NMS Series Photovoltaic Inverter: A Digital-Control Platform That Precisely Optimises Every Kilowatt-Hour
- Opening
The last decade taught the solar industry two hard truths:
- Module prices can fall only so far—below USD 0.20 W⁻¹ the bill-of-materials is dominated by copper, aluminium and semiconductors.
- Module-level efficiency is now 22 %, yet field-wide PR (performance ratio) in large fleets stubbornly hovers around 80 %—the remaining 18 % is lost in wiring, mismatch, soiling, inverter inefficiency and grid curtailment.
The NMS series was conceived to attack the second bucket by treating every kilowatt-hour as a data product. Built around a native 32-bit floating-point DSP and an ARM dual-core safety-MCU, the platform embeds physics-based models, AI-driven forecasting and edge-computing analytics directly into the inverter firmware. The result is 98.7 % Euro efficiency, < 1 % THD, 15 % higher energy capture in shaded scenarios, and O&M cost reductions exceeding 3 ¢ kWh⁻¹ over a 25-year life.
- Architecture Overview
2.1 Hardware stack
- Power stage: 6-pack SiC-MOSFET module (1 200 V, 40 mΩ) switching at 48 kHz; magnetic volume 30 % smaller than 16 kHz IGBT equivalent.
- Controller: TI C2000 TMS320F28388D (800 MIPS) + AM2632 lock-step (400 MHz).
- Sampling: 16-bit SAR ADC, 2 MS s⁻¹ simultaneous on 6 channels; effective resolution 14.2 bits at 50 Hz fundamental.
- Memory: 2 MB on-chip flash + 8 MB external QSPI for datalog, 512 kB ECC SRAM for model scratch-pad.
- Communications: dual Ethernet (100BASE-TX/1000BASE-X), RS-485, CAN-FD, Wi-Fi 6, 4 G LTE, LoRa for sensor leaf nodes.
2.2 Software partitioning
- Real-time core (0–1 kHz): MPPT, current control, islanding detection—certified IEC 60730 Class C.
- Model predictive core (1–100 Hz): shading, thermal, ageing estimator.
- Application core (0.1–1 Hz): data aggregation, web server, OCPP 2.0.1, SunSpec IEEE 1547-2018.
- Digital-Control Philosophy
Traditional PI + hill-climbing MPPT assumes uniform irradiance and static I-V curve. NMS replaces this with a three-tier hierarchy:
Tier 1 – Sub-millisecond current control
Dead-beat controller predicts the grid voltage vector one switching cycle ahead; harmonic spectrum programmable to cancel site-specific background distortion (e.g., 5th from an adjacent steel plant).
Dead-beat controller predicts the grid voltage vector one switching cycle ahead; harmonic spectrum programmable to cancel site-specific background distortion (e.g., 5th from an adjacent steel plant).
Tier 2 – Millisecond energy control
Model-predictive control (MPC) with on-line parameter identification solves the optimisation problem:
min Σ (Ppv – Pgrid)² + λ·(Vdc – Vref)² + μ·THD²
subject to: Ig ≤ 1.1 Inom, dVdc/dt ≤ 2 kV s⁻¹
Weighting factors λ, μ are updated every 10 ms by the AI layer.
Model-predictive control (MPC) with on-line parameter identification solves the optimisation problem:
min Σ (Ppv – Pgrid)² + λ·(Vdc – Vref)² + μ·THD²
subject to: Ig ≤ 1.1 Inom, dVdc/dt ≤ 2 kV s⁻¹
Weighting factors λ, μ are updated every 10 ms by the AI layer.
Tier 3 – Second-to-minute optimisation
Reinforcement learning (RL) agent observes 46 inputs: irradiance GHI & POA, cell temperature, wind, soiling sensors, wholesale price, meter curtailed energy, transformer tap position. Reward = +1 per kWh delivered, –10 per device heating cycle. After ≈ 6 weeks training on a 5 MWp UK site, the agent learnt to pre-heat inverters at 04:00 when forecast morning cloud-passage probability > 0.6, reducing condensation-induced faults 28 %.
Reinforcement learning (RL) agent observes 46 inputs: irradiance GHI & POA, cell temperature, wind, soiling sensors, wholesale price, meter curtailed energy, transformer tap position. Reward = +1 per kWh delivered, –10 per device heating cycle. After ≈ 6 weeks training on a 5 MWp UK site, the agent learnt to pre-heat inverters at 04:00 when forecast morning cloud-passage probability > 0.6, reducing condensation-induced faults 28 %.
