An Inclusive Review of Machine Learning Techniques in Securing Power Systems and Recognition of Cyber Attacks

GrowingDeveloping utilization of environmentally friendly power, opening up of the energy markets, and joining of checking frameworks have opened up potential open doors for a stronger power matrix. Nonetheless, this advancement likewise brings new difficulties, for example, cyberattacks and voltage flimsiness. Opportune location of these issues is critical, and ML approaches have shown adequacy in settling power framework issues. This study looks at momentum research utilizing MLTs - like counterfeit brain connections, decision trees, and help vector machines - to build the unwavering quality and security of energy frameworks. The attention is on things like unique security evaluations, power quality aggravation studies, and cyberattack discovery. The audit centers around the triumphs, approaches, and limitations in making classifiers and utilizing datasets. Besides, it offers a brief outline of profound support learning and support learning in transient steadiness assessment finishing up with bits of knowledge into difficulties and potential future examination headings in this developing field.