Table of Content

05 June 2020, Volume 53 Issue 6

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Research and Practice on the Upgrading for Diamond Distribution Network

RUAN Qiantu, XIE Wei, ZHANG Zheng, ZHU Ruijin, SHI Fangdi, LI Yinong

2020, 53(6):  1-7,63.  DOI: 10.11930/j.issn.1004-9649.202004073

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Urban power distribution networks in some cities currently have the problems such as the low load transfer capability and balance ability between stations, and the low line utilization for direct transmission between substations and users. Based on an investigation of the advantages and disadvantages of medium-voltage distribution networks both at home and abroad, this paper puts forward some ideas for development of diamond distribution network. The structure and characteristics of diamond distribution network are described, and the main technical requirements of primary grid and self-healing system for diamond distribution network are introduced. Through comparison of typical grid structures both at home and abroad in terms of adaptability, safety, flexibility, reliability, and economy, the technical and economic characteristics of the diamond distribution network are analyzed in detail, which provides a solution for the high-quality development of distribution network. Finally, a case study is conducted on an area of Shanghai, in which the evolution process from the current grid to the diamond distribution network is analyzed, and its technical and economic characteristics are verified.

Intelligent Dispatch Decision-Making for UHVDC Blocking Fault Based on Deep Learning

YANG Xiaonan, SUN Bo, LANG Yansheng

2020, 53(6):  8-17.  DOI: 10.11930/j.issn.1004-9649.201910138

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For disposal of the UHVDC blocking faults, this paper proposes a deep-learning-based fault feature modeling method and a post-fault grid dispatching strategy generation method. The proposed intelligent dispatch decision-making is based on the DC fault characteristics and operating environment information of power systems, and the post-fault dispatching strategy is generated through training with the big data driven model. Firstly, based on the fault environment information, the effective fault information is extracted to construct the fault feature model. And then, the principle of deep-learning neural network and the multi-layer perceptron model are introduced, and the idea is proposed to use deep network to extract the running characteristics before and after the training fault and automatically generate the dispatching strategy. Thirdly, the back-propagation algorithm is used to construct the deep learning framework, and the effective fault-disposal strategy is automatically generated by continuously calculating the loss function and the accuracy correction training model. Finally, the effectiveness of the proposed method is verified using the related power system of the Jinsu UHV DC transmission line.

Abnormal Electricity Consumption Behaviors Detection Based on Improved Deep Auto-Encoder

LIN Nvgui, HONG Lanxiu, HUANG Daoshan, YI Yang, LIU Zhixuan, XU Qifeng

2020, 53(6):  18-26.  DOI: 10.11930/j.issn.1004-9649.201910005

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In order to accurately detect the abnormal electricity consumption behaviors for reducing the operating costs of power companies, a detection method of abnormal electricity consumption behaviors is proposed based on the improved deep auto-encoder (DAE). Firstly, the data of normal electricity users are employed as training samples, and the effective features of the data are automatically extracted by AE; and then the data is reconstructed to calculate the detection threshold. Because the effective data characteristics are destroyed by the abnormal behaviors, the abnormal behaviors can be detected through comparing the difference between the reconstruction error and the detection threshold. To improve the feature extraction ability and the robustness of AE network, the sparse restrictions and the noise coding are introduced into the auto-encoder, and the hyper-parameters of AE network are optimized through the particle swarm optimization algorithm to improve the learning efficiency and generalization ability. The proposed model is validated by the electricity consumption dataset of domestic and business users of a region in Fujian province, and the abnormal detection accuracy is higher than 92%, which indicates that the proposed method has a powerful ability in feature extraction and abnormal behavior detection.

