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Load inconsistency has disrupted the power system, causing rotor angle fluctuation that leads to angle instability in the system. This research suggests an innovative proportional integral derivative with filter (PIDF)-based thyristor-controlled series compensator (TCSC) controller that utilise an evolutionary programming sine cosine algorithm (EPSCA) for hybrid optimisation to increase the angle stability of the power system. The challenge of the PIDF-TCSC design is transformed into an optimal control problem with respect to performance indices, such as the maximum imaginary part of system eigenvalues, damping ratio and damping factor, where another multi-objective function is utilised to determine the best stabiliser settings. Eigenvalue analysis is used to conduct the stability study in a linearised paradigm of the single-machine infinite-bus (SMIB) network. The resilience of the PIDF controller was tested using a SMIB power network under various operating circumstances. Simulation results are used to evaluate the system's effectiveness with the proposed optimised PIDF-TCSC controller to that of the system using the proportional integral derivative, proportional integral, and base case PIDF-TCSC approaches. The research findings demonstrate the efficacy of EPSCA in implementing PIDF-TCSC motif and its excellent resilient performance for improving power system stability as related to other strategies in various situations.

Cascading rotor and stator in axial flux permanent magnet (AFPM) machine is a beneficial concept to increase the power density without increasing the diameter of the machine. Multi-stage AFPM can provide higher torque capability in applications requiring high power density. In this regard single-stator double- rotor (SSDR) and double-stator single-rotor (DSSR) are efficient machines compared to single -stage AFPM. Since SSDR and DSSR are also considered modular units to make multi-stage AFPM, like single-stage AFPM, this paper makes a comparison with analysis between SSDR and DSSR AFPM machines. Based on 3-D finite element analysis (FEA), the air-gap magnetic flux, voltage, current, torque, torque ripple, power and power density, efficiency are comparatively investigated to evaluate their performance. Results show that fully coreless topology only be achieved in SSDR AFPM machine with a higher power density. Finally, a prototype multi-stage coreless generator has been developed using a single stator double rotor (SSDR) configuration to justify the fully coreless concept. This prototype coreless generator eliminates eddy current losses and reduces weight, making it a suitable option for certain portable applications.

In this research, a novel initialization strategy for conventional MPPT algorithms is proposed to define the best position for the tracking process to start over the P–V curve. Consequently, the global maximum power point (GMPP) becomes the nearest or first maximum among the existing multiple MPPs under the partial shading condition (PSC). In addition, the step size of the applied conventional algorithm is minimized based on its proximity to the actual GMPP. Therefore, the tracking speed is improved, and the power loss can be reduced by the proposed approach. The major advantages of this approach are eliminating the need to modify the original algorithm, hybridizing with other algorithms, or employing any complex procedures, as in metaheuristic and optimization MPPT algorithms. Hence, it is overcoming the main drawbacks of conventional MPPT. In this work, the proposed initialization approach is applied to the simplest conventional MPPT technique, which is the perturbation and observation (P&O) algorithm, to show the enhancement in performance without the need to introduce any complex processes. MATLAB/Simulink simulation model and hardware implementation based on digital signal processing (DSP) controller TMS320F28335 are two distinct methodologies used to validate the outperformance when applying the proposed initialization technique under PSC and various weather fluctuations. The outcomes show that the proposed initialization technique was successful in extracting the maximum power peak while also improving time response, accuracy, and generating oscillations.

PV systems play a vital role in the global renewable energy sector, and they require accurate modeling and reliable performance to maximize the output power. This research presents a thorough analysis and discussions on the effects of different PV models’ parameters and certain specific faults on the performance and behavior of the photovoltaic systems under different temperature and irradiation conditions. It provides a detailed analysis of how several parameters affect the performance of the PV arrays, for instance, the series resistance, shunt resistance, photocurrent, reverse saturation current, and the diode ideality factor. These parameters were extracted mathematically and verified with the help of wide-ranging simulations and practical experiments. Additionally, the investigation of the effect of DC faults, including line-to-line, line-to-ground, partial shading, and complete shading faults on PV arrays, provides important fundamentals for fault detection and classification, thus improving the efficiency and protection of PV systems. It can, therefore, be stated that the outcomes of this research will assist in the enhancement of PV systems in terms of design, operation, and maintainability of photovoltaic plants, as well as contribute positively to the advancement of sustainable solar energy technology.

