Showing papers by "Gexiang Zhang published in 2015"

Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems

Abstract: This paper proposes a graphic modeling approach, fault diagnosis method based on fuzzy reasoning spiking neural P systems (FDSNP), for power transmission networks. In FDSNP, fuzzy reasoning spiking neural P systems (FRSN P systems) with trapezoidal fuzzy numbers are used to model candidate faulty sections and an algebraic fuzzy reasoning algorithm is introduced to obtain confidence levels of candidate faulty sections, so as to identify faulty sections. FDSNP offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity due to its handling of incomplete and uncertain messages in a parallel manner, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. To test the validity and feasibility of FDSNP, seven cases of a local subsystem in an electrical power system are used. The results of case studies show that FDSNP is effective in diagnosing faults in power transmission networks for single and multiple fault situations with/without incomplete and uncertain SCADA data, and is superior to four methods, reported in the literature, in terms of the correctness of diagnosis results.

A Many-Objective Evolutionary Algorithm With Enhanced Mating and Environmental Selections

Abstract: Multiobjective evolutionary algorithms have become prevalent and efficient approaches for solving multiobjective optimization problems. However, their performances deteriorate severely when handling many-objective optimization problems (MaOPs) due to the loss of selection pressure to drive the search toward the Pareto front and the ineffective design in diversity maintenance mechanism. This paper proposes a many-objective evolutionary algorithm (MaOEA) based on directional diversity (DD) and favorable convergence (FC). The main features are the enhancement of two selection schemes to facilitate both convergence and diversity. In the algorithm, a mating selection based on FC is applied to strengthen selection pressure while an environmental selection based on DD and FC is designed to balance diversity and convergence. The proposed algorithm is tested on 64 instances of 16 MaOPs with diverse characteristics and compared with seven state-of-the-art algorithms. Experimental results show that the proposed MaOEA performs competitively with respect to chosen state-of-the-art designs.

Design and implementation of membrane controllers for trajectory tracking of nonholonomic wheeled mobile robots

Abstract: This paper proposes a novel trajectory tracking control approach for nonholonomic wheeled mobile robots. In this approach, the integration of feed-forward and feedback controls is presented to design the kinematic controller of wheeled mobile robots, where the control law is constructed on the basis of Lyapunov stability theory, for generating the precisely desired veloc- ity as the input of the dynamic model of wheeled mobile robots; a proportional-integral-derivative based membrane controller is introduced to design the dynamic controller of wheeled mobile robots to make the actual velocity follow the desired velocity command. The proposed approach is defined by using an enzymatic numerical membrane system to integrate two proportional- integral-derivative controllers, where neural networks and experts' knowledge are applied to tune parameters. Extensive experi- ments conducted on the simulated wheeled mobile robots show the effectiveness of this approach.

Multicriteria adaptive differential evolution for global numerical optimization

Abstract: Differential evolution DE has become a prevalent tool for global optimization problems since it was proposed in 1995. As usual, when applying DE to a specific problem, determining the most proper strategy and its associated parameter values is time-consuming. Moreover, to achieve good performance, DE often requires different strategies combined with different parameter values at different evolution stages. Thus integrating several strategies in one algorithm and determining the application rate of each strategy as well as its associated parameter values online become an ad-hoc research topic. This paper proposes a novel DE algorithm, called multicriteria adaptive DE MADE, for global numerical optimization. In MADE, a multicriteria adaptation scheme is introduced to determine the trial vector generation strategies and the control parameters of each strategy are separately adjusted according to their most recently successful values. In the multicriteria adaptation scheme, the impacts of an operator application are measured in terms of exploitation and exploration capabilities and correspondingly a multi-objective decision procedure is introduced to aggregate the impacts. Thirty-eight scale numerical optimization problems with various characteristics and two real-world problems are applied to test the proposed idea. Results show that MADE is superior or competitive to six well-known DE variants in terms of solution quality and convergence performance.

