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Parameter optimization of hydro turbine governor PID controller by chaotic improved PSO algorithm

Energy Education Science and Technology Part A: Energy Science and Research 2014年第5期32卷 3949-3962页

作者： Chen, Gonggui Du, Yangwei Song, Peizhu Chongqing University of Posts and Telecommunications Key Laboratory of Industrial Wireless Network and Networked Control of Ministry of Education Chongqing China Chongqing University of Posts and Telecommunications Research Center on Complex Power System Analysis and Control Chongqing China Hubei Minzu University Dept. of Electrical Engineering Enshi China Hubei Energy Group Qiyueshan Wind Power Co Ltd Lichuan China

For overcoming the disadvantages of easily falling into local extremum and easily losing species diversity in traditional particle swarm optimization (PSO) algorithm, a cross improved PSO (IPSO) algorithm is proposed. In addition, in order to enhance the exploitation capacity of IPSO algorithm, it also makes use of chaotic sequence features which is called chaotic IPSO (CIPSO) algorithm. In present paper, mathematical structure model of hydro turbine governor is established, and the integral of absolute value of error as the minimum objective function is adopted to select particles. In order to obtain satisfactory transient dynamic characteristics in process of tuning of PID parameters, the square of output signal after PID tuning and the rise time are proposed to integrate into the augmented objective function, and in order to minimize overshoot to the greatest extent, the penalty mechanism is concerned. These algorithms are performed for optimized designs of governor PID parameters on hydro turbine. Several specific working conditions are modeled and CIPSO algorithm results are compared with those in PSO and IPSO algorithms. It comes out that the CIPSO algorithm has made some improvements whether in convergent speed or convergent accuracy in comparing with other two algorithms. © Sila Science. All rights reserved.

关键词： Governors

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Research on the corrosive effect of inactive sulfur on oil-paper insulation

Research on the corrosive effect of inactive sulfur on oil-p...

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International Conference on High Voltage Engineering and Application, ICHVE

作者： Sihang Gao Jiajun Li Tingjing Ke Yang Liu Dong Ding Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China State Grid Chongqing Electric Power Company Chongqing Chongqing China Baoding Electric Power Corporation Baoding China State Key Laboratory of Transmission & Distribution Equipment and Power System Safety and New technology Chongqing University Chongqing China

ISBN: (数字)9781728155111

ISBN: (纸本)9781728155128

Sulfur corrosion induced by active sulfur has been proved to significantly decrease the insulation performance of oil-immersed equipment. In order to protect the oil-paper insulation from sulfur corrosion, active sulfur is cleared in the refining process of crude oil. Some inactive sulfur with high-efficiency antioxidant is still retained in the mineral oil to improve the oxidation stability of oil, such as thiophenic compounds. However, the corrosive effect of inactive sulfur on the oil-paper insulation has not yet clear. Therefore, thiophene that is the largest content of sulfide in the insulating oil was selected as the research object, a series of accelerated thermal ageing experiments of oil-paper without and with thiophene under the different high temperature were conducted, scanning electron microscope equipped with an energy-dispersive X-ray spectrometer (SEM/EDX) and inductively coupled plasma atomic emission spectrometry (ICP-AES) were used to characterize the corrosion degree of oil-paper insulation. The results show that no corrosive deposit was observed on the surface of oilpaper insulation without thiophene even at very high temperature, while a few corrosive deposit was accumulated on the surface of oil-paper insulation with 500 mg/kg thiophene. The corrosion degree of oil-paper insulation with thiophene had an uptrend as the increase of temperature (130°C-180°C), preliminarily indicating that thiophene may be activated in the oil-paper system under the action of overheating fault of oil-immersed transformer and posed a corrosion threat to oil-paper insulation.

关键词： Copper Sulfur Corrosion Oils Windings Power transformer insulation Oil insulation

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Natural Gas Load Forecasting Based on Multi-Stage Model Identification

Natural Gas Load Forecasting Based on Multi-Stage Model Iden...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Pengbin Zhang Hongrui Wang Kunpeng Xing Mingzhe Yuan State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China GUANGZHOU INSTITUTE OF INDUSTRIAL INTELLIGENCE Guangzhou China China Nuclear Power Engineering Co. Ltd. Shenzhen China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

Natural gas load forecasting is crucial for the safe operation of urban natural gas. Based on the system identification method, the short-term natural gas load forecasting is studied in the paper. Employing a multi-stage identification framework, the initial values of the model parameters using the least squares and the instrumental variables method, while the model parameters are further optimized by the prediction error method to improve the accuracy of the model identification. The proposed method is validated by the simulation based on the public natural gas data in the U.S., demonstrating the algorithm's effectiveness in forecasting the daily natural gas load effectively.

