As the traditional generation is gradually replaced by inverter-based resources, a lack of rotational inertia is now a common issue of modern power systems, which leads to an increasingly larger rate of change of frequency (RoCoF) following contingencies and may result in frequency collapse. As a crucial index of the frequency security and stability of power systems, the accurate estimation of the RoCoF can be a foundation for the development of advanced operations and control techniques of the future power system. This paper firstly analyzes the role of the RoCoF in typical blackouts occurring in recent years and discusses the physical and numerical nature of the RoCoF; then, by introducing the frequency spatial distribution of the power system, the paper discusses the concept of the “center” RoCoF that can present the frequency security and stability of the entire system. The estimation and prediction techniques of the maximal power system RoCoF following a contingency and the existing real-time tracking techniques of the power system RoCoF are comprehensively reviewed. Finally, the open questions and related research topics of the RoCoF estimation are discussed.

Accurate identifying of fault causes and locating fault sites are fundamental requirements to ensure the safe operation of the power grid. Compared with extra-high-voltage transmission lines, digital fault recorders (DFRs) with high sampling rates and information synchronization are rarely applied in high-voltage transmission lines. One-ended DFRs and impedance-based methods are commonly used for fault identification and location. However, the impedance-based fault location and fault cause identification methods have coupling effect due to the fault resistance caused by different causes, which can affect the location accuracy. To address this, a fault cause identification and location method based on pose normalized multioutput convolutional nets (PNMCN) is proposed. First, the fault characteristics displayed by the volt-ampere curve are analyzed based on the fault mechanism. Second, a one-ended volt-ampere curve is used as an input, pose normalization is employed to improve feature extraction for fault resistance, and coupled prior knowledge of fault cause and fault location is used during training of PNMCN to mitigate their mutual effects. Finally, the results of the case study show that the median and mean relative errors of the fault location are 0.68% and 1.16%, respectively, and the fault cause identification accuracy is 99.5% when using one-ended low-sampling frequency data. The proposed method can be used for adaptive reclosing and is convenient for operation and maintenance personnel to detect faults.

To effectively guarantee a secure and stable operation of a smart substation, it is essential to develop a relay protection system considering the real-time online operation state evaluation and the risk assessment of that substation. In this paper, based on action data, defect data, and network message information of the system protection device (PD), a Markov model-based operation state evaluation method is firstly proposed for each device in the relay protection system (RPS). Then, the risk assessment of RPS in the smart substation is carried out by utilizing the risk transfer network. Finally, to highly verify the usefulness and the effectiveness of the proposed method, a case study of a typical 220 kV substation is provided. It follows from the case study that the developed method can achieve a better improvement for the maintenance plan of the smart substation.

Dynamic state estimation (DSE) for generators plays an important role in power system monitoring and control. Phasor measurement unit (PMU) has been widely utilized in DSE since it can acquire real-time synchronous data with high sampling frequency. However, random noise is unavoidable in PMU data, which cannot be directly used as the reference data for power grid dispatching and control. Therefore, the data measured by PMU need to be processed. In this paper, an adaptive ensemble square root Kalman filter (AEnSRF) is proposed, in which the ensemble square root filter (EnSRF) and Sage–Husa algorithm are utilized to estimate measurement noise online. Simulation results obtained by applying the proposed method show that the estimation accuracy of AEnSRF is better than that of ensemble Kalman filter (EnKF), and AEnSRF can track the measurement noise when the measurement noise changes.

Aiming at the problems of insufficient power supply capacity, unbalanced load, and poor safety and reliability of the urban power grid, a flexible DC optimization control strategy is proposed to support the interconnection of power grid zones. Firstly, the steady-state and transient optimal control objectives are established to meet the safety and economic operation requirements of the grid; secondly, comprehensive evaluation indexes are constructed, covering load balancing, network loss, and voltage deviation, and a step-by-step approximation method is used to quickly solve the optimal power of the flexible DC, so as to realize the optimal control of the urban grid in the steady state. Meanwhile, a multi-terminal flexible DC adaptive sag control strategy based on the power margin of the converter station is designed to reasonably allocate the emergency power support in each partition after a fault and reduce the frequency deviation of the heavily loaded nodes. Taking the urban grid as an example, a simulation model of the grid structure with a 750 KV outer ring and a 220 KV inner ring is constructed, and the simulation results verify the effectiveness of the proposed method and its engineering practicability.

A large number of idle defect and fault texts are accumulated in the process of long-term operation, maintenance and repair of power secondary equipment. In order to realize the effective utilization and deep mining of defect texts, a method of defect text classification of secondary equipment based on BiLSTM-Attention is proposed. Firstly, the text is cleaned according to the natural language characteristics of the historical defect texts of the secondary equipment. Secondly, the text content is vectorized through Word2Vec model, and the semantic information of the text is extracted in depth through BiLSTM-Attention to realize the accurate classification of the defect degree of the equipment. Finally, an example is built through the historical defect texts of a power company. The result shows that the proposed model can accurately classify the defect text of power secondary equipment and help operation and maintenance personnel to judge the defect degree quickly and accurately.