Table of Content

05 March 2021, Volume 54 Issue 3

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Model Construction and Pathways of Low-Carbon Transition of China's Power System

ZHANG Yunzhou, ZHANG Ning, DAI Hongcai, ZHANG Siyu, WU Xiaoyu, XUE Meimei

2021, 54(3):  1-11.  DOI: 10.11930/j.issn.1004-9649.202101058

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The goal of carbon peak by 2030 and carbon neutrality by 2060 proposed by Chinese government has brought higher requirements for carbon emission reduction in energy and electricity. The clean and low-carbon transition pathways for the power system need to be explored urgently. Firstly, based on an investigation of the current status of carbon emissions in the energy and power sector, the paper clarifies the role of energy and power in carbon emissions reduction in China. Secondly, according to the carbon development situation of electric power in the near and mid-term, a quantitative calculation model considering the external cost of carbon emissions is constructed. For the long-term low-carbon development scenarios, the synergistic pathways of electricity-hydrogen and electricity-hydrogen-carbon are innovatively proposed, breaking the boundaries of different energy systems. Furthermore, a whole-chain techno-economic evaluation model is built, which is suitable for the diversified utilization of new energy, and has been applied to evaluation of the economic competitiveness of the end products such as hydrogen and methanol.

Cyber-Physical Risk Evolution Analysis of Active Distribution Network under Feeder Control Error

WENG Jiaming, LIU Dong, AN Yu, YIN Haoyang, HUANG Zhi, QIN Han

2021, 54(3):  13-22.  DOI: 10.11930/j.issn.1004-9649.202009100

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In the active distribution network cyber-physical system exists a strong coupling relationship between the cyber system and the physical grid. Under the complex cyber-physical interaction, the cyber system anomalies or failures will directly affect and reduce the operation level of the power grids, and even cause serious cascading failures. Compared to the traditional power system, the risk incentives of the power cyber-physical system are more diversified, the interaction mechanisms are more complicated, and the identification are more difficult. The power system cyber-physical security risk has become one of the fundamental issues in the power system operation. By taking the active distribution network as an example, we establish a risk transfer model for active distribution networks under cyber-attacks to reveal the evolution mechanism of failures in the distribution network cyber-physical system. Finally, a simulation case study is carried out with DIgSILENT to verify the correctness of the proposed model, and some suggestions are proposed on how to prevent cyber-side risks in the future distribution network and improve the level of security risk protection.

Data Security Risk Recognition Algorithm for Energy Cyber Physics System Based on Function Mining

DENG Song, CAI Qingyuan, GAO Kunlun, ZHANG Jiantang, RAO Wei, ZHU Lipeng

2021, 54(3):  23-30,37.  DOI: 10.11930/j.issn.1004-9649.202007135

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Data security risk assessment is essential for the safe and stable operation of energy cyber physics system (CPS). The existing data security risk analysis from the perspective of secondary equipment and information cannot meet the requirements for extensive energy access as well as energy and information interaction between various energy sources in the energy CPS. Firstly, a feature selection algorithm for data security risk elements based on rough set (FSDSRF-RS) is proposed to select the data security risk feature sets in the energy CPS, consequently reducing the dimensions of the data security risk element sets of the energy CPS. And then, a data security risk recognition algorithm for energy cyber physics system based on hybrid gene expression programming (DSRR-HGEP) is proposed. In the DSRR-HGEP, a niche-based population generation strategy and a dynamic adaptive mutation probability adjustment strategy are designed to improve the accuracy and efficiency of data security risk identification. Simulation and experimental results show that the proposed algorithm in this paper has a high recognition and prediction accuracy for the complex and high-dimensional data security risk sets in the energy CPS, and can provide a theoretical support for formulating data security protection strategies of the energy cyber physical system in the future.

