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This paper proposes a fully distributed scheme to solve the day‐ahead optimal power scheduling of networked microgrids in the presence of different renewable energy resources, such as photovoltaics and wind turbines, considering energy storage systems. The proposed method enables the optimization of the power scheduling problem through local computation of agents in the system and private communication between existing agents, without any centralized scheduling unit. In this paper, a cloud‐fog‐based framework is also introduced as a fast and economical infrastructure for the proposed distributed method. The suggested optimized energy framework proposes an area to regulate and update policies, detect misbehaving elements, and execute punishments centrally, while the general power scheduling problem is optimized in a distributed manner using the proposed method. The suggested cloud‐fog‐based method eliminates the need to invest in local databases and computing systems. The proposed scheme is examined on a small‐scale microgrid and also a larger test networked microgrid, including 4 microgrids and 15 areas in a 24‐h time period, to illustrate the scalability, convergence, and accuracy of the framework. The simulation results substantiate the fast and precise performance of the proposed framework for networked microgrids compared with other existing centralized and distributed methods. [ABSTRACT FROM AUTHOR]

The diode‐rectifier (DR)‐based high‐voltage direct current (HVDC) transmission is regarded as a low‐cost alternative to the HVDC transmission for offshore wind farms (OWFs) integration. However, the DR would generate harmonic voltages in the offshore grid, which greatly affects the power quality. To solve this problem, this paper utilizes a parallel‐connected small‐capacity auxiliary converter to suppress the harmonic voltages and provide black‐start energy for the OWFs. In addition, the submodule branches (SMBs) of the auxiliary converter can be used as part of the HVDC chopper. On this basis, a coordinated grid fault ride‐through (FRT) strategy is proposed in this paper. After a grid fault occurs, the HVDC chopper constructed by the auxiliary converter is able to constrain the DC‐link voltage. At the same time, the OWFs are controlled to reduce the output active power. As a result, the cost of the FRT can be significantly reduced. The proposed harmonic voltage suppression and the FRT method are verified through the simulation model in Simulink. [ABSTRACT FROM AUTHOR]

Multi‐terminal high‐voltage direct current technology based on voltage‐source converter stations (VSC‐MTDC) is expected to be one of the most important contributors to the future of electric power systems. In fact, among other features, it has already been shown how this technology can contribute to improve transient stability in power systems by the use of supplementary controllers. Along this line, this paper will investigate in detail how these supplementary controllers affect electromechanical oscillations, by means of small‐signal stability analysis. The paper analyses two control strategies based on the modulation of active‐power injections (P‐WAF) and reactive‐power injections (Q‐WAF) in the VSC stations which were presented in previous work. Both control strategies use global signals of the frequencies of the VSC‐MTDC system and they presented significant improvements on transient stability. The paper will provide guidelines for the design of these type of controllers to improve both large‐ and small‐disturbance angle stability. Small‐signal stability analysis (in Matlab) has been compared with non‐linear time domain simulation (in PSS/E) to confirm the results using CIGRE Nordic32A benchmark test system with a VSC‐MTDC system. The paper analyses the impact of the controller gains and communication latency on electromechanical‐oscillation damping. The main conclusion of the paper is that transient‐stability‐tailored supplementary controllers in VSC‐MTDC systems can be tuned to damp inter‐area oscillations too, maintaining their effectiveness. [ABSTRACT FROM AUTHOR]

DC collection and transmission is one of the major development directions of large‐scale photovoltaic (PV) power system. In order to achieve low‐cost, high‐efficiency and long‐distance transmission of PV power, this paper adopted a DC grid‐connected topology by using multi‐modular cascaded DC‐DC converters, forming an input‐independent and output‐series (IIOS) system. In this system, an LLC type DC‐DC converter based on IGCT and magnetically integrated transformer was proposed in this paper, which can significantly increase the capacity, efficiency and power density of DC‐DC converter. And the equalization unit based on half‐bridge topology was adopted for the case of uneven irradiation of the PV inputs. In addition, this paper proposed an equalization control method based on the Nearest Level Modulation (NLM), which greatly simplifies the design of the acquisition and control system. Furthermore, the parametric design of a 50MW PV system was presented, and the simulation was carried out to verify the feasibility and advantages of the system in this paper. The proposed IIOS system is more suitable for high‐capacity, long‐distance PV power transmission, which can serve as a reference for the development of large‐scale photovoltaic systems. [ABSTRACT FROM AUTHOR]