A Modified Membrane-Inspired Algorithm Based on Particle Swarm Optimization for Mobile Robot Path Planning

Abstract: To solve the multi-objective mobile robot path planning in a dangerous environment with dynamic obstacles, this paper proposes a modified membraneinspired algorithm based on particle swarm optimization (mMPSO), which combines membrane systems with particle swarm optimization. In mMPSO, a dynamic double one-level membrane structure is introduced to arrange the particles with various dimensions and perform the communications between particles in different membranes; a point repair algorithm is presented to change an infeasible path into a feasible path; a smoothness algorithm is proposed to remove the redundant information of a feasible path; inspired by the idea of tightening the fishing line, a moving direction adjustment for each node of a path is introduced to enhance the algorithm performance. Extensive experiments conducted in different environments with three kinds of grid models and five kinds of obstacles show the effectiveness and practicality of mMPSO.

Fuzzy Membrane Computing: Theory and Applications

Abstract: Fuzzy membrane computing is a newly developed and promising research direction in the area of membrane computing that aims at exploring the complex in- teraction between membrane computing and fuzzy theory. This paper provides a comprehensive survey of theoretical developments and various applications of fuzzy membrane computing, and sketches future research lines. The theoretical develop- ments are reviewed from the aspects of uncertainty processing in P systems, fuzzifica- tion of P systems and fuzzy knowledge representation and reasoning. The applications of fuzzy membrane computing are mainly focused on fuzzy knowledge representation and fault diagnosis. An overview of different types of fuzzy P systems, differences between spiking neural P systems and fuzzy reasoning spiking neural P systems and newly obtained results on these P systems are presented.

PMU based Robust Dynamic State Estimation method for power systems

Abstract: An accurate and dynamically robust state estimator is indispensable for the efficient and reliable operation of a power system. In this paper, a state estimation method based on Phasor Measurement Units (PMUs) is proposed for the real-time monitoring of power systems under various operating conditions. This PMU-based Robust Dynamic State Estimator (PRDSE) makes feasible the combination of historical measurements, obtained from the Supervisory Control And Data Acquisition (SCADA) system, with present more precision measurements obtained from PMUs. A new state accuracy-based weighting function is proposed to increase the robustness when the system encounters a large unwanted disturbance. Several IEEE test systems under normal and dynamic operation conditions are used to demonstrate the high performance of the PRDSE. Numerical results show the effectiveness and robustness of the PRDSE.

QEAM: An Approximate Algorithm Using P Systems with Active Membranes

Abstract: This paper proposes an approximate optimization approach, called QEAM, which combines a P system with active membranes and a quantum-inspired evolutionary algorithm. QEAM uses the hierarchical arrangement of the compart- ments and developmental rules of a P system with active membranes, and the objects consisting of quantum-inspired bit individuals, a probabilistic observation and the evolutionary rules designed with quantum-inspired gates to specify the membrane algorithms. A large number of experiments carried out on benchmark instances of satisfiability problem show that QEAM outperforms QEPS (quantum-inspired evolu- tionary algorithm based on P systems) and its counterpart quantum-inspired evolu- tionary algorithm.

Multistage Phasor-aided Bad Data Detection and Identification

Abstract: This paper proposes a multistage Phasor-aided Bad Data Detection and Identification (PBDDI) method to improve state estimation accuracy. This method defines a special kind of innovation— the difference between estimated SCADA measurements (calculated from valid PMU state estimates) and raw SCADA measurements, then proposed statistical residual test is used in a cooperative effort for identifying bad data. The proposed approach has the advantages of handling various kinds of bad data all at once, including bad data smearing effect elimination, critical measurements detection. Besides, the adequate measurements replacement strategy can avoid the multiple round identification process, commonly adopted by the estimation residual analysis of hybrid estimators, resulting in computation reduction. Numerical tests on the IEEE-14 system under various cases verify the effectiveness of proposed method.