D. Devaraj

Optimal Reactive Power Dispatch for Voltage Stability Enhancement Using Real Coded Genetic Algorithm

This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. An improved genetic algorithm which permits the control variables to be represented in their natural form is proposed to solve this combinatorial optimization problem. For effective genetic operation, crossover and mutation operators which can directly operate on floating point number and integers are used. The proposed method has been tested on IEEE 30 bus system and has resulted in loss which is less than the value reported earlier and is well suited for solving the mixed integer optimization problem.

Multi-objective GA with fuzzy decision making for security enhancement in power system

Power system security enhancement is a major concern in the operation of power system. In this paper, the task of security enhancement is formulated as a multi-objective optimization problem with minimization of fuel cost and minimization of FACTS device investment cost as objectives. Generator active power, generator bus voltage magnitude and the reactance of Thyristor Controlled Series Capacitors (TCSC) are taken as the decision variables. The probable locations of TCSC are pre-selected based on the values of Line Overload Sensitivity Index (LOSI) calculated for each branch in the system. Multi-objective genetic algorithm (MOGA) is applied to solve this security optimization problem. In the proposed GA, the decision variables are represented as floating point numbers in the GA population. The MOGA emphasize non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A fuzzy set theory-based approach is employed to obtain the best compromise solution over the trade-off curve. The proposed approach has been evaluated on the IEEE 30-bus and IEEE 118-bus test systems. Simulation results show the effectiveness of the proposed approach for solving the multi-objective security enhancement problem.

Artificial neural network based day ahead load forecasting using Smart Meter data

Recent developments in Power Sector have led to the introduction of Smart Meter with bi-directional communication capabilities between the Utility and the consumers. The data accumulated by these meters are tremendously huge and pose challenges in extracting useful information from them. Accurate load forecasting helps the Utility to schedule their resources in order to balance the supply and demand. Day-ahead load forecasting using smart meter data is significantly important as it greatly affects the resource planning and control operation of the Utility. In this paper, we propose artificial neural network (ANN) technique to forecast average electricity load for every hour on daily basis using Smart Meter data. Initial experimental result is promising with overall accuracy of 70.54% in forecasting.

A novel dual euclidean algorithm for secure data transmission in smart grid system

Smart Grid is a widely distributed automated energy delivery network, which uses two way flows of energy and data to deliver the electricity to the customer with minimum disturbance. The backbone of smart grid is the communication network. The reliability of the smart grid depends on the data received from various distributed domains of the network. Because of the multifaceted nature of the network, the smart grid is highly prone to attacks. Another issue is the generation of vast amount of data. The massiveness of devices and data collected in smart grid makes the system unable to use the existing cryptographic algorithms. So, there is a need for a security algorithm that provides high security as well as executes promptly. In this paper, we propose an efficient security algorithm that can encrypt large amount of data in short time. The algorithm we propose uses the dual Euclidean algorithm with two keys k1 and k2. The keys are distributed by the trusted third party through secure channel. It provides NP-hard complexity and will not be able to disclose without knowing both keys. The security and performance of the algorithm are analyzed by comparing it with the widely used AES algorithm. Simulation results demonstrate the suitability of the proposed scheme for the vast data generating systems.

Support vector machine based intrusion detection system with reduced input features for advanced metering infrastructure of smart grid

Security of communication network is essential for the smooth functioning of smart grid. In this paper, an intrusion detection system is proposed for early detection of threats in advanced metering infrastructure of smart grid. The proposed intrusion detection system has a multi-support vector machine classifier with mutual information based feature selection technique to detect attacks in Neighborhood Area Network (NAN) of smart grid. Mutual information technique selects the input features of classifier by analyzing the relation between different features with attacks. The developed classifier is the integration of multiple support vector machine classifiers in which each classifier detect specific attack only. The performance of developed intrusion detection system is analyzed by training and testing the classifier with ADFA-LD dataset. The proposed classifier outperforms the other machine learning approaches like artificial neural network in the detection of attacks. Simulation results demonstrate that the proposed intrusion detection approach with mutual information is well suitable for detecting attacks accurately in smart grid.

