Lucio Ippolito

Optimisation of energy flow management in hybrid electric vehicles via genetic algorithms

Hybrid electric vehicles powertrain, combining electric motor with an auxiliary power unit, can offer a sensible improvement of the overall vehicle environmental impact achieving at the same time a rational energy employment. The main task of an energy flow management unit is to split the instantaneous vehicle power demand between the internal combustion engine and the electric motor ensuring that the power sources are operated at high efficiency operating points and the related vehicle emissions are minimised. This paper presents an original methodology for the tuning of the characteristic parameters. The proposed methodology identifies, using the genetic algorithm, the value of the energy flow management parameters that minimize the cost function in terms of fuel consumption and emissions. Some interesting simulation results are discussed to prove the validity of the methodology, which contributes to a substantial reduction of the pollutant emissions from hybrid electric vehicles.

Optimal Allocation of FACTS Devices by Using Multi-Objective Optimal Power Flow and Genetic Algorithms

The increases in power flows and environmental constraints are forcing electricity utilities to install new equipment to enhance network operation. Some application of Flexible AC Transmission System (FACTS) technologies to existing high-voltage power systems has proved the use of FACTS technology may be a cost-effective option for power delivery system enhancements. Amongst various power electronic devices, the unified power flow controller (UPFC) device has captured the interest of researchers for its capability of regulating the power flow and minimizing the power losses simultaneously. Since for a cost-effective application of FACTS technology a proper selection of the number and placement of these devices is required, the scope of this paper is to propose a methodology, based on a genetic algorithm, able to identify the optimal number and location of UPFC devices in an assigned power system network for maximizing system capabilities, social welfare and to satisfy contractual requirements in an open market power.In order to validate the usefulness of the approach suggested herein, a case study using a IEEE 30-bus power system is presented and discussed.

Agent-based architecture for designing hybrid control systems

When designing very complex control strategy using hybrid technology, one usually faces the challenge of balancing effective realization of multi-control modeling with design simplicity. To better manage this difficulty we have used the agent paradigm as a simple and powerful bridge between asynchronous/distributed computation and Matlab environment. The proposed architecture has been used to design a complex hybrid control environment using multi-objective, fuzzy c-means, and genetic algorithms optimization to design hybrid control strategies suitable for the energy flows management on board of hybrid electric vehicles.

A genetic-based methodology for hybrid electric vehicles sizing

Abstract As private transport concerns, the global challenge of this millennium is the reduction of carbon dioxide emissions from passenger cars by improving fuel economy without sacrificing the vehicle performance. Hybrid electric vehicles powertrain, combining electric motor with an auxiliary power unit, can improve effectively the vehicle performance and fuel economy, reducing at the same time the effects of the use of private cars on the air quality of the cities. These advantages can be achieved only if the design of the powertrain is inspired to the minimisation of the main figures of merit holding in consideration many general aspects and variables. As supporting methodology in developing this difficult activity, a genetic-based sizing methodology will be presented. It will be aimed to minimise a function objective which takes into account not only technical specifications but also environmental, social, and economic aspects. Some interesting simulation results will be reported to prove the validity of the methodology, which will contribute to a substantial reduction of the pollutant emissions from hybrid electric vehicles.

Parameter identification of power transformers thermal model via genetic algorithms

Abstract Recent studies by various authors have shown as the IEEE Transformer Loading Guide model and the more recent modified equations, proposed by the Working Group K3 of the IEEE ‘Power System Relaying Committee’, are lacking in accuracy in predicting the winding hottest-spot temperature of a power transformer in presence of overload conditions. This is mainly due to the deviation of the parameters of the thermal model of the power transformer in the presence of overload conditions. In the paper, a novel technique to identify the thermal parameters to be used for the estimation of the hot-spot temperature is presented. The proposed method is based on a genetic algorithm (GA) which, working on the load current and on the measured hot-spot temperature pattern, permits to identify a corrected set of parameters for the thermal model of the power transformer. Thanks to data obtained from the experimental tests, the GA based method is tested to evaluate the performance of the proposed method in terms of accuracy.

