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The distribution of transmission lines is complex, influenced by terrain and topography, making their maintenance and management challenging. With the advancement of global mechanization, various types of construction machinery and large‐scale mechanical equipment are continually increasing. Accidental contact of construction machinery near transmission lines poses a serious threat to the stability of the power system. Traditional drones and inspection robots face difficulties in achieving real‐time monitoring of construction machinery and equipment around transmission lines, quickly identifying potential risks. Conventional image processing techniques and convolutional neural networks struggle with effective handling of small targets and densely packed detection tasks involving multiple targets. To address these challenges, this paper proposes an intelligent detection algorithm Swin Transformer attention efficient algorithm‐you only look once (YOLO) (STAE‐YOLO). It is based on the Swin Transformer global self‐attention mechanism, cross‐channel fusion attention mechanism, enhanced small object detection framework, and a focused and efficient regression loss function. The experimental results show that the STAE‐YOLO algorithm model improved by 6.3% in mean average precision, 3.7% in precision, and 3.1% in recall compared to the baseline model. Meanwhile, deploying the window multi‐head self‐attention global self‐attention mechanism in the model can strengthen the global multi‐scale semantic information in detection. [ABSTRACT FROM AUTHOR]

The grid‐following control‐based hybrid cascaded high‐voltage direct current (HC‐HVDC) links may contribute to the worsening of inertia decline and instability issues, especially in situations characterized by a weak grid. To address the challenge, this paper proposes a grid‐forming (GFM) based optimal frequency support scheme (OFSS) for HC‐HVDC integrated wind farms. Firstly, a GFM control for hybrid converter is proposed and its stability under weak grid conditions is evaluated through a small‐signal model. Then, DC current–voltage droop control is introduced to the line commutated converter (LCC). This control approach ensures the consistency of reactive power in the LCC by regulating the DC voltage and current in the same direction, thereby mitigating AC voltage fluctuations. It also facilitates active power control in the LCC in response to grid frequency variations. Furthermore, the OFSS can convey the frequency of receiving‐end grid to the rectifier and wind farms without communication for precise power control. Finally, to validate the effectiveness of OFSS, a point‐to‐point HC‐HVDC and a 4‐machine test model are constructed on PSCAD/EMTDC. The OFSS significantly enhances the frequency support capability of HC‐HVDC links and improves the robustness in weak grid. [ABSTRACT FROM AUTHOR]

This paper proposes a virtual inertia control strategy for wind farms based on auto disturbance rejection controller with artificial bee colony (ABC) algorithm. First, based on the system frequency dynamic response equation, an active disturbance rejection controller is designed according to the non‐linear feedback control law and the extended state observer to improve the anti‐interference capability of the virtual inertia control system. Second, in order to solve the problem of difficulty in tuning the parameters of the active disturbance rejection control (ADRC), an ABC algorithm based on nectar collection behaviour was proposed to iteratively optimize the parameters of ADRC. Finally, the proposed control method is effectively verified based on Matlab/Simulink. The simulation results show that compared with traditional algorithms, the proposed ABC‐ADRC can effectively control the rotor speed to adjust the frequency of the power grid system, achieve power sharing and adaptive noise elimination, reduce the complexity of parameter settings, not only improve anti‐interference ability, but also enhance the robustness of the system. [ABSTRACT FROM AUTHOR]

This paper addressed the challenges associated with the complexity, numerous parameters, computational resource demands, and slow processing speed of transformer fault identification models based on deep learning technologies. Sparse convolutional neural network (CNN) approach is proposed for identifying faults related to dissolved gases in oil. Leveraging an improved Gramian angular field, one‐dimensional fault samples are converted into two‐dimensional feature images and data augmentation is implemented to meet the input requirements of deep learning models. Building upon visual geometry group (VGG)19 and residual networks (ResNet)50 networks for fault diagnosis, sparsity techniques are introduced through pruning, the fusion of convolution layers and batch normalization layers, and parameter quantization. Numerical experiments and performance evaluations on dissolved gas in transformer oil fault data demonstrate that the proposed method effectively reduced model complexity, minimized parameter count, conserved computational resources, and improved processing speed while maintaining a considerable level of fault identification accuracy. This made it applicable to edge computing platforms characterized by small form factors and low power consumption in the power industry. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) diagnostics test is a reliable solution for estimating insulation health conditions in power system components. During laboratory PD diagnostics, the effect of harmonic components in the voltage waveform generated by variable frequency drives (VFD) fed electric motors (EMs) is often overlooked. As VFD‐fed EMs operate at low speeds, the harmonic concentration in the voltage increases. It has been found that PD activity in VFD‐fed EM is significantly affected by the addition of harmonic components in the voltage, thus making it necessary to consider their impact when performing true PD diagnostics. This paper investigates the influence of voltage harmonic distortion produced in VFD‐fed EM on PD severity and the subsequent insulation degradation. Eight VFD‐fed EM are used to study the PD behaviour under different levels of harmonic pollution. The PD characteristics, such as inception voltage, pulse repetition rate, accumulated apparent charge, average discharge current, discharge power, and quadratic rate, are determined. Using probability distribution functions, PD activity and severity at various voltage harmonic levels are estimated. The proposed technique can be used to plan the maintenance activities that ensure the reliability for industrial applications dealing with the voltage harmonic distortion. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy sources and the retirement of conventional generation units have decreased system inertia, making power systems more vulnerable to resilience and stability issues. To address this problem, this paper proposes a novel approach using grid‐supportive loads (GSLs) to provide a fast and concise primary frequency response and a deep deterministic policy gradient agent‐based secondary controller to restore the system frequency to the nominal value. The proposed method is evaluated on the single‐area and multi‐area test systems. The simulation results demonstrate that using GSLs enhances the power system's stability and resilience. Compared to conventional controllers, the frequency nadir is improved with GSLs. Additionally, the proposed method effectively enhances resilience even with high penetration. These findings indicate that the proposed approach can improve the resilience and stability of power systems and provide a promising solution for future power systems. The results of this study underscore the importance of utilizing innovative approaches to enhance the stability and resilience of power systems in the context of high penetration of renewable energy sources and the retirement of conventional generation. [ABSTRACT FROM AUTHOR]

This paper mainly studies a detection method of dynamic load altering attacks (D‐LAAs) in smart grids. First, communication factors are considered, and a smart grid discrete system model under D‐LAA attack is established. Second, for closed‐loop D‐LAAs, an adaptive fading Kalman filter (AFKF) is designed to estimate the states of smart grids with Gaussian noise in real time, and a Euclidean distance ratio detection algorithm based on AFKF is proposed to detect D‐LAAs. Moreover, the proposed detection algorithm can identify D‐LAAs even in the presence of noise in the measurement data, significantly enhancing the speed of attack detection. Finally, take a smart grid with three generators and six buses as an example. Its feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified by simulations. The simulations are carried out through the real‐time hardware‐in‐the‐loop simulation platform, which is mainly composed of StarSim and multi‐tasking devices. [ABSTRACT FROM AUTHOR]