Application of Formal Methods in Power Grid Cyber Physical Systems

HUANG Li, LIANG Yun, HUANG Hui, ZHAO Ruohan

2021, 54(3):  31-37.  DOI: 10.11930/j.issn.1004-9649.202007008

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The embedded terminals in the power grid cyber physical systems need to not only have the ability of information interaction, but also meet the real-time requirements of measurement and control under resource constraints. It is necessary to introduce formal methods to verify the reliability of complex systems with large scale. This paper analyzes the application of formal methods in power grid cyber physical systems, designs and realizes a formal method and model checking software tool for analyzing the information interaction process of embedded system in the power grid cyber physical systems. The application process of the model checking tool is analyzed in detail through a practical case, and the application results show that the formal method can shorten the distance from high level design to code implementation and improve the reliability of the products. The model checking software tool can provide a reference solution to the reliability assurance problem caused by the rapid increase of embedded devices in terms of scale and complexity.

Small Scale Invade-Target Recognition and Location Based on Improved Faster RCNN

MA Jingyi, CUI Haoyang, ZHANG Mingda, SUN Yihui, XU Yongpeng

2021, 54(3):  38-44.  DOI: 10.11930/j.issn.1004-9649.202006190

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In order to realize the recognition and location of dynamic small-scale intrusion targets with the video monitoring system in unattended substations, a fast neural network identification method based on improved Faster RCNN is proposed. In this method, the strong semantic features of the target samples are calculated by constructing the deep convolution network, and the location information is fused using the densely connected transmission channels, so as to obtain the basic backbone network suitable for small target detection. Then, the candidate region of the target is generated with the region proposal network, and the coordinates of the location frame are calculated using the bilinear interpolation method to achieve the accurate positioning at the pixel level. The model is trained based on the actual image sample set, and the improved Faster RCNN detection model is obtained. The experimental results show that the improved method can maintain high accuracy and timeliness in detection of small-scale foreign objects, and has a certain value for engineering application.

Detection Method for Bolts with Mission Pins on Transmission Lines Based on DBSCAN-FPN

ZHAO Zhenbing, ZHANG Shuai, JIANG Wei, WU Peng

2021, 54(3):  45-54.  DOI: 10.11930/j.issn.1004-9649.202005160

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Bolts are the mostly used fasteners on transmission lines, and their defect detection is an important content for transmission line inspection. As the bolt with missing pins are small targets, their positioning is difficult and their features are hard to extract. Aim at this problem, a detection method for bolts with missing pins is proposed based on the DBSCAN algorithm and FPN model. Firstly, the FPN model is used to locate the target area of the bolts with missing pins, and the areas with same morphological structure are clustered based on the DBSCAN clustering algorithm. Then, the FPN model is improved: based on the prior knowledge of bolts, the convolutional network is used to achieve bottom-up feature extraction, the bilinear interpolation method is used to transfer the high-level semantic information of the features to each level from top to bottom, the convolution filtering method is used to laterally strengthen the information fusion of high-level semantic features and high-resolution features, and a more optimized feature expression of bolts with missing pins is obtained. The improved FPN model is used to realize the preliminary detection of the bolts with missing pins. Finally, the DBSCAN clustering algorithm is adopted to screen the preliminary detection results for error detection, thereby achieving the accurate detection of the bolts with missing pins. Experimental results show that the detection accuracy of DBSCAN-FPN reaches up to 76.23% on the self-built data set, with the detection effect better than FPN, R-FCN and Faster R-CNN. The proposed method can effectively improve the detection accuracy of bolts with missing pins, which has practical significance for the operation and maintenance of transmission lines.

Intelligent Storage and Retrieval of Power Accessories Based on Deep Learning and Image Recognition

ZHAO Yongliang, FU Xin, GUO Yang, BIAN Yingying, WANG Sining

2021, 54(3):  55-60.  DOI: 10.11930/j.issn.1004-9649.202006148

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The power accessories have various types and models, and the storage and retrieval management with RFID technology cannot cover them all, which often leads to the inaccuracy and low efficiency of power accessories storage and retrieval, and the management quality not to meet the production requirements. In view of these problems, we carry out a research on intelligent recognition of power accessories based on machine learning and image recognition to correct the deficiency of RFID technology for storage and retrieval management of power accessories. Firstly, the gray-scale processing and binarization methods are used to process the original images, and the minimum circumscribed rectangle is used to correct the original images. Secondly, a deep learning model suitable for identifying power accessories is constructed using CNN and CRNN deep neural networks with combination of CTC loss function, and the suspected accessories are recommended synchronously according to the image recognition coincidence. The images of power accessories are acquired by intelligent equipment, and their name and model are identified in real time using the proposed methods, with prompt of their overall dimension, application scope and product use. The experimental results show that the accuracy of the intelligent recognition of power accessories based on machine learning and image recognition reaches 95%, which significantly improves the intelligent level of warehousing management.