Foreign Body Detection Method for Transmission Equipment Based on Edge Computing and Deep Learning

LU Yanqiao, SUN Cuiying, CAO Hongwei, YAN Hongwei

2020, 53(6):  27-33.  DOI: 10.11930/j.issn.1004-9649.201910011

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Various foreign bodies, such as bird's nests and plastic bags, often appear on transmission equipment. Failure to detect and clean them up in time will cause great potential safety hazards to the transmission system. Therefore, it is necessary to timely detect the presence of foreign bodies on transmission equipment. To solve this problem, a foreign body detection method is proposed based on edge computing and deep learning. Different from the existing method that sends UAV pictures back to the cloud server for processing, this method, by sinking the detection calculation to the edge device, uses the target detection method of Mobilenet and optimized SSD to directly make process calculation in the edge device, and sends the pictures of detected foreign bodies back to the cloud server. The proposed method is about 5 times faster than the VGG-based SSD method and 58 times faster than the Faster-RCNN method in CPU running speed, and 2/9 times of the VGG-based SSD method and 2/29 times of the Faster-RCNN method in model size, with an accuracy of 89%. Compared with the method that sends original data back to the cloud server for processing, the proposed method can reduce the data transmission amount by about 90%. It is concluded that the proposed method can reliably detect foreign bodies on transmission equipment in real time. The detecting system based on this method has been deployed in practice.

Power Communication Network Recovery from Large-Scale Failures Based on Reinforcement Learning

JIA Huibin, GAI Yonghe, LI Baogang, ZHENG Hongda

2020, 53(6):  34-40.  DOI: 10.11930/j.issn.1004-9649.201907078

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Natural disasters or malicious attacks may cause large-scale failures of power communication networks in smart grid systems, which will impose big risks on the security and stability of the power system operation unless the communication network is recovered immediately. In order to solve the recovery problem of power communication network after large-scale failure, under the constraints of limited link recovery resources, a link recovery model for large-scale failures in power communication networks is established with the objective to maximize the recovery amount of failed services. Regarding this model, a heuristic algorithm based on reinforcement learning is proposed, in which the link recovery resources and the degree of importance of the damaged link in the failed service are taken into account to set the reward and penalty functions as well as the selection rules. Then the optimal link recovery combination is obtained through the accumulation of the maximum reward values. The simulation results show that the power communication network failure recovery algorithm proposed in this paper can restore quite considerable failed services quickly with limited resources.

Photovoltaic Inverter Fault Prediction Technology Based on t-SNE Manifold Learning and Fast Clustering Algorithm

ZHANG Xiaochen, ZHU Jinda, YANG Dongmei, CHEN Yonghua, DU Wei

2020, 53(6):  41-47.  DOI: 10.11930/j.issn.1004-9649.201902032

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As the key component of solar photovoltaic power generation system, photovoltaic inverter directly affects the safety and stable operation of power systems. Therefore, a photovoltaic inverter fault prediction technology is proposed based on t-SNE manifold learning and fast clustering algorithm. Firstly, the historical monitoring data of the PV inverter cluster are used to construct the original feature database. Secondly, the t-SNE dimensionality reduction algorithm is applied to extract the main feature matrix of the PV inverter cluster. Thirdly, the cluster center PV inverter at each sampling time is searched by the fast clustering algorithm, and the eccentricity distance of each inverter at the sampling time is calculated respectively. Then, the normalized accumulative eccentricity distance matrix is obtained. By setting a rational warning threshold, the accurate fault prediction is thus realized for photovoltaic inverters. Finally, the algorithm is tested with the collected PV inverter cluster data acquired by the distributed photovoltaic generation monitoring system. The results show that the proposed fault prediction technology can accurately predict the photovoltaic inverter faults in advance, which is helpful to ensure the healthy and stable operation of the equipment.

