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This paper presents a design for the novel intelligent control (NIC) of the stabilization of a doubly fed induction generator (DFIG)-based wind turbine. The intelligent control scheme was developed...

A new approach to rotational speed control structures based on an optimized intelligent recurrent wavelet-based Elman neural network (RWENN) controller used for the integration of offshore wind and wave energy conversion systems driven by a doubly fed induction generator. The nodes connecting the weights of the RWENN are trained online using a backpropagation method. A modified gravitational search algorithm (MGSA) is developed to adjust the learning rates and improve learning capability. The proposed control scheme has improved the real power regulation and dynamic performance of a combined wind and ocean wave energy scheme over a wide range of operating conditions. The performance of this control scheme is assessed by comparing it to a traditional proportional-integral based control scheme in a series of case studies representative of maximum power generation. Simulations are carried out using PSCAD/EMTDC software to verify the robustness of the power electronics converters and the efficiency of the proposed controller under steady state and transient conditions.

In this article, the characteristics of the wave energy converter are considered and a novel dynamic controller (NDC) for a permanent magnet synchronous generator (PMSG) is proposed for Wells turbine applications. The proposed NDC includes a recursive cerebellum model articulation controller (RCMAC) with a grey predictor and innovative particle swarm optimization (IPSO). IPSO is developed to adjust the learning speed and improve learning capability. Based on the supervised learning method, online adjustment law of RCMAC parameters is derived to ensure the system’s stability. The NDC scheme is designed to maintain a supply–demand balance between intermittent power generation and grid power supply. The proposed NDC exhibits an improved power regulation and dynamic performance of the wave energy system under various operation conditions. Furthermore, better results are obtained when the RCMAC is used with the grey predictive model method.

In this article, a novelty control structure of grid-connected doubly-fed induction generator (DFIG) based on a function link (FL)-based Wilcoxon radial basis function network (FLWRBFN) controller is proposed. The back-propagation (BP) method is used online to train the node connecting weights of the FLWRBFN. To improve the online learning capability of FLWBFN, differential evolution with particle swarm optimization (DEPSO) is used to tune the learning rates of FLWRBFN. For high randomness of wave energy generation, the transmission power between generators and electrical grids is easy to unstable and AC bus voltage and DC voltage will also lose constant under the conditions of variable generator speed and variable load. Therefore, the proposed intelligent controller can maintain the above power balance and voltage constant and reduce fluctuation. Finally, PSCAD/EMTDC software is used to simulate and study various cases to confirm the robustness and usefulness of the proposed intelligent control technology applied to an ocean wave energy conversion system.

This study proposes a unified power flow controller-based oscillation damping controller for use with a permanent magnet synchronous generator-based offshore power farm. A novel intelligent damping controller (NIDC) was used to increase the stability of power control and improves the performance, where the proposed NIDC consists of the adaptive critic network, the functional link-based Elman neural network (FLENN), the genetic ant colony optimisation algorithm (GACO) and proportional–integral-derivative (PID) linear damping controller. The PID damping controller analyses complex eigenvalues based on the theory of modern control. The node connecting weights of the FLENN and critic network are trained online. A GACO approach is developed to adjust the learning rates and thus improve the learning ability. The proposed NIDC can achieve better damping characteristics, and the internal power fluctuations can also be effectively alleviated.

This paper proposed the reconfigured unified power flow controller (RUPFC) design with the cascaded H-bridge converter to realize the transferability between the converter branches of shunt and series. An improved recurrent fuzzy neural network (IRFNN) was used to improve the interaction performance of real and reactive power. The flow control for the RUPFC is formed for the series converter. The fast and stable response of dc link voltage and bus voltage of RUPFC control are expected. The proposed control schemes can improve the shortcoming of the conventional power flow controllers, such as small disturbance linearizing methods. Finally, the effectiveness of the proposed scheme is provided to demonstrate in simulation results.

