给已部署的电能质量分类器加不确定度感知解释，提升可靠性。
Post-Hoc Uncertainty-Aware Explanations for Deployed Power Quality Disturbance Classifiers via Laplace Approximation
- 用拉普拉斯近似将模型转为贝叶斯后验，无需重训练。
- 生成带置信区间的定位图，低分位数增强显著事件定位。
- 适合电网监测中需可信解释的场景，可识别噪声与分布偏移影响。
深度学习分类器在电能质量扰动（PQD）识别中表现优异，但现有解释方法仅输出单一确定性归因图，且无可靠性度量。本文提出一种针对已训练PQD分类器的后处理贝叶斯解释（B-explanation）方法。通过计算高效的拉普拉斯近似，将训练好的网络转化为近似参数后验，无需重训练；再通过后验样本传播遮蔽敏感性，生成扰动定位图的概率分布。该分布的百分位数总结可得无分布假设的覆盖区间：低百分位数的共识摘要显著强化对跌落、浪涌和振荡瞬态等显著事件的定位能力，区间宽度反映每处归因的可靠性，其余扰动类型呈现类别依赖行为。解释发散性在注入测量噪声及合成到真实场景迁移时上升，与预测不确定性互补。在包含15类扰动的合成基准和现场记录的跌落数据上进行实验，与蒙特卡洛丢弃法和深度集成在统一评估协议下比较，对比确定性遮蔽、LIME和SHAP的定位精度与忠实性指标，并分析解释生成的计算成本，适用于电网监控应用。
原文摘要 · Abstract (English)
Deep learning classifiers achieve high accuracy in power quality disturbance (PQD) recognition, but existing explanation methods return a single deterministic attribution map and provide no measure of its reliability. This paper develops a post-hoc Bayesian explanation (B-explanation) method for trained PQD classifiers. A computationally efficient Laplace approximation converts the trained network into an approximate parameter posterior without retraining, and occlusion sensitivity is propagated through posterior samples to produce a distribution over disturbance-localization maps. Percentile summaries of this distribution yield explanations with distribution-free coverage bands: consensus summaries at low percentiles sharpen localization significantly for distinctive events such as sags, swells, and oscillatory transients, the band width indicates the reliability of each attribution, and the remaining disturbance types show class-dependent behavior. Explanation dispersion also increases under injected measurement noise and synthetic-to-field transfer, complementing predictive uncertainty. Experiments on a synthetic benchmark of 15 disturbance classes and on field-recorded sags compare the method with Monte Carlo dropout and deep ensembles under a common evaluation protocol, evaluate it against deterministic occlusion, LIME, and SHAP with localization and faithfulness metrics, and characterize the computational cost of explanation generation for grid monitoring applications.
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