Short-Term Load Forecasting Based on Complementary Ensemble Empirical Mode Decomposition and Long Short-Term Memory

ZHAO Huiru, ZHAO Yihang, GUO Sen

2020, 53(6):  48-55.  DOI: 10.11930/j.issn.1004-9649.201910012

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With the continuous development of power industry, the importance of load forecasting is becoming more and more obvious. As an important part of load forecasting, short-term load forecasting is of great significance to the dispatching and operation of power system and market transactions. Accurate load forecasting is helpful to improve the utilization rate of power generation equipment and the effectiveness of economic dispatching. Because load data are affected by many random factors and have strong nonlinear characteristics, a short-term power load forecasting method is proposed based on complementary ensemble empirical mode decomposition and long short-term memory. A simulation is made of a city’s power load data using the proposed method, and the simulation results are compared with those of other traditional forecasting methods. It is proved that the long short-term memory model has lower error and higher prediction accuracy. At the same time, the prediction results of complementary ensemble empirical mode decomposition and long short-term memory are compared with those of long short-term memory model under other decomposition methods, which has verified that the complementary ensemble empirical mode decomposition method is effective in improving the prediction accuracy.

An Algorithm for Analyzing Typical Transmission Line Icing and Wind Disasters Based on Integration of Fuzzy Comprehensive Evaluation and Support Vector Machine

GU Kaikai, CHEN Kai, GU Ran, PENG Zhonghan, WU Qirui, SONG You

2020, 53(6):  56-63.  DOI: 10.11930/j.issn.1004-9649.201911078

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At present, researches on transmission line faults caused by the coupling effect of icing and wind are few, an innovative algorithm is therefore proposed for analyzing the icing-wind disasters of transmission lines based on integrated fuzzy comprehensive evaluation (FCE) and support vector machine (SVM) method. Firstly, by analyzing the influencing factors and their types of typical ice-wind disasters, the key influencing indicators are extracted using FCE method, and the key indicators of wind speed and direction are corrected. And then, based on extraction of five indicators that are highly correlative to disasters, including temperature, relative humidity, wind speed, wind direction and landforms, a nonlinear SVM model with RBF kernel function is proposed for disaster analysis of small samples. Finally, the training samples and test samples are established from historical icing-wind caused faults and non icing-wind caused fault data. Simulation results show that the ice-wind disaster model established by integrated FCE and SVM can effectively judge the probability of ice-wind disaster, and realize the reliable ice-wind disaster analysis with small samples of data.

The Architecture and Key Technologies of Fault Inversion System for Hybrid UHV AC/DC Power Grid

WANG Bing, JIA Yupei, YAN Jianfeng, JIN Yiding

2020, 53(6):  64-71.  DOI: 10.11930/j.issn.1004-9649.201911114

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The security and stability problems of power grid have become increasingly prominent for the complexity and fragility of large-scale hybrid AC/DC. In order to quickly and accurately simulate the fault characteristics of power grid, a fault inversion method is proposed for the hybrid UHV AC/DC power grid. A complete and accurate fault inversion model is generated by integrating the mode offline model of Power System Data Base (PSDB) with the real-time online model of Smart Grid Dispatching and Control System, which can not only solve the problem of missing transient parameters of the real-time online model, but also overcome the difficulty that the mode offline model cannot reflect the power flow of power grid in real-time. Besides, this model shortens fault inversion time from 2 to 3 weeks to a few seconds, as a result, greatly improving the working efficiency of the computation staff and meeting the requirements of timeliness and accuracy of fault inversion calculation. The model is used to simulate the power grid, and the results shows that it can accurately simulate the changes of electricity volume in the process of power grid fault. The system has been installed successfully and put into use in State Grid Simulation Center. The fault inversion analysis is carried out for State Grid Dispatch Center and Central China power grid, and the results have verified the effectiveness of the proposed scheme.