The virtual synchronous generator (VSG) is a good solution for stabilizing the power system with high penetration of renewable energy. However, in case of serious unbalanced voltage disturbance/fault, the conventional VSG may lose voltage, inertia, and damping support characteristics to the grid and even can cause disconnection of renewable energy. This paper proposes an improved fault control strategy for VSG with the coordination of STATCOM. The proposed method can provide sufficient voltage support while keeping continuous system inertia and damping support under severe unbalanced fault. In the paper, an improved VSG and STATCOM control topology based on positive and negative sequence current control are first proposed so as to keep the damping and inertia support to grid during the grid fault. Secondly, the voltage support control method for the VSG with improved topology during unbalanced fault is introduced, which can achieve multiple control objectives, in terms of voltage support, current limitation, and active power output simultaneously. Then a coordination control scheme of improved VSG and STATCOM is developed so as to optimize the maximum control objectives in all possible scenarios, especially in the case of severe unbalanced fault. Finally, the effectiveness of the method is verified by using the MATLAB/SIMULINK simulation platform. [ABSTRACT FROM AUTHOR]

To handle the challenges attributed to the distributed generators, the protection system of distribution networks needs to be evolved. Recent protection schemes modify the standard characteristics of overcurrent relays, called non‐standard characteristics. These non‐standard characteristics are mostly case‐dependent and have limitations in solving the variety of protection challenges attributed to distributed generators. This paper analyses the performance of six voltage‐based non‐standard characteristics on photovoltaic distributed generators (PVDG) penetrated 3‐bus, 8‐bus, and IEEE 30‐bus distribution grid, as well as the 9‐bus Canadian radial distribution grid with synchronous distributed generator, wind turbine generator, and PVDG penetrations. Besides that, the optimum coordination of these non‐standard characteristics‐based overcurrent relays is a complex problem due to the involvement of additional controlling parameters. To find the best suitable method, a comparative analysis of the genetic algorithm, differential evolution, artificial bee colony, harmony search algorithm, firefly algorithm, cuckoo search algorithm and particle swarm optimization are performed for optimum relay coordination. The outcomes of the paper are thoroughly discussed for future research on non‐standard characteristics development. [ABSTRACT FROM AUTHOR]

Two extreme cases of static stability are voltage stability and power angle stability. The general stability analysis only focuses on one of the voltage stability or power angle stability. The stability limit and state change are studied for a certain kind of stability characteristics. In this paper, the stability limits of static voltage and power angle are derived, respectively. Therefore, different stability characteristics and their interrelationships can be further studied. The index to judge the dominant stability characteristic is defined. The proposed index only needs the system wide‐area measurement information to quickly and accurately judge the dominant static stability characteristics. The dominant instability (DI) characteristic index proposed in this paper is equal to 1 or −1, which can help to quickly identify whether the power angle stability or voltage stability is dominant. The effectiveness of the method is proved by a simple system. [ABSTRACT FROM AUTHOR]

As an important form of renewable energy utilization, microgrid (MG) is considered to be the main bearing form of distributed generation in the future. One of the most concerning issues in the operation of MG is how to realize its economic dispatch (ED). Nowadays, distributed algorithms have been increasingly used to solve the economic dispatch problem of MG. However, the MG based on the distributed optimization architecture must bear higher cyber‐attack risks. To address this issue, this paper investigates the distributed robust ED problem of MG. Firstly, a multi‐objective dispatch model of MG using a linear weighted sum (LWS) algorithm is developed, which considers the environmental and economic costs. On this basis, an event‐triggered fully distributed algorithm is proposed, which can effectively reduce communication times. Furthermore, an attack resilient strategy against false data injection (FDI) attacks is implemented in the proposed fully distributed algorithm, which has strong robustness against various colluding attacks and non‐colluding attacks, and can eliminate incorrect measurement of incremental cost and power generation data. Finally, the effectiveness of the proposed distributed control strategy is demonstrated through case studies in this paper. [ABSTRACT FROM AUTHOR]

The droop mechanism is widely utilized in a stand‐alone microgrid (MG) to regulate power‐sharing among distributed generators (DG). However, over the years, the droop phenomena are continually modified to lessen the deviation in voltage and frequency parameters caused due to classical droop. This study suggests computing the droop coefficient for voltage–current (V–I) droop to take into account for proportional power distribution among DGs. In grid utility networks, the conservation voltage reduction (CVR) strategy is widely used to curtail the use of energy. Hence, this paper investigates the CVR's performance for stand‐alone MG by performing the adaptive vector control scheme in the two‐phase d–q reference frame. In addition to it, the paper is intended to develop a coordinated control strategy for stand‐alone MG involving classical P–f droop and self‐sustained V–I droop employed to perform the function of CVR during peak demand and overloading conditions. Further, a load‐shedding control approach is also considered to cut out some segments of load during overloading conditions to operate the MG in the stable zone. The validation of the proposed strategy is conducted on MATLAB/Simulink software. [ABSTRACT FROM AUTHOR]

