Changes In Load Demand Research Articles

AGC in the Multi-Area Thermal System with Integration of Distribution Generation on the Frequency of the System Using the Classical PID & Hybrid Neuro-Fuzzy Controllers

In the essential operation and control of the intelligent power plants, the primary goal is to balance both powers generated on the generation side with the power demand on the demand side. But, the structural variation of the power systems with a continuing escalation of customer demand is changing load demand continuously. Due to these problems, it is a continuous requirement to control and accommodate the escalation power need of the customer. The system’s frequency is affected by disturbance and mismatching between power generation and power demand. The novel design of a hybrid Neuro-fuzzy system is used in the Automatic Generation Control (AGC) model of two equal-areas power systems. Installing non-conventional energy sources allows power producers and consumers to prefer power transactions in an economic scenario. The Distribution Generation (DG) is incorporated into the proposed model. The first design, the two equal-area AGC, uses a classical PID Controller considering DG. Second, the two equal- areas AGC design is investigated by considering Adaptive Neuro-Fuzzy Interface (ANFIS) with integrated Distributed generation. The simulation results show that an ANFIS incorporated with a DG-based PID controller exhibits more robust performance than the studied PID without and with the DG-based method. The reduction in vibration amplitude and the damping time provided by a PID-ANFIS-based AGC controller with the integration DG is 83.75%, much less than Classical Controllers.

Techno-Economic Feasibility of Off-Grid Renewable Energy Electrification Schemes: A Case Study of an Informal Settlement in Namibia

This paper examines different off-grid renewable energy-based electrification schemes for an informal settlement in Windhoek, Namibia. It presents a techno-economic comparison between the deployment of solar home systems to each residence and the supplying power from either a centralized roof-mounted or ground-mounted hybrid microgrid. The objective is to find a feasible energy system that satisfies technical and user constraints at a minimum levelized cost of energy (LCOE) and net present cost (NPC). Sensitivity analyses are performed on the ground-mounted microgrid to evaluate the impact of varying diesel fuel price, load demand, and solar photovoltaic module cost on system costs. HOMER Pro software is used for system sizing and optimization. The results show that a hybrid system comprising a solar photovoltaic, a diesel generator, and batteries offers the lowest NPC and LCOE for both electrification schemes. The LCOE for the smallest residential load of 1.7 kWh/day and the largest microgrid load of 5.5 MWh/day is USD 0.443/kWh and USD 0.380/kWh, respectively. Respective NPCs are USD 4738 and USD 90.8 million. A sensitivity analysis reveals that variation in the fuel price and load demand changes linearly with system costs and capacities. However, reducing the PV module price in an energy system that includes wind and diesel power sources does not offer significant benefits. Furthermore, deploying an energy system that relies on fossil fuels to each residence in an informal settlement is not environmentally responsible. Unintended negative environmental impacts may result from the mass and simultaneous use of diesel generators. Therefore, a microgrid is recommended for its ability to control the dispatch of diesel generation, and its scalability, reliability of supply, and property security. A roof-mounted microgrid can be considered for piloting due to its lower initial investment. The electricity tariff also needs to be subsidized to make it affordable to end-users. Equally, government and community involvement should be prioritized to achieve long-term economic sustainability of the microgrid.

Robust Control for Optimized Islanded and Grid-Connected Operation of Solar/Wind/Battery Hybrid Energy

Wind and solar energy systems are among the most promising renewable energy technologies for electric power generations. Hybrid renewable energy systems (HRES) enable the incorporation of more than one renewable technology, allowing increased reliability and efficiency. Nevertheless, the introduction of variable generation sources in concurrence with the existing system load demand necessitates maintaining the power balance between the components of the HRES. Additionally, the efficiency of the hybrid power supply system is drastically affected by the number of converters interfacing its components. Therefore, to improve the performance of the HRES, this paper proposes a robust sliding mode control strategy for both standalone and grid-connected operation. The control strategy achieves maximum power point tracking for both the renewable energy sources and stabilizes the DC-bus and load voltages irrespective of the disturbances, change in load demand, variations of irradiance level, temperature, and wind speed ensuring an efficient energy management. Furthermore, the solar PV system is directly linked to the DC-bus obviating the need for redundant interfacing boost converters with decreased costs and reduced system losses. Lyapunov candidate function is used to prove the asymptotic stability and the convergence of the entire system. The robustness of the proposed control strategy is tested and validated under various conditions of HRES, demonstrating its efficacy and performance under various conditions of the HRES.

