprehensive investigation of the input features has resulted in introducing the most appropriate feature set and the most effective search procedure in estimating the instability time. The simulation results show an acceptable accuracy in estimation of voltage instability time. [ABSTRACT FROM AUTHOR]

• Presenting a single end-based fault location method for hybrid parallel HVAC/HVDC overhead transmission lines (HPOTL). • Accurate modeling of HPOTL considering electromagnetic coupling between AC and DC lines. • Low sampling frequency and high accuracy in fault location estimation. • Determining the faulty half of transmission line using a robust method. • The proposed method is robust and independent of fault resistance, noise and fault angle. In this paper, a single ended traveling wave based fault location method is presented for Hybrid Parallel HVAC/HVDC Overhead Transmission Lines (HPOTLs) on the same tower using Discrete Wavelet Transform (DWT). In HPOTLs, the AC and DC transmission lines are adjacent to each other from the sending to the receiving end of the lines and there is electromagnetic coupling between them. Fault detection and classification algorithms in HPOTLs are presented in another paper. Therefore, in order to summarizing, in this paper, it is assumed that the fault is detected in the HVAC or/and HVDC lines and faulty line is identified. HPOTL faults include AC faults, DC faults and AC/DC intersystem faults. AC and DC voltage signals on sending end of the transmission line with a sampling frequency of 100 kHz are used for fault location in this paper. In order to minimize the noise effect, the squared wavelet transform coefficients (WTC2) is used for proposed fault location method. Finally, Bewley-Lattice diagrams are checked for the traveling wave pattern and locate the fault. At the end of paper, it has been shown that the proposed method has high accuracy, noise immunity, robustness against fault inception angles, and high impedance faults. [ABSTRACT FROM AUTHOR]

This paper presents a new design of the hybrid AC/DC microgrid for optimization of the performance of the smart grid. This design by using the capacity of the DC microgrid and introducing a new unified power quality conditioner, named UPQC-DC, and providing appropriate control schemes for two back to back interface converter between the AC and DC microgrids, presents important characteristics such as power quality improvement, power flow control, reactive power compensation, and elimination of power swings. The proposed method guarantees the realization of all aforementioned goals in both grid connected and isolated modes for the first time. Several computer simulations are performed using Matlab/Simulink software for verifying the proposed scheme. [ABSTRACT FROM AUTHOR]

Considering the advantages of DC microgrids, the extension of the conventional AC distribution grid can be implemented using a DC microgrid. This justifies the realization of a hybrid AC/DC microgrid. In the present study, a new global solution is presented to improve the power quality and to fully compensate the reactive power of an AC microgrid using DC bus capacity while introducing a new design for a hybrid AC/DC microgrid. In the new design, back- to-back connections of two series and parallel converters, as well as the presentation of new controllers and simultaneous utilization of an earthing switch, are proposed. The proposed method guarantees the quality of the delivered voltage to consumers and the drawn current from the network according to the IEEE-519 and IEEE-1159 standards under different power quality problems (e.g., interruptions, sags, harmonics, and any variation in voltage/current signals from pure sinusoidal). Through the proposed design of the new hybrid AC/DC microgrid, as a new feature, the operation of the network in islanded mode can be achieved in accordance with power quality standards even in the worst load quality conditions. It should be noted that in common hybrid microgrids in islanded mode, the delivered voltage quality is proportional to the quality of the consumer's load current. Another possibility of the proposed design is the instantaneous VAR compensation of nonlinear and induction loads of consumers to keep the power factor of the distribution transformer close to unit value. Simulation results indicate that there are acceptable levels of compensation for different types of power quality problems. Total harmonic distortions and total demand distortions are below 3% in both the grid-connected and isolated modes of the hybrid AC/DC microgrid. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid islanding detection approach for microgrids (MGs) with multiple connection points to smart grids (SGs) which is based on the probability of islanding (PoI) calculated at the SG side and sent to the central control for microgrid (CCMG). The PoI values are determined using a combination of passive, active, and communication islanding detection approaches based on the utility signals measured at the SGs sides which are processed by discrete wavelet transform using an artificial neural network (ANN). If \textPoI{\rm{ANN}} is larger than the threshold value (indicating high possibility of islanding) then a more accurate approach based on fuzzy network is used to recompute it (\textPoI{\rm{FUZZY}}) where the fuzzy parameters are determined by an adaptive neuro-fuzzy inference system. In the proposed technique, an active islanding is only performed when PoI is high and the amplitudes of the disturb signals are proportional to \textPoI{\rm{FUZZY}}. Furthermore, if the PoI is not correctly received by CCMG, two auxiliary tests will be performed in the MG side to detect islanding. These tests include an intentional passive islanding detection in a short preset time and an active islanding detection with disturb signals proportional to the calculated PoI. Detailed simulations are performed and analyzed to evaluate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

