Modern power sector requires grid observability under all scenarios for its ideal functioning. This enforces the operator to incorporate state estimation solutions based on a priori measurements to deduce the corresponding operating states of the grid. The key principle for such aforementioned algorithms lies on an occurrence of an over determined class of system having an ample redundancy in the measurements. Operators employ state forecasting solutions to counter the loss of real-time measurements. This work encompasses a critical comparison between several machine learning models along with ARIMA and time delayed neural network architecture for proper forecasting of operating states under normal as well as contingency scenarios. To showcase the efficacy of the proposed approach, this work incorporates a comprehensive comparison between them based on RMSE, MSE and MAE index. Copula-based synthetic data generation based on Gaussian multivariate distribution of the a priori measurements and operating states along with optimal hyper-parameter tuning of the models have shown the effectiveness of such algorithms in predicting future state estimates. The proposed machine learning models can be also seen to showcase an effective forecasting strategy under varying noise scenarios. This work also showcases the implementation of the models for real-time state forecasting strategy having computational times in the order of micro seconds. All the simulations have been carried out on the standard IEEE 14 bus test bench to support the former proposals.

现代电力部门需要在所有场景下都具有电网可观测性，以实现其理想功能。这迫使运营商结合基于先验测量的状态估计解决方案来推断电网的相应运行状态。此类上述算法的关键原理在于出现在测量中具有充足冗余的过度确定的系统类别。运营商采用状态预测解决方案来应对实时测量的损失。这项工作包括对几种机器学习模型以及 ARIMA 和时间延迟神经网络架构之间的关键比较，以正确预测正常和应急情况下的运行状态。为了展示所提议方法的有效性，这项工作结合了基于 RMSE、MSE 和 MAE 指数的它们之间的综合比较。基于先验测量和操作状态的高斯多元分布以及模型的最佳超参数调整的基于 Copula 的合成数据生成已经显示了此类算法在预测未来状态估计方面的有效性。还可以看到所提出的机器学习模型在不同的噪声场景下展示了一种有效的预测策略。这项工作还展示了具有微秒级计算时间的实时状态预测策略模型的实现。所有的模拟都是在标准的 IEEE 14 总线测试台上进行的，以支持以前的建议。基于先验测量和操作状态的高斯多元分布以及模型的最佳超参数调整的基于 Copula 的合成数据生成已经显示了此类算法在预测未来状态估计方面的有效性。还可以看到所提出的机器学习模型在不同的噪声场景下展示了一种有效的预测策略。这项工作还展示了具有微秒级计算时间的实时状态预测策略模型的实现。所有的模拟都是在标准的 IEEE 14 总线测试台上进行的，以支持以前的建议。基于先验测量和操作状态的高斯多元分布以及模型的最佳超参数调整的基于 Copula 的合成数据生成已经显示了此类算法在预测未来状态估计方面的有效性。还可以看到所提出的机器学习模型在不同的噪声场景下展示了一种有效的预测策略。这项工作还展示了具有微秒级计算时间的实时状态预测策略模型的实现。所有的模拟都是在标准的 IEEE 14 总线测试台上进行的，以支持以前的建议。