基于优化极限学习机的非侵入式负荷识别

Non-invasive Load Identification Based on Optimized Extreme Learning Machine

在极限学习机的非侵入式负荷识别算法中,由于输入权值和隐含层阈值的随机产生容易导致误判,鉴于此,提出了一种改进的遗传算法优化极限学习机方法。对遗传算法中选择算子进行改进,改进方法为求解出个体的适应度值,并按从小到大的顺序完成排序,将排完序的种群等分成4份,按照比例从4份中择优组成新种群,对新种群中剩余个体再从适应度较大的部分中择优;结合爬山法获得优化后的权值和阈值,构建优化极限学习机网络对负荷进行识别;利用MATLAB进行仿真验证,验证结果表明:优化后算法与未优化算法相比,负荷识别的准确率提高了约7.41%,体现了更优的分类性能,证明了该算法对负荷识别的有效性。

In the non-invasive load identification algorithm of extreme learning machine,the input weight and hidden layer threshold are generated randomly,which leads to misjudgment.An improved genetic algorithm is proposed to optimize the extreme learning machine.The selection operator in genetic algorithm is improved by solving the fitness value of each individual and completing the sorting in the order from small to large,dividing the sorted population into four parts,selecting the best from these four parts to form a new population according to the proportion,and selecting operators with greater fitness from this new population.The optimized weights and thresholds are obtained by hill-climbing method,and the optimized extreme learning machine network is constructed to identify the load.Through a large number of simulations on MATLAB,the simulation test results show that the accuracy of load identification is improved by about 7.41%compared with the results of non optimization algorithm,presenting better classification performance and verifying the effectiveness of the algorithm for load identification.