Introducing a novel control algorithm and scheduling procedure for optimal operation of energy storage systems

One of the principal purposes of using energy storage systems (ESS) are peak shaving and load smoothing. In the present study, a novel control algorithm has been developed in order to the optimal use of ESS for peak load shaving. First, by comparing the complex control algorithm of the present study with the control algorithm developed by other researchers (simple control algorithm), the superiority and advantage of the present work become clear. To do this, five different load demand profiles have been considered and the mentioned algorithms have been applied to them. The results show that the implementation of the present study novel algorithm on the load demand profiles number one to five reduces the maximum of the required grid power to 6.477%, 11.81%, 12.60%, 10.25%, and 3.000% of their initial value, respectively. Their variance also reduces their initial value to 14.47%, 14.20%, 28.48%, 39.87%, and 54.96%, respectively. On the other hand, the implementation of the simple control algorithm has reduced the maximum of the required grid power to 0.8542% of its initial value only in the fifth profile. But the variance in the first to fifth profiles is reduced by 9.630%, 5.831%, 14.12%, 21.89%, and 52.00% of their initial value, respectively. In the following, by applying the novel control algorithm on the first sample load demand profile and change of various parameters such as ESS capacity, lower and upper limitation of the grid power, etc. the results are extracted and reported in detail.

Cascaded model predictive controller performance for the selection of robust working fluids in absorption refrigeration cycles

• Evaluation of the dynamic performance of 13 novel working fluids. • Cascade model predictive control is of superior performance than cascade PI control. • Systematic ranking of ABR fluids based on setpoint tracking and resource utilization. • A multi-criteria assessment approach for working fluid selection is presented. • Acetaldehyde/Dimethylformamide exhibits superior steady state and dynamic performance. Current working fluid selection approaches for the absorption refrigeration cycle are based exclusively on steady-state cycle performance. In real operating conditions, the cycle is subject to exogenous disturbances, with detrimental effects on their performance, resulting in increased resource utilization or even failure to meet load demands. In the present work, the problem of working fluid evaluation for the absorption refrigeration cycle is investigated, by considering both the steady state and the dynamic performance of the system. The evaluation of 13 novel, organic working fluids is performed utilizing a cascaded proportional-integral and a cascaded model predictive controller, implemented on a single-effect absorption refrigeration cycle. A set of previously validated non-linear, dynamic process models, developed in ASPEN Plus are used to create the linear models required for the model predictive controller. The closed loop dynamic performance is evaluated based on speed of response, resource utilization and deviations from the desired operating point (setpoint), while operating under a disturbance scenario involving load demand changes. Multi-criteria assessment results indicate that the cascaded model predictive control is considerably more consistent than the cascade PI controller. The novel mixture of acetaldehyde/dimethylformamide exhibits superior performance than the conventional NH 3 /H 2 O mixture, by 57% in speed of response and 76% in resource utilization. The mixture ιs also 25% and 12% better than NH 3 /H 2 O in steady-state cost per ton of cooling and coefficient of performance, indicating high economic potential and robustness for single-effect ABR systems.

Frequency regularization of a linked wind\u2013diesel system using dual structure fuzzy with ultra-capacitor

Microgrids (μ-grids) are gaining increased interest around the world for supplying cheap and clean energy. In this paper, a μ-grid comprising a wind turbine generator (WTG) and diesel generator (DG) is considered. It is one of most practical and demanding systems suitable for the present energy crisis in isolated or rural areas. However, wind energy is intermittent in nature while load demand changes frequently. Therefore, a µ-grid can experience large frequency and power fluctuations. The speed governor of the DG tries to minimize the frequency and power deviations in µ-grid though its operation is slow and cannot adequately minimize system deviations. The paper proposes a novel arrangement based on a dual structured fuzzy (DSF) whose structure changes according to the switching limit with a reduced rule base. It has the capability to switch between proportional and integral actions and hence improves the frequency regularization of the μ-grid. The proposed strategy is tested in a μ-grid and the results considering step load alteration, load alteration at different instants of time, nonstop changing load request are compared with some of the well published methods to validate the effectiveness and simplicity of the present design. In addition, it shows that ultra-capacitor establishment in a μ-grid has a positive impact in minimizing system deviations with DSF for the studied cases.