A New Method for Predicting the Monthly Fault Number of Watt-hour Meters Based on Time Series

LI Yuan, ZHENG Angang, TAN Huang, CHEN Hao, CHENG Shuya, CAI Hui, WANG Lixin

2020, 53(6):  72-80.  DOI: 10.11930/j.issn.1004-9649.201910016

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The existing watt-hour meter fault prediction models in the State Grid information system are relatively simple and insufficient, and there is no specific model for predicting the monthly fault number of watt-hour meters. Based on time series, an integrated time series prediction model is established for an accurate prediction of the monthly fault number of batch watt-hour meters. Firstly, the moving average sequence is calculated for the monthly fault number of watt-hour meters to remove small fluctuations. And then, the ARIMA model or exponential smoothing model is selected to predict the moving average sequence according to the long-term trend of the sequence. Finally, the reverse moving average is used to realize the accurate short-term prediction of the monthly fault number of the whole batch of watt-hour meters. By comparison with the BP neural network model, the practicability and accuracy of the proposed time series model is verified. On this basis, a monthly fault prediction model is established. The measurement asset management departments can use the proposed method to predict the number of faulted watt-hour meters, and prepare the stock according to the prediction results, consequently improving the rationality of resource allocation and work efficiency.

The Online Connection of MMC to DC-grid Based on DC Breaker

YUAN Bin, MEI Nian, YUE Bo, LI Tan, WEI Zheng

2020, 53(6):  81-86,96.  DOI: 10.11930/j.issn.1004-9649.201909066

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The fast and reliable online connection of modular multilevel converter (MMC) to DC grid is one of the problems needing to be solved urgently in practice. Since the islanded converter station cannot be started up by AC grid, it is necessary to start up the converter by the DC grid and realize its connection to the grid. In order to limit the charging current at the DC side, the DC breaker can be used to pre-charge the converter. The feasibility is studied to use step by step switching on of DC breaker to realize the online connection of islanded converter station. An innovative calculation method is also proposed to calculate the current stress of DC breaker and the MOV energy in the process of step by step switching on, and is applied to the calculation of the electrical stress of DC breaker in Zhangbei DC project. The Zhangbei flexible DC grid project is modelled and simulated based on PSCAD/EMTDC, and the results validate the applicability in DC grid of the proposed online DC grid connection method and the correctness of the proposed calculation method.

Wide-Area Distributed Photovoltaic Power Generation Monitoring and Output Estimation

PEI Zheyi, LIANG Zhifeng, LU Weihua, LI Xintong, WANG Zhenhao

2020, 53(6):  87-96.  DOI: 10.11930/j.issn.1004-9649.201912164

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In order to accurately evaluate the impact of photovoltaic output on the operation of power system, get the real-time data of distributed generation, and provide the real-time operation status of power grid for dispatching decision-making, the wide area distributed photovoltaic power generation monitoring and output estimation are studied in this paper. Firstly, a distributed photovoltaic integrated information modeling method for global power estimation is proposed, and a wired and wireless panoramic monitoring for wide area distributed photovoltaic power generation is realized; Secondly, the research on distributed photovoltaic output characteristics under different scenarios is carried out. With insight on the impact of scenarios including component types, installation methods, geographical locations, light resource distribution and terrain conditions on distributed photovoltaic output, the aggregation analysis on distributed photovoltaic output is carried out and the global output estimation method is proposed; Thirdly, a system for aggregation analysis on wide area distributed photovoltaic power generation and estimation of photovoltaic global output is developed and applied as demonstration in the provincial dispatching center.

A Cross-Correlation Analysis of Irradiation, Temperature and Wind Speed Based on Detrended Cross-Correlation Method

WU Xiaosheng, JIANG Yuewen

2020, 53(6):  97-106,123.  DOI: 10.11930/j.issn.1004-9649.201809009

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Exploiting the interconnections among meteorological factors properly can reduce the operating costs and enhance the stability of the system. In this paper, the measured data of irradiation, wind velocity and temperature at three different areas in the United States are used to explore the long-range auto correlation and the interactions between these factors. First of all, the detrended fluctuation analysis method (DFA) is adopted to analyze the long-range auto correlation of these meteorological factors at three areas, respectively. Moreover, the detrended cross-correlation analysis method (DCCA) and the coefficient for reciprocity of DCCA is used to quantify the interactions of these three factors at different scales. Finally, several wind-solar hybrid generation scenarios are developed to evaluate the general and seasonal characteristics of the complementarity between wind and solar energy. The results verify the validity and necessity of the proposed model.

