The work introduces a new methodology to solve multiobjective DER accommodation problem of distribution systems by combining a technique for order of preference by similarity to ideal solution (TOPSIS) and an improved elephant herding optimization (EHO) technique. A complex real-life multiobjective distributed energy resources (DERs) planning problem is formulated and solved using the proposed method. The aim is to determine the optimal sites and sizes of DERs to maximize the overall benefits of utility and consumers. The proposed technique is productively implemented on three small to large-scale benchmark test distribution systems of 33-bus, 118-bus and 880-bus. The optimal solutions obtained are compared with the methods available in the literature. The comparison shows that the proposed optimization method is promising.

In this paper, new metaheuristic technique Cuckoo Search Optimization (CSO) is explored for optimal Power System Stabilizer (PSS) designing of 10-machines, 39-bus New England power system and results are compared with well established techniques, i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and new Harmony Search Optimization (HSO). An eigenvalue based multi-objective function is used for simultaneous control of damping factor and damping ratio to damp out low frequency local and inter-area modes of oscillations. The PSS parameters are so intended that unstable and/or lightly damped mode eigenvalues are shifted to a specified D-shape zone in the left half of the s-plane. The controller designed using GA, PSO, HSO and CSO are named GAPSS, PSOPSS, HSOPSS and CSOPSS respectively. The success of all designed PSSs controllers are evaluated by eigenvalues analysis, non-linear simulations and performance indices for various operating conditions under different scenarios of severe disturbances and their comparative analysis is observed. The robustness of all designed PSSs controllers are observed by testing them on unseen operating conditions and results are compared for the same. It is observed that the damping performance and small-signal stability enhancement using CSOPSS is superior to GAPSS, PSOPSS and comparable with HSOPSS.

Power system monitoring and control in real-time is a challenging task for modern power system due to large operational constraints. The deployment of Phasor Measurement Units (PMUs) at key locations provides an opportunity for devising effective power system monitoring and control measures. In this study, a new method is proposed to determine the real-time transient stability status and identification of the coherent generator groups by predicting the rotor angle values following a large disturbance through Radial Basis Function Neural Network (RBFNN). The first six cycle of synchronously sampled post-fault data measurements from PMUs consisting of rotor angles and voltages of generators are taken as the input to the neural network to predict the future state of the system. The proposed method can also determine the synchronism state of the individual machine in real-time. The proposed scheme is demonstrated on IEEE-39 bus test system at different operating conditions