A hybrid algorithm, biogeography based optimization with simulated annealing (SABBO), is proposed in this paper by integrating the SA strategy into a basic BBO algorithm to solve combined heat and power unit economic dispatch (CHPED) problem. In this modified algorithm, a simulated annealing operator is used to randomly perturb the best individual retains after migration and mutation operating in the basic BBO. Elitism solution is further optimized to enhance the algorithm convergence capacity, and to prevent the algorithm from getting trapped into local optimum. Several benchmark functions are then taken to test the proposed method's optimal capacity, and the results show that SABBO could significantly outperform the basic BBO algorithm and mostly as the best solution listed BBO algorithms with different versions. Then, various systems are taken to tested SABBO algorithm, compared with the basic BBO and algorithms from references. Test results indicate that the SABBO is better than others to solve CHPED problems including large-scale ones. Further, the energy saving potential is calculated based on the whole plant's feasible operating region to work as guidance for economic operation.

The high-quality operation of a superheated steam temperature (SST) system is a core fact of the safety, economy, and stability of thermal power units. How to improve the control performance of an SST system under large-varying operating conditions is becoming a research hotspot. To solve this challenge, this paper proposes a proportional integral derivative (PID) control strategy based on integral gain scheduling. Based on the introduction of the SST system and classical model under typical operating conditions, the control difficulties of the SST system are analyzed theoretically. Then, a PID control strategy, based on integral gain scheduling, is introduced for the cascade control structure, and the stability of the proposed control strategy is analyzed by calculating the PID stability region. Finally, the effectiveness of the proposed method is verified under nominal and uncertain conditions, where the proposed method could obtain satisfactory tracking and disturbance rejection control performance. Simulation results show the valuable application prospects of the proposed method.

A steam turbine is one of the components in a power plant. Considered as a strong coupling system, steam turbines may possess correlation information among the operation parameters. In this article, a correlation extraction method based on mutual information theory is proposed for performance monitoring. First, five functions between two variables including linear and nonlinear relationships are tested to validate the proposed method. In the first industrial case study, a sensor fault detection based on the governing stage pressure is conducted on the abnormal fluctuation mutual entropy values calculated by operation data. The results show the proposed method may effectively be used to detect sensor errors and locate the first error sample. Furthermore, uncertainty analysis on the heat rate is studied to sort the importance degree of the relative parameters. The mutual information entropy difference values are then calculated to quantify the sensitivity degree of the operation parameters to the heat rate index. The results in this case study are in accordance with the sequences by the Guide to the Expression of Uncertainty Measurement (GUM) and Monte Carlo (MC) methods. In conclusion, mutual information entropy may hence be used as a new solution for turbine monitoring, combined with information theory.

This paper proposes a clustering framework with updating strategy in data stream for working condition classification and monitoring in a power plant air preheater system. A weight factor is added into the basic entropy fuzzy clustering method (EFC) for the statistical characteristics. Moreover, a framework is proposed to deal with data stream with updating block and off-line block. Four real-life data sets from UCI Machine Leaning Repository are adopted to evaluate the effectiveness (outlier detection rate and false alarm rate) of data mining framework with weighted EFC (W-EFC). These experiment results verify that the proposed framework dealing with data stream based on W-EFC has high clustering capacity and decrease noise data influence to a certain extent. Then, a field data set of the air preheater system is obtained for power plant application analysis. The clustering results can represent different working conditions and different working levels during long running time and thus can be taken as guidance for real-time operation monitoring.

Gas-steam combined cycle units, with gas as fuel, are more efficient than the thermal units. The output of the combined system includes gas turbine and steam turbine load. With the load variation during the operation, it is difficult for the main equipment to keep running under the economic load all the time, and the fuel consumption will increase compared with the optimal working conditions. In this paper, special two-one economic consuming models are established as the base of dispatch problems, with the steam extraction flow and low-pressure water extraction flow as the model input, while the heat output as the model output. A hybrid algorithm is proposed in this paper by integrating the SA strategy into the basic BBO algorithm, called SABBO algorithm, to solve optimization problem. In this modified algorithm, a simulated annealing operator is used to randomly perturb the best individual retains after migration and mutation operating in the basic BBO. A benchmark function is then taken to test the proposed method’s optimal capacity. The proposed algorithm is then used in load optimal distribution model for gas-steam combined cycle units (one-one units and one two-one units). The optimal fuel flow calculated with SABBO algorithm by Optimal 2 is 202.17 t/h, lower than 206.63 t/h by Optimal 1 when power and heat demands are 1000 MW and 575 MW, respectively. Moreover, optimization results by BBO and SABBO algorithms under Optimal 2 solution with demands as [1000, 575], [1000, 700], and [800, 575] MW are listed in the paper. This case tells SABBO algorithm under Optimal 2 solution can be used in gas-steam combined cycle units dispatch problems for lower fuel flow consumption as 202.17 t/h, 211.01 t/h, and 171.76 t/h.