This paper summarizes our recent work on Grid‐Forming Inverter (GFM) application to power systems. We have developed a novel design of GFM, a single‐phase synchronous inverter (SSI) for the conventional 100/200V distribution network based on the concept of "non‐interference core (NIC) dynamic model." This paper first explains the design concept of NIC‐SSI and a simulation model of SSI for power system analysis. Then the stabilization effect is investigated, where the installation of SSI on the single‐phase consumer side is assumed as a new concept. The improvement of frequency and transient stability are evaluated. The SSI model is verified by comparing the developed root mean square (RMS) simulation, hardware‐in‐the‐loop (HIL) simulation, and the experiment using SSI hardware. The simulation results show that the SSI has the considerable ability of grid stabilization. Singlephase micro‐grid (SMG) operations using SSIs are also presented. The effectiveness of SMG operations is shown based on laboratory experiments. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RES) are increasingly being developed and used to address the energy crisis and protect the environment. However, the large‐scale integration of wind and solar energy into the power grid is still challenging and limits the adoption of these new energy sources. Microgrids (MGs) are small‐scale power generation and distribution systems that can effectively integrate renewable energy, electric loads, and energy storage systems (ESS). By using MGs, it is possible to consume renewable energy locally and reduce energy losses from long‐distance transmission. This paper proposes a deep reinforcement learning (DRL)‐based energy management system (EMS) called DRL‐MG to process and schedule energy purchase requests from customers in real‐time. Specifically, the aim of this paper is to enhance the quality of service (QoS) for customers and reduce their electricity costs by proposing an approach that utilizes a Deep Q‐learning Network (DQN) model. The experimental results indicate that the proposed method outperforms commonly used real‐time scheduling methods significantly. [ABSTRACT FROM AUTHOR]

With the development of the energy structure of the power system, the characterization of the observed oscillation in the power system has steadily evolved. With the increasingly high penetration of distributed energy sources and power electronic devices, such as the photovoltaic, wind power and charging pile, the basic mechanism, frequency ranges and propagation of oscillation would become more dynamic and complex. Therefore, the traditional parameter estimation, as well as the suppression methods would also suffer from dynamic oscillations. To this end, this paper first investigates the oscillation propagation and typical events caused by the oscillations. Next, the oscillation model estimation method including the model‐based and data‐driven based methods is discussed. The benefits and difficulties of various parameter estimate techniques are highlighted. Under this context, the validity and limitations of the current power system stability are summarized. Furthermore, the different oscillation damping and mitigation measures are compared, and the major adaptability and limitations are listed for the specific application scenarios. Conclusions are drawn, and the wideband oscillation becomes the future research objective where the future work is presented at the end of the paper. [ABSTRACT FROM AUTHOR]

This paper presents a method for optimal reconfiguration of smart grids following the occurrence of short circuit faults. Due to restoration delays, the aim of the proposed approach is to save the maximum possible number of loads by forming stable islands and serving loads with Distributed Energy Resources (DERs). The islanding plan aims to prevent island instability and to help DERs continue supplying the maximum number of loads by the optimal network reconfiguration. Fault isolation is carried out by the protection system and the proposed procedure is commenced right after the fault isolation by controlling the condition of the network remote‐controlled switches. The proposed islanding plan is a novel method by this paper in the management of the postfault conditions of smart grids. Furthermore, a Q‐learning reinforcement approach is presented as the optimization tool because of its great capability and fast response for the determination of optimal reconfiguration. Numerous simulation tests for various fault locations on a 6‐bus and a 33‐bus test networks show the effectiveness of the proposed method in the improvement of postfault network reliability and sustainability. [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]

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]

Voltage stability assessment based on machine learning has become an important challenge in power systems. This paper presents real‐time short‐term voltage stability (STVS) assessment based on phasor measurement unit (PMU) data and machine learning (ML). The database is created through time series of measurement data to involve system time‐temporal and dynamics. Then multiple operating states of the power system are classified through the calculation of the Lyapunov exponent and dynamic voltage index according to the database. This paper presents a weighted combination of random forest (RF) and LightGBM (LGBM) classifiers to train a time‐series database. One of the main advantages of this paper is using the gradient concept in data preprocessing, which has enhanced performance metrics and reduced the defect of data noise. Also, hyperparameter optimization is conducted to improve machine performance. Studies on the IEEE 118bus and a real local grid (RLG) demonstrate that the proposed method improves the performance metrics such as accuracy and F1‐score. Also, this approach is robust against PMU data noise and topology changes in the network. [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]

