The state of charge (SOC) estimation of the battery is one of the important functions of the battery management system of the electric vehicle, and the accurate SOC estimation is of great significance to the safe operation of the electric vehicle and the service life of the battery. Among the existing SOC estimation methods, the unscented Kalman filter (UKF) algorithm is widely used for SOC estimation due to its lossless transformation and high estimation accuracy. However, the traditional UKF algorithm is greatly affected by system noise and observation noise during SOC estimation. Therefore, we took the lithium cobalt oxide battery as the analysis object, and designed an adaptive unscented Kalman filter (AUKF) algorithm based on innovation and residuals to estimate SOC. Firstly, the second-order RC equivalent circuit model was established according to the physical characteristics of the battery, and the least square method was used to identify the parameters of the model and verify the model accuracy. Then, the AUKF algorithm was used for SOC estimation; the AUKF algorithm monitors the changes of innovation and residual in the filter and updates system noise covariance and observation noise covariance in real time using innovation and residual, so as to adjust the gain of the filter and realize the optimal estimation. Finally came the error comparison analysis of the estimation results of the UKF algorithm and AUKF algorithm; the results prove that the accuracy of the AUKF algorithm is 2.6% better than that of UKF algorithm.

In order to improve the accuracy of fault diagnosis on wind turbines, this paper presents a method of wind turbine fault diagnosis based on ReliefF algorithm and eXtreme Gradient Boosting (XGBoost) algorithm by using the data in supervisory control and data acquisition (SCADA) system. The algorithm consists of the following two parts: The first part is the ReliefF multi-classification feature selection algorithm. According to the SCADA history data and the wind turbines fault record, the ReliefF algorithm is used to select feature parameters that are highly correlated with common faults. The second part is the XGBoost fault recognition algorithm. First of all, we use the historical data records as the input, and use the ReliefF algorithm to select the SCADA system observation features with high correlation with the fault classification, then use these feature data to build the XGBoost multi classification fault identification model, and finally we input the monitoring data generated by the actual running wind turbine into the XGBoost model to get the operation status of the wind turbine. We compared the algorithm proposed in this paper with other algorithms, such as radial basis function-Support Vector Machine (rbf-SVM) and Adaptive Boosting (AdaBoost) classification algorithms, and the results showed that the classification accuracy using “ReliefF + XGBoost” algorithm was higher than other algorithms.

Aiming at the problems of transient over-current and over-voltage in the switching process of AC/DC hybrid microgrid in grid-connected mode and island mode, which leads to the sudden change of transmission power and seriously affects the power transmission quality. In this paper, an improved seamless switching control strategy of droop control with disturbance observer is designed. The main work is as follow: according to the diffident power transmission calculation methods of droop control in grid-connected mode and island mode, the adaptive switching is realized by detecting the current and frequency at Point of Common Coupling(PCC) of public power grid. Secondly, the switching from island mode to grid-connected mode will cause impact due to the difference of frequency, phase and amplitude between microgrid and public grid, and even cause system collapse. So the pre-synchronization control of frequency, phase and amplitude is designed. Based on the droop control, the disturbance observation(DOB) is added, which can quickly track the sudden change of system current, and suppress the sudden change by the difference between the tracking value and the actual value, so as to realize the smooth switching from island to grid. Finally, the effectiveness and feasibility of the control strategy are verify in Matlab/Simulink.

With the rapid development of renewable energy technologies, islanded DC microgrids have received extensive attention in the field of distributed power generation due to their plug-and-play, flexible operation modes and convenient power conversion, and are likely to be one of the mainstream structures of microgrids in the future. The islanded DC microgrid contains multiple distributed power generation units. The battery energy storage system (BESS) is the main controlled unit used to smooth power fluctuations. The main parameter of concern is the state of charge (SOC). In order to maintain the stability of the microgrid, this paper takes the islanded DC microgrid as the research object and designs a control strategy based on the SOC of the BESS. Additionally, in the control strategy, the BESS’s energy balance control strategy and the microgrid’s operation control strategy are emphatically designed. The designed BESS control strategy adjusts the droop coefficient in real time according to the SOC of the battery energy storage unit (BESU), and controls the charge and discharge power of the BESU to achieve the SOC balance among the BESUs. The microgrid operation control strategy takes the energy storage system (ESS) as the main controlled unit to suppress power fluctuations, and distributes the power of distributed power sources according to the SOC of the BESS to achieve power balance in the microgrid, and control the DC bus voltage fluctuation deviation within 4.5%.

An effective Supervisory Control and Data Acquisition (SCADA) system can improve the reliability, safety and economic benefits of a microgrid operation. In this research, the lower central controller and upper WEB (World Wide Web) monitoring system are connected by the SCADA system, which is the hub of a microgrid intelligent monitoring platform. This system contains a set of specific functions programmed by Java as a middleware and can provide communication and control functions between the central controller and the upper monitoring system. For the sake of security and stability of the microgrid, the SCADA system realizes business processing on real-time data acquisition and storage, load balancing and resource recovery, concurrent security processing, and control instruction parsing and transmission. All those functions were tested and verified in actual operation.

To enhance the accuracy of water quality turbidity detection and address the challenges associated with drinking water safety, this study proposes a high-precision turbidity sensor detection approach. However, in practical water quality monitoring, the design of the water quality turbidity monitoring system encounters difficulties arising from air bubbles in the water affecting beam refraction, excessive beam scattering angles impacting detection accuracy, and the need for precise water quality turbidity measurements. To tackle these issues, this study presents a novel design comprising a pressurized air-removing device and an innovative mechanical structure, effectively eliminating difficult-to-remove air bubbles in the water channel. Furthermore, a lens with calibration is devised to effectively address the problem of excessive beam scattering angles. Based on the 90° scattering method, the designed sensor achieves an impressive detection limit of 0.001 nephelometric turbidity unit (NTU) and a concentration range of 0–100 NTU. The results demonstrate a significant improvement in the accuracy and interference reduction ability of the sensor designed in this study. The developed high-precision turbidity sensor offers promising potential for advancing water quality monitoring technologies and ensuring safe drinking water for the public.