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.

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.

The combined cooling, heating and power (CCHP) system not only has high energy efficiency but also has different load structures. Traditional separate production (SP) system and power supply system do not consider the land cost in terms of the environmental benefits, and in the aspect of the power supply reliability, the grid-connected inverter cost is also ignored. Considering the deficiency of the traditional energy supply system, this paper builds the CCHP system construction cost model. The particle swarm optimization (PSO) is adopted to find out the minimum value of the construction cost, and the optimal system construction scheme is constructed from three aspects which are system reliability, economic benefits and environmental benefits. In this paper, the typical daily data, as well as the meteorological data and the load data, in the last four years are taken as experimental dataset. The experimental results show that compared with the traditional SP system and power supply system, the CCHP system established in this paper not only achieves lower cumulative investment cost, but also has a good power supply reliability and environmental benefits.

In microgrids, paralleled converters can increase the system capacity and conversion efficiency but also generate zero-sequence circulating current, which will distort the AC-side current and increase power losses. Studies have shown that, for two paralleled three-phase voltage-source pulse width modulation (PWM) converters with common DC bus controlled by space vector PWM, the zero-sequence circulating current is mainly related to the difference of the zero-sequence duty ratio between the converters. Therefore, based on the traditional control ideal of zero-vector action time adjustment, this paper proposes a zero-sequence circulating current suppression strategy using proportional–integral quasi-resonant control and feedforward compensation control. Firstly, the dual-loop decoupled control was utilized in a single converter. Then, in order to reduce the amplitude and main harmonic components of the circulating current, a zero-vector duty ratio adjusting factor was initially generated by a proportional–integral quasi-resonant controller. Finally, to eliminate the difference of zero-sequence duty ratio between the converters, the adjusting factor was corrected by a feedforward compensation link. The simulation mode of Matlab/Simulink was constructed for the paralleled converters based on the proposed control strategy. The results verify that this strategy can effectively suppress the zero-sequence circulating current and improve power quality.

At present, the remote meter reading problem for a large number of existing domestic diaphragm gas meters has not been reliably solved. The diaphragm gas meter uses a physical structure to measure gas consumption, so the main problem of remote meter reading is the digitization of gas consumption. Although it is possible to digitize the consumption data by modifying the internal structure of the gas meter or adding sensors, thereby realizing the function of remote meter reading, the modification process is complicated, the modification cost is high, and manual meter reading can only be used for the existing diaphragm gas meters. For this reason, this paper proposes an indirect measurement method of gas consumption based on gas pressure signal detection. The method can digitalize the metering results of the mechanical diaphragm gas meter and develop a state detection function by analyzing the pressure signal of the outlet of the meter without modifying the internal structure of a meter, thus addressing the key difficulties in the construction of a gas meter remote meter reading system. The experimental results show that this method has small calculation error and high reliability.