This study analyses the local microclimate information to discover the ideal Agrivoltaic system tilt-orientation angles and the influence of crops on energy production. The study’s objectives are twofold: (1) to provide a comprehensive method for determining the ideal tilt-orientation angles of PV modules that would both match the demands of crop-light requirements and optimize the energy output, and (2) to develop a mathematical model that considers the integration of crops within the AVS when projecting energy output. The simulation utilized a mix of PV tilt angle 0°-90° with orientation 0°-359° and hourly local meteorological data over one year. The study’s findings showed that local microclimate data can be used to anticipate the tilt-orientation angle of PV modules to fulfil crop-light demand. The maximum solar irradiance collected on the PV module is 1819 kW/m2/year at a tilt angle of 8° and orientation of 187°. In comparison, 40.92% of light reduction is observed below the same combination. Furthermore, it was discovered that cultivation of Andrographis paniculata using the same combination produced higher yields than cultivation in open areas. Next, AVS installation also reduces the temperature of the PV module by 1.28 °C and increases the efficiency of the PV module by approximately 0.82%. Following that, the LER value for AVS recorded at 2.17. In conclusion, this AVS model offers enormous potential to predict ideal PV tilt-orientation and assessing the effect of crops on energy output.

A fuel cell (FC) is one of the most viable solutions to the current energy crisis and environmental pollution problem. It can be applied in power generation, portable power supply, as well as in hybrid vehicles. Due to the versatile nature of this energy source and non-dependency over the environmental conditions, FCs can be a significant source of alternative energy source in the future. Researchers are working on power conversion topologies and control strategies development for FC application for the past decade. However, the latest summary of the progress in this sector is not available in the literature. This article is aimed at discussing the various control techniques and energy management systems reported in the literature. The configurations are categorized according to their nature, application area, and performance. Different energy management strategies used in fuel cell-based systems are considered here for the qualitative analysis. The essential characteristics such as the advantages, disadvantages, and improvement opportunities of various energy management strategies of fuel cell vehicles are discussed from the control system point of view. Finally, the recent trends in the development of fuel cell hybrid energy management systems are presented to improve the performance based on the above qualitative analysis. This article will help the researchers in this field to know the current status, future trend and the future research directions in fuel cell research for power generation.

Abstract In recent years, coreless axial‐flux permanent‐magnet (AFPM) machines have been gaining attention. Being coreless, these machines do not experience eddy current (hence hysteresis losses) and has lower cogging torque leading to higher efficiency. However, limitations such as high leakage flux and weak mechanical structure impede its wide application. The results of a systematic review on coreless AFPM machine research is presented, wherein a total of 123 studies have been selected through the Preferred Reported Items for Systematic Reviews and Meta‐Analysis (PRISMA) protocol. About 55% of the articles have been published between 2017 and 2021, indicating recent attention of the researchers on coreless AFPM machines. More than 70% (87 out of 123) of the records have been published by IEEE, which proves the quality and acceptance of the studies. In two‐thirds (77 out of 123) of the studies, single‐stator double‐rotor has been adopted as the machine topology, and in 71.8% (84 out of 123) cases, trapezoidal shape magnets have been used in conventional array. The key research areas on coreless AFPM identified through this review are to produce an efficient design for multiphase and multi‐stage topology and develop lightweight rotor and stator structures.

