In recent years, the installation of distributed generation (DG) of renewable energies has grown rapidly. When the penetration of grid-integrated DGs are getting high, the voltage and frequency of the power system may cause deviation. We propose an algorithm that reduces voltage and frequency deviation by coordinating the control of multiple battery energy storage systems (BESSs). The proposed algorithm reduces the total number of charging and discharging times by calculating the sensitivity coefficient of BESS at different nodes and then selecting the appropriate BESSs to operate. The algorithm is validated on a typical distribution testing system. The results show that the voltage and frequency are controlled within the permissible range, the state of charge of BESSs are controlled within the normal range, and the total number of charging and discharging cycles of BESSs are reduced.

The growing penetration of photovoltaic (PV) systems may cause an over-voltage problem in power distribution systems. Meanwhile, charging of massive electric vehicles may cause an under-voltage problem. The over- and under-voltage problems make the voltage regulation become more challenging in future power distribution systems. Due to the development of smart grid and demand response, flexible resources such as PV inverters and controllable loads can be utilized for voltage regulation in distribution systems. However, the voltage regulation needs to calculate the nonlinear power flow; as a result, utilizing flexible resources for voltage regulation is a nonlinear scheduling problem requiring heavy computational resources. This study proposes an intelligent search algorithm called voltage ranking search algorithm (VRSA) to solve the optimization of flexible resource scheduling for voltage regulation. The VRSA is built based on the features of radial power distribution systems. A numerical simulation test is carried out on typical power distribution systems. The VRSA is compared with the genetic algorithm and voltage sensitivity method. The results show that the VRSA has the best optimization effect among the three algorithms. By utilizing flexible resources through demand response, the tap operation times of on-load tap changers can be reduced.

The large-scale integration of photovoltaic generation and electric vehicles results in over-voltage, under-voltage, and voltage unbalance problems in three-phase distribution networks. The power consumption of the air conditioners (ACs) is large among the daily electrical appliances and can be regulated to provide voltage regulation services. To protect occupants’ privacy and utilize the existing dispatch strategy of the battery, we propose a thermal battery equivalent model of the inverter-based AC to regulate the active power and provide a voltage regulation service. Moreover, an improved Newton-Raphson power-flow algorithm is developed to reduce the computational burden. The affinity propagation method is applied to cluster ACs according to different operating states. Thus, the ACs’ operating frequencies are adjusted to reset the corresponding operating power. The feasibility of the thermal battery equivalent model and voltage regulation algorithm is verified based on case studies utilizing the revised IEEE 33-node three-phase power distribution network.

A sensorless control system of a permanent magnet synchronous motor based on an extended Kalman filter (EKF) algorithm faces problems with inaccurate or mismatched process noise statistics. This problem affects the performance of the filter, resulting in an inaccurate estimation of motor speed. To address the above problem, this paper proposes a parameter-adaptive Kalman filter algorithm that does not depend on precise noise system covariance. This method can significantly reduce the negative impact of the noise statistical mismatch on motor speed estimation. In addition, the method uses adaptive covariance prediction and removes the original covariance checks in the EKF, thus reducing the calculation burden. The simulation results show that, compared with the traditional EKF algorithm, the algorithm proposed in this article can effectively reduce the steady-state jitter and improve the filtering adaptability and calculation accuracy.

With the development of the economy, electricity demand continues to increase, and the time for electricity consumption is concentrated, which leads to increasing pressure on the voltage regulation of the distribution network. For example, a large number of electric vehicles charging during a low-price period may cause the problem of under-voltage of the distribution network. On the other hand, the penetration of distributed power generation of renewable energy may cause over-voltage problems in the distribution network. This study proposes a Stackelberg game model between the distribution system operator and the load aggregator. In the Stackelberg game model, the distribution system operator affects the users’ electricity consumption time by issuing subsidies to decrease the frequency of voltage violations. As the representative of users, the load aggregator helps the users schedule the demand during the subsidized period to maximize profits. Case studies are carried out on the IEEE 33-bus power distribution system. The results show that the time of the subsidy can be optimized based on the Stackelberg game model. Both the distribution system operator and the load aggregator can obtain the optimal economic profits and then comprehensively improve the operating reliability and economy of the power distribution system.

In the indoor laser simulation localization and mapping (SLAM) system, the signal emitted by the LiDAR sensor is easily affected by lights and objects with low reflectivity during the transmission process, resulting in more noise points in the laser scan. To solve the above problem, this paper proposes a clustering noise reduction method based on keyframe extraction. First, the dimension of a scan is reduced to a histogram, and the histogram is used to extract the keyframes. The scans that do not contain new environmental information are dropped. Secondly, the laser points in the keyframe are divided into different regions by the region segmentation method. Next, the points are separately clustered in different regions and it is attempted to merge the point sets from adjacent regions. This greatly reduces the dimension of clustering. Finally, the obtained clusters are filtered. The sets with the number of laser points lower than the threshold will be dropped as abnormal clusters. Different from the traditional clustering noise reduction method, the technique not only drops some unnecessary scans but also uses a region segmentation method to accelerate clustering. Therefore, it has better real-time performance and denoising effect. Experiments on the MIT dataset show that the method can improve the trajectory accuracy based on dropping a part of the scans and save a lot of time for the SLAM system. It is very friendly to mobile robots with limited computing resources.