The ability to withstand and recover from disruptions is essential for seaport energy systems, and in light of the growing push for decarbonization, incorporating clean energy sources has become increasingly imperative to ensure resilience. This paper proposes a resilience enhancement planning strategy for a seaport multi‐energy system that integrates various energy modalities and sources, including heating, cooling, hydrogen, solar, and wind power. The planning strategy aims to ensure the reliable operation of the system during contingency events, such as power outages, equipment failures, or extreme weather incidents. The proposed optimization model is designed as a mixed‐integer nonlinear programming formulation, in which McCormick inequalities and other linearization techniques are utilized to tackle the model nonlinearities. The model allocates fuel cell electric trucks (FCETs), renewable energy sources, hydrogen refueling stations, and remote control switches such that the system resilience is enhanced while incorporating natural‐gas‐powered combined cooling, heating, and power system to minimize the operation and unserved demand costs. The model considers various factors such as the availability of renewable energy sources, the demand for heating, cooling, electricity, and hydrogen, the operation of remote control switches to help system reconfiguration, the travel behaviour of FCETs, and the power output of FCETs via vehicle‐to‐grid interface. The numerical results demonstrate that the proposed strategy can significantly improve the resilience of the seaport multi‐energy system and reduce the risk of service disruptions during contingency scenarios. [ABSTRACT FROM AUTHOR]

The efficient, reliable, and resilient supply of electricity has become essential for social and economic well‐being of the modern society. However, more frequent occurrence of extreme weather events has exposed inequity in the planning and operation practices of power distribution systems, evidenced in higher vulnerability and longer power interruptions for some parts of the grid as compared to others. This paper proposes an equity‐aware power distribution system restoration model in an effort to ensure a more equitable yet resilient power distribution operation after outages. To this end, the proposed equity‐aware distribution system restoration model balances the efficiency of the restoration operation and the equitable allocation of distributed energy resources among affected customers after an outage, while prioritizing the critical infrastructure (e.g. hospitals). The results demonstrate the effectiveness of the proposed framework to ensure a more equitable restoration process as measured by the proposed fairness and restoration performance indices. [ABSTRACT FROM AUTHOR]

This paper proposes a situational awareness‐based method of load control for safe electricity consumption by industrial users to assess the risk of regulated load equipment within industrial users and to ensure their safe use of electricity. Firstly, in the situational perception stage, collecting key parameters such as controllable equipment within industrial users, and constructing a risk indicator system for industrial users in the process of load control from four perspectives: the degree of controllability of load equipment, the correlation and mutual influence of load equipment, the uncertainty of self‐provided power and economic losses; Then, in the situational understanding and presentation stage, a risk assessment method that integrates a self‐learning weight model and a cloud model is proposed to analyze the risk of industrial users under control; Finally, in the situational guidance stage, based on the assessment results of the risk situational of industrial users, industrial users' safe electricity consumption is controlled in multiple rounds to achieve the objective of maintaining safe and reliable operation of the power grid by accepting control at a low‐risk level. The simulation of the industrial users of steel mills is used to verify the rationality and effectiveness of the method proposed here. [ABSTRACT FROM AUTHOR]

Distributed energy resources (DERs) provide flexible load restoration strategies, which can effectively enhance the resilience of active distribution systems (ADSs). Whereas, the widespread DERs in ADSs complicates the supply‐demand relationship and make the system resilience difficult to access. Therefore, this paper proposes a simulation‐based resilience assessment algorithm of ADSs considering the microgrid formation based on grid‐edge DERs. Microgrid formation is used to depict the resilience gain of grid‐edge DERs on ADSs. Specifically, a resilience assessment framework for ADSs is firstly proposed, where the uncertainty of component state and supply‐demand is modelled based on probability statistics. Then the mixed integer linear programming is used to search for optimal load restoration strategies with microgrid formation. On this basis, a set of resilience indices are defined to quantitatively analyse the resilience of ADSs, and a resilience assessment algorithm with uncertainty scenario generation is proposed to obtain these indices. Furthermore, extensive numerical results based on a modified IEEE 123‐bus feeder validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The high penetration of distributed generation systems poses challenges in effectively managing both DC bus voltage and power‐sharing in DC microgrids (MGs). To ensure precise voltage regulation across various distributed generation systems and maintain overall system stability, this paper studies the modelling and control of an islanded DC MG described by a set of nonlinear‐nonaffine differential equations. In the proposed DC MG, a battery storage, a photovoltaic (PV) power plant, and a back‐end power converter with DC loads have been connected to a common DC bus. First, a dynamic model of a DC MG was developed. Then, to find the maximum power point of solar PV, a straightforward method based on an artificial neural net is employed. Afterward, a nonlinear local state feedback controller through output regulation theory is applied to achieve voltage stability, overcoming the drawbacks of droop‐based techniques. Moreover, partial state feedback capability simplifies controller design. To verify the effectiveness of the proposed approach, various scenarios, including sudden load variation, illumination change, and changes in the MG configuration, have been examined. Finally, numerical studies were carried out in a simulation environment to demonstrate the superiority of the proposed nonlinear control scheme over constant feedback control. [ABSTRACT FROM AUTHOR]

This paper proposes a recurrent neural network based model to segment and classify multiple combined multiple power quality disturbances (PQDs) from the PQD voltage signal. A modified bi‐directional long short‐term memory (BI‐LSTM) model with two different types of attention mechanisms is developed. Firstly, an attention gate is added to the basic LSTM cell to reduce the training time and focus the memory on important PQD signal part. Secondly, an attention layer is added to the BI‐LSTM to obtain the more important part of the voltage signal by assigning weightage to the output of the BI‐LSTM model. This attention gate applied in the LSTM cells improves effective and decisive key information extraction from future and past states of the PQD signal and the addition of attention layer improves the overall decision capability of the model, saving computation time, and increasing PQD classification accuracy. Finally, a SoftMax classifier is applied to classify the combined PQD signal in 96 different combinations. The proposed BI‐LSTM model with attention gate and attention layer mechanism is compared to different baseline models based on recurrent neural network and convolution neural network (CNN). From the simulation study, it is inferred that with the proposed method, the multiple combined PQD signals are easily segmented from the voltage signal which makes the process of PQD classification more accurate with less computation complexity and in less time as compared to popular signal processing based approaches for which classification of multiple PQDs is computationally difficult. [ABSTRACT FROM AUTHOR]