• Providing a bi-level framework for resilience enhancement of integrated energy systems. • Reducing the deviation of the final schedule of hubs from their requested schedule. • 52.71% reduction of FLS in the network by deploying MDGs and MESS. • Reducing the FLS in the network through hardening and DFR. • 408.22 kW reduction in FLS in industrial hubs through DLC program implementation. This study develops a comprehensive model for enhancing the resilience of integrated electricity and natural gas networks in the presence of industrial, commercial and residential hubs. Distribution system hardening is used as a long-term plan to improve system resilience, while a bi-level operation model is proposed to enhance real-time resilience. In the proposed model, in the upper level, the hubs perform their optimal scheduling and send it to distribution system operator (DSO). Then, in the lower level, the DSO performs network scheduling with regard to hubs' schedules, and determines limitations and power/gas shortages during emergency conditions. It should be noted that DSO performs network scheduling with the possibility of distribution feeder reconfiguration (DFR) and deploying mobile distributed generations (MDGs) and mobile energy storage systems (MESSs). Finally, the limitations are sent to the upper level and the hubs perform their scheduling according to the new condition. The proposed model is implemented on a 69-bus electricity network integrated with a 14-node gas network, and the results show that the proposed model not only reduces the deviation of the final schedule of hubs from their requested schedule but also significantly reduces the amount of forced load shedding (FLS) in the network. [ABSTRACT FROM AUTHOR]

Based on differential algebraic (DA) subsystem models of power system components including the nonlinear controllers, which are called component structural complex models, this paper presents a model reduction method (MRM). For the dynamic differential equations, based on the property that the nonlinearity of the controller compensates that of the component completely (or partially), the presented method uses the linear dynamic equations to describe the compensated dynamics. For the algebraic connection equations, the new defined state variables are used to substitute the original state variables for keeping the connection relation invariable. By selecting generators and static var compensators (SVC) as examples, the paper presents the basic procedure of the proposed reduction method for power system component complex models in detail. The numerical simulation indicates that the derived simplified models matches the original one very well in both dynamic response behaviors and the connection relation with the external system. [ABSTRACT FROM AUTHOR]

• Implementation of a flexible-reliable operation strategy by MG operator (MGO) on a green microgrid participated in the DA energy market. • Simultaneous modeling of operation, reliability, flexibility, economic, and environmental indices in MG power management scheme. • Integration of MG operation and data forecasting problems to achieve reliable network scheduling results in the DA energy market. • Using the hybrid ABC+SCA algorithm to reach the optimal solution with unique response conditions. This paper presents the flexible-reliable operation (FRO) of microgrids (MGs) constrained to supplying clean energy. The scheme minimizes the total expected costs of the MG and sources operation, MG reliability, and MG flexibility and is subject to power flow equations, operation, environmental, and reliability constraints, and formulation of sources and energy storage-based active loads. The problem is mixed-integer nonlinear programming (MINLP). To achieve an optimal reliable solution with unique response conditions, a combination of an artificial bee colony (ABC) and sine-cosine algorithm (SCA) is utilized in this study. In the given scheme, stochastic programming is employed to model the uncertainties of load, energy price, renewable power, and availability of MG equipment. Furthermore, mean and standard deviation values of the above-mentioned first three uncertainty parameters are obtained using the data forecasting method based on the hybrid ABC+SCA method, adaptive network-based fuzzy inference system (ANFIS), and wavelet transform (WT). Finally, by implementing the proposed strategy on a standard MG, the capabilities of the proposed scheme in improving economic, environmental, operation, reliability, and flexibility conditions of the network are validated. [ABSTRACT FROM AUTHOR]

