It is a challenge problem how to deal with the uncertainty in fault diagnosis of power systems. To solve the challenge problem, this paper introduces an interval-valued fuzzy spiking neural P system (IVFSNP system), where the interval-valued fuzzy logic is integrated into spiking neural P systems to characterize the uncertainty. Based on the IVFSNP system, a fuzzy reasoning algorithm is presented, and the corresponding fault diagnosis model is developed. IVFSNP system is capable of describing the incomplete and uncertain fault signals from a supervisory control and data acquisition system equipped together with electric power systems. In order to evaluate the availability and effectiveness of the proposed fault diagnosis model, two case studies of fault diagnosis of a transmission network are discussed and analyzed, including complex and multiple fault situations with the incomplete and uncertain status signals. The results of the case studies demonstrate that IVFSNP system can be used to diagnose the faulty sections in power transmission networks accurately and effectively.

The power coordination control of a photovoltaic/battery microgrid is performed with a novel bio-computing model within the framework of membrane computing. First, a neural-like P system with state values (SVNPS) is proposed for describing complex logical relationships between different modes of Photovoltaic (PV) units and energy storage units. After comparing the objects in the neurons with the thresholds, state values will be obtained to determine the configuration of the SVNPS. Considering the characteristics of PV/battery microgrids, an operation control strategy based on bus voltages of the point of common coupling and charging/discharging statuses of batteries is proposed. At first, the SVNPS is used to construct the complicated unit working modes; each unit of the microgrid can adjust the operation modes automatically. After that, the output power of each unit is reasonably coordinated to ensure the operation stability of the microgrid. Finally, a PV/battery microgrid, including two PV units, one storage unit, and some loads are taken into consideration, and experimental results show the feasibility and effectiveness of the proposed control strategy and the SVNPS-based power coordination control models.

Compared with the theoretical research, the application research of membrane computing was started late. Firstly, cell-like P system is selected as a computational framework for data clustering based on studies of previous membrane clustering algorithm in this paper. Then, particle swarm optimization algorithm is used as the optimization algorithm to construct the membrane algorithm and the parallel computing characteristic of programmable logic device FPGA is used to realize data clustering. Finally, experimental results show that FPGA processor can realize the characteristics of parallel computing while the system operates the membrane clustering algorithm, which can improve the speed of operation at the same time. Besides, the proposed method can be used in practical engineering systems.

Combining interval-valued fuzzy numbers with spiking neural P systems (SN P systems, in short), an extended SN P system model is developed for fault diagnosis of power systems, called fuzzy reasoning spiking neural P systems with interval-valued fuzzy numbers (ivFRSN P systems, in short). The ivFRSN P systems can better characterize uncertain alarm information in power systems. Firstly, the modeling approach and fuzzy reasoning algorithm are developed. Secondly, the corresponding fault diagnosis models are discussed. Finally, the fault diagnosis of a six-bus 69kV distribution system is used as an example, including single fault with device failure and multiple faults, to demonstrate the availability and effectiveness of the proposed fault diagnosis model based on ivFRSN P systems.

Based on triangular fuzzy spiking neural P systems (TFSNP systems, in short), a fault diagnosis method for power system is presented in this paper. First, triangular fuzzy number (TFN) is integrated into spiking neural P systems (SNP systems, in short) to propose the TFSNP systems. Afterward, modeling and fuzzy reasoning methods based on TFSNP systems are developed. Finally, TFSNP systems are used for fault diagnosis in power system. A fault diagnosis example for ring network of the voltage level with 220 kV is used to demonstrate the availability and effectiveness of the proposed fault diagnosis model.

Based on the fuzzy knowledge, this paper presents a cell-like fuzzy P system (CFPS for short). The CFPS is mainly characterized by the introduction of the fuzzy concept and includes the fuzzy catalyst. Afterwards, the definition and the operation process of the CFPS are elaborated. Based on the CFPS, this paper realizes its application in the control of micro-grid. It takes micro-grid system frequency and current work status as inputs. The CFPS gives the decision-making to choice the reasonable working conditions which include the control of the distributed power and the switching of loads in the micro-grid. Firstly, detailed reasoning is presented to prove the rationality and feasibility of the proposed control thoughts. Then, MATLAB simulation verifies the decision made by the CFPS is correct and the application is rationality which is aimed to achieve a stable energy management and control micro-grid system steady. Experimental results show that CFPS can manage micro-grid effectively, and play a role in load shifting by energy management to stabilize the frequency of feeder.