Power Quality Estimations for Unknown Binary Combinations of Electrical Appliances Based on the Step-by-Step Increasing Model Complexity

Smart detached houses, contingent on renewable energy (RE), are subjected to an unstable power supply of the intermitted nature. The power quality (PQ) norms define allowable variances in the characteristics of electrical systems to ensure their functioning without malfunction. The estimation and optimization of PQ parameters on day bases are inevitable in the regulation of systems to comply with the specified standards and allow the fault-free operation of electrical equipment. Measurements of all PQ states are impossible for dozens of eventual grid-attached power consumers defined by their binary load patterns. Specific demands and uncertain RE can lead to system instability and unacceptable PQ. Self-optimizing models based on artificial intelligence (AI) can estimate the next PQ states in real off-grids where power is induced only by chaotic RE sources. A new proposed multistage prediction scheme allows incremental improvements in the accuracy of AI models beginning their development with binary coded data only. The number of selected PQ inputs gradually increased in the next estimate for the initial equipment in demand. Historical records include complete training PQ data for all parameters, but only “1/0” switch-on load sequences are available at prediction times. The most valuable PQ outputs are modeled in the previous stages to process their supplementary series in the next prediction. More capable models, applied to previously approximated PQ data, are able to better compute the PQ output in the secondary steps. Complementary PQ inputs are supplied with the new processing data, which were unknown in the previous stage. The growing number of input features enables a more complex representation of the target quantity in each iteration. Advanced input selection and data reevaluation can additionally improve model discriminability for unseen active load patterns. It can be applied in modeling unknown states of various dynamical systems, initially defined only by series of binary or inadequate input data, to improve the results.依托可再生能源（Renewable Energy, RE）的智能独栋住宅，其供电具有间歇性且稳定性不佳。电能质量（Power Quality, PQ）规范定义了电气系统特性的允许偏差范围，以保障系统正常运行而非出现故障。按日开展电能质量参数的估算与优化，是系统合规运行、保障电气设备无故障运转的必要环节。对于大量以二元负荷模式为特征的并网电力用户而言，无法直接采集所有电能质量状态数据。特殊用电需求与不稳定的可再生能源供给，可能引发系统失稳与不合格的电能质量问题。基于人工智能（Artificial Intelligence, AI）的自优化模型，可在仅由随机波动的可再生能源电源供给电力的离网场景中，对下一时刻的电能质量状态进行估算。本文提出的新型多阶段预测方案，可在初始仅使用二进制编码数据构建模型的基础上，逐步提升人工智能模型的预测精度。针对目标用电设备的后续预测中，所选取的电能质量输入特征数量将逐步增加。历史数据集包含所有参数的完整训练用电能质量数据，但在预测阶段仅能获取“1/0”形式的负荷投切序列。在前期阶段先对最具价值的电能质量输出变量进行建模，以此为后续预测阶段提供补充序列信息。将性能更优异的模型应用于前期已近似得到的电能质量数据，可在后续迭代步骤中更精准地计算电能质量输出结果。前期阶段未获取的新处理数据，将作为补充电能质量输入特征投入后续计算。随着输入特征数量的逐步增加，每一轮迭代中目标变量的表征复杂度也随之提升。借助先进的输入特征选择与数据重评估方法，可进一步提升模型对未见有功负荷模式的判别能力。该方案可应用于各类仅通过二元输入数据或不充分输入数据进行初始定义的动态系统未知状态建模，从而优化建模效果。