The cuckoo search (CS) algorithm is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real- world optimization problem. In this paper, a modified CS algorithm is applied to determine the feasible optimal solution of the economic load dispatch problem considering various generator constraints. In order to solve the problem of being trapped into the local optima, a new mutation scheme, inspired by the DE/current-to-gr_best/1, is incorporated in standard CS to enrich the searching behavior and solution quality and to avoid being trapped into local optimum. In order to verify the performance of our approach, the proposed approach is tested with two power system cases consisting of 3, 6, 15, and 40 thermal units with generator constraints. State-of-the-art optimization techniques are compared with simulation results obtaining proposed method to illustrate the efficiency of the proposed method and to show that the improved CS could be used as a reliable tool for solving economic load dispatch problems with generator constraints. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Highlights: [•] Propose the simultaneous coordinated design of the STATCOM and PSS. [•] Consider the internal AC and DC voltage controllers of the STATCOM. [•] Introduce the seeker optimization algorithm to coordinated design. [•] Investigate the performance of proposed method in terms of the ITAE and FD indices. [•] Show the robustness of proposed method over wide range of operating conditions. [ABSTRACT FROM AUTHOR]

Highlights: [•] Introduce the εv-MOGA to solve emission, reserve and ELD problem. [•] Form the εv-MOGA based on the idea of ε-dominance. [•] Consider the valve-point, prohibited zones, frequency, and practical constraints. [•] Increase the social welfare for consumers and GENCOs by frequency constraints. [•] Illustrate the more speed and effectiveness of the εv-MOGA than other algorithm. [Copyright &y& Elsevier]

• Designing a new optimal wide-area measurement-based damping controller for UPFC. • Application of the DHP method from ACD family to have real-time adaptive controller. • Adoption of ANFIS in the DHP method to optimize the controller parameters. • Investigation of the performance of the proposed ANFIS-DHP method in lRAN. This study presents a new optimal supplementary neuro-controller design for unified power flow controllers (UPFCs) using wide-area signals. The new design is oriented by the dual heuristic programming (DHP) method, which is a powerful adaptive critic procedure. The proposed controller injects additional signals to the UPFC series inverter to enhance power system stability. A model, action and critic neural networks are also designed for optimizing the neuro-controller. To have an accurate dynamic response of multi-machine power systems, the concepts of two-machine equivalent model (TMEM) and center of inertia (COI) are used to train the adaptive critic design (ACD) neural networks. The ANFIS structure is applied to the suggested DHP technique based on the selected input signals to improve the dynamic performance of the applied DHP controller. The designed optimal controller is applied on a real interconnected power system between Tehran and Khuzestan regions in Iran power grid. The proposed ANFIS-DHP controller performance is compared with various controllers, such as PI-Genetic, PI-Lyapunov and other intelligent approaches. The dynamic responses of the proposed controller are found to be the most effective in increasing system stability and damping out system inter-area oscillations. [ABSTRACT FROM AUTHOR]

Improving the accuracy of solar power forecasting has become crucial for dealing with the negative effects of the integration of continually increasing solar power into power systems. This is a more challenging task when historical solar radiation data has not been recorded and no specific sky imaging equipment is available. This paper proposes a univariate data-driven method to improve the accuracy of very short-term electrical solar power forecasting. This approach includes defining new features that efficiently tackle the nonlinear characteristics of electrical solar power. An instance-based variable selection is also used to identify the best relevant features. Three state-of-the-art learning algorithms (i.e. neural networks, support vector regression, and random forest) have been used and compared as prediction algorithms of the proposed method. The effectiveness of the proposed approach is evaluated in a 15-min ahead prediction trial using a real solar power dataset and against three evaluation measures. The results show the proposed method significantly enhances the performance of very short-term solar power forecasting. • An accurate very short-term method for solar power forecasting is developed. • The method merely relies on historical solar power data. • The method requires no specific equipment, sensor data, or weather predictions. • The method enhances the performance of very short-term solar power forecasting. [ABSTRACT FROM AUTHOR]