Data Middle Platform Construction and Application of Intelligent Power Stations

WANG Yi, WANG Zhiwei, HE Xin

2021, 54(3):  61-67,176.  DOI: 10.11930/j.issn.1004-9649.202006220

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In order to solve such problems as data island and low data utilization rate of existing systems for intelligent power station construction, a data middle platform is designed. Based on the key technologies of data collection, data lake, data extraction and analysis, data service delivery and data visualization, a data middle platform of intelligent power station is established, which realizes the ability of multi-source heterogeneous data fusion as well as sharing and service, and is of great significance to improving the operation and management level of power stations. Finally, the paper introduces the typical application scenarios of intelligent power station data middle platform, which has a certain reference values for construction and application of the data middle platform.

Evolution Strategy of 4G Wireless Private Network to 5G in Power CPS Environment

ZHAO Hongda, CHEN Chen, WANG Zhe, WANG Haiyong

2021, 54(3):  68-79.  DOI: 10.11930/j.issn.1004-9649.202005018

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To build an electric power wireless private network with high-efficiency connection, large-capacity bandwidth and low-latency communication is an important guarantee for the reliable operation of power cyber physical system (CPS). It is therefore necessary to study the electric power wireless private network in the environment of electric power CPS. Firstly, we analyze the demands of power business for communication network under the power CPS environment, and studies the communication network architecture of 5G communication technology combined with the characteristics of power business. Secondly, based on the demand of electric power business and the characteristics of 5G communication technology, the evolution goal of 4G wireless private network to 5G is proposed, and a step-by-step evolution path is constructed. Thirdly, the evolution strategy for 4G wireless private network terminal, base station, backhaul network and core network is proposed respectively. Finally, the evolution strategy of power wireless private network is forecasted.

A RF Fingerprint Based EIoT Device Identification Method

LIU Ming, LIU Nian, HAN Xiaoyi, PENG Linning, FU Hua, CHEN Yicong

2021, 54(3):  80-88.  DOI: 10.11930/j.issn.1004-9649.202005053

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Due to the openness nature of the communication medium, the wireless communication of Electric Internet of Things (EIoT) devices faces severe security risks. It is proposed in this paper to adopt the natural radio frequency fingerprint (RFF) of wireless devices as the basis for IoT device identification, and use the deep learning techniques to achieve bi-directional device identification, so as to enhance the communication security at the wireless access stage. Based on the technical thinking of edge computing and the bi-directional channel reciprocity of communication, the proposed method offloads the learning task of IoT terminals to the base station so that the proposed RFF-based device identification method can meet the demands of computation, storage, and power consumption of the IoT scenarios. Experimental results show that the proposed method can achieve good device identification performance.

5G-V2X-Based Vehicle Charging Station Planning Method Considering Different Land-Use Types

XIONG Ke, WU Siyu, ZHENG Haina, WANG Rui, ZHANG Yu

2021, 54(3):  89-98.  DOI: 10.11930/j.issn.1004-9649.202005060

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With the development of the 5G technology, much progress has been made in vehicle to everything (V2X) and the Internet of Vehicle. Mobile users could quickly obtain relevant physical facility information, and facility operators could better allocate scheduling resources based on the road network information. In the city planning, different area has different functions. From the view of electrical vehicle, two area will own the same characteristic in the arriving distribution if they belong to the same function. In addition, the grid capacity, land restriction, cost and price will have similar patterns. Based on the area characteristics and the V2X assistance, the station network model was proposed in this paper. Concerning about the aim of commercial charging network, we optimize the charging station network within a giving area with the objective of retained profits and the constraints of grid capacity and land. A charging network strategy which is on the basis of station parameter and parking distribution is devised. The strategy includes two parts, the first part calculates the optimal charger number in the given station; the second part removes and aggregates some candidate station to optimal the charging network station. The M/M/S/K queue model and user charging intention curve are introduced to picture the user behavior in charging station. The impacts of critical parameters of charging network on the system performance is provided.

