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2024 2nd International Conference on Power, Grid and Energy Storage, PGES 2024

作者： Cheng, Yan Dong, Cun Sun, Shumin Wang, Chenglong Song, Yu Wu, Shouyuan Huan, Jiafei Liu, Yu State Grid Shandong Electric Power Company Electric Power Research Institute China National Electric Power Dispatching and Control Center Xicheng District Beijing100031 China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Shandong Province Jinan250061 China North China Branch of State Grid Corporation of China China

The secondary frequency modulation of the unit, that is, the automatic generation control (AGC), is the process of increasing or decreasing the power of the power generation unit by the power plant unit according to the dispatching command issued by the power grid dispatching center, and then using the frequency modulator to restore the frequency. Based on the principle of AGC, this paper establishes a frequency dynamic response model of the secondary frequency modulation of the traditional unit and the wind storage participation system including load frequency control (LFC) and traditional proportional-integral-derivative (PID) controllers, determines the frequency modulation control strategy of wind storage, and uses the distribution mode based on Area control Error (ACE) signal to assist in the secondary frequency modulation. In order to solve the parameter optimization problem of the classical PID controller when the wind-storage combined system participates in the secondary frequency modulation under the large load disturbance, a parameter optimization model of the controller with PID was established, and the ITAE evaluation index was finally selected as the fitness function by comparing different fitness evaluation indexes. To overcome the problem that the conventional Particle Swarm Optimization (PSO) is prone to local extremum, the inertia weights and learning factors are adjusted nonlinearly to balance and optimize the global and local optimization capabilities and improve the search efficiency, and the parameters of the PID controller are optimized by the improved PSO algorithm. The simulation results show that the proposed algorithm improves the secondary frequency modulation effect under the background of a high proportion of renewable energy connected to the power grid. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Design of Load Frequency controller for Power system Based on Equivalent Input Disturbance Method

Design of Load Frequency Controller for Power System Based o...

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New Energy and Energy Storage system control Summit Forum (NEESSC)

作者： Jianrong Zhang Hang Xu Hongzhang Wang Chuanke Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798350389012

ISBN: (纸本)9798350389029

In the power system, due to the instability of the external environment, safe and stable operation cannot always be guaranteed, as it frequently encounters varying degrees of external disturbances. In order to mitigate the effects of these disturbances on the system, the power system can operate safely and stably, In this paper, a power system load frequency control (LFC) based on Equivalent Input Disturbance (EID) method is proposed from the perspective of active disturbance suppression. Firstly, the disturbances present in the environment are unified as external disturbances, and then a single area the electric power system model with external disturbance was constructed. Then, a controller algorithm based on EID was designed using EID equivalent processing method and linear matrix inequality method to address the issue of external interference in the power system. Finally, a numerical example is used to validate the effectiveness and accuracy of the controller by simulating with two aspects, fixed perturbation and random perturbation, respectively.

关键词： Perturbation methods Process control Interference Power system stability control systems Linear matrix inequalities Numerical models Frequency control Load modeling Energy storage

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Source-Load-Storage Coordinated Optimal Scheduling Model Considering Admissible Region of Net Load

Source-Load-Storage Coordinated Optimal Scheduling Model Con...

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IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Fengbin Sun Mingqiang Wang Caishuai Liang Xia Zhang Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China Luyuan Electric Group Co. LTD Jinan China

ISBN: (数字)9798350373479

ISBN: (纸本)9798350373486

With the increasing penetration of renewable energy, the urgent issue of maximizing the integration of renewable energy while meeting economy security and reliability requirements needs to be addressed. Based on the theory of admissible region, integrating models of renewable energy, energy storage systems, and demand response, this paper proposes a source-load-storage coordinated optimal scheduling model considering admissible region of net load. The model uses the generation-load transfer factors based on decoupled linearized power flow to construct power flow constraints, enabling coordinated optimization of active power, reactive power, and voltage in the power system. Uncertainty constraints in the model are transformed into deterministic constraints based on affine strategy, and the entire model is transformed into a bilinear programming model using duality theory and power circle linearization, facilitating solution using mature commercial solvers. Finally, the effectiveness and superiority of the model are verified on the IEEE-RTS system.

关键词： Renewable energy sources Analytical models Reactive power Uncertainty Biological system modeling Computational modeling Optimal scheduling

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Forecasting-Aided Voltage Estimation for Distribution Network Considering the Integrated Management of Virtual Power Plants Platform

Forecasting-Aided Voltage Estimation for Distribution Networ...