Fuzzy Gain-Scheduling PI Controller Design for Large-Scale Wind Turbine

LI Gengda, LIN Zhongwei, CHEN Zhenyu, DUAN Zhenqing, CHEN Baowei

2020, 53(6):  107-113.  DOI: 10.11930/j.issn.1004-9649.201903100

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When the wind speed exceeds the rated value, usually the blade pitch controller will be put into the operation of large-scale wind turbine. Specifically, the blade pitch angle is adjusted to maintain appropriate angle of attacking on the blade. Thus, the aerodynamic torque captured from the wind can be regulated to achieve stable power output. In combination with fuzzy control logic and conventional PI control, this paper proposes the design of fuzzy-PI controller and fuzzy gain-scheduling PI controller respectively. Both controllers are applied in the blade pitch control process for the stabilized power output. Under the Simulink-FAST interface, the proposed strategies were tested on a 5 MW onshore wind turbine model. Simulation results show that the novel controller can significantly shorten the time duration for adjustment and reduce the overshoot such that satisfactory control effect can be achieved under the proposed strategies.

Impact Analysis of Market-Driented Carbon Emission Reduction Policies in Power Generation Industry Based on System Dynamics

ZHANG Jinliang, ZHOU Xiuxiu

2020, 53(6):  114-123.  DOI: 10.11930/j.issn.1004-9649.201905026

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Under the background of carbon emission reduction and electricity market reform, the market-based carbon emission reduction policy in power generation industry has a significant impact on the process of electricity market reform and the effect of carbon emission reduction. For this reason, by taking the market-based carbon emission reduction policy in power generation industry as a research object, this paper constructs a system dynamics model based on green certificate trading market, carbon emission trading market, generation right trading market and power market, and analyzes the impact of different policy scenarios on carbon emission reduction and power market. The results indicate that the green certificate trading market has a significant role in promoting the development of renewable energy; the carbon emission trading market has a restraining effect on the development of thermal power, and increases the feed-in tariffs; the generation right trading market can alleviate the rise of electricity price and carbon price, but it will impact the green certificate market, resulting in the decline of green certificate price.

Research on Brazil's Electricity Market Trading Mechanism and Its Enlightenment to China

ZHU Yongjuan, CHEN Ting

2020, 53(6):  124-132.  DOI: 10.11930/j.issn.1004-9649.202003174

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Electricity trading is an important part of the electricity market and the core of the electricity market operation. Up to now, the construction of China's power market is still in its infancy, the provincial trading centers have not been established for a long time, and the trading mechanism has not been fully established. Brazil, as one of the BRICS countries, has a high degree of similarity to the development of China. There are also many similarities in the development trajectory of the power industry. The rapid establishment and efficient operation of the electricity market are of great reference to the current construction of the electricity market in China. This article studies the power market trading mechanism in Brazil for many years from the aspects of regulators, participants, market mechanisms, types of trading markets, and supporting measures, summarizes its operating experience, analyzes the similarities and differences between the Brazilian power market trading mechanism and China's power market transactions, and in the adaptability of China, the empirical enlightenment beneficial to the construction of China's electricity market is finally proposed.