Deregulation of electric power market and aggregation of renewable resources raise the need for new hour-ahead load forecasting models. This paper proposes a new hybrid data-driven method for hour-ahead electrical load forecasting based on innovative features that represents the nonlinear and dynamic characteristics of electrical load. These features predict hourly load changes and improve the accuracy and performance of STLF. These innovative features first construct the pool of features along with historical load variables. Then, a feature selection method called RReliefF is used for choosing most relevant features and finally, a multi-layer perceptron neural network is employed as a forecasting engine—due to its advantages such as self-organization, fault tolerance and ease of integration in existing technologies. The efficiency of the proposed model is evaluated through various comparative experiments and compared with benchmark models using the three years' real energy market data from New England ISO by four evaluation criteria. The results demonstrate the superiority of proposed method in forecasting performance for the period of analysis including 12 test months as well as special days. • Proposes a STLF method based on innovative features, feature selection and NN. • Innovative features efficiently identify the dynamic characteristics of load data. • The model is also suitable for STLF in holidays and special days. • The results clearly confirm the accuracy of method using four evaluation criteria. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive characteristic for eliminating the adverse effects of fault resistance and static synchronous compensator (STATCOM) on the performance of distance relays in transmission line and collector lines of doubly‐fed induction generator (DFIG)‐based wind farms in the case of single‐phase‐to‐ground resistive faults. The proposed method improves the performance in distance relaying and maintains the coordination between relay pairs in the transmission line and wind farm collector lines. In this method, an adaptive coefficient that can modify the zone settings of conventional distance relays is added to their characteristic to improve the accuracy and coordination under the mentioned conditions. Using analytical relationships and numerical results from simulations in Matlab/Simulink environment, the efficiency of the proposed method is confirmed. [ABSTRACT FROM AUTHOR]

• Accurate modeling of HPOTL considering electromagnetic coupling between AC and DC lines. • Simulating all types of faults in HPOTLs, including AC, DC and inter-system faults. • Complete investigation of induced components in AC and DC transmission lines in HPOTLs in different fault conditions with different fault resistances and locations. • Investigating the possibility of the impact of faults in one of the transmission lines (AC or DC) on the adjacent line protection system. • Investigating the most severe AC, DC and inter-system faults from the point of view of induction of voltage and current components in the adjacent line. The use of Hybrid Parallel HVAC/HVDC Overhead Transmission Lines (HPOTL) on the same tower can improve the power transfer capacity and transmission line stability and reduce power transient swing, but it will also present some challenges. The most important one is the electromagnetic coupling between the HVAC and HVDC lines due to the proximity of the lines. The electromagnetic coupling phenomenon induces different voltage and current components in the lines for different conditions, including the fundamental frequency component in the DC line and the zero sequence component in the AC line. The magnitude of the induced components is much higher in fault conditions than in normal conditions. The electromagnetic coupling between the HVDC and HVAC lines has a significant influence on their equipment and protection systems. In this paper, the induced components in the normal and various fault conditions in HPOTL are investigated with the detailed modeling of the HPOTL. In addition, the effect of faulty phase(s) or pole(s) for AC, DC, and AC/DC intersystem faults, fault location, and fault resistance on the induced component in transmission lines are investigated. Simulation results show that the electromagnetic coupling can cause the mal-operation of the protection system in healthy lines. [ABSTRACT FROM AUTHOR]

This paper presents the optimal scheduling of active distribution network (ADN) with linear and non‐linear loads in the presence of an integrated unit of electric spring and electric vehicle parking lot (IUEE). The proposed scheme is formulated in the form of three‐objective optimization, which minimizes the expected operating cost of the ADN, voltage deviation function (VDF), and the total harmonic distortion (THD) of the voltage in separate objective functions. Constraints include harmonic power flow (HPA) equations, operation and power quality limits, and operation model of the IUEE. This scheme has uncertainties of load, energy price, and energy demand of EVs, which are modelled here using stochastic programming. Next, the sum of the weighted functions method as a subset of the Pareto optimization technique is adopted to extract the integrated single‐objective problem. Then, Sine‐Cosine Combined Algorithm (SCA) and Gray Wolf Optimization (GWO) are used to achieve an optimal solution. In the end, by examining the numerical results obtained from the implementation of the proposed scheme on a 69‐bus ADN, the capability of the design in enhancing the economic situation, operation, and power quality of the ADN compared to network power flow studies obtained by optimal scheduling of the IUEE is confirmed. [ABSTRACT FROM AUTHOR]