关键词： Load forecasting Simulation Load shedding Predictive models Prediction algorithms system identification Reliability Forecasting Natural gas Load modeling

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Influence of the pyrolysis of benzotriazole passivator on the characteristics of dissolved gas in the insulating oil

Influence of the pyrolysis of benzotriazole passivator on th...

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International Conference on High Voltage Engineering and Application, ICHVE

ISBN: (数字)9781728155111

ISBN: (纸本)9781728155128

Insulation failures of oil-immersed power transformer induced by sulfur corrosion can be controlled by adding the metal passivator into insulating oil. However, it has been reported that the addition of benzotriazole and its derivative passivators into insulating oil would cause abnormal generation of dissolved gases in the oil during the operation of oil-immersed transformer. Therefore, this paper investigated on the effect of passivator on the characteristics of dissolved gas in the oil under the action of thermal field. Benzotriazole (BTA) that widely used in the oil-immersed transformer was selected as the research object, the thermogravimetric analysis of BTA was conducted, and a series of accelerate thermal ageing experiments of oil-paper insulation with and without BTA were also conducted, the composition and content of dissolved gases in oil were measured and analyzed. The results show that BTA was pyrolyzed continuously as the temperature increase. The addition of BTA in oil accelerated the contents of H2, CO and CO2 and decelerated the content of hydrocarbon gases, which lead to abnormal generation of dissolved gases in oil. All of these may interfere the operation condition evaluation of oil-immersed transformer based on DGA technology.

关键词： Oils Gases Aging Power transformer insulation Oil insulation Thermal conductivity Partial discharges

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An Improved Chimp Optimization Algorithm for Short-term Hydrothermal Scheduling

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第3期49卷 666页

作者： Chen, Gonggui Wang, Shumin He, Yongsheng Shang, Wanfeng Long, Hongyu Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing University of Posts and Telecommunications Chongqing400065 China State Grid Chongqing Electric Power Company Chongqing400015 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Shenzhen518055 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China

The short-term hydrothermal scheduling (STHS) is a complex non-linear, non-convex and high-dimensional mathematical optimization problem. Due to the fact that the original chimp optimization algorithm (ChOA) is likely to fall into local optimum and has the disadvantage of low population diversity, an improved chimp optimization algorithm (IChOA) is proposed to solve the STHS problem. Firstly, Logistic-Tent chaotic mapping is employed to initialize the population and increase the population diversity. Then a new nonlinear convergence factor is introduced to make the optimization search process more applicable to STHS problems. Next, particle swarm algorithm (PSO) and gravitational search algorithm (GSA) are combined to enhance the shortcoming and balance the global search ability and local exploration ability of ChOA. Moreover, Cauchy mutation and opposition-based learning strategy are supplemented as perturbation interference when the optimal position is not updated to improve the ability to leap out of the local optimum. Finally, a graded optimization strategy is adopted for the constraint handing of dynamic reservoir balance and thermal power balance. Three standard hydrothermal power systems are utilized to verify the practicability and validity of the proposed method. Results of simulations reveal that IChOA has significant competitive advantages in addressing the STHS problem. © 2022, IAENG International Journal of Computer Science. All Rights Reserved.

关键词： Particle swarm optimization (PSO)

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Kernel-Based Regularized Continuous-Time system Identification from Sampled Data

Kernel-Based Regularized Continuous-Time System Identificati...

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IEEE Conference on Decision and control

作者： Xiaozhu Fang Biqiang Mu Tianshi Chen School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Key Laboratory of Systems and Control of CAS Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The identification of continuous-time (CT) systems from discrete-time (DT) input and output signals, i.e., the sampled data, has received considerable attention for half a century. The state-of-the-art methods are parametric methods and thus subject to the typical issues of parametric methods. In the last decade, a major advance in system identification is the so-called kernel-based regularization method (KRM), which is free of the issues of parametric methods. It is interesting to test the potential of KRM on CT system identification. However, very few results have been reported, mainly because the estimators have no closed forms for general CT input signals, except for some very special cases. In this paper, we show for KRM that the estimators have closed forms when the DT input signal has the typical intersample behavior, i.e., zero-order hold or band-limited, and this paves the way for the application of KRM for CT system identification. Numerical Monte Carlo simulations show that the proposed method is more robust than the state-of-the-art methods and more accurate when the sample size is small.