Deteriorated power system components have a higher probability of failure than new components. Still, the reliability of supply analyses traditionally models all components of the same type with the same probability of failure, and thus neglects the effect of deteriorated components. This paper presents a methodology to integrate a condition‐dependent component probability of failure model into a power system reliability analysis. The component state is described by a semi‐Markov process, and the paper shows how this, under reasonable assumptions, can be approximated by a Markov process. The Markov assumption simplifies the analysis and allows the model to include preventive retirement and be calibrated to statistical data. A case study using statistical data for Norwegian power transformers shows that, in the Norwegian power system, the proportion of failures that are due to the poor condition is small, partly due to the common strategy of preventive retirement. However, if the condition of the transformers were worse, the impact of poor conditions can be considerable. The methodology further enables the identification of the transformers that contribute most to the risk to the reliability of supply. The paper thus highlights the importance of accounting for the component condition in strategic decisions such as long‐term renewal planning [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]

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]

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]

The high‐voltage direct current (HVDC) power transmission short‐circuit fault protection is a big challenge, and it is the main obstacle to promote the HVDC multi‐terminal networks. A natural commutation current topology (NCCT) of hybrid HVDC circuit breaker (DCCB) integrated with limiting fault current is proposed in this paper, which shares several obvious merits, such as lower peak fault current, shorter fault isolation time and low turn‐off loss. The main contributions in this paper are made as follows. (1). A second‐order RLC circuit in novel NCCT circuit breaker is utilized as the current limiting circuit to dramatically reduce the fault current rising slope and its peak value. (2) A asynchronous NCCT circuit breaker has been investigated to broaden the proposed NCCT circuit breaker's optimal working area. (3) By taking China Zhangbei 500 kV four‐terminal DC grid as an example, an optimization design technique of the asynchronous NCCT‐DCCB is developed to demonstrate the process of the theoretical analysis and parameter design. The correctness of the proposed NCCT‐DCCB and the feasibility of the optimization design technique are confirmed by EMT simulation on PSCAD/EMTDC and hardware‐in‐loop experiment on PLECS‐RtBox. [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]

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]

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]

Peak‐valley period partition of load curve is a key aspect of time‐of‐use (ToU) tariff to improve power load characteristics, such as shifting peak loads towards valley time periods. Fuzzy clustering algorithm is an effective and popular method commonly used to solve the peak‐valley period partition of load curves, but it still encounters the difficulty of dividing some data within the boundary regions of different time periods. Therefore, this paper presents a peak‐valley period partition and abnormal time correction scheme for ToU tariffs under typical daily load curves based on improved fuzzy C‐means (FCM) clustering algorithm. In order to improve the accuracy of peak‐valley period partition, modified fuzzy membership functions are proposed to improve the initialization of FCM clustering, and a loss function‐based method is presented for calculating the fuzzy parameters of those membership functions. To resolve the problem of abnormal time partitioning within the boundaries of different time periods, an abnormal time period recognition model and a correction model based on fuzzy subsethood are proposed to obtain the final corrected peak‐valley time period partitioning results. On the MATLAB R2020b platform, the effectiveness of the proposed method is verified through two real daily load curves with a time resolution of 5 min. [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]

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]

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]

Earth faults is a challenging fault type to locate in resonant grounded networks due to their naturally low fault current, and the problem increases with an increased fault impedance. This paper describes the detailed implementation and laboratory testing of a method for detection, location and clearing of earth faults with very small fault currents. The method consists of two indicators used in the fault detection stage, where their simultaneous operation ensures selective fault detection and faulty feeder selection. One of these indicators also enables continuous fault indication throughout a sectionalizing process. The laboratory tests demonstrate that both indicators function as intended, and it is the current sensors which ultimately limit the attainable sensitivity. Faults up to 15 kΩ were detected successfully in the laboratory network based on phase current measurements, while the sectionalizing indicator showed much higher sensitivity and functioned as intended in a 50 kΩ fault. Measurements from one field test in a 22 kV network corroborate the laboratory results and demonstrate the expected earth fault indicator response. [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]

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]

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]

