The hardware equipment and monitoring systems used in various stages of production of renewable energy stations are provided by different manufacturers, resulting in the data format and communication protocols between various business systems can’t be standardized. This data non-uniformity makes the business systems can only run independently of each other, bringing a lot of inconvenience to the operation and maintenance and coordinated control of the field operation and maintenance personnel, and part of the system even falls into the situation of no one maintenance. Based on the data characteristics of renewable energy stations, an integrated monitoring scheme of renewable energy is proposed based on pre-processing data framework. By introducing the architectural design and workflow, a comprehensive data processing driver applicable to the integrated monitoring of renewable energies and data storage technology based on a hybrid architecture is proposed, the implementation of the data acquisition, standardization and storage ideas are described in detail, and application cases is combined to reflect the system performance improvement brought by this solution. This solution can be adapted to renewable energy monitoring needs of different scales, ensure the real-time and security of massive data processing under high concurrency processing scenarios, and significantly improve the data analysis capability of renewable energy stations, providing an important reference for the development and improvement of related systems.

The rapid and comprehensive determination of coal quality is of great significance for the optimization of boiler combustion and the digital transformation of coal-fired power plants. Laser-induced breakdown spectroscopy (LIBS) has the potential to be applied effectively in the rapid determination of coal quality. In order to meet the application goal of rapid coal inspection, 46 sets of spectral data of coal samples from different power plants were collected by the experimental device of coal particle flow LIBS, and the research of simultaneous rapid inspection of multiple indicators of coal quality by combining LIBS with machine learning was carried out systematically. In view of the considerable spectral fluctuations observed in the particle flow state, the number of single-pulse acquisitions was optimized. In addition, invalid spectral screening, spectral averaging and spectral normalization data preprocessing methods were established. Furthermore, four machine learning algorithms (PLSR, SVR, PSO-SVR, and LSTM) and four spectral feature inputs (full spectra, eigenbands, intensity integration, and PCA extraction) were compared in terms of their performance in predicting multiple indicators of coal quality. The results demonstrate that the uncertainty of the spectral signals can be maintained at a maximum of 5% when 200 single-pulse spectra are collected for spectral averaging in a single test. The PSO-SVR algorithm exhibits the most optimal prediction performance in the quantitative analysis of coal quality indicators, and the PCA algorithm reduces the dimensionality of the spectral data, which reduces the amount of model computation and at the same time improves the prediction performance of the model, and the model established by combining both of them has the best performance, the root mean square error (RMSEP) of the coal heat content is 0.289 MJ/kg, and the mean absolute error (MAE) is 0.231 MJ/kg. The coal carbon mass fraction, ash content and volatile matter content are also predicted satisfactorily, with the RMSEP of 0.987%, 1.310% and 1.612%, and the MAE of 0.839%, 1.014%, and 1.033%, respectively. The results show that, combined with appropriate machine learning algorithms, the LIBS technique can achieve simultaneous accurate and rapid determination of multiple indicators of coal quality, which has a broad application prospect in the scenario of efficient and clean coal utilization.

Ammonia cofiring in coal-fired boilers is one of the promising technical routes for decarbonization of existing coal-fired power plants. However, ammonia cofiring may potentially result in drastic increase of NO_x emissions due to its high nitrogen content. Effective control of NO_x emissions is thus one of the key factors that affect the technical feasibility of ammonia cofiring in coal-fired boilers. The formation of NO_x during ammonia-coal cofiring is affected by the ammonia combustion as well as its interaction with the coal combustion process. There are significant differences in volatile content of different coal types which may strongly affect the NO_x formation characteristics of ammonia cofiring. For this reason, the NO_x formation characteristics of ammonia cofiring with bituminous and lean coals that have distinct volatile contents are investigated by an experimental rig that allows for flexible control of the combustion environment of ammonia. The results show that, the NO_x emissions from bituminous and lean coals show different trends with the increase of ammonia cofiring ratio under different ammonia cofiring modes. Because of the significant differences in volatile matter content between bituminous and lean coals and the consequent differences in the amount of O₂ consumption, the distributions of O₂ concentration in the furnace are substantially different between the two coals. This results in different competition relationships between the NO formation and reduction reactions of ammonia in the furnace, which consequently leads to different NO_x formation and emission characteristics between the two coals.

It is of great significance to carry out health condition assessment and fault early warning of auxiliary equipment for safe operation of thermal power units in new power system. By taking the forced draft fan of a supercritical 660 MW thermal power unit as the research object, a method to construct dynamic memory matrix based on multiple characteristic parameters is proposed. The application shows that the proposed method can improve calculating speed of model effectively while ensuring the accuracy of calculated results. This work also presents a calculation method of weighted coefficients to modify the multivariate state estimation technique (MSET). The global similarity and parameter similarity indexes are introduced for fault early warning and recognition. An early fault warning model based on dynamic matrix and weighted MSET is utilized to simulate faults of forced draft fan. The results indicate that the weighted MSET model can not only improve the prediction accuracy of abnormal parameters under fault conditions effectively, but also reduce the influence of abnormal parameters on the predicted results of normal parameters. Consequently, the model proposed can realize both early warning of forced draft fan faults and recognition of abnormal parameters.

