Renewable energy plays an increasingly important role in achieving sustainable development, ensuring energy security, and controlling greenhouse gas emissions. Solar energy is widely used source for electricity production due to continuous advancements in solar power technology and decreasing costs. Additionally, advancements in PV panel and inverter designs have made it possible to invest in low-insolation areas. Partial shading conditions (PSCs) can significantly reduce the energy output of photovoltaic (PV) systems. Conventional and advanced MPPT systems often fail to operate PV systems at their peak performance during PSCs due to the conduction mode of bypassi diodes, which can trap the PV system at a low power point. However, global peak searching tools can enable the operation of PV systems at the GMPP. It is important to note that frequent use of these tools may decrease the output of PV systems, as they force the system to operate outside its power region during the scanning of the I-V curve to determine the GMPP. Therefore, global peak searching tools should only be deployed when PSCs occur. In this thesis, a simple and accurate method for detecting PSCs is proposed by monitoring the sign of voltage changes. The method predicts a PSC if the sign of successive voltage changes remains the same for a certain number of successive changes. The proposed method was tested on two types of PV array configurations (series and series-parallel) with various emulated shading patterns. The method successfully and timely identified all emulated shading patterns and outperformed a detection method based on monitoring the normalized change in power. It can be used to trigger GMPP searching techniques for improving the output of PV systems under PSCs. Machine learning tools, such as fuzzy logic control and neural networks, have been used in numerous studies to enhance conventional MPPT methods and optimize PV systems under PSCs. However, these methods are complex to implement as they require intensive calculations and exhibit limitations in ensuring GMPP operation under all PSC conditions. To address this, a light and fast GMPP searching method based on Bald Eagle Searching (BES) is proposed in this thesis. The BES method locates the maximum value in three stages: selecting space, searching in space, and swooping. The first stage of the BES method is utilized to design the proposed GMPP method. MATLAB results demonstrate that the BES technique outperforms Cuckoo Search (CS) and Particle Swarm Optimization (PSO) methods, reducing search time significantly and successfully finding the GMPP in all simulated PSC cases. The proposed method is simple, easy to implement, and only requires a single tuning parameter, distinguishing it from PSO and CS methods. A validation method using a real-time digital simulator (RTDS) confirms the capability of the proposed method to find the GMPP within a reasonable time. MPPT methods aim to maximize the output power of PV systems under changing meteorological conditions. The performance of these methods depends on the algorithm's complexity and the number of variable inputs used to obtain the MPP value. However, they tend to oscillate around the MPP during steady-state operations, resulting in energy waste. Moreover, traditional MPPT methods do not perform optimally under PSCs. To address these issues, modifications are proposed to the global maximum power point bald eagle search-based (GMPP BES) method to make it function as an MPPT method as well. The modifications enable the GMPP BES method to detect minor changes in insolation and temperature by monitoring the PV array output voltage and trigger the search for the suitable MPP voltage accordingly. The RTDS simulation results demonstrate the modified GMPP BES method's ability to accurately and timely locate MPP values under the changes in insolation and ambient temperature. The results show that the proposed method outperforms the perturb and observe (P&O) method in responding to changes in insolation and ambient temperature, and it effectively reaches correct MPP values with minimal oscillation around the MPP. Therefore, the proposed method is considered a practical solution for solar farms aiming to harvest large amounts of energy. The suggested GMPP tracking method is implemented at the Sultan Qaboos University Hybrid Station Lab using a real experimental test platform. Various experimental conditions, including switching between partial shading and no shading situations, as well as changes in temperature and irradiance, are conducted to evaluate the performance of the proposed GMPP tracking method. Based on the experimental results, the GMPP tracking method demonstrates excellent tracking performance under diverse operating conditions.