In order to solve the collaborative optimization scheduling of multi‐microgrid under the high penetration rate of new energy, this paper considered the energy interaction between micro‐grids in multi‐microgrid and the relationship between new energy consumption and electricity cost, constructed a collaborative scheduling model considering both micro‐grid load and main grid wind and optical energy storage, proposed objective function based on economic cost, and improved Genetic Algorithm (GA). The elite thought and catastrophe thought are used to optimize the selection operation, and the particle swarm optimization algorithm is used to optimize the mutation operation. Furthermore, three scenarios were selected to verify the effectiveness of the proposed model and the improved GA. The results show that the proposed model can ensure the stable operation of the multi‐microgrid system with a high proportion of new energy access and reduce the operation cost. In addition. the improved algorithm solves the problems of easy to fall into local optimum and slow iteration speed, and has better performance in solving the model. [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 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 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]

This paper introduces a new dynamic wide‐area cooperative protection based on cooperative control of distributed multi‐agent systems and graph theoretical methods. For power system cooperative protection, a new voting method is designed that manages each agent's cooperation according to each protection area. Cooperative protection areas work together and make a wide area of cooperative protection for the specific protection scheme. As the power system configuration varies, the voting management and the wide‐area cooperative protection dynamically change. The proposed protection method decisions are collective, while a centralized master does not exist, and local information is used. A fault detection code and a wide‐area fault detection code are presented for fault detection. The suggested dynamic wide‐area cooperative protection scheme is tested on an IEEE 39‐bus test system. The proposed protection performance is studied as backup protection for distance relays during normal, stable power swings, unstable power swings, and load encroachment conditions. [ABSTRACT FROM AUTHOR]

Low and medium speed magnetic levitation traffic with short power supply distance and complex grounding network structure, prone to power supply rail grounding faults. However, the existing fault location methods do not accurately locate the fault point, making it difficult for the protective device to act to cut off the fault. To address the above problems, this paper builds a dynamic simulation model of the low and medium speed magnetic levitation power supply rails to study the distribution characteristics of the fault traveling waves after a ground fault occurs in the power supply rails, and analyses the generation mechanism of the traveling wave spectrum through formula calculation. First, the difference in current between the positive and negative bus bars is analyzed to determine whether a ground fault has occurred. Second, the direction of the current difference between stations is compared to locate the faulty section. Finally, the fault distance is calculated from the frequency difference of the fault voltage at the double‐ended station. Through simulation, the method is validated to be unaffected by fault location, fault transition resistance, noise interference, and is applicable to short circuit faults caused by lightning strikes, and the ranging error always remains within 20 m. The method has strong robustness, can effectively solve the problem of protection misoperation and accurately locate the fault point. It is suitable for low and medium speed magnetic levitation transportation power supply rail ground fault. [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 power system, optimal sizing of hybrid energy systems (HESs) is a vital and challenging issue. Optimal sizing leads to designing a cost‐effective and reliable power generation system. In such a problem, modelling the load uncertainty helps the planner to make appropriate decisions for optimizing the performance of HESs against possible changes of the electrical load. In this paper, techno‐economic and optimal sizing of an off‐grid photovoltaic‐diesel generator‐fuel cell (PV‐diesel‐FC) HES is investigated in two frameworks, risk‐neutral strategy and risk‐averse strategy, where the load uncertainty is modelled by information gap decision theory. To size the HES, objective function is defined as the total net present cost and with respect to a desirable loss of power supply probability, size of the system components is optimally determined. In the risk‐averse framework, the sizing problem is solved with different critical tolerance levels of the objective function and the results are evaluated. Over the case study, simulation results show that at risk‐averse framework, optimal combination of PV, diesel generator and FC leads to a cost‐effective and reliable HES. [ABSTRACT FROM AUTHOR]

A large‐scale AC/DC hybrid power network has recently been constructed in China because of the fast development of high voltage direct current (HVDC) technology. The receiving AC system must have sufficient system strength to guarantee the safe operation of the DC side. The paradox is that raising the AC side system's strength will increase the short‐circuit current (SCC) level, endangering the safety of the system. In order to resolve the contradiction between the system strength of multi‐infeed DC receiving‐end power networks and SCC, this paper proposes an optimal transmission switching strategy. Firstly, the system strength is characterised by the multi‐infeed short‐circuit ratio (MISCR), followed by constructing a comprehensive constraint set that considers the MISCR and SCC simultaneously. Secondly, to optimize SCC with MISCR and N−1 safety constraints, a mixed integer linear programming model is developed. Finally, to enhance the computing performance for large power systems, a new connectivity constraint set and decision space reduction techniques are provided. The proposed method is tested for effectiveness using the IEEE 30‐bus and IEEE 118‐bus systems. [ABSTRACT FROM AUTHOR]

