Unit Commitment Using Embedded Systems

AbstractUnit commitment problem helps in deciding which electricity generation unit should be running in each period so as to satisfy a predictably varying demand for electricity. Unit Commitment enables uninterruptible power to be delivered to consumers using the principle of minimum operating cost. In this paper a laboratory prototype for unit commitment is developed using embedded systems. In this work, the unit commitment problem is solved using dynamic programming approach. The generators are switched ON and OFF on a priority basis to minimize the total operating cost of the generating units. An Embedded Development Kit(EDK) is used for the prototype which supports micro framework technology. The laboratory prototype is tested for various combinations of generating units

The Optimal combination: Grammatical Swarm, Particle Swarm Optimization and Neural Networks.

Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks

Steady state load shedding to mitigate blackout in power systems using an improved harmony search algorithm

Generation contingencies in a power system lead to under-frequency and low voltages owing to active and reactive power deficiencies. Load shedding is considered as a last alternative to avoid the cascaded tripping and blackout in power systems during generation contingencies. It is essential to optimize the amount of load to be shed in order to prevent excessive load shedding. To minimize load shedding, this paper proposes the implementation of music inspired optimization algorithm known as improved harmony search algorithm (IHSA). The optimal solution of steady state load shedding is carried out by squaring the difference between the connected and supplied power (active and reactive). The proposed algorithm is tested on IEEE 14, 30 and 118 bus test systems. The viability of the proposed method in terms of solution quality and convergence properties is compared with the other conventional methods reported earlier

CSO BASED ENERGY EFFICIENT CLUSTER PROTOCOL FOR WIRELESS SENSOR NETWORKS

In wireless sensor networks (WSNs) energy saving has become one of the prime optimization problem and clustering technique has been considered as most efficient techniques to achieve the same. The selection of cluster heads (CHs) plays a vital role in hierarchical based WSNs as it consume more energy owing to its additional duty of receiving, aggregating the data from the cluster member nodes and transmitting the same to the base station (BS). Improper selection of CHs causes impact on network life time. In order to have an energy efficient network a suitable optimization algorithm is to be adopted to select the CHs. We propose a cluster protocol based on Cat Swarm Optimization (CSO) algorithm to reduce the energy consumption during cluster setup phase and transmission phase. The CSO cluster protocol is developed by considering intra-cluster distance of nodes to CH and residual energy of cluster member nodes. The algorithm is tested extensively on various scenarios of WSNs, varying number of sensor nodes and the CHs. The energy efficient scheme of proposed CSO performance is compared with other well-known protocols such as Low Energy Adaptive Clustering Hierarchy -Centralized (LEACH-C) and Particle Swarm Optimization (PSO) based protocol to prove the superiority of it

OPTIMAL LOAD SHEDDING BASED ON LINE VOLTAGE STABILITY INDEX USING HARMONY SEARCH ALGORITHM

Modern power systems have been operated close to their limits for reasons of economic viability. Consequently, a small increase in the load may lead to the Maximum Loading Point (MLP) of the system resulting in voltage collapse. Under such circumstances, the buses for load-shed have been selected based on line voltage stability index and its sensitivities at the operating point. This avoids voltage collapse and improves the system stability. Computational algorithms for minimum load-shed have been developed using the heuristic technique, Harmony search (HS) algorithm. The algorithm proposed in the present paper is implemented on the standard IEEE 14-bus and 25-bus test systems to obtain the optimal load shedding at the selected buses when the systems are operated at their MLP. The effectiveness and efficiency of the proposed method are established by improvements in the line voltage stability index and the bus voltages

A hybrid method for optimal load shedding and improving voltage stability

In this paper, a hybrid method is proposed for reducing the amount of load shedding and voltage collapse. The hybrid method is the combination of Genetic Algorithm (GA) and Neural Network (NN). The GA is used by two stages, one is to frame the optimization model and other stage is to generate data set for developing the NN based intelligent load shedding model. The appropriate buses for load shedding are selected based on the sensitivity of minimum eigenvalue of load flow Jacobian with respect to the load shed. The proposed method is implemented in MATLAB working platform and the performance is tested with 6 bus and IEEE 14 bus bench mark system. The result of the proposed hybrid method is compared with the GA based optimization algorithm. The comparison shows that, the proposed method ensures voltage stability with minimum loading shedding

Multi objective Flower Pollination Algorithm for solving capacitor placement in radial distribution system using data structure load flow analysis

The radial distribution system is a rugged system, it is also the most commonly used system, which suffers by loss and low voltage at the end bus. This loss can be reduced by the use of a capacitor in the system, which injects reactive current and also improves the voltage magnitude in the buses. The real power loss in the distribution line is the I2R loss which depends on the current and resistance. The connection of the capacitor in the bus reduces the reactive current and losses. The loss reduction is equal to the increase in generation, necessary for the electric power provided by firms. For consumers, the quality of power supply depends on the voltage magnitude level, which is also considered and hence the objective of the problem becomes the multi objective of loss minimization and the minimization of voltage deviation. In this paper, the optimal location and size of the capacitor is found using a new computational intelligent algorithm called Flower Pollination Algorithm (FPA). To calculate the power flow and losses in the system, novel data structure load flow is introduced. In this, each bus is considered as a node with bus associated data. Links between the nodes are distribution lines and their own resistance and reactance. To validate the developed FPA solutions standard test cases, IEEE 33 and IEEE 69 radial distribution systems are considered