One of the most common and deadly diseases in the world is lung cancer. Only early identification of lung cancer can increase a patient’s probability of survival. A frequently used modality for the screening and diagnosis of lung cancer is computed tomography (CT) imaging, which provides a detailed scan of the lung. In line with the advancement of computer-assisted systems, deep learning techniques have been extensively explored to help in interpreting the CT images for lung cancer identification. Hence, the goal of this review is to provide a detailed review of the deep learning techniques that were developed for screening and diagnosing lung cancer. This review covers an overview of deep learning (DL) techniques, the suggested DL techniques for lung cancer applications, and the novelties of the reviewed methods. This review focuses on two main methodologies of deep learning in screening and diagnosing lung cancer, which are classification and segmentation methodologies. The advantages and shortcomings of current deep learning models will also be discussed. The resultant analysis demonstrates that there is a significant potential for deep learning methods to provide precise and effective computer-assisted lung cancer screening and diagnosis using CT scans. At the end of this review, a list of potential future works regarding improving the application of deep learning is provided to spearhead the advancement of computer-assisted lung cancer diagnosis systems.

Metaheuristic optimization is considered one of the most efficient and powerful techniques of recent decades as it can deal effectively with complex optimization problems. The performance of the optimization technique relies on two main components: exploration and exploitation. Unfortunately, the performance is limited by a weakness in one of the components. This study aims to tackle the issue with the exploration of the existing jellyfish search optimizer (JSO) by introducing a hybrid jellyfish search and particle swarm optimization (HJSPSO). HJSPSO is mainly based on a JSO structure, but the following ocean current movement operator is replaced with PSO to benefit from its exploration capability. The search process alternates between PSO and JSO operators through a time control mechanism. Furthermore, nonlinear and time-varying inertia weight, cognitive, and social coefficients are added to the PSO and JSO operators to balance between exploration and exploitation. Sixty benchmark test functions, including 10 CEC-C06 2019 large-scale benchmark test functions with various dimensions, are used to showcase the optimization performance. Then, the traveling salesman problem (TSP) is used to validate the performance of HJSPSO for a nonconvex optimization problem. Results demonstrate that compared to existing JSO and PSO techniques, HJSPSO contributes in terms of exploration and exploitation improvements, where it outperforms other well-known metaheuristic optimization techniques that include a hybrid algorithm. In this case, HJSPSO secures the first rank in classical and large-scale benchmark test functions by achieving the highest hit rates of 64% and 30%, respectively. Moreover, HJSPSO demonstrates good applicability in solving an exemplar TSP after attaining the shortest distance with the lowest mean and best fitness at 37.87 and 36.12, respectively. Overall, HJSPSO shows superior performance in solving most benchmark test functions compared to other optimization techniques, including JSO and PSO. As a conclusion, HJSPSO is a robust technique that can be applied to solve most optimization problems with a promising solution.

In this paper, a perturb and observe (P&O) based voltage regulator (POVR) and a capacitor compensator (CC) circuit are proposed for the implementation on 31-level asymmetrical switch-diode based multi-level DC-link (MLDCL) inverter. Since the application of MLDCL in a standalone photovoltaic (PV) system requires constant DC voltages from PV panels, the POVR strategy is deployed to regulate the voltage along with the capability to deliver the maximum power at full load. Boost DC-DC converters are used as the interface between the panels and the inverter for the POVR operation. The results show that POVR is capable of achieving the desired fixed DC voltages even under varying environmental and load conditions, with a steady 230 V at the output. At full load, the standalone system successfully delivers 97.21% of the theoretical maximum power. Additionally, CC is incorporated to mitigate voltage spikes at the output when supplying power to inductive loads. It successfully eliminates the spikes and also reduces the total harmonic distortion (THD) of output current and voltage from more than 10% to less than 5%, as recommended in IEEE 519 standard.

