To solve the problem of low recognition accuracy of transformer insulation oil gas fault diagnosis, the slime mold algorithm (SMA) is improved by the reverse learning strategy to form the improved slime mold algorithm (ISMA), thus to improve the global optimization ability and optimize the support vector machine (SVM). An ISMA-SVM optimized fault diagnosis model is established, and the sample set is used for learning and training. The diagnosis and recoginition results are compared with that of the greywolf algorithm (GWO-SVM) and the particle swarm optimization (PSO-SVM), it shows that the accuracy of the ISMA-SVM fault diagnosis and recognition is 93.3%, which is 6.66 and 10.66 percentage points higher than that of the GWO-SVM and PSO-SVM, respectively.

The variable operating condition of thermal power units makes the data show multi-modal characteristics, which leads to the decrease of prediction accuracy of the regression soft sensor model based on shallow network structure. An improved BP neural network (back propagation neural network, BPNN) soft sensor method is studied. Firstly, the original data features are extracted by using the strong deep learning ability of stacked sparse autoencoder (SSAE), and then the extracted features are analyzed by BPNN. The experimental results show that, the mean square error of the SSAE+BPNN soft sensor method is 0.135 8×10^–3 and the square correlation coefficient is 0.983 2. It is proved that its prediction accuracy and generalization ability are significantly better than those of BPNN. It is applied to the soft sensor of carbon content in fly ash of a flexible peak-shaving 660 MW ultra-supercritical generator set, and the average relative error of the prediction results is 0.91%, the overall relative error is less than±5%, indicating the method has good engineering application value.

Micro-mixing combustion is a kind of potential technology to reduce pollutant emissions, and the microtube structure as a basic unit has significant influence on combustion characteristics. Taking the single microtube model burner as the object, the computational fluid dynamics (CFD) method is employed to analyze methane micro-mixing combustion characteristics with different microtube diameters, rear-section lengths, and chamber-to-tube area ratios, under the air-preheated and pressurized condition and the atmospheric condition. The numerical simulation results show that, the flame length increases obviously under the atmospheric condition, and the increase rate is greatly affected by the microtube diameter and the area ratio. With the increase of microtube diameter, the flow and temperature fields in the flame chamber are similar, and the dimensionless flame length does not change significantly. However, when the microtube diameter is smaller, the change of flame length is smaller between two working conditions, and the flexibility is better. With the increase of microtube rear-section length, the turbulence intensity at the microtube outlet reduces greatly, and the flame length increases obviously, but the change trend of flame length is consistent under two working conditions. With the increase of chamber-to-tube area ratio, the flame length decreases at first and then increases. When the area ratio is 4.0~9.0, the flame length is relatively short, and the combustion performance is relatively good. The results have reference value for the gas turbine combustor, and also have certain reference significance for free jet flame.

Large-scale grid connection of new energy power generation makes cogeneration units need flexible operation to smooth out the fluctuation brought by new energy access. In order to meet the control requirements of flexible operation of the unit and the safety of the unit operation, a flexible control strategy for cogeneration units based on flexibility evaluation is designed. Firstly, according to the response characteristics of electrical load, thermal load and throttle pressure of the unit, a calculation method of flexibility evaluation indexes which can represent the load adaptability and operation safety of the unit are proposed, and these indexes are used as the basis for selecting the optimal flexibility factors. Then, the influence of flexibility factor on the system performance under a single working condition is simulated. Finally, on the basis of determining the optimal flexibility factors under different working conditions, the adaptive flexibility factors that changing with working conditions are designed and applied to the flexible control. The simulation results show that, the control strategy can not only achieve a better control effect when the unit runs under large-scale conditions change, but also achieve a better control for the unit after the low-pressure cylinder is cut off, which can meet the control requirements of flexible operation of the unit and ensure the safety of the unit operation..

As a nondestructive testing method, magnetic particle testing is widely used in power industry. The comprehensive sensitivity of the magnetic particle detection can be tested by A1 standard shims. However, at present, the evaluation of the clarity of magnetic trace on standard shims still depends on subjective judgment of the tester. In order to eliminate the subjective factors in the evaluation, this paper proposes a standard magnetic trace evaluation method based on machine vision. Based on Python programming language and OpenCV function library, the initial obtained magnetic trace is processed by image correction, magnetic trace extraction, quantitative analysis and evaluation using computer program. On this basis, the influence of the thickness of non-magnetic layer on the comprehensive sensitivity of magnetic particle detection is investigated using this method. It is shown that the magnetic trace evaluation method based on machine vision is more objective and accurate than the conventional manual evaluation.