5G-Enabled Electricity Internet of Things: the Network Architecture and Key Technologies

XIONG Ke, ZHANG Ruichen, WANG Rui, ZHONG Guidong, ZHANG Yu

2021, 54(3):  99-108.  DOI: 10.11930/j.issn.1004-9649.202006230

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As a combination of the Internet of Things and the smart grid, the Electricity Internet of Things (EIoT) could effectively integrate various communication resources and power resources to improve the informatization level of the power grid and the utilization of existing equipment. The EIoT would be a typical example of the Smart Energy Internet, which enjoys the features of comprehensive perception, efficient response, and flexible processing. The fifth-generation mobile communication network (5G) has the characteristics of "high rate, high capacity, high reliability, low latency and low energy consumption". The key technologies of 5G can satisfy the power grid requirements of "mass equipment access, mass power data transmission, power grid reliability, flexible cooperative response, and long equipment life". In this work, the basic concept of EIoT is presented. Then, the overall structure of EIoT based on 5G is constructed. Next, the 5G applications and its key technologies in EIoT are summarized. Finally, future research directions on 5G technology and EIoT are discussed.

A Method for Assessing Harmonic Emission Level of Photovoltaic Access Point Considering System-Side Harmonic Voltage Fluctuations

ZHANG Xu, XU Yonghai, LIU Ziteng, JIANG Haiwei, TAO Shun

2021, 54(3):  109-116.  DOI: 10.11930/j.issn.1004-9649.202003086

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With the extensive application of photovoltaic inverter, the harmonic generated during its operation overlaps with the background harmonic of the system at the access point, which causes harmonic amplification and even resonance problems. It is therefore of great significance for the prevention of harmonic problems to evaluate the harmonic voltage emission level of the access point. Different from the traditional non-linear load, the photovoltaic inverter is connected to the power grid after being filtered by filter, which may cause the PV-side harmonic impedance at the point of common coupling (PCC) not to meet the condition of the traditional power grid that requires the PV-side harmonic impedance to be much greater than the system-side harmonic impedance. At the same time, the continuous access of power electronic equipment leads to a large number of harmonics in the system, and the fluctuation of harmonic voltage increases gradually. Therefore, according to the parameters of photovoltaic inverters and filters and the harmonic impedances of the lines, the photovoltaic-side harmonic impedance is estimated firstly. Secondly, the harmonic voltage and harmonic current data of the PCC are classified according to the logistic regression, and the data sets with basically identical background harmonic voltages are selected. And then the system-side harmonic impedance is estimated using partial least square method, so as to reduce the influence of system-side harmonic voltage fluctuation. Lastly, based on the estimation of photovoltaic-side and system-side harmonic impedance, the harmonic voltage emission level is evaluated. Compared with other methods, the proposed method is proved to be superior and accurate.

Transferred Ground Potential Characteristics of Independent Ground Networks Caused by One Artificially Triggered Lightning

CAO Xuefen, CHEN Shaodong, LIANG Hongyu, GAO Wenjun, LI Hejie, CAI Zhanwen

2021, 54(3):  117-124.  DOI: 10.11930/j.issn.1004-9649.202007068

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Based on 10 return strokes of one artificially triggered lightning T179, the interaction of two independent ground networks and the characteristics of transferred ground potential(TGP) under the action of lightning current are analyzed, and the energy of SPD impacted by TGP is calculated and evaluated through the impulse low-voltage power surge protector (SPD). The results show that the generated peak TGP voltage at the ground network 40 m away is relatively large, reaching –10.9 kV on average. There is a very good positive correlation between the peak TGP voltage and the peak triggered lightning current with the correlation coefficient R² reaching 0.97. The energy of impacting SPD is relatively small, with the average value of 10 return strokes being 1.5 J. It is therefore very safe to install SPD surge protector for electronic equipment.

