基于IEC 60870-5-104协议的调度命令发送方法研究

对IEC 60870-5-104协议的调度命令进行了研究，提出了一种基于现有IEC 60870-5-104协议的调度命令发送新方法。此技术充分利用了IEC 60870-5-104协议的文件发送功能，通过远程终端单元（RTU）向电厂发送包含调度命令的文本文件。考虑到调度命令在电力系统和电力市场结算系统安全方面的重要性，将该方法应用在电网调度自动化 SCADA 系统中，可以实现交换数据的更高可用性。     

Statistics of ceramic strength: Use ordinary Weibull distribution function or Weibull statistical fracture theory?

“Weibull statistics” for strength distribution analysis refers to either the ordinary Weibull distribution function or the Weibull statistical fracture theory. The ordinary Weibull distribution function is an empirical distribution function on an equal footing with other type of classical empirical distributions such as normal and log-normal distributions for fitting the statistical data of various random variables nonexclusive to materials strength. It has no explicit physical meaning and cannot be used for size scaling and prediction of strength. The Weibull statistical fracture theory is a weakest-link statistical fracture model for a solid with the strength distribution of an elemental volume being described by the ordinary Weibull distribution function. It has the capability of size scaling and prediction of strength for specimens with different geometries and different loading modes. The three-parameter Weibull statistical fracture theory in uniaxial flexure of prismatic beams is reformulated and validated by both numerical and real strength experiments of different ceramics.     

Monte Carlo simulation-based probabilistic assessment of DG penetration in medium voltage distribution networks

With the growing use of renewable energy sources, Distributed Generation (DG) systems are rapidly spreading. Embedding DG to the distribution network may be costly due to the grid reinforcements and control adjustments required in order to maintain the electrical network reliability. Deterministic load flow calculations are usually employed to assess the allowed DG penetration in a distribution network in order to ensure that current or voltage limits are not exceeded. However, these calculations may overlook the risk of limit violations due to uncertainties in the operating conditions of the networks. To overcome this limitation, related to both injection and demand profiles, the present paper addresses the problem of DG penetration with a Monte Carlo technique that accounts for the intrinsic variability of electric power consumption. The power absorbed by each load of a medium voltage network is characterized by a load variation curve; a probabilistic load flow is then used for computing the maximum DG power that can be connected to each bus without determining a violation of electric constraints. A distribution network is studied and a comparison is provided between the results of the deterministic load flow and probabilistic load flow analyses.     

Integrated sizing and scheduling of wind/PV/diesel/battery isolated systems

In this paper we address the optimal sizing and scheduling of isolated hybrid systems using an optimization framework. The hybrid system features wind and photovoltaic conversion systems, batteries and diesel backup generators to supply electricity demand. A Mixed-Integer Linear Programming formulation is used to model system behavior over a time horizon of one year, considering hourly changes in both the availability of renewable resources and energy demand. The optimal solution is achieved with respect to the minimization of the levelized cost of energy (LCOE) over a lifetime of 20 years. Results for a case study show that the most economical solution features all four postulated subsystems.     

Acceptance of biomass plants – Results of a longitudinal study in the bioenergy-region Altmark

The present contribution addresses the results of a longitudinal study in a ‘bioenergy-region’ concerning the public acceptance of biomass plants and the corresponding influencing factors. Using a standardized questionnaire, 423 persons were polled between 2009 and 2011 on three points of measurement in four places in the bioenergy-region Altmark. One main result of the study is that the reported public acceptance remains constantly high over time; nevertheless it became evident that the respective influencing factors differ in their strength, whereas the perceived regional benefit shows a strong connection to the reported public acceptance of biomass plants at each point of measurement. Concluding the research results, the acceptance of biomass plants doesn't seem to be a fixed construct, but has to be seen in context of the respective experiences with plants on a local level over time.In addition to the local population, key actors of the regional biomass sector were also interviewed (N = 26). The analyses show significant differences in the perception and evaluation of the current informational level between the population and the key actors. Furthermore, the key actors estimated the utilisation of biomass even more positively and expected a greater ‘signal function’ of the bioenergy-region-project compared to the population.     

