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The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

Different fish species and different growth stages require different amounts of fish pellets. Excessive fish pellets increase the cost of aquaculture, and the leftover fish pellets sink to the bottom of the fish farm. This causes water pollution in the fish farm. Weather changes and providing too many or too little fish pellets affect the growth of the fish. In light of the abovementioned factors, this article uses the artificial intelligence of things (AIoT) precision feeding management system to improve an existing fish feeder. The AIoT precision feeding management system is placed on the water surface of the breeding pond to measure the water surface fluctuations in the area of fish pellet application. The buoy, with s built-in three-axis accelerometer, senses the water surface fluctuations when the fish are foraging. Then, through the wireless transmission module, the data are sent back to the receiver and control device of the fish feeder. When the fish feeder receives the signal, it judges the returned value to adjust the feeding time. Through this system, the intelligent feeding of fish can be achieved by adjusting the amount of fish pellets in order to reduce the cost of aquaculture.

The objective of this paper is to study smart home network systems and the application of LabVIEW to develop a human machine interface (HMI), so that traditional instrument panels can be replaced with virtual panels to reduce the consumption of hardware resources. For energy efficiency, MATLAB’s fuzzy toolbox is used as the computing center, which is applied to the lighting system and air-conditioning system. This paper also uses LabVIEW’s common gateway interface (CGI) tools to develop remote monitoring functions, as well as to embed the network-related syntax into the web pages. The user can not only use computer equipment, but can also use their mobile devices to connect to the networks and conduct remote monitoring, which enhances convenience and security. This paper is finally tested with actual cases, and the electricity consumption with and without fuzzy logic control is compared. The test results show that fuzzy logic control can reduce electricity consumption. As well as using computers to test the remote monitoring functions, cell phones and pads were used.

This paper presents a short-term wind power forecasting model for the next day based on historical marine weather and corresponding wind power output data. Due the large amount of historical marine weather and wind power data, we divided the data into clusters using the data regression (DR) algorithm to get meaningful training data, so as to reduce the number of modeling data and improve the efficiency of computing. The regression model was constructed based on the principle of the least squares support vector machine (LSSVM). We carried out wind speed forecasting for one hour and one day and used the correlation between marine wind speed and the corresponding wind power regression model to realize an indirect wind power forecasting model. Proper parameter settings for LSSVM are important to ensure its efficiency and accuracy. In this paper, we used an enhanced bee swarm optimization (EBSO) to perform the parameter optimization for LSSVM, which not only improved the forecast model availability, but also improved the forecasting accuracy.

In this article, an Enhanced Radial Fuzzy Wavelet Neural Network with Sliding Mode (RFWNNSM) and hill-climb searching (HCS) maximum power point tracking (MPPT) strategy are proposed for a switched ...

This paper proposes a design of robust intelligent control for stabilization of grid-connected microgrid (MG) system, consisting of photovoltaic (PV), wind power (WP), and fuel cell (FC). Since intermittent power generations from WP and PV sources are unpredictable and variable, these result in power fluctuations in a MG. To stabilize power fluctuations, an intelligent controller was proposed, which consists of the modified general regression neural network (GRNN) and the radial basis function network-sliding mode (RBFNSM) for stabilization control of microgrids incorporating PV and wind generation. With proper control, a distribution static var compensator (SVC) integrated with MG is able to significantly enhance the dynamic security of the power system. Simulation studies have been done to verify the performance and robustness of the proposed method against various loading conditions and it is found that the designed control scheme enhances the stability.

With the increased loading of transmission lines, the voltage stability problem has become a critical issue for most power system planners and operators. Some important studies related to the voltage stability indices used in the electric power systems are first sorted. Then, a new approach for voltage stability is developed to find the weak lines in this paper. The new method is based on the practical line ABCD parameters, the power factor of receiving end, and the power angle between the sending end and the receiving end. This concept of integrated transmission line transfer index (ITLTI) is derived for a radial transmission system and later applied to a large system. However, ITILI index has a more integrated content and should be a better solution for on line voltage stability evaluation. The new ITLTI index and other existing techniques are demonstrated and compared with numerical studies by IEEE 14-bus test system, using gradually increase reactive load.