In this paper, an alternative control technique for producing reference current signal to manage operation of three-phase three-wire parallel-connected active power filter (PAPF) is presented. In the context of generating reference current, time-domain synchronous reference frame (SRF) technique has commonly been recognized to provide the benefit of control simplicity. However, its control structure is rather rigid which restricted its flexibility to be applied in different power system configurations. For instance, SRF technique which is initially designated to work with three-phase system cannot be applied directly to single-phase network without intensive modification to its overall control structure. Hence, in this work, a control technique named as modular fundamental element detection (modular-FED) which exhibits modular structure is proposed, to manage mitigation of harmonic current under unbalanced and/or distorted grid. The design concept of the proposed technique was modeled using MATLAB-Simulink software. Two types of highly nonlinear loads are applied to assess the effectiveness of the proposed modular-FED technique under various unbalanced and/or distorted grids. In-depth comparative analysis with the standard SRF technique is performed to evaluate the benefits of using the proposed technique. The presented simulation findings confirmed superiority of the proposed technique in controlling a PAPF to mitigate harmonics contained in the supply grid.

This study presents a hybrid optimization technique to optimize stator and rotor slots of induction motor (IM) design for electric vehicle (EV) applications. The existing meta-heuristic optimization techniques for the IM design, such as genetic algorithm (GA) and particle swarm optimization (PSO), suffer premature convergence, exploration and exploitation imbalance, and computational burden. Therefore, this study proposes a new hybrid optimization technique called opposition-based jellyfish search optimization (OBJSO). This technique adopts opposition-based learning (OBL) into a jellyfish search optimization (JSO). Apart from that, a multi-objective formulation is derived to maximize the main performance indicators of EVs, including efficiency, breakdown torque, and power factor. The proposed OBJSO is used to solve the optimal design of stator and rotor slots based on the formulated multi-objective. The performance is compared with conventional optimization techniques, such as GA, PSO, and JSO. OBJSO outperforms three other optimization techniques in terms of average fitness by 2.2% (GA), 1.3% (PSO), and 0.17% (JSO). Furthermore, the convergence rate of OBJSO is improved tremendously, where up to 13.6% reduction in average can be achieved compared with JSO. In conclusion, the proposed technique can be used to help engineers in the automotive industry design a high-performance IM for EVs as an alternative to the existing motor.

This paper proposes a novel robust thyristor-controlled series compensator (TCSC) controller based on proportional integral derivative with filter (PIDF) and uses a new hybrid optimisation evolutionary programming sine cosine algorithm (EPSCA) to improve the power system’s angle stability. The problem of the PIDF-TCSC design is transformed into an optimisation problem based on performance indices, such as damping factor, damping ratio, and eigenvalues, where the multi-objective function is employed to obtain the optimal stabiliser parameters. To examine the robustness of PIDF-TCSC, it was tested on a single-machine infinite-bus power system under different operating conditions. The performance of the system with the PIDF-TCSC controller was compared with the simulation results, and the results obtained with the proposed EPSCA were compared with those obtained with SCA, moth flame optimisation, and EP-based PIDF-TCSC methods. Simulation results showed the effectiveness of EPSCA for the PIDF-TCSC design and the superior robust performance for the enhancement of power system stability compared with other techniques in different cases.

This paper presents a modified Levy particle swarm optimization (MLPSO) to improve the capability of maximum power point tracking (MPPT) under various partial shading conditions. This method is aimed primarily at resolving the tendency to trap at the local optimum particularly during shading conditions. By applying a Levy search to the particle swarm optimization (PSO), the randomness of the step size is not limited to a specific value, allowing for full exploration throughout the power-voltage (P-V) curve. Therefore, the problem such as immature convergence or being trapped at a local maximum power point can be avoided. The proposed method comes with great advantages in terms of consistent solutions over various environmental changes with a small number of particles. To verify the effectiveness of the proposed idea, the algorithm was tested on a boost converter of a photovoltaic (PV) energy system. Both simulation and experimental results showed that the proposed algorithm has a high efficiency and fast-tracking speed compared to the conventional HC and PSO algorithm under various shading conditions. Based on the results, it was found that the proposed algorithm successfully converges most rapidly to the global maximum power point (GMPP) and that the tracking of GMPP under complex partial shading is guaranteed. Furthermore, the average efficiency for all test conditions was 99% with a tracking speed of 1.5 s to 3.0 s and an average output steady-state oscillation of 0.89%.

