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Epoxy‐impregnated paper transformer bushings are prone to water absorption due to the presence of water‐attracting moieties in the cellulose chemical structure and epoxy resin, leading to moisture‐induced insulation defects. To understand the mechanism of moisture's impact on epoxy‐impregnated paper, this study developed a testing platform that integrated moisture diffusion, adsorption, and dielectric properties of insulating materials. Insulation specimens made of epoxy resin and epoxy‐impregnated paper were prepared and their water diffusion characteristics were investigated using the Fick diffusion model and the Langmuir model, respectively. The Guggenheim–Anderson–de Boer equation was used to examine the vacuum vapour adsorption characteristics of the materials, providing a theoretical foundation for examining the form of water present within them. The dielectric response of materials with varying moisture levels was then tested to study the effect of water on the dielectric spectrum curve. Using the Extended Derivative Method, the characteristic frequency reflecting the water content of the epoxy‐impregnated paper was extracted. The results demonstrate that 0.01‐Hz characteristic frequency provides superior accuracy for moisture assessment in epoxy resins compared to 50 Hz, and extracting and fitting the relaxation peak characteristic frequency in epoxy‐impregnated paper moisture evaluation yields higher accuracy than using tan δ at the power frequency. [ABSTRACT FROM AUTHOR]

The insulation paper is a crucial factor for evaluating the insulation status of transformers. The traditional evaluation methods for insulation paper are dissolved gas component content analysis (CO and CO2) and furfural (2‐FAL) content analysis in oil. The detection principle of the former leads to its low accuracy, while the field application effect of the latter is not ideal due to the easy oxidation of furfural and low content. Methanol, result of its good stability and high production compared with other marker products (CO, CO2, 2‐FAL) has been investigated as a novel marker for aging evaluation of transformer insulating paper in recent years. The results of the investigation indicated that there is a good correlation between the content of methanol in oil and the degree of polymerization under laboratory conditions, which reflected the insulation aging state of the transformer insulating paper more accurately. This paper summarizes the current research status of methanol formation mechanism, detection methods and aging evaluation in insulating oil, and points out the key problems to be solved and development prospects, hoping to provide relevant reference for aging evaluation of oil immersed power transformer. [ABSTRACT FROM AUTHOR]

TiO2 nanoparticles can improve the electrical performance of oil paper insulation. In this study, 10 nm TiO2 nanoparticles were prepared and dispersed into transformer oil, and then immerse pressboards in it to obtain nanofluid impregnated pressboards (NP). Its thermal aging was accelerated together with the reference sample at 130°C for 36 days. Analyzing the acid value in the oil, polymerization degree, surface topography, AC breakdown strength, surface flashover characteristics and surface potential decay characteristics. It was observed that TiO2 nanoparticles can reduce the acid value and improve AC breakdown strength of oil. However, the degree of polymerization and surface appearance of NP and oil impregnated pressboards (OP) during thermal aging did not differ significantly. During the aging process, the energy level of pressboard surface traps shifted first towards the shallow and then gradually towards the deep. During all the aging stages, the surface flashover voltage of NP was always higher or close to that of OP. The mechanism of TiO2 nanoparticles improving the breakdown and surface flashover characteristics of oil–paper insulation during thermal aging is discussed from the perspective of water molecules and aging by‐products adsorption and introduction of shallow traps by nanoparticles. [ABSTRACT FROM AUTHOR]

Arc discharge in the power transformer can bring enormous economic losses and safety risks. The transformer protection system for arc discharge faults is still not ideal. The physical characteristics of the whole process from partial discharge defects transition to arc faults are not fully recognized, mainly because it is difficult to establish and sense the relevant characteristic parameters. This paper built a research platform for studying the transition process from partial discharge to early arc discharge based on fibre‐optic ultrasonic, temperature, pressure sensors with traditional electrical voltage and current sensors. Five discharge models are designed and the changing pattern of multiple parameters in the transition process of different models is studied. The experiment results show that the amplitude and frequency of acoustic waves drop significantly after the arc breakdown. Ultrasonic and high‐frequency current sensing in the early warning of arc fault each has its own limitations. The pressure caused by the arc is not only related to the arc energy but also related to the discharge types. The overall oil temperature change after the early arc discharge is small and hard to detect. This work highlights the promise of using fibre‐optic sensors for physical process research within a harsh electromagnetic environment. [ABSTRACT FROM AUTHOR]

In order to solve the problems of oil‐paper insulation deterioration of oil‐immersed power transformers, including the instability of performance degradation quantity at the early stage, non‐linear deterioration process and uncertainty of life prediction, an improved Wiener stochastic process based on strong tracking filter (STF) is proposed to predict the remaining life of transformer oil‐paper insulation in this paper. Firstly, in order to grasp the dynamic process from healthy state to current deterioration state, a deterioration model is established based on Wiener random process. Secondly, in order to improve the accuracy, the initial values of model parameters are estimated based on expectation maximization (EM) algorithm, then with the increase of monitoring data, model parameters are updated by using STF algorithm, and the probability density function of remaining life is derived to predict the remaining life of oil‐paper insulation. Finally, the accuracy of the proposed method is verified by using furfural content as performance degradation index and accelerated thermal aging experimental data. Compared with the Bayesian–Wiener algorithm, the MSE of the proposed algorithm is reduced to 0.2138 and its MAPE is reduced more than 4%. Besides, the proposed method has the advantages of strong robustness and low uncertainty of residual life prediction. [ABSTRACT FROM AUTHOR]

In order to cope with the current engineering demand for increasing the current‐carrying capacity of resin impregnated paper (RIP) bushing, an optimization scheme of RIP bushing was proposed in this paper. Simulation and experimental studies were carried out to verify the rationality of the scheme. An improved simulation method was proposed. To ensure as much accuracy as possible, the physical model of simulation considers the influence of heat conduction, thermal convection and thermal radiation process according to the actual operation environment. To boost the computational efficiency, the improved method simplified the relationship between temperature and heating power of the current‐carrying conductor as a fitting formula to avoid the iterative calculation between electromagnetic field simulation and temperature field simulation. The temperature rise test was carried out according to the engineering requirement and the standard international electro technical commission (IEC) 60137:2008. The simulation and experimental results show that the RIP bushing can carry a current with the heating power of its conductor equivalent to that generated by a 50‐Hz current of 6736 A while running in a converter station. It is a breakthrough of the current‐carrying capacity of high voltage bushing. Meanwhile, the regularity of temperature distribution of the RIP bushing was discussed. [ABSTRACT FROM AUTHOR]

