Nowadays, the integration of renewable energy sources such as solar, wind, etc. into the grid is recommended to reduce losses and meet demands. The application of power electronics devices (PED) to control non-linear, unbalanced loads leads to power quality (PQ) issues. This work presents a hybrid controller for the self-tuning filter (STF)-based Shunt active power filter (SHAPF), integrated with a wind power generation system (WPGS) and a battery storage system (BS). The SHAPF comprises a three-phase voltage source inverter, coupled via a DC-Link. The proposed neuro-fuzzy inference hybrid controller (NFIHC) utilizes both the properties of Fuzzy Logic (FL) and artificial neural network (ANN) controllers and maintains constant DC-Link voltage. The phase synchronization was generated by a self-tuning filter (STF) for the effective working of SHAPF during unbalanced and distorted supply voltages. In addition, STF also does the work of low-pass filters (LPFs) and HPFs (high-pass filters) for splitting the Fundamental component (FC) and Harmonic component (HC) of the current. The control of SHAPF works on d-q theory with the advantage of eliminating low-pass filters (LPFs) and phase-locked loop (PLL). The prime objective of the projected work is to regulate the DC-Link voltage during wind uncertainties and load variations, and minimize the total harmonic distortion (THD) in the current waveforms, thereby improving the power factor (PF).Test studies with various combinations of balanced/unbalanced loads, wind velocity variations, and supply voltage were used to evaluate the suggested method’s superior performance. In addition, the comparative analysis was carried out with those of the existing controllers such as conventional proportional-integral (PI), ANN, and FL.
This paper proposes a generalized soccer league optimization (SLO) based load flow (LF) method suitable for both transmission and distribution systems. The LF problem is formulated as an optimization problem of lowering the sum of squares of active and reactive power mismatches at all busses, while taking the net corrections of bus voltage angles and magnitudes as unknown decision variables. The formulated problem is then solved using SLO, a population-based algorithm imitated from the behavior of team players of soccer league competition. The performances in respect of accuracy, robustness to different line r/x ratios, and computational efficiency are studied on six standard IEEE transmission and distribution systems and the results are presented.
In order to minimize losses in the distribution network, integrating non-conventional energy sources such as wind, tidal, solar, and so on, into the grid has been proposed in many papers as a viable solution. Using electronic power equipment to control nonlinear loads impacts the quality of power. The unified power quality conditioner (UPQC) is a FACTS device with back-to-back converters that are coupled together with a DC-link capacitor. Conventional training algorithms used by ANNs, such as the Back Propagation and Levenberg–Marquardt algorithms, can become trapped in local optima, which motivates the use of ANNs trained by evolutionary algorithms. This work presents a hybrid controller, based on the soccer league algorithm, and trained by an artificial neural network controller (S-ANNC), for use in the shunt active power filter. This work also presents a fuzzy logic controller for use in the series active power filter of the UPQC that is associated with the solar photovoltaic system and battery storage system. The synchronization of phases is created using a self-tuning filter (STF), in association with the unit vector generation method (UVGM), for the superior performance of UPQC during unbalanced/distorted supply voltage conditions; therefore, the necessity of the phase-locked-loop, low-pass filters, and high-pass filters are totally eliminated. The STF is used for separating harmonic and fundamental components, in addition to generating the synchronization phases of series and shunt filters. The prime objective of the suggested S-ANNC is to minimize mean square error in order to achieve a fast action that will retain the DC-link voltage’s constant value during load/irradiation variations, suppress current harmonics and power–factor enhancement, mitigate sagging/swelling/disturbances in the supply voltage, and provide appropriate compensation for unbalanced supply voltages. The performance analysis of S-ANNC, using five test cases for several combinations of loads/supply voltages, demonstrates the supremacy of the suggested S-ANNC. Comparative analysis was carried out using the GA, PSO, and GWO training methods, in addition to other methods that exist in the literature. The S-ANNC showed an extra-ordinary performance in terms of diminishing total harmonic distortion (THD); thus PF was improved and voltage distortions were reduced.
Nowadays, integration of renewable sources into the local distribution system and the nonlinear behavior of advanced power electronic equipment have made a large impact on the power quality (PQ). The unified power quality conditioner (UPQC) is a multifunctional FACTS device, which is a combination of both shunt active filter and series active filters via a common DC link. Presently, the artificial intelligence is playing a vital role in the development of the intelligent control methods. Traditional training methods of artificial neural network (ANN) like back propagation and Levenberg-Marquardt may get stuck in local optimal solution which leads to the invention of ANN trained optimally by metaheuristic algorithms. This paper develops a firefly algorithm-trained ANN (FF-ANNC) controller for the shunt active filter and proportional integral controller (PI-C) for the series active filter of the UPQC integrated with the solar energy system and battery energy storage via boost converter (B-C) and buck boost converters (B-B-C). The main aim of the proposed FF-ANNC is to reduce the mean square error (MSE) thereby achieving the constant DC link capacitor voltage (DLCV) during load and irradiation variations, reduction of imperfections in current waveforms, improvement in power factor (PF), and mitigation of sag, swell, disturbances, and unbalances in the grid voltage. The working of developed FF-ANNC was tested on five test studies with different types of loads and source voltage balancing/unbalancing conditions. However, to demonstrate supremacy of the suggested FF-ANNC, a comparative study with the training methods like genetic algorithm (GA) and ant colony optimization (AC-O) and also with other methods that exist in literature like PI-C, fuzzy logic controller (FL-C), and artificial neuro fuzzy interface system (ANFI-S) was conducted. The proposed method reduces the total harmonic distortion to 2.39%, 2.32%, 2.27%, 2.45%, and 2.66% which are lower than the existing methods that are available in literature. The FF-ANNC shows an excellent performance in reducing voltage fluctuations and total harmonic distortion (THD) successfully and thereby improving PF.
Placement of thyristor-controlled series compensator (TCSC) devices at appropriate lines reduces the net transmission loss (NTL) through injecting suitable series voltage in the transmission lines. The classical approaches for placing TCSCs in the power network may not provide optimal solution and face intricacies in solving the problem with multifarious constraints and vehemently place all the allotted TCSCs in the network. This paper presents a method employing improved harmony search optimization (MHSO), an evolutionary algorithm, for solving TCSC problem (TCSCP) and places the vital number of SVCs from the allotted ones. This paper presents the solution of TCSCP problem of 14, 30 and 57 bus systems and compares the performances in various aspects with existing TCSCP methods.
Distribution power flow (DPF) and distribution generation (DG) placement are important problems in modern distribution systems (DSs). The DPF problem is modelled as an optimization problem of minimizing the node power mismatches, while considering the corrections of node voltages as solution variables. The node locations and DG ratings of DG placement (DGP) problem are considered as optimization parameters with an objective of minimizing the network loss (NL). The soccer game optimization (SGO) models the movements of soccer game players by "move-off" and "move-forward" phases, and has the drawback of performing simple arithmetic average for representing random stochastic movements of players during its "move-forward" phase. This paper endeavours to first remodel the move-forward phase by adapting Levy Flight mechanism to simulate the random jumping action of players to a long distance in getting the ball and scoring a goal, and then develop new modified SGO (MSGO) based methods for solving the formulated DPF and DGP problems. The simulation study exhibited that the proposed DPF method is 751 and 666 times faster than the NR PF technique and the DGP method is able to save the NL by 65% and 69% for 33 and 69 node systems respectively.
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