Fractional slot concentrated winding (FSCW) permanent magnet (PM) machines are gaining more attention for low‐speed applications because of their great advantages. This paper proposes a new structure for an FSCW double rotor axial flux PM synchronous machine (DRAFPMSM) with counter‐rotating rotors. In addition, an analytical formula for the Back EMF waveform of the proposed structure is presented. The accuracy of the formula is investigated by designing a 40 W sample machine. The rotors of this machine rotate with speeds of 600 and 428 rpm in opposite directions. The Back EMF of the sample machine is calculated using the suggested formula. In addition, the finite element method (FEM) is used to determine the Back EMF waveform. Comparison of the obtained results confirms that the new formula yields the Back EMF precisely such that the results well match to those of FEM. The effect of the relative position of two rotors on the Back EMF waveform is investigated using the proposed formula. In addition, the optimum relative position of two rotors for minimizing the Back EMF THD of the case study machine is determined. [ABSTRACT FROM AUTHOR]

Conventional power systems function under the assumption that loads are uncontrollable, and that the generation control is the primary means of preserving system voltage, frequency, and stability. Thanks to recent advancements of power electronics technology and communication schemes, demand‐side resources are now capable of providing fast frequency regulation. Controllable loads can provide upward/downward reserve during frequency excursions. In this paper, the authors consider collective contribution of large clusters of controllable loads which modulate their aggregate demand power to regulate the primary frequency. Koopman model predictive control is designed to handle local frequency variations caused by various disturbances at each load bus, considering uncertain load models. The efficacy of the proposed method has been validated using the New‐England power system considering two scenarios, namely, load variation, and generation outage. [ABSTRACT FROM AUTHOR]

As renewable power generation increases in distribution networks, the real‐time power balance is becoming a tough challenge. Unlike simple peak‐load shedding or demand turn‐down scenarios, generation following (GF) requires persistent and precise control due to the temporal response performance of controlled resources. This motivates a comprehensive control design considering the temporal response limits and execution performance of air conditioner clusters (ACCs) when providing GF. Accordingly, this paper proposes a self‐constraint model predictive control (SMPC) that properly allocates the generation following task among different ACCs, consisting of three main parts: response rehearsal, distributed consistency‐based power allocation, and real‐time task execution. Specifically, the rehearsal knowledge of ACCs is evaluated by introducing model predictive control (MPC) to track power signals with different values and thus obtain prior factors, including the upward/downward limits and control cost function. On this basis, the coherence of the incremental response costs of different clusters is achieved to allocate the GF signals. Once the optimised following signals (OFS) are obtained, a real‐time MPC for generation following task execution is employed, where the OFS are used as reference and the upward/downward limits are used as constraints. Simulations are conducted to verify the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the rapid development of high‐speed railway, the auto‐transformer traction network (ATN) is also expanding. However, due to the improved performance of electric multiple units (EMUs) and the non‐linear relationship between impedance and ground fault distance, distance protection has limited coverage and cannot accurately locate the fault distance by measuring impedance. This paper presents a model of the ATN considering the presence of EMUs, which effectively demonstrates the characteristics of current distribution under the ground faults. Furthermore, a ground fault identification method based on multi‐terminal currents is proposed to enhance the accuracy of fault identification and differentiate faults and EMUs. The mapping relationship between current distribution and fault location is derived, and an accurate fault location method is proposed. Compared to existing methods, this method is not affected by EMUs and AT parameters. The fault identification and location scheme for ATN employs multi‐terminal synchronous information. Finally, through simulation and experimental setups, case studies have been conducted to verify the accuracy and robustness of the proposed method in various fault scenarios. Certainly, this research has significant application prospects in fault analysis and detection for HSR. [ABSTRACT FROM AUTHOR]

In the case of a single‐phase grounding fault in the distribution network, the transient zero‐sequence current (TZSC) tends to be non‐linear and non‐stationary. The faulty line selection is relatively difficult. The distributed power access further brings many difficulties to faulty line selection. This work proposes a novel method of faulty line selection using spatial image generation and deep learning. At first, the optimal smooth denoising model can be used to smooth the zero‐sequence current for each line, reducing the external environment electromagnetic interference. Then, the treated zero‐sequence current is mapped into the colorful floral hoop image by using symmetrized Hilbert transform pattern (SHTP). The SHTP transforms the one‐dimensional time domain signal into the two‐dimensional space domain image, enhancing invisible information and obtaining more abundant feature information. Finally, the deep features of the SHTP floral hoop image are extracted by means of deep learning method. In order to improve the faulty line selection universality, a mixed sample library containing three different topologies is established, including the 10 kV radial distribution network, IEEE‐13 node model, IEEE‐34 node model and StarSim platform. The comparisons show that the proposed method has a more noticeable visualization effect on fault features, higher classification precision rate, and better anti‐noise performance. [ABSTRACT FROM AUTHOR]

This paper proposes a method for recognising the fault type using a set of diagnostic criteria. Existing approaches to the interpretation of dissolved gas analysis (DGA) results usually rely on single diagnostic criteria. The proposed method solves the problems of incomplete use of diagnostic information and discrepancies in the norms and criteria for recognising the same fault inherent in the existing methods. The feature of the method is the analysis of the compliance of the DGA results of the diagnosed equipment with the ones of the equipment with a confirmed diagnosis from the fault database. This approach outperforms the existing methods in the number of faults recognised, allowing a wider range of faults to be detected, including combined faults. In addition, the method provides an opportunity to identify possible causes and localise the fault. This feature is unique and provides a significant advantage when interpreting DGA results. The proposed method demonstrates significantly higher accuracy (100%) in recognising various fault types, including combined faults, in comparison to existing methods (about 50%). The proposed method has a 60% success rate in determining the cause and location of a fault, but this figure depends on the database with faults of various types used. [ABSTRACT FROM AUTHOR]

The indirect benefits of battery energy storage system (BESS) on the generation side participating in auxiliary service are hardly quantified in prior works. Nevertheless, the configuration of BESS could be affected by its indirect benefits. In this paper, the authors purpose a quantitative economic evaluation method of BESS considering the indirect benefits from the reduction in unit loss and the delay in investment. First, the authors complete further the cost model of BESS for frequency and peak regulation based on the whole life cycle theory. Second, the authors quantify the indirect benefits of BESS in thermal power plants based on the theory of rotor fatigue life loss and establish a benefits model that considers the unit loss reduction during frequency regulation and the delay in investment during peak regulation. Finally, the authors propose a set of indexes for economic evaluation of the thermal power plant with BESS. The simulation results show that the total benefits of BESS can be improved effectively by considering the indirect benefits from unit loss reduction and the delay in investment, proving the effectiveness of the proposed approach which can be meaningful for the future investment in BESS on the generation side. [ABSTRACT FROM AUTHOR]