The introduction of solid dielectrics to electrode surfaces will change the motion behaviours of free metallic particles and in turn affect partial discharge (PD) characteristics in transformer oil. To estimate the influence, the metallic particles movement in flowing oil was compared between different cases with and without insulating paper covering electrodes here. When the electrodes were bare, particles executed up‐down movements repeatedly in the vertical direction. As the electrodes were paper‐covered, the particles would settle on the paper surface for a period of time. Simultaneously, the PD characteristics caused by metallic particles were measured. When the electrodes were bare, the discharges concentrated in 0–90° and 300–360° and the PD magnitude reached 390 pC. When the double electrodes were paper‐covered, the PD magnitude decreased by 67% and the concentration phases of PDs would migrate to 45° and 160°. In order to analyse the experimental results, a simulation model with a paper‐covered boundary was constructed. Simulated trajectories of particles were in accord with the experiment results. Base on the model, the particle motion characteristics were obtained and the electric field distribution between the particle and electrode was calculated. Finally, the influence of paper‐covering on PD characteristics was discussed. [ABSTRACT FROM AUTHOR]

The heterocharge accumulated in the oil‐paper insulation system of the converter transformer will enhance the local electric field and cause insulation deterioration. Numerical simulation is an effective method to analyze heterocharge. Currently, the numerical simulation of heterocharge is mainly based on the bipolar charge transport (BCT) model and the impurity ion transport (IIT) model. However, the effects of temperature, conductivity, and thickness of oil‐impregnated paper on charge transport have not been considered in these models. In view of this, a modified charge transport (MCT) model by introducing the Maxwell–Wagner model and heat conduction equation is proposed in this paper based on the BCT model and IIT model. Then, the influence of multi‐factors (temperature gradient, moisture content, and oil‐impregnated paper thickness) on heterocharge accumulation is investigated by using the MCT model. The simulation results obtained by the MCT model are more consistent with the experimental results than the BCT model, which proves the accuracy and superiority of the MCT model. Moreover, the increase in temperature gradient, moisture content, and oil‐impregnated paper thickness will enlarge the ion dissociation rate, conductivity, and trap density, and thus promote the generation of heterocharge. In that respect, the MCT model is expected to optimize the insulation design and provide a reference for space charge dynamic simulation. [ABSTRACT FROM AUTHOR]

As one of the main reasons for the deterioration of Nomex paper and charge migration, partial discharge (PD) plays a critical role during PD degradation. In this study, the PD characteristics of multilayer oil paper are focused on. In order to explore the characteristics of charge migration, the experiments of the relative permittivity, conductivity, isothermal surface potential decay, scanning electron microscopy and Fourier infrared spectroscopy have been carried out. The results show that the discharge branches of the second layer are two and three times longer than those in the first layer. In addition, the trap level of the first layer for PD degradation samples increases with the degree of PD degradation. The charges captured by deep traps provide seed charges for PD on the surface of the first layer. The charge transportation will accelerate the formation of shallow traps. The charge distribution and shallow traps contribute to the expansion of discharge branches. [ABSTRACT FROM AUTHOR]

The accuracy and feasibility of the traditional XY model of transformer oil–paper insulation system are disputable since it ignored the effect of non‐uniform aging and conductance on dielectric response property. Given this issue, this work attempted to report a modified XY model to overcome these defects. The novelty of this work is in the exploration of the modified XY model as a potential tool for establishing the quantitative correlation among the complex relative permittivity of the liquid insulation, solid insulation, and liquid–solid composite insulation. In that respect, the presented verification experiments proved the feasibility and accuracy of the reported model. The contributions are expected to provide a theoretical basis for the condition evaluation of transformer solid insulation under non‐uniform aging conditions. [ABSTRACT FROM AUTHOR]

This paper introduces a virtual consensus‐based wide area differential protection method through cooperative control concepts and graph theory. Using multi‐agent systems eliminates the need for a direct telecommunication connection between the protective relays to implement the proposed differential protection. In addition, applying telecommunication subgraphs facilitates the establishment of numerous differential protection areas. Collaboration between protected areas is facilitated by common agents, establishing a wide‐area cooperative protection network. The capability of the network operator to define various protection areas and the collaboration between these areas ensure the versatility of the proposed method for various protection purposes. The present paper primarily represents the application of the proposed protection system as a wide‐area supervisor protection for distance relays. Performance evaluations on an IEEE 39‐bus test system illustrate the method's effectiveness in various scenarios. The results show enhanced power system stability and resilience, particularly when traditional methods struggle to detect power swings with high impedance variation rates. [ABSTRACT FROM AUTHOR]

The integration of distributed energy resources and smart grid technologies has increased the vulnerability and complexity of modern power systems, especially in the face of extreme events. Enhancing the resilience of power systems is crucial to ensure continuous and reliable electricity supply during and after such events. This paper proposes a preventive framework to enhance the resilience of networked microgrids (NMGs) during extreme events. The framework integrates optimal energy management strategies and preventive resilience enhancement measures, focusing on the coordination and utilization of hydrogen (H2) energy systems and controllable distributed generators (CDGs). The framework leverages capacity‐based signals to prompt H2 system owners and CDG operators to prepare for disruptions by fully charging H2 storage tanks and pre‐scheduling CDG commitments. By utilizing this proactive energy scheduling, critical loads can be supplied through stationary fuel cells and CDGs, improving overall network resilience. The proposed resilient energy management approach combines optimization techniques to improve the resilience of NMGs and reduce their dependence on the main grid. Numerical simulations demonstrate the effectiveness of the proposed linear model in enhancing the resilience of NMGs and improving their operational performance during extreme events. The contributions of this paper include advancements in understanding NMGs, the development of a preventive framework, integration of H2 energy systems and CDGs, and proposal of effective signalling models. [ABSTRACT FROM AUTHOR]

Planning maintenance of medium voltage overhead lines is one of the most important issues in the studies on power distribution network, which will prevent and reduce unwanted interruption. In this paper, long‐term maintenance of medium voltage networks was planned by multi‐objective function, including an extended mixed‐integer linear model to optimize costs, energy not supplied (ENS), and average interruption duration index (SAIDI). In addition, the uncertainty about the annual growth rate of the load, the increase in the cost of goods and services and the increase in the selling price of energy as well as various constraints are all included in the desired objective function, which is one of the main innovations of this paper compared to other published studies. To apply the uncertainties, information gap decision theory (IGDT) has been used, and to solve the objective functions, LP‐Metric method has been used. The proposed method was implemented on the standard 11‐bus RBTS network by MATLAB and GAMS. The results showed that three different long‐term maintenance plans proposed here lead to the optimization of the annual maintenance costs of network, reduction in ENS and interruption, and increase in the reliability of the network based on the uncertainty of each feeder. [ABSTRACT FROM AUTHOR]