As a clean and efficient power generation technology, solar thermal complementary combined cycle power generation (ISCC) system is concerned widely. By taking the ISCC system containing three-pressure reheat combined cycle and trough solar field as the research object, and based on the conventional trough ISCC system (using solar energy to replace the high pressure evaporator (system 1)), two new trough ISCC system are presented, namely using solar heat collection field to replace the high and medium pressure evaporators simultaneously (system 2), and using solar heat collection field to replace the high, immediate and low pressure evaporators simultaneously (system 3). Moreover, the dynamic performances of the above three ISCC systems are analyzed under the operation condition of typical day. The results show that, with proper operation strategy presented above, the three systems can not only realize safe and stable operation, but also reduce the impact of solar energy varies on the ISCC system output power. Under the operation condition of typical day, the second type of ISCC system (system 2) has better thermal performance.

With the proposal of "carbon peaking and carbon neutral", the total installed capacity of renewable energy power units continues to increase. This may greatly challenge the safety and stability of power grid. The characteristics of flywheel energy storage system (FESS) are fast response, unlimited times of charge and discharge and deep depth of discharge. FESS has been widely used in frequency modulation and frequency safety improvement of power grid. In order to make full use of the advantage of flywheel energy storage in auxiliary frequency modulation of the power grid, an adaptive coordinated droop control strategy of primary frequency regulation coordinated with thermal power units was designed, which realized the power cooperative adaptive adjustment of the combined coal-fired thermal power units and storage systems. Simulation results show that, the proposed control strategy can effectively improve the frequency modulation performance of the combined fire-storage system. Compared with the conventional droop control, the maximum dynamic frequency difference and steady frequency difference of the system reduces by 29.00% and 25.50% respectively, which eases the frequency modulation pressure of thermal power unit, and is benefit to safety and stability of the thermal power unit.

Blade fractures and cracks occurred on low pressure second last stage moving blade of a steam turbine before and after the blade optimization. In order to find out the cause of this type blade failures and prevent subsequent reoccurrence, the blade failure, operating parameters and historical records were checked, and the materials and fractures of some failed blades were analyzed through physical and chemical inspection. Moreover, the centrifugal stress of the blade and the vibration characteristics of the gear train before and after optimization were numerically analyzed by finite element method. The results show that, the blade fracture is a high peripheral fatigue fracture. Before optimization, the main reason for cracks and fractures at the connection transition between the top of the inner cambered surface and the shroud on the steam outlet side of the blade is that the blade has a large torsional recovery under working conditions, resulting in severe compression of the shroud, and stress concentration and fatigue damage occur at the connection transition between the top of the inner cambered surface and the shroud on the steam outlet side. The unreasonable design of blade root structure is the main factor for high cycle fatigue cracking of blade root, while the vibration of the sixth pitch diameter of the first stage of blade impeller system falling into the "3-coincide point" resonance area is the secondary factor for blade failure. After optimization, the main reason for the fracture is the unreasonable design of the blade root structure, and the vibration of the eleventh pitch diameter of the second stage of the blade impeller system falling into the "3-point" resonance area is the secondary factor causing the blade root failure.

With high power generation efficiency and low carbon emission, (ultra) supercritical unit is the main type in China's development of thermal power units. Optimization on feed water operating condition of the (ultra) supercritical unit is of great guiding significance. The optimal control indexes of different feed water operating conditions of (ultra) supercritical units are investigated by in-situ sampling test, and the influence of centralized sampling on feed water quality is researched. Moreover, the intelligent control system for feed water operating condition is developed. The results show that, the optimal target value of feed water conductivity is 6.7 μS/cm under AVT(O) condition, the optimal target value of feed water dissolved oxygen is 15 μg/L and the optimal target value of feed water conductivity is 3.0 μS/cm under OT condition. Long sampling pipeline of feed water will cause the deposition of iron corrosion products, and interfere with the judgment of unit corrosion. The chromates produced by adding oxygen to feed water is only related to the oxidation of the sampling tube and has nothing to do with the water vapor system.

By taking a supercritical 600 MW opposed firing boiler as the research object, the temperature deviation law of main steam on both sides is analyzed and verified through hydrodynamic modeling, and a technical scheme is put forward to optimize the temperature deviation of main steam from the perspective of water side. After adopting this steam temperature optimization scheme, the main steam temperature deviations on both sides of the boiler reduced by 44.6%, 95.8% and 28.0% respectively under 50%, 75% and 100% BRL conditions. This scheme can realize safe and economical operation of the unit, and has good guidance and reference value for the same type of boiler.