This paper proposes a recurrent neural network (RNN) based model to segment and classify multiple combined multiple power quality disturbances (PQDs) from the PQD voltage signal. A modified bi-directional long short-term memory (BI-LSTM) model with two different types of attention mechanism is developed. Firstly, an attention gate is added to the basic LSTM cell to reduce the training time and focus the memory on important PQD signal part. Secondly, attention layer is added to the BI-LSTM to obtain the more important part of the voltage signal by assigning weightage to the output of the BI-LSTM model. Finally a SoftMax classifier is applied to classify the combined PQD signal in 96 different combinations. The performance of proposed BI-LSTM model with attention gate and attention layer mechanism is compared with the performance of baseline models based on recurrent neural network (RNN) and convolution neural network (CNN). With this model, the PQD signal is easily segmented from the voltage signal which makes the process of PQD classification more accurate with less computation complexity and in less time.

This paper considers the variability in the impact of multi-dimensional meteorological information on power load in different regions. To improve the accuracy of load forecasting in the spatial dimension, the method of spatio-temporal fusion (SF) of multi-dimensional meteorological information is proposed. The Copula theory is applied to analyze the nonlinear coupling of meteorological information such as wind speed, rainfall, temperature, and sunshine intensity from multiple meteorological stations with the power load and to achieve spatio-temporal fusion. In the time dimension, the core parameters of the variational mode decomposition (VMD) are improved by the marine predators algorithm (MPA). The weighted permutation entropy (WPE) is used to construct the MPA-VMD fitness function for the adaptive decomposition of the load sequence. In addition, the input sets of the LSTM model and MPA-LSSVM model are constructed by combining each component of the time dimension and each meteorological information of 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 show that the proposed forecasting method is better than the traditional forecasting method and effectively improves the accuracy of power load forecasting.

In modern power systems, the impact of random power sources and loads on power systems is increasing, resulting in threatened system frequency security. However, traditional methods are often not comprehensive in modelling randomness, and there is no analytical method to assess the frequency response of power systems under uncertain power disturbances. In this paper, the power disturbance in a power system is regarded as an interval random quantity. Based on a general model of the system frequency response (SFR-G) and the theory of stochastic processes, an analytical prediction method of the power system frequency deviation under uncertain power disturbance is proposed. Through further deduction, the allowable range of power disturbance under the premise of secure frequency deviation can also be defined, which provides a reference for frequency security and accident prevention in power systems. Numerical simulations demonstrate that the proposed analytical method can delimit the response interval of system frequency deviation well, and the backstepping calculation can also delimit the allowable range of power disturbance to ensure the secure operation of the system.

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

Natural disasters have posed great challenges to the power system in recent years. This paper proposes an emergent trading framework that uses parking lots as resources to provide power support to critical loads in a blackout due to typhoon. Firstly, a distribution line fault model under typhoon is established to create possible fault scenarios with the typhoon trajectory data. Subsequently, an evolutionary Stackelberg game-based trading model is proposed to maximize all stakeholders' economic benefits while reducing the critical load loss for all chosen scenarios, leading to enhanced system resilience. At the same time, a benefit allocation mechanism and free-riding penalty are incorporated in the framework to motivate players' participation while limiting the negative effect of free-riders. Further, an iterative evolutionary-Stackelberg solution set-up is applied to obtain the equilibria of the proposed framework. Finally, a modified IEEE 69-bus system is used to illustrate and validate the proposed framework.

In order to build UHV AC transmission lines at high altitudes, it is necessary to analyze the audible noise characteristics of the existing EHV transmission lines at high altitude areas to guide the design. The typical test data of audible noise for 750 kV single circuit transmission lines measured at the Guanting long-term observation station, located at an altitude of 1854 m, were collected. Two methods of sorting the test data of audible noise and extracting valid data were given. Then the levels of audible noise under different weather conditions were analyzed, which showed that audible noise was raised with the increase of instantaneous rainfall and snowfall. Typical frequency spectrums of audible noise under different weather were also obtained, the pure tone at 100 Hz was more prominent under rainy and snowy weather, and the pure tone oscillation attenuated at different positions. Audible noise at different locations presented the better attenuation characteristics with the increase of distance during rainy days. The statistical results and calculated difference of audible noise over 5.5 hours with continuous rainfall were obtained. For the quiet plateau areas, the calculated difference between audible noise during rainy days and fair weather can be taken as 25 dB.

In future modern power systems, reliability and resilience could be an extreme challenge caused by the stability issues of the bidirectional power converters (BPCs). The non-linear dynamics of DC link voltage (DCLV) of BPCs in interaction with the existing linear control schemes may decrease the stability margin and cause operating-point-dependent instability issues. Existing approaches may solve this issue by reducing the DCLV control loop bandwidth, which considerably degrades the system performance. To tackle this issue, first, the root cause of the instability challenge is analytically investigated, and then, a non-linear stabilizer control scheme based on Lyapunov theorem is proposed. Considering the non-linear dynamic of the BPCs and the interaction between dynamics of DC link voltage and AC currents in the proposed stabilizer, it guarantees the stability of the converter in both directions of power flow and the full range of loading conditions. The performance of the proposed scheme is verified through simulation of the system under various operating conditions, considering uncertainties, disturbances, and short-circuit events, and comparing it with that of prevalent controllers.

