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In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.

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In [25], an ANN model was employed for the prediction of daily mean wind speed of 11 locations in India where actually measured wind data are not available. The authors used meteorological variables of the target locations from NASA surface meteorology and solar energy database, and the prediction accuracy is compared with measured wind data that was collected from a nearby meteorological station in Hamirpur. A hybrid method consists of wavelet transform (WT), ANFIS, SVM, and GS was proposed in [26] for 6h ahead wind power forecasting. This study showed that the proposed method can predict wind power with MAPE of 12.16% to 13.83%. More literature review regarding the application of soft computing methodologies for the prediction of medium-term wind speed and power can be found in the ref. [21].

Long-term prediction of wind speed has become a research hotspot in many different areas such as restructured electricity markets, energy management, and wind farm optimal design. Although ANFIS merges the learning power of the ANNs with the knowledge representation of fuzzy logic, there are still some difficulties in ANFIS in constructing membership functions (MFs). The difficulty of using ANFIS in constructing membership functions lies in tuning the function to build the best model with high accuracy and better performance. Therefore, this study proposed hybrid ANFIS; ANFIS-PSO, ANFIS-GA, and ANFIS-DE to predict long-term (monthly and weekly) wind power density for four different places in Malaysia namely; Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas. The main benefit of combining these three techniques (PSO/GA/DE) with ANFIS is to reduce the error rates by tuning and optimizing the membership functions. Besides, this study examined the wind speed prediction capabilities of the proposed models for the locations where measured wind data are not available, and the result of the wind speed extrapolation is compared with the measured wind data collected from the nearby meteorological station.

Typically, the variation of wind speed at daytime follows the 1/7th power law whereas, when the temperature become stable or better at night time, the wind speed close to the ground usually subsidies and at turbine altitudes, it does not decrease that much or may even increase. Thus, the daily average wind speed data collected from the meteorological stations were adjusted at turbine hub height of 50m using the power law. The power law for wind speed adjustment at the different hub height is defined as [12]:(1)where v is the wind speed at is desired height h and vo is the wind speed at measured height ho. While α is the power law coefficient. The exponent (α) is an empirically derived coefficient that varies depending upon the stability of the atmosphere. For neutral stable conditions, it is approximately 1/7, or 0.143, which is commonly assumed to be constant in wind resource assessments. This is because the differences between the two levels are not usually so great as to introduce substantial errors into the estimation (usually

The wind energy potential assessment is very important for independent power producer and governmental organization to determine how efficiently wind power can be extracted from a certain location. The wind power density (WPD) is the key assessment parameter in wind potentiality analysis. Therefore, an efficient soft computing technique based on ANFIS-PSO, ANFIS-GA, ANFIS-DE and standalone ANFIS prediction models were developed in this paper to predict long-term (monthly and weekly) average wind power density of four different locations in Malaysia. The choice of the ANFIS technique was made due to its simplicity, reliability as well as its efficient computational capability; its ease of adaptability to optimization and other adaptive techniques, and its adaptability in handling complex parameters. The most significant advantage of hybrid ANFIS is that PSO/GA/DE tune the membership functions of the ANFIS model to ensure minimum error. The prediction models were trained and tested using wind speed data collected from meteorological stations of the underlying locations and measured wind power density. Moreover, different training and testing data size were applied to the prediction models to obtain best data size that provides a minimal error. The first 80% of data used for training and remaining 20% data for testing provide the optimal error in WPD prediction. Based on the result from best data size, there is no model that performed uniformly superior to other for all locations in both training and testing stages. Overall, ANFIS-PSO and ANFIS-GA out-performed ANFIS standalone and ANFIS-DE. Therefore, the results and analysis confirmed that the proposed hybrid ANFIS, especially ANFIS-PSO and ANFIS-GA have the excellent capability to predict the WPD with higher accuracy and precision. Other soft computing techniques applicable to wind speed and power density prediction for other parts of the world can be developed and compare with hybrid ANFIS in the further study.

A suggestion that permanent quarters may be obtained where a transmitting and receiving station can be established is being investigated and a report is expected to be made at the next meeting. With this move in prospect, Gordon Hammond, of Ashland, offered the club the use of his two transmitters, which are of 7.5 watts and 75 watts of power.

Reclosers are used throughout the power distribution system, from the substation to residential utility poles. They range from small reclosers for use on single-phase power lines, to larger three-phase reclosers used in substations and on high-voltage power lines up to 38,000 volts.

You have to open the case and remove (bridge) the input diode, which you will find directlyin between the power connector and the input capacitors.Care: There are no thermal reliefs so you need a quite powerful soldering station.Unsolder the SMD diode, place a wire across the pads and solder that. 041b061a72


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