Multi-period wind integrated optimal dispatch using series PSO-DE with time-varying Gaussian membership function based fuzzy selection

Wind power has emerged as the most promising option for providing sustainable eco-friendly power supply to the modern world. Due to its unpredictable nature, integration of wind power into the conventional power grid is a very challenging task having dynamic characteristics. Due to the inherent uncertainty associated with wind availability, additional spinning reserve needs to be scheduled in order to maintain security and supply reliability. Multi-period multi-objective optimal dispatch (MPMOOD) is presented for wind integrated power system with reserve constraints. The complex relationship between wind power availability, spinning reserve allocation and their impact on economic/environmental cost are analysed using an elaborate model.A new multi-objective Series PSO-DE (SPSO-DE) hybrid algorithm is proposed where the two paradigms, differential evolution (DE) and particle swarm optimization (PSO) share domain information and maintain a synergistic cooperation to overcome their individual weaknesses. For multi-objective (MO) problems, the selection operation in SPSO-DE is replaced by a 5-class time-varying fuzzy selection mechanism (TVFSM) to avoid saturation and to increase Pareto diversity. To promote convergence towards the central part of the Pareto front and to quickly isolate the boundary solutions, Guassian membership function is employed. Elitism is applied to preserve good solutions and momentum operation is used to stop premature convergence. The proposed method expedites the search for the best solution, i.e. the solution which satisfies all the objectives of the MO problems. To test the performance and computational efficiency, the proposed method is applied on two standard test power systems.     

Posture Control for Personal Mobility Robot Using Feedback Compensation with Unstable Pole

This paper presents a novel posture control approach using feedback compensation with an unstable pole. For a narrow and small personal mobility robot (PMR), this is needed in order to control its posture for achieving quick turning and high acceleration. However, in the conventional control approach that uses the posture angle as a controlled variable, the zero moment point (ZMP) does not settle to the desired point in the following cases: an unknown disturbance force given to the PMR, a fluctuation in the center of gravity of the PMR, and a change in the conditions between the tires and the road surface. In this paper, a novel control method using feedback compensation with an unstable pole is proposed to achieve the desired ZMP in the steady state. The proposed controller changes the control input for the actuator of posture control to zero in order to achieve the desired posture angle. The effectiveness of the proposed approach is verified through experiments using a prototype of the PMR.     

A novel quasi-oppositional harmony search algorithm and fuzzy logic controller for frequency stabilization of an isolated hybrid power system

The intermittent wind power in isolated hybrid distributed generation (IHDG) may cause serious problems associated with frequency (f) and power (P) fluctuation. Energy storage devices such as battery, super capacitor, and superconducting magnetic energy storage (SMES) may be used to reduce these fluctuations associated with f and P. This paper presents a study of IHDG power system for improving both f and P deviation profiles with the help of SMES. The studied IHDG power system is consisted of wind turbine generator and diesel engine generator. Both f and P control problems of the studied power system model are addressed in presence or absence of SMES. Fuzzy logic based proportional–integral–derivative (PID) controller with SMES is used for the purpose of minimization of f and P deviations. The different tunable parameters of the PID controller and those of the SMES are tuned by a novel quasi-oppositional harmony search algorithm. Performance study of the IHDG power system model is carried out under different perturbation conditions. The results demonstrate minimum f and P deviations may be achieved by using the proposed fuzzy logic based PID controller along with SMES.     

Energy storage requirements of dc microgrids with high penetration renewables under droop control

Energy storage is a important design component in microgrids with high penetration renewable sources to maintain the system because of the highly variable and sometimes stochastic nature of the sources. Storage devices can be distributed close to the sources and/or at the microgrid bus. In addition, storage requirements can be minimized with a centralized control architecture, but this creates a single point of failure. Distributed droop control enables a completely decentralized architecture but, the energy storage optimization becomes more difficult. This paper presents an approach to droop control that enables the local and bus storage requirements to be determined. Given a priori knowledge of the design structure of a microgrid and the basic cycles of the renewable sources, the droop settings of the sources are determined that minimize both the bus voltage variations and overall energy storage capacity required in the system. This approach can be used in the design phase of a microgrid with a decentralized control structure to determine appropriate droop settings as well as the sizing of energy storage devices.     

An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.     

Fault‐tolerant control of distributed systems by information pattern reconfiguration

Given a fault‐tolerance strategy and a distributed control system, the set of recoverable faults depends on the information pattern that is implemented. This paper shows that fault‐tolerance can be achieved, when possible, by the reconfiguration of the information pattern. A necessary and sufficient recoverability condition is provided, and sub‐optimal solutions are developed for actuator and sensor faults under system reconfiguration. Copyright © 2014 John Wiley & Sons, Ltd.     

Control-faced dynamics with deformation compatibility for a 5-DOF active over-constrained spatial parallel manipulator 6PUS–UPU

Taking into account the effect of structural compliance, inverse dynamics of the active over-constrained parallel manipulator 6PUS–UPU with five degrees of freedom is solved in this article. Firstly, the relationship between driving forces and actuated force screws of each limb is derived. Then the coordination of elastic deformation between limbs which consider the effect of gravity and inertia is acquired. Finally the unique solution of driving forces for the active over-constrained parallel manipulator is derived by incorporating the force equilibrium equation of the moving platform. To validate the theoretical derivation, dynamics simulation model of manipulator based on rigid–flexible mixed structure is shown and numerical examples are given. Comparison with the traditional method of dynamics based on pseudo-inverse is also made. Finally, a feasible experimental method, as an effective test to the theoretical calculation, is proposed and applied on the prototype.     

建筑电气工程中低压配电系统的安装与调试

低压配电系统对建筑电气工程是非常重要的，本文对其安装做论述，针对各种建筑物对配电的要求，对系统做合理的调试，最终实现建筑实体安全用电，同时也确保低压配电系统的稳定性。     

基于学生群体心理测试系统的设计

心理测试的根本目的是为了让人们在测试过程中，产生某些特定行为，即个体对测试题目的反应，并根据这些行为反应来推论其相应的心理特性。在一定程度上帮助我们了解人类的心理活动，并从中得到帮助。因此，本文对基于学生群体心理测试系统的设计进行深入研究。     

Root cause analysis of process fault conditions on an industrial concentrator circuit by use of causality maps and extreme learning machines

Model-based fault diagnosis tends to be too expensive or time-consuming to apply in the mineral processing industries, owing to the complexity and variability of operations. In contrast, data-based methods are inexpensive, but do not exploit the availability of first principle knowledge of plant operations. In this investigation, the use of process causality maps in conjunction with data-based fault diagnosis is considered as a hybrid methodology that can leverage the advantages of both approaches. Extreme learning machine algorithms are used to implement the data-based component of the approach. These algorithms can be deployed rapidly on large-scale systems and have the ability to deal with highly nonlinear systems. Two different variants are considered, viz. one used in combination with principal component analysis, as well as one with a bagging algorithm for fault diagnosis and applied to an industrial concentrator circuit in South Africa. The use of process causality maps led to significantly more effective fault diagnosis, while the use of extreme learning machines in combination with principal component analysis likewise allowed markedly better fault detection and diagnosis. In contrast, fault diagnosis with the bagging approach did not perform particularly well, owing to the high degree of correlation between the variables, which made it difficult to isolate individual causal variables.