This paper develops the study about parking spaces detection in indoor environments, implementing a wireless sensor networks (WSNs) based on Zigbee technology. With the popularity of digital communications technology, there are many people engaged in wireless sensor network applications. Indoor positioning technology is very important area among wireless sensor network applications. In this paper, the fuzzy model converted the Received Signal Strength Indicator (RSSI) into the distance. We combine fuzzy with Minimum Mean Square Error (MMSE) algorithm estimation for positioning.

An induction generator (IG) speed drive with the application of an optimal controller and a proposed General Regression Neural Network (GRNN) controller is introduced in this paper. Grid connected wind energy conversion system (WECS) present interesting control demands, due to the intrinsic nonlinear characteristic of wind mills and electric generators. The GRNN with adaptive ant colony optimization (AACO) torque compensation is feed-forward to increase the robustness of the wind driven induction generator system. An optimal control loop for the wind power system is designed. The optimality of the whole system is defined in relation with the trade-off between the wind energy conversion maximization and the minimization of the induction generator torque variation that is responsible for the frequency fluctuations. This is achieved by using a combined optimization criterion, resulting in a LQ tracking problem with an infinite horizon and a measurable exogenous variable (wind speed). The proposed controller is designed to drive the turbine speed to extract maximum power from the wind and adjust to the power regulation.

In recent years, based on the rising of global personal safety demand and human resource cost considerations, development of unmanned vehicles to replace manpower requirement to perform high-risk operations is increasing. In order to acquire useful resources under the marine environment, a large boat as an unmanned surface vehicle (USV) was implemented. The USV is equipped with automatic navigation features and a complete substitute artificial manipulation. This USV system for exploring the marine environment has more carrying capacity and that measurement system can also be self-designed through a modular approach in accordance with the needs for various types of environmental conditions. The investigation work becomes more flexible. A catamaran hull is adopted as automatic navigation test with CompactRIO embedded system. Through GPS and direction sensor we not only can know the current location of the boat, but also can calculate the distance with a predetermined position and the angle difference immediately. In this paper, the design of automatic navigation is calculated in accordance with improved Elman neural network (ENN) algorithms. Takagi-Sugeno-Kang (TSK) fuzzy and improved ENN control are applied to adjust required power and steering, which allows the hull to move straight forward to a predetermined target position. The route will be free from outside influence and realize automatic navigation purpose.

A grid-connected wind-photovoltaic (PV) hybrid power system is proposed, and the steady-state model analysis and the control strategy of the system are presented in this paper. The system consists of the PV power, wind power, and an intelligent power controller. The General Regression Neural Network (GRNN) algorithm applied to PV generation system which has non-linear characteristic and analyzed performance. A high-performance on-line training radial basis function network-sliding mode (RBFNSM) algorithm is designed to derive the turbine speed to extract maximum power from the wind. To achieve a fast and stable response for the power control, the intelligent controller consists of a RBFNSM and a GRNN for maximum power point tracking (MPPT) control. The pitch angle of wind turbine is controlled by RBFNSM, and the PV system uses GRNN, where the output signal is used to control the boost converters to achieve the MPPT. The simulation results confirm that the proposed hybrid generation system can provide high efficiency with the use of MPPT.

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 with modified particle swarm optimization (MPSO) regulating controller for the sensorless control of a permanent magnet synchronous generator (PMSG). The MPSO is adopted in this study to adapt the learning rates in the back-propagation process of the RBFN to improve the learning capability. 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.

A sensorless vector-control strategy for an induction generator (IG) operating in a grid-connected variable speed wind energy conversion system is presented. The sensorless control is based on a model reference adaptive system (MRAS) observer for estimating the rotational speed. An on-line training recurrent neural network (RNN) controller using back-propagation learning algorithm with particle swarm optimization (PSO) is designed to allow the rotational speed adjustment for power regulation. The node connecting weights of the RNN are trained online by back-propagation (BP) methodology. The PSO is adopted to adjust the learning rates in the BP process to improve the learning capability. The proposed output maximization control is achieved without mechanical sensors such as wind speed or position sensor, and the new control system will deliver maximum electric power with light weight, high efficiency, and high reliability. The concept has been developed and analyzed using a turbine directly driven IG.

