US 8,933,572 B1Granted

Adaptive superconductive magnetic energy storage (SMES) control method and system

AssigneeKing Fahd University of Petroleum and Minerals

InventorsAbu Hamed Abdur-Rahim, Muhammad Haris Khan

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Abstract

The adaptive superconductive magnetic energy storage (SMES) control method and system control a SMES device connected to a power generation system. A radial basis function neural network (RBFNN) connected to the controller adaptively adjusts gain constants of the controller. A processor executes an improved particle swarm optimization (IPSO) procedure to train the RBFNN from input-output training data created by the IPSO, and thereafter generate starting weights for the neural network. Tests carried out show that the proposed adaptive SMES controller maintains the DC capacitor voltage constant, thus improving the efficiency of wind energy transfer. The power output (reactive and real) of the SMES device improves the voltage profile following large voltage dips and provides added damping to the system.

Claims16

Dependent claims

In a wind turbine electrical power generator system that includes a superconductive magnetic energy storage (SMES) device, a SMES controller, and a neural network executing on a processor connected to the SMES controller, an adaptive superconductive magnetic energy storage (SMES) control method, comprising the steps of:

comparing a power generator reference power and voltage signal with a measured power and voltage signal from the electrical power generator system in order to generate a reference error signal based on any differences between the reference and the measured signals;
transmitting the reference error signal to an input of the SMES controller;
the SMES controller, responsive to the reference error signal, commanding the SMES device to change its real power contribution, ΔPSM, and its reactive power contribution, ΔQSM, to the power generator system;
sending signals from the neural network to the SMES controller to adaptively adjust gain and time constant controller parameters, Kpg, Kvg, Tpg, Tvg of the SMES controller, where Kpg is SMES controller gain (generator real power), Kvg is SMES controller gain (generator real voltage), Tpg is SMES controller time response (generator real power) and Tvg is SMES controller time response (generator real voltage);
generating starting weights for the neural network;
and
adaptively and iteratively adjusting the neural network weights responsive to error signals generated from differences between output power of the power generator system and the power generator reference power.

4 more elements

A wind turbine electrical power generator system, comprising:

a wind turbine electrical power generator;
a superconductive magnetic energy storage (SMES) device operably connected to the wind turbine electrical power generator;
a SMES controller operably connected to the SMES device;
a processor operably connected to the SMES controller, the processor having means for operating a neural network, the neural network including means for adaptively adjusting gain and time constant controller parameters, Kpg, Kvg, Tpg, Tvg of the SMES controller, where Kpg is SMES controller gain (generator real power), Kvg is SMES controller gain (generator real voltage), Tpg is SMES controller time response (generator real power) and Tvg is SMES controller time response (generator real voltage);
means for comparing a power generator reference power and voltage signal with a measured power and voltage signal from the wind turbine electrical power generator system in order to generate a reference error signal based on any differences between the reference and the measured signals;
means for transmitting the reference error signal to an input of the SMES controller;
means for commanding the SMES device to change its real power contribution, ΔPSM, and its reactive power contribution, ΔQSM, to the power generator system responsive to the reference error signal;
means for generating starting weights for the neural network;
and
means for adaptively adjusting the neural network weights responsive to error signals generated from differences between output power of the power generator system and the power generator reference power.

7 more elements

Dependencies are inferred from the claim text.