Understanding a person’s attitude or sentiment from their facial expressions has long been a straightforward task for humans. Numerous methods and techniques have been used to classify and interpret human emotions that are commonly communicated through facial expressions, with either macro- or micro-expressions. However, performing this task using computer-based techniques or algorithms has been proven to be extremely difficult, whereby it is a time-consuming task to annotate it manually. Compared to macro-expressions, micro-expressions manifest the real emotional cues of a human, which they try to suppress and hide. Different methods and algorithms for recognizing emotions using micro-expressions are examined in this research, and the results are presented in a comparative approach. The proposed technique is based on a multi-scale deep learning approach that aims to extract facial cues of various subjects under various conditions. Then, two popular multi-scale approaches are explored, Spatial Pyramid Pooling (SPP) and Atrous Spatial Pyramid Pooling (ASPP), which are then optimized to suit the purpose of emotion recognition using micro-expression cues. There are four new architectures introduced in this paper based on multi-layer multi-scale convolutional networks using both direct and waterfall network flows. The experimental results show that the ASPP module with waterfall network flow, which we coined as WASPP-Net, outperforms the state-of-the-art benchmark techniques with an accuracy of 80.5%. For future work, a high-resolution approach to multi-scale approaches can be explored to further improve the recognition performance.

Low frequency oscillations in large power systems may result in system instability under large disturbances. Power system stabilisers (PSS) play an effective role in damping these low frequency oscillations by injecting a modulating signal in the excitation loop of a synchronous machine. A new metaheuristic optimisation algorithm termed the sine cosine algorithm (SCA) was proposed for optimising PSS controller parameters to obtain an optimal solution with the damping ratio as an objective function. The SCA technique was examined on a single machine infinite bus (SMIB) system under distinct loading situations and matched with a moth flame optimisation technique and evolutionary programming to design a robust controller of PSS. The simulation was accomplished using a linearised mathematical model of the SMIB. The performance of a designed lead lag-controller of PSS was demonstrated using eigenvalue analysis with simulations, showing promising results. The dynamic performance was validated with respect to the damping ratio, the eigenvalue’s location in the s-plane and rotor angle deviation response to demonstrate system stability.

Traditional transmission line ratings are limited by a set of fixed conservative weather assumptions that are also known as static thermal rating (STR). Owing to STR, new line corridors are continuously required to address increasing electricity demands while minimizing the curtailment of renewable energy sources (RES). However, the expansion of an electricity network is expansive, long, and limited due to the scarcity in land and space. To overcome this issue, researchers have proposed a dynamic thermal rating (DTR) system that can increase the capacity of existing transmission lines. Research has shown that actual line ratings are higher than STR most of the time. The potential of using the DTR system to increase the reliability of power systems is therefore significant. Almost every country has begun the process of increasing the integration of RES, and consequently, the DTR system has become increasingly important. Exploring and reviewing critical studies on the DTR system are thus beneficial for researchers who are interested in the developments of DTR technology. This review paper begins by comparing the two main DTR system standards. Then, monitoring technologies of the DTR system are reviewed. Notable research on the reliability impacts of the DTR system on electrical networks are surveyed. Interactions with wind power and other smart grid technologies are also examined, and the concept of power system reliability is briefly discussed.

This paper discusses the use of a stochastic metaheuristic population-based optimization algorithm known as the sine cosine algorithm (SCA) to design the parameters of a power system stabilizer (PSS) for damping electromechanical oscillations in a single machine connected to a large power system. The design of the PSS parameters was formulated as an optimization problem to minimize the objective function value. The SCA was used to obtain the best values of the PSS parameters under the objective function. Simulation was carried out by a linearized power system model. The lead lag controller was used as the PSS structure and the results from that were compared with those obtained by moth flame optimization and evolutionary programming. The results showed that the SCA is more effective than are the other techniques in exploration and exploitation to tune the PSS parameters and enhance the power system stability by damping oscillations in a range of loading conditions.