This article presents the design of a hybrid fuzzy sliding mode loss-minimisation control for the speed of a permanent magnet synchronous generator (PMSG) and a high-performance on-line training recurrent neural network (RNN) for the turbine pitch angle control. The back-propagation learning algorithm is used to regulate the RNN controller. The PMSG speed uses maximum power point tracking below the rated speed, which corresponds to low- and high-wind speeds, and the maximum energy can be captured from the wind. The sliding mode controller with an integral-operation switching surface is designed, in which a fuzzy inference mechanism is utilised to estimate the upper bound of uncertainties.

A stand-alone hybrid power system is proposed in this paper. The system consists of solar power, wind power, diesel engine, and an intelligent power controller. MATLAB/Simulink was used to build the dynamic model and simulate the system. To achieve a fast and stable response for the real power control, the intelligent controller consists of a radial basis function network (RBFN) and an improved Elman neural network (ENN) for maximum power point tracking (MPPT). The pitch angle of wind turbine is controlled by the ENN, and the solar system uses RBFN, where the output signal is used to control the dc/dc boost converters to achieve the MPPT.

English letters cannot be recognized by the Hopfield Neural Network if it contains noise over 50%. This paper proposes a new method to improve recognition rate of the Hopfield Neural Network. To advance it, we add the Gaussian distribution feature to the Hopfield Neural Network. The Gaussian filter was added to eliminate noise and improve Hopfield Neural Network’s recognition rate. We use English letters from ‘A’ to ‘Z’ as training data. The noises from 0% to 100% were generated randomly for testing data. Initially, we use the Gaussian filter to eliminate noise and then to recognize test pattern by Hopfield Neural Network. The results are we found that if letters contain noise between 50% and 53% will become reverse phenomenon or unable recognition [6]. In this paper, we propose to uses multiple filters to improve recognition rate when letters contain noise between 50% and 53%.

Tourists traveling abroad, always forget the assembling time or the events of on the wrong tour bus, resulting in delay follow-up trip. Therefore, in order to solve the above problem, this paper uses RFID, infrared sensors, LabVIEW and RF radio frequency technology to develop a tour management system. This system can replace the guides automatically roll call and can display the number of tourists get on and off. The guide can also be set destination of residence time via RF technology, so that tourists be able to remember the assembling time and timely reminders, will not delay to follow-up trip. This system allows the tour guide will be more efficient to master time for management and follow-up trip.

Human life has changed since energy scarcity and climate change. We consider that building of high energy consumption. In this study, we propose a method to solve this problem by ZigBee Wireless Sensor Network (WSN). We use ZigBee WSN and Hall current transducer to monitor power consumption of load for each sensor node. If the power consumption of load is higher than maximal power, the ZigBee WSN will cut off the load to avoid disaster. We consider that ZigBee module must be small, so we develop own ZigBee Module that call “ZBee”. In this study, we develop the friendly user interface (UI) by National Instruments (NI) LabVIEW. The host join to ZigBee WSN, tablet, computer and smart phone by way of Internet. The manager can achieve energy saving effect by the system. The complete design has been installed in our laboratory. The main purpose is convenience to prevent extra power waste.

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.

Part 9: ATIMCN; International audience; IEEE 802.11e Enhanced Distributed Coordination Function (EDCF) is a new wireless technology in wireless local area networks (WLANs). It defines a new supplement to the existing IEEE 802.11 MAC protocol. In IEEE 802.11e EDCF, the aim is providing a QoS support in WLANs. While the system services different Access categories (ACs), IEEE 802.11e EDCF does not perform well under high load conditions. In order to improve the efficiency, we pay attention to the EDCF of IEEE 802.11e with Block_Ack mechanism. We first proposed a markov chain model and studied the behavior. We extend the model to support IEEE 802.11e EDCF, and presented a more accurate analysis under non-ideal channel environment. We also compared it with that without Block_Ack mechanism under channel error environment.

