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SSRN 2023年

作者： Liu, Yizhuo Lv, You Fang, Fang Qiu, Tian Liu, Jizhen Fang, Xinglong School of Control and Computer Engineering North China Electric Power University Changping District Beijing102206 China Key Laboratory of Power Station Energy Transfer Conversion and System North China Electric Power University Changping District Beijing102206 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Changping District Beijing102206 China

Accurate and timely assessment of the flame combustion process in the furnace of coal-fired power plants is crucial for the secure and economic operation of the units. This paper proposes an unsupervised quantitative assessment method based on flame images for assessing the combustion process of a flame to address the problems that most of the current methods are highly dependent on human experience and difficult to classify the state in detail. Attention mechanism is introduced into the stacked convolutional autoencoder (SCAE) feature extraction network to obtain the latent features of the flame images. Principal component analysis (PCA) is used to reduce the dimensionality of the sparse latent feature matrix for obtaining 1D combustion features. The extracted 1D features are smoothed and normalized, and a combustion state index is obtained to reflect the degree of combustion in the furnace. The variance of the combustion state index within a sliding window is used to determine the fluctuation of the flame state as the combustion stability result. The flame images of a 660 MW coal-fired power plant under different combustion conditions and the variables related to the combustion process in the distributed control system (DCS) are captured to verify the effectiveness of the proposed method. The MSE, PNSR, and SSIM of the flame images reconstructed by the feature extraction network and the original images are 1.082×10-4，39.97dB and 0.9854, with higher accuracy compared to other autoencoder (AE) networks. 12 of the 14 absolute values of the correlation coefficients between the variables related to the combustion process and the extracted combustion state index exceed 0.7. Results suggested that the proposed method can reflect the combustion process in the furnace, which verifies the validity and reliability. © 2023, The Authors. All rights reserved.

关键词： Principal component analysis

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Categorical Spatial Interpolation of Solar Irradiance for Regional Distributed Photovoltaic power Forecasting

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IEEE Transactions on Sustainable Energy 2025年

作者： Ruan, Chenglong Li, Kangping Li, Zhenghui Huang, Chunyi Wang, Fei Shanghai Jiao Tong University College of Smart Energy Shanghai200240 China Shanghai Jiao Tong University Shanghai Non-Carbon Energy Conversion and Utilization Institute Shanghai200240 China Ministry of Education Shanghai Jiao Tong University Key Laboratory of Control of Power Transmission and Conversion Shanghai200240 China North China Electric Power University Department of Electrical Engineering Baoding071003 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China Hebei Key Laboratory of Distributed Energy Storage and Microgrid North China Electric Power University Baoding071003 China

High-spatial-resolution (HSR) solar irradiance forecast data is important for regional distributed photovoltaic (PV) power forecasting. Distributed PV sites are widely geographically distributed and the cost of obtaining HSR irradiance forecast data with full regional coverage is very high. Improving spatial resolution through interpolation based on limited sparse irradiance forecast reference points is a low-cost and feasible approach. However, existing irradiance spatial interpolation methods would produce large errors under variable weather conditions (e.g., cloudy) with limited reference points, because local abrupt changes in irradiance usually occur under such weather conditions due to cloud movement. This paper proposes a categorical spatial interpolation method to improve the accuracy of distributed PV power forecasting with limited sparse irradiance forecast data. The method first converts satellite-derived shortwave radiation images into binary irradiance maps through adaptive thresholding, then trains a 3D U-net model to predict future irradiance category probabilities for each grid. These probability maps dynamically govern two parallel interpolation processes: one using reference stations in predicted clear-sky regions, another utilizing stations in cloudy zones, with final irradiance values determined by probability-weighted fusion. Case studies on a real dataset verify the effectiveness and superiority of the proposed method. © 2025 IEEE.

关键词： Solar irradiance

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Study on multi-timescale cascading failure in multi-infeed HVDC systems 5

Study on multi-timescale cascading failure in multi-infeed H...

