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Abstract This study introduces a strategy for the pre‐allocation and dispatch of mobile power sources (MPSs) within a power‐transportation coupled network, targeting efficient distribution network fault recovery under extreme weather conditions. The strategy incorporates the Dijkstra algorithm and blind number theory to analyse the MPS driving path and generate fault scenarios respectively, considering the effects of extreme weather on traffic capacity and distribution network fault rate. The MPS pre‐allocation model, formulated to minimize the load outage loss, MPS investment, and driving fuel cost, provides a foundation for optimization of the dispatch process. The dispatch optimization model adapts to dynamic information from the power‐transportation coupled network to make decisions on load restoration sequencing and MPS driving path. The effectiveness of the proposed strategy is demonstrated through an application to the IEEE 33‐node distribution system and a corresponding 33‐node transportation network.

With the ever-increasing scale of power systems, stability analysis and control usually bear heavy storage and massive calculation burdens. In view of this, the model order reduction technique proves valuable by constructing a low-dimensional approximate model of the original system, which is crucial for efficiently handling large-scale systems. Balanced truncation (BT), a famous model reduction method, confronts practical limitations as it requires the systems to be stable and cannot deal with unstable models. Therefore, a parallel linear balanced truncation method for power systems based on extended Krylov subspace (EKS) is proposed in this work. Besides extending the BT method to unstable systems by α-shift, the key contribution also lies in strategies to enhance the convergence of the EKS method, whereupon the algorithm improvements include effective and efficient techniques for solving dual Lyapunov equations, and parallel acceleration of the singular value decomposition. The results of the simulation case verify that the proposed method can effectively improve the convergence of the EKS method by increasing α-shift, and the improvement work in this paper reduces the total time consumption of the BT method by about 26 %–33 % of the original, exhibiting better calculation efficiency. In addition, the case studies show that the simplified model still retains the time-domain and frequency-domain response characteristics of the original high-dimensional model.

With the increasing penetration of renewable energy, the adaptability of the existing substation planning model in terms of capacity and quantity of transformer needs to be further studied when preferring large-capacity substations. Considering the variations of renewable energy penetration rate and load, this paper proposes a method to optimize the total capacity of substations in distribution networks. This paper introduces the influence of renewable energy access on power supply reliability and introduces the idea of partitioning for economic analysis of reliability. An economic analysis model for simultaneously optimizing the capacity and quantity of substation transformer in the distribution network is constructed, taking into account the effects of reducing net load and enhancing the reliability of the distribution feeders resulting from renewable energy access to the medium and low voltage side of the substation. Various wiring means of the distribution network are retained and the impact of renewable energy access on the reliability of the network power supply is quantified. The optimization model is solved by the multivariate universe optimizer(MVO) algorithm with stronger optimality finding capability and short solving time. Finally, the case study results of a regional distribution network are employed to demonstrate and verify the validity and rationality of the method.

With the growing usage of renewable energy technologies like wind power and photovoltaics, and the increased integration of FACTS (Flexible Alternating Current Transmission System) devices into the power system, the transient characteristics of the evolving power system undergo changes that require appropriate analysis methods. As a new type of FACTS device, TCPST (Thyristor Controlled Phase Shifting Transformer) is introduced to facilitate the superior operation of renewable energy sources by controlling the power flow of access branches. From the perspective of branches, the dynamic characteristics of renewable energy sources and TCPST might be uniformly studied and simply calculated in the transient stability analysis. Therefore, the paper proposes a transient stability analysis method for wind power systems with TCPST based on the branch potential energy index. First, considering the variation characteristics of branch potential energy, this paper employed the stability index to analyze the transient stability of branches and the system by finding vulnerable lines. Then, based on the stability index, the paper investigates the influence of wind turbine removal, the installation and control parameter adjustment of TCPST on the transient stability of wind power systems. The simulation results in PSAT demonstrate that the removal of a proper amount of wind turbine post-fault and the installation of TCPST can improve the transient stability of the wind power system. Furthermore, it can also be improved by reasonably adjusting the TCPST control parameters. All simulation results are verified by the time domain simulation method.