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IEEE Student Conference on Electric Machines and systems

作者： Yujian Jia Zhao Liu Changtao Kan Yue Yu Changgang Li State Grid Jinan Power Supply Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350366945

ISBN: (纸本)9798350366952

As more distributed energy sources integrate into distribution networks, issues such as voltage fluctuations and reduced observability become increasingly prominent. Therefore, this paper proposes an algorithm for forecasting-aided state estimation (FASE) in virtual power plants' integrated operation management platform. Firstly, a Bidirectional Long Short-Term Memory (BiLSTM)-Extended Kalman Filter (EKF) algorithm for distribution network FASE is introduced. Secondly, the integrated management of virtual power plant platforms (IM-VPPP) enhances the observability of distribution networks. To evaluate the performance of the proposed method, simulations are conducted using the IEEE 33-bus standard distribution system. Results demonstrate significant advantages of the algorithm in terms of accuracy and robustness.

关键词： Voltage measurement Voltage fluctuations Noise Distribution networks Virtual power plants Robustness Kalman filters State estimation Observability Standards

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Visualization of CNN Transient Voltage Classification Based on Feature Recognition and Enhancement

Visualization of CNN Transient Voltage Classification Based ...

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2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021

作者： Li, Zhendong Liu, Yutian An, Jun Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan China Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control Renewable Energy Technology Ministry of Education Jilin China

ISBN: (纸本)9781665414395

The deep learning algorithm represented by CNN has achieved remarkable results in complex feature extraction, classification and regression. Since the CNN adopts an end-to-end training process, the carrier of acquisition knowledge is a high-dimensional non-linear function, which is hard to understand by humans. Therefore, it is difficult to be widely used in online decision-making engineering scenarios such as the fast assessment of power system transient voltage stability. In this paper, the key input elements extracted by CNN are identified by Grad-CAM, and the key features captured by CNN in the assessment process are enhanced based on AM, so as to finally obtain the visual expression of key areas and variables of the original input data. Besides, a sample construction method based on single line diagram is proposed to improve the visualization effect of Grad-CAM and AM. Simulation results and analyses of IEEE 39bus test system demonstrates the effectiveness of the proposed method. © 2021 IEEE.

关键词： Visualization

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Research on Synthetic Data Generation and Multi-modal Target Detection

Research on Synthetic Data Generation and Multi-modal Target...

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Chinese control and decision Conference, CCDC

作者： Jiexi Liu Chenyu Wang Lei Xu Beijing Institute of Technology Beijing China Key Laboratory of Complex System Intelligent Control and Decision Beijing China

ISBN: (纸本)9781665478977

Target Detection is one of the most important tasks in Computer Vision, which has broad application prospects in many scenes. For the past few years, great progress has been made in this field with the rapid development of deep learning technology. However, it’s still a big challenge at complex scenarios such as dim environment for traditional single-modal visible images. To address this problem, researchers introduce thermal images as additional modal in consideration of that thermal cameras are less susceptible to interference and explore how to fuse the two modalities information effectively. Nevertheless, the lack of large labeled and high-quality visible-thermal datasets hampers the usage of convolutional neural networks for detection. Therefore, we propose to use image-to-image translation model combined with a differentiable data augmentation method to generate fake thermal images from labeled visible images and use multi-modal target detection model to prove the validity of the method. Our experiment results show that our method can provide us a large labeled dataset of synthetic visible-thermal image pairs with better generalization and the introduction of thermal modality can obtain a better performance than single modality.

关键词： Deep learning Computer vision Fuses Object detection Interference Detectors Data models

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Low-Carbon Dispatch Strategy for Virtual Power Plant Based on Bagging Ensemble Learning Forecasting Method

Low-Carbon Dispatch Strategy for Virtual Power Plant Based o...

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IEEE Student Conference on Electric Machines and systems

作者： Guang Li Xin Dong Li Li Yue Yu Changgang Li State Grid Jinan Power Supply Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350366945

ISBN: (纸本)9798350366952

Virtual power plants (VPPs) struggle with peak-shaving due to unreliable distributed generation (DG) data and the intermittent nature of wind and solar power, impacting carbon emissions. This paper proposes a low-carbon dispatch strategy for VPPs based on a Bagging ensemble forecasting model. Firstly, a VPP dispatch framework based on DG output and load forecasting is established, and a Bagging ensemble forecasting model with feature selection is proposed. Secondly, the control characteristics of flexible industrial, commercial, and residential loads are analyzed to establish a control cost model, which is then integrated with the balanced relationship between wind, photovoltaic, and energy storage, as well as the carbon emission mechanism, to formulate a low-carbon dispatch model. Finally, the simulation results show that the proposed strategy can improve the accuracy of DG output and load forecasting in complex feature environments while realizing the interaction between source-grid-load and reducing the generation and operation costs of the grid.

