Comparison of conventional and soft computing-based power control methods in mobile communications systems

 In this paper, we give a comparison between the conventional power control scheme and soft computing-based approaches in a mobile communications application. At the base station, the `bang–bang' control strategy and a neural network-based prediction control method are employed. In addition, full power command transmission mode, single-bit command transmission mode, and fuzzy logic-based power command enhancement unit are considered. Based on simulation experiments, we quantitatively evaluate the performance of various combinations of these control methods and command transmission modes. Conclusions on the optimal configuration are finally drawn.     

Characterization of fuzzy clustering algorithms in terms of entropy of probabilistic sets

We discuss a method of evaluating fuzzy clustering algorithms. Each of them generates a partition matrix of a data set with the entries lying in the [0, 1] interval and expressing the grade of belonging of the object to the clusters detected. Membership functions of the same cluster are interpreted as probabilistic sets in the sense of Hirota. This makes it possible to characterize the clusters by means of the entropy of the corresponding probabilistic sets. Moreover, the mutual entropy of pairs of probabilistic sets provides an index for evaluating the degree of interaction between clusters.     

基于微波谐振器的谷物湿度检测系统

设计和研制一种能连续测量谷物湿度的微波检测系统.提出利用微波谐振器的干扰特性测量谷物的湿度,并设计专用于谷物湿度检测的中心开通孔的微波谐振器.当谷物颗粒通过微波谐振器的中孔时,其质量和湿度都令谐振器的谐振频率产生偏移和功率衰减,谷物的质量和湿度两者所引起的谐振频率偏移和微波功率衰减是相对独立的,在同一湿度下谷物的质量仅仅与微波功率的衰减量有关.因此在得到谐振频率的偏移量和微波功率的衰减量后,就可以计算谷物的湿度.在大量试验的基础上,对试验数据进行模糊聚类,从而对微波谐振器的频偏和功率衰减特性进行建模,据此设计并研制了谷物湿度的微波连续检测系统.试验调试表明:该检测系统适宜于谷物的连续湿度检测,检测精度达到92%以上.     

基于ANFIS的长期电力负荷预测模型

某电力公司的长期负荷预测模型,利用自适应神经网络模糊推理系统(ANFIS)建立.其结构分为计算输入的模糊隶属度、每条规则适用度、适用度归一化、每条规则输出及模糊系统输出5层.系统网络中含14个待定的前件和后件参数.采用Matlab编程,通过Sugeno和evalfis函数训练ANFIS,按指定指标得到这些参数,实现模糊预测.     

Variable-order fractional derivatives and their numerical approximations

This paper addresses the different possible definitions of variable-order derivatives and their numerical approximations; both approximations based upon the definitions and approximations consisting of non-linear transfer functions (in particular combining existing approximations of constant-order fractional derivatives, such as the Crone approximation, with fuzzy logic) are considered. There are different possible configurations, implementing variable-order fractional derivatives both with and without memory of past values of the time-dependent differentiation order.     

Tuning fuzzy PD and PI controllers using reinforcement learning

In this paper, we propose a new auto-tuning fuzzy PD and PI controllers using reinforcement Q-learning (QL) algorithm for SISO (single-input single-output) and TITO (two-input two-output) systems. We first, investigate the design parameters and settings of a typical class of Fuzzy PD (FPD) and Fuzzy PI (FPI) controllers: zero-order Takagi–Sugeno controllers with equidistant triangular membership functions for inputs, equidistant singleton membership functions for output, Larsen’s implication method, and average sum defuzzification method. Secondly, the analytical structures of these typical fuzzy PD and PI controllers are compared to their classical counterpart PD and PI controllers. Finally, the effectiveness of the proposed method is proven through simulation examples.     

基于模糊神经网络直接转矩控制系统设计

针对传统的直接转矩控制中存在转矩、定子磁链波动大的问题,提出了一种结合空间矢量调制的模糊神经网络直接转矩控制方法。它运用模糊神经网络得到期望的任意相位空间电压矢量,再结合空间矢量调制直接对逆变器的开关状态进行控制,以达到减小转矩、定子磁链波动的目的。最后以TMS320F240为核心搭建了一个直接转矩控制系统,通过采集实验数据,分析该直接转矩控制系统与传统的直接转矩控制系统相比磁链和转矩波动明显减少,并具良好的响应性。     

Volt/Var control in a distribution system by a fuzzy optimization approach

This paper presents a fuzzy optimization approach for solving the Volt/Var control problem in a distribution system with uncertainties. Wind turbines are being considered in the study distribution system. The main purpose is to find an optimum combination of tap position for the main transformer under load tap changer (ULTC) and on/off status for switched capacitors in a day to minimize the voltage deviation on the secondary bus of the main transformer, reactive power flow through the main transformer and real power loss on feeders. When performing the Volt/Var control problem in conventional methods, the hourly load and wind speed must be forecasted to prevent errors. However, actually there are always errors in these forecasted values. A characteristic feature of the proposed fuzzy optimization approach is that the forecast hourly load and wind speed errors can be taken into account using fuzzy sets. Fuzzy set notations in the load demand, wind speed, voltage deviation on the secondary bus, reactive power flow through the main transformer and total real power loss on feeders are developed to obtain the optimal dispatching schedule under an uncertain environment. To demonstrate the effectiveness of the proposed method, the Volt/Var control problem is performed in a distribution system within the service area of Yunlin District Office of Taiwan Power Company (TPC). The results show that a proper dispatching schedule for ULTC position and capacitor switching operation can be reached using the proposed method.     

Control of a DC/DC converter by fuzzy controller for a solar pumping system

The exploitation of the solar energy is very significant for the very sunny countries. Moreover the dryness phenomenon in these country is imposes more and more the use of pumping plants. The storage of the water in insulated basins from the wells has a double advantage. On the one hand, it is a technical storage solution of the solar energy collected by the photovoltaic panels. On the other hand, it is a hygienic way out to supply water for the rural population.In our work, we propose a technique for the identification of the maximum power point (MPP) based on fuzzy logic. This method is used to generate the cyclic ratio to operate the switcher within the maximum power of a photovoltaic array (PVA).For simulation purpose we made a complete modeling of the entire system. The system carried out consists of a photovoltaic array supplying, through a DC converter, a direct current (DC) engine coupled to a centrifugal pump. Our experimental bench consists of two principal units. A DC converter module composed of IGBT power transistors. And a processing module connected to a PC serial port, handling the input signals delivered by photovoltaic generator and controlling the power unit.The obtained experimental results confirm the simulation result which is very satisfactory and show the utility of the fuzzy controller for the optimization of the system.     

Optimal power flow by a fuzzy based hybrid particle swarm optimization approach

This paper presents a fuzzy based hybrid particle swarm optimization (PSO) approach for solving the optimal power flow (OPF) problem with uncertainties. Wind energy systems are being considered in the study power systems. OPF is an optimization problem which minimizes the total thermal unit fuel cost, total emission, and total real power loss while satisfying physical and technical constraints on the network. When performing the OPF problem in conventional methods, the load demand and wind speed must be forecasted to prevent errors. However, actually there are always errors in these forecasted values. A characteristic feature of the proposed fuzzy based hybrid PSO method is that the forecast load demand and wind speed errors can be taken into account using fuzzy sets. Fuzzy set notations in the load demand, wind speed, total fuel cost, total emission, and total real power loss are developed to obtain the optimal setting under an uncertain environment. To demonstrate the effectiveness of the proposed method, the OPF problem is performed on the IEEE 30- and 118-Bus test systems.