Design, Analysis and Implementation of Bidirectional DC–DC Converters for HESS in DC Microgrid Applications

This research proposes an enhanced converter for a hybrid energy storage system (HESS) for a multi-input bidirectional DC–DC power converter (MIPC). When batteries are used for energy storage, their charge and discharge rates are low, putting the battery under current stress and shortening its life. Because of their increased power density, supercapacitors (SCs) can react quickly to abrupt fluctuations and solve this problem. SCs, on the other hand, cannot be utilized for storage since they cannot provide power for prolonged periods of time. Batteries and supercapacitors are employed together in HESSs because their opposing characteristics make them an ideal pair for energy storage. An MIPC is used to connect the HESS to the DC microgrid. The MIPC allows for decoupled battery and SC power regulation, as well as energy transfer across storage devices inside the system. A controller has been developed to regulate both HESS charging and discharging operations, making it a unified controller for DC microgrid applications. The proposed model predictive control (MPC) provided better DC grid voltage restoration to step change in PV generation and load demand over the traditional proportional integral (PI) control scheme. The MPC method minimizes current strains, extends battery life and enhances overall system performance in response to a step change in PV power and load demand as well as providing quicker DC grid voltage control. Simulation and experimental data for the proposed controller were created by varying PV generation and load demand, resulting in faster DC link voltage regulation.

Cost Functions for Generation Dispatching in Microgrids for Non-Interconnected Zones in Colombia

Generation dispatching is a challenge in islanded microgrids due to the operational and economic restrictions in isolated zones. Furthermore, the impact of usual operational network changes in topology, load demand, and generation availability may become significant considering the grid size. This research paper presents a detailed multiple cost function modeling methodology of an optimal power flow algorithm applied to a non-interconnected zone in Colombia. The optimal power flow (OPF) formulation includes cost functions related to renewable resources as presented in the isolated zone and a complete model of the charging and discharging of batteries. Additionally, the flexibility of the proposal is tested using three different network topologies with a characteristic daily load curve from the zone. The main contribution of this paper lies in the implementation of an optimal power flow including cost functions of renewable sources for isolated microgrids. A test case for a non-interconnected zone in Colombia is performed for various operation cases.

Study of power management of standalone DC microgrids with battery supercapacitor hybrid energy storage system

<p>In the last years, renewable energy (RE) is increasing widely in the energy sector, and microgrid technology is overgrowing. In this paper, stand-alone microgrid using solar photovoltaic (PV) energy as a source of renewable energy is simulated to provide power for direct current (DC) loads with hybrid energy storage system (HESS) which consists of battery and supercapacitor bank. The proposed microgrid system is tested under various cases of load and variable irradiance to confirm and validate the proposed management strategy to remain the DC bus voltage within a stable limit. The performance of DC microgrid is comparing with and without supercapacitor (SC) bank and notes a desirable decrease in the magnitude of transient voltage when using HESS. The sun power SPR-E19-320 standard was simulated to analyze system performance taking into account the constant load demand. Note that HESS helps reduce transient of DC voltage very effectively in all situations. Very large transients arise due to sudden changes in load demand is also compensated by HESS. The results obtained indicate that the stand-alone DC microgrid with HESS is very beneficial for reducing transient of DC-link voltage that occurs due to sudden change in load or fault. The proposed system is performed by MATLAB/Simulink environment.</p>

Coordinate control and energy management strategies for a grid-connected hybrid system

This paper explains how to operate a networked hybrid system. A Proton Exchange Membrane Fuel Cell (PEMFC) and a hybrid photovoltaic (PV) array system are taken into account. The hybrid system can run in Feeder Flow Control (FFC) or Unit Capacity Control (UPC) mode. Changes in load demand are adjusted by the main grid because the hybrid source's output is controlled by reference power in UPC mode. Renewable energy is widely used. When light and temperature fluctuate, a PV battery uses Maximum Power Point Tracking (MPPT) to give maximum power to a load. Photovoltaic electricity is uncontrollable because its output varies with the outside and cell temperature. PEMFC should be added to the hybrid system to overcome its limitations. The output of the hybrid source can be controlled by adjusting the output power of the frequency converter. As a result, it is necessary to set a reference value for the output of the hybrid source. In Feeder-Flow Control (FFC) mode, the feeder flow is controlled to remain constant, the hybrid source takes on additional load, and as a result, the feeder reference power needs to be understood. This method reduces load shedding and boosts electricity under high loads. Under light load, the hybrid power supply switches to UPC mode. When the load requirement is near the border of the mode change, UPC or FFC mode is used. Hysteresis eliminates power reference fluctuations. The proposed operation strategy increases system performance, system stability, and mode transitions by consistently operating the PV array at maximum output power and the Photovoltaic–Fuel Cell (PVFC) within its highefficiency range.

