Distribution network automation is considered by power supply companies as an effective investment strategy to improve reliability and service quality. Switching devices and protective devices play an important role in the distribution automation system (DAS). This paper presents a novel method to optimize placement of fault indicators and sectionalizing switches in distribution networks with branch lines. The objective function of the proposed method includes the total cost of fault indicators and sectionalizing switches as well as interruption cost. Among different automation equipment, this paper considers fault indicators and remote controlled switches. Besides, manual switches are taken into account since their number and location have a significant impact on the optimal placement problem. Mixed-integer linear programming is used to model the problem, and the proposed model can be solved by large-scale commercial solvers. The solution to the problem is composed of the optimal number and location of fault indicators and sectionalizing switches. The validity of the proposed method is demonstrated by relevant case studies and sensitivity analysis. Moreover, the proposed method is applied to a real distribution network to verify its practicability.

Compared to the load characteristics of normal working days, weekend load characteristics have a low level of load and are sensitive to meteorological conditions, which influences the accuracy of short-term weekend-load forecasting. To solve this problem and to improve the accuracy of short-term weekend-load forecasting, a Semi-parametric weekend-load forecasting method based on the interaction between meteorological and load is proposed in this paper. The main work is shown as follows: (1) through separating weekend-load from normal-load and analyzing the correlation between meteorological factors and daily maximum load, the meteorological factors with parameter characteristics and non-parameter characteristics can be screened out; (2) a short-term weekend-load forecasting model is built according to Semi-parametric regression theory which can express the coupling relation between meteorology and load more realistically; (3) the effect of temperature accumulation is also considered to correct the forecasting model. The proposed method is proved by implementing short-term weekend-load forecasting on the real historical data of the Southern Power Grid in China. The result shows that the 96-point mean load forecasting accuracy obtained by this model can meet the requirement of power network operation.

Line loss management and evaluation is a core issue in the management of power grid projects. However, with the deepening of Chinas power system reform, there are too many human factors in the traditional line loss management and evaluation system. There is a lack of analysis and evaluation of the correlation between indicator data and line loss, and between indicator data and data. It is necessary to establish a new indicator relationship. This article applies knowledge graph technology to the selection of evaluation indicators of line loss management. This strategy uses recursive feature elimination and random forest fusion algorithm to filter evaluation indicators and builds intelligent evaluation indicators for line loss lean management system. Finally, the combination of the indicator system and the evaluation method is applied to the evaluation of line loss management in the municipalities of the China Southern Power Grid Municipal enterprises. The analysis of comparative examples shows that the constructed lean and intelligent evaluation index system has a better prediction accuracy than other methods, and can objectively and reasonably reflect the actual status of line loss management. Also, it can be used as a basis for future line loss management evaluation. It can be used as a decision basis for future line loss management evaluation.

As the scale of the power system continues to expand and the energy situation changes dramatically, the existing automatic generation control (AGC) strategy needs to be optimized and improved. The current grid AGC mainly adopts closed-loop PI control. By learning an excellent data set that incorporates the characteristics of PI control and DFT control, this paper proposes a real-time AGC strategy based on a deep forest network. The strategy selects the controller with better control performance in each assessment period as the controller for the assessment cycle for power deviation regulation studies. The simulation results show that the strategy can achieve real-time AGC regulation with a lower number of actions and outperform any of the learned strategies.

Transient stability is an important factor for the stability of a power system. With improvements in voltage levels, and the expansion of power network scales, the problem of transient stability is particularly prominent. When a power system circuit fails, if the operation time of the relay protection device is higher than the critical clearing time (CCT), the relay protection device cannot cut the fault line in a timely manner. It is essential to forecast and adjust the CCT to improve the stability of the system; therefore, a method is proposed in this paper to predict and evaluate the critical clearing time using the broad learning system (BLS). The sensitivity of the critical clearing time can be easily calculated based on the prediction results of the critical clearing time using BLS. Moreover, the critical clearing time can be modified using the BLS correction control model. The proposed method was verified using a 4-machine 11-node system and a 10-machine 39-node system. According to the experimental results, the proposed model can predict, evaluate, and correct the CCT very well.