Interpolation Methods for Binary and Multivalued Logical Quantum Gate Synthesis

A method for synthesizing quantum gates is presented based on interpolation methods applied to operators in Hilbert space. Starting from the diagonal forms of specific generating seed operators with non-degenerate eigenvalue spectrum one obtains for arity-one a complete family of logical operators corresponding to all the one-argument logical connectives. Scaling-up to n-arity gates is obtained by using the Kronecker product and unitary transformations. The quantum version of the Fourier transform of Boolean functions is presented and a Reed-Muller decomposition for quantum logical gates is derived. The common control gates can be easily obtained by considering the logical correspondence between the control logic operator and the binary propositional logic operator. A new polynomial and exponential formulation of the Toffoli gate is presented. The method has parallels to quantum gate-T optimization methods using powers of multilinear operator polynomials. The method is then applied naturally to alphabets greater than two for multi-valued logical gates used for quantum Fourier transform, min-max decision circuits and multivalued adders

Fuzzy modelling using a simplified rule base

Transparency and complexity are two major concerns of fuzzy rule-based systems. To improve accuracy and precision of the outputs, we need to increase the partitioning of the input space. However, this increases the number of rules exponentially, thereby increasing the complexity of the system and decreasing its transparency. The main factor behind these two issues is the conjunctive canonical form of the fuzzy rules. We present a novel method for replacing these rules with their singleton forms, and using aggregation operators to provide the mechanism for combining the crisp outputs

Automating virtual power plant decision making with fuzzy logic and human psychology

This paper presents a Virtual Power Plant (VPP) decision making approach which uses fuzzy logic and a novel “insecurity” metric, based on human psychology. The VPP approach is modelled as a multi-agent system, which aims to minimize carbon emissions and/or energy cost, using an aggregation structure similar to energy or carbon markets. The “insecurity factor” reflects the operational flexibility of micro-generators, translated to a numerical value through fuzzy logic. The system was able to create a functional internal VPP market, where the micro-generators were trading autonomously according to external price signals and taking into account their own needs and limitations, as well as short-term forecasts

Integrated Optimal Design of a Passive Wind Turbine System: An Experimental Validation

This work presents design and experimentation of a full passive wind turbine system without active electronic part(power and control). The efficiency of such device can be obtained only if the system design parameters are mutually adapted through an Integrated Optimal Design (IOD) method. This approach based on multiobjective optimization, aims at concurrently optimizing the wind power extraction and the global system losses for a given wind speed profile while reducing the weight of the wind turbine generator. It allows us to obtain the main characteristics (geometric and energetic features) of the optimal Permanent Magnet Synchronous Generator (PMSG) for the passive wind turbine. Finally, experiments on the PMSG prototype built from this work show a good agreement with theoretical predictions. This validates the design approach and confirms the effectiveness of such passive device

Smoothing Control of Wind Farm Output Fluctuations by Fuzzy Logic Controlled SMES

Due to random variations of wind speed, the output power and terminal voltage of a fixed speed wind generator fluctuate continuously. These irregularities in power output are affecting both the power quality and reliability. It is reported that STATCOM/SMES (Superconducting Magnetic Energy Storage) system can significantly decrease voltage and output power fluctuations of grid connected fixed speed wind generator. But the main problem in wind generator output power smoothing is the choice of the reference output power, because it corresponds to energy storage capacity. The storage capacity of SMES that is sufficient for the smoothing control but as small as possible is very important, considering cost effectiveness. In this paper, a fuzzy logic controlled STATCOM/SMES system is proposed, in which both SMA (Simple Moving Average) and EMA (Exponential Moving Average) are used to generate output power reference. Real wind speed data are used in the simulation analyses, whichvalidate the effectiveness of the proposed control strategy. Simulation results clearly show that the proposed STATCOM/SMES system can smooth well the wind generator output power and also maintain the terminal voltage at rated level in both cases when SMA or EMA is used to generate output reference power. Finally, it is shown that reference output power generated by EMA provides better performance with reduced SMES storage capacity than that of output power generated by SMA.Keywords: Minimization of fluctuations, fixed speed wind generator, STATCOM/SMES, simple moving average (SMA) and exponential moving average (EMA), and wind farm (WF).DOI:http://dx.doi.org/10.11591/ijece.v1i2.18

Appropriate choice of aggregation operators in fuzzy decision support systems

Fuzzy logic provides a mathematical formalism for a unified treatment of vagueness and imprecision that are ever present in decision support and expert systems in many areas. The choice of aggregation operators is crucial to the behavior of the system that is intended to mimic human decision making. This paper discusses how aggregation operators can be selected and adjusted to fit empirical data&mdash;a series of test cases. Both parametric and nonparametric regression are considered and compared. A practical application of the proposed methods to electronic implementation of clinical guidelines is presented<br /

Optimal Sizing and Location of Static and Dynamic Reactive Power Compensation

The key of reactive power planning (RPP), or Var planning, is the optimal allocation of reactive power sources considering location and size. Traditionally, the locations for placing new Var sources were either simply estimated or directly assumed. Recent research works have presented some rigorous optimization-based methods in RPP. Different constraints are the key of various optimization models, identified as Optimal Power Flow (OPF) model, Security Constrained OPF (SCOPF) model, and Voltage Stability Constrained OPF model (VSCOPF). First, this work investigates the economic benefits from local reactive power compensation including reduced losses, shifting reactive power flow to real power flow, and increased transfer capability. Then, the benefits in the three categories are applied to Var planning considering different locations and amounts of Var compensation in an enumeration method, but many OPF runs are needed. Then, the voltage stability constrained OPF (VSCOPF) model with two sets of variables is used to achieve an efficient model. The two sets of variables correspond to the “normal operating point (o)” and “collapse point (*)” respectively. Finally, an interpolation approximation method is adopted to simplify the previous VSCOPF model by approximating the TTC function, therefore, eliminating the set of variables and constraints related to the “collapse point”. In addition, interpolation method is compared with the least square method in the literature to show its advantages. It is also interesting to observe that the test results from a seven-bus system show that it is not always economically efficient if Var compensation increases continuously