The existing relay protections for line‐commutated converter (LCC)‐ultra high voltage DC (UHVDC) transmission lines are insufficient in protection sensitivity and operation speed. The travelling wavefront fault distance information can be used to improve protection performance, which has been used in protection of voltage source converter ‐high voltage DC (HVDC) transmission lines. The adaptability of travelling wavefront protection in LCC‐UHVDC transmission line is analyzed in this paper. First, the conditions that need to be met for the travelling wavefront protections to be able to effectively recognize faults are analyzed. Eight applicability conditions of wavefront protection are revealed from the perspective of travelling wavefront generation and practical application requirements. Then the degree of match between the applicability conditions and the conditions of LCC‐UHVDC system is analyzed as a basis for assessing the adaptability of travelling wavefront protection in the LCC‐UHVDC system. It is pointed out that the index coefficient‐based wavefront protections are not available for LCC‐UHVDC transmission lines since the hardware sampling rate of protection device is insufficient, while first peak time of transient voltage (FPTV)‐based wavefront protection is available for LCC‐UHVDC transmission lines if it has enough anti‐noise ability. Finally, an enhanced anti‐noise algorithm is proposed for the FPTV‐based travelling wavefront protection. The adaptability of the travelling wavefront protection is verified by hardware‐in‐the‐loop test and filed recording experimental data. [ABSTRACT FROM AUTHOR]

The occurrence of inter‐area oscillations in a power system is challenging to prevent. Thus, it is imperative to dampen inter‐area oscillations due to their potential to cause various instability problems. In this study, the Fuzzy Elman Wavelet Neural Network (FEWNN) was created as a novel neuro‐fuzzy network that combines the beneficial features of the Wavelet Neural Network (WNN), Takagi‐Sugeno‐Kang (TSK) fuzzy model, and cross‐coupled feedbacks inspired by the Elman Neural Network (ENN). Compared to alternative fuzzy wavelet networks, the representations of high‐order nonlinear dynamic systems in the proposed novel FEWNN are more effective, the training speed and computational power are greater, and interval type‐2 fuzzy membership functions are able to manage uncertainties in the power system. This paper uses FEWNN to develop a novel method for damping inter‐area oscillations caused by flexible alternating current transmission systems (FACTS) employing a supplementary damping controller (SDC) based on direct adaptive control theory (DACT). The proposed SDC is integrated into a Static Synchronous Series Compensator (SSSC) in a two‐machine two‐area power system and Kundor's four‐machine two‐area power system, as well as a Unified Power Flow Controller (UPFC) in Kundor's four‐machine two‐area power system. In conclusion, the damping power of the system is compared to that of other SDCs presented in recent studies. The MATLAB/SIMULINK environment was used to conduct all simulations for this work. [ABSTRACT FROM AUTHOR]

Emergence of different stakeholders with contrary objectives in active distribution network (ADN) requires for a fair network expansion planning that considers all of the contrary objectives. This paper provides an expansion planning model for ADN strategic visioning from electric distribution stakeholders' organization (EDSO) perspective, which takes into account the objectives of all stakeholders. The stakeholders are network owner, active customers (ACs), consumers, and environmental regulatory body (ERB). The proposed model is multistage bi‐level, where the upper‐level minimizes the present value of the total costs imposed to all ADN stakeholders and the lower‐level minimizes the total generation costs within the distribution‐level electricity market. The Karush–Kuhn–Tucker optimality conditions and the linearization techniques are employed through which the model is converted to mixed integer linear programming which can be tackled effectively. The model provides a visionary expansion plan that policymaker must legislate to realize it. Effectiveness of the model in providing a visionary expansion plan considering EDSO's perspective in making cost effective planning decisions per social welfare goals is verified through several case studies on 33‐bus standard test system. According to the simulation results, transition from DSO to EDSO in the presence of low‐capacity ACs improves social welfare by 5%. But this improvement is up to 70% in the presence of high‐capacity ACs. [ABSTRACT FROM AUTHOR]

Transmission lines may suffer from pin defects, necessitating regular patrols and maintenance. The traditional maintenance approach not only involves a substantial workload and low efficiency but also poses a threat to the safety of maintenance personnel. To address the issue of pin defects in high‐voltage transmission lines, this paper introduces the design of an intelligent pin‐eliminating robot. The research focuses on two main aspects: firstly, analyzing the transmission line environment and employing Solidworks software to design the mechanical structure of the robot. Subsequently, a static stress analysis of the robot's structure is conducted to ensure the rationality of the design. The second aspect addresses the impact of the robot on the electric field distribution of the transmission line in high electric field intensity areas and whether the robot itself experiences discharge phenomena. COMSOL simulation software is utilized to globally calculate and analyze the electric field of the robot. Research results indicate that the threshold for the curvature radius of the pin‐eliminating robot's tip is 2.5mm, and values below this threshold may lead to the occurrence of discharge phenomena. The optimized pin‐eliminating robot, under normal operation, does not exhibit discharge phenomena in a 500kV strong electric field. [ABSTRACT FROM AUTHOR]

This paper proposes a method based on scenario optimization and meta‐heuristic algorithm to optimize the power system stabilizers (PSSs) for enhancement of small‐signal stability (SSS) considering power system uncertainties such as photovoltaic generation, wind turbine generation, and load. The proposed method consists of two stages. In the first stage, the uncertainties are modeled using probability functions and scenarios are created based on them. These scenarios are then used to obtain a joint probability where the most likely probabilities are chosen for the second stage. The second stage utilizes these critical scenarios to formulate an optimization problem. The objective function of the optimization problem is to enhance system's small signal stability margin with PSS parameters as constraints. The proposed method is then solved using a new meta‐heuristic optimization algorithm known as Jelly Search Optimizer to obtain the optimized parameter of PSSs. The proposed method is tested on a two‐area and five‐area system, and the results show that the system SSS margin is improved for the majority of uncertainty situations. Moreover, the proposed method is around 1.6 times faster compared to the conventional probability method when tested in the modified two‐area system while 5.7 times faster when tested in the modified five‐area system. [ABSTRACT FROM AUTHOR]