- Physics-Enhanced MPPT
4.1 Partial-shading model
The firmware holds an n-diode electrical model for every module string. Irradiance is inferred from open-circuit voltage using the linear coefficient –0.35 % K⁻¹; temperature from embedded NTC thermistors. A grey-box solver estimates the global MPP within 0.5 V (≈ 0.02 % power) in < 200 ms after a step change.
4.2 Power-gradient constraint
In weak-light sunrise/sunsets dP/dV noise is large. NMS introduces a variable perturbation ΔV = k · ln(1 + G/100) where k = 0.8 V. This cuts hunting loss from 1.2 % to 0.3 %.
In weak-light sunrise/sunsets dP/dV noise is large. NMS introduces a variable perturbation ΔV = k · ln(1 + G/100) where k = 0.8 V. This cuts hunting loss from 1.2 % to 0.3 %.
4.3 Split-array operation
When snow covers the lower third of a tracker, the upper strings may exceed 850 V. The controller temporarily splits the array into two electrical sections with independent MPPTs while still feeding a common DC-link—achieved by phase-shifting the two interleaved boost stages 180°. Energy gain 4–7 % in Nordic winter conditions.
When snow covers the lower third of a tracker, the upper strings may exceed 850 V. The controller temporarily splits the array into two electrical sections with independent MPPTs while still feeding a common DC-link—achieved by phase-shifting the two interleaved boost stages 180°. Energy gain 4–7 % in Nordic winter conditions.
- Thermal Digital-Twin
5.1 Real-time thermal map
Finite-element ladder (RC) network with 12 nodes (MOSFET junction, substrate, base-plate, heat-sink, ambient) runs at 1 kHz. Parameters extracted from impedance spectroscopy during factory burn-in.
5.2 Lifetime model
Coffin-Manson + Arrhenius for bond-wire, Basquin for PCB traces. Accumulated damage D = Σ (ΔTj)^β · exp(–Ea/kTmean) with β = 4.3, Ea = 0.32 eV. When D > 0.2, the inverter automatically throttles 5 %, extending calculated life 40 %.
Coffin-Manson + Arrhenius for bond-wire, Basquin for PCB traces. Accumulated damage D = Σ (ΔTj)^β · exp(–Ea/kTmean) with β = 4.3, Ea = 0.32 eV. When D > 0.2, the inverter automatically throttles 5 %, extending calculated life 40 %.
5.3 Validation
In a 12-month desert pilot (Ta 48 °C, full-power 9 h day⁻¹), predicted junction temperature matched thermocouple within 2 °C; predicted 15-year wear 0.18, aligning with 0.2 limit.
In a 12-month desert pilot (Ta 48 °C, full-power 9 h day⁻¹), predicted junction temperature matched thermocouple within 2 °C; predicted 15-year wear 0.18, aligning with 0.2 limit.
- Grid-Services & Power-Quality Engine
6.1 Harmonic cancellation
Selective harmonic mitigation (SHM) shifts switching angles to place notches at 5th, 7th, 11th, 13th. Residual current THD < 1 % at 50 % load, outperforming IEEE 1547-2018 < 5 % requirement.
6.2 STATCOM mode
Reactive range 0.6 → 0.9 p.f. lead/lag at 50 % active power. Response time 30 ms using feed-forward decoupled dq control.
Reactive range 0.6 → 0.9 p.f. lead/lag at 50 % active power. Response time 30 ms using feed-forward decoupled dq control.
6.3 Frequency-watt & volt-watt
Configurable droop 0–10 % Hz⁻¹; dead-band ±5 mHz for UK National Grid, ±15 mHz for ERCOT. Over-frequency throttling reduces curtailed wind in weak-grid regions.
Configurable droop 0–10 % Hz⁻¹; dead-band ±5 mHz for UK National Grid, ±15 mHz for ERCOT. Over-frequency throttling reduces curtailed wind in weak-grid regions.
- Edge AI & Forecasting
7.1 Irradiance now-casting
A 3-layer LSTM trained on GOES-16 satellite 5-min cadence predicts GHI 0–2 h ahead with RMSE 42 W m⁻². The inverter pre-positions DC voltage to the forecast MPP, saving 100 ms tracking delay—worth 0.15 % daily energy.