With the continuous advancement of new energy commercialization, new energy projects planned urgently need to operate online to obtain benefits. However, large new energy projects require high standards in grid-connected facilities, which makes electric companies consuming all new energies face more and more technical and management challenges. In order to promote the development of the new energy industry and guide the decision of electric companies and the authorities, this paper forms the orderly access of different new energy projects within the same grid as an externality problem based on the analysis of planning technicalities and economics theory. Then, several compensation countermeasures to solve the proposed externality problem among electric companies and multiple new energy projects put in use in different periods are proposed by constructing an intergenerational externalities indicator evaluation model. For a practical case of new energy projects respectively integrated with two voltage levels, calculated and analyzed the externality indicators in the featured scheme. The sensitivity of parameters such as the cost of new energy generation to the calculation results is analyzed for the externality index calculation results of new energy projects. Finally, based on the results of the externality index calculation and sensitivity analysis, the compensation countermeasures to solve the externality problem of new energy project commissioning are proposed in a targeted manner.

Abstract With large‐scale wind farms connected to AC/DC network, the transient stability assessment (TSA) of the power system becomes more and more difficult. Among them, estimating the region of attraction (ROA) of the equilibrium point is a traditional but still challenging problem. Based on the Lyapunov stability theory, this paper proposes a new approach to obtain the enlarged estimation of the ROA. The optimization and updating strategy of shape function in the sum of squares (SOS) optimization problem are studied to reduce the conservatism of estimation result. In the proposed method, the gravitational search algorithm (GSA) is employed to optimize the coefficients of initial shape function to improve estimation performance. Based on the time‐domain simulation (TDS) of expected faults, the fitness value for shape function optimization is calculated using the values of state variables at the fault clearing time and the system stability information. Furthermore, the optimal Lyapunov function is computed by introducing the update condition of shape function and adjusting the iteration strategy of the ROA estimation algorithm. Finally, the proposed method is applied to a two‐machine‐infinite‐bus system and a more complex nine‐bus AC/DC system with wind power. And the effectiveness of the proposed method is verified by comparing with the existing ROA estimation methods.

In this paper, a resilient control strategy is proposed to improve the stability of frequency and voltage recovery for the islanded microgrid (MG) under hybrid cyber attacks. To deal with the common false data injection attacks (FDI) and denial of service attacks (DoS) in MGs, the proposed resilient control strategy utilizes the observers to accurately estimate the potential FDI signals on both the sensors and actuators of each distributed generation unit (DG) and reconstruct the unavailable states in the system to enhance the system’s ability actively. The ultimate uniform boundedness (UUB) of the system under hybrid cyber attacks is proved by the Lyapunov stability theory. Finally, an islanded MG system is established in MATLAB/SIMULINK, and multiple scenarios are simulated to verify the effectiveness of the method.

In a distribution system, sparse reliable samples and inconsistent fault characteristics always appear in the dataset of neural network fault detection models because of high impedance fault (HIF) and system structural changes. In this paper, we present an algorithm called Generative Adversarial Networks (GAN) based on the Reptile Algorithm (GANRA) for generating fault data and propose an evolution strategy based on GANRA to assist the fault detection of neural networks. First, the GANRA generates enough high-quality analogous fault data to solve a shortage of realistic fault data for the fault detection model’s training. Second, an evolution strategy is proposed to help the GANRA improve the fault detection neural network’s accuracy and generalization by searching for GAN’s initial parameters. Finally, Convolutional Neural Network (CNN) is considered as the identification fault model in simulation experiments to verify the validity of the evolution strategy and the GANRA under the HIF environment. The results show that the GANRA can optimize the initial parameters of GAN and effectively reduce the calculation time, the sample size, and the number of learning iterations needed for dataset generation in the new grid structures.

Phase Shifting Transformer (PST) can help improve the power flow distribution of the transmission section, which can increase the wind power consumption of the grid. In order to adapt the PST allocation to the grid evolution, this paper presents a dynamic programming method to allocate PST in each planning stage of the grid optimally. The optimal allocation model of PST under a single grid seeks to maximize the wind power consumption and the Total Transfer Capacity (TTC) between areas. A calculation method for TTC of grids containing PST and wind power is proposed. The Non-Dominated Sorting Genetic Algorithm II (NSGA2) is used to solve the Pareto sets under each planning stage of the grid. Then, the optimal planning path of PST is derived based on dynamic programming. The superiority of the proposed method is demonstrated by comparing the IEEE-118 system results of dynamic and static programming.