This paper presents a multi‐objective restoration scheme for improving the resilience of integrated electricity and natural gas distribution systems against extreme weather events. The coupling constraints of electricity and gas networks are tackled properly using a linearized optimal power flow (OPF). Distributed generators, power‐to‐gas facility, rescheduling of generation/storage units, and microgrid formation are employed as operational resources/measures for restoring the integrated energy system after the event landfall. An adaptable directed multi‐commodity flow‐based microgrid formation is utilized, that is, the network configuration is dynamically changed in accordance with time‐variant load priority weights. The proposed method was successfully examined on an integrated electricity and natural gas distribution system comprised of the modified IEEE 33‐bus distribution network and a 14‐node natural gas distribution network. Numerical results showed that using microgrid formation increased the supplied critical load of integrated electricity and natural gas distribution system by about 16%. Moreover, due to making benefit of the power‐to‐gas unit, the supplied critical load increased by about 12.3%. respectively. While utilizing energy storage systems along with the power‐to‐gas unit facilitated the exchange of energy between the power distribution network and natural gas distribution network regarding time‐variant load priority weights, the supplied critical load increased by about 13%. [ABSTRACT FROM AUTHOR]

This paper proposes a nonlinear analysis of the parameter estimation problem in overhead and submarine power cable applications. The proposed methodology is able to estimate both positive and zero sequence parameters. Also, the measurement model incorporates random uncertainty in a rigorous manner, addressing the issue of noise propagation after applying symmetrical components. Three nonlinear estimation methods were studied to address the nonlinear least squares problems. Several noise levels and sample sizes were considered to demonstrate the robustness of the proposed approach. Finally, the methods were validated using both a simplified 2‐bus system and the IEEE 14‐bus system. The results obtained demonstrated significantly better performance compared to similar results found in the technical literature. [ABSTRACT FROM AUTHOR]

The restoration control of the modern alternating current–direct current (AC–DC) hybrid power grid after a major blackout is difficult and complex. Taking into account the interaction between the line‐commutated converter high‐voltage direct current (LCC‐HVDC) and the AC power grid, this paper proposes a novel optimization method of restoration path to reconfigure the skeleton network for the blackout power grid. Based on the system strength, the supporting capability of the AC power grid for the LCC‐HVDC is first analysed from the aspects of start‐up and operation of LCC‐HVDCs. Subsequently, the quantitative relationship between the restoration path and the restoration characteristic of LCC‐HVDC is derived in detail based on the system strength indices of the short‐circuit capacity and the frequency regulation capability. Then, an optimization model of restoration path considering non‐tree paths is formulated and a feasible optimization algorithm is proposed to achieve the optimal path restoration scheme. A modified IEEE 39‐bus system and a partial power grid of Southwest China are simulated to show that the proposed method is suitable for the restoration of AC–DC power grids and can improve restoration efficiency. This research can be an important guidance for operators to rapidly restore the AC–DC power grid. [ABSTRACT FROM AUTHOR]

This paper proposes a new adaptive nonlinear extended state observer‐based model predictive controller (ANLESO‐MPC) for residual current compensation (RCC) inverters to compensate fault characteristics in resonant grounded power systems (RGPSs). In the proposed scheme, the adaptation feature is used to adapt the adjustable inductor appearing in the dynamical model of RGPSs by satisfying the desired fault current compensation requirement which in turn will ensure the resonance condition. Since the adapted parameter is used in the control signal, the ANLESO‐MPC offers robustness against parameter sensitivity. The neutral voltage is considered as a disturbance and the change in this voltage as an extended state to estimate this disturbance while incorporating the adapted parameter so that the adaptive nonlinear extended state observer (ANLESO) provides robustness against disturbances. Finally, the model predictive controller (MPC) is designed for the RCC inverter utilizing estimated disturbances and adapted parameters to fully achieve the fault current compensation. The performance of the controller is evaluated using simulations in MATLAB/Simulink and OPAL‐RT real‐time platforms under varying fault impedances as the fault characteristics change with variations in the fault impedance. Results are also compared with a conventional MPC where the proposed ANLESO‐MPC seems more effective to compensate fault characteristics. [ABSTRACT FROM AUTHOR]

The accurate classification of power quality disturbances (PQDs) is crucial for advancing real‐time monitoring and classification systems within the modern power grid. The proposed system must ensure dependable, safeguarded, and stable operating conditions amidst diverse power quality issues. This paper presents an approach to classifying power quality disturbances using a deep learning model that synergizes deep convolutional neural networks (DCNN) and Bidirectional Long Short‐Term Memory (BiLSTM). This amalgamation effectively extracts and classifies disturbance signals in real time, grounded on noise levels. The initial feature extraction from the signal is accomplished through a time‐frequency matrix. Subsequently, secondary extraction employs the BiLSTM layer to intricately and significantly classify disturbances in the power signal. This aids in transforming high‐dimensional matrices into a reduced set for enhanced performance. The detailed classification is facilitated by the softmax layer. The simulation results support the power quality evaluations under varied constraints and underscore the substantial classification of power quality disturbances through the DCNN‐BiLSTM algorithm, in comparison to alternative classification algorithms in terms of computational speed and accuracy. [ABSTRACT FROM AUTHOR]

The commutation failure is the most prevalent fault in line‐commutated converter based high‐voltage direct current systems, which may result in transient overvoltage on the sending‐side system. Overvoltage level evaluation has become a crucial task for power industries to assess the tripping risk of large‐scale wind turbines and implement effective stability control measures. In this paper, a derivation of the mathematical relationship between the reactive power consumed by the rectifier and AC voltage is presented firstly, along with an analytical expression for the peak value of transient overvoltage. Secondly, decision tree (DT) model is adopted to extract the mapping relationship between transient overvoltage and massive electrical quantities of power grids. The common DT algorithm is transformed by modifying the error weight assignment, which reflects the error tolerances for different actual overvoltage regions. On this basis, an overvoltage analysis method integrating the model‐driven and data‐driven techniques is proposed, and the improved DT algorithm is applied to fast error correction, enhancing the interpretability of regression prediction results. Case studies were performed in the simulation system of Northwest China local power grid with transient overvoltage problems, and the simulation results verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In the scientific literature, it has been a common assumption that electric vehicles (EVs) draw a constant current during the whole charging session. In reality, EV charging profiles are not linear, and the non‐linearities have recently gained more attention. However, a thorough analysis of the influences of different charging profile modelling methods is not yet carried out. This paper aims to fill this gap by comparing experimental measurements of four commercial EVs and results of a developed simulation model that considers different charging profile modelling methods. According to the results, the use of linear charging profiles may lead to notable modelling inaccuracies (error > 30%) whereas the use of measurement‐based non‐linear charging profile models yields relatively accurate results (error mostly ≤ 3.5%). The results also demonstrate that the use of a simple, but justified, bilinear charging profile model is likely to be sufficiently accurate in most scenarios. [ABSTRACT FROM AUTHOR]