The development of renewable energy will increase the demand for flexible resources in power systems due to the strong uncertainties. To allocate resources and cope with these uncertainties, it is beneficial to apply robust coordinated energy storage and transmission expansion planning (ES&TEP). The large candidate line set can significantly increase the computational complexity of robust optimization models. However, there is currently no suitable network search space reduction (NSSR) method to address this problem. This paper designs a NSSR method based on redundancy constraint identification that only requires information on component power ranges rather than fixed power curves, which can be well adapted to robust optimization. Additionally, this method requires only basic numerical calculations and ensures convergence within a finite number of iterations. The classical stochastic robust ES&TEP model and the column and constraint generation (C&CG) algorithm are used to validate the effectiveness of the NSSR method. In the case studies, the numerical results confirm the rationality and effectiveness of the approach proposed in this paper. By selecting appropriate parameters, the candidate line set can be reduced by over 70% and the computational efficiency can be improved by an order of magnitude. [ABSTRACT FROM AUTHOR]

The coupling capacitance between the line and the transducer cannot be ignored when making non‐contact voltage measurements on low‐voltage three‐phase power lines. This leads to an increase in the number of unknown capacitances, and the number of correlatable response voltage equations is less than the number of unknown coupling capacitances. In this paper, a simplified decoupling method is proposed based on the characteristics of triangular three‐phase lines erected in distribution areas. Firstly, the equivalent circuit measured by the voltage sensing mechanism during three‐phase operation is established, and the three‐phase line response voltage equation is derived. Secondly, this paper proposes a three‐phase line response voltage decoupling algorithm after designing the decoupling matrix. Then, this paper uses the least squares method to solve the coefficients in the equations. Finally, the three‐phase voltage fluctuations of the actual low‐voltage stations are simulated by simulation. The measured sensor voltage values are substituted into the algorithm to invert and calculate the three‐phase line voltage. The three‐phase phase error is less than 2°. The amplitude error is less than 1.77%. The experiment verifies the stability and accuracy of the proposed decoupling method. [ABSTRACT FROM AUTHOR]

The utilization of accurate models is crucial in the various stages of development for photovoltaic (PV) systems. Modelling these systems effectively allows developers to assess new modifications prior to the manufacturing phase, resulting in cost and time savings. This research paper presents a viable approach to accurately estimate both static and dynamic PV models. The proposed estimation method relies on a novel and enhanced optimization algorithm called leader artificial ecosystem‐based optimization (LAEO), which improves upon the original artificial ecosystem‐based optimization (AEO). The proposed LAEO algorithm integrates the adaptive probability (AP) and leader‐based mutation‐selection strategies to enhance the search capability, improve the balance between exploration and exploitation, and overcome local optima. To evaluate the effectiveness of LAEO, it was tested on 23 different benchmark functions. Additionally, LAEO was applied to estimate the parameters of static three‐diode PV models, as well as integral‐order and fractional‐order dynamic models. This paper showcases practical implementations of photovoltaic (PV) parameter estimation in various scenarios, including the static three‐diode model, dynamic integral order model (IOM), and fractional order model (FOM). The results were assessed from various angles to examine the precision, performance, and stability of the LAEO algorithm. [ABSTRACT FROM AUTHOR]

Accurate power load prediction is an important guide for power system planning and operation. High‐ or low‐load prediction results will affect the operation of the power system. In recent years, deep learning technology represented by convolution neural network (CNN) and transformer has been proved to be suitable for power load prediction. This paper proposes a new short‐term power load hybrid forecasting model, called channel enhanced attention (CEA) and temporal convolutional network (TCN)‐based transformer comprehensive forecasting model. This method combines the short‐term feature extraction ability of TCN with the long‐term dependent capture ability of transformer for short‐term load forecasting. And the CEA designed in this study is added to improve the prediction accuracy. On the same dataset, the designed model predicts power load mean square errors of 0.056 and 0.146 for the next 24 h and the next week, respectively, which is 0.002 to 0.073 and 0.012 to 0.024 lower than the baseline model. The experimental results show that the hybrid short‐term power load prediction model proposed in this paper is significantly better than the existing methods. The predicted curve is in agreement with the actual charge change, which provides a good guidance for short‐term power load prediction. [ABSTRACT FROM AUTHOR]

In the hydrogen‐based integrated energy system (HIES), there exists a hydrogen trading market where hydrogen producers and consumers are distinct stakeholders. Current research in hydrogen trading predominantly focuses on high‐cost green hydrogen (GH), which is not aligned with the current trend of utilizing hydrogen from multiple sources. To address this, this paper proposes a hydrogen trading strategy between the virtual hydrogen plant (VHP) and electro‐hydrogen energy system (EHES) based on a bi‐level model, considering the synergy of GH produced from electrolyzers and blue hydrogen (BH) derived from natural gas in the HIES. In the VHP level, the objective is to maximize profit from hydrogen sales, allowing for the determination of hydrogen prices. In the EHES level, the goal is to minimize the cost of energy supply, leading to the formulation of GH and BH purchasing plans based on hydrogen prices. Additionally, this paper incorporates a risk‐averse model from the information gap decision theory (IGDT) to account for the impact of wind power output uncertainties in the VHP level. Subsequently, leveraging the Karush–Kuhn–Tucker (KKT) conditions of the EHES level, the bi‐level problem is transformed into a solvable single‐level mathematical program with equilibrium constraints (MPEC), with the non‐linear equilibrium constraints linearized. The proposed bi‐level optimization model is validated through case studies encompassing industrial and residential hydrogen utilization within the HIES. The outcomes confirm the rationality of the proposed model, demonstrating that, in comparison to exclusively trading GH, the coordinated GH and BH trading can increase the profit of the VHP by 2.7% and reduce the costs of the EHES by 8.5%. [ABSTRACT FROM AUTHOR]