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5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Song, Kaige Wang, Xiaohui Xie, Zhenjian Li, Chen Chen, Qichao Wang, Fei Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education School of Electrical Engineering Jinan China Economic and Technological Research Institute State Grid Jiangsu Electric Power Co. Ltd. Nanjing China State Grid Economic and Technological Research Institute CO . Ltd State Grid Corporation of China Bejing China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

With more power electronics deployed in power system, the propagation of cascading failure become more complex and it is necessary to investigate the mechanism of cascading failure in the novel power system. This paper reveals a new cascading failure mode in multi-infeed high voltage direct current (HVDC) systems. Firstly, the mechanism of multi-timescale cascading failure is proposed. Then the simulation results of multi-infeed HVDC systems which is carried out in power system department-Bonneville power administration (PSD-BPA) indicate that the dominant cascading failure modes in different timescales are different and they are coupling with each other. Finally, several dominant factors of different timescale cascading failure modes are pointed out. © 2022 IEEE.

关键词： Failure modes

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A Novel Triple Capacitor Multi-Level Submodule Mmc Topology with Fault Current Clearing Capability

SSRN

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SSRN 2023年

作者： Sun, Yinfeng Jia, Guobin Hao, Jiaqi Lu, Mingzhi Li, Guoqing Wang, Zhenhao Wu, Xueguang Modern Power System Simulation Control and Green Energy New Technol. Key Lab. The Ministry of Education Northeast Electric Power Univ. Jilin132012 China State Grid Smart Grid Research Institute Beijing China

In the high voltage direct current system based on modular multilevel converter (MMC-HVDC), the half-bridge submodule (HBSM) has become the preferred submodule for significant projects due to its convenient control and low cost. However, the DC shortcircuit protection performance of MMC-HVDC will seriously restrict its development in areas such as overhead lines, high voltage, and large capacity. This article proposes a triple capacitor multi-level submodule (TCMSM) based on a modular multilevel converter with DC fault-blocking capability. It utilizes at least one-third of the module capacitance in the bridge arm to reverse, suppress the DC fault current and turn off the diode. At the same time, the unidirectional conductivity of the diode can also prevent arc reignition at the fault point. The article also conducted in-depth research on the DC short-circuit fault elimination mechanism of this topology, and compared its performance with other topologies. Through analysis, it can be concluded that TCMSM reduces the initial investment and operating costs by reducing the number of devices inside the module while possessing the fault current blocking ability. Finally, MATLAB/Simulink was used to simulate and verify the proposed new topology, and its performance was analyzed. © 2023, The Authors. All rights reserved.

关键词： Topology

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Dissipative state Feedback of General Linear Time-Delay systems: Applications of the Kronecker-Seuret Decomposition Concept

SSRN

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SSRN 2024年

作者： Feng, Qian Zheng, Wei Xing Wang, Xiao Yu Xiao, Feng School of Control and Computer Engineering North China Electric Power University Beijing China School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia State Key Laboratory of Alternative Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

Stabilizing autonomous linear time delay systems, particularly when addressing an unlimited number of pointwise and distributed delays (DDs) under dissipative constraints, poses a significant challenge. Existing solutions are often hindered by theoretical limitations, numerical obstacles, or an inability to address the complexities of the delay integral kernels. In this paper, we propose a unified framework to tackle the above problem by employing the concept of the Kronecker-Seuret decomposition (KSD) for matrix-valued functions, which we recently have developed for the analysis of complex delay structures in coordination with the Krasovski\u{\i} functional approach. Our strategy can simultaneously address two distinct control problems, where the matrix kernels of DDs can contain an unlimited number of square-integrable functions. We show in detail how the KSD can factorize and approximate different kernel functions simultaneously without introducing conservatism. Furthermore, the use of KSD also enables us to construct complete-type functionals, whose integral kernels can include any number of weakly differentiable and linearly independent functions, underpinned by the utilization of novel integral inequalities derived from the least-squares principle. The solution to each synthesis problem comprises two theorems accompanied by an iterative algorithm, which can be utilized as a single package to compute controller gains, thus eliminating the need for nonlinear solvers. We present the testing results of two challenging examples, which could not be addressed by existing methods, to demonstrate the effectiveness of our methodology. Additionally, the paper reviews recent advancements in the research of time-delay systems, providing a valuable reference for both emerging and established researchers. © 2024, The Authors. All rights reserved.