The drastic increase in distributed energy resources (DERs) leads to challenges in the operation of distribution systems worldwide. While several solutions for grid monitoring and control are available on the market and in literature, this research is the first of its kind aiming to supervise the grid by providing a modular configurable unified hardware and software architecture. The control algorithms are configured using data models according to IEC 61850‐7‐3 and IEC 61850‐7‐4. The novel system architecture is a portable, modular and flexible architecture that aggregates smart grid control functions onto a standardised hardware platform, emphasising the need for hardware independence. The central controller contains several smart grid control functions and the various field devices are distributed across the distribution grid. This paper deals with the simulation of different real‐world distribution grids on the Real‐Time Simulator (RTS) and experimental verification of the control algorithms. Smart grid control functions such as Coordinated Voltage Control (CVC) and Optimal Power Flow (OPF) are experimentally verified on a German grid. The grid dynamics are compared when the central controller executes the CVC against the OPF implementations. The experimental results, advantages and challenges of each control are presented here. The results also showed the variation in grid behaviour when the control parameters were varied. The paper also shows that the algorithm and the choice of the control parameters depend upon the distribution grid's complexity and the system operator's individual needs. The results illustrate the potential of such a universal distribution automation solution for system operators worldwide. [ABSTRACT FROM AUTHOR]

The effective design of controlling parameters in AC–DC conversion in power cables has played an important role in transmission security. The state‐of‐the‐art studies determining the voltage level and ampacity of cables are mostly based on the core temperature at 70 ℃. Accordingly, this paper proposed a technique to determine the cable DC voltage level and ampacity of AC–DC conversion by taking operational temperature, the steady and the transient state electric field intensity constraints and the critical reversal temperature difference (CRTD) into account. The results of the research indicated that the voltages of 66 kV AC cable can be transformed to ±55 kV, ±50 kV and below, the voltages of 35 kV AC cable can be transformed to ±50 kV, ±30 kV and below, the voltages of 10 kV AC cable can be transformed to ±20 kV, ±10 kV and below. Although one of three phases is idle, the capacity of transmission line has improved effectively, and the idle phase could be an alternate to enhance the reliability. Compared with AC operation, the maximum transmission capacity of DC operation increased on different levels. The design results and the approaches of AC‐DC conversion in this paper can contribute a valuable recommendation to the electrical engineering field. [ABSTRACT FROM AUTHOR]

In a high proportion of new energy grids or a 100% new energy source of power grid, the dynamic performance of PMSG under fault conditions is very important. However, the current small signal model lacks detailed and deep analysis of a permanent‐magnet synchronous generator (PMSG)‐based wind power generation under LVRT control. Here, a small signal model of a PMSG under pre‐fault, fault and after fault failure recovery stages are established. This paper employs linear analyses to characterize the dynamic performance of a PMSG‐based wind power generation system under LVRT control and practical generator characteristics. In particular, the conflict between frequency regulation and LVRT is discussed based on the constructed model. On this basis, the impact of dc‐link bandwidth on the system performance during failure recovery stage is discussed, and the active damping method is analysed to improve the performance of PMSG during failure and failure recovery stages. Finally, time‐domain simulations are utilized to verify the analyses and models discussed. The research results in this paper are helpful to promote and improve the performance of PMSG during LVRT conditions. [ABSTRACT FROM AUTHOR]

In this paper, a collaborative stochastic expansion planning model of cyber‐physical system (CPS) with resilience constraints is proposed. The model can reduce the coupling risk and enhance the resilience under extreme scenarios, from the perspective of the structural/functional coupling between the physical and cyber systems. The model is to collaboratively optimize expansion transmission line siting, expansion communication fibre siting, and service routing distribution to minimize total investment cost. Constraints are divided into conventional constraints and resilience constraints. In the resilience constraints, the impact of cyber failures on the dispatch strategy is analyzed, and the load shedding in stochastic scenarios is limited to guarantee the resilience of the planning scheme. Particularly, to reach a stable solution for the stochastic planning, a mixed scenario set is proposed based on the probabilistic typhoon model and the complex network theory. Finally, the model is solved by the progressive hedging (PH) algorithm. The case studies of the IEEE RTS‐79 test system demonstrate that, compared with the traditional independent planning model, the model proposed in this paper performs better in limiting load loss and enhancing resilience under extreme scenarios. [ABSTRACT FROM AUTHOR]

The intermittent access of renewable energy sources leads to a significant increase in fault short‐circuit currents in power grid. DC superconducting fault current limiters (SFCL) can limit short‐circuit currents in flexible DC transmission systems, which work in combination with direct current circuit breaker (DCCBs) to protect the grid system effectively. However, for the parameter design of DC superconducting fault current limiter (DCSFCL), it is difficult to balance the economics of the device and its adaptability to different fault situations. In this paper, a cross‐mapping design methodology for the DCSFCL is proposed and applied in the Nan'ao flexible DC transmission project, China. A cross‐mapping method is designed combining the critical parameters of DCSFCL and the system parameters. A three‐terminal simulation model of the flexible DC transmission system is constructed through MATLAB/Simulink and the design process of DCSFCL parameters is introduced. A cyclic calculation approach is proposed for the design of DCSFCL parameters through three fault conditions of the power system. Meanwhile, impact current short‐circuits test and artificial ground short‐circuit test were conducted on the prototype. Test results show that the designed DCSFCL meets the requirements of practical applications. [ABSTRACT FROM AUTHOR]

For future multi‐terminal HVDC networks, protection systems based on DC circuit breakers (DCCBs) are envisioned to limit the loss of power transmission towards AC grid(s). Yet, HVDC protection system design is considered a major challenge—especially in a multi‐vendor setup. In particular, protection systems shall enable HVDC converters to ride through DC faults without protective blocking. Several studies have performed sensitivity analyses to determine the DCCB properties and current‐limiting inductor properties that are required to enable converter fault‐ride‐through (FRT), but the HVDC converters themselves have been simplified and kept identical. However, in a multi‐vendor setup, the exact converter design is not known at the time of DC protection planning. Therefore, this paper investigates the impact of different converter properties on the DC‐FRT behaviour, and reveals implications on HVDC protection design. Firstly, analytical approaches are proposed to mathematically derive potential impact factors on the converters' FRT characteristics, also considering different converter control architectures. Secondly, a comprehensive EMT study demonstrates a significant impact of the converter parameters on the FRT behaviour. Analytical approaches and EMT results are compared with regard to limitations and applicability, such that the paper's findings can support the development of functional requirements for multi‐vendor multi‐terminal HVDC networks. [ABSTRACT FROM AUTHOR]