Extended fuzzy c-means and genetic algorithms to optimize power flow management in hybrid electric vehicles

The need for personal transportation must be harmonized by considering the impact of so huge number of vehicles on the environment. The adoption of hybrid electric vehicles can provide a sensible improvement from an environmental viewpoint, but at the same time makes more difficult the definition and implementation of the overall powertrain control mechanism. In fact, powertrain control problems are known to be very complex due to conflicting requirements, and this difficulty augments in case of hybrid electric vehicles. Most of the features of the future hybrid electric vehicles are enabled by a new energy flow management unit designed to split the instantaneous power demand between the internal combustion engine and the electric motor, ensuring both an efficient power supply and reduced emissions. Classic approaches that rely on static thresholds, optimized on a fixed drive cycle, cannot face the high dynamicity and unpredictability of real-life drive conditions. The need to actually control a real vehicle stimulates the research of innovative methodologies for the real-time identification of the operating points of each energy source. This paper is framed into this context: after a brief discussion about a non-conventional formalization of the energy flows problem based on a multiobjective function, a knowledge-based control system for splitting the vehicles power demand between the engine and motor is presented. The proposed approach exploits a fuzzy clustering criterion that combined with a genetic algorithm, permits to achieve better results, both in terms of a reduced computational effort and an improved efficiency of the control system over various driving cycles. To validate the proposed approach, simulation tests and comparisons with other energy management strategies are discussed.

Using multi-objective optimal power flow for reducing magnetic fields from power lines

Abstract Over the past several years, concerns have been raised over the possibility that the exposure to 50/60 Hz electromagnetic fields (EMFs) from power lines, substations, and other power sources may have detrimental health effects on living organisms. As a result of these concerns, some European States, as Belgium, Italy, Switzerland and Estonia, have set limits, which are more stringent then the Council Recommendation making reference to the precautionary principle. This stricter legislation is leading not only to an ambiguous legal situation but, above all, to controversy, delay, and costs increases in the construction of utility lines and facilities. Consequently, a number of techniques for mitigating EMFs associated with power lines have been proposed, but many of them are mainly applicable to future constructions and may not be appropriate for existing transmission or distribution lines due to high implementation cost. From these considerations, the study analyses the feasibility of using optimal power flow (OPF) for limiting EMF levels. The mitigation is obtained solving a multi-objective optimal power flow (MO-OPF) problem with a specific objective function for the EMFs. In order to validate the usefulness of the approach suggested herein, a case study using a modified IEEE 30-bus power system is presented and discussed.

The use of affine arithmetic for thermal state estimation of substation distribution transformers

Thermal protection of mineral‐oil‐filled substation distribution transformers is of critical importance in power systems. The failure of such a transformer is a matter of significant concern for electrical utilities, not only for the consequent severe economic losses, but also because the utility response to a customer during outage condition is one of the major factors in determining the overall customer attitude towards the utility. Therefore, it is essential to estimate the thermal state of transformers during load cycling and, in presence of overload conditions, to evaluate the need to reduce the load current or to install another transformer bay. A method of solving the transformers thermal model, considering explicitly the source of uncertainty affecting its parameters, is required. In this paper, such an activity is developed by an interval‐based approach, which provides the calculation of the inner and outer solution in the hot‐spot temperature or top‐oil temperature estimation process, keeping track of correlation between uncertain quantities.

Achieving transparency and adaptivity in fuzzy control framework: an application to power transformers predictive overload system

From a technologic point of view, the problem of fuzzy control deals with the real implementation of a controller on a specific hardware. Today, the market of micro-controller offers different solutions able to implement a fuzzy controller varying from application domains to programming language support. Considering the integration issue, made easier from the cheap network infrastructure, there is the need to empower practical approaches suitable to support various and different components ruled by advanced (fuzzy) control strategies. In this work we first present a general Web-based architecture that supports a high integration of heterogeneous and increasingly complex control systems, and then we focus on a Takagi-Sugeno-Kang (TSK) fuzzy model able to reproduce the thermal behaviour of mineral-oil-filled power transformers for implementing a protective overload system. The TSK fuzzy model, working on the load current waveform and on the top oil temperature (TOT), gives an accurate global prediction of the hot-spot temperature (HST) pattern. In order to validate the usefulness of the approach suggested herein, some data cases, derived from various laboratory applications, are presented to measure the accuracy and robustness of the proposed fuzzy model.

Arcing In AC Railways: A MathematicalApproach

The growing use of electronic devices for communication and control in the new AC railways suggests a deepened study of electromagnetic interferences. Amongst the dynamic phenomena the more interesting one is the interference produced by the arcing due to the pantograph de-wiring from the contact wire. This phenomenon determines interference with a wide frequency spectrum up to several GHz, and with a not negligible magnitude. The analysis of this kind of phenomenon represents a complex issue, due to the lack of experiences and mathematical models able to describe completely and in a easy way the electromagnetic phenomena and its interaction with the electric environment. Starting from the results of previous studies, the paper shows an accurate arc resistance model for AC systems. The obtained arc model is used in an high frequency model of an AC railways and it takes into account both the power supply system and the on board traction drive. The results of some simulations are presented to highlights the EMIs (Electromagnetic Interferences) due to the arc ignition. An analysis of the voltages and currents of the system are carried out to evaluate the propagation of the disturbance along the line and the effects on other electric vehicles running on the track. Transactions on the Built Environment vol 34,