Data-Driven Multi-Mode Adaptive Operation of Soft Open Point With Measuring Bad Data

The high penetration of distributed generators (DGs) deteriorates the uncertainty of active distribution networks (ADNs). Soft open points (SOPs) can effectively improve flexibility and deal with operational issues in ADNs. However, the formulation of SOP control strategies depends on the accurate mechanism model. Data-driven method can utilize only measuring data to conduct operation and becomes a promising way. In practical conditions, the measuring data may suffer from bad data and measuring errors, which poses a challenge to meet the diverse operational requirements. This paper proposes a data-driven multi-mode adaptive control method for SOP with measuring bad data. First, considering the inaccurate network parameters and quality of measuring data, a robust data-driven framework for SOP operation is proposed based on robust hierarchical-optimization recursive least squares (HO-RLS). Then, a multi-mode control strategy for SOP is proposed to adapt to the diverse operational requirements. A dynamic triggering mechanism is designed to achieve adaptive mode switching. The case studies on practical distribution networks show that the proposed method can fully explore the benefits of SOP to improve the operational performance of ADNs. The potential limitations are discussed to enhance practicality.