关键词： Accuracy Monte Carlo methods Numerical simulation Robustness system identification Standards

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A Family of Hyper-parameter Estimators for Regularized Linear system Identification

A Family of Hyper-parameter Estimators for Regularized Linea...

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IEEE Conference on Decision and control

作者： Meng Zhang Tianshi Chen Biqiang Mu Key Laboratory of Systems and Control of CAS Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Beijing China School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

Hyper-parameter estimation is one of the fundamental issues for kernel-based regularized system identification methods. Empirical Bayes (EB) estimator and Stein's unbiased risk estimator (SURE) are two popular hyper-parameter estimators, but they both have advantages and disadvantages. Specifically, EB is not asymptotically optimal in the mean squared error (MSE) sense but SURE is, while SURE is more sensitive to ill-conditioned regression matrix but EB is more robust. In this paper, to find a better estimator by combining their strength and mitigating their weakness, we propose a family of hyper-parameter estimators by linking EB and SURE estimators together through an index. The finite sample and asymptotic properties of this family of estimators have been established. The Monte Carlo simulation results show that there does exist a ‘middle’ hyper-parameter estimator in this family that is superior to the EB and SURE.

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Encoding Space to Count Multi-Targets with Multiplexed Binary Infrared Sensors

Encoding Space to Count Multi-Targets with Multiplexed Binar...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Longxiang Luo Yang Xiao Wei Liang Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Department of Computer Science The University of Alabama Tuscaloosa AL USA

ISBN: (数字)9781728152127

ISBN: (纸本)9781728152134

Recently, many researchers multiplex binary infrared sensors in object tracking, habitat monitoring, and atypical behavior detection. Due to the binary digit output of binary sensors, multiplex binary sensors may lead to count the wrong number of targets when three or more targets present in the field of interest (FOI). We call this as the invisible targets' problem. To enhance the sensing results of sensors, a reference structure tomography technique is used to segment and code the FOI by modulating the sensing view of sensors. In this paper, we propose a subregion coding method to count targets moving in the FOI. Hexagon modulators are designed to make their projections segment the FOI into hexagon cells. We also propose a signature construct scheme to code cells and a encoder to count the number of targets in the FOI. Experiment results show that the accuracy to correctly count targets of our method is around 90% which is much better than 45% of a conventional method.

关键词： Modulation Monitoring Shape Sensor systems Encoding Intelligent sensors

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Nonlinear steam generator feedwater system identification based on wavelet neural network

Nonlinear steam generator feedwater system identification ba...

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10th International Symposium on Test Automation & Instrumentation (ISTAI 2024)

作者： Hongrui Wang Mingzhe Yuan Pengbin Zhang Kunpeng Xing Guangzhou Institute of Industrial Intelligence Guangzhou People's Republic of China Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang People's Republic of China China Nuclear Power Engineering Co. Ltd Shenzhen People's Republic of China

ISBN: (数字)9781837241910

Steam generator is the key equipment in the large and complex nuclear power plant, and its water supply system has the characteristics of time-varying, large delay, nonlinear. In this paper, a segmented linearized model based on wavelet neural network is developed for a steam generator feedwater system of a nuclear power plant. All the data of the automation stages of the steam generator, which are steam flow rate, feedwater flow rate and liquid level from 20% load to 100% load, are collected and divided into six stages based on the characteristics of the data. For the first four stages of data, the feedwater system was modelled using the Hammerstein-Wiener method based on wavelet neural network, which was used as the nonlinear input and nonlinear output module of Hammerstein-Wiener. And for the remaining two stages of data, the nonlinear autoregressive models based on with exogenous (ARX) inputs model approach was modelled using nonlinear ARX as the underlying framework of wavelet neural network. The proposed model can be used for model predictive control to switch between different models based on load changes. Through the verification of simulation experiments, the built model can accurately reflect the trend of actual data, and the predicted values have high accuracy and reliability.

关键词：

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MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection

MARS: Multi-Agent Deep Reinforcement Learning for Real-Time ...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Long Cheng Haoyang He Yan Gu Qingzhi Liu Zhiming Zhao Fang Fang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing China Information Technology Group Wageningen University and Research Netherlands Research Group of Multi-Scale Networked Systems University of Amsterdam Netherlands

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of $53.18 \%$ and costs by $61.98 \%$ compared to basic techniques, and achieving $20.26 \%$ and $25.71 \%$ improvements over the latest advanced methods, respectively.

关键词： Training Cloud computing Privacy Costs Scalability Deep reinforcement learning Dynamic scheduling Real-time systems Complexity theory Protection

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