With the rapid development of active distribution networks, the “petal”-type distribution network has become the mainstream power supply structure. To ensure the safe and reliable power supply of a distribution system, power control methods for active distribution networks should be further studied. An electromagnetic rotary power flow controller (RPFC) is a feasible solution for active distribution network power control. However, when testing the effectiveness of the PQ decoupled control method for RPFC based on instantaneous reactive power theory, difficulties were encountered with the synchronous control of the rotor position angle of two rotating-phase transformers, and the accuracy of power control was unsatisfactory. Given this condition, PQ control is improved in three ways. First, the system periodic oscillation problem is solved via variable speed control. Second, the servo motor–rotary phase-shifting transformer synchronous rotation scheme is designed, reducing power control error and improving stability. The overshoot phenomenon in power control is improved using the variable domain fuzzy proportional–integral adaptive method. Experimental results show that the proposed advanced control scheme exhibits good dynamic and static performance in power control scenarios and achieves effective improvement of RPFC.

High level of fault currents has become one of the most challenging issues, along with increased electrical energy consumption and power grid expansion. However, some of the previous work was limited to installing limiters on designs and installing them on small plans. In this study, the optimal placement of a resistive-type superconducting fault current limiter (RSFCL) to achieve good power system transient stability is proposed. The proposed method has several advantages. First, procedure fault analysis is applied to consider the magnitude of the short-circuit currents and the line located close to the generator for the placement of the device. Compared with other methods, it can directly find the exact line to be installed RSFCL. Second, the stability assessment is validated using the time domain simulation by installing limiters with varying capacities in any location. Third, parameters were analysed to evaluate the proposed method’s effectiveness, including angular speed, rotor angular velocity, and generator rotor angle in a multi-machine system. The result reveals that the placement of the limiter on the line with the most significant short-circuits value on the line closest to the generator increases the power system stability more than the placement in other locations.

Power systems around the world are rapidly transitioning to much higher shares of inverter-based resources (IBRs) with few synchronous generators remaining online. IBRs and synchronous generators have fundamentally different dynamic performance characteristics resulting in a difference in the overall power system dynamic performance. IBRs are generally more flexible and controllable than synchronous generators, however at the same time exhibit significantly more complex control systems. Furthermore, new and emerging capabilities are being developed progressively and in particular the so-called grid-forming inverters. Grid-forming inverters (GFM) offer several new capabilities not previously possible with conventional grid-following inverters (GFL). However, they are not well understood currently when applied in a mega scale and moving forward when they will likely take over the role synchronous generators have been performing for several decades as the workhorse of system security support. Key questions currently in the technical community include the extent to which GFM shall be similar or different to each of the synchronous machines and conventional GFL, and how various control strategies can assist in maximising the grid support capabilities sought and minimise or ideally eliminate any adverse impacts. The objective of this special issue is to provide insights into some of these unknowns.

Energy routers based on the electronic power transformer are suitable for the AC-DC hybrid grid with multiple voltage levels, but their structures are complex. This paper proposes a novel energy router based on the multi-winding line frequency transformer. By a combination of a multi-winding line frequency transformer and power electronic devices, the proposed energy router can take advantage of the high reliability of the multi-winding line frequency transformer and the high controllability of power electronic devices. The proposed energy router is suitable for the AC-DC hybrid grid with multiple voltage levels and has the characteristic of a simple structure. The simulations and experimental results demonstrate the effectiveness of the proposed energy router.

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

Recently, power interruptions with high impact effects are increased by occurring natural disasters. Furthermore, the uncertain nature of data creates the considerable challenges for enhancing the resiliency of power distribution systems after occurring events. Regarding this, this paper presents a robust optimization approach for resiliency improvement in power distribution system. The robust approach uses the crew teams for switching action as restoration process, demand response programs and Mobile Generators (MGs) simultaneously for improving the resiliency with considering the uncertainty of electrical load and electrical price. The objective function is tri-level consists of minimum, maximum and minimum function. The first level is minimum function that is for minimizing cost of commitment of CHPs with considering location of MGs and reconfiguration structure in power distribution systems. The second level is maximum function that is for finding the worst-case scenario of the uncertainty variables. The third level is minimizing total operation cost in the condition with worst scenario of stochastic data by using demand response programs. The aforementioned algorithm is implemented on an IEEE 33-bus test distribution system with four different cases. Furthermore, several cases and sensitivity analysis are accomplished in order to show the efficiency of the proposed model.