Theoretical Study and Experimental Research on Collision Fault of Shaft System of Siemens 1 000 MW Steam Turbine Unit

CUI Yahui, YAO Jianfei, XU Yatao, QU Fulai, SUN Peng, WANG Zenghong

2020, 53(6):  133-139.  DOI: 10.11930/j.issn.1004-9649.201910064

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During the shutdown process of a Siemens 1 000 MW steam turbine unit, the vibration occurred at the high-pressure rotor with the abnormal increase of its eccentricity, which has imposed high risk of permanent bending in the high-pressure rotor and endangered the operation safety of the unit. In this paper, the dynamic model of the shafting of the unit is established by using the finite element method. Then, the dynamic response of the high-pressure rotor with respect to dynamic and static frictions is analyzed and the rubbing fault characteristics of the high-pressure rotor are obtained. In addition, the abnormal vibration of the high-pressure rotor during the shutdown process of the unit is numerically simulated. The research results show that the rubbing of the gland seal during the shutdown process of the unit is the major cause for the abnormalities of the high-pressure rotor. Through the radial clearance test to adjust the radial clearance between the high pressure rotor and the cylinder, the problem has been solved successfully. Therefore, the high risk of permanent bending of the high-pressure rotor is eliminated.

Determination of the Optimal Capacity of Peaking Electric Boiler in CHP Unit

CAO Lihua, PAN Tongyang, SI Heyong, JIANG Tieliu, CAO Xing, ZHAO Jinfeng

2020, 53(6):  140-146.  DOI: 10.11930/j.issn.1004-9649.201811092

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The cogeneration unit is equipped with an electric boiler for thermal electro-coupling to promote the integration of new energy such as wind power. To determine the optimal capacity configuration of the medium-electric boiler in the thermal power plant, the concept of the feature day is introduced, and the constraints during the boiler operation are elaborated in this paper. Then a mathematical model is established by setting peaking increment and economic net present value of the unit as the objective functions respectively. The results show that for the 330 MW unit, when the heating supply of the electric boiler reaches 52 MW, the maximum peak-increasing increment can be obtained if the investment cost is not considered. The electric heating power of the electric boiler is 178.28 MW by then. While in the case of the investment cost taken into account, with the heating power of the electric boiler reaching 49 MW, the electric heating power of the electric boiler is 168 MW and the maximum economic net present value is achieved.

Primary Frequency Regulation Modeling of Deep Peak Regulation Unit Based on Improved Group Optimization Algorithm

YU Guoqiang, CUI Xiaobo, SHI Yiyue, TANG Keyi, ZHANG Tianhai

2020, 53(6):  147-152.  DOI: 10.11930/j.issn.1004-9649.201910003

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Under deep peak regulation state, the performance of primary frequency regulation of thermal power units has undergone significant changes. In order to figure out the changes of generator primary frequency regulation output and provide reference for the power grid to assess the performance of primary frequency regulation of deep peak regulation unit, the model structure is determined based on the prior knowledge of the primary frequency regulation model. Then the unknown parameter values in the model are derived by using an improved group optimization algorithm with better global search capability. Steady state values are incorporated into the identification parameters in order to eliminate modeling error which was introduced from manually selected steady state values. In this way, both the initial state and the end state converge to the steady state as required in conventional model identification process. The model parameter calculation results show that under deep peak regulation state, the capability margin of primary frequency regulation is higher than that under the normal load conditions. Therefore, relevant parameters need to be adjusted to better utilize the primary frequency regulation capability of the deep peak regulation unit.

Research on the Frequency Control Strategy of Hydro-Thermal Power Generating Units

TANG Yaohua, GUO Weimin, CUI Yang

2020, 53(6):  153-160,178.  DOI: 10.11930/j.issn.1004-9649.201907127

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Since being a large number of hydro power units in the Hami-Zhengzhou UHVDC transmission network, it is of great significance for the safety and stability of high-power transmission to master the primary frequency characteristics and differences of hydro power and thermal power. In this paper, the Hami-Zhengzhou UHVDC transmission power grid is taken as a typical research object. The influence of different control parameters on the primary frequency control under the condition of higher hydropower load ratio is simulated and analyzed. The joint adjustment scheme between thermal power unit and hydropower unit is given. The control strategy and feasibility suggestions are put forward.