With the continuous and rapid growth of electric vehicle (EV) ownership, a large number of disorderly charging loads accessed to the regional grid may lead to problems such as widening of the peak-to-valley load difference and deterioration of power quality. This study proposes an optimal EV charging and discharging regulation strategy based on dynamic regional dispatching price to give full play to the EV vehicle-to-grid (V2G) regulation potential and reasonably regulate EV charging and discharging behavior to ensure safe, high-quality, and economic operation of the grid. First, the EV travel chain model is constructed according to the travel behavior pattern of EV users to obtain the spatial and temporal distribution characteristics of EV charging and discharging loads. Second, considering the difference in regional grid operation states, a dynamic update strategy of regional dispatching price integrating power grid spatial and temporal information is proposed. Furthermore, considering the characteristics of EV mobile energy storage and user demand, an EV V2G optimal regulation model that takes into account the interests of distribution system operators, EV aggregators, and EV users is established to effectively guide the regulation of EV charging and discharging behavior. Finally, the simulation results show that the proposed strategy can effectively reduce the load deviation of each regional grid, operation cost of EV aggregators, and charging cost of EV users and significantly improve the safety and economy of the power system.

To cope with the volatility and randomness of wind power, photovoltaic (PV) power, and load demands in the islanded microgrid, and also to ensure the safety and economic operation of the islanded microgrid system. A collaborative Distributed model predictive control (Di-MPC) based voltage optimization control strategy is proposed, which considers the strong coupling characteristic of active and reactive power due to the impedance ratio of islanded microgrid, and the requirements of real-time and robustness in the optimization as well. By coordinating the controllable devices in the source-grid-load side of the islanded microgrid, the proposed strategy aims to make full use of the voltage regulation capability of each controllable device. Firstly, by considering the different operating characteristics of the controllable devices, a multi-time scale distributed voltage optimal control model is established. It divides the optimal control process into long-time scale and short-time scale and optimizes for respective objective functions and control variables in different time scales. Secondly, a collaborative Di-MPC-based voltage optimal control strategy is proposed. With the proposed collaboration mechanism, the power output increments of the distributed generators (DGs) are solved in the short-time scale, and the errors in the long-time scale control are also fixed. Finally, the simulation results show that compared with a traditional optimal control method and a centralized model predictive control (CMPC) method, the proposed voltage optimization control strategy can effectively reduce the voltage deviation and fluctuation at each node while ensuring the economic operation of the islanded microgrid system.

With the trend of electronization of the power system, a traditional serial numerical algorithm is more and more difficult to adapt to the demand of real-time analysis of the power system. As one of the important calculating tasks in power systems, the online solution of Lyapunov equations has attracted much attention. A recursive neural network (RNN) is more promising to become the online solver of the Lyapunov equation due to its hardware implementation capability and parallel distribution characteristics. In order to improve the performance of the traditional RNN, in this study, we have designed an efficient vectorization method and proposed a reduced-order RNN model to replace the original one. First, a new vectorization method is proposed based on the special structure of vectorized matrix, which is more efficient than the traditional Kronecker product method. Second, aiming at the expanding effect of vectorization on the problem scale, a reduced-order RNN model based on symmetry to reduce the solution scale of RNN is proposed. With regard to the accuracy and robustness, it is proved theoretically that the proposed model can maintain the same solution as that of the original model and also proved that the proposed model is suitable for the Zhang neural network (ZNN) model and the gradient neural network (GNN) model under linear or non-linear activation functions. Finally, the effectiveness and superiority of the proposed method are verified by simulation examples, three of which are standard examples of power systems.

Phase-shifting transformer (PST) is one of the flexible AC transmission technologies to solve the problem of uneven power transmission. Considering that PST can also be used as a regulation means for the economic operation of the system, it is necessary to study the power flow optimization of power systems with PST. In order to find a more efficient power flow optimization method, an improved genetic algorithm including a data-driven module is proposed. This method uses the deep belief network (DBN) to train the sample set of the power flow and obtains a high-precision proxy model. Then, the calculation of the DBN model replaces the traditional adaptation function calculation link which is very time-consuming due to a great quantity of AC power flow solution work. In addition, the sectional power flow reversal elimination mechanism in the genetic algorithm is introduced and appropriately co-designed with DBN to avoid an unreasonable power flow distribution of the grid section with PST. Finally, by comparing with the traditional model-driven genetic algorithm and traditional mathematical programming method, the feasibility and the validity of the method proposed in this paper are verified on the IEEE 39-node system.

With the interconnection of large power networks, transient voltage stability of AC/DC power systems has been paid more attention in recent years. To investigate main influence of generating facility, high-voltage direct current (HVDC) and motor load for transient voltage stability, from the perspective of the generalised branch potential energy, the new indexes, and analysis method is proposed here. First, an energy function for AC/DC system is constructed with major dynamic components, namely classical HVDC control systems and motors in third-order model. Accordingly, the influence effects of the above major dynamic elements are investigated based on indexes from the corresponding generalised branch potential energy. With the rule of change in transient potential energy distribution in the network and information in stability margins, an evaluating procedure is established. Here, the judgment of transient voltage stability employs a heuristic energy function method. Finally, simulation tests on three-machine system with a HVDC transmission line are conducted in PSAT package, and the obtained results demonstrate the feasibility and effectiveness of the proposed method.