With the increasing rate of population in big cities around the world, the tendency to build new buildings in the suburb of main cities or to build large apartments in the main cities has been highlighted. In this regard, building residential complexes has seen a dramatic increase in these areas as it makes it possible to build a large number of residential units within a reasonable space. Although these complexes have brought numerous benefits, they are some challenges regarding their construction processes. One main concern associated with these complexes is how to optimally install energy components such as transformers, combined heat and power (CHP) units, boilers etc., in the shared area of apartments in the residential complex. To address this issue, this paper models the energy system of a residential complex as an energy hub and proposes a novel framework to obtain the optimal planning of such an energy hub. In order to address the conflicting desires of the residential complex's builders and the future residents of the residential units, a multi‐objective (MO) optimization problem has been considered in the proposed method that simultaneously optimizes the investment costs, operation costs, and the reliability of energy supply. In this regard, a Multi‐objective Particle Swarm Optimization (MOPSO) algorithm combined with classical linear programming (LP) optimization method has been proposed to solve the MO optimization problem. In order to demonstrate the effectiveness of the proposed method, a case study including a residential complex with 300 residential units is considered, and the proposed method is implemented in this case study. The numerical results show that the proposed framework can appropriately optimize investment costs, operation costs, and the reliability index simultaneously, and the obtained Pareto frontier gives the investors the freedom to opt for any point from this surface. [ABSTRACT FROM AUTHOR]

The damage of wind turbines suffered from lightning strikes has been a key issue for the safe and reliable operation of wind farms. Accurate determination of the annual lightning flash number to a wind turbine is essential for designing proper lightning protection measures. The interaction of downward and multiple upward leaders (MULs) is studied in this paper, which also considers the stochastic nature and branched behaviour of the lightning attachment phenomenon. Firstly, with an improved stochastic lightning model, the relationship among the striking distance, the height of wind turbines and the return stroke current is established. Moreover, a modified method for predicting the annual lightning flash number strikes to a wind turbine is proposed. The simulation results show that the striking distance and the collection area not only depend on the return stroke current, but also on the height and blade angle. Besides, the comparations between the calculation results and field statistics indicate that the conventional electric geometry model is not satisfied with the need of lightning protection for wind turbines. Note that there is only a difference of 4% between the modified method and the observation value. These quantitative researches provide the guidance of calculation results for the optimization of the blade lightning protection system (LPS) design. [ABSTRACT FROM AUTHOR]

Electric arc furnace (EAF) is one of the most disturbing loads in the power system as it is highly time‐varying and nonlinear. In this paper, new models based on the instantaneous EAF voltage and current, recorded from EAFs in Mobarakeh steel company in Isfahan/Iran are proposed. The focus of this paper is on developing efficient models based on the Schwarz model where three modified types are proposed. The original Schwarz model has four constant parameters. Unlike the original Schwarz model which uses invariant parameters, the time‐varying nature of the arc's parameters is considered in the proposed models. The first modified model has two constant model orders and two time‐varying coefficients. Additionally, to further enhance the model's accuracy, in the second modified model, six parameters include three constant model orders, and three time‐varying coefficients exist. The third model uses all the original model's parameters as time‐series parameters. The parameters of the time‐varying model are derived using autoregressive moving average (ARMA) models. The accuracy of the proposed model is evaluated by calculating the active power, harmonics, instantaneous flicker, and short‐term flicker. Finally, three MATLAB codes are provided to generate the model parameters time series. [ABSTRACT FROM AUTHOR]

The secular increase of load causes the evolving power system to operate near the critical margins. In such attitudes, the probability of cascading line outages, as the major reason for recent blackouts, is much more than expected. As a result, an innovative approach for optimal AC transmission expansion planning (TEP) to minimize the risk of cascading blackouts is presented in this paper. The proposed multi‐objective AC‐TEP problem minimizes the expected energy not supplied due to the total load shedding imposed on the system to mitigate the risk of cascading line outages along with the total investment and operational costs of the new transmission lines planned to be optimally added to the system. This probabilistic procedure is based on self‐organized criticality and considers the actual aspects of the power system such as reactive power and voltage concerns in the planning stage. The obtained Pareto front set provides the opportunity for the planners to choose the most preferred solution. The proposed method is implemented and verified in the IEEE 118‐bus test system and compared with two types of TEP approaches. The first one is a previously reported version of this work which was a simplified DC model of the problem and the second one is the conventional AC‐TEP model. The obtained results show the benefits of the proposed AC model on the mitigation of cascading line outages in a more realistic framework, considering the impact of reactive power and voltage stability concerns. An innovative approach for optimal AC transmission expansion planning (AC‐TEP) to minimize the risk of cascading blackouts is presented in this paper. The proposed multi‐objective AC‐TEP problem minimizes the expected energy not supplied due to the total load shedding imposed on the system to mitigate the risk of cascading line outages along with the total investment and operational costs of the new transmission lines planned to be optimally added to the system. This probabilistic procedure is based on self‐organized criticality and considers the actual aspects of the power system such as reactive power and voltage concerns in the planning stage. [ABSTRACT FROM AUTHOR]

Undervoltage load shedding (UVLS) is the last line of defense to ensure the safe and stable operation of the power system. The existing UVLS technique has difficulty adapting and generalizing to new topology variation scenarios of the power network, which greatly affects the reliability of the control strategy. This paper proposes a UVLS emergency control scheme based on a graph deep reinforcement learning method named GraphSAGE‐D3QN (graph sample and aggregate‐double dueling deep q network). During offline training, a GraphSAGE‐based feature extraction mechanism of the power grid with topology variation is designed that can better capture the changes in system state characteristics. A novel reinforcement learning framework based on D3QN is developed for UVLS modeling, which reduces overestimations of control action and leads to a better control effect. Then, online emergency decision‐making is achieved. The simulation results on the modified IEEE 39‐bus system and IEEE 300‐bus power system show that the proposed UVLS scheme can always provide more economical and reliable control strategies for power networks with topology variations and achieves better benefits in both adaptability and generalization performances for previously unseen topology variation scenarios. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic interactions between large‐scale PV plants and nearby synchronous generators. To this end, a comprehensive model of a PV plant, including the PV arrays characteristic and their voltage controller, DC/DC converter, DC‐link dynamic, DC/AC inverter controllers, and output filter, is obtained. Then, the eigenvalues of the PV plant model are calculated, and it is revealed that the PV plant has a dynamic mode in the sub‐synchronous oscillations (SSOs) range. Furthermore, the modal analysis method is employed, and it is shown that the inductance of the DC/DC converter and the DC‐link capacitor significantly contribute to the PV sub‐synchronous mode. Also, it is shown that this mode oscillations penetrate the grid and increase the risk of sub‐synchronous resonance (SSR). Then, the mechanism of dynamic interactions between large‐scale PV plants and torsional modes of nearby synchronous generators is revealed. This paper also investigates the required conditions for these interactions and shows that solar irradiation level and the DC‐link capacitance are the essential factors that can intensify these interactions. [ABSTRACT FROM AUTHOR]