Galloping Equation and Primary Resonance Investigation of Overhead Transmission Lines

MIN Guangyun, LIU Xiaohui, SUN Ceshi, CAI Mengqi

2021, 54(3):  125-131.  DOI: 10.11930/j.issn.1004-9649.202005106

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The galloping of overhead transmission lines is one of the main causes for line damages. How to accurately describe the galloping of transmission lines is a worthy topic. Firstly, a partial differential equation of a transmission line is derived with Hamiltonian variational principle. And then the equation is nondimensionalized, and the modal function and linear frequencies of the transmission line are calculated under in-plane symmetrical mode and anti-symmetric mode. The partial differential equation is transformed into ordinary differential equation with Galerkin method. Finally, the influence of Irvine parameters on amplitude-frequency response is analyzed with the method of multiple scales. From the curves of the amplitude-frequency response, it is found that the larger the Irvine parameters are, the stronger the nonlinear effects are and the more remarkable the jump phenomenon is. When the primary resonance occurs, the amplitude of galloping is mainly determined by the first-order modal function, and the amplitude caused by the higher-order modal function is much smaller than that caused by the first-order modal function.

A Short-Term Load Interval Forecasting Method Based on EEMD-SE and PSO-KELM

ZHANG Lin, LIU Jichun

2021, 54(3):  132-140.  DOI: 10.11930/j.issn.1004-9649.202001103

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Accurate load forecasting plays an important role in power system. In recent years, a large number of load forecasting studies show that compared with point forecasting, interval forecasting of load can effectively ensure the safe operation of power system. This paper presents a short-term load interval forecasting method based on EEMD-SE and PSO-KELM. Firstly, the ensemble empirical mode decomposition (EEMD) is used to decompose the original load series into a series of subseries. Then, the sample entropy (SE) is used to calculate and quantify the complexity of the series, and the series with small SE values are reconstructed. Finally, the particle swarm optimization (PSO) is used to optimize the weight of output layer of kernel extreme learning machine (KELM), and a prediction model is established to reconstruct the interval of each subseries. The proposed model was tested with the actual load data of a city in South China in different seasons under different nominal confidence, and the simulation results show that compared with other prediction methods, the proposed method has better performance in interval reliability and width.

Short-Term Power Load Forecasting Method based on Glowworm Swarm Optimization Algorithm

FAN Haihong

2021, 54(3):  141-148.  DOI: 10.11930/j.issn.1004-9649.202011132

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With the rapid development of the power industry, the power load prediction is becoming more and more important in recent years, and short-term load prediction plays an extremely important role in dispatching and market operation of the power system. Power load prediction can effectively improve the utilization of power generation equipment. The selective ensemble learning method based on Kappa statistic and the glowworm swarm optimization algorithm (GSO) to forecast short-term load is proposed. This proposed method firstly generates multiple learners by bootstrap sampling, and then use glowworm swarm optimization algorithm to select some learners with large differences and high accuracy to participate in the integration. Compared with a single learner, the accuracy of the proposed method is significantly improved. The daily average load curves of two enterprises in Wuhan from 2015 to 2016 are used as a case study to carry out load forecasting. Comparing with other forecasting methods, the prediction accuracy of the proposed method is proved to be higher.