Integration of Distributed Energy Resources (DERs) in power systems exacerbates the existing information problems between power utilities and regulators. DER policies oblivious to the trilemma of information asymmetry between power utilities, DER aggregators, and regulators result in distorted price signals to DER investors, and socially inefficient DER roll-out. Therefore, in this paper, we propose a game-theoretic approach for modeling information asymmetry in distribution network information and consumer data between the DER aggregators and the power utilities. The proposed framework is based on Single Leader Single Follower (SLSF) games, reformulated as Mathematical Programs with Equilibrium Constraints (MPECs), and solved using the Scholtes’s relaxation technique. Our results, based on the 7-bus Manhattan power network, show that unless the DER aggregators have complete information about the distribution network characteristics, the welfare along with the realized installed capacity of DERs in the system decreases. Moreover, progressively decreasing DER investment costs alleviate the effects of information asymmetry, suggesting that early adopters face disproportionately high welfare losses attributed to incomplete information between the DER stakeholders. Hence, policy interventions to alleviate the rampant information problems are imperative to ensure an optimal DER roll-out.

As electric vehicles(EVs) begin to participate in the peak regulating auxiliary service market, it has become a major problem that how can price aggregators maximize the peak shaving capacity provided by EVs and maximize their own interests. This paper proposes a bargaining game pricing method based on the psychology cost of EVs and the risk assessment of aggregators. First of all, according to the impact of users’ participation in peak shaving on the battery life of EVs, the impact of participation in peak shaving on users’ original travel plans and time, and the impact of aggregator pricing on users’ psychology, the comprehensive psychology cost of EV users is obtained. Then, based on the user’s psychology cost and the law of gravitation, the evaluation scheme for the peak shaving capacity of EVs is obtained. On the basis of conditional value at risk(CVaR), the mixed CVaR is obtained by considering the behavior of users who may chase risks. Based on the mixed CVaR, the risk assessment of aggregators’ participation in the peak regulating auxiliary service market is carried out. According to the above information, the aggregators and the EV teams are engaged in a bargaining game based on the peak shaving pricing problem, which is divided into complete information game and incomplete information game. Finally, the feasibility of the proposed method is verified by an example analysis.

A novel parameter estimation method is proposed for the permanent magnet synchronous generator (PMSG), which is implemented by an enhanced self-learning particle swarm optimization algorithm with Levy flight (SLPSO), and the problem of lower parameter estimation precision of standard PSO is obviated. This method injects currents of different intensities into the d-axis in a time-sharing manner to solve the problem of equation under-ranking, and the mathematical model for full-rank parameter estimation is developed. The speed term of PSO is simplified to expedite the convergence of PSO, and a strategy with Chaotic decline for the inertia weight of PSO is adopted to strengthen its ability to jump out of the local optimum. Moreover, the self-learning dense fleeing strategy (SLDF) is proposed where particles perform diffusion learning based on population density information and Levy flight, the evolutionary unitary problem and human intervention in the evolutionary process is averted. Furthermore, the memory tempering annealing algorithm (MTA) and greedy algorithm (GA) is integrated into the algorithm, MTA can facilitate the exploration of potentially better regions, and GA for local optimization enhances the convergence speed and accuracy in late stage of the algorithm. Comparing the proposed method with several existing PSO algorithms through simulation and experiments, the experimental data show that the proposed method can effectively track variable parameters under different working conditions and has better robustness.

Generally, wind speed alters continuously during a day and lead to fluctuating output power of a wind farm. Connecting such a wind farm to the transmission line of grid causes a malfunction in the performance of the backup zone of the distance relays due to the random nature of generation and the number of connected wind turbines (wind farm penetration). In this study, a new adaptive setting groups-based method has been proposed to set the second zone of the distance relay in the grid with a wind farm equipped with a doubly fed induction generator (DFIG). In this method, we also taken into account the uncertainty of wind fluctuations and the availability of wind turbines. A Markov model is developed to implement these features. To determine the impedance setting of each of the set groups of the second zone in the distance relays of the remote bus, the obtained impedances are clustered in four clusters by using the combination of K-means and particle swarm optimization (PSO) algorithms. Finally, details of the analysis results of this issue have been compared with other methods available in the literature. The validity and accuracy of the proposed approach also have been assayed and confirmed.

This paper proposes an intelligent approach based on the empirical Fourier decomposition (EFD) to identify harmonic sources at the point of common coupling (PCC) when different inverter-based distributed generations (DGs) like microturbine (MT), Battery energy storage system (BESS), photovoltaic (PV), superconducting magnetic energy storage (SMES), wind turbine with a permanent magnet synchronous generator (PMSG), and doubly-fed induction generator (DFIG) wind turbine are presented. In order to decrease memory storage and computational burden, strife feature selection is used. Applying just voltage signals consumes less processing time and decreases measurement devices. Moreover, the whale optimization algorithm (WOA) as the optimizer of the parameters of the support vector machine (SVM) classifier is used. Consequently, the results from the proposed method can be helpful for both engineers and researchers to plan and develop a better strategy to mitigate harmonic distortion.