Effects of Lay-up Protective Agent on Water and Steam TOCi During Unit Start-up and Lay-up Processes

DENG Yuqiang, QI Ji, WANG Qiliang, QI Dongdong, SONG Fei, LI Haiyang, CHENG Jiaqing, WANG Dongmei, MA Yanbin

2020, 53(6):  161-165,196.  DOI: 10.11930/j.issn.1004-9649.201902097

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Although protective agents have been applied in many power plants to inhibit corrosion during unit shut-down process, related research and operation experience seem to be inadequate regarding the effect of the protective agents containing organic compound on the TOCi (total organic carbon ion) values of steam and water in boiler. Thus experimental studies have been performed for several protective agents used in subcritical, supercritical and ultra-supercritical units in the aspects of the TOCi and cation conductivity values changes. This study result revealed that TOCi and cation conductivity of feedwater and main steam would exceed standard values severely. Specifically, the TOCi concentration would reach more than ten times of the standard limit while the cation conductivity would be more than three times of the maximum limit value respectively, which definitely increased the risk of corrosive hazard of steam and water system. Therefore, the protective agent free of chained carbon component should be selected whenever possible. If organic agents had to be a choice, the TOCi and cation conductivity must be placed under supervisory control during shutdown or initial start-up processes so as to provide directions for the addition of agents as well as the system flushing operation. It was also suggested that condensate polishing system should be put into operation during unit lay-up and start-up process such that the TOCi values of steam and water could be constrained within the standard range.

Development and Application of Reverse Lancing Equipment for the Secondary Side of Steam Generator in Nuclear Power Plants

WU Weirang, TAO Yuchun, LUO Wei, YU Tong, ZHAO Qingsen

2020, 53(6):  166-171.  DOI: 10.11930/j.issn.1004-9649.201905050

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It is absolutely necessary to expand high pressure lancing area in order to ensure the operation safety of steam generator transfer heat tubes in nuclear power plants. Considering the structure at the secondary side of the steam generator, and in compliance with the guidelines of reducing cost and increasing the compatibility, the reverse lancing technology is studied on conventional tubesheet lancing equipment. And the equipment applicable for the entire area between flow distribution baffle and the first tube support plate was developed by way of parameter matching, device design, verification calculation, etc. After full simulation testing, the equipment has been put into service in the nuclear power plant successfully. It is shown that the above area can be cleaned entirely by taking advantage of the technology and equipment of the reverse lancing developed. Furthermore, the volume of the sludge removal increases significantly, with all result index meet the requirements.

Research on Oxygenated Treatment for High Pressure Heater Drainage Water of Ultra Supercritical Units

MENG Long, JIA Lan, ZHANG Wenshuai, YANG Shuiyang, LV Peng, LI Junwan

2020, 53(6):  172-178.  DOI: 10.11930/j.issn.1004-9649.201902099

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In order to avoid the potential risk of extensive oxidation skin peeling in the superheater and reheater that arises from dosing high concentration oxygen (a.k.a high oxygenated treatment), some power plants adopt the low concentration oxygenated treatment (a.k.a. low oxygenated treatment). However, this type of low oxygenated treatment of the feed water cannot solve the corrosion problem of the high-pressure heater drainage system. Therefore full protective oxygenated treatment of "feed water treatment with low concentration oxygen plus high pressure heater drainage treatment with individual dosing oxygen" is developed and applied to a 660 MW ultra-supercritical unit. The results show that with the implementation of this full protective oxygenated treatment the corrosion problem of water steam system can be addressed without taking the potential risk of oxygenated treatment using high concentration oxygen. Oxygenated treatment can significantly reduce the content of iron corrosion products especially those of suspended iron. The iron concentration of high pressure heater drainage is less than 1 μg/L after oxygenated treatment. Also air is the safest medium for oxygenated treatment, which has negligible influence on water quality(such as hydrogen conductivity and pH value).