7.2 Price arbitrage
Where dynamic tariffs exist (e.g., Spain PVPC), the agent curtails during negative price (< –0.02 € kWh⁻¹) and discharges battery (if installed) during peaks (> 0.20 € kWh⁻¹). Pay-back of embedded computer (< 35 USD) < 4 months.
Where dynamic tariffs exist (e.g., Spain PVPC), the agent curtails during negative price (< –0.02 € kWh⁻¹) and discharges battery (if installed) during peaks (> 0.20 € kWh⁻¹). Pay-back of embedded computer (< 35 USD) < 4 months.
- Cyber-Security by Design
8.1 Trusted boot
Hardware root-of-measurement (RoT) in MCU calculates SHA-256 hash of firmware; if ≠ OEM signature, inverter operates in safe mode (stand-alone, 50 Hz, 230 V) until field visit.
8.2 Encrypted comms
TLS 1.3 with AES-256-GCM for cloud; ChaCha20 for resource-constrained local SCADA. Certificate rotation every 90 days.
TLS 1.3 with AES-256-GCM for cloud; ChaCha20 for resource-constrained local SCADA. Certificate rotation every 90 days.
8.3 Audit trail
1 MB circular buffer stores 5-year tamper-evident log (RFC 3161) for insurance and warranty claims.
1 MB circular buffer stores 5-year tamper-evident log (RFC 3161) for insurance and warranty claims.
- Modular Power Shelf Concept
The NMS control board is agnostic to power stacks:
- 3 kW single-phase, 6 kW split-phase, 10 kW three-phase use identical digital PCB.
- Firmware auto-detects stack (eeprom in gate-driver) and loads appropriate thermal model.
- Utilities can stock one spare controller for mixed fleet, cutting holding cost 22 %.
- Field Validation
10.1 5 MWp flat-roof industrial, Jiangsu
- 1-year PR: 84.7 % (vs 79.2 % with legacy central inverters)
- MPPT energy gain: 3.1 %
- Soiling recovery via mid-day sweep: +1.4 %
- Pay-back time: 2.3 years on 0.04 USD kWh⁻¹ premium tariff.
10.2 50 kW residential cluster, California
- Shading from palm trees 08:30–10:00; MLPE-equivalent function in firmware recovers 12 % morning energy.
- Rule-21 voltage support avoided 18 utility tap-changer operations in 6 months.
10.3 Micro-grid, Norwegian island
- 100 % renewable for 72 h in February; inverter operated as grid-forming, frequency stability ±0.01 Hz.
- Battery-less black-start achieved using only 15 % inverter overload capability.
- Life-Cycle Economics
Assumptions: 1 MWp DC, 800 kWac NMS, 0.06 USD kWh⁻¹ PPA, 5 % WACC.
- Extra CAPEX vs commodity inverter: +0.02 USD W⁻¹
- Annual energy uplift: 18 MWh yr⁻¹
- O&M saving (predictive, fewer truck rolls): 4 kUSD yr⁻¹
- 25-year NPV benefit: +0.11 USD W⁻¹
IRR improvement: +2.1 %—sufficient to move a project from marginal to bankable in emerging markets.
- Road-Map
2025-Q4: silicon-carbon hybrid FET (1.2 kV, 6 mΩ) → target 99 % Euro efficiency.
2026-Q2: AI model compressed to run on RISC-V co-processor (< 0.5 W) enabling mass-market micro-inverter adoption.
2026-Q4: embedded solid-state DC breaker (4 kV, 100 A) for arc-fault protection without external fuses. - Conclusion
The NMS series demonstrates that the next frontier in PV economics is not higher cell efficiency but the algorithmic mastery of every electron between the module and the meter. By fusing high-frequency SiC power stages with cloud-grade computing, the platform delivers:
- 2–4 % extra kWh in real-world irradiance,
- sub-1 % THD without external filters,
- revenue-grade metering and cyber-security ready for capital markets,
- and a software upgrade path that improves, rather than depreciates, with age.
In an industry where hardware margins are compressing rapidly, the NMS digital-control suite offers manufacturers and developers a defensible value proposition: kilowatt-hours as a service, precision-optimised one digital watt at a time.
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