The world today is plagued with problems of increased transmission and distribution (T&D) losses leading to poor reliability due to power outages and an increase in the expenditure on electrical infrastructure. To address these concerns, technology has evolved to enable the integration of renewable energy sources (RESs) like solar, wind, diesel and biomass energy into small scale self-governing power system zones which are known as micro-grids (MGs). A de-centralised approach for modern power grid systems has led to an increased focus on distributed energy resources and demand response. MGs act as complete power system units albeit on a small scale. However, this does not prevent them from large operational sophistication allowing their independent functioning in both grid-connected and stand-alone modes. MGs provide greater reliability as compared to the entire system owing to the large amount of information secured from the bulk system. They comprise numerous sources like solar, wind, diesel along with storage devices and converters. Several modeling schemes have been devised to reduce the handling burden of large scale systems. This paper gives a detailed review of MGs and their architecture, state space representation of wind energy conversion systems & solar photovoltaic (PV) systems, operating modes and power management in a MG and its impact on a distribution network.

This paper proposes a hybrid learning algorithm based super twisting sliding mode control (STSMC) of a hybrid wind/photovoltaic (PV) power system for grid connected applications. The gating pulses of the voltage source converter (VSC) are generated by employing adaptive reweighted zero attracting least mean square (RZA-LMS) algorithm. The control law acquiring the super-twisting algorithm generates a continuous and saturated control signal to regulate a hybrid system influenced by disturbances. The proposed control injects sinusoidal currents into the grid with low total harmonic distortion (THD) which improves the steady state & dynamic performance of the system by mitigating power system problems like harmonic injections besides giving satisfactory results under dynamic loading, varying wind speeds and solar insolation. It is a chattering free control which enhances the quality of disturbance rejection and sensitivity to parameter variation. It also caters to abnormal conditions like voltage distortions, DC link variations and reduces the latter by a factor of 80 V besides reducing switch stress by a factor of 5 V. This control exhibits robustness against model uncertainties and external disturbances. Also, the loss component is reduced which decreases the unmodelled losses. It also ensures efficient power flow between the grid, hybrid source and the load. The efficacy of the system is verified in MATLAB/Simulink. Improvements are also observed during dynamic conditions in terms of reduced fluctuations, steady state error and peak overshoot.

This work proposes the techno-economic analysis of a three-phase, three wire grid integrated third-generation perovskite solar cells-wind energy conversion system (PSC-WECS) controlled by a two-level voltage source converter (VSC) which is driven by the application of polynomial zero-attracting least mean square (PZA-LMS) algorithm. The proposed algorithm enhances the dynamic performance of the system by introducing sinusoidal grid currents with low total harmonic distortion (THD) which enhances system performance by providing acceptable outcomes under dynamic loading, fluctuating wind velocity, and solar insolation by compensating for load reactive power and balancing load and power demands at the point of coupling. This work replaces the conventional silicon PVs with PSCs to improve the economics of power generation thanks to their higher efficiency. For relative comparisons, the performance of the proposed 36kWPSC-WECS is compared with the conventional silicon PV-WECS. The results reveal that the proposed system has a low net present cost (NPC) of $68843 compared to the conventional system which costs $160917, translating to a reduction of 58% in total cost. Sensitivity Analysis (SA) is performed to analyse the dependence of optimal solutions on the uncertainty of key variables namely NPC, cost of energy (COE), windspeed & insolation. The results show that the total NPC is more sensitive to the COE and the average wind speed as compared to the other variables. Moreover, the area covered by the proposed system is reduced by 50% while as the cost of power generation is reduced by 72% due to the high efficiency of PSCs. The results obtained in MATLAB/Simulink and HOMER to determine the optimum quantity of the renewable energy sources (RES) model demonstrate the effectiveness of the recommended system.

This work proposes a hydrological cycle-based optimisation technique for performance enhancement of a non-negative least mean mixed norm (NNLMMN) based voltage source control (VSC) for a three-phase grid integrated wind/photovoltaic (PV) system. Water cycle algorithm (WCA) is employed to tune the proportional–integral (PI) controller parameters of VSC to regulate voltage variations at DC link generated during dynamic load and wind conditions. This results in generation of a specific loss component (I_loss) of current to boost the performance of VSC. The NNLMMN control deals with system identification troubles with a non-negativity limitation in varied Gaussian noise conditions. It generates accurate I_loss to upgrade the ability of VSC to accurately extricate weight signals and the fundamental load current component in terms of improvement in DC link voltage (vdc) and total harmonic distortion. The proposed algorithm mitigates problems like voltage fluctuations besides performing operations like reactive power compensation, enhancement of power quality, load and power balancing at the point of coupling during steady-state and dynamic conditions. In conjunction with the NNLMMN algorithm, the proposed technique reduces the surplus mean square error iteratively, resulting in a compact expression of step size. The proposed topology stands out with an improved performance of vdc under wind gusts and the well-known bell-shaped irradiance curve. For relative evaluations, the performance of non-optimised PI and particle swarm optimised PI has been added to analyse the improved response of vdc and I_loss. Extensive simulation results obtained in MATLAB/Simulink are exhibited to corroborate the efficacy of the suggested system.

This paper presents the design and analysis of a dynamic voltage restorer (DVR) based on a transistor-clamped H-bridge (TCHB) multilevel inverter with reduced number of switches. The DVR uses a renewable energy source—specifically, a solar PV array integrated with a battery energy storage system—as the DC voltage supply. A recently developed multilevel inverter (MLI) topology called the TCHB inverter has fewer components and relatively lower power losses than other conventional topologies with the same output quality. In this work, five level inverter-based DVR is used for 11 kV line. The proposed quick-propagation (Q-prop) control algorithm provides improved estimation accuracy, a faster rate of weight convergence, simplicity and an increase in the system’s robustness to changing parameters. The performance of the proposed system is assessed in the face of voltage-based power quality (PQ) problems like voltage sag, swell, harmonics, voltage imbalance, and their combinations. The performance of the suggested system is then compared with traditional methods. A digital simulator is used to experimentally validate the results in real time. The system is simulated in MATLAB/Simulink.