With the development of the energy internet, the standalone wind–solar–battery hybrid power system has gradually become an effective means for the complete utilisation of clean energy for power generation. The allocation of the various types of power sources for ensuring a reliable power supply and for improving the economic efficiency of a system is a crucial issue in the system planning and design stages. The models of the power load and the power sources, containing random components, are first established in this study, based on which the power supply reliability and constraint conditions for the equipment quantities are considered for establishing various evaluation indexes for improving the system performance. Then, according to the combinations of the natural selection particle swarm optimisation algorithm and the weight coefficient transform method, a multi‐objective optimisation algorithm is proposed for optimising the configuration of a standalone wind–solar–battery hybrid power system. Finally, it is demonstrated by an example that the optimisation method not only meets the requirements of the multi‐objective optimisation system but is also significant in reducing energy wastes and fluctuations, cost saving, and in ensuring the reliability of the power supply.

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Abstract

With the increasing penetration of distributed generation (DG) in power systems, the voltage instability problem of power systems is receiving a great deal of attention. As new power electronics equipment, an electric spring (ES) can effectively restrain the bus voltage fluctuation caused by DG, and in addition achieve the new control paradigm of load demand following power generation. In this article, an adaptive voltage‐regulation control strategy of ES is proposed, in which the proportional integral (PI) postgain changes dynamically according to the real‐time output current of the ES. The simulation results show that, when the proposed control strategy is applied, bus voltage can still be stabilized effectively by the ES under the condition that noncritical loads change. The proposed control strategy brings the adaptive voltage‐regulation capability of the ES, and therefore, it can be in series with multiple noncritical loads reliably and safely, and accordingly the costs can be greatly reduced. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Nowadays the demand of power supply reliability has been strongly increased as the development within power industry grows rapidly. Nevertheless such large demand requires substantial power grid to sustain. Therefore power equipment's running and testing data which contains vast information underpins online monitoring and fault diagnosis to finally achieve state maintenance. In this paper, an intelligent fault diagnosis model for power equipment based on case-based reasoning (IFDCBR) will be proposed. The model intends to discover the potential rules of equipment fault by data mining. The intelligent model constructs a condition case base of equipment by analyzing the following four categories of data: online recording data, history data, basic test data, and environmental data. SVM regression analysis was also applied in mining the case base so as to further establish the equipment condition fingerprint. The running data of equipment can be diagnosed by such condition fingerprint to detect whether there is a fault or not. Finally, this paper verifies the intelligent model and three-ratio method based on a set of practical data. The resulting research demonstrates that this intelligent model is more effective and accurate in fault diagnosis.