Bit masking based secure data aggregation technique for Advanced Metering Infrastructure in Smart Grid system

A Smart Grid consists of Advanced Metering Infrastructure (AMI), Phasor Measurement Unit (PMU), communication network, etc along with traditional grid equipment to deliver power with minimum disturbances. Advanced Metering Infrastructure (AMI) is a core component of smart grid that supports various applications like collecting meter reading, monitoring, on-demand applications, etc. It depends on communication network for its appropriate functioning. As like other communication networks, AMI communication network also suffer from various cyber security threats. A security framework is needed for secure communication of data and control messages. Existing security algorithms are not suitable for smart grid data communication because of their large execution time and the requirement of more memory. In this paper, a security framework is proposed using bit masking technique for collecting the smart meter data in AMI communication. The proposed scheme provides trust services, confidentiality and integrity through data encryption and authentication services. The proposed scheme is simulated in Matlab Simulink to evaluate its performance in terms of speed and memory requirement and is compared with the widely used AES algorithm. The simulation results show that the proposed scheme is most suitable for wireless mesh network of AMI communication, and its performance is better than AES algorithm.

An Intelligent Algorithm for Joint Routing and Link Scheduling in AMI with a Wireless Mesh Network

Advanced metering infrastructure (AMI) of smart grids is a network that integrates the consumer directly with the smart grid communication infrastructure. AMI generates a meter that reads the datagram periodically. This unpredictable data flow needs link scheduling between the smart meter and the data collector. The AMI network is scalable because any number of smart meters can be added, as well as removed. So, the routing method should ensure reliability even in node/link loss in the path. An intelligent link scheduling algorithm is discussed in this chapter. This method addresses the scheduling of multiple links in a single slot with assurance for immediate reception of the packets. This method is applied on an AMI network that is deployed using a wireless mesh network (WMN). The paths between the smart meters and data aggregator are identified using a hybrid wireless mesh routing protocol (HWMP). The results show that the proposed link scheduling method can ensure faster packet delivery with a desirable time slot length according to the available traffic.

Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection

Abstract Security is a prime challenge in wireless mesh networks. The mesh nodes act as the backbone of a network when confronting a wide variety of attacks. An intrusion detection system provides security against these attacks by monitoring the data traffic in real time. A support vector machine for intrusion detection in wireless mesh networks is proposed in this paper. The redundant and irrelevant variables in the monitored data affect the accuracy of attack detection by the system. Hence, feature selection techniques are essential to improve the performance of the system. In this paper, a novel intrusion detection system with genetic-algorithm-based feature selection and multiple support vector machine classifiers for wireless mesh networks are proposed. The proposed system selects the informative features of each category of attacks rather than the features common to all the attacks. The proposed system is evaluated using intrusion datasets generated by simulating a wireless mesh network in Network Simulator 3 and by considering packet delivery ratio, delay, etc. as the parameters. The experimental results have demonstrated that the proposed system exhibits a high accuracy of attack detection and is suitable for intrusion detection in wireless mesh networks.

Genetic algorithm-based optimisation of load-balanced routing for AMI with wireless mesh networks

Abstract The advanced metering infrastructure (AMI) in a smart grid contains hardware, software, and other electronic components connected through a communication infrastructure. AMI transfers meter-reading data between a group of smart meters and a utility centre. Herein, a wireless mesh network (WMN) with a random mesh topology is used to deploy the AMI communication network. In a WMN, paths are identified using a hybrid wireless mesh routing protocol (HWMP) with a load balancing feature called load aware-HWMP (LA-HWMP). These paths reduce the demand on links with a minimal air time metric; however, the delay in the data transmission of certain smart meters is high, given the large number of retransmissions caused by packet drop. To avert this problem and enhance the end-to-end delay, a genetic algorithm is applied on the LA-HWMP to obtain the optimal path. The optimisation process will result in the selection of paths with minimal delay. The genetic algorithm is developed with a rank-based selection, a two-point crossover, and a random reset mutation with a repair function to eliminate duplicate entries. The proposed method is compared with the HWMP, the LA-HWMP, and a state-of-the-art method that uses a combination of the ant colony algorithm and simulated annealing (ACA-SA) for AMI networks of different sizes. The obtained results show that the path identified by the proposed method yields a shorter delay and higher throughput than paths identified using the other methods.

Study on the impact of under voltage ride through charactertics of larger PV penetrations on the system transient stability

With the increasing demand of electricity, many countries have been moving towards interconnecting the renewable energy to the existing power grid. In this framework, photovoltaic energy has been most popular. In this work, the impact of grid connected photovoltaic (PV) on the system transient stability is assessed by numerical simulation using matlab/simulink. The effect of under voltage ride through (UVRT) characteristics of different PV penetration levels on the system transient stability is investigated via 9-bus 3 machine test system. Result shows that as we increase the size of PV installation into the grid, critical clearing times (CCT) are proportionally increased. CCT increase is more favorable to enhance the power system transient stability.