Energy Management of an Isolated Wind/Photovoltaic Microgrid Using Cuckoo Search Algorithm

This paper introduces a renewable-energy-based microgrid that includes Photovoltaic (PV) energy and wind energy generation units. Also, an energy storage system is present. The proposed microgrid is loaded with a constant load impedance. To improve the performance of the proposed microgrid, an optimal control algorithm utilizing Cuckoo Search Algorithm (CSA) is adapted. It has many merits such as fast convergence, simple tunning, and high efficiency. Commonly, the PV and wind energies are suitable for supplying loads under normal conditions. However, the energy storage system recovers the excess load demand. The load frequency and voltage are regulated using the CSA optimal controller. The microgrid responses with the introduced optimal controller are measured under step changes in load demand, wind power, and the PV irradiation level. Matlab simulations are carried out to test the proposed system performance. The simulation results showed that the proposed microgrid fed the load with Alternating Current (AC) power of constant amplitude and frequency for all disturbances. Moreover, the required load demand has been perfectly compensated. Moreover, the performance of the storage system is excellent with the unstable wind speed and variable solar irradiation. Also, the results with the optimal CSA controller are compared to those with the Particle Swarm Optimization (PSO) algorithm at the same conditions. It is also found that the optimal CSA controller provides better responses.

Real-Time Shipboard Power Management Based on Monte-Carlo Tree Search

The increasing concern in reducing greenhouse gas emissions and improving the fuel efficiency of marine transportation leads to a higher demand for intelligent power management systems (PMS). Unlike offline PMS with prior knowledge on the load profiles, real-time PMS is more challenging because of unknown load profiles. Two typical real-time PMS methods are equivalent consumption minimization strategy (ECMS) and model prediction control (MPC). However, ECMS can only make instantaneous decisions without the ability to handle large load demand changes, and MPC normally relies on pre-defined input references to guide the optimization. To alleviate these problems, in this paper, a Monte-Carlo Tree Search (MCTS) based method is proposed with a reward function guided by worst-case to minimize the fuel consumption. Meanwhile, a Siamese learning-based model is integrated with MCTS to improve its performance further. To ensure the sustainability of the power for unknown profiles, a time-dependent SoC-shore constraint is introduced, which intends to fully recharge the battery when leaving each of the shore connection stations. This constraint has not been considered in the previous real-time PMS studies. The experimental results demonstrate that the proposed method outperforms other methods on both fuel consumption minimization and the SoC-shore constraint fulfillment.

Design of single- and multi-loop self-adaptive PID controller using heuristic based recurrent neural network for ALFC of hybrid power system

This paper presents a novel heuristic based recurrent Hopfield neural network (HNN) designed self-adaptive proportional-integral-derivative (PID) controller for automatic load frequency control of interconnected hybrid power system (HPS). The control problem is conceptualized as an optimization problem and solved using a heuristic optimization technique with the aim of minimizing the Lyapunov function. Initially, the energy function is formulated and the differential equations governing the dynamics of HNN are derived. Then, these dynamics are solved using hybrid particle swarm optimization-gravitational search algorithm (PSO-GSA) to obtain the initial solution. The effectiveness of the controller is tested for two-area system considering the system non-linearities and integration of plug-in-electric vehicle (PEV). Further, to improve the speed of response of the system, the cascade control scheme is proposed using the presented approach of heuristic based HNN (h-HNN). The efficacy of the method is examined in single- and multi-loop PID control of three-area HPS. The performance of propounded control schemes is compared with PSO-GSA and generalized HNN based PID controller. The results obtained show that the response of proposed controller is superior in terms of transient and steady state performance indices measured. In addition, the control effort of suggested cascade controller is much reduced compared with other controllers presented. Furthermore, the self-adaptive property of the controller is analyzed for random change in load demand and their corresponding change in gain parameters are recorded. This reveals that the proposed controller is more suitable for stable operation of modern power network with green energy technologies and PEV efficiently.