The conventional steam valve predominantly manages power regulation tasks while also addresses limited-scale primary frequency regulation. Condensate throttling can change energy distribution of the unit to a certain extent, and serve as a potential and selectable auxiliary frequency regulation method. To delve deeper into the frequency regulation capability of condensate throttling, the working principle and advantages are dissected, and static and dynamic models of the condensate throttling system are established, the frequency regulation characteristics are analyzed and the frequency modulation boundary conditions are outlined. To comprehensively enhance the dynamic performance of the condensate throttling primary frequency regulation, the system dynamic model is linearized, and then a model-switching fuzzy predictive control strategy is proposed. In this control strategy, fuzzy logic is introduced into the predictive controller algorithm to dynamically adjust control weighting coefficients in real-time, and predictive models are dynamically switched according to operational changes to enhance control quality. Case studies based on actual data from a certain 600 MW unit are conducted, indicating the static and dynamic frequency modulation capabilities of condensate throttling increase with the unit load, which has engineering application value in primary frequency regulation. Compared with the conventional PID and self-tuning fuzzy parameter PID controllers, the proposed control strategy exhibits superior adjustment time and performance metrics under various operating conditions, demonstrating better adaptability to changes in operating conditions.

Grid-forming energy storage system is expected to solve the problems like insufficient frequency modulation resources and disturbance resistance decline of large-scale new energy-based power grid. However, its active support ability is greatly limited due to its inherent power coupling characteristics, plus power overshoot, oscillation and even instability problems are most likely to occur with parameter variation. To solve this problem, a full-order small signal model for grid-forming energy storage system is developed, its frequency response characteristics are analyzed according to the output power state space model and its characteristic roots. On this basis, by analyzing the sensitivity and participation factors of the state space matrix parameters, the stable boundary of the grid-forming energy storage converter is given, and the influence of key parameters of the grid-forming energy storage converter on its dynamic power coupling, frequency support and other grid related characteristics is clarified. The simulation and semi physical experimental results have verified the correctness and feasibility of the theoretical analysis, providing a basis for the design of grid connected parameters and stable operation of grid-forming energy storage converters.

To address the electricity consumption challenges in remote regions beyond the reach of power grid, a novel off-grid microgrid system integrating wind and solar energy with flywheel storage technology is introduced. Research is conducted on the optimal capacity configuration of distributed power sources, and power output models are developed for wind turbines, photovoltaic arrays, energy storage flywheels, and diesel generators. With economic and reliability indicators as objective functions, and environmental protection indicators as constraints, a capacity optimization simulation for the microgrid system is conducted. By employing the improved NSGA-II algorithm, a multi-objective bi-level coordinated optimization of the microgrid system is performed, resulting in a Pareto optimal solution through multi-objective optimization. The TOPSIS method is utilized for decision-making, ultimately identifying the optimal solution tailored for the microgrid system. The data and conclusions obtained from this study have reference value for engineering applications in related fields.

The technology of grid-forming energy storage can form a voltage source, which can support the stable operation of large power grids. The technology of grid-forming energy storage is an effective means to support the stable operation of high proportion of new energy connected to the grid. Based on this, the operation mechanism of grid-forming energy storage to support the stability of high proportion of new energy connected to the grid is analyzed. According to the principle and characteristics of grid-forming energy storage technology, five commonly used technologies to improve the stability of the grid are compared and. A model building idea of grid-forming energy storage system considering multi-time-varying parameters is proposed. Moreover, the scheme of grid-forming energy storage technology supporting high proportion of new energy grid-connected and the mechanism analysis of massive grid-forming energy storage equipment grid-connected oscillation is proposed. In addition, the system impedance dynamic identification technology based on signal injection is studied, and then the impedance reconstruction technical route of massive grid-forming energy storage equipment grid-connected system is proposed.

Under the background of “dual-carbon” goal, the combined heat and power units with once through boilers are required to have the ability to quickly change load. To address this need, a strategy based on linear time-varying model predictive control (LTV-MPC) for coordinating electricity and heat to change unit load is proposed, which can simultaneously utilize boiler heat storage and heat network heat storage to improve the rate of variable load. Firstly, the deviation of the heat load signal is integrated to establish an equivalent heat load model, which is used as one of the controlled variables in the prediction model. Then, by taking rapid load tracking, stable unit operation, and timely compensation for heating as the objective of MPC rolling optimization, the optimal control law for each moment is solved online, and then it is applied to the unit. In addition, the operational constraints of the unit are explicitly addressed to ensure that changes in the heating extraction flow rate do not affect the operational stability of the low-pressure cylinder. Finally, simulation verification is conducted on a 350 MW unit, and the results show that this strategy can accurately track the 5%Pe/min variable load command. Furthermore, the heat load recovery time is reduced by 26% compared with that of the electric heating coordinated variable load strategy based on PID. The simulation results have verified the superiority of the proposed strategy in improving the fast load changing capacity of heating units.

Superheated steam temperature is crucial for safety and economy of coal-fired power units. However, the large inertia and strong uncertainty of superheated steam temperature system make it difficult to control. To solve these difficulties, a cascade control structure based on modified active disturbance rejection control is proposed. The inner loop uses a conventional PI controller and the outer loop uses a modified active disturbance rejection controller. An engineering tuning method for modified active disturbance rejection control is provided, and a response curve to optimize the compensation time constant is designed to address the difficulty of obtaining the compensation time constant. Finally, the advantages of the proposed control strategy in tracking and disturbance rejection performance under large-scale variable loads are verified through comparative simulations and practical engineering applications. The operational data of engineering applications shows that the proposed method can ensure smaller maximum positive and negative deviations, average absolute deviation, and deviation standard deviation, which has significant advantages and potential for engineering applications.