Volume 22, Issue 4 - Special Issue Title: Emerging Sustainable Green Energy Technologies for Smart Grid; Guest Editors: Surender Reddy Salkuti, Papia Ray and Sandeep Vuddanti

In this article, a hybrid approach is implemented namely, neural network training (NNT) based machine learning (ML) estimator inspired by artificial neural network (ANN) and self-adaptive neuro-fuzzy inference system (ANFIS) to tackle the voltage aggravations in the power distribution network (DN). In this work, potential of swarm intelligence technique namely particle swam optimization (PSO) is analysed to obtain an optimum prediction model with certain modifications in training algorithm parameters. In practice, when the systems are continuously subjected to parametric changes or external disturbances, then ample time is dedicated to tune the system to regain its stable performance. To improve the dynamic performance of the system intelligence-based techniques are proposed to overcome the shortcomings of conventional controllers. So, gain tuning process based on the intelligence system is a desirable choice. The statistical tools are used to proclaim the effectiveness of the controllers. The obtained MSE, RMSE, ME, SD and R were evaluated as 0.0015959, 0.039949, −0.00089838, 0.039941 and 1 in the training phase and 0.0015372, 0.039207, −0.0005657, 0.039203 and 1 in the testing phase, respectively. The results revealed that the ANFIS-PSO network model could accomplish a better DC voltage regulation performance when it is compared to the conventional PI. The proposed intelligence strategies confirm that the predicted DVR model based on NNT-ML and ANFIS has faster convergence speed and reliable prediction rate. Moreover, the simulation results show that the dynamic response is improved with proposed PSO based NNT based ML and ANFIS (Takagi-Sugeno) that significantly compensates the voltage based PQ issues. The proposed DVR is actualized in MATLAB/SIMULINK platform.

Optimal placement of Distributed Generation (DG) is a crucial challenge for Distribution Companies (DISCO’s) to run the distribution network in good operating conditions. Optimal positioning of DG units is an optimization issue where maximization of DISCO’s additional benefit due to the installation of DG units in the network is considered to be an objective function. In this article, the self adaptive levy flight based black widow optimization algorithm is used as an optimization strategy to find the optimum position and size of the DG units. The proposed algorithm is implemented in the IEEE 15 and PG & E 69 bus management systems in the MATLAB environment. Based on the simulation performance, it has been found that with the correct location and size of the DG modules, the distribution network can be run with maximum DISCO’s additional benefit.

Intelligent threshold value improves the speed and accuracy of any fault detector unit. But in the literature, any such traditional threshold selection mechanism is not available to implant in the fault detector unit of protective relay. This paper introduces a swarm intelligence-based threshold setting mechanism for fault detector. In this context, the Euclidean distance approach (EDA) with a single detection index is used for transmission network protection. The detection index function is formulated with fault parameters on a priority base. Further, the modified particle swarm optimization technique with adaptive inertia strategy is used to identify the optimal threshold value of detection index function. With swarm intelligence based optimal threshold, numerous cases are investigated on a test power system and obtained results show the efficacy of the method.

Currently, electrical distribution networks (EDNs) have used modern technologies to operate and serve many types of consumers such as renewable energy, energy storage systems, electric vehicles, and demand response programs. Due to the variability and unpredictability of these technologies, all these technologies have brought various challenges to the operation and control of EDNs. In this case, in order to operate effectively, it is inevitable that effective power redistribution is required in the entire network. In this paper, a multi-objective based dynamic optimal network reconfiguration (DONR) problem is formulated using power loss and voltage deviation index considering the hourly variation of load, photovoltaic (PV) power, and electric vehicle (EV) fleet load in the network. This paper introduces recently introduced meta-heuristic butterfly optimization algorithm (BOA) and it’s improve variant of self-adaptive method (SABOA) for solving the DONR problem. The simulation study of IEEE 33-bus EDN under different conditions has proved the effectiveness of DONR, and its adoptability for real-time applications. In addition, by comparing different performance indicators (such as mean, standard deviation, variance, and average calculation time) of 50 independently run simulations, the efficiency of SABOA can be evaluated compared with other heuristic methods (HMs). Comparative studies show that SABOA is better than PSO, TLBO, CSA and FPA in the frequent occurrence of global optimal values.

Electric vehicles (EVs) load and its charging methodologies play a significant role in distribution system planning. The inaccurate modelling of EV load may overload the distribution system components, increase in Network Power Loss (NPL) and Maximum Voltage Deviation (MVD). The Constant Power (CP) load model is more popularly used to model both the conventional and EV loads in the distribution system. But the CP load modelling cannot provide accurate information of EV charging process. In this paper, the EV load is modelled as constant Impedance-constant Current-constant Power (ZIP), Exponential, Constant Current and Constant Power load models and the conventional loads are modelled as Residential–Industrial–Commercial (RIC) and Constant Power load models. With these EV and conventional load models, the optimal site and size of Fast Charging Stations (FCSs) in the distribution system have been determined. Further, to analyse the impact of load of FCSs in the distribution system, the distribution indices are calculated. The multi-objective hybrid SFL-TLBO algorithm has been used to determine the optimal location and size FCSs with the minimization of NPL, MVD and EV User Cost (EVUC) in the distribution system. To consider the uncertainty of the initial SOC of EVs, the Monte-Carlo simulation technique has been used. These studies have been carried out on 38-bus distribution system and substantiate results are presented.