Determining the appropriate size of the electrical energy storage system of an energy process based on a solid oxide fuel cell and wind turbine

Fuel cells are efficient and popular technologies, and have recently shown remarkable performance and expansion. Their use in coupled energy systems can result in significant performance of the energy system. In addition, the problem of fuel cell reliability (due to changes in load demand due to reduced fuel supply) can be solved by using it in integrated systems. The energy system proposed in this research comprised of power generation system and energy storage system. The power generation system includes a solid oxide fuel cell (SOFC), and thermoelectric and thermionic generators. The energy in hydrogen fuel is converted to power and heat by the fuel cell. The thermionic generator utilizes the waste heat of the fuel cell and generates power. The thermoelectric generator is then coupled to the thermionic and generates more power using its dissipated heat. This leads to increased power generation and energy system performance. The electrical energy obtained from the power generation system is stored by the energy storage system (via pumped hydro-compressed air system) for peak hours. In a scenario based on the benefit of clean energy, the performance of an electrolyzer coupled with a wind turbine to supply the fuel required by the SOFC is presented. The results showed that the power generation system is capable of generating 533.4 W of electricity (with an output efficiency of 76.6%). The contribution of fuel cell and thermoelectric and thermionic generators in power generation is 81.5, 5, and 13.5%, respectively. Furthermore, the storage efficiency and capacity of the system are 60% and 477.85 liters, respectively. It was also found that on annual average about 26.5 g/h of hydrogen fuel can be obtained from a wind-based hydrogen production system. This amount is about 11% more than the SOFC needs.

Frequency stabilization in interconnected power system using bat and harmony search algorithm with coordinated controllers

Modern power system faces excessive frequency aberrations due to the intermittent renewable generations and persistently changing load demands. To avoid any possible blackout, an efficient and robust control strategy is obligatory to minimize deviations in the system frequency and tie-line. Hence, to achieve this target, a new two-degree of freedom-tilted integral derivative with filter (2DOF–TIDN) controller is proposed in this work for a two-area wind-hydro-diesel power system. To enhance the outcome of the proposed 2DOF–TIDN controller, its gain parameters are optimized with the use of a newly designed hybrid bat algorithm-harmony search algorithm (hybrid BA–HSA) technique. The effectiveness and superiority of hybrid BA–HSA tuned 2DOF–TIDN is validated over various existing optimization techniques like cuckoo search (CS), particle swarm optimization (PSO), HSA, BA and teaching learning-based optimization (TLBO). To further refine the system outcome in the dynamic conditions, several flexible AC transmission systems (FACTS) and superconducting magnetic energy storage (SMES) units are adopted for enriching the frequency and tie-line responses. The FACTS controllers like static synchronous series compensator (SSSC), thyristor-controlled phase shifter (TCPS), unified power flow controller (UPFC) and interline power flow controller (IPFC) are employed with SMES simultaneously. The simulation results disclose that the hybrid BA–HSA based 2DOF–TIDN shows superior dynamic performance with IPFC–SMES than other studied approaches. A sensitivity analysis is examined to verify the robustness of proposed controller under ±25% changes in loading and system parameters.

A Design and Modelling of PV-Diesel Hybrid Energy System with Fuzzy Logic Controller

— Renewable energy, such as wind and photovoltaic arrays, has become the core energy in micro-grids used for supplying remote areas and areas suffering from electricity outages. Many limitations, such as weather fluctuations and low efficiency, lead to designing and developing a hybrid energy system. The integration of photovoltaic arrays and diesel gensets has become one of the most common approaches to generating electricity for remote communities. Although the integration of photovoltaic arrays and diesel gensets has the potential to reduce the cost of electricity production by harnessing free energy from the sun to reduce the power generated by diesel engines, it tends to complicate the control of the entire system due to the intermittent nature of the renewable energy sources and changing load demand. This paper proposes a fuzzy logic controller of PV-Diesel hybrid energy system, which is used as an effective tool in facilitating optimum power-sharing between the PV power source, charging, and discharging batteries and diesel generator as a backup based on the dynamics of the available PV energy at any time. Optimizing power control in the PV-Diesel hybrid energy system is key to minimizing the cost of power generation and maximizing the overall efficiency of the PV-Diesel hybrid energy system. The MATLAB-Simulink is used to design the fuzzy logic controller of the PV-Diesel hybrid energy system and to validate its performance. Five scenarios during the day have been tested to show the performance of the fuzzy logic controller. The results showed the accurate controlling of the power flow in the PV-Diesel hybrid system and the power saved about 2%.

