▍1. Modul 1
praktikum dasar sistem kendali teknik elektro
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▍2. connection model of MV distribution
说明： 在不同供电区域负荷密度和不同变电所容量条件下 ,对中压配电网常见的几种接线模式进行了经济性和可靠性计算分析 ,研究其随负荷密度和变电所容量变化的趋势 ,以及在相同条件下不同接线模式之间的比较。(Under the conditions of load density and different substation capacity in different power supply areas, the economic and reliability calculations of several common wiring modes of medium voltage distribution network are carried out, and the trend of load density and capacity change of substation is studied. And a comparison between different wiring modes under the same conditions.)
▍4. MPC of wind power cluster
该文基于模型预测控制理论， 提出一种考虑多时空尺度协调的风电集群有功分层预测控制方法。 该方法将风电集群在空间尺度上分为三层控制层，将超短期风电功率预测值在时间尺度上逐层细化，并对各层滚动优化环节进行了优化建模。 仿真分析中通过与常用有功分配方法对比， 结果表明文中所提方法在保证系统安全运行和满足集群计划的同时，能够提高风电消纳能力，合理利用风能资源和分配集群内风电功率。(Based on the model predictive control theory, this paper proposes a wind power cluster active layered predictive control method that considers multi-temporal scale coordination. The method divides the wind power cluster into three layers on the spatial scale, and predicts the ultra-short-term wind power prediction value on a time scale, and optimizes the rolling optimization of each layer. Compared with the commonly used active power allocation method in the simulation analysis, the results show that the proposed method can improve the wind power consumption capacity, rationally utilize the wind energy resources and allocate the wind power in the cluster while ensuring the safe operation of the system and meeting the cluster plan.)
▍5. heavy overload forecasting based on RF
针对使用传统分类器预测配变重过载会因为重过载样本率较低而带来的总正确率很高，重过载预测正确率却很低这一问题，将重抽样与随机森林理论引入分类模型中，构建重抽样-随机森林分类器对配变重过载进行预测。结果表明，新方法在预测配变日重过载类型、重过载开始与结束时间、重过载严重程度方面有较高的准确率。(For the problem of using the traditional classifier to predict the distribution of heavy overload, the total correct rate is high because of the low overload sample rate, and the accuracy of the heavy overload prediction is very low. The resampling and random forest theory are introduced into the classification model. In the construction, the re-sampling-random forest classifier is used to predict the heavy-duty overload. The results show that the new method has higher accuracy in predicting the type of daily variable overload, the start and end time of heavy overload, and the severity of heavy overload.)
▍6. Control Systems Engineering 7th Edition 2015
control system engineering ogata
▍7. MPC of wind power cluster
说明： 该文基于模型预测控制理论， 提出一种考虑多时空尺度协调的风电集群有功分层预测控制方法。 该方法将风电集群在空间尺度上分为三层控制层，将超短期风电功率预测值在时间尺度上逐层细化，并对各层滚动优化环节进行了优化建模。 仿真分析中通过与常用有功分配方法对比， 结果表明文中所提方法在保证系统安全运行和满足集群计划的同时，能够提高风电消纳能力，合理利用风能资源和分配集群内风电功率。(Based on the model predictive control theory, this paper proposes a wind power cluster active layered predictive control method that considers multi-temporal scale coordination. The method divides the wind power cluster into three layers on the spatial scale, and predicts the ultra-short-term wind power prediction value on a time scale, and optimizes the rolling optimization of each layer. Compared with the commonly used active power allocation method in the simulation analysis, the results show that the proposed method can improve the wind power consumption capacity, rationally utilize the wind energy resources and allocate the wind power in the cluster while ensuring the safe operation of the system and meeting the cluster plan.)
▍8. heavy overload forecasting based on RF
说明： 针对使用传统分类器预测配变重过载会因为重过载样本率较低而带来的总正确率很高，重过载预测正确率却很低这一问题，将重抽样与随机森林理论引入分类模型中，构建重抽样-随机森林分类器对配变重过载进行预测。结果表明，新方法在预测配变日重过载类型、重过载开始与结束时间、重过载严重程度方面有较高的准确率。(For the problem of using the traditional classifier to predict the distribution of heavy overload, the total correct rate is high because of the low overload sample rate, and the accuracy of the heavy overload prediction is very low. The resampling and random forest theory are introduced into the classification model. In the construction, the re-sampling-random forest classifier is used to predict the heavy-duty overload. The results show that the new method has higher accuracy in predicting the type of daily variable overload, the start and end time of heavy overload, and the severity of heavy overload.)
▍9. Control Systems Engineering 7th Edition 2015
说明： control system engineering ogata
▍10. DMPC used in AGC
针对风电等间歇性电源大规模并网后，系统在满足安全性和 可 靠 性 的 前 提 下 自 动 发 电 控 制系统调节速率和调节精度的协调性问题，将基于分布式模型预测控制算法引入自动发电系统，该算法提高了自动发电系统调节速度和调节精度之间的协调性，可有效平抑大规模风电接入带来的系统频率波动和联络线功率偏移，为改善自动发电系统性能提供了新的思路。(After the large-scale grid connection of intermittent power supply such as wind power, the coordination problem of the adjustment rate and adjustment precision of the automatic power generation control system is satisfied under the premise of satisfying the safety and reliability, and the distributed model predictive control algorithm is introduced into the automatic power generation system. The algorithm improves the coordination between the adjustment speed and the adjustment precision of the automatic power generation system, and can effectively suppress the system frequency fluctuation and the tie line power offset brought by large-scale wind power access, and provides a new idea for improving the performance of the automatic power generation system. .)
▍11. reactive power optimization of DG
建立了基于模型预测控制的配电网多时间尺度无功优化模型，包含日前优化调节层和实时滚动调控层。日前优化侧重于运行经济性，协调配合不同类型无功设备进行大尺度无功调节，并预留充足动态无功储备响应动态调控，降低运行风险；实时滚动调控侧重于系统运行可靠性。(A multi-time scale reactive power optimization model for distribution network based on model predictive control is established, including the optimization adjustment layer and the real-time rolling control layer. A few days ago, optimization focused on operational economy, coordinated with different types of reactive devices for large-scale reactive power regulation, and reserved sufficient dynamic reactive power reserve response dynamic control to reduce operational risks; real-time rolling control focused on system operational reliability.)
▍12. reactive power optimization of DG
说明： 建立了基于模型预测控制的配电网多时间尺度无功优化模型，包含日前优化调节层和实时滚动调控层。日前优化侧重于运行经济性，协调配合不同类型无功设备进行大尺度无功调节，并预留充足动态无功储备响应动态调控，降低运行风险；实时滚动调控侧重于系统运行可靠性。(A multi-time scale reactive power optimization model for distribution network based on model predictive control is established, including the optimization adjustment layer and the real-time rolling control layer. A few days ago, optimization focused on operational economy, coordinated with different types of reactive devices for large-scale reactive power regulation, and reserved sufficient dynamic reactive power reserve response dynamic control to reduce operational risks; real-time rolling control focused on system operational reliability.)
▍13. Automatic segmentation TPS Kamwa2007
Automatic segmentation TPS Kamwa2007
▍15. Automatic segmentation TPS Kamwa2007
说明： Automatic segmentation TPS Kamwa2007
▍17. A numerical method for analysis of Korres2003
A numerical method for analysis of Korres2003
▍18. A numerical method for analysis of Korres2003
说明： A numerical method for analysis of Korres2003
▍19. A fast method for topological Mori1991
A fast method for topological Mori1991
▍20. A fast method for topological Mori1991
说明： A fast method for topological Mori1991
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