Power transformers play a critical role in the performance of power systems. This equipment is costly due to significant copper and iron prices and manufacturing costs. Therefore, maintenance and protection of such equipment is essential. Despite its robust performance, maloperation of differential protection (DP) in transformers may cause operational challenges to power system operators. The differential relay may operate incorrectly after the transformer energization leads to an inrush current (IC) and the relay identifies the event as an internal fault, and consequently issues the trip command. The other case of maloperation includes, but not limited to, a moment when the current transformer saturates due to an external fault. In this paper, a novel approach for DP is proposed, that is based on signal processing methods. In this paper, variational mode decomposition (VMD) and the deep neural network are implemented by using the convolutional neural network (CNN) and bi‐directional long short‐term memory (BiLSTM) models. The VMD decomposes differential current signal (DCS) to intrinsic mode functions with corresponding narrow‐band property frequency spectrums, which provides more detailed information about signal characteristics in different frequency bands. At the next stage, an effective feature for the BiLSTM is extracted by the CNN with the convolutional layers to classify events and proper discrimination. Extensive simulations on a 500 MVA transformer in MATLAB demonstrate the effectiveness of the proposed protection approach to differentiate ICs from internal and external faults with 99.8% accuracy in less than 1/8th of a power cycle. [ABSTRACT FROM AUTHOR]

Increasing the penetration level of distributed generation (DG) units requires overcoming the technical challenges associated with their integration into the distribution systems, especially protection problems. Change in the current profile of the distribution system due to the presence of DG units disrupts the operation of the conventional fuse saving coordination (FSC) scheme. The first objective of this paper is to provide an overview of the state‐of‐the‐art of FSC schemes in distribution systems with distributed generators that has not been systematically presented yet. In addition to comparing the features of reliability, cost, speed, implementation, calculation burden, and requirements, the impact of presence of distributed generations on the performance of the conventional FSC scheme is investigated in details. The second objective of this paper is to propose an FSC restoration scheme for minimizing the challenges of previous works. Using a quasi‐voltage current term, the proposed scheme modifies the adjustable time coefficient of the recloser in two ways of pro and plus. The former scheme provides an approximate FSC with a simple setting while the latter scheme provides complete coordination at the expense of a more complex setting. No need for voltage measurement makes its implementation practical in available distribution systems. The effective performance of the proposed FSC scheme is verified through extensive simulation studies in the ETAP environment. [ABSTRACT FROM AUTHOR]

Stability issues can significantly increase the risk of hybrid microgrids (HMGs), particularly during island mode operation. The dynamic performance of the system can induce constraints and stability margins that may elevate the loss of load probability. This paper presents a new stability‐oriented risk assessment model that bridges the conventional reliability models, stability, and system risk. The proposed model ensures the risk of the system by considering the redesign or reconfiguration of HMGs to address stability issues. First, the interlinking converters (ICs) DC‐link voltage stability is analysed to determine the acceptable power flow margins in rectifying and inversion mode. Next, the new general risk assessment model is introduced. The results show that the stability margin significantly increases the risk of the HMG, particularly when considering the aging of converters. The study also examines the impact of various load characteristics and ICs with different numbers but the same total size. In some cases, the risk is acceptable for the desired loads, or it can be reduced to an admissible level by reconfiguring the ICs. Finally, the paper demonstrates the effectiveness of the proposed model in the optimal design of HMGs, aiming to guarantee the system's risk. [ABSTRACT FROM AUTHOR]

With the diversification of users' energy demands, accurate load forecasting is an important prerequisite for optimal scheduling and economic operation of the system, but a single‐load forecasting method cannot effectively predict multi‐energy loads accurately. Therefore, this paper proposes a multi‐energy load forecasting method based on bidirectional long short‐term memory (BiLSTM) and parallel feature extraction networks. Firstly, residual network and convolutional block attention module were used to extract the spatial coupling features of multi‐energy load data. Secondly, BiLSTM is used to capture the temporal features and long‐term dependencies in the load data, and the spatial coupling features are fused to obtain non‐linear prediction results. Finally, the non‐linear prediction results and the linear prediction results obtained by using multi‐energy linear regression were linearly superimposed to obtain the final prediction results. In this paper, IES load data of Tempe Campus of Arizona State University was used to verify and compared with several existing methods, and the results showed that Weighted Mean Absolute Percentage Error decreased by more than 20%. [ABSTRACT FROM AUTHOR]

With the increasing integration of distributed energy resources (DERs) into distribution systems, the optimization of system operation has become complex, facing challenges such as inadequate consideration of market participants' benefits, poor computational efficiency, and data privacy concerns. This paper introduces the concept of a virtual power plant (VPP) as a solution for energy integration and management. To strike a balance between operational safety and the interests of market participants, a dual‐layer model is proposed. This model considers the benefits of both Distribution System Operators (DSO) and VPP, while also enhancing the consideration of distribution network constraints. The DSO considers AC optimal power flow and utilizes penalty functions to ensure network security in case of violations. To enhance computational efficiency and privacy, the paper presents the parameter‐sharing twin delayed deep deterministic policy gradient approach. This approach allows multiple intelligent agents to share a neural network model, effectively reducing the computational load. During the training process, only essential data is exchanged among the agents, ensuring the privacy of sensitive information. The effectiveness of the proposed model and the algorithm is validated through a case study on an IEEE 33‐node system. [ABSTRACT FROM AUTHOR]

This paper proposes a new multistage AC model for transmission expansion planning that finds an optimal combination of transmission lines, fault current‐limiting high‐temperature superconducting cables, and multiple distributed generations (DGs). On this basis, the proposed model, from a new perspective, allows for simultaneous improvement of the short‐circuit level and grid‐scale flexibility (GFLX) under both normal and fault conditions. The objective function to be minimized includes not only the net present worth of the total investment and operation costs but also the congestion‐induced GFLX degradation measure. This model also takes the AC power balance and flow relationships, equipment capacity limits, nodal voltage bounds, DG penetration level limit, as well as discrete logical and financial restrictions together into account with the short‐circuit level constraint. To overcome the complexity of solving the resultant non‐convex mixed‐integer non‐linear optimization problem, a multi‐objective integer‐coded melody search algorithm is employed, followed by a fuzzy satisfying decision‐making mechanism to obtain the final optimal solution. The exhaustive case studies conducted on the IEEE 24‐ and 118‐bus test systems verify the efficacy of the newly developed model in terms of cost‐effectiveness, flexibility, and short‐circuit level suppression when facing different normal and fault conditions. [ABSTRACT FROM AUTHOR]

A machine learning‐based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs). Traditionally, a communication‐less technique known as the hybrid droop method has been employed to decrease PC and PLL in DC MGs. However, achieving the desired reduction in either PC or PLL requires arbitrary adjustments of weighting coefficients for each distributed generator in the conventional hybrid droop method. To address this challenge, this paper introduces a systematic approach that capitalizes on the benefits of artificial intelligence to accurately predict both the PC and PLL in a DC MG. Furthermore, an optimization technique relying on the gradient descendent method is employed to independently optimize both PC and PLL for each scenario. The effectiveness of the proposed method is confirmed through a comparative study with classical and hybrid droop coordination schemes under various scenarios such as rapid load changes. [ABSTRACT FROM AUTHOR]