In recent years, wind power has played a significant role in energy generation of micro-grids (MGs). However, randomness nature of wind speed leading to uncertainty in wind power forecast, imposes some problems such as overestimating wind power on optimized scheduling of MG. In this paper, we propose an adaptive probabilistic concept of confidence interval (APCCI) to address these problems. The main purpose of the proposed APCCI is to modify the risk we endure to schedule wind power with other distributed energy resources (DERs) in order to degrade the unnecessary rigors and upgrade the other ones. The forecasting method which is used in this paper is artificial neural network (ANN). In order to increase the accuracy of forecasting, wavelet decomposition (WD) is applied to the wind power time series then the results are sent to ANN. After that, dependable levels for the predicted wind power based on APCCI are obtained. An energy storage system (ESS) is utilized not only to decrease the impact of forecasting errors on the MG but also to increase the flexibility of the planning. A comprehensive formulation with operational constraints is employed to model the optimization problem. An economic dispatch based non-dominated sorting genetic algorithm II (EDNSGA-II) is proposed and applied to solve the multi-objective optimization problem. The optimization algorithm produces some alternatives which consist of different combination of objectives (cost and emission). Techniques for order preference by similarity to an ideal solution (TOPSIS) method is utilized to make a compromised decision between the alternatives. Eventually, the proposed algorithm is applied to a typical MG which consists of micro turbine (MT), fuel cell (FC), photo voltaic (PV), wind turbine (WT) and energy storage system (ESS). Evaluation of the results show that the proposed APCCI works well and can adapt the level of confidence interval in various situations. Moreover, the results confirm the superiority of WNN over ANN. The results also show that the proposed EDNSGA-II is more efficient in comparison with the well-known NSGA-II. [ABSTRACT FROM AUTHOR]

An ultra‐high‐speed, accurate and reliable algorithm is proposed in this study for the identification and faulted phases selection of inter‐circuit faults. For this purpose, modal components of current samples just before they arrive to the relay location are extracted. Teager energy operator is used here for the extraction of current travelling waves (TWs). The amplitude of the TWs in different modal components and their ratios in similar modal components are used for the inter‐circuit fault identification and finding the faulted phases. Test signals are simulated in PSCAD/EMTDC software and the algorithm is implemented in MATLAB. Results show that the proposed algorithm is a really ultra‐high‐speed algorithm which can be reliably used for the identification and classification of the inter‐circuit faults. [ABSTRACT FROM AUTHOR]

In this paper, a new fuzzy controller for a three-phase and three-wire distribution static compensator (DSTATCOM) is presented to improve power quality. A three-leg voltage source converter based DSTATCOM is used to control the harmonic currents for two nonlinear loads. In this control system, gate pulses for IGBT switches are provided by a hysteresis controller, using samples of source voltages and currents. The derived reference currents, obtained by the direct method of reference current estimation with two PI and Fuzzy control models are simulated in a MATLAB/ Simulink environment and each performance is analyzed. Comparisons show that the fuzzy controller performs better than a classic PI controller. [ABSTRACT FROM AUTHOR]

• A new deep-stacked autoencoder model is proposed for short-term load forecasting. • A stack of autoencoders is developed to extract more latent relevant features. • The challenge of setting the random initial weights of the deep models is solved. • The proposed model is equipped with multi-stage temporal attention mechanisms. • The proposed model has good robustness against various data partitioning. This paper presents an innovative univariate Deep LSTM-based Stacked Autoencoder (DLSTM-SAE) model for short-term load forecasting, equipped with a Multi-Stage Attention Mechanism (MSAM), including an input AM and several temporal AM in the pre-training phase. The input AM is used to capture the high-impact load sequence time steps of univariate input data. It should be noted that the model's performance is improved by increasing the network depth; however, finding the optimal network parameters is a challenging task due to the random assignment of the initial weights of the network. An unsupervised greedy layer-wise pre-training structure equipped with the MSAM is expanded to solve setting the random initial weight problem of the DLSTM-SAE model. The multi-stage temporal AM in the pre-training structure leads the DLSTM-SAE to properly learn the time dependencies related to remarkably long sequence input data and capture the temporal merit features lied in the LSTM memory. The performance of the proposed model is evaluated through various comparative tests with current prevalent models using actual energy market data New England ISO using three criteria indexes. The results show the superiority of the proposed model and its robustness in offline and online load forecasting. [ABSTRACT FROM AUTHOR]

