This simulator models an illustrative 293 W, 60-cell PV module feeding a bounded, averaged boost converter into an ideal absorbing 48 V DC bus at 96% efficiency. A digital controller samples voltage and current and adjusts the PV-voltage reference — compare perturb-and-observe against incremental conductance, and see exactly how sampling rate, perturbation size, plant response and shading can mislead either one.
• A real-time 3D cutaway of the module and boost-converter stage, with home view, rear/front view toggle, auto-rotate and expand-3D scene tools, plus tappable components with anchored callout labels. • Tracking algorithm selector: perturb and observe (P&O), incremental conductance, or manual PV-voltage control, plus a fault detector (none, sensor invalid, gate driver failure) and a disable/enable-converter toggle. • Time and scan controls: run time, step 200 ms, advance 5 seconds, a timed global scan (a 45-point sweep, not an instantaneous jump), pin/clear comparison curve, and a half-irradiance cloud fixture. • A Measurements & trends tab with a panel I-V curve (present curve, pinned comparison, operating point, independently calculated global maximum), a P-V curve showing where local trackers can settle at a weaker peak, a power-response-over-time trend, a PV-voltage-versus-controller-reference chart (actual voltage in blue, reference in amber, showing how slow plant response or overly fast sampling can mislead the tracker), the full equation set (P=VI, dP/dV, the P&O and incremental-conductance decision rules, the ideal boost relation, duty-cycle bounds and tracking-capture ratio), and a Controller decision trace listing each sample's measured Δ values and the resulting action, newest first. • Controller and converter measurements showing that delivered bus power is 96% of harvested PV power through the bounded averaged boost stage. • An Experiments & tests tab with 14 guided experiments (P&O convergence, incremental conductance, manual operating point, coarse/fine perturbations, sampling too quickly, noisy sensors, shaded local trap, scan under partial shade, cloud arrival, hot PV cells, darkness, sensor failure, gate failure), a Verification bench that runs automated model checks, and a diagnostic challenge. • A Learn & assess tab with a 7-question knowledge-check quiz and a written model-scope statement referencing MathWorks MPPT-algorithm and Sandia PVPMC single-diode materials.
Perturb-and-observe simply nudges the PV-voltage reference in one direction and checks whether measured power rose or fell on the next sample; a fall reverses direction. Incremental conductance instead estimates the local slope dP/dV from consecutive current and voltage samples (using the identity that at a smooth maximum, dI/dV = −I/V) and adjusts the reference based on that slope's sign — in principle this can converge more directly, but both algorithms only ever see local information from real, noisy sensor samples.
That local-only view is exactly what the guided experiments are built to expose: sampling too quickly relative to the plant's settling time constant can make the controller react to a transient rather than a settled reading; oversized perturbations overshoot the peak and oscillate around it instead of settling there; and substring shading — modeled with the same three-substring bypass-diode behavior as the panel model — can create a second local peak where either algorithm gets trapped. The simulator's timed global scan exists specifically to show what the true global maximum is, as an independent reference the controller itself is never given.
The Controller decision trace is a direct window into the algorithm's reasoning: each row shows the measured Δvoltage, Δcurrent and Δpower for that sample alongside the action taken and the resulting new reference, so you can trace exactly why the tracker moved the way it did — including sensor-noise-driven decisions when the noise setting is nonzero. The tracking-capture ratio compares total harvested energy against total available MPP energy over the run, giving a single number for how well a given algorithm and configuration performed.
This is a teaching model: the PV side reuses the same illustrative single-diode, three-bypass-substring module as the standalone panel simulator, the converter is a first-order averaged voltage response (not a switching or capacitor-energy solver) at a constant 96% efficiency into an ideal absorbing 48 V bus, and the global scan is a timed 45-point sweep that can miss narrow peaks and aborts if conditions change mid-scan — it is not an instantaneous jump to the answer. Sensor noise is repeatable uniform error; plotted power always reflects the true physical value, not the noisy measurement.
Perturb-and-observe (P&O) simply perturbs the voltage reference and reverses direction if power fell on the next sample. Incremental conductance instead estimates the local power-voltage slope from consecutive current/voltage samples and follows its sign, using the fact that at a smooth maximum the slope is zero. Both are selectable modes in the simulator so you can compare their behavior side by side.
Substring shading (modeled with the same bypass-diode behavior as the standalone panel simulator) can create two power peaks. Both P&O and incremental conductance only see local slope information from real samples, so they can settle at the nearer, weaker peak instead of the true global maximum — which is exactly what the "shaded local trap" experiment and the timed global scan are built to demonstrate.
It runs a 45-point sweep across the PV-voltage range to find an independent reference for the true global maximum power point — it is not an instantaneous jump, takes real simulated time, can miss narrow peaks, and aborts if the environment changes mid-scan. It exists to show what the controller is not directly given, not to replace the tracking algorithms.
The converter is a first-order averaged voltage response at a constant 96% efficiency into an ideal absorbing 48 V bus, not a switching or capacitor-energy solver. It excludes battery charging dynamics, semiconductor switching, thermal hotspots, output-bus dynamics and protection certification. Sensor noise is repeatable uniform error, and the plotted power is always the true physical value.