Hybrid Intelligent Control Method Research Articles

Hybrid Intelligent Control Method to Improve the Frequency Support Capability of Wind Energy Conversion Systems

This paper presents a hybrid intelligent control method that enables frequency support control for permanent magnet synchronous generators (PMSGs) wind turbines. The proposed method for a wind energy conversion system (WECS) is designed to have PMSG modeling and full-scale back-to-back insulated-gate bipolar transistor (IGBT) converters comprising the machine and grid side. The controller of the machine side converter (MSC) and the grid side converter (GSC) are designed to achieve maximum power point tracking (MPPT) based on an improved hill climb searching (IHCS) control algorithm and de-loaded (DL) operation to obtain a power margin. Along with this comprehensive control of maximum power tracking mode based on the IHCS, a method for kinetic energy (KE) discharge control of the supporting primary frequency control scheme with DL operation is developed to regulate the short-term frequency response and maintain reliable operation of the power system. The effectiveness of the hybrid intelligent control method is verified by a numerical simulation in PSCAD/EMTDC. Simulation results show that the proposed approach can improve the frequency regulation capability in the power system.

Hybrid intelligent control of substrate feeding for industrial fed-batch chlortetracycline fermentation process

The lack of accurate process models and reliable online sensors for substrate measurements poses significant challenges for controlling substrate feeding accurately, automatically and optimally in fed-batch fermentation industries. It is still a common practice to regulate the feeding rate based upon manual operations. To address this issue, a hybrid intelligent control method is proposed to enable automatic substrate feeding. The resulting control system consists of three modules: a presetting module for providing initial set-points; a predictive module for estimating substrate concentration online based on a new time interval-varying soft sensing algorithm; and a feedback compensator using expert rules. The effectiveness of the proposed approach is demonstrated through its successful applications to the industrial fed-batch chlortetracycline fermentation process.

Application of Case-based Reasoning and Iterative Learning to Laminar Cooling Process Control

In some previous study, the strip coiling temperature prediction compensator and batch to batch compensator cannot obtain good compensating results due to the manually adjusted weight parameters for index feature of the casebased reasoning (CBR) system. And exact match and effective iteration cannot be done for the lack of initial operating condition matching algorithm. For this reason, a method based on neural network technology is proposed to learn the weights parameters of the index features of CBR system, with an initial operating condition matching algorithm that uses iterative learning technique to improve prediction compensator and the batch to batch compensator. The proposed hybrid intelligent control method is applied to a large domestic steel plant, and the results show that the strip coiling temperature control error decrease 1.63 ◦C and the hit rate increased 14.5% where the coiling temperature errors are controlled in the range of ± 10 ◦C.

Hybrid intelligent control for optimal operation of shaft furnace roasting process

A hybrid intelligent control method is proposed to control the technical indices into their desired range. This is realized by on-line adjusting the set-points of control loops for optimal operation of the shaft furnace in response to changes in operating points. The controller consists of six modules, namely a pre-setting model for control loop set-points, a predictive model for technical index, a feedforward compensator using predictive model, a feedback compensator, a fault working situation diagnosis and a fault tolerant control model. The proposed approach has been successfully applied to the roasting process of shaft furnace in a mineral processing plant in China and its efficiency has been verified by the practical application results.

Hybrid Intelligent Optimal Control Method for Operation of Complex Industrial Processes

摘要: 工业过程运行的优化控制的目标是将反应产品在加工过程中的质量、效率、消耗的工艺指标控制在目标值范围内. 由于复杂工业过程的工艺指标难于在线测量且与控制回路输出之间的动态特性具有强非线性、强耦合、难以用精确模型描述、随生产边界条件变化而变化的综合复杂性,因此, 难以采用已有优化控制方法, 运行控制只能采用人工设定的控制方式. 由于人工控制不能及时准确地随运行工况调整设定值, 难以将工艺指标控制在目标值范围内, 甚至造成故障工况.本文提出了根据运行工况实时调整控制回路设定值, 通过控制系统跟踪调整后的设定值, 将工艺指标控制在目标值范围内的过程优化运行的混合智能控制方法. 该方法由控制回路预设定模型、前馈补偿与反馈补偿器、工艺指标预报模型、故障工况诊断和容错控制器组成. 在某选矿厂22台竖炉组成的焙烧过程的应用案例, 证明了所提出方法的有效性. 关键词: 复杂工业过程 / 过程运行 / 工艺指标 / 智能优化控制

Hybrid Intelligent Control for Optimal Operation of Shaft Furnace Process

The extensively used shaft furnace in the ore concentration industry is an important facility that turns the weak-magnetic low-grade hematite ore into strong-magnetic one. During the operation of the shaft furnace roasting process, the optimal control objective is to control the technique indices, namely the magnetic tube recovery ratio (MTRR) that represents the quality, the efficiency, and the consumption of the product processing, into its targeted ranges. However, due to the complex dynamics between the MTRR and the control loops, such a control objective is by far difficult to achieve by the existing control methods, thus only manual control is adopted. In this paper, a hybrid intelligent control method for the optimal process operation is proposed with the purpose of controlling the technique indices into the desired range by on-line adjusting the set-points of the control loops. The proposed method was applied to the roasting process undertaken by 22 shaft furnaces in the ore concentration plant of Jiuquan Steel & Iron Ltd in China. The application results show that the MTRR is controlled to the targeted range with 2% increase; the faulty working-conditions are eliminated, which boosts the equipment operation ratio by 2.98%, resulting in a raise of 0.57% in the concentrated grade and 2.01% in the metal recovery ratio.