Decision‐dependent (endogenous) uncertainties (DDUs), as a new type of uncertainties revealed recently, couple dispatch decisions with uncertainty parameters and thus render power system dispatch more challenging. However, most previous works handled various DDUs via stochastic programming (SP) or robust optimization (RO) in a two‐stage framework, which undoubtedly introduces the drawbacks of SP and RO, and cannot meet the nonanticipativity requirements in power scheduling. In this paper, a multistage distributionally robust optimization (DRO) method for generation dispatch with demand response (DR) is proposed considering the DDUs of deferrable loads and the decision‐independent (exogenous) uncertainties (DIUs) of wind power and regular loads. By analyzing the structure of decision‐dependency parameters, a novel data‐driven decision‐dependent ambiguity set is proposed, which provides a generic framework for formulating DDUs and DIUs simultaneously. Then a multistage DRO model with nested max‐min structure is developed to integrate the merits of DRO and nonanticipativity into generation dispatch. The proposed model is solved by tailored reformulation method and improved stochastic dual dynamic integer programming (SDDiP). Case studies illustrate the effectiveness of the proposed approach by comparing with the multistage SP, RO, and decision‐independent DRO methods. [ABSTRACT FROM AUTHOR]

Microgrids are a new concept resulting from decentralized power sources, which have changed energy utility grid topography. As this promising field is penetrated, control must be streamlined immediately. This paper analyses key control mechanisms in energy utility networks, including centralized, decentralized, and distributed setups. The focus is on the auxiliary category as the various benefits and drawbacks of each approach is investigated. Information interchange architectures that enable multiple control techniques in the auxiliary control domain are examined using distributed energy networks. The research emphasizes the necessity of auxiliary control in ensuring reliable and effective utility infrastructure operation but notes the lack of a comprehensive scholarly reference. Additionally, the study explores key concerns in utility grids' auxiliary control mechanisms, emphasizing information‐sharing system limits. The goal is to demonstrate the importance of auxiliary control and evaluate its challenges, such as latency, which require new solutions for a seamless transition to a sustainable energy future through rigorous academic assessment. At the auxiliary level, complex, decentralized regulatory mechanisms are recommended to handle transmission channel bandwidth and operational overhead in energy utility networks. These innovative measures have the potential to transform the fundamental structure of our energy landscape, heralding in unprecedented efficiency and sustainability. [ABSTRACT FROM AUTHOR]

With the development of smart meters and other intelligent electronic devices, more and more data‐driven fault location methods based on wide area measurement are emerging. However, these diagnostic methods for dealing with the whole tested system often appear complex. This paper presents an innovative subsystems‐based fault location strategy in distribution grid by the sparsity promoted Bayesian learning algorithm. To avoid taking measurement for the whole distribution system, the fault‐included subsystem is selected according to the distribution characteristics of negative sequence voltage. Then the data for fault location is measured by allocating meters in subsystem, which can reduce the number of required meters. For accurately estimating the fault location, a sparse prior is proposed for the Bayesian learning, which could improve the accuracy of the fault location algorithm by about 4%. The performance is tested on a 12.66‐kV, 69‐bus distribution system in response to various fault scenarios. The results show that the accuracy of the proposed method for the fault section location can reach 90%. It also verifies the robustness and accuracy for fault line location, faced different fault types, fault resistance, noise, etc. [ABSTRACT FROM AUTHOR]

This paper conducts the frequency‐dependent grounding system impact on externally gapped line arresters (EGLAs) operation and their placement effects on the lightning performance of a transmission line (TL). The study involves placing EGLAs at different phases of a 400‐kV double‐circuit TL with two downstream shield wires and selecting the optimal state with fewer back‐flashover incidents. The results show that using a non‐linear grounding system leads to a higher back return current from insulators or EGLAs, resulting in a higher flashover rate compared to the frequency grounding system (FGS). Installing four EGLAs in the middle and upper phases of the TL provides satisfactory lightning protection, and the flashover probability is zero and 4.2% for the FGS and the non‐linear grounding system, respectively. A sensitivity analysis including soil resistor variation, economic analysis, and grounding electrode configuration has been performed that confirms installing downstream shield wires and EGLAs is an effective and economical technique for lightning protection. [ABSTRACT FROM AUTHOR]