Analysis and Improvement of Active Arc Suppression Algorithm Considering Effect of Distribution Line Parameters

ZHU Zhanchun, WEN Shengxue, TANG Jinrui, RAN Ronghua, YUAN Chengqing, YANG Hualin, MOU Hua, DENG Congzhong

2021, 54(3):  149-158.  DOI: 10.11930/j.issn.1004-9649.201912001

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Considering the influence of self impedance and phase-to-phase coupling of the distribution feeders, an analysis is made on the full-compensation zero-residual-current arc-suppression algorithms for the single-phase grounding faults of active inverters, and some ideas are proposed for their improvement. An equivalent injection current formula is derived for the active inverter based on full-compensation fault arc-suppression, which shows that the active arc-suppression injection current is determined by the faulted phase voltage and the zero-sequence input impedance at the fault position. Based on the analysis of the active-current and active-voltage arc-suppression algorithms, it is proposed to optimize active-current arc-suppression algorithm by correcting current flow through the arc-suppression coil, measuring the fault resistance and locating the fault section. The active voltage arc-suppression algorithm can be optimized by such methods as monitoring the load current and locating the single-phase grounding fault position. The results of this study can provide an important theoretical basis for improving the active arc suppression algorithms for single-phase grounding faults in the distribution networks.

Black-Start Scheme Evaluation Based on Improved ELECTRE Method and Comprehensive Weight

XIANG Yinxing, LIN Jikeng

2021, 54(3):  159-167.  DOI: 10.11930/j.issn.1004-9649.201907094

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The scientificity and rationality of a black-start scheme directly affects the rapidity and effectiveness of system recovery. Therefore, it is particularly important to select an optimal black-start scheme from multiple feasible restoration options through evaluation. An evaluation method is proposed based on combination of comprehensive weight method and improved ELECTRE method, and applied to evaluation of black-start schemes. Firstly, the comprehensive weight is obtained by combining the objective weight and expert weight according to a certain proportion, of which the objective weight is obtained by traditional entropy weight method, and the expert weight of evaluation indexes is obtained by ratio scale method. Then, the qualitative index of the black-start evaluation scheme is described using triangular fuzzy number and 1-9 scale method, and then it is combined with the ELECTRE method, which overcomes the drawback that ELECTRE method cannot adapt to the evaluation of qualitative indexes and is solely dependent on the expert weight. The proposed method in this paper realizes the reasonable combination of subjective weight and objective weight, and improves the ELECTRE method, which makes the evaluation of black-start scheme more convenient and the obtained results more reasonable. Finally, a case study has verified the validity and correctness of the method.

Research and Implementation of In-Depth Frequency Regulation Technology with Heater Removal

CHEN Huanle, GUI Yishu, CHEN Wei, WANG Yang, LU Honglin, MING Lei

2021, 54(3):  168-176.  DOI: 10.11930/j.issn.1004-9649.202009007

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Poor frequency regulation performance has been linked to the limited heat storage capacity of ultra-supercritical boilers under in-depth frequency regulation working conditions in the event of low frequency failure of power grid. To address this issue, the authors proposed an in-depth frequency regulation technology in which the heat storage of regenerative system based on heater removals are fully utilized to participate in grid frequency control. In addition, the field testing was carried out on a typical 660 MW ultra-supercritical unit and the responding characteristics with respect to different heater removals as well as their impacts on the safe and stable operation of the unit were obtained. Accordingly, this paper designed the heater-removal-based frequency modulation strategies and then conducted tests for verification. The test results show that under the premise of ensuring the safe and stable operation of the unit, the proposed technology can effectively improve the unit performance of response to frequency modulation. Therefore, it can be implemented as a new manner to enhance the primary frequency regulation capability of thermal power units operated under large frequency difference conditions in the power grid.

Effect of Inlet Air Heating on the Performance of Gas-Steam Combined Cycle Power Plant Unit

ZHANG Tao, LIU Zhitan, GAN Xue, FU Zhongguang, YAN Zhiyuan, ZHU Hongfei, QIU Zhenbo