In distribution networks, among the planning problems, optimal placement of medium voltage to low voltage (MV/LV) transformers is a vital and challenging issue. Electrical load uncertainty is an important factor that affects the result of this planning problem. This paper investigates optimal allocation of service transformers with respect to the load uncertainty modelled by information gap decision theory (IGDT). For this aim, the planning problem is solved in risk‐neutral (RN) and risk‐averse (RA) frameworks. In RN strategy, objective function is defined to minimize the cost of service transformers and low voltage feeders as well as the cost of power losses. On the other hand, in RA strategy, objective function is defined to maximize the radius of the uncertainty in such a way that any deviation of the uncertain parameter results in an objective function value that is not worse than the critical limit. The optimization problem is solved by crow search algorithm (CSA) and particle swarm optimization (PSO) and the results are compared. In mid‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 5.89%, 13.64%, 21.37%, 28.97%, 34.39% and 43.46%, respectively. In long‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 6.92%, 13.33%, 20.39%, 27.03%, 34% and 40.46%, respectively. Moreover, on average, CSA finds more promising results than PSO. [ABSTRACT FROM AUTHOR]

Existing interturn fault detecting methods rely on winding impedance, winding current, and dissolved gases. They are effective only when the insulation is severely damaged. This paper proposes a novel detection method based on fusion analysis of electrothermal characteristics including winding currents, temperatures of four areas on the tank wall, top oil and ambient temperatures, which can identify the interturn fault at an early stage. When an incipient interturn fault occurs, the heat generated by the faulty turns is transferred to the oil and tank wall, leading to an increase in top oil and tank wall temperatures. Thus, the incipient fault can be detected by analysing these electrothermal characteristic parameters. Borrowing the idea of digital twin (DT), this method establishes a high‐fidelity simulation model to simulate the transformer electrothermal characteristics under different operating conditions. Afterward, an intelligent neural network is adopted to extract the quantitative relationship between the eight feature attributions and fault conditions. Finally, this neural network is utilized to detect the incipient interturn fault for the transformer entity. Case studies are conducted on a 100 kVA transformer with oil natural air natural (ONAN) cooling mode. The detection accuracy is improved by 68.5% compared to the winding current‐based method. [ABSTRACT FROM AUTHOR]

This paper presents a state‐space average model of a three‐level photovoltaic (PV) inverter to understand short‐circuit currents transient characteristics. Analytical solution of the model is also derived. The proposed model is validated with PV system controller hardware in‐loop (HIL) test method. The errors between simulation and HIL tests results under various conditions are evaluated, testifying the validity of the proposed model. With the model and analytical solution, the decaying time constants of sub‐transient and transient processes are derived, which shows that the time constants of a three‐level PV inverter are not fixed and they vary with voltage dips. The analytical expression of short‐circuit currents is also formulated. The findings from this study are essential to short‐circuit current calculations of a power grid with large‐scale PV plants, coordination of power system protections, and grid planning. [ABSTRACT FROM AUTHOR]

Precisely locating the voltage sag sources is significant for improving power quality and clarifying the responsibilities of both power supply and consumption. However, the existing location models are complex, computationally intensive, and influenced by the measurement error and fault type, which have low positioning efficiency and large errors. Therefore, this paper proposes a method for locating voltage sag sources based on power polarity and twin characteristics matching. The method firstly uses the polarity of the sequence sag component disturbance reactive power at the monitoring point to determine the sag line. Secondly, the basic realization method of the twin power system is discussed, and the twin characteristics are defined by combining the positive and negative sequence sag component disturbance reactive power and the twin database is established. Thirdly, the spectral norm and Pearson product‐moment correlation coefficient are used to define the data matching degree, and the precise location model of the sag source is established with the objective function of maximizing the data matching between the twin characteristics of the preset and actual sag. Finally, the Improved Whale Optimization Algorithm is used to solve the location model. The correctness and superiority of the proposed method is verified based on IEEE33 node modelling. [ABSTRACT FROM AUTHOR]

The explosion accident due to the internal arc fault in the metal‐enclosed switchgear cannot be ignored. Therefore, improving the ability of switchgear to resist the arc fault has important significance. In this paper, a passive protection measure by installing the buffer volume (BV) was proposed. The short‐circuit arcing tests were carried out in the small‐scale switchgear compartments, and the pressure rise was measured in the different models. The changes of pressure rise before and after installing BV were analyzed. Then, the calculation methods of pressure rise in the compartments and the mechanical response of the enclosure were put forward. By calculating the distributions of the pressure rise and temperature rise in the compartments, as well as the time‐varying displacement and stress of the enclosure, the pressure relief effect of the BV has been discussed. Results show that after installing the BV, the pressure rise, displacement, and stress are all decreased obviously in the initial of arcing. Hence, the arc fault protection effect for the switchgear can be improved by properly installing the BV. [ABSTRACT FROM AUTHOR]

Contemporary distribution networks can be seen with diverse dispatchable and non‐dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non‐dispatchable resources can provide several techno‐economic and social benefits. Since battery energy storage systems (BESSs) and microturbine units are capital intensive. A thorough investigation of their coordinated scheduling on a purely economic basis will be an interesting and challenging task while considering dynamic electricity price and uncertainty of renewable power generation and load demand. This paper proposes a new methodology for optimal coordinated scheduling of BESSs and microturbine units considering existing renewable energy resources and dynamic electricity price to maximize daily profit function of the utility. In this study, a recently explored modified African buffalo optimization algorithm is employed. The key attributes of the proposed methodology are comprised of mean price‐based adaptive scheduling embedded within a decision mechanism system to maximize arbitrage benefits. Decision mechanism system keeps a track of system states as a‐priori thus guides the artificial intelligence‐based solution technique for sequential optimization. This may also reduce the computational burden of complex real‐life engineering optimization problems. Further, a novel concept of fictitious charges in coordination with BESS management algorithm is proposed to restrict the counterproductive operational management of BESSs. The application results investigated and compared on a benchmark 33‐bus test distribution system highlights the importance of the proposed methodology. [ABSTRACT FROM AUTHOR]