Analysis on the Factors Affecting the Degree of Superheat in Wet Plume Elimination

WANG Gan, WANG Shaoquan, GAO Xiang, LI Jianguo, ZHOU Linhai, FENG Guohua, ZHANG Chengwei, CHEN Tiejiong

2020, 53(6):  179-184.  DOI: 10.11930/j.issn.1004-9649.201903041

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The demand for industrial wet plume elimination has been rising sharply in the current environmental protection strategy. In this paper the influence of environmental conditions are analyzed quantitatively and comprehensively, such as ambient temperature, ambient relative humidity and air pressure, as well as the flue gas temperature on the degree of superheat, which is the key parameter in wet plume elimination technology. The results show that the degree of superheat decreases exponentially with the increase of ambient temperature or the decrease of air pressure, and increases exponentially with the increase of ambient relative humidity or flue gas temperature. When ambient temperature is above 10 ℃, the sensitivity of ambient relative humidity to the degree of superheat is slightly higher than that of ambient temperature. As the fluctuations of air pressure may cause drastic changes of the degree of superheat, the variations of regional air pressure should be taken into fully consideration in the design for different projects. Since the degree of superheat also varies significantly with flue gas temperature, the flue gas temperature should be monitored under strict control in wet plume elimination such that the technical advantages of Cooling &Reheating method could be fully utilized.

Test Method for Ammonia Injection Optimization of SCR Denitrification System Based on CFD Simulation Calculation

WANG Weiqun, ZHANG Lei, HUANG Zhijun, FU Gaojian, LI Guoqi

2020, 53(6):  185-190.  DOI: 10.11930/j.issn.1004-9649.201910095

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In this paper, a method of simulation calculation with computational fluid dynamics (CFD) is presented to precisely adjust the ammonia injection grid valve by introducing tracer gases into the denitrification system. Through full-scale modeling of the SCR system, and by replacing the original flue gas with the equivalent tracer gases in some ammonia-spraying branches, numerical simulation is conducted for the turbulent flow and multi-component diffusion of the flue gas in the SCR reactor. Then the concentration distribution of tracer gases at the outlet section of the denitrification system are calculated and the corresponding influencing areas of the ammonia-spraying branches at the outlet can be obtained. In this way the operators can be instructed to accurately change the ammonia flow rate by adjusting the opening of each ammonia spray branch so as to make the NO_x concentration evenly-distributed at the outlet section of the reactor. Finally, the verification test is carried out on the inlet flue duct of the SCR system of the tower boiler of a 1 000 MW unit. The test results show that this method can improve the efficiency and accuracy of typical tests, and therefore can effectively provide the guidelines for the field ammonia spraying optimization test.

Deactivation of Honeycomb SCR Catalysts in High-Arsenic Coal-Fired Power Plant

YAO Yan, MA Yunlong, YANG Xiaoning, ZHANG Huadong, WANG Lipeng

2020, 53(6):  191-196.  DOI: 10.11930/j.issn.1004-9649.201903095

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In this paper, the arsenic deactivation of honeycomb Selective Catalytic Reaction (SCR) catalysts in Huolinhe coal-fired power plant were studied. The denitrification activity of catalyst were tested and assessed at different time points of its life cycle. In addition, by utilizing X-ray fluorescence spectrometer, N₂ adsorption-desorption, mercury porosimetry and automatic chemisorption analyzer (NH₃-TPD and H₂-TPR), the chemical compositions and physicochemical properties of the catalysts were explored and analyzed. As it is shown from the results, the deposition of arsenic in the surface and micro pores of the catalysts led to the decrease of specific surface area and acidity of the catalyst, which was the major reason for the poisoning of the catalyst. Bench-scale catalyst test results show that the activity of catalysts dropped down to 69% of the new catalysts after only about 1 000 hours service, suggesting a very fast deactivation process compared to normal catalyst. To extend the catalyst lifetime, certain procedures for catalyst lifetime managements were proposed to slow down the arsenic poisoning process in the coal-fired power plant fueled with high-arsenic coal.