Titik Khawa Abdul Rahman

Economic load dispatch via an improved Bacterial Foraging Optimization

An economic dispatch is one of the most important research areas in power system. This is because an optimal power dispatch will contribute to a sound power system load management. As a result, the solution can ease the cost of fuel without ignoring the power operation constraints and system losses. Adaptive Tumble Bacterial Foraging Optimization (ATBFO) and Adaptive Mutation Bacterial Foraging Optimization (AMBFO), which are originally from basic Bacterial Foraging Optimization (BFO), are considered as alternative algorithms to minimize fuel cost. Thus, the two techniques are compared under the same parameters to determine the best optimal result. After several analyses on the results obtained, it was found that ATBFO outperformed AMBFO. Both of these adaptive bacterial foraging optimization techniques are tested on 26 bus Reliability test system using MATLAB R2009b on MS Window 7.

Cuckoo Search technique for active load and loss allocation in transmission line with load increase condition

These Active load and loss allocation has become a concern in power system with the introduction of deregulation. Deregulation often gives opportunity for the end user to choose their own supplier which has brought the importance of active load and loss allocation in transmission line. Thus, this paper proposed a Cuckoo Search technique as a tool to allocate both active load and losses in transmission line. The technique has been tested in normal and load increase condition. Cuckoo Search is a simple technique that did not involve complex mathematical procedures but provides a better performance in terms of accuracy and computational time. The allocation is done by treating the active load and loss as an optimization problem. This paper presented the result obtained from IEEE-30 bus system in both normal and load increase condition. In addition, comparative study has been conducted with Genetic Algorithm technique.

A novel hybrid method of LSSVM-GA with multiple stage optimization for electricity price forecasting

Predicting price has now become an important task in the operation of electrical power system. Day-ahead prediction provides forecast prices for a day ahead that is useful for daily operation and decision-making. The main challenge for day ahead price forecasting is the accuracy and efficiency. Lower accuracy is produced due to the nature of electricity price that is highly volatile compared to load series. Hence, some researchers have developed complex procedures to produce accurate forecast while considering significant features and optimum parameters. Therefore, a multistage optimization for hybrid Least Square Support Vector Machine (LSSVM) and Genetic Algorithm (GA) model is developed in this study to provide an accurate price forecast with optimized parameters and input features. So far, no literature has been found on multistage feature and parameter selections using the methods of LSSVM-GA for day-ahead price prediction. All the models are examined on the Ontario power market; which is reported as among the most volatile market worldwide. A huge number of features are selected by two stages of optimization to avoid from missing any important features. The developed LSSVM-GA shows higher forecast accuracy with lower complexity than the existing models.

Multi objective Adaptive Tumbling Bacterial Foraging in VAR Solutions for Sustainable Power System Operation

Received Dec 04, 2017 Revised Jan 11, 2018 Accepted Apr 15, 2018 Wireless Body Area Networks (WBANs) are fundamental technology in health care that permits the information of a patient’s essential body parameters to be gathered by the sensors. However, the safety and concealment defense of the gathered information is a key uncertain problem. A Hybrid Key Management (HKM) scheme [13] is worked based on Public Key Cryptography (PKC)-authentication scheme. This scheme uses a oneway hash function to construct a Merkle Tree. The PKC method increase the computational complexity and lacking scalability. Additionally, it increases expensive computation, communication costs and delay. To overcome this problem, Robust Security for Protected Health Information by ECC with signature Hash Function in WBAN (RSP) is proposed. The system employs hash-chain based key signature technique to achieve efficient, secure transmission from sensor to user in WBAN. Moreover, Elliptical Curve Cryptography algorithm is used to verifies the authenticate sensor. In addition, it describes the experimental results of the proposed system demonstrate the efficient data communication in a network.A Weblogs contains the history of User Navigation Pattern while user accessing the websites. The user navigation pattern can be analyzed based on the previous user navigation that is stored in weblog. The weblog comprises of various entries like IP address, status code and number of bytes transferred, categories and time stamp. The user interest can be classified based on categories and attributes and it is helpful in identifying user behavior. The aim of the research is to identifying the interested user behavior and not interested user behavior based on classification. The process of identifying user interest, it consists of Modified Span Algorithm and Personalization Algorithm based on the classification algorithm user prediction can be analyzed. The research work explores to analyze user prediction behavior based on user personalization that is captured from weblogs.Wireless Body Area Networks (WBANs) are fundamental technology in health care that permits the information of a patient’s essential body parameters to be gathered by the sensors. However, the safety and concealment defense of the gathered information is a key uncertain problem. A Hybrid Key Management (HKM) scheme [13] is worked based on Public Key Cryptography (PKC)-authentication scheme. This scheme uses a oneway hash function to construct a Merkle Tree. The PKC method increase the computational complexity and lacking scalability. Additionally, it increases expensive computation, communication costs and delay. To overcome this problem, Robust Security for Protected Health Information by ECC with signature Hash Function in WBAN (RSP) is proposed. The system employs hash-chain based key signature technique to achieve efficient, secure transmission from sensor to user in WBAN. Moreover, Elliptical Curve Cryptography algorithm is used to verifies the authenticate sensor. In addition, it describes the experimental results of the proposed system demonstrate the efficient data communication in a network.

Support vector machine for day ahead electricity price forecasting

Electricity price forecasting has become an important part of power system operation and planning. In a pool- based electric energy market, producers submit selling bids consisting in energy blocks and their corresponding minimum selling prices to the market operator. Meanwhile, consumers submit buying bids consisting in energy blocks and their corresponding maximum buying prices to the market operator. Hence, both producers and consumers use day ahead price forecasts to derive their respective bidding strategies to the electricity market yet reduce the cost of electricity. However, forecasting electricity prices is a complex task because price series is a non-stationary and highly volatile series. Many factors cause for price spikes such as volatility in load and fuel price as well as power import to and export from outside the market through long term contract. This paper introduces an approach of machine learning algorithm for day ahead electricity price forecasting with Least Square Support Vector Mac...