The paper presents a ground fault location algorithm (GFLA) in double‐circuit transmission lines (DCTL) with a T‐off connection to an industrial microgrid (IMG) containing electric arc furnaces (EAF), renewable distributed generations (RDG), and static VAR compensator (SVC). The design presented utilizes the information on the current and voltage phasors of one side of the transmission lines (TL) during the fault period, and there is no need for the availability of information on the type of faulty phase(s) and even the faulty section. To find the exact location of the fault in this design, the voltage phasor equations governing the fault point in positive, negative, and zero‐sequence (PNZS) domains, the zero‐sequence current (ZSC) equation of the fault point, and the boundary condition equation of the fault point have been used. The simulation system is implemented in Simulink/MATLAB using accurate modelling of all network components. The algorithm has been tested and evaluated in different fault conditions, including various locations, phase types, phase‐to‐ground connection resistance, occurrence time, and microgrid (MG) operation modes. The successful results of various tests of the proposed design as well as the analysis of various sensitivities confirm the satisfying performance of the suggested algorithm. [ABSTRACT FROM AUTHOR]

Proper current sharing, DC bus voltage deviation reduction, and SOCs balancing, along with ensuring stability are the vital challenges of DC microgrids control algorithms. Addressing these challenges without communication links and a central controller is one of the priorities of control methods. Motivated by the above mentions, this paper presents a novel communication‐free control method. In this regard, a new parameter called "virtual current" is defined according to the unit current and its SOC. Then using a piecewise droop curve and the droop curve shift technique, the virtual current for each unit is determined. The units control coefficients and the relationship of the virtual current are allocated based on the location and power of the loads and RESs such that in the worst case; 1) SOCs are converged; 2) the DC bus voltage deviation is reduced; and 3) the current is appropriately distributed. The simulation and experimental results confirm that the proposed method can balance SOCs like SOC‐based methods and share power properly like piecewise droop methods while reducing DC bus voltage deviation. [ABSTRACT FROM AUTHOR]

The increasing integration of microgrids into distribution networks has highlighted the significance of evaluating and managing intelligent microgrids from both technical and economic perspectives. In this paper, a decentralized approach using agents is employed to optimize the operation of an intelligent microgrid within the telecommunications platform. The decentralized control method comprises two layers. The first layer represents the main microgrid, which includes loads and their controllers, as well as renewable and conventional resources. In the secondary layer, there is a telecommunication platform in which agents can operate as a control processor along with the means of communication. It should be noted that agents interact with the primary layer and neighbouring agents and exchange information with each other. This exchange takes place until the best state of optimization for the power supply occurs. In this study, the operation cost is calculated for decentralized control rules and considering telecommunication links. Also, the effect of performance on cost reduction is examined and compared with normal conditions and centralized methods. It can be seen that the operation cost of the network has decreased to 9.034% after the implementation of the mentioned method in comparison with the normal condition and it has decreased to 6.957% in comparison with the centralized method. Then, using a demand side management program, the cost will be reduced by 2.5%. In the next step, the uncertainty of available resources is taken into account where the uncertainties increase the cost by 7.8%. [ABSTRACT FROM AUTHOR]

With the increasing application of renewable energy sources (RESs), more and more grid‐connected voltage‐source converters (VSCs) in the grid need to operate in a "grid‐forming" manner. However, when large disturbances occur in the grid, grid‐connected converters may lose synchronization and cause the current to exceed set limits. To ensure the stable and safe operation of VSC during large disturbances and to improve the power supply capacity during voltage dips of RES, this paper proposes an emergency control method that takes into account the virtual synchronous generator (VSG) current limit and transient stability based on a large‐signal mathematical model of the VSG during fault and normal states. The method divides the operation area of the VSG into non‐emergency area and emergency area. If the operating point is in the non‐emergency region after a fault, only the active power reference value is changed to avoid current exceeding the limit and transient instability; if the operating point is in the emergency region, the current exceeding the limit and transient instability are avoided by changing the active power reference value and adding in the virtual impedance. Finally, the effectiveness of the proposed control strategy is verified by simulation. [ABSTRACT FROM AUTHOR]

There exist problems of hard coordination and threshold setting as well as low sensitivity in overcurrent relay due to the bidirectional and small fault current when distributed generators are integrated into the distribution network. This paper introduces ferrite rings (FRs) to act as boundaries of distribution feeders, and a peak time of voltage traveling wave‐based protection is proposed. The characteristics of FRs are analyzed and their feasibility as line boundary is validated. The fault characteristics of internal and external faults are analyzed when FRs are installed at both ends of the protected feeder. The results show that the peak time of the voltage travelling wave of the internal fault is far smaller than that of the external faults. According to the difference, the fault detection method and the protection scheme are proposed. Simulations in PSCAD and experiment validate that the proposed protection principle can reliably distinguish between internal and external faults. Besides, the proposed protection scheme has higher sensitivity compared to ROCOV‐based protection. [ABSTRACT FROM AUTHOR]

The rapid growth of offshore wind power has resulted in a mismatch between generation and demand because of its variability. To quantify the maximum wind power penetration of the transmission network, the dispatchable region is defined as the largest region in the uncertainty space. The security distance is defined as the minimum distance from the operating point to the boundaries of the dispatchable region. System operators can use security distance as a metric to assess the flexibility of the power system. This paper proposes a method to construct the dispatchable region for the AC/DC hybrid system with VSC‐HVDC by outer convex relaxation firstly. The second‐order cone relaxation is employed to reformulate non‐convex and non‐linear power flow equations. Next, a polyhedral approximation is adopted to obtain the convex hull of the dispatchable region. Subsequently, an efficient algorithm known as adaptive constraint generation (Ad‐CG) is introduced to calculate the boundaries of the dispatchable region. Furthermore, solving the Chebyshev centre problem determines the minimal security distance. The modified IEEE 5‐bus and 39‐bus system is used for validating effectiveness of the proposed method and evaluating the impact of the converter reactive power compensation capacity and generation dispatch on the dispatchable region. [ABSTRACT FROM AUTHOR]

Affected by inverter station commutation failure and fast control, line fault characteristics are obviously different from traditional AC transmission line fault characteristics. The existing protection methods for AC line connected to line commutated converter based high voltage DC (LCC‐HVDC) inverter station (ACLs) have the problems of complex setting, weak anti‐noise and high demand for data synchronization. This paper proposes a pilot protection scheme based on high‐frequency transient current waveform similarity. Firstly, with analyzing the fault traveling wave propagation process, the obvious differences are found in the transient current waveforms at both ends of the line when internal and external faults occur. Then the similarity of transient current waveforms is measured with the improved longest common similar subsequence (LCSS) method. And the weak high‐frequency current is extracted with the improved synchronous squeezed S‐transform (SST) method. Finally, a large number of power systems computer aided design (PSCAD) simulation results show that the proposed protection method does not require strict data synchronization, has strong anti‐noise performance, and is tolerant to the influence of abnormal point fluctuations. [ABSTRACT FROM AUTHOR]

