Juel Rana

Levenberg–Marquardt neural network to estimate UPFC-coordinated PSS parameters to enhance power system stability

AbstractDue to the presence of weak tie line interconnections, small signal oscillations are created in power system networks. Damping out these oscillations is one of the most crucial issues to be settled down for the stability of power system industry. The employment of flexible AC transmission systems (FACTS) may suppress these oscillations effectively in addition to the enhancement of power transfer capability. Unified power flow controller (UPFC) is one of those FACTS devices which are installed in the powers grids, which ensures proper functionality of high-voltage transmission lines. To select the proper parameters of power system stabilizer (PSS) when applied with UPFC is a challenge in this field which can be represented as a multi-objective optimization problem. This work aims to optimize the PSS parameters of power network incorporating UPFC using the artificial neural network (ANN) in real time to damp out the small signal oscillations with a view to enhancing the stability of the power system where the Levenberg–Marquardt (LM) algorithm is used as the training algorithm. System eigenvalues obtained from ANN-tuned PSS coordinated with UPFC and the fixed gain conventional PSS with UPFC are compared to investigate the efficiency of the proposed technique for different loading conditions. Additionally, the comparison has been made in time domain simulation results which prove the superiority of the proposed technique over conventional technique. Moreover, the satisfactory values of statistical performance measures validate the efficacy of the prediction capability of the proposed LM-NN approach.

Optimal placement of Phasor Measurement Units for transmission grid observability

Phasor Measurement Unit (PMU) provides both magnitude and phase information of current and voltage signals with appropriate time stamp which is very useful in controlling power system networks in real time. Consequently, PMU is considered as one of the most significant measurement devices for complete observability of the future electricity grids. But placing PMU in every bus of the network is not economically viable. This paper aims to place PMUs in different buses of electric network to ensure full network observability employing five evolutionary, colony and swarm optimization algorithms namely backtracking search algorithm (BSA), differential evolution (DE), artificial bee colony (ABC), particle swarm optimization (PSO) and invasive weed optimization (IWO). The proposed algorithms have been tested on IEEE 14-bus, 30-bus, 39-bus and 57-bus test networks. The obtained results illustrate the compatibility of the proposed schemes in compared to each other as well as in compared to available techniques in literatures.

Direct control of three-phase smart load for neutral current mitigation

Electric Spring (ES), a power electronic based device, has been recently developed to improve various attributes of future power systems with high penetration of intermittent renewable energy sources. ES is connected in series with non-critical load to form an adaptive load, known as smart load. This configuration has been successfully used to mitigate voltage and frequency fluctuations, ensure demand side management, and improve power quality. It has also been used to reduce three-phase power imbalance and so the adverse effect of neutral current is eliminated. In this work a novel control scheme, based on run time impedance measurement, is proposed to mitigate neutral current from unbalanced three-phase system. Mathematical analysis is also given and corresponding simulations are carried out to substantiate the theoretical framework. Simulation results show the efficacy of presented technique under various unbalanced loading conditions.

Power system stability enhancement by designing optimal PSS employing backtracking search algorithm

Interconnected power systems are critically vulnerable to instability due to the disturbances in any one portion of the system. Even low-frequency oscillations and small disturbances may lead to an outage of the system. Properly tuned Power System Stabilizer (PSS) is an effective tool for the suppression of these oscillations. This paper proposes a novel approach to tune proportional-integral (PI), proportional-integral-derivative (PID) and lead-lag PSS for a single machine infinite bus (SMIB) network by using backtracking search algorithm (BSA) to damp out low-frequency oscillation. The efficacy of BSA tuned PSS has been investigated by comparing the simulation results with fixed gain conventional PSS. In addition, the damping ratio and Eigenvalues of the same network have been compared with genetic algorithm (GA) based tuned PSS.

Switching signal reduction of load aggregator with optimal dispatch of electric vehicle performing V2G regulation service

Environmental concerns over the production of greenhouse gas have led to the development of environmental friendly transportation such as electric vehicle (EV). As the number of EVs is increasing day by day, it will have a great impact on grid operation and electricity market. Significant amount of EV charging during peak hour will cause branch congestions and low voltage. Moreover, high penetration of EVs plays an important role in altering electricity price. Thus, optimal scheduling of EVs charging is inevitable from the perspective of both system reliability and market. Load aggregators can combine the capacities of many EVs to participate in wholesale energy market. In this paper, optimal dispatch algorithm of EVs is developed and tested on a system consisting 1000 EVs. The advantage of the algorithm is that it requires less number of communication signals and less expensive infrastructure. EVs are turned on and turned off in binary fashion based on priority to follow the regulation signal.