Distribution feeders form the last leg of the bulk power system and have the responsibility of providing reliable power to the customers. These feeders experience voltage drops due to a combination of feeder length, load distribution, and other factors. Traditionally, voltage drop was a major concern. Now, due to an ever-increasing PV penetration, overvoltage has also become a major concern. This limits the amount of solar PV that may be integrated. Few solutions exist to improve the voltage profile, where the most common is the use of voltage control devices like shunt capacitors and voltage regulators. Due to a large number of design parameters to be considered, the determination of the numbers and locations of these devices is a challenging problem. Significant research has been done to address this problem, utilizing a wide array of optimization techniques. However, many utilities still determine these locations and numbers manually. This is because most algorithms have not been adequately validated. The validation of a voltage profile design (VPD) algorithm has been presented here. The validation of this algorithm was carried out on a set of statistically relevant feeders. These feeders were chosen based on the results obtained from a feeder taxonomy study using clustering analysis. The algorithm was found to be effective in enhancing the amount of solar PV a feeder may host, while still maintaining all the voltages within the ANSI standard limits. Furthermore, the methodology adopted here may also be used for the validation of other algorithms. / Master of Science / Utilities have the responsibility of providing reliable power supply to their customers. Traditionally, bulk power was generated and transmitted over long distances incurring losses and voltage drops along the way. Now, with the integration of distributed energy resources, particularly solar photovoltaic (PV) generators at the customer locations, overvoltage has also become a problem. This requires adoption of measures which can help in maintaining the voltages within standard limits. Several options exist to compensate for these voltage issues, the most commonly used is voltage control devices like capacitor banks and voltage regulators. However, determining the required numbers of these devices and their appropriate locations is a challenging problem. Even though a number of algorithms have been proposed to give automated solutions to this problem, most utilities still use a manual approach. This is because these algorithms have not been validated on a statistically relevant set of feeders. To solve this issue, the validation of a voltage profile design (VPD) algorithm is presented in this thesis. The ability of this algorithm to enhance the amount of PV that may be connected to a distribution network has been validated on a set of feeders. The feeders were chosen based on the results obtained from clustering analysis, a machine learning concept. The cost effectiveness of this algorithm has also been investigated and significant savings were observed. Furthermore, the methodology adopted here can be easily extended for the validation of other algorithms as well.