• Double-circuit grounded cross-country fault location in GUPFC-compensated DCTLs. • The proposed algorithm is independent of the GUPFC's operating point and parameters. • No GUPFC output current/voltage is measured in the proposed algorithm. • The algorithm is based on phasor theory; easily applicable in industrial relays. • The performance of the proposed method is very accurate and desirable. This paper presents an accurate fault location algorithm for double-circuit grounded cross-country (DCGCC) faults in double-circuit transmission lines (DCTLs) compensated by a generalized unified power flow controller (GUPFC). The algorithm incorporates synchronous current and voltage phasors at both end buses of the line. The proposed algorithm determines the accurate location of simultaneous faults occurring in both line circuits in three basic steps. In the first step, the faulty sections are identified using a zero-sequence current analysis. In the second step, the accurate location of a fault in the circuit on which only a series converter is installed is calculated. Next, by employing the calculated zero-sequence current of this fault point, the zero-sequence current component of the other line circuit, which is equipped with series and shunt converters, is obtained. Finally, the accurate fault location in this circuit is calculated. The calculation process and the derived equations for all DCGCC fault types are described in detail. The GUPFC considered in this study along with the test network is modeled in MATLAB/Simulink environment. The simulation results confirm the accurate performance of the proposed algorithm for various fault types occurring in different operating conditions. [ABSTRACT FROM AUTHOR]

• Novel fault classification algorithm (FCA) for GUPFC-compensated transmission lines. • FCA is based on the loci of active and reactive powers calculated using PMU signals. • Presenting faulty area and faulty circuit detection algorithms using new indices. • The proposed method is robust and independent of the operating modes of the GUPFC. • Easily applicable in regular fault locators available in the protection industry. The paper presents a fault area detection and classification algorithm in the phasor domain for double-circuit lines compensated by a generalized unified power flow controller (GUPFC). In the first step, by defining and using a voltage index, the algorithm determines the side of the GUPFC in which the fault has occurred. In the second step, by defining and employing a current index, the algorithm will be able to identify the circuit of the line on which the fault has happened. Finally, the fault is classified. In this step, the algorithm detects the faulty phase by analyzing the loci of three-phase active and reactive powers at the line terminal. The algorithm uses the current and voltage phasors of the line terminals measured by the phasor measurement units (PMUs) in one cycle before and during the fault. The GUPFC and the test system have been modeled in MATLAB/Simulink. The presented algorithm is tested and evaluated under different conditions including different fault types and fault resistances for all normal series and shunt fault types. The results obtained from simulations verify the accurate performance of the algorithm for various fault conditions. [ABSTRACT FROM AUTHOR]

Summary: The use of phase shifting transformer (PST) is one of the common methods of power flow control in complex transmission systems. Changing the power distribution in lines by the PST will change the voltage and the current signals of the power system. Loss of excitation relay in a synchronous generator is one of the important relays of the generator that measures the voltage and current signals of generator output to identify the loss of excitation of the generator. This study shows that the presence of PST in the transmission line interferes with the performance of the loss of excitation (LOE) relay. The LOE relay in this paper is of the Berdy type, which detects loss of excitation by measuring the voltage and current of the generator and calculating the impedance from them. In this paper, the way this impedance is calculated has been modified, as this impedance is calculated from the modified values of the active and reactive powers of the generator. As a result, it has been shown that with the help of this method, the effect of PST on the operation of the LOE relay is significantly eliminated. In the proposed method, PST information is not required to be sent to the relay location, and so the communication channel is not needed. A decision tree‐based method is used as a backup protection to guarantee proper operation in power swing conditions. [ABSTRACT FROM AUTHOR]

This paper provides an algorithm of fault resistance compensation for digital ground distance relay considering the voltage and current transformer effects. Performance of the conventional ground distance relaying manner is adversely affected by different ground faults and also typical type, called a simultaneous open conductor and ground fault. The proposed scheme by using local-end data only, has shown satisfactory performances under wide variations in fault location, with different values of fault resistance and having positive and negative of power transfer angle. The presented method which has been carried out on the IEEE 14 bus benchmark is executed in PSCAD/EMTDC and MATLAB software, and the results show the accurate performance of mentioned configuration. [ABSTRACT FROM AUTHOR]

Due to the limited operations of three-phase machines in open-phase fault conditions, the multiphase induction generators are now introduced in high-performance power generation systems. In the case of six-phase induction generators, the operation with opened phases is associated with torque and consequently power oscillations even in steady state conditions. Previous works have reported different methods to reduce these oscillations but they only deal with sensored control issues. Therefore, this paper deals with on-line modification of a sensorless control system applied to a squirrel cage six-phase induction generator (SC6PIG) when one to three phases of the machine are lost. Designing ten different off-line Luenberger speed observers as well as ten control systems corresponding to the different possible fault types and switching between them as a function of a detection system is proposed in this paper. Furthermore, on-line imposing of the nonzero initial conditions induces a smooth switching between the observers as well as the controllers with minimum associated transient at switching instants. The proposed strategy is tested on a wind turbine simulator driven by a permanent magnet dc motor. The experimental results in healthy and faulty modes assess the validity of the proposed strategy. [ABSTRACT FROM AUTHOR]