The IEEE 802.11 protocol has achieved worldwide acceptance with WLANs with minimum management and maintenance costs. In order to maximize the throughput, stations had to spend many times to find out the best contention window. In this paper, we modified the Wangi¦s model and took account of the freezing of the back off counter when channel is busy. In the modified model, it is easily to obtain the optimal contention window for a given network scale. The selection of optimal contention window is just based on the network scales. Simulation results showed that the throughput is the same as Wangi¦s. But, the optimal contention window is less than Wangi¦s.

An induction generator (IG) speed drive with the application of a sliding mode controller and a proposed recursive least square (RLS) estimator is introduced in this paper. The rotor inertia constant, friction constant, and the disturbed mechanical torque of the IG are estimated by the proposed RLS estimator, which is composed of the RLS algorithm and a torque observer. The integral proportional (IP) speed controller can be on-line designed according to the estimated rotor parameters. Then the observed disturbance torque is fed forward to increase the robustness of the IG speed drive system. A hybrid controller is designed to drive the turbine speed to extract maximum power from the wind and adjust to the power regulation.

With the increasing variety of multimedia applications, it is needed to develop a mechanism for the quality of service (QoS) to support different types of traffic. IEEE 802.11e EDCF is a new wireless technology which is an enhanced version of DCF. There have been many performance studies of IEEE 802.11e EDCF and proposed an accurate Markov chain model to depict the IEEE 802.11e EDCF characteristics. However, the Markov chain model neglected the channel error condition. In other words, the Markov chain can not truly reflect the real operation of EDCF. In this paper, we modify the Ziouva and Antonakopoulous's (ZA's) model and then extend the model to support IEEE 802.11e EDCF. We study the behavior of the station within a Markov chain model. We have theoretically analyzed the saturation throughput of the Markov chain model with both the ACK and RTS/CTS CSMA/CA mechanisms under a channel error scenario. We also have compared it with the Markov chain model that exists under an ideal channel scenario.

We use one year's subset to train the Support Vector Machines (SVM) and the next year's data was used for testing with Kernel Principal Components Analysis (KPCA). This is clearly not optimal for a non-stationary time series such as we have here nevertheless the MAPE of peak load data set with back-propagation neural network [Chuang et al., 1998] is 3.0 and Support Vector Machine is 2.6.

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.

The purpose of this paper is to research the radio effective zones related to the service of a radio system for the dispatch of trolleys and the operation of level-crossings in the service of Taiwan Railways. The service uses radio and radio propagation to dispatch messages to trolleys and level-crossings throughout the railway system when the critical situation is occurred or the emergency button is pressed by users to allow the train driver to break subsequently. In order to estimate the radio effective zone, we perform some realistic measurements related to level-crossings and trolleys. Then, the data is analyzed using a combination of support vector machine (SVM) and polynomial approximation to estimate the radio effective zone, and create a new optimal model specific to the needs of Western Line, Pingtung Line and the Kaohsiung City local line in the Taiwan Railway context.

A VLSI design of a 12/spl times/12-bit parallel inner-product processor (IPP) for two's complement multiplication is presented. The designed architecture and its driving algorithm makes an efficient use of all the array cells in order to reduce the computation time. Therefore, the time-area complexity of the designed IPP is proportional to N/spl times/m/sup 2spl times/T/sub FA/ for large N and is m times smaller than for a sequential computation on a single multiplier. An inner product of two N-dimensional vectors with m-bit words requires area proportional to m(m/2), and the delay time of (N+2m+log/sub 2/N)/spl times/T/sub FA/, where T/sub FA/ denotes the delay of a full adder. The design has been fabricated in 2-/spl mu/m CMOS double-metal technology. An example application of the designed IPP for solution of a triangular system is presented. >