基于因子隐马尔可夫模型的负荷分解方法及灵敏度分析

Load Disaggregation Method Based on Factorial Hidden Markov Model and Its Sensitivity Analysis

负荷分解是智能电网的关键技术,对负荷预测、需求侧管理及电网安全有重要意义。传统负荷分解方法的准确率受限于负荷特征的维度、采样频率和负荷的稳定性。文中提出了基于因子隐马尔可夫模型的负荷分解方法,利用该模型对负荷进行建模,对Viterbi算法进行了扩展并求解负荷状态,进而基于整数规划实现对总负荷的最优分配。该方法对负荷数据的稳定性和采样频率不敏感,可适用于家居和工业电力用户。同时,深入研究了Viterbi算法求解最优状态与观测扰动之间的影响关系,并进一步得到最优状态对于当前观测的允许扰动范围,这对负荷分解最优状态的可靠性评估有重要意义。

As the key technology in smart grid, load disaggregation is important for the tasks such as load forecasting, demand side management and power security. The accuracy of the traditional methods subjects to the dimension of load signatures, the sampling frequency and the stability of load profile. In this paper, a Factorial Hidden Markov Model（ FHMM） based load disaggregation method is proposed, which contains the load state disaggregation through extended Viterbi algorithm and the load allocation based on integer programming. The load is modeled as FHMM, and we extend the Viterbi algorithm to solve the FHMM directly. The proposed method is insensitive to the stability and accuracy of power data, so it is suitable for the residential and industrial devices. Meanwhile, through the sensitivity analysis of Viterbi algorithm, the relationship between the optimal states and the disturbance of the observation is established, which is significant for the reliability evaluation of the optimal states.