A new traveling-wave-based algorithm is proposed in this paper for the protection of transmission lines. In the proposed algorithm, Teager energy operator is used to extract traveling waves. Teager energy operator has a high computational efficiency and extracts traveling waves with high resolution. The amplitude of the first detected traveling wave as well as that of the traveling waves which are detected in 2 L / v and 2( L − x )/ v seconds (“ L ” is the line length, x is an initial guess about the fault location, and “ v ” is the traveling wave propagation speed) after the first one are used to discriminate internal and external faults. Then the traveling wave propagation speed and the time difference between the first and the second traveling waves are used to find the fault location. This process can also be used for the internal fault identification. Finally, two decision trees and the amplitude of initial current traveling waves in different modes are used for the fault type classification and faulted phase selection. The feasibility of the proposed algorithm is investigated by test signals which are simulated in PSCAD/EMTDC, and the algorithm is implemented in MATLAB. [ABSTRACT FROM AUTHOR]

This study proposes an intelligent traveling‐wave (TW)‐based protection algorithm for parallel transmission lines. In the proposed algorithm, Karenbauer's phase to modal transform is applied on three phase current signals. Teager energy operator is then applied on the modal components to extract TWs. First extracted TW of each modal component along with well‐designed fuzzy systems are used for internal fault identification and fault type classification. In order to find the fault location, the time difference between the first and the second TWs and the TW propagation speed are utilised. Test signals are generated in PSCAD/EMTDC software and the algorithm is implemented in MATLAB. Results show that the proposed algorithm is an ultra‐high‐speed algorithm for the reliable protection of parallel transmission lines. [ABSTRACT FROM AUTHOR]

This paper provides an algorithm of fault resistance compensation for digital ground distance relay considering the voltage and current transformer effects. Performance of the conventional ground distance relaying manner is adversely affected by different ground faults and also typical type, called a simultaneous open conductor and ground fault. The proposed scheme by using local-end data only, has shown satisfactory performances under wide variations in fault location, with different values of fault resistance and having positive and negative of power transfer angle. The presented method which has been carried out on the IEEE 14 bus benchmark is executed in PSCAD/EMTDC and MATLAB software, and the results show the accurate performance of mentioned configuration. [ABSTRACT FROM AUTHOR]

• A phasor-based fault location algorithm (FLA) is proposed in a three-terminal transmission line (TL) connected to an industrial microgrid. • The FLA does not require the parameters of the TL. • The FLA does not require the data of faulty phase detection. • The FLA does not require to detect the faulty section in three-terminal TLs. • The FLA is robust to the variations of different parameters of the industrial microgrid and uncertainties of renewables-based DGs. The connection of large industrial microgrids to three-terminal transmission lines greatly complicates the protection scheme of the lines due to the uncertainty in generating renewable energy sources, the probability of unintentional islanding, the abrupt switching of sensitive and critical loads, the influence of supply and demand management programs, and bidirectional active and reactive power flow. This paper presents a fault location algorithm for three-terminal transmission lines connected to an industrial microgrid that does not need the detection of faulty section, fault classification, and transmission line parameters. The proposed method uses voltage and current phasors of transmission lines measured by phasor measurement units (PMUs). One of the salient features of the proposed algorithm is that it does not need to determine the faulty section before the fault location operation. Furthermore, since power transmission lines are always exposed to changing climatic conditions and aging, their electrical characteristics vary over time in the long run compared to their rated value. Hence, this leads to the malfunction of protection schemes. This is solved in this study as the proposed fault location algorithm is independent of line impedances. On the other hand, the dependence of conventional fault location algorithms on fault classification data reduces the reliability of fault location schemes, making these algorithms dependent on fault classification data. However, the present study overcomes this problem as the algorithm is independent of the data of the faulty phase(s) detection. The proposed scheme is fully implemented in the MATLAB software environment, and the performance of the algorithm is analyzed for various fault types under different conditions. The validity of the algorithm is shown by various tests and sensitivity analyses. [ABSTRACT FROM AUTHOR]