Cloud energy storage (CES) in the power systems is a novel idea for the consumers to get rid of the expensive distributed energy storages (DESs) and to move to using a cloud service centre as a virtual capacity. Although the different characteristics and applications of the energy storages are reviewed in some papers, there is no review study on the CES concepts, formulations, applications, and challenges. Therefore, the main contribution of this paper is to review the applications of the CES and its technical challenges in the power systems. For this purpose, the concept and fundamentals of the CES, as well as their role in supporting the consumers and the power network, are described first. The flow of information in a CES is then discussed, and the roles of the operator, consumers, and facilities, as the main sectors of the CES are explained. The existing studies are classified and discussed regarding the different applications of the CES in the power systems and their drawbacks are highlighted. The operation and planning (feasibility) problems of the CES are investigated. Reviewing the existing studies shows that comprehensive models are required to address the energy management (EM) and feasibility analysis of the CES applications. To address this challenge, the general formulations are presented for the planning and the operation scheduling problems of the CES. In addition, addressing different CES applications in the power systems leads to some technical challenges which are described. Finally, future directions are suggested for potential researchers to continue the studies on the CES integration and application. [ABSTRACT FROM AUTHOR]

As electric vehicles (EVs) begin to participate in the peak‐shaving auxiliary service market, the question of how price aggregators can maximize the peak‐shaving capacity provided by EVs and maximize their own profit has become a major problem. This paper proposes a bargaining game pricing method based on the psychological cost of EVs and the risk assessment of aggregators. First, the comprehensive psychological cost of EV users is obtained based on the impact of users' participation in peak shaving on the battery life of EVs and on users' original travel plans and time, and the impact of aggregator pricing on users' psychology. Then, based on users' psychological cost and the law of gravitation, the evaluation scheme for the peak‐shaving capacity of EVs is obtained. On the basis of conditional value at risk (CVaR), the mixed CVaR is obtained by considering the risk‐chasing behaviour of users. Based on the mixed CVaR, the risk assessment of aggregators' participation in the peak‐shaving auxiliary service market is carried out. According to the above information, the aggregator and EV groups are engaged in a bargaining game based on the peak‐shaving pricing problem, which is divided into complete information game and incomplete information game. Finally, the feasibility of the proposed method is verified by an example analysis which is run by the MATLAB R2019b. The method proposed in this paper provides aggregators with a complete peak‐shaving electricity pricing scheme, effectively improving the dispatching capacity of EVs. It is helpful to rationalize the pricing of aggregators and maximize their profits. EVs can also obtain satisfactory profits, attracting more EVs to participate in peak shaving, and reduce the pressure on power grid peak shaving. [ABSTRACT FROM AUTHOR]

This paper proposes and develops the idea of using a community supercapacitor (SC) in an islanded DC multiple nano‐grids (MNG) system. In the proposed structure, the community SC works in tandem with the community/cloud battery energy storage system (CBESS) of the DC MNG. This combination forms a grid‐forming battery‐supercapacitor cloud hybrid energy storage system (CHESS), which is responsible for maintaining the voltage stability and power balance at the common DC bus of the MNG system. Also, to effectively utilize the SC capacity, this paper proposes a modified control structure for each DC nano‐grid enabling the local BESS units to coordinate with the community SC. Then, it is shown that, in the proposed grid‐forming CHESS technology, the output power of all the local and community BESS units has significantly smoother power variations leading to a higher battery lifetime. Additionally, it is shown that the proposed CHESS technology can improve the voltage stability of the system leading to higher voltage quality. Moreover, it is discussed analytically that the proposed CHESS technology requires less energy storage capacity for the community SC compared to its equivalent MNG with a distributed SC architecture. Finally, these results are verified by simulating two case‐study MNGs in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

The fault of the tie line between the photovoltaic (PV) station and the grid is a serious fault for the PV station. It will cause the PV station to operate into an unintentional island. The uncontrolled Island voltage and frequency will inevitably lead to the disconnection of the inverter in the station. This situation will bring great losses to PV operators and impact to the power grid. In order to deal with the tie line fault, this paper analyzes the operation characteristics of PV stations in case of tie line fault firstly. Then a tie line fault ride‐through method based on cooperative strategy of small capacity energy storage (ES), relay protection and PV inverters is proposed. The islanding switching control strategies of PV and ES are designed respectively. The cooperative strategy of protection, PV controller and ES controller is formulated as well. The real‐time digital simulator (RTDS) closed‐loop test platform including line protection device, ES controller and PV controller is built to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Demand response programs (DRPs) have paved a meaningful role in the power supply–demand balance in a smart grid. Also, a residential community with the presence of renewable energy sources (RESs) and electric vehicles (EVs) provides a new way to tackle growing concerns about energy efficiency and environmental pollution. The inherent uncertainty of RESs generation and EVs behaviour leads to difficulty in the economic scheduling of the demand side. Different types of uncertainty modelling have been investigated, such as Monte Carlo (MC) simulation, fuzzy method, and robust optimization. They are faced with many scenarios and computational complexity. This paper uses the information gap decision theory (IGDT) method to study variations of uncertainty radius on residential community electricity costs. Therefore, to achieve an optimal strategy for scheduling the appliances considering the deep uncertainties of RESs and EVs, a novel IGDT‐based demand response scheduling for a residential community is proposed. Impacts of different levels of uncertainties are studied. The simulation results depict the privileges of the proposed method when confronting deep uncertainties. By increasing the radius of the uncertainty of RES and the initial charge of EVs, energy consumption costs grew 20% and 2%, respectively, which indicates the system operator can manage the costs effectively. [ABSTRACT FROM AUTHOR]

This paper extends the study of a zero‐sequence impedance‐based fault location method (FLM) designed for medium voltage (MV) distribution feeders whose principle has been presented in a previous work Bach et al. This method is able to locate any type of earth fault with any type of neutral grounding inside a convex set of nodes forming the solution area. The total length of the lines inside this area is statistically computable a priori and depends only on the number and location of additional voltage measurements deployed on some remote end secondary substations. To be efficiently applied to the most challenging rural feeders presenting a lot of ramifications, simulations have shown a need for synchronized measurements, such as the ones obtained using phasor measurement units. Besides, this paper addresses the coupling potential of this method with more traditional ones such as Takagi‐based FLMs to enhance the performances of both, solving the multiple estimation problem while potentially locating the fault within one node. Finally, an extensive sensitivity analysis has been carried out, showing a good robustness with respect to impedance estimation errors or fault impedance values while showing the need for synchronization, especially on some remote measurement locations. [ABSTRACT FROM AUTHOR]