This paper presents a novel modified Levy flight optimization for a photovoltaic PV solar energy system. Conventionally, the Perturb and Observe (P&O) algorithm has been widely deployed in most applications due to its simplicity and ease of implementation. However, P&O suffers from steady-state oscillation and stability, besides its failure in tracking the optimum power under partial shading conditions and fast irradiance changes. Therefore, a modified Levy flight optimization is proposed by incorporating a global search of beta parameters, which can significantly improve the tracking capability in local and global searches compared to the conventional methods. The proposed modified Levy flight optimization is verified with simulations and experiments under uniform, non-uniform, and dynamic conditions. All results prove the advantages of the proposed modified Levy flight optimization in extracting the optimal power with a fast response and high efficiency from the PV arrays.

Renewable energy (RE) has become a focal point of interest as an alternative source of energy to the traditional fossil fuel and other energy sources due to the fact that it is more environmentally friendly, abundant and economically feasible. Many countries aggressively promote feed-in tariff schemes and solar photovoltaic (PV) systems have become one of the fastest growing RE sources that can be integrated into the grid distribution network. This paper reviews the recent development of grid-connected PV (GPV) generation systems comprising of several sub-components such as PV modules, DC-DC converter, maximum power point tracking (MPPT) technique, and an inverter. In addition, various grid synchronization and islanding detection methods are elaborated. The future key challenges to build a smart and efficient GPV generation system were also presented.

In this paper, we propose enhanced adaptive step size Perturb and Observe (P&O) maximum power point tracking (MPPT) with properly organized comparison sequences which lead to achieving the actual maximum power point (MPP) effectively in the presence of partial shading conditions, taking into account the optimization of all aspects of high-performance MPPT to be novel, simpler, fast, and accurate, with the best efficiency reaching up to almost 100%. In this study, the proposed algorithm, along with a boost converter, was designed and simulated in MATLAB/Simulink to validate the performance of the suggested technique. Four different levels of partial shading conditions were considered for system examination: weak, moderate, and two different levels of strong shading. Each case was applied separately first and then combined in a sequence arrangement to provide robust and comprehensive testing which can provide a guaranteed assessment of the proposed algorithm. The performance of the suggested technique is discussed and compared with that of conventional P&O and conventional incremental conductance (IC) MPPT techniques. The failure of the conventional techniques to work efficiently in the presence of partial shading conditions was observed from the simulation results. Meanwhile, the success of the proposed technique and its high performance were clearly confirmed under partial shading conditions with no increase in complexity or convergence time.

This manuscript presents a significant work in improving the current harmonics extraction algorithm and indirectly improving the injection current produced by a single-phase Photovoltaic Shunt Active Power Filter (PV SAPF). Improvement to the existing adaptive linear neuron (ADALINE) technique has been carried out, leading to the formation of a simpler ADALINE; it is expected to perform as fast as the current harmonics extraction algorithm. Further analysis on the DC link capacitor control algorithm, called “self-charging with step size error cancellation”, was also done to inspect the performance of the algorithm in a single-phase photovoltaic shunt active power filter system. Both algorithms, configured in single-phase PV SAPF, were simulated in MATLAB/Simulink (R2012b). A laboratory prototype was developed, and the algorithms were computed on a TMS320F28335 Digital Signal Processing (DSP) board for hardware implementation purposes. From the acquired results, the simpler ADALINE algorithm has effectively performed with lower total harmonic Distortion (THD) and outstanding compensation. The established algorithm of self-charging with step size error cancellation works well with single-phase PV SAPF and has shown less overshoot, a fast response time, and minimal energy losses.