关键词： state feedback

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Methodology for Capacity Credit Evaluation of Physical and Virtual Energy Storage in Decarbonized power system

arXiv

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arXiv 2023年

作者： Qi, Ning Li, Peng Cheng, Lin Zhang, Ziyi Huang, Wenrui Yang, Weiwei State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Tsinghua University Beijing100084 China Co. Ltd Tongzhou District Beijing101100 China

Energy storage (ES) and virtual energy storage (VES) are key components to realizing power system decarbonization. Although ES and VES have been proven to deliver various types of grid services, little work has so far provided a systematical framework for quantifying their adequacy contribution and credible capacity value while incorporating human and market behavior. Therefore, this manuscript proposed a novel evaluation framework to evaluate the capacity credit (CC) of ES and VES. To address the system capacity inadequacy and market behavior of storage, a two-stage coordinated dispatch is proposed to achieve the trade-off between day-ahead self-energy management of resources and efficient adjustment to real-time failures. And we further modeled the human behavior with storage operations and incorporate two types of decision-independent uncertainties (DIUs) (operate state and self-consumption) and one type of decision-dependent uncertainty (DDUs) (available capacity) into the proposed dispatch. Furthermore, novel reliability and CC indices (e.g., equivalent physical storage capacity (EPSC)) are introduced to evaluate the practical and theoretical adequacy contribution of ES and VES, as well as the ability to displace generation and physical storage while maintaining equivalent system adequacy. Exhaustive case studies based on the IEEE RTS-79 system and real-world data verify the significant consequence (10%-70% overestimated CC) of overlooking DIUs and DDUs in the previous works, while the proposed method outperforms other and can generate a credible and realistic result. Finally, we investigate key factors affecting the adequacy contribution of ES and VES, and reasonable suggestions are provided for better flexibility utilization of ES and VES in decarbonized power system. © 2023, CC BY.

关键词： Behavioral research

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Evaluating The Aggregated Frequency Regulation Capability of Energy Storage Clusters

Evaluating The Aggregated Frequency Regulation Capability of...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Jiaxiong Xiang Bo Yang Shibo Wang Zhi Zhang Zheyi Pei Kecheng Li Guangqi Zhou Ming Zhou State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China State Grid Shandong Electric Power Research Institute Jinan China State Grid Corporation of China Beijing China China Electric Power Research Institute Beijing China

With the integration of a large number of wind and solar new energy power generation into the power grid, the system faces frequency security issues. Energy storage stations (ESS) can effectively maintain frequency stability due to their ability to quickly adjust power. Due to the differences in the state of each ESS and the topology of the power grid, it is difficult to evaluate the frequency support capability of the energy storage cluster (ESC) in real-time. This paper proposes a real-time evaluation model for the aggregated frequency support capability of ESC. The evaluation indicators for inertia support and primary frequency regulation (PFR) capability is proposed respectively. Considering the aggregation effect of ESC, the evaluation of frequency support ability for ESC is transformed into an optimization problem. This article establishes evaluation models for the inertia support capability and primary frequency regulation capability of ESC, respectively. In the evaluation model, we establish frequency response models for ESS based on virtual inertia and droop control, considering the differences in the states of each ESS and network security constraints. The case study shows that the model can evaluate the frequency support capability of energy storage clusters based on their real-time status. When considering network constraints, the supporting capacity of ESC exhibits spatial distribution characteristics.