2021, 54(3):  177-184.  DOI: 10.11930/j.issn.1004-9649.201911021

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Taking a 200 MW-class gas-steam combined cycle power plant unit(GSCC) as the research object, the exhaust heat in the tail flue of the HRSG was utilized to heat the feed water, which was directed afterwards into the gas-water heat exchanger to raise the inlet air temperature of the compressor. The parameter changes before and after the operation of the inlet air heating system were simulated by Aspen Plus and furthermore, the influence of inlet air heating on the performance of the GSCC at partial load was analyzed. Results show that when the inlet temperature of the compressor rise from 12.5 ℃ to 35 ℃ under the load of 50%, 75% and 87% of GSCC, the load rate of the gas turbine increases by 0.08, 0.12 and 0.15 respectively, while the fuel flow rate drops down by 0.11 kg/s, 0.13 kg/s and 0.10 kg/s respectively, and the combined cycle efficiency goes up by 1.04%, 1.03%, and 0.73% respectively. Under 95% load, the load rate of the gas turbine increases from 0.95 to the maximum 1.00, and the combined cycle efficiency increases first before taking a dip. Under 100% load, the load rate of the gas turbine remains unchanged at 1.00, while the heat efficiency of GSCC decreases continually.

Experimental Research on Vibration Control of Ultra Low Nitrogen Gas Boilers

JI Haimin, LI Wenfeng, YANG Dong, ZHANG Jihao, DONG Fangqi, ZHOU Qulan, ZHANG Yifan

2021, 54(3):  185-190.  DOI: 10.11930/j.issn.1004-9649.202003166

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In recent years, the state and local administration of China have issued related policies successively in which it is required for gas boilers to achieve ultra-low nitrogen emissions. At present, the technical route of low-nitrogen gas burner coupled with flue gas recirculation system is chosen by most of the retrofits of high-power gas-fired boilers. However, boiler vibration generally occurs after the retrofit. On the basis of two types of high-power gas-fired boiler low-nitrogen retrofit projects, the effects of three major factors, i.e., the fuel ratio of the low-nitrogen gas burner, the oxygen content of the combustion air, and the combustion flame length on the vibration of the boiler were studied respectively. And then, the technical retrofit principles and specific design requirements applicable for this type of boiler combustion system are proposed, which will provide strong technical support for the follow-up retrofit tasks.

Analysis on the Optimization of SCR Denitrification System Based on Life Cycle Cost

YAN Min, ZHANG Yang, GUO Bowen, ZHU Yue

2021, 54(3):  191-196.  DOI: 10.11930/j.issn.1004-9649.201907031

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Under the circumstances of tough environmental protection, high investment and operation cost of environmental protection facilities and intense competition in the electricity market, the transition of SCR denitrification device in domestic thermal power plant from extensive operation to refined operation is urgently needed. In this article, the life cycle cost is introduced into SCR denitrification system. Then the life cycle cost model of SCR denitrification system is established based on the characteristics of system investment and operation. Through the analysis of the major factors affecting each individual cost component, the approaches and measures of controlling and optimizing the life cycle cost are explored. The results can assist electric companies in sustainable development and rational utilization of SCR denitrification system.

Research on Data-Driven Optimal Operation of Slurry Circulation Pump

XU Zunyi, LIU Wenhui, ZHANG Xuran, ZHANG Haiyan

2021, 54(3):  197-204.  DOI: 10.11930/j.issn.1004-9649.202002141

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As the main power consumption equipment in the limestone-gypsum wet flue gas desulfurization system of thermal power plants, optimizing the operation of the slurry circulation pump is of great practical significance to reduce power consumption and improve economic efficiency of the power plant. In this paper, according to the current operation status of slurry circulation pumps in thermal power plants, an operation optimization strategy of slurry circulation pumps based on historical operating data is proposed incorporated with the combination of clustering and classification method. Specifically, the fuzzy C-means clustering algorithm and the combinatory evaluation method based on the weighted fusion of analytic hierarchy process and entropy weight method are applied to automatically search the optimal historical operation of the slurry circulation pump under different operating conditions, such that the SVM classifier is trained with the best historical operation record. Then by using the genetic algorithm for parameter optimization and the real-time operation data as input, to implement the recommended operations of the slurry circulation pump, the optimization of its operation can be achieved. The simulation experiments based on the onsite measurement data from a power plant show that by using this slurry circulation pump operation optimization method, the power consumption of the slurry circulation pump can be reduced by about 21.55%, which can provide the kind of valuable reference for energy saving and emission reduction in thermal power plants.