Photovoltaic is one of the renewable energies in generating electricity. The MPPT as effective technique can improve the performance of photovoltaic cells. Key problem in photovoltaic systems is that it does not achieve much power since the various weather conditions effects on photovoltaic power cells in working such as solar radiation or rainy and cloudy weather. Therefore, the MPPT in PV cells runs and leads to change during the day and seasons. On the other hand, providing an appropriate controller for the MPPT is too important. Here, main purpose is applying the robust controller, which is tuned by firefly algorithm to optimize MPPT in photovoltaic systems. The main reason to propose this method is some defects in previous controllers such as low stability, low battery lifetime, inability to combine AC/DC and low efficiency. The suggested model is performed by MATLAB/Simulink software. Finally, the FLFA model has higher efficiency and best dynamic response than other methods.

The back-up third zone unit of distance relay may issue nuisance tripping command during different system critical events like power swing, voltage stress/instability and load encroachment. Since, such events are system symmetry phenomenon, the protection unit will not always able to discern them from three phase (symmetrical) fault in the distance relay third-zone region. The conventional relay algorithm fails to make a proper decision and results in unwanted tripping. This may further lead to system blackout. To mitigate this problem, a new approach based on the average rate of change of current (ARCC) is proposed. The method will work in integration with the conventional distance relay impedance setting algorithm to provide better results. The main advantage of the method is that it considers only local end current information. Within one power cycle, an accurate decision can be taken by the protective relay. The proposed method is validated considering IEEE 39-bus New England test system and the Indian regional power grid model modeled using EMTDC/PSCAD software. The simulation results for various critical cases and comparative analysis with existing methods shows effectiveness of the proposed method.

The multiphase induction motor can be a highly preferable choice for the EV application, where the reliability and safety of passengers are one of the major concerns. The main objective of this article is to design high performance fault tolerant control of five phase induction motor (FPIM) drive to enhance the dynamic performance under healthy, faulty and fault tolerant modes of operation under arbitrary open phase fault condition. First, a simple method of modeling open phase fault in any phase of the FPIM drive is proposed. The most frequently occurring open phase faults due to the power electronics switch failure in the inverters are considered for the analysis. A generalized method of modeling arbitrary open phase fault in FPIM drive using back EMF calculations in faulty phase is suggested. The developed method utilizes the same transformation matrix for any open phase fault such that the minimum modification in control strategy is required under fault condition. Thereafter, the control technique is detailed. The rotor field oriented control (RFOC) of FPIM with fuzzy logic speed controller is developed to generate optimal torque reference. The design of FLC is detailed with fine-tuned scaling factor, rule base and membership function to obtain robust performance of the drive under healthy and fault tolerant modes of operation. The complete RFOC scheme for 2 HP FPIM drive is built in MATLAB/SIMULINK incorporating fuzzy logic based controller to verify its performance. The comparison of transient and steady state response and robustness (to the load disturbance as well as fault) obtained using developed fuzzy logic based controller with those using conventional PI controller is presented to prove the efficiency of the suggested controller under various speed torque profiles. The effectiveness of the proposed fuzzy based fault tolerant controller for FPIM drive is validated using OP4510 controller based real time HIL simulator.

Multilevel inverters (MLIs) have formed a new wave of interest in research and industry. Switched capacitor-based multilevel inverters are used to avoid the need for multiple separated DC sources compared to cascaded MLIs. However, the inclusion of several capacitors creates problems such as high inrush current, voltage imbalance. To avoid these drawbacks, this paper proposes an isolation-based scheme by using a flyback converter in the switched capacitor multilevel inverter. Further, the overall topology provides step-up AC voltage across the load from a single DC source with fewer power switches. To generate a step-up five-level voltage across the load, switched capacitor-based multilevel inverter needs six power switches and only one capacitor. To get the appropriate switching operation to generate the NL- levels, phase disposition pulse width modulation (PD-PWM) has been developed. The extended nine-level S 2 -MLI is also discussed in this paper under different conditions as change in input source voltage and dynamic load change. Moreover, to prove the superior performance of switched-capacitor single DC source multilevel inverter (S 2 -MLI), comparative analysis with existing single DC source MLI has been performed. The effectiveness and feasibility of the proposed topology are tested with varieties of loads by simulation using Matlab/Simulink. To validate the simulation results, hardware implementation has been done of five-level S 2 -MLI considering resistive and motor load by using DSpace 1103 controller.

The goal towards attaining a sustainable future has led to the rapid increase in the integration of converter control based generators (CCBGs). The low inertia response characteristics of CCBGs and the weak tie lines in interconnected systems pose a huge threat to Small-Signal Stability (SSS). Adequate damping of low-frequency oscillations (LFO) is pivotal in ensuring the maximum power transfer through the critical transmission corridors. These operational issues become more serious with the significant reduction in system inertia as a result of the high penetration of CCBGs. Therefore, appropriate control techniques are an absolute requirement for preventing LFOs from limiting the penetration of CCBGs in interconnected networks. This may also eventually lead to revisions in grid codes mandating CCBGs to provide auxiliary damping control. But, the progressive addition of multiple damping controllers for specific target modes can lead to the drifting of eigenvalues (EVs) associated with other electromechanical modes (EMs) in the system. This is due to the adverse interactions between multiple damping controllers in the uncoordinated control approach and may result in deteriorating SSS. Therefore, this paper proposes a simultaneous coordinated control among Battery Energy Storage System (BESS), Wind Turbine Generators (WTG) and Power System Stabilizer (PSS) for enhancing SSS in networks with high wind penetration by considering both inter-area (IA) and local modes. The performance of the proposed coordinated control is corroborated using IEEE 68 bus system for multiple operating scenarios for which the critical modes in the system have the lowest damping index (DI). The effectiveness of modulating the active power, reactive power and simultaneous modulation of both active and reactive power injected by BESS along with a dual-channel Optimized WTG Damping Controller (DOWDC) and PSS is evaluated. The impact of the different coordinated control strategies on voltage dynamics is also investigated. The simulation results validate the better performance of the proposed coordinated control over uncoordinated control approaches.