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Data Fitting Method of Customer Electric Energy Consumption Based on Orthogonal Polynomial Neural Network

Data Fitting Method of Customer Electric Energy Consumption ...

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China International Electrical and Energy Conference (CIEEC)

作者： Hong Yu Xi Chen Letian Zhang Lin Wei Linlin Yang Zixu Wang 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 State Grid Shandong Electric Power Company Marketing Service Center (Metrology Center) State Grid Shandong Electric Power Company Jinan China

The electric energy consumption data of marketoriented customers must be perfectly metered or accurately fitted, that is essential for the construction of electricity spot market. The electricity consumption behavior of market-oriented customers is complex and variable, to address the issue that the patterns of the electricity consumption data are difficult to be accurately characterized, an orthogonal polynomial neural network-based data fitting model of electric energy consumption is established. The model is implemented using Chebyshev orthogonal polynomials, Hermite orthogonal polynomials, Legendre orthogonal polynomials, and Laguerre orthogonal polynomials respectively, while the neural network weight coefficients are trained by gradient descent algorithm. The results show that the fitting effect of the same type of electricity consumption data differs significantly among different implementation methods. The neural network models using Hermite orthogonal polynomials and Laguerre orthogonal polynomials have higher fitting accuracy than other models. It is an effective way to achieve accurate data fitting of customer electricity consumption by selecting the corresponding fitting method according to the type of electricity consumption behavior.

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Research on Stepwise Clustering and Fitting Method for Wind power Based on Discrete Wavelet Transform

Research on Stepwise Clustering and Fitting Method for Wind ...

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power system and Green Energy Conference (PSGEC)

作者： Haijing Zhang Hong Yu Lin Wei Yuxi Wang Peng Wang Letian Zhang Yang Yang State Grid Shandong Electric Power Company Marketing Service Center (Metrology Center) State Grid Shandong Electric Power Company Jinan China 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

The participation of new energy reseurces in market trading needs accurate metering of new energy power generation. In order to establish a verification method for wind power generation metering data, a step-by-5tep k-means clustering algorithm based on discrete wavelet transform is proposed to explore the variation laws of wind power fluctuation, so that a basis for missing data fitting and abnormal data correction of wind power generation curves can be built. The frequency domain analysis of wind power generation data is carried out by using discrete wavelet transform, and the generation curve is decomposed into different frequency ranges. For the frequency domain results after multiple wavelets transforms, step-by-tep k-means clustering is carried out from the lowest frequency component to the highest frequency component. This processing method can etfetively ensure that the information of stroke fluctuation is not lost in the clustering process. Typical curves extracted based on clustering are used to fit the missing data. The proposed method is simulated by using the wind power generation data of some wind farms in East China, and the effectiveness of the proposed algorithm is verified.

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Risk Assessment of Demand Response based on Distributed Privacy Data Acquisition

Risk Assessment of Demand Response based on Distributed Priv...

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China International Electrical and Energy Conference (CIEEC)

作者： Bo Liang Hanqi Li Guanting Chen Lei Xie Chang Liu Yankun Feng Shengnan Cao Yang Yang State Grid Shandong Electric Power Company Marketing Service Center (Metrology Center) State Grid Shandong Electric Power Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education(Shandong University) Shandong University Jinan China

As demand response(DR) is gradually progressing toward the extensive participation and lower barrier to entry, a trend of diversification has been observed in DR participants, which greatly aggravates the unpredictability of its effect. Risk assessment of DR becomes the necessary link of future organization DR. However, due to the conflict between the centralized risk assessment and the customer privacy, the risk assessment of DR has been limited by insufficient data support. To solve this, a data acquisition and risk assessment method for DR is proposed in the paper. Decentralized access to privacy data is designed, so that customers' appeal for data privacy protection has been met, as well as the risk assessment of DR is supported by more data. The risk assessment method and its implementation by the blockchain are described. The simulation results verify the feasibility and effectiveness of the method.

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