after reading the below paragraphs please write the introduction in your on words(with 100% plagiarism free)

WIDE-AREA measurement systems (WAMSs) are prevalently deployed in modern power systems. Because of their high sampling precision and high reporting rate, synchrophasor measurements (SMs) collected by phasor measurement units (PMUs) can provide abundant information about the operating condition of power systems. Hence, SMs often serve as input for many energy management applications, such as situational awareness, wide-area protection, and post contingency analysis. However, in practice, communication contingencies and cyber attacks may severely degrade the quality of SMs. On the one hand, accidental network congestion or deliberate communication blocking may cause some samples to be lost in the received time-series. On the other hand, random noises, electromagnetic interference, or malicious data tampering attacks may lead to modified data points . Both cases pose significant impact on the normal functioning of SM-based applications, which will further threaten the reliability and stability of power systems. For this reason, adequate attention should be paid to the potential risks of data loss and modification of SM time-series.

In the existing literature, some papers focus on the detection of corrupted fragments of SM time-series. Methods based on deep autoencoder, Kalman filter, cumulative sequential deviation analysis, support vector machine, robust principal component analysis (RPCA), and extreme learning machine have been proposed. However, these methods can neither locate nor recover the corrupted samples. Some researchers go a step further and investigate data recovery approaches. These approaches can be classified as model-based, machine-learning-based, and data-mining-based. Model-based data recovery methods are often coupled with state estimation. In SM-based state estimation is introduced for correcting corrupted synchrophasors. The Sparsity of the Jacobian matrix and short-term prediction function are utilized to eliminate anomalies and restore PMU data However, model-based approaches require detailed configuration of power systems, and will fail when the measurements are not sufficient to ensure system observability