The Helan section of the third drainage ditch in Ningxia is selected as the research object, and its water environmental capacity is analyzed; The Daily discharge and water level are calculated according to the measured data; Then the daily concentration values of the main water quality indexes are simulated by one-dimensional hydrodynamic water quality mathematical model; Finally, the dynamic water environment capacity is calculated. The results show that from May to October 2020, the main water quality indicators exceeding the standard in Helan section of the third drainage ditch were total nitrogen and five-day biochemical oxygen demand. Their water environmental capacity was −11.8744 t and −67.1173 t, respectively. Chemical oxygen demand and total phosphorus exceeded the standard severely in some months. There are problems of eutrophication and high organic content in drainage ditches. The primary pollution sources are aquaculture wastewater form fishery, farmland drainage, seasonal flood, and domestic sewage. It is suggested to take preventive measures such as source control, process blocking, and end treatment.
Water quality directly determines our living environment. In order to establish a more scientific and reasonable water quality evaluation model, it needs a lot of data support, but it will lead to a large increase in the calculation time of the evaluation model. This paper proposes an improved particle swarm optimization SVM model (CPOS-SVM) to solve this problem. In this paper, the Pareto optimal solution concept is used to sparsely process the training set, which can ensure that the number of training sets is reduced without loss of data characteristics, thus reducing the training time. In order to solve the problem of the kernel parameter g and penalty factor c on the SVM algorithm, which affects the accuracy of the SVM model but it is difficult to select why, a particle swarm optimization algorithm is used in this paper to optimize the kernel parameter and penalty factor and improve the accuracy of the model. In this paper, 480 sets of data from Ming Cui Lake from 2014 to 2022 are taken as the research object, and examples are analyzed in MATLAB 2020a. The results show that the training time of the CPOS-SVM model can be completed within 2 s and does not increase with the increase of data volume. Meanwhile, by comparing the SVM model, POS-SVM model, and POS-BP model, training time increases dramatically with the amount of data. The accuracy of the POS-SVM model is the highest, and the accuracy of the CPOS-SVM model is basically consistent with that of the POS-SVM, reaching 94%, while the accuracy of the SVM model and the POS-BP model are slightly worse. This indicates that the CPOS-SVM model has good application value in water quality evaluation.
In island mode, voltage source inverter (VSI) supports the frequency and voltage of microgrid. After the complex load is connected, the VSI control performance is degraded, and the output voltage has deviation, negative sequence, waveform distortion and other problems, which further deteriorate the power quality of the microgrid. Different from the traditional strategy that only focus on a single problem, the strategy proposed in this paper can deal with these three power quality problems simultaneously. In this paper, a self‐learning sliding mode control strategy is proposed. First, a nonlinear smooth function is used to design an expansion observer, which can estimate the expansion state of the internal uncertainties and external disturbances of the control system. Second, the extended observer is combined with the self‐learning synovial control technology to realize the self‐learning synovial disturbance rejection control of VSI control strategy. This strategy can improve the stability of voltage control under various working conditions without precise mathematical model. The simulation results show that, compared with the traditional control strategy, this strategy has good robustness under different working conditions.
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