Vincenzo Galdi

Designing an Adaptive Fuzzy Controller for Maximum Wind Energy Extraction

The wind power production spreading, also aided by the transition from constant to variable speed operation, involves the development of efficient control systems to improve the effectiveness of power production systems. This paper presents a data-driven design methodology able to generate a Takagi-Sugeno-Kang (TSK) fuzzy model for maximum energy extraction from variable speed wind turbines. In order to obtain the TSK model, fuzzy clustering methods for partitioning the input-output space, combined with genetic algorithms, and recursive least-squares optimization methods for model parameter adaptation are used. The implemented TSK fuzzy model, as confirmed by some simulation results on a doubly fed induction generator connected to a power system, exhibits high speed of computation, low memory occupancy, fault tolerance, and learning capability.

Optimal Decentralized Voltage Control for Distribution Systems With Inverter-Based Distributed Generators

The increasing penetration of distributed generation (DG) power plants into distribution networks (DNs) causes various issues concerning, e.g., stability, protection equipment, and voltage regulation. Thus, the necessity to develop proper control techniques to allow power delivery to customers in compliance with power quality and reliability standards (PQR) has become a relevant issue in recent years. This paper proposes an optimized distributed control approach based on DN sensitivity analysis and on decentralized reactive/active power regulation capable of maintaining voltage levels within regulatory limits and to offer ancillary services to the DN, such as voltage regulation. At the same time, it tries to minimize DN active power losses and the reactive power exchanged with the DN by the DG units. The validation of the proposed control technique has been conducted through a several number of simulations on a real MV Italian distribution system.

A smart strategy for voltage control ancillary service in distribution networks

The expected impact of distributed generation (DG) into Smart Grid represents a great challenge of the future for power systems. In particular, the integration of DG based on renewable energy sources (RESs) in distribution networks, without compromising the integrity of the grid, requires the development of proper control techniques to allow power delivery to customers in compliance with power quality and reliability standards. This paper proposes a coordinated local control approach that allows distribution system operator (DSO) and independent power producers (IPPs) to obtain benefits offering the voltage regulation ancillary service to DSO and maximizing allowable active power production for each RES unit belonging to the same IPP. The control is based on a cooperation of data transfer between DSO and IPPs. In order to realize such cooperation, a nonlinear constrained optimization problem is formulated and solved by sequential quadratic programming (SQP) method. The validation of the proposed control technique has been conducted through several time series simulations on a real MV Italian distribution system.

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.

A new algorithm for steady state load-shedding strategy

This paper deals with a steady state load-shedding strategy considering the effect of the demand priorities on the operation of the power system during emergencies. The load-shedding problem is solved by a new algorithm (the Alliance Algorithm) based on the “alliance among tribes” (a tribe or an alliance is a possible solution with a proximity degree to the optimum). The obtained results prove additional enhancements compared to other heuristic approaches. The case studies are carried out on 30 and 57 bus IEEE test networks.

Distributed Generation and local voltage regulation: An approach based on sensitivity analysis

The increasing diffusion of distributed generation plants in recent years could introduce problems concerning voltage regulation in Medium Voltage (MV) radial distribution networks. Among the various control techniques able to perform voltage regulation, local sensitivity analysis based ones are suitable to regulate voltage profiles within standard limits. This paper presents a decentralized sensitivity based control technique able to regulate voltage profiles at buses where wind power generators are connected, taking into account capability curves constraints of wind turbines. The implemented control strategy allows voltage regulation avoiding, as much as possible, both disconnection of the wind turbine and reduction of active power production. Validation of the proposed control algorithm has been carried out by simulations run on a real MV Italian radial distribution network.

Optimal fuzzy controller for voltage control in distribution systems

The increasing diffusion of distributed generation plants in recent years highlights problems concerning voltage regulation in medium voltage (MV) radial distribution networks. Among various possible control techniques able to regulate voltage profiles, intelligent systems based ones seems to be very promising. In particular, fuzzy control techniques are very interesting for a wide range of applicative fields like power distribution systems control, allowing regulation of voltage profiles handling uncertainty/vagueness and imprecise information. This paper presents a decentralized fuzzy based control technique finalized to realize a local regulation of voltage profiles at buses where wind power generators are connected, in order to avoid their disconnection. Validation of the proposed control system has been carried out by simulations conducted on a real MV Italian radial distribution system.

Evaluation of a Neural-Network-Based adaptive Beamforming Scheme with Magnitude-Only Constraints

In this paper, we present an adaptive beamforming scheme for smart antenna arrays in the presence of several desired and interfering signals, and additive white Gaussian noise. As compared with standard schemes, the proposed algorithm minimizes the noise and interference contributions, but enforces magnitude-only constraints, and exploits the array-factor phases in the desired-signal directions as further optimization parameters. The arising nonlinearly constrained optimization problem is recast, via the Lagrange method, in the unconstrained optimization of a non-quadratic cost function, for which an iterative technique is proposed. The implementation via artificial neural networks is addressed, and results are compared with those obtained via standard schemes.

Impact analysis of distributed PV and energy storage systems in unbalanced LV networks

The integration of distributed PV units in LV distribution networks with co-located energy storage systems (ESSs) allows increasing the amount of consumed local electrical energy while significantly raising the PV penetration. The installation of ESSs may in addition promote the self-consumption of energy and reduce the mismatch between the demand and the PV power generation making such power source dispatchable. A Monte Carlo simulation is performed by varying residential load profiles, sizes and locations of PV units and ESSs in order to assess the impact that a local and independent control of co-located PV units and ESSs have on the grid. Time series unbalanced power flow simulations are carried out considering different scenarios on a typical LV Italian distribution network. The results are evaluated in terms of benefits on voltage profiles pointing out a significantly reduction of voltage problems on the network at each penetration level.