The integration of the renewable energy sources (RESs) into the power grid, drives a significant transformation in the conventional power generation landscape. This transition from traditional synchronous generators to inverter based RESs introduces unique challenges in maintaining the grid frequency stability due to the reduced system inertia. The inherent stochastic nature of the RES power generation, load demand, and grid inertia includes further complexity in the assessment of frequency stability. Existing studies have limitations, including neglecting the stochastic nature of RES generation and load demand fluctuations, relying on limited metrics, and lacking a comprehensive day‐to‐day assessment. To address these shortcomings of the existing approaches, this paper introduces a novel methodology for assessing frequency stability in power grids with high RES penetration. It proposes three indices for evaluating grid frequency sensitivity, resiliency, and permissibility amidst varying RES integration. Utilizing a stochastic approach, the study incorporates uncertainties in RES generation and load demand, offering a comprehensive framework for day‐to‐day frequency stability analysis. Additionally, it presents a systematic method to ascertain the necessary inertial support for maintaining desired frequency reliability in RES‐dominated grids. The effectiveness of these methodologies is validated through a case study on a modified IEEE 39‐bus test system, demonstrating their applicability in ensuring reliable grid operation under high RES scenarios. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the fault current contributions of small‐scale single‐phase photovoltaic inverters under grid‐connected operation and their potential impact on the protection of distribution systems. To conduct this analysis, an autotransformer‐based voltage dip generator is proposed as a means to test the photovoltaic inverters' contribution to short‐circuit currents. Laboratory tests are then performed to obtain the short‐circuit current contribution of eight single‐phase photovoltaic inverters. Using the short‐circuit current data obtained, a behaviour model is developed and simulated on Matlab‐Simulink. The model is then applied to a real distribution system and case studies are conducted to simulate the potential impacts on the protection system. Results indicate that while the massive penetration of small‐scale single‐phase photovoltaic inverters can alter the protection system's operating time, the impacts are not significant. Only in very specific scenarios, such as events related to high impedance faults, some impact can be observed. In these situations, the presence of photovoltaic inverters further complicates the already difficult task of identifying high impedance faults through conventional overcurrent protections. This study provides valuable insights into the integration of photovoltaic inverters into distribution systems, and can aid in the development of effective protection measures for future grid designs. [ABSTRACT FROM AUTHOR]

The Fractional Frequency Transmission System (FFTS) operates with low frequency to decrease the electrical length of AC transmission lines, consequently enhancing transmission capacity. The integration of renewable energy sources via FFTS will significantly impact power system planning. This paper presents a Fractional Frequency Transmission Expansion Planning (FF‐TEP) model, which considers the interaction between FFTS and the industrial‐frequency power grid. This model addresses the challenges posed by the integration of offshore wind power through FFTS, and takes several key factors into account, including the active power coupling of Modular Multilevel Matrix Converter (M3C), inverter capacity, and converter transformer capacity. To address the non‐convexity caused by AC power flow, this paper introduces a Genetic‐Particle Swarm Optimization algorithm (G‐PSO) with dynamic penalties tailored to the M3C structure. This approach incorporates both crossover and mutation operators from Genetic Algorithms (GA) into Particle Swarm Optimization (PSO). The penalty function dynamically adapts according to the M3C structure, resulting in improved global optimization while maintaining computational efficiency. The results of two case studies demonstrate that FFTS can significantly enhance transmission capacity while reducing cable charging power. This reduces investment costs and decreases voltage offset issues. [ABSTRACT FROM AUTHOR]

The objective of this paper is to determine the transient characteristics and sites of the pre‐breakdown (pre‐BD) in the spark conditioning under power frequency voltage (PFV) in vacuum. A multi‐angle optical path system was designed and made the determination of the pre‐BD sites in spark conditioning possible, through one camera at two viewing angles. The experimental results indicate that, in the spark conditioning under PFV, the pre‐BDs continuously occurred at a random moment. Within several half‐waves, the number of the pre‐BDs could reach dozens of times, which was the key for the treatment of the electrode surface in conditioning. Under the application of PFV, the single pre‐BD occurred at a random moment, in which it would last for several microseconds and the peak value of the current would reach several amperes. Moreover, the pre‐BD sites randomly distributed in the surface of the electrode and almost covered the whole surface of the electrode. The pre‐BDs took place independently in both time and space, in which there should be no effect between each other of two sequential pre‐BDs. To sum up, the transient characteristics and the sites of the pre‐BDs determined in this paper could reveal the physical mechanism of the pre‐BD on the treatment of electrode surface in the conditioning process, which is quite significant to improve the BD voltage of vacuum interrupter. [ABSTRACT FROM AUTHOR]

A power system stabilizer (PSS) is a control system integrated into the control structure of specific generation units within AC grids. It monitors current, voltage, and machine shaft speed. Analysing these variables, the PSS generates appropriate control signals to the voltage regulator unit, aiming to damp system oscillations. With the advancement of high‐voltage direct current (HVDC) overlaid high‐voltage alternative current (HVAC) grids, it is anticipated that direct current power system stabilizers (DC‐PSS) will be developed to perform a similar role as their AC counterparts. DC‐PSS will be responsible for monitoring and controlling DC voltage levels, ensuring stable operations. This paper focuses on DC‐PSS in HVDC grids, designed to ensure stable operation and mitigate voltage fluctuations. Unlike conventional AC power systems, HVDC includes only DC voltage and power. The input signal for DC‐PSS is the variations in DC voltage, and the output signal is proportional to the power changes at the specific bus where the DC‐PSS is installed, aiming to minimize DC voltage oscillations. These characteristics pose significant challenges in DC‐PSS. The paper addresses the challenges and highlights issues such as inertia and low‐frequency oscillations associated with DC‐PSS. Various control methods are presented and a comparison is made among these methods. [ABSTRACT FROM AUTHOR]

Residential buildings may use energy storage, flexible loads, and renewable energy sources to reduce energy consumption and increase demand side flexibility. The flexibility of a single building can be coordinated with other facilities in a transactive energy (TE) market to reduce energy costs. In addition, cloud energy storage (CES) has been proposed to provide storage services for residential buildings with more economic benefits than individual energy storage units in recent years. Although the TE market and CES implementation have received much attention in previous works, a suitable structure for CES participation in TE market has not been addressed. Furthermore, previous studies ignored all or some sources of uncertainties in the TE decision making process. This paper presents a stochastic optimization model in a transactive energy framework based on a distributed optimization algorithm for peer‐to‐peer energy trading using the alternating direction method of multipliers in the presence of CES. This paper considers the uncertainties of the inflexible load demand, renewable energy generations, and market prices using an artificial neural network‐based scenario generation and reduction methodology. Numerical results show improvements toward addressing the challenges of the uncertainties while maximizing the CES's owner revenue and minimizing the customers' costs in the proposed model. [ABSTRACT FROM AUTHOR]

