Power system planning method based on machine learning

• Main Authors: LI BING, WANG HAIBO, TIAN CHUNQING, LIN JIADIAN
• Format: Patent
• Language: Chinese; English
• Published: 25.08.2020
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

The invention discloses a power system planning method based on machine learning. The method includes: determining the number of newly-built substations and the capacity of each substation by utilizing a greedy method; and on the basis, solving the position of the newly-built transformer substation and the power supply range of each transformer substation in each stage by utilizing a Hopfield neural network algorithm, then reducing the capacity of the transformer substation according to the actual power supply condition of each transformer substation, finally determining that the optimal solution is met, establishing a model and optimizing. According to the invention, the artificial neural network applied to power grid planning is mainly a Hopfield network; the artificial neural network has excellent characteristics of strong nonlinear mapping capability, large-scale synergistic effect, clustering effect, parallelism, fault tolerance, robustness, no need of data normalization processing and the like, is suita