CAJ | 학술논문

针对智能算法与历史大数据相结合进行多变量系统辨识过程中不能精确量化每个子系统数学模型的问题，提出了一种有效的数据并行优化计算的解决方案。在辨识过程中，为了解决量子粒子群算法(quantum particle swarm optimization ，QPSO)收敛速度和寻优精度方面的不足，提出了一种改进的 QPSO 算法--双量子粒子群算法(double quantum particle swarm optimization ，D-QPSO)。该算法对粒子种群编码和原有的进化搜索策略同时进行了量子化处理，经过测试函数实验，改进的算法在搜索能力上优于 PSO 和QPSO算法。最后利用现场运行历史数据，通过D-QPSO算法进行参数估计，将设计的解决方案应用于热力发电厂负荷控制系统的传递函数辨识中，得到的模型为控制器的设计与优化奠定了基础。
침대지능산법여역사대수거상결합진행다변량계통변식과정중불능정학양화매개자계통수학모형적문제，제출료일충유효적수거병행우화계산적해결방안。재변식과정중，위료해결양자입자군산법(quantum particle swarm optimization ，QPSO)수렴속도화심우정도방면적불족，제출료일충개진적 QPSO 산법--쌍양자입자군산법(double quantum particle swarm optimization ，D-QPSO)。해산법대입자충군편마화원유적진화수색책략동시진행료양자화처리，경과측시함수실험，개진적산법재수색능력상우우 PSO 화QPSO산법。최후이용현장운행역사수거，통과D-QPSO산법진행삼수고계，장설계적해결방안응용우열력발전엄부하공제계통적전체함수변식중，득도적모형위공제기적설계여우화전정료기출。
To overcome the drawback of imprecise quantification for each subsystem during the process of multivariable system identification with intelligent algorithm and historical data, an effective optimized data parallel computing solution was proposed. In order to improve the convergence speed and precision of quantum particle swarm optimization (QPSO) during the identification, a new improved QPSO algorithm named double quantum particle swarm optimization (D-QPSO) was presented. The particle’s encoding mechanism and the evolutionary search strategy were simultaneously quantized in D-QPSO algorithm. Several benchmark test functions were used to test the proposed D-QPSO algorithm, which verified that the new algorithm was superior to standard PSO and QPSO in search capabilities. Finally, the proposed scheme was used in transfer function identification of the thermal power plant load control system, and the parameters of the model were estimated by the D-QPSO algorithm and historical data. The identification results laid the foundation for load control system design and optimization.