Dynamic voltage restorer (DVR) is a compensation device that can effectively improve power quality, and it is widely used to compensate the voltage sag on sensitive loads. However, DVR will affect the operation of the sensitive loads in parallel at the same busbar (such parallel are defined as adjacent loads) in the process of improving sensitive load voltage quality. With the externality theory, this paper proposes a solution to clarify the influence of DVR on the adjacent load and handle the possible controversy. This paper first establishes a simple system that consists of a DVR, sensitive loads, and adjacent loads. Based on the equivalent network, the influence of the DVR on the adjacent load is analyzed in terms of voltage change and power consumed. The externality boundaries are clearly defined when considering network parameters in order to fairly deal with the interaction of multi entities. Afterwards, an optimization compensation strategy that is based on externality theory is proposed. Finally, the simulation on compensation strategy is tested on the MATLAB/Simulink platform to demonstrate the feasibility and effectiveness of the proposed method.

A centralized secondary control is utilized in a DC islanded microgrid to fine-tune voltage levels following the implementation of droop control. This is done to avoid conflicts between current allocation and voltage adjustments. However, because it introduces a single point of failure, a distributed secondary control is preferred. This paper introduces a consensus-based secondary distributed control approach to restore critical bus voltages to their nominal values and properly distribute current among converters. The critical bus takes the lead in voltage adjustments, with only connected energy resources contributing to regulation. The microgrid is represented as an undirected graph to facilitate consensus building. Two adjustment terms, dv and di, are generated to assist in returning voltage to its nominal level and correctly allocating current among energy resources. To enhance consistency and improve controller performance compared to those reported in existing literature, all buses are connected to a leader node. In the event of the failure of all converters except one, voltage can still be effectively restored. MATLAB-Simulink simulations are conducted on two medium-voltage DC (MVDC) microgrids to validate the efficiency of the proposed control method. The results confirmed that the proposed control method can effectively maintain voltage stability and enhance the precise distribution of current among agents by 8%. [ABSTRACT FROM AUTHOR]

In this paper, simulation modeling was carried out using Sentaurus Technology Computer-Aided Design. Two types of high electron mobility transistors (HEMT), an AlGaN/GaN/AlGaN double heterojunction and AlGaN/GaN single heterojunction, were designed and compared. The breakdown characteristics and damage mechanisms of the two devices under the injection of high-power microwaves (HPM) were studied. The variation in current density and peak temperature inside the device was analyzed. The effect of Al components at different layers of the device on the breakdown of HEMTs is discussed. The effect and law of the power damage threshold versus pulse width when the device was subjected to HPM signals was verified. It was shown that the GaN HEMT was prone to thermal breakdown below the gate, near the carrier channels. A moderate increase in the Al component can effectively increased the breakdown voltage of the device. Compared with the single heterojunction, the double heterojunction HEMT devices were more sensitive to Al components. The high domain-limiting characteristics effectively inhibited the overflow of channel electrons into the buffer layer, which in turn regulated the current density inside the device and improved the temperature distribution. The leakage current was reduced and the device switching characteristics and breakdown voltage were improved. Moreover, the double heterojunction device had little effect on HPM power damage and high damage resistance. Therefore, a theoretical foundation is proposed in this paper, indicating that double heterojunction devices are more stable compared to single heterojunction devices and are more suitable for applications in aviation equipment operating in high-frequency and high-voltage environments. In addition, double heterojunction GaN devices have higher radiation resistance than SiC devices of the same generation.

In this manuscript, we present a novel deployment protection method aimed at safeguarding aeronautical radio altimeters (RAs) from interference caused by fifth-generation (5G) telecommunication base stations (BSs). Our methodology involves an integrated interference model for defining prohibited zones and utilizes power control and angle shutoff methods to mitigate interference. First, to ensure reliable protection, we define both horizontal and vertical prohibited zones and investigate their variations to immunize RA against 5G interference. Second, we validate the effectiveness of the model in various operational scenarios, analyzing the influence of factors such as base station types, antenna parameters, flight altitude, and aircraft attitudes to cover a wide range of real-world scenarios. Third, to mitigate interference, we propose and analyze the power control and angle shutoff methods through simulation for the RMa prohibited zone. Our results demonstrate the efficacy of the deployment protection method in safeguarding RAs from 5G interference, providing guidance for interference protection during civil aviation operations and base station deployment near airports.