Electric power utilities across the globe are facing higher demand for electricity than ever before, while juggling to balance environmental conservation with transmission corridor expansions. Demand side management (DSM) and dynamic thermal rating systems (DTR) play an important role in alleviating some of the challenges faced by electric power utilities. In this paper, various DSM measures are explored and their interactions with the application of the DTR system in the transmission network are examined. The proposed modelling of DSM in this paper implements load shifting on load demand curves from the system, bus and load sector levels. The correlation effects of line ratings are considered in the DTR system modelling as the weather that influences line ratings is also correlated. The modelling of the line ratings was performed using the time series method, the auto regressive moving average (ARMA) model. Both the DSM and the DTR systems were implemented on the modified IEEE reliability test network. The modification was achieved by developing a load model starting from the perspective of the load sectors at each bus and a new collective hourly load curve for the system was obtained by combining the loads at all buses. Finally, the results in this paper elucidate the interaction of DSM and DTR systems.

Harmonic distortion in power networks has greatly reduced power quality and this affects system stability. In order to mitigate this power quality issue, the shunt active power filter (SAPF) has been widely applied and it is proven to be the best solution to current harmonics. This paper evaluates the performance of the modified synchronous reference frame extraction (MSRF) algorithm with fuzzy logic controller (FLC) based current control pulse width modulation (PWM) inverter of three-phase three-wire SAPF to mitigate current harmonics. The proposed FLC is designed with a reduced amount of membership functions (MFs) and rules, and thus significantly reduces the computational time and memory size. Modeling and simulations of SAPF are carried out using MATLAB/Simulink R2012a with the power system toolbox under steady-state condition, and this is followed with hardware implementation using a TMS320F28335 digital signal processor (DSP), Specrum Digital Inc., Stafford, TX, USA. The results obtained demonstrate a good and satisfactory response to mitigate the harmonics in the system. The total harmonic distortion (THD) for the system has been reduced from 25.60% to 0.92% and 1.41% in the simulation study with and without FLC, respectively. Similarly for the experimental study, the SAPF can compensate for the three-phase load current by reducing THD to 5.07% and 7.4% with and without FLC, respectively.

This paper presents improvement of a harmonics extraction algorithm, known as the fundamental active current (FAC) adaptive linear element (ADALINE) neural network with the integration of photovoltaic (PV) to shunt active power filters (SAPFs) as active current source. Active PV injection in SAPFs should reduce dependency on grid supply current to supply the system. In addition, with a better and faster harmonics extraction algorithm, the SAPF should perform well, especially under dynamic PV and load conditions. The role of the actual injection current from SAPF after connecting PVs will be evaluated, and the better effect of using FAC ADALINE will be confirmed. The proposed SAPF was simulated and evaluated in MATLAB/Simulink first. Then, an experimental laboratory prototype was also developed to be tested with a PV simulator (CHROMA 62100H-600S), and the algorithm was implemented using a TMS320F28335 Digital Signal Processor (DSP). From simulation and experimental results, significant improvements in terms of total harmonic distortion (THD), time response and reduction of source power from grid have successfully been verified and achieved.

This paper presents a hybrid maximum power point tracking (MPPT) method to detect the global maximum power point (GMPP) under partially shaded conditions (PSCs), which have more complex characteristics with multiple peak power points. The hybrid method can track the GMPP when a partial shadow occurs either before or after acquiring the MPP under uniform conditions. When PS occurs after obtaining the MPP during uniform conditions, the new operating point should be specified by the modified linear function, which reduces the searching zone of the GMPP and has a significant effect on reducing the reaching time of the GMPP. Simultaneously, the possible MPPs are scanned and stored when shifting the operating point to a new reference voltage. Finally, after determining the possible location of the GMPP, the GMPP is obtained using the modified P&O. Conversely, when PS occurs before obtaining the MPP, the referenced MPP should be specified. Thus, after recognizing the possible location of the GMPP, the modified P&O can be used to obtain the GMPP. The simulation and experimental implementations for the proposed algorithm are performed with different scenarios of shadowing under different irradiations, which clearly indicate that the proposed method is robust and has a fast tracking speed. Moreover, this work presents the load sizing method for PSCs to avoid controller failure when detecting the GMPP. Additionally, in this paper, the user-friendly method for programming the digital signal processing (DSP) via Simulink/MATLAB is presented in detail.