The protection of traditional high‐voltage capacitor banks relies on an unbalance relay which operates when an internal fuse is blown. However, the unbalance relay cannot indicate the cause of the fault. Thus, an operator wastes time and human resources investigating the fault issues. In this paper, a fault which occurred in a 230‐kV power system of Electricity Generating Authority of Thailand was observed by performing simulations using the Power Systems Computer Aided Design (PSCAD) program. The study system based on the double bus station and 72 MVAR capacitor banks was installed in the form of a back‐to‐back topology. Three scenarios were considered: normal condition, fault occurrence in one capacitor bank and fault occurrence in both capacitor banks. Current characteristics such as the current phase and difference in unbalance current were considered. In addition, discrete wavelet transform was applied to solve the ambiguity of current generated from the PSCAD. The authors' results suggest that identifying fault events using a coefficient of wavelet is more efficient than relying on the current amplitude. The findings mentioned in this paper can be applied in a traditional power system protection scheme to enhance a system's reliability. [ABSTRACT FROM AUTHOR]

Technically, residential energy management systems are fundamental sectors in the smart grids for implementing demand response programs in the layer of households for managing energy consumption and reducing energy bills. The paper proposes a novel energy management scheme that takes production and usage into account based on a heuristic searching operation. In addition to modelling the grid, renewable energy sources, batteries, and electric vehicles, various kinds of electrical and thermal devices have been examined, including air conditioners, water heaters, vacuum cleaners etc. A method is developed for solving the objective constraint issue in a smart home in order to reduce energy consumption and determine feasible operation states among the various loads. Moreover, this paper proposes a grey wolf optimization method for solving the issue over a longer simulation period. Various cases were examined to evaluate the effectiveness of this suggested robust optimization algorithm. The outcomes show that the suggested model could not only reduce energy costs significantly but has also shown good performance for energy management purposes. [ABSTRACT FROM AUTHOR]

This paper proposes a method to enhance the accuracy of power load forecasting by considering the variability in the impact of multi‐dimensional meteorological information on power load in diverse regions. The proposed method employs spatio‐temporal fusion (SF) of multi‐dimensional meteorological information and applies the Copula theory to analyze the non‐linear coupling of meteorological information from multiple stations with power load to achieve SF in the spatial dimension. To enhance the accuracy of load forecasting in the time dimension, this paper improves the core parameters of the variational mode decomposition (VMD) using the marine predators algorithm (MPA) and utilizes the weighted permutation entropy (WPE) to construct the MPA‐VMD fitness function for the adaptive decomposition of the load sequence. Moreover, this paper constructs input sets for the long short‐term memory model and the MPA‐LSSVM model by combining each component of the time dimension and each meteorological information of the spatial dimension to obtain the prediction results of each component. The prediction model corresponding to each component is selected according to the evaluation index and reconstructed to obtain the overall prediction results. The analysis results demonstrate that the proposed forecasting method outperforms the traditional forecasting method and effectively enhances the accuracy of power load forecasting. [ABSTRACT FROM AUTHOR]

The papers address the following key areas: ' B I Applications of IoT and digital twin in electrical power systems: A comprehensive survey i b ': This research paper reviews the applications of the Internet of Things (IoT) and digital twin technology in electrical power systems. With the growth of Internet of Things (IoT) techniques, applications have become smarter, and linked gadgets allow them to be used in many parts of power systems. ' B I A new low-cost and low-power industrial Internet of Things infrastructure for effective integration of distributed and isolated systems with smart grids i b ': This research paper provides an Internet of Things (IoT) infrastructure solution based on a newly designed low-cost microcontroller-based IoT remote terminal unit (RTU) to integrate new, old, and conventional sites of existing grids with smart grids. [Extracted from the article]

This paper proposes a novel primary frequency regulation (PFR) scheme for multi‐infeed high voltage direct current (HVDC) (MIDC) system. First, a dynamic model for frequency response in the MIDC system is carried out to involve line‐commutated‐coveter (LCC)‐HVDCs in frequency regulation. Then, a disturbance observer (DO) is proposed to estimate both the power imbalance and the full order states of the system. By combining a Kalman filter, this DO gets an accurate estimation quickly with local frequency measurement. On the basis of the estimated disturbance and state variables, feedforward control schemes can be concluded to fully utilize the fast adjustability of LCC‐HVDCs. In this paper, a model predictive control based scheme is designed to coordinate LCC‐HVDCs under constraints of HVDC capacity and frequency boundaries. As a result, the optimal power‐sharing property of LCC‐HVDCs is achieved to enhance the PFR dynamic performance of the AC main system. Further, the stability of the proposed method is analysed using the Lyapunov method. Finally, numerical simulations are concluded to verify the effectiveness and the merits of the proposed method, by comparing it to typical droop controls. [ABSTRACT FROM AUTHOR]

With the rapid increase of wind power penetration, the frequency indicators of power system, encompassing frequency deviation and rate of change of frequency (RoCoF), are prone to exceed the pertinent relay thresholds, thus leading to serious power outages. Wind farms can participate in primary frequency regulation (PFR) to alleviate the above problem. However, wind farms reserve capacity (WFRC) presented by overspeed control and pitch angle control is the important factor for frequency safety especially when large disturbance occurs, and it varies obviously in different conditions. Thus, WFRC should be utilized optimally to avoid insufficient or excessive response of wind farms in PFR. To overcome the challenge, this paper proposes frequency‐trajectory‐oriented control (FTOC) for wind farms based on a two‐fold optimization framework, seeking to obtain the best frequency control performances in PFR through realizing the optimal compromise between frequency results and the consumption of WFRC. First, when the frequency indicators deviate significantly from the normal values, the optimal frequency trajectory is planned periodically according to the local standards and operation conditions, thereby minimizing the total consumption of WFRC on the premise of frequency safety. Second, based on model predictive control and a real‐time power system parameters estimator, the total reference power of wind farms, which helps the frequency track the latest planned trajectory, is optimally distributed among wind generators according to their reserve capacity. In this case, through the optimal frequency trajectory planning and tracking, the new method in this paper not only effectively prevents the frequency results from exceeding the local standards under diverse conditions, but also finds the optimal balance among frequency indicators safety, frequency trajectory smoothness, and the reserve capacity consumption of wind power generators. Finally, the simulation results prove the effectiveness and advancement of FTOC. [ABSTRACT FROM AUTHOR]