Total power deficiency estimation of isolated power system network using full-state observer method

<span>An isolated electrical network with an independent local distributed generator is very sensitive towards the contingencies between load demand and supply. Although the network system has less complexity in term of structure, its stability condition is crucial due to its stand-alone operating condition. The total power deficit in the network gives the important information related to the dynamical frequency responses which may directly affect the system’s stability level. In this paper, the approach to estimate the total power deficiency for the isolated electrical network was presented by utilized the Luenberger observer method. Although the power deficit is not the state variable in the network mathematical model, the solution of estimation problem was feasible by introducing the new variable using additional dummy system. The simulation was carried out by using MATLAB/Simulink environment and the designed estimator was verified using multifarious load demand changes. The results show that the estimated signal was successfully tracked the expected actual signal with minimum error.</span>

ORCA Algorithm for Unit Commitment Considering Electric Vehicle Inclusion

The most cost-effective operation of a power system is achieved in practice by combining different types of producing units to create a committed power plan to satisfy load demand changes at all periods of operation. To reduce overall costs while upholding set limitations, power outputs from generating units are distributed based on load demand at a certain moment. Economic Dispatch models are used to account for changes in load demand to compute the overall cost variations of operation to fulfill a unit commitment (UC). Orca Algorithm is used in this work to solve the UC problem with the IEEE-62 bus system as the model, where loads are linked with flexible loads where the flexible load in this study is determined by the driving habits of an electric vehicle (EV). The simulation results show that the Orca Algorithm solves the problem in the fewest iterations possible. Computations used to compute load demand changes over all periods are fast and smooth, with high convergence. UC problem is carried out in various power outputs, and total operating expenses. Furthermore, the EV has different driving characteristics as well as power users to cover the entire route for driving patterns with one-way and two trips.

A Hybrid Metaheuristic Optimization Approach for the Synthesis of Operating Procedures for Optimal Drum-Boiler Startups

A steam generator serves as a power generation equipment that uses the expansive power of steam to generate electricity. The startup process of a steam generator plays an important process in a power plant to adjust its electricity generation in response to changes in load demand. As renewable generation plants increase, the levels of variability in electricity production increase. Fast startups become instrumental as they enable traditional power generation plants to provide the quantity of electricity missing when variable renewable energies cannot satisfy the load demand. The drum boiler is one of the main pieces of equipment involved in the startup process of a steam generator. However, if the startup process is carried out too fast, excessive thermal stresses may occur, thus provoking damage to the components of the drum boiler. This paper proposes a dynamic optimization methodology to synthesize operating procedures that minimize the startup time of the drum boiler while avoiding the excessive formation of thermal stresses. Since valve operations influence the time-varying behavior of the steam, dynamic simulation is needed in order to evaluate the operating procedure. The proposed algorithm is based on two important elements of two metaheuristic algorithms: the acceptance probability of the simulated annealing algorithm and the tabu search memory structures. A case study evaluates the proposed approach by comparing it against results previously published in the literature.

Machine Learning-Based Classification of Electrical Low Voltage Cable Degradation

Low voltage distribution networks have not been traditionally designed to accommodate the large-scale integration of decentralized photovoltaic (PV) generations. The bidirectional power flows in existing networks resulting from the load demand and PV generation changes as well as the influence of ambient temperature led to voltage variations and increased the leakage current through the cable insulation. In this paper, a machine learning-based framework is implemented for the identification of cable degradation by using data from deployed smart meter (SM) measurements. Nodal voltage variations are supposed to be related to cable conditions (reduction of cable insulation thickness due to insulation wear) and to client net demand changes. Various machine learning techniques are applied for classification of nodal voltages according to the cable insulation conditions. Once trained according to the comprehensive generated datasets, the implemented techniques can classify new network operating points into a healthy or degraded cable condition with high accuracy in their predictions. The simulation results reveal that logistic regression and decision tree algorithms lead to a better prediction (with a 97.9% and 99.9% accuracy, respectively) result than the k-nearest neighbors (which reach only 76.7%). The proposed framework offers promising perspectives for the early identification of LV cable conditions by using SM measurements.