Most of the OBU (On Board Unit) devices of vehicles use MCU to control the infrared transmission and reception for the infrared-oriented parking systems. However, the more battery power of the OBU device will be dissipated. In this paper, a non-processor OBU device is proposed to reduce its standby power consumption. Besides, when the OBU device receives an infrared induced signal to cause transmitting an infrared identification signal, the data collision also can be avoided by the design of the delay control circuit to further increase the stability of identification.

This paper proposes the design of a closed-loop vector control structure based on Recurrent Functional Link based Elman Neural Network (RFLENN) for a Static Synchronous Series Compensator (SSSC), linking to a Seashore Wave Energy System (SWES) driven Doubly-Fed Induction Generator (DFIG) is connected. A RFLENN controller for a SSSC in order to reduce the power fluctuations, voltage support and damping for a hybrid power system. The proposed RFLENN is a functional link-based recurrent Elman Neural Network. Analysis of the performance of the proposed controller shows that it can achieve better damping characteristics. The internal power fluctuations to the power system can effectively stabilize the network under unstable conditions. This paper presents a closed-loop vector control structure based on RFLENN for a grid-connected SWES driven DFIG. This paper presents the transient stability improvement and power-flow control results of a DFIG-based SWES connected to a SSSC connected in series with one of two parallel transmission lines.

A hybrid power control system is proposed in the paper, consisting of solar power, wind power, and a diesel-engine. To achieve a fast and stable response for the real power control, an intelligent controller was proposed, which consists of the Wilcoxon (radial basis function network) RBFN and the improved (Elman neural network) ENN for (maximum power point tracking) MPPT. The pitch angle control of wind power uses improved ENN controller, and the output is fed to the wind turbine to achieve the MPPT. The solar array is integrated with an RBFN control algorithm to track the maximum power. MATLAB (MATrix LABoratory)/Simulink was used to build the dynamic model and simulate the solar and diesel-wind hybrid power system.

This paper endeavors to apply a novel intelligent damping controller (NIDC) for the static synchronous compensator (STATCOM) to reduce the power fluctuations, voltage support and damping in a hybrid power multi-system. In this paper, we discuss the integration of an offshore wind farm (OWF) and a seashore wave power farm (SWPF) via a high-voltage, alternating current (HVAC) electric power transmission line that connects the STATCOM and the 12-bus hybrid power multi-system. The hybrid multi-system consists of a battery energy storage system (BESS) and a micro-turbine generation (MTG). The proposed NIDC consists of a designed proportional–integral–derivative (PID) linear controller, an adaptive critic network and a proposed functional link-based novel recurrent fuzzy neural network (FLNRFNN). Test results show that the proposed controller can achieve better damping characteristics and effectively stabilize the network under unstable conditions.

A microgrid distribution system is proposed in the paper, consisting of solar power, wind power, microturbine power and a battery energy storage system (BESS). By using the proposed algorithm, a more efficient network configuration can be obtained. The problem is optimized in a stochastic searching manner similar to that of the evolutionary programming. A direct building algorithm for microgrid distribution power flow analysis is proposed in this paper. Simulation results show that the proposed algorithm has advantages than the earlier developed algorithms. The optimization strategy is general and can be used to solve other hybrid power system optimization problems as well.

The static synchronous series compensator (SSSC) is a series controller of Flexible AC Transmission Systems (FACTS). It can be controlled by thyristors, it also has the ability of fast control adjustments and high frequency operation. Through impedance compensation, it is able to control the magnitude and directions of the real power flow in the transmission system. In order to achieve a fast and steady response for real power control in power systems, this paper proposed a unified intelligent controller, which consists of Fuzzy Neural Network (FNN) and Ant Colony Optimization plus Genetic Algorithms (GACO) for the SSSC to provide better control features for real power control in the dynamic operations of power systems. Finally, the simulation results of the proposed controllers are compared with the conventional proportional plus integral (PI) controllers to demonstrate the superiority and effectiveness of the unified intelligent controller.