Rules discovery in fuzzy classifier systems with PSO for scheduling in grid computational infrastructures

Particle swarm optimization (PSO) is a bio-inspired optimization strategy founded on the movement of particles within swarms. PSO can be encoded in a few lines in most programming languages, it uses only elementary mathematical operations, and it is not costly as regards memory demand and running time. This paper discusses the application of PSO to rules discovery in fuzzy classifier systems (FCSs) instead of the classical genetic approach and it proposes a new strategy, Knowledge Acquisition with Rules as Particles (KARP). In KARP approach every rule is encoded as a particle that moves in the space in order to cooperate in obtaining high quality rule bases and in this way, improving the knowledge and performance of the FCS. The proposed swarm-based strategy is evaluated in a well-known problem of practical importance nowadays where the integration of fuzzy systems is increasingly emerging due to the inherent uncertainty and dynamism of the environment: scheduling in grid distributed computational infrastructures. Simulation results are compared to those of classical genetic learning for fuzzy classifier systems and the greater accuracy and convergence speed of classifier discovery systems using KARP is shown.     

A novel fast distance relay for series compensated transmission lines

This paper presents a fast distance relay for series compensated transmission lines based on the R–L differential-equation algorithm using the theory of equal transfer process of transmission lines. The measuring distances based on the proposed algorithm can fast approach the actual value of fault distance when a fault occurs in front of the series capacitor. When a fault occurs behind of the series capacitor, the fault loop, including the series capacitor, does not match the R–L transmission line model, so the measuring distances fluctuate severely. Based on this, the relative position of the fault with respect to the series capacitor can be judged effectively according to the fluctuation range of the measuring distances, and the accurate fault location can be obtained fast. A variety of PSCAD/EMTDC simulation tests show that the new relay has fast operating speed and high accuracy when applied to the long series compensated transmission lines.     

Protection of power transformer using multi criteria decision-making

Power transformers are protected by different relays that operate independently. Malfunction of each relay has a major role in reducing the reliability of the protection system. In order to mitigate the main drawbacks of the power transformer relays, an overall protection scheme is presented in this paper. This scheme proposes a novel multi criterion algorithm using decision-making based on fuzzy logic. In this paper the outputs of restricted earth fault relay and a directional check unit, are combined with the output of the differential protection relay. Therefore, problems that are pertaining to independent operation of each relay have been mitigated and the relays cover protection blind spots of each other. The improved power transformer protection (IPTP) scheme enhances the sensitivity and reliability of the power transformer protection. Extensive simulations are used to measure the effectiveness and merit of the proposed IPTP relay. The above efforts result in a multi criteria approach for protection of power transformers.     

An improved partheno genetic algorithm for multi-objective economic dispatch in cascaded hydropower systems

The multi-objective economic dispatch (MOED) problem in cascaded hydropower systems is a complicated nonlinear optimization problem with a group of complex constraints. In this paper, an improved partheno genetic algorithm (IPGA) for resolving the MOED problem in hydropower energy systems based on the non-uniform mutation operator is proposed. In the new algorithm, the crossover operator is removed and only mutation operation is made, which makes it simpler than GA in the genetic operations and not generate invalid offspring during evolution. With the help of incorporating greedy selection idea into the non-uniform mutation operator, IPGA searches the solution space uniformly at the early stage and very locally at the later stage, which makes it avoid the random blind jumping and stay at the promising solution areas. Finally, the proposed algorithm is applied to a realistic hydropower energy system with two giant scale cascaded hydropower plants in China. Compared with other algorithms, the results obtained using IPGA verify its superiority in both efficiency and precision.     

Simulation and experimental study of the NOx reduction by unburned H2 in TWC for a hydrogen engine

The unburned H₂ can be used to reduce NO emission in conventional TWC (three-way catalyst) for a hydrogen internal combustion engine when it works at equivalence ratio marginally higher than the stoichiometric ratio. To explore the effects and feasibility of this reaction, a Perfectly Stirred Reactor simulation model of TWC has been built with simplified mechanisms. Experiments on a 2.3 L turbocharged hydrogen engine are used to verify the conclusion. It shows that rising initial temperature accelerates the reduction of NO and the maximum reaction rate occurs at 400 °C temperature. The conversion efficiency of NO remains approximately 0 when temperatures below 300 °C. The efficiency reaches a peak value of approximately 98% with 400 °C and declines gradually. The unburned H₂ to NO mixing ratio greater than 1.5 in TWC guarantees 100% NO conversion efficiency. The experiments indicate that the NOx concentration decreases from 2056 ppm to 41 ppm at the stoichiometric ratio after the treatment of TWC and NOx reaches 0 ppm with a rich ratio. Results also demonstrate that the suitable reaction temperatures for TWC locate in the range of 400 °C–500 °C. Therefore, if the temperature and the mixing ratio are appropriate, it can achieve zero emissions with NOx reduction by unburned H₂ in conventional TWC for a hydrogen engine.