Intelligent Optimal Control Method Research Articles

Technical, economic, and environmental assessment of the distribution power system with the application of renewable energy technologies

Renewable energy technologies (RETs) have been universally accepted as the prospective choice to increase the reliability of the power system and reduce the effects of emission cost (EC), fuel cost (FC), maintenance cost (MC), power loss (PL), total annual cost (TAC) and voltage deviation (VD). The performance indicators are used in the study to assess the effects of diesel generator (DG), electric storage system (ESS), photovoltaic (PV) and wind turbine generator (WTG) on the environmental, economic, and technical performance of a microgrid system with modified Roy Billiton Test System (RBTS) bus 4 distribution network. The proposed model is utilized to achieve the goals of the study by minimizing the costs that are identified with the power outages, TAC, MC, EC, FC, PL, and VD of the system with the application of an intelligent optimal control method based on the fmincon solver. The outcomes of the study can be used as benchmarks to assess the cost-effectiveness of increasing the capacity of universal power supply and resolve the universal power crisis owing to the depletion of fossil fuels. The study provides vital information to enhance the efficiency of the power system and the guidelines to promote the development of sustainable energy.

Two-parameter cooperative intelligent optimal control of chaotic motion for a vibro-impact system with soft constraint

For the chaotic motion control of a single-degree-of-freedom vibro-impact system with soft constraint, two-parameter cooperative intelligent optimal control method based on LRS-QPSO (i.e., Local Rotational Search Quantum Particle Swarm Optimization) algorithm optimization SVM (i.e., Support Vector Machines) was proposed. Firstly, in the parameter plane (ω,ζ), the motion distribution of the system and the change of the maximum Lyapunov exponent is obtained by numerical simulation, and the correlation between parameters change and the transition from chaotic motion to periodic motion is analyzed. Secondly, by introducing ω and ζ into the construction of the input variables of the SVM controller, a chaotic motion two-parameter cooperative intelligent optimal control strategy is proposed to meet the requirements of expected control objectives. Therefore, by the virtue of nonlinear mapping characteristics of SVM, the combined effects of ω and ζ on the dynamic state transition of the system can be reflected in the output variables of the controller. Finally, with single parameter ω and single parameter ζ as the research object, the bifurcation diagram, Lyapunov exponential spectrum, and controlled periodic motion orbit diagram are analyzed, and the advantages of two-parameter cooperative intelligent optimal control are compared and analyzed.

Optimal Load-Shedding Control of a Microgrid Power System

In this paper, an intelligent optimal control method is applied to solve the load-shedding problem which has an effect of reduction in fuel cost for the operating period of a diesel generator system unit integrated into a microgrid (MG) system. The optimal control has been implemented firstly to reduce the operating cost of the diesel generator and secondly to secure the proper operation of the distribution system by keeping the power balance of the MG system within a positive range of its acceptable value with the objective to meet the maximum demand side capacity. This paper has implemented a new intelligent method to solve the load shedding and saving cost of the diesel generator fuel problems in MG power system. The application of additional renewable energy systems in an MG power system has an impact on the reduction of the fuel cost of the diesel generator and implies at the same time adjustments in the fluctuations in relative excess energy generation, which has been maintained within a range of approximately thirty percent of the normal value of supply side of the MG power system.

Intelligent optimal control system for ball mill grinding process

Operation aim of ball mill grinding process is to control grinding particle size and circulation load to ball mill into their objective limits respectively, while guaranteeing producing safely and stably. The grinding process is essentially a multi-input multi-output system (MIMO) with large inertia, strong coupling and uncertainty characteristics. Furthermore, being unable to monitor the particle size online in most of concentrator plants, it is difficult to realize the optimal control by adopting traditional control methods based on mathematical models. In this paper, an intelligent optimal control method with two-layer hierarchical construction is presented. Based on fuzzy and rule-based reasoning (RBR) algorithms, the intelligent optimal setting layer generates the loops setpoints of the basic control layer, and the latter can track their setpoints with decentralized PID algorithms. With the distributed control system (DCS) platform, the proposed control method has been built and implemented in a concentration plant in Gansu province, China. The industrial application indicates the validation and effectiveness of the proposed method.

Optimal operation control of the raw slurry blending process using the case-based reasoning and neural network

Raw slurry blending process is a key unit in the sintering alumina industry. In this blending process, raw materials are grinded and blended in the mills to produce raw slurry. The optimal operation control objective is to make the quality indices of raw slurry into their targeted ranges. Flow rates of raw materials are the key factors that affect the quality indices of raw slurry. So, in order to realise the optimal operation control objective, right set points of flow rates must be obtained. However, due to the dynamics between quality indices and control loops of flow rates with complex natures, such as strong non-linearity, heavy coupling and difficulty of description by the accurate model, such a control objective is difficult to be realised by existing control methods. An intelligent optimal control method, which is comprised of the setting layer and the loop control layer, is proposed. In the setting layer, case-based reasoning (CBR) and neural network are adopted to obtain right set points of the control loops. In the loop control layer, the actual flow rates of raw materials follow their set points obtained from the setting layer. At last, the results of industry experiments have proven the effectiveness of the proposed method.

A hybrid intelligent optimal control method for complex flotation process

In mineral industry, flotation is used to separate utilised ore from gangue. The relationships between the technical indexes, i.e. the concentrate grade and the tailing grade, and the reagent feeding appear to have strong non-linearities and uncertainties in dynamic behaviours, which can hardly be described using any accurate mathematical model. For non-automatic and non-precision of manual control of reagent feeding, technical indexes fluctuate and are difficult to be controlled within their target ranges. This article has proposed a novel hybrid intelligent optimal control method which consists of a flotation reagent intelligent setting model, a slurry consistency controller, a supply ore mass controller, an air flow rate controller and a flotation level controller, among which the flotation reagent intelligent setting model is the key which consists of a unit reagent pre-setting model-based Rule-based reasoning (RBR), a feedback compensator and a feed forward compensator RBR, and a reagent feeding computational model. The method can automatically adjust the reagent feeding when the work-condition varies, so that the technical indexes can be controlled within their target ranges. The successful application has shown that the proposed method has practical significance and high potential of being further applied in optimal control of mineral industry.

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