This paper presents maximum-power-point-tracking (MPPT) based control algorithms for optimal wind energy capture using radial basis function network (RBFN) and a proposed torque observer MPPT algorithm. The design of a high-performance on-line training RBFN using back-propagation learning algorithm regulating controller for the sensorless control of a permanent magnet synchronous generator (PMSG). The PMSG is controlled by the loss-minimization control with MPPT below the base speed, which corresponds to low and high wind speed, and the maximum energy can be captured from the wind. Then the observed disturbance torque is feed-forward to increase the robustness of the PMSG system.

This paper presents an advance stratagem to prevent contingencies and solve the optimal problem of economic and security dispatch. The dispatch takes outage and limitation of technical constraints into account. In order to ensure state steady stability for accidents, preventive control algorithm (PCA) based on the generalized reduced gradient method processing a preventive contingency set jointly. The technique determines the active power dispatch and voltage of each generating unit before contingent condition so as to minimize the energy re-dispatch cost subject to dispatch, network, and security constraints. A secure process is proposed to control the power system and provide operators with emergency suggestions in deregulated market.

This paper presents the design of an on-line training fuzzy neural network (FNN) using back-propagation learning algorithm with modified particle swarm optimization (MPSO) regulating controller for the induction generator (IG). The MPSO is adopted in this study to adapt the learning rates in the back-propagation process of the FNN to improve the learning capability. The proposed output maximization control is achieved without mechanical sensors such as the wind speed or position sensor, and the new control system will deliver maximum electric power with light weight, high efficiency, and high reliability. The estimation of the rotor speed is designed on the basis of the sliding mode control theory.

This paper presents an intelligent solar charging system with fuzzy logic control method. With the scarce energy source and the worsening environmental pollution, how to create and use a clean and never exhausted energy is becoming very important day by day. This solar charging system is composed of a solar cell, a charger, batteries, a buck converter and a digital signal processor. In the meantime, it also combines the fuzzy logic method with the tactics of charging to improve the efficiency of charging, suppress the abnormal battery temperature rise, lengthen the battery's life, and reduce the waste used. Finally, experimental and simulation results are shown to demonstrate the effectiveness and validity of the system.

A new model based on Probabilistic neural network (PNN) and Polynomial Fitting Approaches (PFA) for radio field strength prediction has been developed. This paper researches the radio field strength, related to the service of a radio system for the operation of set points in the radial networks. The service uses radio propagation to dispatch messages to set points. In order to estimate the radio field strength, we performed some realistic measurements related to set points. Then, the data was analyzed using a combination of Probabilistic Neural Network and Polynomial approximations to estimate the radio field strength, and to create a new optimal model specific to the needs of radial networks.

Voltage security is a crucial issue in power systems especially under heavily loaded condition. In the new scheme of restructuring, voltage stability problem becomes even more serious. To solve the problem, we integrate reactive power compensation concept by Static Synchronous Compensator (STATCOM) with Equivalent - current Injection (ECI). We derive a new STATCOM with ECI model. This paper shows the application of Ant Colony Optimization (ACO) plus Genetic Algorithms (GA) for optimal capacity and location of a new STATCOM with ECI model in a power system. Finally simulation shows the optimal location and capacity of new STATCOM with ECI model to enhance power system voltage stability by using GACO. The proposed method demonstrates the improvement of voltage stability margin.

The static synchronous series compensator (SSSC) is a series controller of Flexible AC Transmission Systems (FACTS). It can be controlled by thyristors, it also has the ability of fast control adjustments and high frequency operation. Through impedance compensation, it is able to control the magnitude and directions of the real power flow in the transmission system. In order to achieve a fast and steady response for real power control in power systems, this paper proposed a unified intelligent controller, which consists of Fuzzy Neural Network (FNN) and Genetic Algorithms (GA) for the SSSC to provide better control features for real power control in the dynamic operations of power systems. Finally, the simulation results of the proposed controllers are compared with the conventional proportional plus integral (PI) controllers to demonstrate the superiority and effectiveness of the unified intelligent controller.