关键词： Analytical models Costs Carbon dioxide Predictive models Wind power generation Virtual power plants Forecasting Wind forecasting Bagging Load modeling

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H∞ state estimation for delayed neural networks via variable-augmented-based free-weighting matrices method

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Journal of the Franklin Institute 2025年第7期362卷

作者： Chang, Xu-Kang He, Yong School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

The issue of H∞ state estimation for neural networks with time-varying delays is investigated in this study. Firstly, an augmented Lyapunov-Krasovskii functional (LKF) with two delay-product-type terms is constructed to enhance the consideration of system state, delay, and delay derivative information. Furthermore, for taking into account more effective information, the LKF is augmented with both single and double integral variables. Accordingly, the LKF derivative becomes a higher-order term that exhibits nonlinear characteristics of the time delay. To solve the nonlinear problem, a variable-augmented-based free-weighting-matrices (VAFWMs) approach is employed to transform the nonlinear term into a linear form and provides more freedom in obtaining less-conservative results. Consequently, two improved H∞ state estimation criteria are derived. Lastly, a numerical example is provided to demonstrate the merits and effectiveness of the presented methods. Meanwhile, to prove the practical viability of the presented methods, this study extends the methods to a real-world quadruple-tank process system. © 2025 The Franklin Institute

关键词： Lyapunov functions

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Multi-level Voltage Coordination Optimization Strategy of Distribution Network Considering Multiple Devices

Multi-level Voltage Coordination Optimization Strategy of Di...

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IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Caishuai Liang Mingqiang Wang Fengbin Sun Xia Zhang Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China Luyuan Electric Group Co. LTD Jinan China

ISBN: (数字)9798350373479

ISBN: (纸本)9798350373486

The presence of wind power, photovoltaic, and other distributed energy sources within the distribution network. The power quality of the distribution network degrades as the proportion of distributed energy increases. To address the uncertainty introduced by the integration of distributed energy, it is essential to coordinate and optimize the multi-voltage levels within the distribution network. Currently, most studies focus on achieving voltage control in the distribution network by minimizing either network loss or voltage fluctuation, often limiting consideration to a single voltage stage. Minimizing network loss can enhance the overall operational cost efficiency of the distribution network, but it has a limited impact on suppressing voltage fluctuation. Hence, this paper initially establishes a two-layer voltage control model for the distribution network through priority target programming. The first layer employs the minimum voltage deviation as the objective function to meet the 10-0.38 kV voltage control requirements. The second layer minimizes the total operational cost of the distribution network, incorporating network loss costs as the objective function to meet economic operational requirements. The programming is subsequently implemented in GAMS. Lastly, the effectiveness of the proposed model is validated using the IEEE-33 node system.

关键词： Costs Voltage fluctuations Fluctuations Uncertainty Distribution networks Transforms Programming

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A Fault Diagnosis Method of Actuator via Single Class Probability Density Learning 18th

A Fault Diagnosis Method of Actuator via Single Class Prob...

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18th Chinese intelligent systems Conference, CISC 2022

作者： Wang, Na Li, Yang Peng, Kun School of Control Science and Engineering Tiangong University Tianjin300387 China Tianjin Key Laboratory of Intelligent Control of Electrical Equipment University Tianjin300387 China Key Laboratory of low light level electromechanical system technology of Ministry of education Tianjin University Tianjin300387 China

ISBN: (纸本)9789811962028

A fault diagnosis method via Single Class Probability Density Learning for the actuator is proposed in this paper. Firstly, for the normal current data of the original actuator, the Fuzzy c-Means approach is combined with the matrix transformation. Then the Single Class Learning process is realized. Thus the distribution centers of the normal current data and the Probability Density control Threshold are obtained. On this basis, the fault diagnosis model is established. Secondly, for the actuator data to be tested, the Principal Component Analysis is adopted to reduce their dimension. Then the Affinity Propagation Clustering method is improved and the new centers of clustering are acquired by the test data. Additionally, the original distribution centers of current are compared with the new centers of clustering. So the actuator fault is diagnosed quickly. Finally, by some actuator measured data simulation, the effectivity of the proposed approach is verified in construct with the traditional methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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